\n A távoli fa
\n Darabos Dániel, 2012
\n\n\n '''.strip())\nlines.append(prev)\n\nwith file('index.html', 'w') as f:\n f.writelines(lines)\n\nwith file('a-tavoli-fa.css', 'w') as f:\n f.write('''\n\n@media (min-width: 768px) {\n .container {\n max-width: 600px;\n margin-left: auto;\n margin-right: auto;\n }\n .story {\n font-size: 18px;\n }\n}\n\n@media screen {\n body {\n background: rgb(250, 240, 220);\n color: rgba(0, 0, 0, 0.6);\n }\n}\n\n.story, .title {\n font-family: 'Lora', serif;\n}\n\n.title {\n text-align: center;\n margin-top: 4em;\n margin-bottom: 4em;\n}\n.title h1 {\n margin-bottom: 0;\n}\n.title h2 {\n font-size: 18px;\n margin-top: 0;\n}\n\n.story {\n text-align: justify;\n}\n\n.story p {\n margin-top: 0;\n margin-bottom: 0;\n line-height: 1.4;\n}\n.story p.indent {\n text-indent: 2em;\n}\n\n.story .separator,\n.story .the-end {\n text-align: center;\n margin-top: 2em;\n margin-bottom: 2em;\n}\n\n.story .verse {\n margin: 2em;\n margin-left: 4em;\n text-indent: -2em;\n text-align: left;\n}\n\n.story .verse .noindent {\n margin-top: 1em;\n}\n\n.story .bchri7t- {\n font-style: italic;\n}\n\n '''.strip() + '\\n')\n", "sub_path": "iras/a-tavoli-fa/fixer.py", "file_name": "fixer.py", "file_ext": "py", "file_size_in_byte": 3375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pyphen.Pyphen", "line_number": 5, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}]}
+{"seq_id": "198572445", "text": "import argparse\nimport socket\nimport time\nimport os\nfrom constants import CHUNK_SIZE\n\ndef get_timestamp():\n return int(round(time.time()*1000))\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"-H\", \"--host\", default=\"127.0.0.1\")\n parser.add_argument(\"-P\", \"--port\", type=int, default=\"8080\")\n\n return parser.parse_args()\n\ndef main():\n args = parse_arguments()\n host = args.host\n port = int(os.environ.get(\"PORT\", args.port))\n address = (host, port)\n print(f\"adress - host: {host}, port: {port}\")\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n print('Binding...')\n sock.bind(address)\n print('Binding ok')\n print('Liten...')\n sock.listen(1)\n print('Liten ok')\n\n while True:\n conn, addr = sock.accept()\n if not conn:\n break\n\n print(f\"{get_timestamp()} - Accepted connection from {addr}\")\n\n bytes_received = 0\n\n size = conn.recv(CHUNK_SIZE).decode()\n print(f\"Size? - {size}\")\n if not size:\n continue\n size = int(size)\n print(f\"Size - {size}\")\n conn.send(b'start')\n\n print(f\"Receiving...\")\n while bytes_received < size:\n data = conn.recv(CHUNK_SIZE)\n bytes_received += len(data)\n print(f\"data: {data}\")\n\n print(f\"Receiving ok\")\n\n # Send number of bytes received\n conn.send(str(bytes_received).encode())\n\n\n sock.close()\n\nif __name__ == \"__main__\":\n print(\"Inilcializando server...\")\n main()\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "time.time", "line_number": 8, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 25, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 25, "usage_type": "attribute"}, {"api_name": "constants.CHUNK_SIZE", "line_number": 42, "usage_type": "argument"}, {"api_name": "constants.CHUNK_SIZE", "line_number": 52, "usage_type": "argument"}]}
+{"seq_id": "531426763", "text": "# Author: Johan Burke\n\nimport argparse\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-v', '--verbose', help = 'print more detailed debug information',\n action = 'store_true')\n parser.add_argument('-d', '--driver', type = str, choices = ['text_driver'],\n help = 'Select a driver to use. If none is specified, defaults to text_driver', default = 'text_driver')\n return parser.parse_args()\n\ndef main():\n args = parse_args()\n exec(\"from jargon.driver import \" + args.driver)\n exec(args.driver + \".driver_main(args)\")\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}]}
+{"seq_id": "380980108", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('home', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='CategoryHeads',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(unique=True, max_length=100)),\n ],\n options={\n 'verbose_name': 'category head',\n 'verbose_name_plural': 'category heads',\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='CategoryOptions',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=100)),\n ('priority', models.IntegerField(default=0)),\n ('head', models.ForeignKey(to='home.CategoryHeads')),\n ],\n options={\n 'verbose_name': 'category option',\n 'verbose_name_plural': 'category options',\n },\n bases=(models.Model,),\n ),\n migrations.AlterUniqueTogether(\n name='categoryoptions',\n unique_together=set([('head', 'title')]),\n ),\n ]\n", "sub_path": "home/migrations/0002_auto_20140929_2259.py", "file_name": "0002_auto_20140929_2259.py", "file_ext": "py", "file_size_in_byte": 1461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 40, "usage_type": "name"}]}
+{"seq_id": "545301678", "text": "import unittest\nimport json\nimport main\n\n\nclass DecideTestCase(unittest.TestCase):\n\n def setUpMockData1(self):\n self.testfile1 = open(\"MockData/mockdata1.json\")\n self.inp = json.load(self.testfile1)\n self.setUpGlobalVariables()\n\n def setUpMockData2(self):\n self.testfile2 = open(\"MockData/mockdata2.json\")\n self.inp = json.load(self.testfile2)\n self.setUpGlobalVariables()\n\n def setUpMockData3(self):\n self.testfile3 = open(\"MockData/mockdata3.json\")\n self.inp = json.load(self.testfile3)\n self.setUpGlobalVariables()\n\n def setUpGlobalVariables(self):\n main.NUMPOINTS = self.inp[\"NUMPOINTS\"]\n main.POINTS = self.inp[\"POINTS\"]\n PARAMETERS_T = self.inp[\"PARAMETERS_T\"]\n main.PI = self.inp[\"PI\"]\n main.EPSILON = PARAMETERS_T[\"EPSILON\"]\n main.A_PTS = PARAMETERS_T[\"A_PTS\"]\n main.B_PTS = PARAMETERS_T[\"B_PTS\"]\n main.C_PTS = PARAMETERS_T[\"C_PTS\"]\n main.D_PTS = PARAMETERS_T[\"D_PTS\"]\n main.E_PTS = PARAMETERS_T[\"E_PTS\"]\n main.F_PTS = PARAMETERS_T[\"F_PTS\"]\n main.G_PTS = PARAMETERS_T[\"G_PTS\"]\n main.K_PTS = PARAMETERS_T[\"K_PTS\"]\n main.N_PTS = PARAMETERS_T[\"N_PTS\"]\n main.Q_PTS = PARAMETERS_T[\"Q_PTS\"]\n main.QUADS = PARAMETERS_T[\"QUADS\"]\n main.AREA1 = PARAMETERS_T[\"AREA1\"]\n main.AREA2 = PARAMETERS_T[\"AREA2\"]\n main.DIST = PARAMETERS_T[\"DIST\"]\n main.RADIUS1 = PARAMETERS_T[\"RADIUS1\"]\n main.RADIUS2 = PARAMETERS_T[\"RADIUS2\"]\n main.LENGTH1 = PARAMETERS_T[\"LENGTH1\"]\n main.LENGTH2 = PARAMETERS_T[\"LENGTH2\"]\n\n main.LCM = self.inp[\"LCM\"]\n main.PUV = self.inp[\"PUV\"]\n\n def testDecide(self):\n self.setUpMockData1()\n self.testfile1.close()\n self.assertTrue(main.decide())\n\n self.setUpMockData2()\n self.testfile2.close()\n self.assertTrue(main.decide())\n\n self.setUpMockData3()\n self.testfile3.close()\n self.assertFalse(main.decide())\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "decide_test.py", "file_name": "decide_test.py", "file_ext": "py", "file_size_in_byte": 2081, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "main.NUMPOINTS", "line_number": 24, "usage_type": "attribute"}, {"api_name": "main.POINTS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "main.PI", "line_number": 27, "usage_type": "attribute"}, {"api_name": "main.EPSILON", "line_number": 28, "usage_type": "attribute"}, {"api_name": "main.A_PTS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "main.B_PTS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "main.C_PTS", "line_number": 31, "usage_type": "attribute"}, {"api_name": "main.D_PTS", "line_number": 32, "usage_type": "attribute"}, {"api_name": "main.E_PTS", "line_number": 33, "usage_type": "attribute"}, {"api_name": "main.F_PTS", "line_number": 34, "usage_type": "attribute"}, {"api_name": "main.G_PTS", "line_number": 35, "usage_type": "attribute"}, {"api_name": "main.K_PTS", "line_number": 36, "usage_type": "attribute"}, {"api_name": "main.N_PTS", "line_number": 37, "usage_type": "attribute"}, {"api_name": "main.Q_PTS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "main.QUADS", "line_number": 39, "usage_type": "attribute"}, {"api_name": "main.AREA1", "line_number": 40, "usage_type": "attribute"}, {"api_name": "main.AREA2", "line_number": 41, "usage_type": "attribute"}, {"api_name": "main.DIST", "line_number": 42, "usage_type": "attribute"}, {"api_name": "main.RADIUS1", "line_number": 43, "usage_type": "attribute"}, {"api_name": "main.RADIUS2", "line_number": 44, "usage_type": "attribute"}, {"api_name": "main.LENGTH1", "line_number": 45, "usage_type": "attribute"}, {"api_name": "main.LENGTH2", "line_number": 46, "usage_type": "attribute"}, {"api_name": "main.LCM", "line_number": 48, "usage_type": "attribute"}, {"api_name": "main.PUV", "line_number": 49, "usage_type": "attribute"}, {"api_name": "main.decide", "line_number": 54, "usage_type": "call"}, {"api_name": "main.decide", "line_number": 58, "usage_type": "call"}, {"api_name": "main.decide", "line_number": 62, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 66, "usage_type": "call"}]}
+{"seq_id": "111712527", "text": "# !/usr/bin/env python\n# coding: utf-8\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\ninstall_requires = [\n 'pampy',\n 'blinker'\n]\n\nsetup(\n name='flask_requests',\n version='0.0.14',\n author='kougazhang',\n author_email='kougazhang@gmail.com',\n url='https://github.com/kougazhang',\n description=\"egret, flask authentication used mongodb stored\",\n packages=find_packages(),\n install_requires=install_requires,\n include_package_data=True,\n)\n\n", "sub_path": "pypi_install_script/flask_requests-0.0.14.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "setuptools.setup", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 19, "usage_type": "call"}]}
+{"seq_id": "136949479", "text": "from __future__ import division, print_function, unicode_literals\n\nimport sys, os, re, subprocess, stat, pwd, grp, platform\n\nfrom os import getcwd as get_cwd\nfrom os.path import exists as path_exists\nfrom os.path import isdir as path_isdir\n\ndef extract_point_value(s):\n m = re.search(r'(\\d+)\\s?pts?', s, flags=re.I)\n if m:\n return int(m.group(1))\n else:\n return 0\n\ndef homedir():\n return os.path.expanduser('~/')\n\ndef path_exists(filepath):\n expanded_path = os.path.expanduser(filepath)\n return os.path.exists(expanded_path)\n\ndef path_isdir(filepath):\n expanded_path = os.path.expanduser(filepath)\n return path_exists(expanded_path) and os.path.isdir(expanded_path)\n\ndef path_isfile(filepath):\n return path_exists(filepath) and not path_isdir(filepath)\n\ndef mkdir(filepath):\n expanded_path = os.path.expanduser(filepath)\n try:\n os.makedirs(expanded_path)\n except:\n pass\n return expanded_path\n\ndef check_output(*popenargs, **kwargs):\n r\"\"\"Run command with arguments and return its output as a byte string.\n Backported from Python 2.7 as it's implemented as pure python on stdlib.\n >>> check_output(['/usr/bin/python', '--version'])\n Python 2.6.2\n \"\"\"\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n error = subprocess.CalledProcessError(retcode, cmd)\n error.output = output\n raise error\n return output\n\ndef get_current_user():\n u = check_output(['whoami'])\n return re.sub(r'\\s+', '', str(u))\n\ndef get_perms(filepath):\n expanded_path = os.path.expanduser(filepath)\n st = os.stat(expanded_path)\n return oct(stat.S_IMODE(st.st_mode))\n\ndef user_exists(username):\n try:\n pwd.getpwnam(username)\n return True\n except KeyError:\n return False\n\ndef group_exists(groupname):\n try:\n grp.getgrnam(groupname)\n return True\n except KeyError:\n return False\n\ndef group_has_member(username, groupname):\n try:\n g = grp.getgrnam(groupname)\n return username in g.gr_mem\n except KeyError:\n return False\n\ndef get_input(msg, allow_blank=False, validation_funct=None):\n try:\n ask = raw_input\n except NameError:\n ask = input\n while True:\n result = ask(msg)\n if allow_blank==False and not result:\n continue\n elif result and validation_funct:\n if not validation_funct(result):\n continue\n else:\n break\n return result\n\ndef which(program):\n def is_exe(fpath):\n return os.path.isfile(fpath) and os.access(fpath, os.X_OK)\n fpath, fname = os.path.split(program)\n if fpath:\n if is_exe(program):\n return program\n else:\n for path in os.environ[\"PATH\"].split(os.pathsep):\n path = path.strip('\"')\n exe_file = os.path.join(path, program)\n if is_exe(exe_file):\n return exe_file\n return None\n", "sub_path": "lab_toolkit/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "re.search", "line_number": 10, "usage_type": "call"}, {"api_name": "re.I", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 33, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 44, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 51, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 62, "usage_type": "call"}, {"api_name": "stat.S_IMODE", "line_number": 63, "usage_type": "call"}, {"api_name": "pwd.getpwnam", "line_number": 67, "usage_type": "call"}, {"api_name": "grp.getgrnam", "line_number": 74, "usage_type": "call"}, {"api_name": "grp.getgrnam", "line_number": 81, "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": "os.access", "line_number": 104, "usage_type": "call"}, {"api_name": "os.X_OK", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.pathsep", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}]}
+{"seq_id": "55890999", "text": "from argparse import ArgumentParser\nfrom utils.file_utils import read_by_file_suffix\n\nif __name__ == '__main__':\n parser = ArgumentParser()\n parser.add_argument('--file', type=str, required=True)\n args = parser.parse_args()\n\n test_log = list(read_by_file_suffix(args.file))[0]\n for prediction_name, prediction_results in sorted(test_log.items()):\n print('{0}: Accuracy -> {1:.5f}'.format(prediction_name, prediction_results['ACCURACY']))\n", "sub_path": "src/scripts/read_test_log.py", "file_name": "read_test_log.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "utils.file_utils.read_by_file_suffix", "line_number": 9, "usage_type": "call"}]}
+{"seq_id": "394399429", "text": "NEWS_2_READ = 6\n\n\ndef write_2_csv(my_dict, path='./output/articles_details.csv'):\n import csv\n import os\n\n try:\n if os.path.exists(path):\n with open(path, 'a', encoding='utf-8', newline='') as csv_file:\n fieldnames = ['Datetime', 'Headline', 'Author Name', 'Author Image', 'Author Twitter', 'Content']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writerow({\n 'Datetime': my_dict['datetime'],\n 'Headline': my_dict['headline'],\n 'Author Name': my_dict['author_name'],\n 'Author Image': my_dict['author_image'],\n 'Author Twitter': my_dict['author_twitter'],\n 'Content': my_dict['content']\n })\n csv_file.close()\n else:\n with open(path, 'w', encoding='utf-8', newline='') as csv_file:\n fieldnames = ['Datetime', 'Headline', 'Author Name', 'Author Image', 'Author Twitter', 'Content']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writeheader()\n writer.writerow({\n 'Datetime': my_dict['datetime'],\n 'Headline': my_dict['headline'],\n 'Author Name': my_dict['author_name'],\n 'Author Image': my_dict['author_image'],\n 'Author Twitter': my_dict['author_twitter'],\n 'Content': my_dict['content']\n })\n csv_file.close()\n except:\n raise RuntimeError('Error when saving to CSV file.')\n return\n\n\ndef save_img_2_disk(soup):\n from urllib import request\n import os\n\n try:\n for a in soup.find('div', itemprop='articleBody').find_all('a', href=True):\n ref = a['href']\n if '.jpg' in ref or '.png' in ref or '.gif' in ref:\n request.urlretrieve(ref, './output/' + os.path.basename(ref))\n break\n del ref\n except:\n raise RuntimeError('Error when saving image to disk.')\n return\n\n\ndef get_data(soup, info):\n\n try:\n info['datetime'] = (soup.find(\"time\", itemprop='datePublished').get_text()).replace('\\n', '')\n info['headline'] = soup.find('title').get_text()\n info['content'] = str.encode(str(soup.find('body')).replace(' ', ''))\n\n if soup.find('div', class_='headshot') is None:\n info['author_image'] = 'N/A'\n info['author_name'] = 'HEDGEYE Guest'\n info['author_twitter'] = 'N/A'\n else:\n info['author_image'] = soup.find('div', class_='byline').find('img')['src']\n info['author_name'] = soup.find('div', class_='full-name').get_text()\n info['author_twitter'] = soup.find('div', class_='twitter-handle').find('a').get_text()\n\n return info\n except:\n raise RuntimeError('Error getting data.')\n\n\nif __name__ == '__main__':\n import requests\n from bs4 import BeautifulSoup\n\n page = requests.get('https://app.hedgeye.com/insights/all?type=insight')\n\n if page.status_code != 200:\n print('Something is wrong with the website, HTML error: {}'.format(page.status_code))\n else:\n soup = BeautifulSoup(page.content, 'html.parser')\n articles_href = soup.find_all('a', class_='thumbnail-article__title-link', href=True)\n hrefs = [article['href'] for article in articles_href[:NEWS_2_READ]]\n del articles_href\n\n for ref in hrefs:\n url = 'https://app.hedgeye.com' + ref\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}\n article_url = requests.get(url, headers)\n\n if article_url.status_code != 200:\n print('Something is wrong with the Article website, HTML error: {}'.format(article_url.status_code))\n else:\n article_soup = BeautifulSoup(article_url.content, 'html.parser')\n\n info = dict()\n info = get_data(article_soup, info)\n write_2_csv(info)\n del info\n save_img_2_disk(article_soup)\n", "sub_path": "simple_collection_process/web_scrapping.py", "file_name": "web_scrapping.py", "file_ext": "py", "file_size_in_byte": 4264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.exists", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 12, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.urlretrieve", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 49, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 82, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 100, "usage_type": "call"}]}
+{"seq_id": "317303050", "text": "import torch\nfrom model.generator import Generator\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nimport random\nimport picture_processor\nimport os\nimport wget\n\n#Download model if it is not already downloaded\nif not os.path.isfile('generator.pkl'):\n url = 'https://f000.backblazeb2.com/file/b2mt-public/ecm3401/generator.pkl'\n wget.download(url)\n\nno_face = Image.open(\"model/no-face.png\") #Default image for if no face was detected\nimage_size = 512\n\ntransform_picture = transforms.Compose([transforms.Grayscale(num_output_channels=1),\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.5), std=(0.5))])\n\n#Load the generator model\nG = Generator(1,1)\nG.cpu()\nG.load_state_dict(torch.load('generator.pkl', map_location=torch.device('cpu')))\n\n'''\nFunction for processing a image and feeding it into the network\n:param picture: File stream with the picture\n:return: Portrait drawing PNG image encoded as a base64 string\n'''\ndef process(picture):\n #open the picture and find a face\n picture = Image.open(picture.stream)\n picture = picture_processor.process(picture)\n\n if picture: #transform image and feed to network if face is found\n picture = picture.resize((image_size,image_size), Image.BILINEAR)\n picture = transform_picture(picture).float()\n picture = Variable(picture, requires_grad=True)\n picture = picture.unsqueeze(0)\n\n gen_image = G(Variable(picture.cpu()))\n gen_image = gen_image.cpu().data\n\n img = Image.fromarray(gen_image[0][0].numpy()*255).convert(\"L\")\n else: #if no face is found return default image\n img = no_face\n\n #encode picture as a base64 string\n output_buffer = BytesIO()\n img.save(output_buffer, format=\"png\")\n byte_data = output_buffer.getvalue()\n base64_str = base64.b64encode(byte_data)\n \n return base64_str\n", "sub_path": "Extension/model/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.isfile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 21, "usage_type": "name"}, {"api_name": "torchvision.transforms.Grayscale", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "model.generator.Generator", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "picture_processor.process", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 41, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 49, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 54, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 57, "usage_type": "call"}]}
+{"seq_id": "246593946", "text": "\"\"\"Microscope Processing\nJonathan Mortlock 25/06/20\n\n\"\"\"\n\nimport os\nimport sys\nimport numpy as np\nfrom collections import OrderedDict\nimport time\nfrom scipy.signal import find_peaks\nfrom scipy.stats import norm\nfrom skimage.filters import threshold_minimum\nfrom astropy.stats import binom_conf_interval\nfrom imageanalysis.analysis import Analysis, BOOL\nimport logging\nlogger = logging.getLogger(__name__)\n\n\n\nclass image_handler(Analysis):\n \"\"\"Class to handle image metadata\n (NB Slightly different implementation to Tweezers)\n Inherits the types and stats dictionaries, and reset_arrays, \n load, and save methods from Analysis. \n \"\"\"\n def __init__(self):\n super().__init__()\n self.types = OrderedDict([('File ID', int), # number ID of image\n ('Atom Number', int), # Total occupied\n ('Lattice 00',float),\n ('Lattice 01',float),\n ('Lattice 10',float),\n ('Lattice 11',float),\n ('Offset 0',float),\n ('Offset 1',float),\n ('RL iterations',int),\n ('Threshold',float),\n ('Include', BOOL)])# whether to include in further analysis\n self.stats = OrderedDict([(key, []) for key in self.types.keys()]) \n \n self.delim = ' ' # delimieter to use when opening image files\n self.bias = 697 # bias offset from EMCCD\n self.peak_indexes = [0,0] # indexes of peaks in histogram\n self.peak_heights = [0,0] # heights of peaks in histogram\n self.peak_widths = [0,0] # widths of peaks in histogram\n self.peak_centre = [0,0] # peak position in counts in histogram\n self.fidelity = 0 # fidelity of detecting atom presence\n self.err_fidelity = 0 # error in fidelity\n # self.mask = np.zeros((1,1))# normalised mask to apply to image for ROI\n self.xc = 1 # ROI centre x position \n self.yc = 1 # ROI centre y position\n self.roi_size = 1 # ROI length in pixels. default 1 takes top left pixel\n self.pic_width = 512 # number of pixels along horizontal axis of an image\n self.pic_height= 512 # number of pixels along vertical axis of an image\n self.thresh = 1 # initial threshold for atom detection\n self.fid = 0 # file ID number for the next image\n self.ind = 0 # number of images processed\n self.im_vals = np.array([]) # the data from the last image is accessible to an image_handler instance\n self.bin_array = [] # if bins for the histogram are supplied, plotting can be faster\n", "sub_path": "microscope/microscopeImagehandler.py", "file_name": "microscopeImagehandler.py", "file_ext": "py", "file_size_in_byte": 2766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "imageanalysis.analysis.Analysis", "line_number": 21, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 29, "usage_type": "call"}, {"api_name": "imageanalysis.analysis.BOOL", "line_number": 39, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}]}
+{"seq_id": "370231060", "text": "from itchat.content import *\nfrom utilities import *\nfrom ProcessInterface import ProcessInterface\nfrom pymongo import MongoClient\nfrom datetime import datetime\nimport itchat\nimport re\n\nclass QAPlugin(ProcessInterface):\n def __init__(self, onlyOwerUse = False):\n self.client = MongoClient()\n self.collName = 'QA'\n self.coll = self.client[dbName][self.collName]\n self.onlyOwerUse = onlyOwerUse\n logging.info('QAPlugin initialized.')\n\n def process(self, msg, type):\n if type != TEXT:\n return\n\n content = msg['Content']\n destinationChatroomId = msg['FromUserName'] if re.search('@@', msg['FromUserName']) else msg['ToUserName']\n if content.startswith('/qa '):\n\n BlockChatRoomIds = None\n #BlockChatRoomIds = getChatroomIdByName([u'知乎最牛逼兄弟会没有之一'])\n\n if (BlockChatRoomIds is not None) and (destinationChatroomId in BlockChatRoomIds):\n return\n\n qa = content.split()\n if len(qa) == 3:\n question = qa[1]\n answer = qa[2]\n\n SpecialChatRoomIds = getChatroomIdByName(['qqq', u'知乎最牛逼兄弟会没有之一'])\n if (SpecialChatRoomIds is not None) and (destinationChatroomId in SpecialChatRoomIds):\n if self.onlyOwerUse:\n if 'ChatRoomOwner' in msg['User']:\n ower = msg['User']['ChatRoomOwner']\n else:\n ower = msg['User']['MemberList'][0]['UserName']\n if msg['ActualUserName'] != ower:\n itchat.send(u'我只听群主的话！', destinationChatroomId)\n return\n\n #destinationChatroomId = msg['FromUserName'] if re.search('@@', msg['FromUserName']) else msg['ToUserName']\n res = list(self.coll.find({'question': question, 'to': msg['User']['NickName']}))\n if len(res) == 1:\n itchat.send(u'我知道答案，不用你教我~', destinationChatroomId)\n else:\n timestamp = time()\n rtime = datetime.utcfromtimestamp(timestamp)\n r = {\n 'question': question,\n 'answer': answer,\n 'from': msg['ActualNickName'],\n 'fromId': msg['ToUserName'],\n 'to': msg['User']['NickName'] if 'User' in msg and 'UserName' in msg['User'] else 'N/A',\n 'timestamp': timestamp,\n 'time': rtime\n }\n self.coll.insert(r)\n itchat.send(u'so easy! 我记住了，不信你考我！', destinationChatroomId)\n logging.info('QA remember:{0}:{1}'.format(question, answer))\n else:\n res = list(self.coll.find({'question': content, 'to': msg['User']['NickName']}))\n if len(res) == 1:\n itchat.send(res[0]['answer'], destinationChatroomId)\n", "sub_path": "QAPlugin.py", "file_name": "QAPlugin.py", "file_ext": "py", "file_size_in_byte": 3130, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "ProcessInterface.ProcessInterface", "line_number": 9, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 11, "usage_type": "call"}, {"api_name": "re.search", "line_number": 22, "usage_type": "call"}, {"api_name": "itchat.send", "line_number": 44, "usage_type": "call"}, {"api_name": "itchat.send", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "itchat.send", "line_number": 64, "usage_type": "call"}, {"api_name": "itchat.send", "line_number": 69, "usage_type": "call"}]}
+{"seq_id": "497113369", "text": "\"\"\"\nGraph Attention Networks in DGL using SPMV optimization.\nMultiple heads are also batched together for faster training.\nReferences\n----------\nPaper: https://arxiv.org/abs/1710.10903\nAuthor's code: https://github.com/PetarV-/GAT\nPytorch implementation: https://github.com/Diego999/pyGAT\n\"\"\"\n\nimport argparse\nimport numpy as np\nimport networkx as nx\nimport time\nimport torch\nimport torch.nn.functional as F\nfrom dgl import DGLGraph\nfrom dgl.data import register_data_args, load_data\nfrom gat import GAT\nfrom utils import EarlyStopping\n\nfrom sklearn.metrics import precision_recall_fscore_support as score\nfrom sklearn.metrics import classification_report as report\n\ntorch.manual_seed(2)\n\n\ndef accuracy(logits, labels):\n _, indices = torch.max(logits, dim=1)\n correct = torch.sum(indices == labels)\n return correct.item() * 1.0 / len(labels)\n\n\ndef evaluate(model, features, labels, mask):\n model.eval()\n with torch.no_grad():\n logits = model(features)\n logits = logits[mask]\n labels = labels[mask]\n\n _, indices = torch.max(logits, dim=1)\n correct = torch.sum(indices == labels)\n\n # Statistics\n precision, recall, fscore, support = score(labels, indices)\n\n # Accuracy\n acc = correct.item() * 1.0 / len(labels)\n\n class_based_report = report(labels, indices)\n\n return acc, precision, recall, fscore, support, class_based_report\n\n\ndef main(args):\n # load and preprocess dataset\n data = load_data(args)\n features = torch.FloatTensor(data.features)\n labels = torch.LongTensor(data.labels)\n if hasattr(torch, \"BoolTensor\"):\n train_mask = torch.BoolTensor(data.train_mask)\n val_mask = torch.BoolTensor(data.val_mask)\n test_mask = torch.BoolTensor(data.test_mask)\n else:\n train_mask = torch.ByteTensor(data.train_mask)\n val_mask = torch.ByteTensor(data.val_mask)\n test_mask = torch.ByteTensor(data.test_mask)\n num_feats = features.shape[1]\n n_classes = data.num_labels\n n_edges = data.graph.number_of_edges()\n print(\n \"\"\"----Data statistics------'\n #Edges %d\n #Classes %d \n #Train samples %d\n #Val samples %d\n #Test samples %d\"\"\"\n % (\n n_edges,\n n_classes,\n train_mask.int().sum().item(),\n val_mask.int().sum().item(),\n test_mask.int().sum().item(),\n )\n )\n\n if args.gpu < 0:\n cuda = False\n else:\n cuda = True\n torch.cuda.set_device(args.gpu)\n features = features.cuda()\n labels = labels.cuda()\n train_mask = train_mask.cuda()\n val_mask = val_mask.cuda()\n test_mask = test_mask.cuda()\n\n g = data.graph\n # add self loop\n g.remove_edges_from(nx.selfloop_edges(g))\n g = DGLGraph(g)\n g.add_edges(g.nodes(), g.nodes())\n n_edges = g.number_of_edges()\n # create model\n heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]\n model = GAT(\n g,\n args.num_layers,\n num_feats,\n args.num_hidden,\n n_classes,\n heads,\n F.elu,\n args.in_drop,\n args.attn_drop,\n args.negative_slope,\n args.residual,\n )\n print(model)\n if args.early_stop:\n stopper = EarlyStopping(patience=100)\n if cuda:\n model.cuda()\n loss_fcn = torch.nn.CrossEntropyLoss()\n\n # use optimizer\n optimizer = torch.optim.Adam(\n model.parameters(), lr=args.lr, weight_decay=args.weight_decay\n )\n\n # initialize graph\n dur = []\n for epoch in range(args.epochs):\n model.train()\n if epoch >= 3:\n t0 = time.time()\n # forward\n logits = model(features)\n loss = loss_fcn(logits[train_mask], labels[train_mask])\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if epoch >= 3:\n dur.append(time.time() - t0)\n\n train_acc = accuracy(logits[train_mask], labels[train_mask])\n\n if args.fastmode:\n val_acc = accuracy(logits[val_mask], labels[val_mask])\n else:\n val_acc, _, _, _, _, _ = evaluate(\n model, features, labels, val_mask\n )\n if args.early_stop:\n if stopper.step(val_acc, model):\n break\n\n print(\n \"Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | TrainAcc {:.4f} |\"\n \" ValAcc {:.4f} | ETputs(KTEPS) {:.2f}\".format(\n epoch,\n np.mean(dur),\n loss.item(),\n train_acc,\n val_acc,\n n_edges / np.mean(dur) / 1000,\n )\n )\n\n print()\n if args.early_stop:\n model.load_state_dict(torch.load(\"es_checkpoint.pt\"))\n\n print()\n acc, precision, recall, fscore, support, class_based_report = evaluate(\n model, features, labels, test_mask\n )\n print(\"\")\n print(\"--- Test STATISTICS ---\")\n print(\"Test Precision\", precision)\n print(\"Test Recall\", recall)\n print(\"Test F-Score\", fscore)\n print(\"Test Support\", support)\n print(\"\")\n\n print(\"--- AVERAGE STATISTICS ---\")\n print(\"Average Accuracy\", acc)\n print(\"Average Precision\", precision.mean())\n print(\"Average Recall\", recall.mean())\n print(\"Average F-Score\", fscore.mean())\n print(\"Average Support\", support.mean())\n\n print(class_based_report)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description=\"GAT\")\n register_data_args(parser)\n parser.add_argument(\n \"--gpu\",\n type=int,\n default=-1,\n help=\"which GPU to use. Set -1 to use CPU.\",\n )\n parser.add_argument(\n \"--epochs\", type=int, default=800, help=\"number of training epochs\"\n )\n parser.add_argument(\n \"--num-heads\",\n type=int,\n default=4,\n help=\"number of hidden attention heads\",\n )\n parser.add_argument(\n \"--num-out-heads\",\n type=int,\n default=1,\n help=\"number of output attention heads\",\n )\n parser.add_argument(\n \"--num-layers\", type=int, default=1, help=\"number of hidden layers\"\n )\n parser.add_argument(\n \"--num-hidden\", type=int, default=8, help=\"number of hidden units\"\n )\n parser.add_argument(\n \"--residual\",\n action=\"store_true\",\n default=False,\n help=\"use residual connection\",\n )\n parser.add_argument(\n \"--in-drop\", type=float, default=0.4, help=\"input feature dropout\"\n )\n parser.add_argument(\n \"--attn-drop\", type=float, default=0.25, help=\"attention dropout\"\n )\n parser.add_argument(\"--lr\", type=float, default=1e-2, help=\"learning rate\")\n parser.add_argument(\n \"--weight-decay\", type=float, default=5e-4, help=\"weight decay\"\n )\n parser.add_argument(\n \"--negative-slope\",\n type=float,\n default=0.2,\n help=\"the negative slope of leaky relu\",\n )\n parser.add_argument(\n \"--early-stop\",\n action=\"store_true\",\n default=False,\n help=\"indicates whether to use early stop or not\",\n )\n parser.add_argument(\n \"--fastmode\",\n action=\"store_true\",\n default=False,\n help=\"skip re-evaluate the validation set\",\n )\n args = parser.parse_args()\n print(args)\n\n main(args)\n", "sub_path": "gat/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 7348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.manual_seed", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 50, "usage_type": "call"}, {"api_name": "dgl.data.load_data", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.BoolTensor", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.BoolTensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.BoolTensor", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.ByteTensor", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.ByteTensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.ByteTensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.cuda.set_device", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 91, "usage_type": "attribute"}, {"api_name": "networkx.selfloop_edges", "line_number": 100, "usage_type": "call"}, {"api_name": "dgl.DGLGraph", "line_number": 101, "usage_type": "call"}, {"api_name": "gat.GAT", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn.functional.elu", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 113, "usage_type": "name"}, {"api_name": "utils.EarlyStopping", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 127, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 174, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 200, "usage_type": "call"}, {"api_name": "dgl.data.register_data_args", "line_number": 201, "usage_type": "call"}]}
+{"seq_id": "233457913", "text": "from django.utils.html import strip_tags\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template.loader import render_to_string\nfrom .models import Subscriber\n\n\n# Create your views here.\n\nclass SubscribeAPI(APIView):\n authentication_classes = ()\n permission_classes = []\n\n def post(self, request):\n data = request.data\n try:\n Subscriber.objects.get(email=data['email'])\n return Response({'is_subscribed': True}, status.HTTP_200_OK)\n except:\n try:\n data = request.data\n email = Subscriber(email=data['email'])\n email.save()\n subject, from_email, to = 'Subscribe', 'hello@solutions-eg.org', data['email']\n text_content = 'Subscribe'\n # ==========================================================================\n html_content = 'You have subscribed to solutions-eg successfully '\n # ============================================================================\n msg = EmailMultiAlternatives(\n subject, text_content, from_email, [to])\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()\n # mail = data['email'].split('@')\n # message = render_to_string('sub_email.html', {\n # 'mail': mail[0],\n # 'image_domain': 'https://scarabaeus-sacer.com',\n # })\n # # Strip the html tag. So people can see the pure text at least.\n # text_content = strip_tags(message)\n # msg = EmailMultiAlternatives(\n # 'Subscribe', text_content, 'admin@scarabaeus-sacer.com', [data['email']])\n # msg.attach_alternative(message, \"text/html\")\n # msg.send()\n return Response(status.HTTP_200_OK)\n except Exception as e:\n print(e)\n return Response({'exception': str(e)}, status.HTTP_200_OK)\n\n\nclass ContactUs(APIView):\n authentication_classes = ()\n permission_classes = []\n\n def post(self, request):\n try:\n data = request.data\n subject, from_email, to = 'Contact', 'hello@solutions-eg.org', 'hello@solutions-eg.org'\n text_content = 'Contact Email'\n html_content = 'Name: ' + data['full_name']\n html_content += '
Country: ' + data['country']\n html_content += '
Email: ' + data['email']\n html_content += '
Phone: ' + data['phone']\n html_content += '
Service: ' + data['service']\n html_content += '
Message: ' + data['message']\n # ============================================================================\n msg = EmailMultiAlternatives(\n subject, text_content, from_email, [to])\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()\n return Response(status.HTTP_200_OK)\n except Exception as e:\n print(e)\n return Response({'is_error': True}, status.HTTP_200_OK)\n", "sub_path": "mailer/subscribe/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Subscriber.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Subscriber.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Subscriber", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Subscriber", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMultiAlternatives", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 52, "usage_type": "name"}, {"api_name": "django.core.mail.EmailMultiAlternatives", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}]}
+{"seq_id": "197905778", "text": "from setuptools import setup, find_packages\nfrom distutils.extension import Extension\nimport numpy as np\n\next_modules = [\n Extension(\n \"bayesbridge.random.tilted_stable.tilted_stable\",\n sources=[\"bayesbridge/random/tilted_stable/tilted_stable.c\"],\n include_dirs=[np.get_include()]\n ),\n Extension(\n \"bayesbridge.random.polya_gamma.polya_gamma\",\n sources=[\"bayesbridge/random/polya_gamma/polya_gamma.c\"],\n include_dirs=[np.get_include()]\n )\n]\n\nsetup(\n name='bayesbridge',\n version='0.2.0',\n description=\\\n 'Generates posterior samples under Bayesian sparse regression based on '\n + 'the bridge prior using the CG-accelerated Gibbs sampler of Nishimura '\n + 'et. al. (2018). The linear and logistic model are currently supported.',\n url='https://github.com/aki-nishimura/bayes-bridge',\n author='Akihiko (Aki) Nishimura',\n author_email='akihiko4@g.ucla.edu',\n license='MIT',\n packages=find_packages(exclude=['tests', 'tests.*']),\n ext_modules = ext_modules,\n install_requires=[\n 'numpy', 'scipy'\n ],\n zip_safe=False\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "distutils.extension.Extension", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 9, "usage_type": "call"}, {"api_name": "distutils.extension.Extension", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 14, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 18, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "441066421", "text": "from flask import Flask\napp = Flask(__name__)\n\nimport uuid\nid = uuid.uuid4()\n\n@app.route('/')\ndef hello_world():\n import socket\n return 'Dockerized Flask number %s' % id\n\nif __name__ == '__main__':\n app.run(debug=True, host='0.0.0.0', port=5000)\n", "sub_path": "docker-compose-swarm/sample-app/app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Flask", "line_number": 2, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 5, "usage_type": "call"}]}
+{"seq_id": "380809104", "text": "# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n# Generated file, DO NOT EDIT\n# Changes may cause incorrect behavior and will be lost if the code is regenerated.\n# --------------------------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass TaskAgentPoolMaintenanceDefinition(Model):\n \"\"\"TaskAgentPoolMaintenanceDefinition.\n\n :param enabled: Enable maintenance\n :type enabled: bool\n :param id: Id\n :type id: int\n :param job_timeout_in_minutes: Maintenance job timeout per agent\n :type job_timeout_in_minutes: int\n :param max_concurrent_agents_percentage: Max percentage of agents within a pool running maintenance job at given time\n :type max_concurrent_agents_percentage: int\n :param options:\n :type options: :class:`TaskAgentPoolMaintenanceOptions `\n :param pool: Pool reference for the maintenance definition\n :type pool: :class:`TaskAgentPoolReference `\n :param retention_policy:\n :type retention_policy: :class:`TaskAgentPoolMaintenanceRetentionPolicy `\n :param schedule_setting:\n :type schedule_setting: :class:`TaskAgentPoolMaintenanceSchedule `\n \"\"\"\n\n _attribute_map = {\n 'enabled': {'key': 'enabled', 'type': 'bool'},\n 'id': {'key': 'id', 'type': 'int'},\n 'job_timeout_in_minutes': {'key': 'jobTimeoutInMinutes', 'type': 'int'},\n 'max_concurrent_agents_percentage': {'key': 'maxConcurrentAgentsPercentage', 'type': 'int'},\n 'options': {'key': 'options', 'type': 'TaskAgentPoolMaintenanceOptions'},\n 'pool': {'key': 'pool', 'type': 'TaskAgentPoolReference'},\n 'retention_policy': {'key': 'retentionPolicy', 'type': 'TaskAgentPoolMaintenanceRetentionPolicy'},\n 'schedule_setting': {'key': 'scheduleSetting', 'type': 'TaskAgentPoolMaintenanceSchedule'}\n }\n\n def __init__(self, enabled=None, id=None, job_timeout_in_minutes=None, max_concurrent_agents_percentage=None, options=None, pool=None, retention_policy=None, schedule_setting=None):\n super(TaskAgentPoolMaintenanceDefinition, self).__init__()\n self.enabled = enabled\n self.id = id\n self.job_timeout_in_minutes = job_timeout_in_minutes\n self.max_concurrent_agents_percentage = max_concurrent_agents_percentage\n self.options = options\n self.pool = pool\n self.retention_policy = retention_policy\n self.schedule_setting = schedule_setting\n", "sub_path": "vsts/vsts/task_agent/v4_0/models/task_agent_pool_maintenance_definition.py", "file_name": "task_agent_pool_maintenance_definition.py", "file_ext": "py", "file_size_in_byte": 2965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "msrest.serialization.Model", "line_number": 12, "usage_type": "name"}]}
+{"seq_id": "101127228", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 14 06:52:00 2020\n\n@author: CVPR\n\"\"\"\n\n# Make Json\n\n\n# import\nfrom collections import OrderedDict\n\nimport cv2\nimport glob\nimport json\nimport base64\n\n\n# get img, label list\nimage_list = glob.glob(r\".\\attack\\*.png\")\nlabel_list = glob.glob(r\".\\attack\\*.txt\")\n\n\n# get image data\ndef get_image_data(img_path):\n img_info = cv2.imread(img_path)\n\n imagePath = img_path\n img_data = base64.b64encode(open(img_path, \"rb\").read())\n imageHeight = img_info.shape[0]\n imageWidth = img_info.shape[1]\n\n return [imagePath, img_data, imageHeight, imageWidth]\n\n\n#x1, y1, x2, y1, x1, y2, x2, y2\n# get label data\ndef get_label_data(label_path, group_id, shape_type):\n label_list = []\n\n with open(label_path, 'r') as MyFile:\n MyString = MyFile.read()\n sentences = MyString.rstrip().split(\"\\n\")\n\n for sentence in sentences:\n label_list.append(sentence.split(\", \") + [group_id, shape_type])\n \n return label_list\n\n\n# pointsList\ndef point_list(label_data):\n point_list = []\n \n point_list.append([float(label_data[1]), float(label_data[2])])\n point_list.append([float(label_data[3]), float(label_data[4])])\n point_list.append([float(label_data[7]), float(label_data[8])])\n point_list.append([float(label_data[5]), float(label_data[6])])\n\n return point_list\n\n\n# shapesDict\ndef shapes_list(label_data):\n shape_list = []\n\n for i in label_data:\n shapes = OrderedDict()\n\n shapes[\"label\"] = i[-3]\n shapes[\"points\"] = point_list(i)\n shapes[\"group_id\"] = i[-2]\n shapes[\"shape_type\"] = i[-1]\n shapes[\"flags\"] = {}\n\n shape_list.append(shapes)\n\n return shape_list\n\n\n# data 2 Json\ndef data2Json(img_data, label_data):\n file_data = OrderedDict()\n\n file_data[\"version\"] = \"4.5.6\"\n file_data[\"flags\"] = {}\n file_data[\"shapes\"] = shapes_list(label_data)\n file_data[\"imagePath\"] = img_data[0].split(\"/\")[-1]\n file_data[\"imageData\"] = str(img_data[1]).lstrip(\"b'\")\n file_data[\"imageHeight\"] = img_data[2]\n file_data[\"imageWidth\"] = img_data[3]\n\n return file_data\n\n\ngroup_id = None\nshape_type = \"polygon\"\n\n\n# main\nfor i in range(len(image_list)):\n img_data = get_image_data(image_list[i])\n label_data = get_label_data(label_list[i], group_id, shape_type)\n result = data2Json(img_data, label_data)\n\n if i % 10 == 0:\n with open('./valid_result_json/{}.json'.format(image_list[i].split(\"\\\\\")[-1].strip(\".png\")), 'w', encoding=\"utf-8\") as jsonfile:\n json.dump(result, jsonfile, ensure_ascii=False, indent=\"\\t\")\n\n else:\n with open('./result_json/{}.json'.format(image_list[i].split(\"\\\\\")[-1].strip(\".png\")), 'w', encoding=\"utf-8\") as jsonfile:\n json.dump(result, jsonfile, ensure_ascii=False, indent=\"\\t\")", "sub_path": "makeJson.py", "file_name": "makeJson.py", "file_ext": "py", "file_size_in_byte": 2866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "glob.glob", "line_number": 21, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 69, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 84, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 109, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 113, "usage_type": "call"}]}
+{"seq_id": "166754813", "text": "#!/usr/bin/env python3\n\n#\n# MIT License\n#\n# Copyright (c) 2020-2021 EntySec\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n\nimport subprocess\nimport shutil\n\nimport requests\n\nfrom hatsploit.lib.config import Config\nfrom hatsploit.core.cli.badges import Badges\n\n\nclass Update:\n def __init__(self):\n self.config = Config()\n self.badges = Badges()\n\n def check_update(self):\n remote_config = requests.get('https://raw.githubusercontent.com/EntySec/HatSploit/main/hatsploit/config/core_config.yml',\n stream=True).content\n if self.config.get_config_file(remote_config)['details']['version'] != \\\n self.config.core_config['details']['version']:\n return True\n return False\n\n def update(self):\n if self.check_update():\n self.badges.output_process(\"Updating HatSploit Framework...\")\n shutil.rmtree(self.config.path_config['root_path'])\n subprocess.call('pip3 install git+https://github.com/EntySec/HatSploit', shell=False)\n self.badges.output_success(\"HatSploit updated successfully!\")\n return\n self.badges.output_warning(\"Your HatSploit is up-to-date.\")\n", "sub_path": "hatsploit/core/utils/update.py", "file_name": "update.py", "file_ext": "py", "file_size_in_byte": 2231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "hatsploit.lib.config.Config", "line_number": 38, "usage_type": "call"}, {"api_name": "hatsploit.core.cli.badges.Badges", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 52, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 53, "usage_type": "call"}]}
+{"seq_id": "236910300", "text": "# Copyright (c) 2011-2015 Berkeley Model United Nations. All rights reserved.\n# Use of this source code is governed by a BSD License (see LICENSE).\n\nfrom drf_compound_fields.fields import ListField\nfrom rest_framework import serializers\n\nfrom huxley.api import validators\nfrom huxley.api.serializers.fields import DecimalField\nfrom huxley.core.models import School\n\n\nclass SchoolSerializer(serializers.ModelSerializer):\n registered = serializers.DateTimeField(format='iso-8601', required=False)\n fees_owed = DecimalField(read_only=True)\n fees_paid = DecimalField(read_only=True)\n assignments_finalized = serializers.BooleanField(required=False)\n country_preferences = ListField(\n serializers.IntegerField(),\n source='country_preference_ids')\n\n class Meta:\n model = School\n fields = (\n 'id',\n 'registered',\n 'name',\n 'address',\n 'city',\n 'state',\n 'zip_code',\n 'country',\n 'primary_name',\n 'primary_gender',\n 'primary_email',\n 'primary_phone',\n 'primary_type',\n 'secondary_name',\n 'secondary_gender',\n 'secondary_email',\n 'secondary_phone',\n 'secondary_type',\n 'program_type',\n 'times_attended',\n 'international',\n 'waitlist',\n 'beginner_delegates',\n 'intermediate_delegates',\n 'advanced_delegates',\n 'spanish_speaking_delegates',\n 'chinese_speaking_delegates',\n 'country_preferences',\n 'committeepreferences',\n 'registration_comments',\n 'fees_owed',\n 'fees_paid',\n 'assignments_finalized',\n )\n\n def validate_name(self, attrs, source):\n school_name = attrs[source]\n\n if School.objects.filter(name=school_name).exists():\n raise serializers.ValidationError(\n 'A school with the name \"%s\" has already been registered.'\n % school_name)\n\n validators.name(school_name)\n\n return attrs\n\n def validate_state(self, attrs, source):\n school_state = attrs[source]\n\n validators.name(school_state)\n\n return attrs\n\n def validate_country(self, attrs, source):\n school_country = attrs[source]\n\n validators.name(school_country)\n\n return attrs\n\n def validate_primary_phone(self, attrs, source):\n international = attrs['international']\n number = attrs[source]\n\n if international:\n validators.phone_international(number)\n else:\n validators.phone_domestic(number)\n\n return attrs\n\n def validate_address(self, attrs, source):\n school_address = attrs[source]\n\n validators.address(school_address)\n\n return attrs\n\n def validate_city(self, attrs, source):\n school_city = attrs[source]\n\n validators.name(school_city)\n\n return attrs\n\n def validate_zip(self, attrs, source):\n school_zip = attrs[source]\n\n validators.numeric(school_zip)\n\n return attrs\n\n def validate_primary_name(self, attrs, source):\n primary_name = attrs[source]\n\n validators.name(primary_name)\n\n return attrs\n\n def validate_primary_email(self, attrs, source):\n primary_email = attrs[source]\n\n validators.email(primary_email)\n\n return attrs\n\n def validate_secondary_name(self, attrs, source):\n secondary_name = attrs.get(source)\n\n if secondary_name:\n validators.name(secondary_name)\n\n return attrs\n\n def validate_secondary_email(self, attrs, source):\n secondary_email = attrs.get(source)\n\n if secondary_email:\n validators.email(secondary_email)\n\n return attrs\n\n def validate_secondary_phone(self, attrs, source):\n number = attrs.get(source)\n international = attrs['international']\n\n if number:\n if international:\n validators.phone_international(number)\n else:\n validators.phone_domestic(number)\n\n return attrs\n", "sub_path": "huxley/api/serializers/school.py", "file_name": "school.py", "file_ext": "py", "file_size_in_byte": 4214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 13, "usage_type": "name"}, {"api_name": "huxley.api.serializers.fields.DecimalField", "line_number": 14, "usage_type": "call"}, {"api_name": "huxley.api.serializers.fields.DecimalField", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}, {"api_name": "drf_compound_fields.fields.ListField", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 18, "usage_type": "name"}, {"api_name": "huxley.core.models.School", "line_number": 22, "usage_type": "name"}, {"api_name": "huxley.core.models.School.objects.filter", "line_number": 62, "usage_type": "call"}, {"api_name": "huxley.core.models.School.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "huxley.core.models.School", "line_number": 62, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 63, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 63, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 67, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 67, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 74, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 74, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 81, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 81, "usage_type": "name"}, {"api_name": "huxley.api.validators.phone_international", "line_number": 90, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 90, "usage_type": "name"}, {"api_name": "huxley.api.validators.phone_domestic", "line_number": 92, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 92, "usage_type": "name"}, {"api_name": "huxley.api.validators.address", "line_number": 99, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 99, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 106, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 106, "usage_type": "name"}, {"api_name": "huxley.api.validators.numeric", "line_number": 113, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 113, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 120, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 120, "usage_type": "name"}, {"api_name": "huxley.api.validators.email", "line_number": 127, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 127, "usage_type": "name"}, {"api_name": "huxley.api.validators.name", "line_number": 135, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 135, "usage_type": "name"}, {"api_name": "huxley.api.validators.email", "line_number": 143, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 143, "usage_type": "name"}, {"api_name": "huxley.api.validators.phone_international", "line_number": 153, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 153, "usage_type": "name"}, {"api_name": "huxley.api.validators.phone_domestic", "line_number": 155, "usage_type": "call"}, {"api_name": "huxley.api.validators", "line_number": 155, "usage_type": "name"}]}
+{"seq_id": "137061939", "text": "import urllib\nimport urllib.request\nimport argparse\nfrom bs4 import BeautifulSoup\n\n# parser = argparse.ArgumentParser()\n# parser.add_argument(\"URL\", help=\"url\")\n# parser.add_argument(\"text\", help=\"text attribute of anchor node.\", default=False)\n# parser.add_argument(\"title\", help=\"title attribute of anchor node.\", default=False)\n# parser.add_argument(\"lang\", help=\"lang attribute of anchor node.\", default=False)\n# parser.add_argument(\"langhref\", help=\"langhref attribute of anchor node.\", default=False)\n# args = parser.parse_args()\n\n\ndef node_inspecter(host_url, node_type=\"a\", text=False, title=False, lang=False, hreflang=False):\n\n page = urllib.request.urlopen(host_url).read()\n soup = BeautifulSoup(page, features=\"lxml\")\n soup.prettify()\n res = soup.find_all(name=node_type, text=text, title=title, lang=lang, hreflang=hreflang, href=True)\n\n return res\n\n\nif __name__ == \"__main__\":\n\n # res = node_inspecter(args.URL, text=args.text, title=args.title, lang=args.lang, langhref=args.langhref)\n # b\"Fran\\xc3\\xa7ais\".decode(\"utf-8\")\n res = node_inspecter(\"https://www.societegenerale.com/en/home\",\n node_type=\"a\",\n text=\"fr\",\n title=False,\n lang=\"fr\",\n hreflang=False)\n for r in res:\n print(res)\n print(\"\\n\")\n", "sub_path": "node_inspector.py", "file_name": "node_inspector.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "urllib.request.urlopen", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}]}
+{"seq_id": "421453406", "text": "from django.conf.urls import url\nfrom tailored import views\n\napp_name = 'tailored'\nurlpatterns = [\n\turl(r'^$', views.trending, name = 'index'),\n\turl(r'^items/$', views.items, name = 'items'),\n\turl(r'^section/(?P[-\\w]+)/$', views.show_section, name = 'show_section'),\n\turl(r'^user_profile/$', views.add_item, name = \"add_item\"),\n\turl(r'^trending/$', views.trending, name = \"trending\"),\n\turl(r'^category/(?P<title>[-\\w]+)/$', views.show_category, name = 'show_category'),\n\turl(r'^index/$', views.trending, name = 'home_page'),\n\turl(r'^search/new/(?P<search>[-\\w]+)/$', views.new_in, name = 'new_in'),\n\n\turl(r'^home/$', views.home_page, name = 'home_page'),\n\turl(r'^tailored/$', views.search_bar, name = 'home_page'),\n\turl(r'^search/$', views.search_bar, name = 'search'),\n\n\turl(r'^profile/(?P<seller_username>[-\\w]+)/$', views.show_seller_profile, name = 'show_seller_profile'),\n\n\turl(r'^search/(?P<search>[-\\w]+)/$', views.search_bar, name = 'search'),\n\n\turl(r'^profile/(?P<seller_username>[-\\w]+)/$', views.show_seller_profile, name = 'show_seller_profile'),\n\turl(r'^item/(?P<itemID>[-\\w]+)/$', views.show_item, name = 'show_item'),\n\turl(r'^add_item/$', views.add_item, name = 'add_item'),\n\turl(r'^user_profile/edit/$', views.edit_profile, name = 'edit_profile'),\n\turl(r'^edit/(?P<itemID>[-\\w]+)/$', views.edit_item, name = 'edit_item')\n]", "sub_path": "tailored_project/tailored/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "tailored.views.trending", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "tailored.views.items", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "tailored.views.show_section", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "tailored.views.add_item", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "tailored.views.trending", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "tailored.views.show_category", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "tailored.views.trending", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "tailored.views.new_in", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "tailored.views.home_page", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "tailored.views.search_bar", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "tailored.views.search_bar", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "tailored.views.show_seller_profile", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "tailored.views.search_bar", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "tailored.views.show_seller_profile", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "tailored.views.show_item", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "tailored.views.add_item", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "tailored.views.edit_profile", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "tailored.views.edit_item", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tailored.views", "line_number": 27, "usage_type": "name"}]}
+{"seq_id": "550526107", "text": "\nimport argparse\nimport logging\nimport os\nimport sys\nimport wulibs\n\n\nDATABASE_USERNAME = os.getenv(\"DATABASE_USERNAME\")\nDATABASE_PASSWORD = os.getenv(\"DATABASE_PASSWORD\")\nDATABASE_NAME = os.getenv(\"DATABASE_NAME\", \"tcjpr\")\n\nKUBERNETES_NAMESPACE = os.getenv(\"KUBERNETES_NAMESPACE\", \"default\")\n\nDBLAB_ENDPOINT = os.getenv(\"DBLAB_ENDPOINT\", \"10.0.61.51\")\nDBLAB_URL = os.getenv(\"DBLAB_URL\", \"http://dblab.default.svc.cluster.local\")\n\n\ndef delete_dblab_pr_db(pr_number):\n try:\n dblab = wulibs.DatabaseLab(DBLAB_URL)\n dblab.is_reachable()\n\n database_id = f\"pr{pr_number}-anon-db\"\n\n logging.info(f\"deleting database [{database_id}]\")\n dblab.delete_database(database_id)\n\n kube = wulibs.Kubernetes()\n\n logging.info(f\"deleting kubernetes service [{database_id}]\")\n kube.delete_service(KUBERNETES_NAMESPACE, database_id)\n\n except Exception as e:\n logging.exception(e)\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(\n stream=sys.stdout, level=logging.INFO,\n format=\"%(levelname)s - %(message)s\")\n\n parser = argparse.ArgumentParser(description=\"Delete DBLab PR database\")\n parser.add_argument(\"pr_number\", type=int, help=\"The PR number\")\n args = parser.parse_args()\n\n delete_dblab_pr_db(args.pr_number)\n", "sub_path": "wulibs/scripts/delete_dblab_pr_db.py", "file_name": "delete_dblab_pr_db.py", "file_ext": "py", "file_size_in_byte": 1312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "wulibs.DatabaseLab", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "wulibs.Kubernetes", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 41, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 41, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}]}
+{"seq_id": "104739637", "text": "# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author:pylarva\n# bolg:www.lichengbing.com\n\nimport pika\n# connection 一个TCP的连接、\nconnection = pika.BlockingConnection(pika.ConnectionParameters('10.0.0.111'))\n\n# channel 是建立在TCP连接中的一个虚拟连接\nchannel = connection.channel()\n\n# 创建一个queue\nchannel.queue_declare(queue='hello')\n\n# n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.\nchannel.basic_publish(exchange='',\n routing_key='hello',\n body='Hello World!')\nprint(\" [x] Sent 'Hello World!'\")\nconnection.close()", "sub_path": "day12/producer.py", "file_name": "producer.py", "file_ext": "py", "file_size_in_byte": 644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pika.BlockingConnection", "line_number": 8, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 8, "usage_type": "call"}]}
+{"seq_id": "174956449", "text": "from turtle import Turtle\nimport random\nimport colorgram\n\nclass Food(Turtle):\n\n def __init__(self):\n super().__init__()\n self.colors = []\n self.set_colors()\n self.shape(\"circle\")\n self.penup()\n self.shapesize(stretch_len=0.6, stretch_wid=0.6)\n\n self.speed(0)\n self.refresh()\n\n def set_colors(self):\n color_objects = colorgram.extract(f=\"food_colors.png\", number_of_colors=5)\n for color in color_objects:\n self.colors.append(tuple(color.rgb))\n\n def refresh(self):\n rand_color = random.choice(self.colors)\n self.color(rand_color)\n rand_x = random.randint(-250, 250)\n rand_y = random.randint(-250, 250)\n self.goto(rand_x, rand_y)\n", "sub_path": "Snake/food.py", "file_name": "food.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "turtle.Turtle", "line_number": 5, "usage_type": "name"}, {"api_name": "colorgram.extract", "line_number": 19, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "36351268", "text": "#-*- coding: utf-8 -*-\n#@Time :2018/12/6 0:54\n#@Author :yangjuan\n#@Email :269573175@qq.com\n#@File :do_excel_test.py\n\nfrom openpyxl import load_workbook\n\n# 1.打开工作簿\nworkbook = load_workbook('python13.xlsx') #返回的工作簿\n\n# 2.定位表单\nsheet = workbook['Sheet'] #返回一个表单对象\n\n# 3.读值\nurl = sheet.cell(2,1).value\nprint(url)\nprint(type(url))\n\n# 4.获取最大的行列值\nmax_row=sheet.max_row\nmax_col=sheet.max_column\n\nprint(\"行:\",max_row)\nprint(\"列：\",max_col)\n\n# 怎么写\n# PermissionError: [Errno 13] Permission denied: 'python13.xlsx'：保存前需要关闭工作簿\nsheet.cell(18,4).value=\"None\"\nworkbook.save(\"python13.xlsx\")\n\n# 怎么新建Excel\nfrom openpyxl import Workbook\nwb=Workbook()\nwb.save(\"python14.xlsx\")\n\n# file=open(\"python.xlsx\",\"w+\")\n\n# 概念:如何把每一行的数据读取到存到一个字典里面\n# 所有行的数据以字典的格式存在一个列表中\nwb=load_workbook('python13.xlsx') # 1.打开工作簿\nsheet=wb['Sheet'] # 2.定位表单\n\ntest_data=[]\nfor i in range(1,sheet.max_row+1):\n sub_data={}\n sub_data['url']=sheet.cell(i,1).value\n sub_data['http_method']=sheet.cell(i,2).value\n sub_data['param']=sheet.cell(i,3).value\n sub_data['expected']=sheet.cell(i,4).value\n test_data.append(sub_data)\n\nprint(test_data)\n\n\n", "sub_path": "python_interface/python_1203/do_excel_test.py", "file_name": "do_excel_test.py", "file_ext": "py", "file_size_in_byte": 1322, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 10, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 34, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 41, "usage_type": "call"}]}
+{"seq_id": "464869610", "text": "import pygame\nimport math\nimport sys\nfrom pygame.locals import *\nfrom pygame.time import Clock\n\nclass Planet:\n def __init__(self, x, y, vx, vy, radius, color, id):\n self.x = x\n self.y = y\n self.radius = radius\n self.id = id\n self.volume = radius ** 3\n self.mass = math.pi * (self.radius ** 2) * MASS_AREA_RATIO\n self.last_pos = (x, y)\n self.velocity = [vx, vy]\n self.color = color\n print(self.x, self.y)\n\n def update(self):\n self.x += self.velocity[0]\n self.y += self.velocity[1]\n\n for planet in planets:\n if self.id != planet.id:\n dx = planet.x - self.x\n dy = planet.y - self.y\n angle = math.atan2(dy, dx) # Calculate angle between planets\n d = math.sqrt((dx ** 2) + (dy ** 2)) # Calculate distance\n if d == 0:\n d = 0.000001 # Prevent division by zero error\n f = (\n G * self.mass * planet.mass / (d ** 2)\n ) # Calculate gravitational force\n\n self.velocity[0] += (math.cos(angle) * f) / self.mass\n self.velocity[1] += (math.sin(angle) * f) / self.mass\n\n def draw(self):\n pygame.draw.circle(\n display, self.color, (int(self.x), int(self.y)), int(self.radius)\n )\n # pygame.draw.rect(display, (255, 255, 255), self.rect)\n\n\ndef draw():\n # display.fill((25, 25, 25))\n\n for planet in planets:\n planet.draw()\n \n screen.blit(display, (0,0))\n\n pygame.display.update()\n\n\npygame.init()\nclock = pygame.time.Clock()\n\nwidth = 1440\nheight = 820\nWINDOW_SIZE = (width, height)\nscreen = pygame.display.set_mode(WINDOW_SIZE)\ndisplay = pygame.Surface(WINDOW_SIZE)\n\nG = 6.67408 * (10 ** -11) # Gravitational Constant\nMASS_AREA_RATIO = 2 * (10 ** 9) # mass in kilograms to area in pixels\nechelle = 1/800\nearth_distance = 3.84 * (10 ** 5) * echelle\nplanets = []\nplanet_id = 0\nplanets.append(Planet(int(width / 2), height / 2, 0, 0, 6371 * echelle, (19, 14, 209), planet_id))\nplanet_id += 1\nplanets.append(Planet(int(width / 2 + earth_distance), height / 2, 0, 0.235, 1737 * echelle, (100, 100, 100), planet_id))\nplanet_id += 1\n\nwhile True:\n clock.tick(480)\n for event in pygame.event.get():\n if event.type == QUIT:\n sys.exit()\n pygame.quit()\n \n for planet in planets:\n planet.update()\n draw()", "sub_path": "try.py", "file_name": "try.py", "file_ext": "py", "file_size_in_byte": 2464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "math.pi", "line_number": 14, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 28, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 36, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 82, "usage_type": "call"}]}
+{"seq_id": "398968571", "text": "from rest_framework import serializers\nfrom datetime import datetime\nfrom django_server.apps.manage.models import MedOrgPrincipal\n\nclass TendererListSerializer(serializers.Serializer):\n uuid = serializers.UUIDField()\n name = serializers.CharField()\n reg_cap = serializers.FloatField()\n est_date = serializers.DateField()\n count = serializers.FloatField()\n win_amount = serializers.FloatField()\n last_date = serializers.DateField()\n start = serializers.IntegerField()\n op_from = serializers.DateField()\n\n class Meta:\n fields = ('uuid', 'name', 'reg_cap', 'est_date', 'count', 'win_amount', 'last_date', 'start', 'op_from')\n\n\nclass TendereeListSerializer(serializers.Serializer):\n uuid = serializers.UUIDField()\n name = serializers.CharField()\n org_type_name = serializers.CharField()\n org_grade = serializers.CharField()\n count = serializers.FloatField()\n budget = serializers.FloatField()\n last_date = serializers.DateField()\n start = serializers.IntegerField()\n\n class Meta:\n fields = ('uuid', 'name', 'org_type_name', 'org_grade', 'count', 'budget', 'last_date', 'start')\n\n\nclass UserAddCooperationSerializer(serializers.Serializer):\n id = serializers.IntegerField()\n uuid = serializers.SerializerMethodField()\n name = serializers.SerializerMethodField()\n recent_date = serializers.SerializerMethodField()\n recent_event = serializers.SerializerMethodField()\n created_at = serializers.DateTimeField()\n relation = serializers.SerializerMethodField()\n\n class Meta:\n fields = ('id', 'tenderer_uuid', 'tenderee_uuid', 'tenderee_name', 'tenderer_name', 'recent_date', 'created_at')\n\n def get_uuid(self, obj):\n if self.context['request'].query_params.get('tenderee_uuid', ''):\n return obj.tenderer_uuid\n else:\n return obj.tenderee_uuid\n\n def get_name(self, obj):\n if self.context['request'].query_params.get('tenderee_uuid', ''):\n return obj.tenderer_name\n else:\n return obj.tenderee_name\n\n def get_recent_date(self, obj):\n # 获取自建合作网络最新事件, 如果不负责该机构, 事件为自建添加\n self.activity_state = None\n self.relation = None\n try:\n user = self.context['request'].user.user_extra\n if self.context['request'].query_params.get('tenderee_uuid', ''):\n med_org_principal = MedOrgPrincipal.objects.get(user=user, org_uuid=obj.tenderer_uuid)\n self.activity_state = med_org_principal.activity_state\n self.relation = med_org_principal.relation\n return med_org_principal.updated_at\n\n elif self.context['request'].query_params.get('tenderer_uuid', ''):\n med_org_principal = MedOrgPrincipal.objects.get(user=user, org_uuid=obj.tenderee_uuid)\n self.activity_state = med_org_principal.activity_state\n self.relation = med_org_principal.relation\n return med_org_principal.updated_at\n else:\n return obj.created_at\n except Exception as e:\n return obj.created_at\n\n def get_recent_event(self, obj):\n if not self.activity_state:\n return '自建添加'\n return MedOrgPrincipal.ACTIVITY_STATE_CHOICES[self.activity_state]\n\n def get_relation(self, obj):\n return self.relation\n", "sub_path": "scripts/marketbox-medical-svr/django_server/apps/mining/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 3429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 5, "usage_type": "name"}, {"api_name": "rest_framework.serializers.UUIDField", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FloatField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FloatField", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FloatField", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 12, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.UUIDField", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FloatField", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FloatField", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateField", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 41, "usage_type": "name"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal", "line_number": 65, "usage_type": "name"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal.objects.get", "line_number": 71, "usage_type": "call"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal", "line_number": 71, "usage_type": "name"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal.ACTIVITY_STATE_CHOICES", "line_number": 83, "usage_type": "attribute"}, {"api_name": "django_server.apps.manage.models.MedOrgPrincipal", "line_number": 83, "usage_type": "name"}]}
+{"seq_id": "223697999", "text": "from django.shortcuts import render,get_object_or_404\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom datetime import date\nfrom .models import Post,Author,Tag,Comment\nfrom django.views.generic import ListView\nfrom django.views.generic import DetailView\nfrom django.views import View\nfrom .forms import CommentForm\nfrom django.urls import reverse\n# Create your views here.\n\n\nall_posts=[\n# {\n# \"slug\": \"hike-in-the-mountains\",\n# \"image\": \"mountains.jpeg\",\n# \"author\": \"Anupam\",\n# \"date\": date.today().strftime(\"%d/%m/%Y\"),\n# \"title\": \"Mountain Hiking\",\n# \"excerpt\": \"There's nothing like the views you get when hiking in the mountains! And I wasn't even prepared for what happened whilst I was enjoying the view!\",\n# \"content\": \"\"\"\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n# \"\"\"\n# },\n# {\n# \"slug\": \"programming-is-fun\",\n# \"image\": \"coding.jpg\",\n# \"author\": \"Anupam\",\n# \"date\": date.today().strftime(\"%d/%m/%Y\"),\n# \"title\": \"Programming Is Great!\",\n# \"excerpt\": \"Did you ever spend hours searching that one error in your code? Yep - that's what happened to me yesterday...\",\n# \"content\": \"\"\"\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n# \"\"\"\n# },\n# {\n# \"slug\": \"into-the-woods\",\n# \"image\": \"woods.jpeg\",\n# \"author\": \"Anupam\",\n# \"date\": date.today().strftime(\"%d/%m/%Y\"),\n# \"title\": \"Nature At Its Best\",\n# \"excerpt\": \"Nature is amazing! The amount of inspiration I get when walking in nature is incredible!\",\n# \"content\": \"\"\"\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n\n# Lorem ipsum dolor sit amet consectetur adipisicing elit. Officiis nobis\n# aperiam est praesentium, quos iste consequuntur omnis exercitationem quam\n# velit labore vero culpa ad mollitia? Quis architecto ipsam nemo. Odio.\n# \"\"\"\n# }\n\n\n\n\n]\n\ndef getdate(post):\n return post.get('date');\n\n\n\n\n# def starting_page(request):\n# sorted_post = sorted(all_posts,key=getdate)\n# latest_post = sorted_post[-3:] #top 3 posts\n# return render(request,\"blog/index.html\", {\n# \"posts\":latest_post\n# })\n\nclass StartingPageView(ListView):\n template_name = \"blog/index.html\"\n context_object_name = \"posts\"\n ordering = [\"-date\"]\n model = Post\n def get_queryset(self):\n data = super().get_queryset()\n data = data[:3]\n return data\n \n\n\n\n# def starting_page(request):\n# latest_post = Post.objects.all().order_by(\"-date\")[:3]\n# return render(request,\"blog/index.html\", {\n# \"posts\":latest_post\n# })\n\n # latest_post = sorted_post[:3]\n\n\n# def posts(request):\n# all_posts = Post.objects.all()\n# return render(request,\"blog/all-posts.html\", {\n# \"all_posts\": all_posts\n# })\n\n\nclass AllPosts(ListView):\n model = Post\n template_name = \"blog/all-posts.html\"\n context_object_name = \"all_posts\"\n ordering = [\"-date\"]\n\n\nclass SinglePostView(View):\n\n def is_stored_post(self,request,post_id):\n stored_posts = request.session.get('stored_posts')\n is_saved_for_later = False\n if stored_posts is not None:\n is_saved_for_later = post_id in stored_posts\n return is_saved_for_later\n\n def get(self , request,slug):\n post = Post.objects.get(slug=slug)\n post_tags = post.tags.all()\n comment_form = CommentForm()\n\n return render(request,\"blog/post-details.html\",{\n \"post\": post,\n \"post_tags\": post_tags,\n \"comment_form\" : comment_form,\n \"comments\": post.comments.all().order_by(\"-id\"),\n \"saved_for_later\": self.is_stored_post(request,post.id)\n })\n\n def post(self, request, slug):\n comment_form = CommentForm(request.POST)\n post = Post.objects.get(slug=slug)\n if comment_form.is_valid():\n comment = comment_form.save(commit=False)\n comment.post = post\n comment_form.save()\n return HttpResponseRedirect(reverse(\"post-details-page\", args=[slug]))\n else:\n post_tags = post.tags.all()\n return render(request,\"blog/post-details.html\",{\n \"post\": post,\n \"post_tags\": post_tags,\n \"comment_form\" : comment_form,\n \"comments\": post.comments.all().order_by(\"-id\"),\n \"saved_for_later\": self.is_stored_post(request,post.id)\n\n })\n\n \n\n\n# class SinglePostView(DetailView):\n# model = Post\n# template_name = \"blog/post-details.html\"\n# def get_context_data(self, **kwargs):\n# context = super().get_context_data(**kwargs)\n# context[\"post_tags\"] = self.object.tags.all()\n# context[\"comment_form\"] = CommentForm()\n# return context\n \n\n\n# def post_details(request,slug):\n# # identified_post = None\n# # identified_post = Post.objects.all().get(slug=slug)\n# identified_post = get_object_or_404(Post,slug=slug)\n# # for post in all_posts:\n# # if post['slug'] == slug:\n# # identified_post = post\n\n# return render(request,\"blog/post-details.html\", {\n# \"post\" : identified_post,\n# \"post_tags\" : identified_post.tags.all()\n# })\n\n\n\nclass ReadLaterView(View):\n\n def post(self,request):\n stored_posts = request.session.get('stored_posts')\n if stored_posts is None:\n stored_posts = []\n post_id = int(request.POST['post_id'])\n if post_id not in stored_posts:\n stored_posts.append(post_id)\n else:\n stored_posts.remove(post_id)\n request.session[\"stored_posts\"] = stored_posts\n return HttpResponseRedirect('/')\n\n def get(self, request):\n stored_posts = request.session.get('stored_posts')\n context = {}\n if stored_posts is None or len(stored_posts)==0:\n context['has_posts'] = False\n context['posts'] = []\n else:\n context['has_posts']=True \n posts = Post.objects.filter(id__in=stored_posts)\n context['posts'] = posts\n return render(request,\"blog/stored-posts.html\",context)\n \n\n", "sub_path": "django_project/my_site_after_all_changes/blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.views.generic.ListView", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Post", "line_number": 100, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Post", "line_number": 126, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 132, "usage_type": "name"}, {"api_name": "models.Post.objects.get", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 142, "usage_type": "name"}, {"api_name": "forms.CommentForm", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 146, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 155, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 156, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 156, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 161, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 161, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 202, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 214, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 224, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 224, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 224, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 226, "usage_type": "call"}]}
+{"seq_id": "639622019", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: templateaddons/../templateaddons/tests.py\n# Compiled at: 2016-10-21 19:41:55\nfrom django.template import Template, Context\nfrom django.test import TestCase\nfrom django.utils.html import strip_spaces_between_tags\n\nclass TemplateTagTestCase(TestCase):\n \"\"\"\n Base class to test template tags.\n \"\"\"\n\n def validate_template_code_result(self, fixtures):\n \"\"\"\n Validates that the template code in given fixtures match the\n corresponding expected output.\n\n The given 'fixtures' argument is an iterable of 2-items lists matching\n the following scheme::\n\n (\n (template_code_1, expected_output_1),\n (template_code_2, expected_output_2),\n ...\n )\n \"\"\"\n for template_code, valid_output in fixtures:\n t = Template(template_code)\n c = Context()\n output = t.render(c)\n self.assertEquals(output, valid_output)\n\n\nclass AssignTemplateTagTestCase(TemplateTagTestCase):\n \"\"\"Tests the {% assign %} template tag\"\"\"\n\n def test_output(self):\n fixtures = [\n ('{% assign %}1234{% endassign %}', ''),\n ('{% assign name=\"sample\" %}1234{% endassign %}5678{{ sample }}', '56781234'),\n ('{% assign name=\"sample\" %}1234{% endassign %}{% assign name=\"sample\" %}5678{% endassign %}{{ sample }}',\n '5678'),\n ('{% assign silent=1 %}1234{% endassign %}', ''),\n ('{% assign silent=0 %}1234{% endassign %}', '1234')]\n fixtures = [ ('{% load assign %}' + template_code, valid_output) for template_code, valid_output in fixtures ]\n self.validate_template_code_result(fixtures)\n\n\nclass CounterTemplateTagTestCase(TemplateTagTestCase):\n \"\"\"Tests the {% counter %} template tag\"\"\"\n\n def test_output(self):\n fixtures = [\n ('{% counter %}', '0'),\n ('{% counter %}{% counter %}', '01'),\n ('{% counter %}{% counter %}{% counter %}', '012'),\n ('{% counter %}{% counter name=\"c2\" %}{% counter %}{% counter %}', '0012'),\n ('{% counter name=\"c2\" %}{% counter %}{% counter name=\"c2\" %}{% counter name=\"c2\" %}',\n '0012'),\n ('{% counter name=\"c1\" %}{% counter name=\"c2\" %}{% counter name=\"c1\" %}{% counter name=\"c1\" %}{% counter name=\"c2\" %}',\n '00121'),\n ('{% counter start=1 %}{% counter %}', '12'),\n ('{% counter step=4 %}{% counter %}{% counter %}', '048'),\n ('{% counter step=-4 %}{% counter %}{% counter %}', '0-4-8'),\n ('{% counter ascending=1 %}{% counter %}{% counter %}', '012'),\n ('{% counter ascending=0 %}{% counter %}{% counter %}', '0-1-2'),\n ('{% counter ascending=0 step=-1 %}{% counter %}{% counter %}', '012'),\n ('{% counter silent=1 %}{% counter %}{% counter %}', '12'),\n ('{% counter %}{% counter silent=1 %}{% counter %}', '02'),\n ('{% counter silent=1 %}{% counter silent=1 %}{% counter %}', '2'),\n ('{% counter assign=\"c1\" %}{{ c1 }}{% counter %}{% counter assign=\"c1\" %}{{ c1 }}{% counter %}{% counter assign=\"c2\" %}{% counter %}{{ c1 }}{{ c2 }}',\n '0012234524'),\n ('{% counter start=4 step=4 ascending=0 %}{% counter start=8 step=23 ascending=1 %}{% counter %}',\n '40-4')]\n fixtures = [ ('{% load counter %}' + template_code, valid_output) for template_code, valid_output in fixtures ]\n self.validate_template_code_result(fixtures)\n\n\nclass HeadingContextTemplateTagTestCase(TemplateTagTestCase):\n \"\"\"Tests the {% headingcontext %} template tag\"\"\"\n\n def test_output(self):\n fixtures = [\n ('{% headingcontext %}<h1>Test</h1>{% endheadingcontext %}', '<h2>Test</h2>'),\n ('{% headingcontext %}<H1>Test</H1>{% endheadingcontext %}', '<h2>Test</h2>'),\n ('{% headingcontext %}<h1 class=\"test\">Test</h1>{% endheadingcontext %}', '<h2 class=\"test\">Test</h2>'),\n ('{% headingcontext %}<h1>Test</h1>{% endheadingcontext %}', '<h2>Test</h2>'),\n ('{% headingcontext %}<h2>Test</h2>{% endheadingcontext %}', '<h3>Test</h3>'),\n ('{% headingcontext source_level=2 %}<h2>Test</h2>{% endheadingcontext %}', '<h2>Test</h2>'),\n ('{% headingcontext source_level=5 %}<h5>Test</h5>{% endheadingcontext %}', '<h2>Test</h2>'),\n ('{% headingcontext source_level=2 target_level=4 %}<h2>Test</h2>{% endheadingcontext %}',\n '<h4>Test</h4>'),\n ('{% headingcontext source_level=5 target_level=4 %}<h5>Test</h5>{% endheadingcontext %}',\n '<h4>Test</h4>')]\n fixtures = [ ('{% load heading %}' + template_code, valid_output) for template_code, valid_output in fixtures ]\n self.validate_template_code_result(fixtures)\n\n\nclass JavascriptTemplateTagTestCase(TemplateTagTestCase):\n \"\"\"Tests the {% counter %} template tag\"\"\"\n\n def test_output(self):\n fixtures = [\n ('{% javascript_render %}', '')]\n fixtures = [ ('{% load javascript %}' + template_code, valid_output) for template_code, valid_output in fixtures ]\n self.validate_template_code_result(fixtures)\n\n\nclass ReplaceTemplateTagTestCase(TemplateTagTestCase):\n \"\"\"Tests the {% replace %} template tag\"\"\"\n\n def test_output(self):\n fixtures = [\n ('{% replace %}{% endreplace %}', ''),\n ('{% replace search=\"\" replacement=\"\" %}{% endreplace %}', ''),\n ('{% replace search=\"\" replacement=\"\" %}toto{% endreplace %}', 'toto'),\n ('{% replace search=\"\" replacement=\"aa\" %}toto{% endreplace %}', 'toto'),\n ('{% replace search=\"t\" replacement=\"m\" %}toto{% endreplace %}', 'momo'),\n ('{% replace search=\"t\" replacement=\"\" %}toto{% endreplace %}', 'oo'),\n ('{% replace search=\"to\" replacement=\"ma\" %}toto{% endreplace %}', 'mama'),\n ('{% replace search=\"toto\" replacement=\"a\" %}toto{% endreplace %}', 'a'),\n ('{% replace search=\" \" replacement=\"-\" %}t o t o{% endreplace %}', 't-o-t-o'),\n ('{% replace search=\"\\\\n\" replacement=\"\" %}t\\noto{% endreplace %}', 'toto'),\n ('{% replace search=\"[a-z]+\" replacement=\"\" %}Toto{% endreplace %}', 'T'),\n ('{% replace search=\"^.\" replacement=\"A\" %}toto{% endreplace %}', 'Aoto'),\n ('{% replace search=\"to$\" replacement=\"Z\" %}toto{% endreplace %}', 'toZ'),\n ('{% replace search=\"\\\\s+\" replacement=\"-\" %}to\\t \\n\\n \\tto{% endreplace %}', 'to-to'),\n ('{% replace search=\"(to)\" replacement=\"\\\\1a\" %}toto{% endreplace %}', 'toatoa'),\n ('{% replace search=\"([a-z]+)\" replacement=\"*\\\\1*\" %}123abc456def{% endreplace %}',\n '123*abc*456*def*'),\n ('{% replace search=\"(to)\" replacement=\"au\" %}(to)to{% endreplace %}', '(au)au'),\n ('{% replace search=\"(to)\" replacement=\"au\" use_regexp=0 %}(to)to{% endreplace %}',\n 'auto'),\n ('{% filter escape_regexp %}(to){% endfilter %}', '\\\\(to\\\\)')]\n fixtures = [ ('{% load replace %}' + template_code, valid_output) for template_code, valid_output in fixtures ]\n self.validate_template_code_result(fixtures)", "sub_path": "pycfiles/django_templateaddons3-1.0-py2.py3-none-any/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 7109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.test.TestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "django.template.Template", "line_number": 31, "usage_type": "call"}, {"api_name": "django.template.Context", "line_number": 32, "usage_type": "call"}]}
+{"seq_id": "455939930", "text": "from tkinter import *\r\nimport pandas as pd\r\nfrom PIL import ImageTk, Image\r\nfrom tkinter.font import Font\r\nimport mysql.connector \r\n\r\ndef guest(root) :\r\n\troot.destroy()\r\n#\tfrom frontpage import frontpage\r\n\t\r\n\tmydb = mysql.connector.connect(host=\"localhost\",user=\"root\",database = \"josaa\")\r\n\tmycursor = mydb.cursor()\r\n\tmycursor.execute(\"select * from college\")\r\n\tres = mycursor.fetchall()\r\n\troot = Tk()\r\n\troot.title(\"Guest Login\")\r\n\troot.geometry(\"1920x1080\")\r\n\tfont1 = Font(family = \"Times New Roman\",size=20, weight=\"bold\",underline=1)\r\n\thor_frame = Frame(root, height = 15, width = 1700, bg = \"blue\").place(x=0,y=0)\r\n\t\r\n\timg = ImageTk.PhotoImage(Image.open(\"ashok.jpg\"))\r\n\timg1 = ImageTk.PhotoImage(Image.open(\"josaa-logo.png\"))\r\n\tpanel = Label(root, image = img)\r\n\tpanel1 = Label(root, image = img1)\r\n\tpanel.place(x = 150, y = 25)\r\n\tpanel1.place(x = 1200, y = 25)\r\n\tlabel_1 = Label(root, text=\"Government of India\",font=(\"bold\", 10)).place(x=240,y=80)\r\n\tlabel_2 = Label(root, text=\"Ministry Of Human Resource Development\",font=(\"bold\", 12)).place(x=240,y=100)\r\n\tlabel_3 = Label(root, text=\"Joint Entrance Examination (MAINS)\", font=(\"bold\",30), fg= \"blue\").place(x = 480,y = 45)\r\n\t\r\n\tf3 = Frame(root, height = 5, width = 1700, bg = \"grey\").place(x=0,y=175)\r\n\tf1 = Frame(root, height = 18, width = 1700, bg = \"red\").place(x=0,y=180)\r\n#\tButton(root, text='Home',width=18,bg='red',fg='white',command = frontpage.frontpage()).place(x=600,y=180)\r\n\tf2 = Frame(root, height = 5, width = 1700, bg = \"grey\").place(x=0,y=198)\r\n\tf = Frame(root)\r\n\tf.place(relx = 0.5,rely=0.6,anchor = CENTER)\r\n\tname = Label(f, text=\"S.No\",font=(\"bold\", 14)).grid(row=0,column=0)\r\n\tname = Label(f, text=\"College\",font=(\"bold\", 14)).grid(row=0,column=1)\r\n\tname = Label(f, text=\"branch\",font=(\"bold\", 14)).grid(row=0,column=2)\r\n\tname = Label(f, text=\"seats\",font=(\"bold\", 14)).grid(row=0,column=3)\r\n\tfor i in range (0,15):\r\n\t\tfor j in range (0,4):\r\n\t\t\tLabel(f,text=res[i][j],font=(\"bold\", 15)).grid(row=i+1,column=j)\r\n\r\n\troot.mainloop()\r\n\r\n\r\n\r\n\r\n", "sub_path": "guest.py", "file_name": "guest.py", "file_ext": "py", "file_size_in_byte": 2019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 11, "usage_type": "name"}, {"api_name": "tkinter.font.Font", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 21, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 22, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}]}
+{"seq_id": "428124589", "text": "from datetime import datetime, timedelta\nfrom corehq.apps.locations.dbaccessors import get_users_by_location_id\n\nfrom corehq.apps.sms.api import send_sms_to_verified_number\nfrom corehq.util.translation import localize\nfrom dimagi.utils.dates import get_business_day_of_month_before\nfrom corehq.apps.locations.models import SQLLocation\nfrom custom.ilsgateway.tanzania.handlers.keyword import KeywordHandler\nfrom custom.ilsgateway.models import SupplyPointStatus, SupplyPointStatusTypes, SupplyPointStatusValues\nfrom custom.ilsgateway.tanzania.reminders import TEST_HANDLER_HELP, TEST_HANDLER_BAD_CODE, SOH_HELP_MESSAGE, \\\n SUPERVISION_REMINDER, SUBMITTED_REMINDER_DISTRICT, SUBMITTED_REMINDER_FACILITY, \\\n DELIVERY_REMINDER_FACILITY, DELIVERY_REMINDER_DISTRICT, DELIVERY_LATE_DISTRICT, TEST_HANDLER_CONFIRM, \\\n REMINDER_MONTHLY_RANDR_SUMMARY, reports, REMINDER_MONTHLY_SOH_SUMMARY, REMINDER_MONTHLY_DELIVERY_SUMMARY, \\\n SOH_THANK_YOU, LOSS_ADJUST_HELP\nfrom custom.ilsgateway.utils import send_translated_message\n\n\nclass MessageInitiatior(KeywordHandler):\n\n def help(self):\n self.respond(TEST_HANDLER_HELP)\n return True\n\n def get_district_by_name(self, name):\n try:\n return SQLLocation.objects.get(domain=self.domain, name=name)\n except SQLLocation.DoesNotExist:\n return None\n\n def send_message(self, sql_location, message, **kwargs):\n for user in get_users_by_location_id(self.domain, sql_location.location_id):\n send_translated_message(user, message, **kwargs)\n\n def handle(self):\n if len(self.args) < 2:\n return self.help()\n\n command = self.args[0]\n rest = \" \".join(self.args[1:])\n msd_code = self.args[1].lower()\n fw_message = \" \".join(self.args[2:])\n\n try:\n sql_location = SQLLocation.objects.get(domain=self.domain, site_code__iexact=msd_code)\n except SQLLocation.DoesNotExist:\n sql_location = self.get_district_by_name(rest)\n\n if not sql_location:\n self.respond(TEST_HANDLER_BAD_CODE, code=msd_code)\n return True\n\n if command in ['soh', 'hmk']:\n self.send_message(sql_location, SOH_HELP_MESSAGE)\n now = datetime.utcnow()\n SupplyPointStatus.objects.create(location_id=sql_location.location_id,\n status_type=SupplyPointStatusTypes.SOH_FACILITY,\n status_value=SupplyPointStatusValues.REMINDER_SENT,\n status_date=now)\n elif command in ['la']:\n self.send_message(sql_location, LOSS_ADJUST_HELP)\n now = datetime.utcnow()\n SupplyPointStatus.objects.create(location_id=sql_location.location_id,\n status_type=SupplyPointStatusTypes.LOSS_ADJUSTMENT_FACILITY,\n status_value=SupplyPointStatusValues.REMINDER_SENT,\n status_date=now)\n elif command in ['supervision']:\n self.send_message(sql_location, SUPERVISION_REMINDER)\n now = datetime.utcnow()\n SupplyPointStatus.objects.create(location_id=sql_location.location_id,\n status_type=SupplyPointStatusTypes.SUPERVISION_FACILITY,\n status_value=SupplyPointStatusValues.REMINDER_SENT,\n status_date=now)\n elif command in ['randr']:\n if sql_location.location_type.name == 'DISTRICT':\n self.send_message(sql_location, SUBMITTED_REMINDER_DISTRICT)\n status_type = SupplyPointStatusTypes.R_AND_R_DISTRICT\n else:\n self.send_message(sql_location, SUBMITTED_REMINDER_FACILITY)\n status_type = SupplyPointStatusTypes.R_AND_R_FACILITY\n now = datetime.utcnow()\n SupplyPointStatus.objects.create(location_id=sql_location.location_id,\n status_type=status_type,\n status_value=SupplyPointStatusValues.REMINDER_SENT,\n status_date=now)\n elif command in ['delivery']:\n if sql_location.location_type.name == 'DISTRICT':\n self.send_message(sql_location, DELIVERY_REMINDER_DISTRICT)\n status_type = SupplyPointStatusTypes.DELIVERY_DISTRICT\n else:\n self.send_message(sql_location, DELIVERY_REMINDER_FACILITY)\n status_type = SupplyPointStatusTypes.DELIVERY_FACILITY\n now = datetime.utcnow()\n SupplyPointStatus.objects.create(location_id=sql_location.location_id,\n status_type=status_type,\n status_value=SupplyPointStatusValues.REMINDER_SENT,\n status_date=now)\n elif command in ['fw']:\n if fw_message:\n self.send_message(sql_location, fw_message)\n elif command in [\"latedelivery\"]:\n self.send_message(sql_location, DELIVERY_LATE_DISTRICT, group_name=\"changeme\", group_total=1,\n not_responded_count=2, not_received_count=3)\n elif command in [\"randr_report\"]:\n now = datetime.utcnow()\n self.respond(REMINDER_MONTHLY_RANDR_SUMMARY, **reports.construct_summary(\n sql_location.couch_location,\n SupplyPointStatusTypes.R_AND_R_FACILITY,\n [SupplyPointStatusValues.SUBMITTED, SupplyPointStatusValues.NOT_SUBMITTED],\n get_business_day_of_month_before(now.year, now.month, 5)\n ))\n elif command in [\"soh_report\"]:\n now = datetime.utcnow()\n last_month = datetime(now.year, now.month, 1) - timedelta(days=1)\n self.respond(\n REMINDER_MONTHLY_SOH_SUMMARY,\n **reports.construct_summary(\n sql_location.couch_location,\n SupplyPointStatusTypes.SOH_FACILITY,\n [SupplyPointStatusValues.SUBMITTED],\n get_business_day_of_month_before(last_month.year, last_month.month, -1)\n )\n )\n elif command in [\"delivery_report\"]:\n now = datetime.utcnow()\n self.respond(REMINDER_MONTHLY_DELIVERY_SUMMARY,\n **reports.construct_summary(sql_location.couch_location,\n SupplyPointStatusTypes.DELIVERY_FACILITY,\n [SupplyPointStatusValues.RECEIVED,\n SupplyPointStatusValues.NOT_RECEIVED],\n get_business_day_of_month_before(now.year, now.month, 15)))\n elif command in [\"soh_thank_you\"]:\n self.send_message(sql_location, SOH_THANK_YOU)\n\n self.respond(TEST_HANDLER_CONFIRM)\n return True\n", "sub_path": "custom/ilsgateway/tanzania/handlers/messageinitiator.py", "file_name": "messageinitiator.py", "file_ext": "py", "file_size_in_byte": 7199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "custom.ilsgateway.tanzania.handlers.keyword.KeywordHandler", "line_number": 18, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.TEST_HANDLER_HELP", "line_number": 21, "usage_type": "argument"}, {"api_name": "corehq.apps.locations.models.SQLLocation.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "corehq.apps.locations.models.SQLLocation.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "corehq.apps.locations.models.SQLLocation", "line_number": 26, "usage_type": "name"}, {"api_name": "corehq.apps.locations.models.SQLLocation.DoesNotExist", "line_number": 27, "usage_type": "attribute"}, {"api_name": "corehq.apps.locations.models.SQLLocation", "line_number": 27, "usage_type": "name"}, {"api_name": "corehq.apps.locations.dbaccessors.get_users_by_location_id", "line_number": 31, "usage_type": "call"}, {"api_name": "custom.ilsgateway.utils.send_translated_message", "line_number": 32, "usage_type": "call"}, {"api_name": "corehq.apps.locations.models.SQLLocation.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "corehq.apps.locations.models.SQLLocation.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "corehq.apps.locations.models.SQLLocation", "line_number": 44, "usage_type": "name"}, {"api_name": "corehq.apps.locations.models.SQLLocation.DoesNotExist", "line_number": 45, "usage_type": "attribute"}, {"api_name": "corehq.apps.locations.models.SQLLocation", "line_number": 45, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.TEST_HANDLER_BAD_CODE", "line_number": 49, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.tanzania.reminders.SOH_HELP_MESSAGE", "line_number": 53, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects.create", "line_number": 55, "usage_type": "call"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus", "line_number": 55, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.SOH_FACILITY", "line_number": 56, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 56, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.REMINDER_SENT", "line_number": 57, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 57, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.LOSS_ADJUST_HELP", "line_number": 60, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects.create", "line_number": 62, "usage_type": "call"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus", "line_number": 62, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.LOSS_ADJUSTMENT_FACILITY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 63, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.REMINDER_SENT", "line_number": 64, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 64, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.SUPERVISION_REMINDER", "line_number": 67, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects.create", "line_number": 69, "usage_type": "call"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus", "line_number": 69, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.SUPERVISION_FACILITY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 70, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.REMINDER_SENT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 71, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.SUBMITTED_REMINDER_DISTRICT", "line_number": 75, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.R_AND_R_DISTRICT", "line_number": 76, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 76, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.SUBMITTED_REMINDER_FACILITY", "line_number": 78, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.R_AND_R_FACILITY", "line_number": 79, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 79, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects.create", "line_number": 81, "usage_type": "call"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus", "line_number": 81, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.REMINDER_SENT", "line_number": 83, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 83, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.DELIVERY_REMINDER_DISTRICT", "line_number": 87, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.DELIVERY_DISTRICT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 88, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.DELIVERY_REMINDER_FACILITY", "line_number": 90, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.DELIVERY_FACILITY", "line_number": 91, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 91, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects.create", "line_number": 93, "usage_type": "call"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatus", "line_number": 93, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.REMINDER_SENT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 95, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.DELIVERY_LATE_DISTRICT", "line_number": 101, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.REMINDER_MONTHLY_RANDR_SUMMARY", "line_number": 105, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports.construct_summary", "line_number": 105, "usage_type": "call"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports", "line_number": 105, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.R_AND_R_FACILITY", "line_number": 107, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 107, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.SUBMITTED", "line_number": 108, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 108, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.NOT_SUBMITTED", "line_number": 108, "usage_type": "attribute"}, {"api_name": "dimagi.utils.dates.get_business_day_of_month_before", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 113, "usage_type": "call"}, {"api_name": "custom.ilsgateway.tanzania.reminders.REMINDER_MONTHLY_SOH_SUMMARY", "line_number": 115, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports.construct_summary", "line_number": 116, "usage_type": "call"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports", "line_number": 116, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.SOH_FACILITY", "line_number": 118, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 118, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.SUBMITTED", "line_number": 119, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 119, "usage_type": "name"}, {"api_name": "dimagi.utils.dates.get_business_day_of_month_before", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 124, "usage_type": "name"}, {"api_name": "custom.ilsgateway.tanzania.reminders.REMINDER_MONTHLY_DELIVERY_SUMMARY", "line_number": 125, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports.construct_summary", "line_number": 126, "usage_type": "call"}, {"api_name": "custom.ilsgateway.tanzania.reminders.reports", "line_number": 126, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes.DELIVERY_FACILITY", "line_number": 127, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusTypes", "line_number": 127, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.RECEIVED", "line_number": 128, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 128, "usage_type": "name"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues.NOT_RECEIVED", "line_number": 129, "usage_type": "attribute"}, {"api_name": "custom.ilsgateway.models.SupplyPointStatusValues", "line_number": 129, "usage_type": "name"}, {"api_name": "dimagi.utils.dates.get_business_day_of_month_before", "line_number": 130, "usage_type": "call"}, {"api_name": "custom.ilsgateway.tanzania.reminders.SOH_THANK_YOU", "line_number": 132, "usage_type": "argument"}, {"api_name": "custom.ilsgateway.tanzania.reminders.TEST_HANDLER_CONFIRM", "line_number": 134, "usage_type": "argument"}]}
+{"seq_id": "339502532", "text": "#!/usr/bin/python\n\n# Copyright 2009 bjweeks, MZMcBride\n\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\n\nimport ConfigParser\nimport datetime\nimport MySQLdb\nimport os\nimport wikitools\n\nconfig = ConfigParser.ConfigParser()\nconfig.read([os.path.expanduser('~/.dbreps.ini')])\n\nreport_title = config.get('dbreps', 'rootpage') + 'Most-watched pages'\n\nreport_template = u'''\nMost-watched non-deleted pages (limited to the first 1000 entries); data as of <onlyinclude>%s</onlyinclude>.\n\n{| class=\"wikitable sortable\" style=\"width:100%%; margin:auto;\"\n|- style=\"white-space:nowrap;\"\n! No.\n! [[Wikipedia:Namespace|ID]]\n! Page\n! Watchers\n|-\n%s\n|}\n'''\n\nwiki = wikitools.Wiki(config.get('dbreps', 'apiurl'))\nwiki.login(config.get('dbreps', 'username'), config.get('dbreps', 'password'))\n\ndef namespace_names(cursor, dbname):\n nsdict = {}\n cursor.execute('''\n /* mostwatched.py namespace_names */\n SELECT\n ns_id,\n ns_name\n FROM namespace\n WHERE dbname = %s\n AND ns_id >= 0\n ORDER BY ns_id ASC;\n ''', config.get('dbreps', 'dbname'))\n for row in cursor.fetchall():\n ns_id = str(row[0])\n ns_name = str(row[1])\n nsdict[ns_id] = ns_name\n return nsdict\n\nconn = MySQLdb.connect(host='sql-s3', db='toolserver', read_default_file='~/.my.cnf')\ncursor = conn.cursor()\nnsdict = namespace_names(cursor, config.get('dbreps', 'dbname'))\ncursor.close()\nconn.close()\n\nconn = MySQLdb.connect(host=config.get('dbreps', 'host'), db=config.get('dbreps', 'dbname'), read_default_file='~/.my.cnf')\ncursor = conn.cursor()\ncursor.execute('''\n/* mostwatched.py SLOW_OK */\nSELECT\n wl_namespace,\n wl_title,\n COUNT(*)\nFROM watchlist\nJOIN page\nON wl_namespace = page_namespace\nAND wl_title = page_title\nWHERE wl_namespace mod 2 = 0\nAND wl_namespace >= 0\nGROUP BY wl_namespace, wl_title\nORDER BY COUNT(*) DESC, wl_title ASC\nLIMIT 1000;\n''')\n\ni = 1\noutput = []\nfor row in cursor.fetchall():\n ns_id = row[0]\n page_namespace = str(row[0])\n page_title = u'%s' % unicode(row[1], 'utf-8')\n if ns_id == 6 or ns_id == 14:\n page_title = '[[:%s:%s]]' % (nsdict[page_namespace], page_title)\n elif ns_id == 0:\n page_title = '[[%s]]' % (page_title)\n else:\n page_title = '[[%s:%s]]' % (nsdict[page_namespace], page_title)\n watchers = row[2]\n table_row = u'''| %d\n| %s\n| %s\n| %s\n|-''' % (i, page_namespace, page_title, watchers)\n output.append(table_row)\n i += 1\n\ncursor.execute('SELECT UNIX_TIMESTAMP() - UNIX_TIMESTAMP(rc_timestamp) FROM recentchanges ORDER BY rc_timestamp DESC LIMIT 1;')\nrep_lag = cursor.fetchone()[0]\ncurrent_of = (datetime.datetime.utcnow() - datetime.timedelta(seconds=rep_lag)).strftime('%H:%M, %d %B %Y (UTC)')\n\nreport = wikitools.Page(wiki, report_title)\nreport_text = report_template % (current_of, '\\n'.join(output))\nreport_text = report_text.encode('utf-8')\nreport.edit(report_text, summary=config.get('dbreps', 'editsumm'), bot=1)\n\ncursor.close()\nconn.close()\n", "sub_path": "general/mostwatched.py", "file_name": "mostwatched.py", "file_ext": "py", "file_size_in_byte": 3533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "ConfigParser.ConfigParser", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "wikitools.Wiki", "line_number": 43, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 64, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 112, "usage_type": "call"}, {"api_name": "wikitools.Page", "line_number": 114, "usage_type": "call"}]}
+{"seq_id": "234792120", "text": "from sys import argv\nimport csv\nimport platform\nimport os.path\nfrom os import path\nimport argparse\nfrom my_module import write_list_to_file\n\nlst = []\n\n\ndef write_filenames_to_file(folderpath):\n entries = os.listdir(folderpath)\n write_list_to_file('test.csv', entries)\n\n\ndef write_filenames_to_file_recursive(folderpath):\n root = folderpath\n for root, dirs, files in os.walk(\".\", topdown=False):\n for name in files:\n lst.append(os.path.join(root, name))\n for name in dirs:\n lst.append(os.path.join(root, name))\n # entries = os.listdir(folderpath)\n # for entry in entries:\n # if os.path.isdir(entry):\n # write_filenames_to_file_recursive(entry)\n # else:\n # lst.append(entry)\n write_list_to_file('test.csv', lst)\n\n\ndef read_first_line_from_files(*files):\n for file in files:\n for ele in file:\n with open(ele) as f_obj:\n content = f_obj.readline()\n print(content.rstrip())\n\n\ndef print_lines_with_email(*files):\n for file in files:\n for ele in file:\n with open(ele) as f_obj:\n content = f_obj.readlines()\n for line in content:\n if '@' in line:\n print(line.rstrip())\n\n\ndef print_lines_with_markdown(*files):\n for file in files:\n for ele in file:\n with open(ele) as f_obj:\n content = f_obj.readlines()\n for line in content:\n if '#' in line:\n lst.append(line)\n write_list_to_file('test.csv', lst)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='My Utils module')\n parser.add_argument(\"--write\", help='Function takes a path to a folder and writes all filenames in the folder to a specified output file')\n parser.add_argument(\"--write_recursive\", help='Function takes a path to a folder and write all filenames recursively (files of all sub folders to)')\n parser.add_argument(\"--read_first_line\", nargs='*', help='Function takes a list of filenames and print the first line of each')\n parser.add_argument(\"--print_emails\", nargs='*', help='Function takes a list of filenames and print each line that contains an email (just look for @)')\n parser.add_argument(\"--write_markdowns\", nargs='*', help='Function takes a list of md files and writes all headlines (lines starting with #) to a file')\n\n args = parser.parse_args()\n \n if args.write:\n write_filenames_to_file(argv[2])\n if args.write_recursive:\n write_filenames_to_file_recursive(argv[2])\n if args.read_first_line:\n read_first_line_from_files(argv[2:])\n if args.print_emails:\n print_lines_with_email(argv[2:])\n if args.write_markdowns:\n print_lines_with_markdown(argv[2:])\n", "sub_path": "uge2/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "my_module.write_list_to_file", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.walk", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "my_module.write_list_to_file", "line_number": 30, "usage_type": "call"}, {"api_name": "my_module.write_list_to_file", "line_number": 59, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 73, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 75, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 77, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 79, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "name"}]}
+{"seq_id": "31049588", "text": "\nfrom tweepy.streaming import StreamListener\nfrom tweepy import OAuthHandler, Stream, API, Cursor\n\nimport app_credentials\nimport os\nimport re\nimport sys\nimport threading, time\nimport boto3\nfrom tqdm import tqdm\n\nclass StdOutListener(StreamListener):\n\n\tdef __init__(self, path, filename, time_limit):\n\t\tsuper(StdOutListener, self).__init__()\n\t\tself.start_time = time.time()\n\t\tself.limit = time_limit\n\t\tself.path = path\n\t\tself.filename = filename\n\t\tself.id = 0\n\t\n\tdef on_status(self, data):\n\t\tif (time.time() - self.start_time) < self.limit:\n\t\t\tif self.id == 0:\n\t\t\t\twith open(self.path+self.filename, 'w', encoding=\"utf-16\") as f:\n\t\t\t\t\tf.write(\"id,comment,creation_time,source,tweet_id,user,user_id\\n\")\n\t\t\t\t\tf.write(\"%s,%s,%s,%s,%s,%s,%s\\n\" % (self.id, re.sub(r'[,\\n]',' ', data.text), data.created_at, data.source.replace(',', ' '), data.id_str, data.user.name.replace(',', ' '), data.user.id_str))\n\t\t\telse:\n\t\t\t\twith open(self.path+self.filename, 'a', encoding=\"utf-16\") as f:\n\t\t\t\t\tf.write(\"%s,%s,%s,%s,%s,%s,%s\\n\" % (self.id, re.sub(r'[,\\n]',' ', data.text), data.created_at, data.source.replace(',', ' '), data.id_str, data.user.name.replace(',', ' '), data.user.id_str))\n\n\t\t\tself.id = self.id + 1\n\t\t\t#print(data.text)\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef on_error(self, status):\n\t\tif status == 401:\n\t\t\tprint(status)\n\t\t\treturn False\n\n\t\tprint(status)\n\t\treturn False\n\ndef background(stream):\n\tstream.filter(track = ['George Floyd', 'Donald Trump', 'BlackLivesMatters', 'Bryanna Taylor', 'Police'], languages = ['en'], is_async=True)\n\ndef wait(minutes):\n\tfor i in tqdm(range(60*minutes)):#five minutes\n\t\ttime.sleep(1) #update each second\n\n\nif __name__ == '__main__':\n\tMINUTES = 30\n\tFILE_PATH = './'\n\tFILE_NAME = 'comments.csv'\n\n\tlistener = StdOutListener(FILE_PATH, FILE_NAME,60*MINUTES)\n\t#listener = StdOutListener()\n\tauth = OAuthHandler(app_credentials.CONSUMER_KEY, app_credentials.CONSUMER_SECRET)\n\tauth.set_access_token(app_credentials.ACCESS_TOKEN, app_credentials.ACCESS_TOKEN_SECRET)\n\tapi = API(auth)\n\t\n\tstream = Stream(api.auth, listener)\n\n\ttry:\n\t\tbackground(stream)\n\t\t#tweet_capturer = threading.Thread(name = 'background', target = background, args=[stream])\n\t\t#tweet_capturer.start()\n\t\twait(MINUTES)\n\texcept:\n\t\tstream.disconnect()\n\n\t#after creation of file publish to s3\n\tprint(\"Publicando a bucket\")\n\tS3_BUCKET = \"cmurill5tmp\"\n\ts3_client = boto3.client('s3')\n\n\ts3_client.upload_file(FILE_PATH+FILE_NAME,S3_BUCKET,FILE_NAME)\n\n\n\t#finaliza ejecucion\n\tprint(\"Ejecución terminada\")\n\n\n\n\t\n\n\n", "sub_path": "tweeter_streamer.py", "file_name": "tweeter_streamer.py", "file_ext": "py", "file_size_in_byte": 2505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tweepy.streaming.StreamListener", "line_number": 13, "usage_type": "name"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 28, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 62, "usage_type": "call"}, {"api_name": "app_credentials.CONSUMER_KEY", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app_credentials.CONSUMER_SECRET", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app_credentials.ACCESS_TOKEN", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app_credentials.ACCESS_TOKEN_SECRET", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 64, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 66, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 79, "usage_type": "call"}]}
+{"seq_id": "479819837", "text": "from datetime import datetime\n\nfrom flask import session\n\nfrom . import document\nfrom .. import models\nfrom ..utils import log\n\ndb = models.db\nDocument = document.Document\nLog = log.Log\n\nclass Userdocument():\n\n @staticmethod\n def get_contributor_ids(doc_id):\n return [d.user_id for d in models.Userdocument.query \\\n .filter_by(document_id=doc_id).all()]\n\n @staticmethod\n def get_editable(id, user_id):\n return models.Userdocument.query.filter_by(document_id=id, \n user_id=user_id).first() or session['user_admin']\n\n @staticmethod\n def add(user_id, document_id):\n try:\n if Document.get_editable(document_id):\n userdocument = models.Userdocument.query.filter_by(\n document_id=document_id, user_id=user_id).first()\n if not userdocument:\n userdocument = models.Userdocument(user_id=user_id, \n document_id=document_id)\n db.session.add(userdocument)\n db.session.commit()\n data = {'status':'ok', \n 'owner': int(user_id)==int(session['user_id'])}\n else:\n data = {'status':'error'}\n\n except Exception:\n Log.e()\n return {'status':'error', 'message': 'Error adding the contributor.'}\n return data\n\n @staticmethod\n def delete(user_id, document_id):\n try:\n userdocument = Userdocument.get_editable(document_id, user_id)\n userdocument_count = models.Userdocument \\\n .query.filter_by(document_id=document_id).count()\n if userdocument and userdocument_count > 1:\n db.session.delete(userdocument)\n db.session.commit()\n data = {'status':'ok'}\n else:\n data = {'status':'error'}\n\n except Exception:\n Log.e()\n return {'status':'error', 'message': 'Error removing the contributor.'}\n return data", "sub_path": "sematia/controllers/userdocument.py", "file_name": "userdocument.py", "file_ext": "py", "file_size_in_byte": 2097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "utils.log.Log", "line_number": 11, "usage_type": "attribute"}, {"api_name": "utils.log", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 37, "usage_type": "name"}]}
+{"seq_id": "52705878", "text": "#!/usr/bin/python\n#\n# Copyright (c) 2018 Zim Kalinowski, <zikalino@microsoft.com>\n#\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n 'status': ['preview'],\n 'supported_by': 'community'}\n\n\nDOCUMENTATION = '''\n---\nmodule: azure_rm_webappservicecertificateorder\nversion_added: \"2.8\"\nshort_description: Manage Azure App Service Certificate Order instance.\ndescription:\n - Create, update and delete instance of Azure App Service Certificate Order.\n\noptions:\n resource_group:\n description:\n - Name of the resource group to which the resource belongs.\n required: True\n certificate_order_name:\n description:\n - Name of the certificate order.\n required: True\n kind:\n description:\n - Kind of resource.\n location:\n description:\n - Resource Location.\n - Required when C(state) is I(present).\n certificates:\n description:\n - State of the Key Vault secret.\n distinguished_name:\n description:\n - Certificate distinguished name.\n validity_in_years:\n description:\n - Duration in years (must be between 1 and 3).\n key_size:\n description:\n - Certificate key size.\n product_type:\n description:\n - Certificate product type.\n choices:\n - 'standard_domain_validated_ssl'\n - 'standard_domain_validated_wild_card_ssl'\n auto_renew:\n description:\n - <code>true</code> if the certificate should be automatically renewed when it expires; otherwise, <code>false</code>.\n csr:\n description:\n - Last CSR that was created for this order.\n state:\n description:\n - Assert the state of the App Service Certificate Order.\n - Use 'present' to create or update an App Service Certificate Order and 'absent' to delete it.\n default: present\n choices:\n - absent\n - present\n\nextends_documentation_fragment:\n - azure\n - azure_tags\n\nauthor:\n - \"Zim Kalinowski (@zikalino)\"\n\n'''\n\nEXAMPLES = '''\n - name: Create (or update) App Service Certificate Order\n azure_rm_webappservicecertificateorder:\n resource_group: NOT FOUND\n certificate_order_name: NOT FOUND\n'''\n\nRETURN = '''\nid:\n description:\n - Resource Id.\n returned: always\n type: str\n sample: id\nstatus:\n description:\n - \"Current order status. Possible values include: 'Pendingissuance', 'Issued', 'Revoked', 'Canceled', 'Denied', 'Pendingrevocation', 'PendingRekey',\n 'Unused', 'Expired', 'NotSubmitted'\"\n returned: always\n type: str\n sample: status\n'''\n\nimport time\nfrom ansible.module_utils.azure_rm_common import AzureRMModuleBase\nfrom ansible.module_utils.common.dict_transformations import _snake_to_camel\n\ntry:\n from msrestazure.azure_exceptions import CloudError\n from msrest.polling import LROPoller\n from msrestazure.azure_operation import AzureOperationPoller\n from azure.mgmt.web import WebSiteManagementClient\n from msrest.serialization import Model\nexcept ImportError:\n # This is handled in azure_rm_common\n pass\n\n\nclass Actions:\n NoAction, Create, Update, Delete = range(4)\n\n\nclass AzureRMAppServiceCertificateOrder(AzureRMModuleBase):\n \"\"\"Configuration class for an Azure RM App Service Certificate Order resource\"\"\"\n\n def __init__(self):\n self.module_arg_spec = dict(\n resource_group=dict(\n type='str',\n required=True\n ),\n certificate_order_name=dict(\n type='str',\n required=True\n ),\n kind=dict(\n type='str'\n ),\n location=dict(\n type='str'\n ),\n certificates=dict(\n type='dict'\n ),\n distinguished_name=dict(\n type='str'\n ),\n validity_in_years=dict(\n type='int'\n ),\n key_size=dict(\n type='int'\n ),\n product_type=dict(\n type='str',\n choices=['standard_domain_validated_ssl',\n 'standard_domain_validated_wild_card_ssl']\n ),\n auto_renew=dict(\n type='str'\n ),\n csr=dict(\n type='str'\n ),\n state=dict(\n type='str',\n default='present',\n choices=['present', 'absent']\n )\n )\n\n self.resource_group = None\n self.certificate_order_name = None\n self.name = dict()\n\n self.results = dict(changed=False)\n self.mgmt_client = None\n self.state = None\n self.to_do = Actions.NoAction\n\n super(AzureRMAppServiceCertificateOrder, self).__init__(derived_arg_spec=self.module_arg_spec,\n supports_check_mode=True,\n supports_tags=True)\n\n def exec_module(self, **kwargs):\n \"\"\"Main module execution method\"\"\"\n\n for key in list(self.module_arg_spec.keys()) + ['tags']:\n if hasattr(self, key):\n setattr(self, key, kwargs[key])\n elif kwargs[key] is not None:\n self.certificate_distinguished_name[key] = kwargs[key]\n\n dict_camelize(self.certificate_distinguished_name, ['product_type'], True)\n\n response = None\n\n self.mgmt_client = self.get_mgmt_svc_client(WebSiteManagementClient,\n base_url=self._cloud_environment.endpoints.resource_manager)\n\n resource_group = self.get_resource_group(self.resource_group)\n\n old_response = self.get_appservicecertificateorder()\n\n if not old_response:\n self.log(\"App Service Certificate Order instance doesn't exist\")\n if self.state == 'absent':\n self.log(\"Old instance didn't exist\")\n else:\n self.to_do = Actions.Create\n else:\n self.log(\"App Service Certificate Order instance already exists\")\n if self.state == 'absent':\n self.to_do = Actions.Delete\n elif self.state == 'present':\n if (not default_compare(self.certificate_distinguished_name, old_response, '', self.results)):\n self.to_do = Actions.Update\n\n if (self.to_do == Actions.Create) or (self.to_do == Actions.Update):\n self.log(\"Need to Create / Update the App Service Certificate Order instance\")\n\n if self.check_mode:\n self.results['changed'] = True\n return self.results\n\n response = self.create_update_appservicecertificateorder()\n\n self.results['changed'] = True\n self.log(\"Creation / Update done\")\n elif self.to_do == Actions.Delete:\n self.log(\"App Service Certificate Order instance deleted\")\n self.results['changed'] = True\n\n if self.check_mode:\n return self.results\n\n self.delete_appservicecertificateorder()\n # This currently doesnt' work as there is a bug in SDK / Service\n if isinstance(response, LROPoller) or isinstance(response, AzureOperationPoller):\n response = self.get_poller_result(response)\n else:\n self.log(\"App Service Certificate Order instance unchanged\")\n self.results['changed'] = False\n response = old_response\n\n if self.state == 'present':\n self.results.update({\n 'id': response.get('id', None),\n 'status': response.get('status', None)\n })\n return self.results\n\n def create_update_appservicecertificateorder(self):\n '''\n Creates or updates App Service Certificate Order with the specified configuration.\n\n :return: deserialized App Service Certificate Order instance state dictionary\n '''\n self.log(\"Creating / Updating the App Service Certificate Order instance {0}\".format(self.certificate_order_name))\n\n try:\n response = self.mgmt_client.app_service_certificate_orders.create_or_update(resource_group_name=self.resource_group,\n certificate_order_name=self.certificate_order_name,\n certificate_distinguished_name=self.name)\n if isinstance(response, LROPoller) or isinstance(response, AzureOperationPoller):\n response = self.get_poller_result(response)\n\n except CloudError as exc:\n self.log('Error attempting to create the App Service Certificate Order instance.')\n self.fail(\"Error creating the App Service Certificate Order instance: {0}\".format(str(exc)))\n return response.as_dict()\n\n def delete_appservicecertificateorder(self):\n '''\n Deletes specified App Service Certificate Order instance in the specified subscription and resource group.\n\n :return: True\n '''\n self.log(\"Deleting the App Service Certificate Order instance {0}\".format(self.certificate_order_name))\n try:\n response = self.mgmt_client.app_service_certificate_orders.delete(resource_group_name=self.resource_group,\n certificate_order_name=self.certificate_order_name)\n except CloudError as e:\n self.log('Error attempting to delete the App Service Certificate Order instance.')\n self.fail(\"Error deleting the App Service Certificate Order instance: {0}\".format(str(e)))\n\n return True\n\n def get_appservicecertificateorder(self):\n '''\n Gets the properties of the specified App Service Certificate Order.\n\n :return: deserialized App Service Certificate Order instance state dictionary\n '''\n self.log(\"Checking if the App Service Certificate Order instance {0} is present\".format(self.certificate_order_name))\n found = False\n try:\n response = self.mgmt_client.app_service_certificate_orders.get(resource_group_name=self.resource_group,\n certificate_order_name=self.certificate_order_name)\n found = True\n self.log(\"Response : {0}\".format(response))\n self.log(\"App Service Certificate Order instance : {0} found\".format(response.name))\n except CloudError as e:\n self.log('Did not find the App Service Certificate Order instance.')\n if found is True:\n return response.as_dict()\n\n return False\n\n\ndef default_compare(new, old, path, result):\n if new is None:\n return True\n elif isinstance(new, dict):\n if not isinstance(old, dict):\n result['compare'] = 'changed [' + path + '] old dict is null'\n return False\n for k in new.keys():\n if not default_compare(new.get(k), old.get(k, None), path + '/' + k, result):\n return False\n return True\n elif isinstance(new, list):\n if not isinstance(old, list) or len(new) != len(old):\n result['compare'] = 'changed [' + path + '] length is different or null'\n return False\n if isinstance(old[0], dict):\n key = None\n if 'id' in old[0] and 'id' in new[0]:\n key = 'id'\n elif 'name' in old[0] and 'name' in new[0]:\n key = 'name'\n else:\n key = list(old[0])[0]\n new = sorted(new, key=lambda x: x.get(key, None))\n old = sorted(old, key=lambda x: x.get(key, None))\n else:\n new = sorted(new)\n old = sorted(old)\n for i in range(len(new)):\n if not default_compare(new[i], old[i], path + '/*', result):\n return False\n return True\n else:\n if path == '/location':\n new = new.replace(' ', '').lower()\n old = new.replace(' ', '').lower()\n if new == old:\n return True\n else:\n result['compare'] = 'changed [' + path + '] ' + str(new) + ' != ' + str(old)\n return False\n\n\ndef dict_camelize(d, path, camelize_first):\n if isinstance(d, list):\n for i in range(len(d)):\n dict_camelize(d[i], path, camelize_first)\n elif isinstance(d, dict):\n if len(path) == 1:\n old_value = d.get(path[0], None)\n if old_value is not None:\n d[path[0]] = _snake_to_camel(old_value, camelize_first)\n else:\n sd = d.get(path[0], None)\n if sd is not None:\n dict_camelize(sd, path[1:], camelize_first)\n\n\ndef main():\n \"\"\"Main execution\"\"\"\n AzureRMAppServiceCertificateOrder()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "library/azure_rm_webappservicecertificateorder.py", "file_name": "azure_rm_webappservicecertificateorder.py", "file_ext": "py", "file_size_in_byte": 13365, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "ansible.module_utils.azure_rm_common.AzureRMModuleBase", "line_number": 124, "usage_type": "name"}, {"api_name": "azure.mgmt.web.WebSiteManagementClient", "line_number": 199, "usage_type": "argument"}, {"api_name": "msrest.polling.LROPoller", "line_number": 240, "usage_type": "argument"}, {"api_name": "msrestazure.azure_operation.AzureOperationPoller", "line_number": 240, "usage_type": "argument"}, {"api_name": "msrest.polling.LROPoller", "line_number": 266, "usage_type": "argument"}, {"api_name": "msrestazure.azure_operation.AzureOperationPoller", "line_number": 266, "usage_type": "argument"}, {"api_name": "msrestazure.azure_exceptions.CloudError", "line_number": 269, "usage_type": "name"}, {"api_name": "msrestazure.azure_exceptions.CloudError", "line_number": 284, "usage_type": "name"}, {"api_name": "msrestazure.azure_exceptions.CloudError", "line_number": 304, "usage_type": "name"}, {"api_name": "ansible.module_utils.common.dict_transformations._snake_to_camel", "line_number": 363, "usage_type": "call"}]}
+{"seq_id": "219651703", "text": "import networkx as nx\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport powerlaw\nfrom pathlib import Path\nfrom collections import Counter\n# import the fitting module\nimport statFitting\n\ndef calculateNMSE(outd1, outd2):\n #outd1 and outd2 should be of form\n #{out-degreeVal : countWval}\n # Missing lists of keys that need 0 counts to satisfy distribution discrepancy\n missing1 = [k for k in outd1 if k not in outd2]\n missing2 = [k for k in outd2 if k not in outd1]\n # 0 buffering for distribution comparisons\n for item in missing1:\n outd2[item] = 0\n for item in missing2:\n outd1[item] = 0\n\n tuples1 = sorted(outd1.items())\n tuples2 = sorted(outd2.items())\n filtertuples1 = [k[1] for k in tuples1]\n\n n2 = float(sum(filtertuples1))\n n2A = np.ones(len(filtertuples1)) * n2\n normalfiltertuples = filtertuples1 / n2A\n\n filtertuples2 = [k[1] for k in tuples2]\n dist1 = np.array(normalfiltertuples)\n dist2 = np.array(filtertuples2)\n sub = dist2 - dist1\n square = sub ** 2\n expect = square.mean()\n rootexpect = math.sqrt(expect)\n nmse = []\n for i in range(len(normalfiltertuples)):\n if normalfiltertuples[i] == 0.0:\n nmse.append(0)\n else:\n nmse.append(rootexpect / normalfiltertuples[i])\n #nmse = [rootexpect / normalfiltertuples[i] for i in range(len(tuples2)) if not normalfiltertuples[i] == 0]\n degrees = [k[0] for k in tuples1]\n return nmse, degrees\n\n\n# Indicator functions\n\n\ndef outDegreeIndicator(outdegree, j, node):\n if(outdegree[node] == j):\n return 1\n return 0\n\n\ndef inDegreeIndicator(indegree, j, node):\n if(indegree[node] == j):\n return 1\n return 0\n\n# Calculating S for the steady state probability\n\n\ndef calculateS(degree, selected, w, n):\n ret = 0\n for item in selected:\n if (item in degree): # temp soln: why key in selected not in key in degree?\n added = degree[item]\n ret += (1.0 / (w + added))\n else:\n print('key not in degree dictionary');\n print(item);\n return ret / n\n\n# Steady state probability of sampling a node\n\n\ndef piFunc(degree, item, w):\n if item in degree:\n dg = degree[item]\n else:\n dg = 0\n return (w + dg)\n\n# Driver function to do in-degree and out-degree sample distribution estimators\n\n\ndef distributionEstimatorOut(outdegreeDict, dd2, selected, w, maxout):\n phi = {}\n n = float(len(selected))\n print(\"number of items in selected = \");\n print(n);\n S = calculateS(dd2, selected, w, n)\n # -- for debug purpose only -- #\n print(\"Steady state probability = \");\n print(S);\n # -- for debug purpose only -- #\n print(\"maxout = \");\n print(maxout);\n for i in range(maxout + 1): # i = [0, maxout)\n ret = 0\n for item2 in selected:\n indicator = outDegreeIndicator(outdegreeDict, i, item2)\n pi = piFunc(dd2, item2, w)\n pi *= S\n ret += (indicator / pi)\n ret /= n\n phi[i] = ret\n return phi\n\n\ndef distributionEstimatorIn(indegreeDict, dd2, selected, w):\n phi = {}\n n = float(len(selected))\n S = calculateS(dd2, selected, w, n)\n in_degree_vals = sorted(set(indegreeDict.values()))\n for item in in_degree_vals:\n ret = 0\n for item2 in selected:\n indicator = inDegreeIndicator(indegreeDict, item, item2)\n pi = piFunc(dd2, item2, w)\n pi = pi * S\n ret += (indicator / pi)\n ret = ret / n\n phi[item] = ret\n return phi\n\n\n# Computes out_degree distribution, in_degree distribution\n# and clustering coefficient of sampled graph after DURW\ndef graphSampleStatistics(origG, sampledG, selected, inFile, w, outdegree, maxout, findDeg = False):\n od = origG.out_degree()\n id = origG.in_degree()\n dd2 = sampledG.degree() \n out_degree = distributionEstimatorOut(\n od, dd2, selected, w, maxout)\n \n in_degree = distributionEstimatorIn(\n id, dd2, selected, w)\n outKeys = [float(x) for x in out_degree.keys()]\n outVals = list(out_degree.values())\n inKeys = [float(x) for x in in_degree.keys()]\n inVals = list(in_degree.values())\n # ---- finding the best degree\n if (findDeg == True): \n bestDeg = statFitting.findDegree(outKeys, outVals); \n print('Best fitting degree suggested = ')\n print(bestDeg)\n else:\n bestDeg = 4;\n # ---- stat fitting for outdegree distribution ----\n # First draw the sample to derive the fitting polynomial \n # Then use the test data points to test the fitting polynomial \n # Finally plot the sample, test, and errors \n S, T = statFitting.drawSample(outKeys, outVals);\n coefs, error = statFitting.polyFit(S[0], S[1], bestDeg);\n xdt, ydt, yfit = statFitting.polyTest(coefs, T[0], T[1]);\n xdt2, ydt2, yfit2 = statFitting.polyTest(coefs, S[0], S[1]);\n title = 'Curve Fit For Test Data Out Degree'\n statFitting.graphStat(xdt, ydt, yfit, error, title, test = True)\n title = 'Absolute Error For Test Data Fitting Out Degree' \n statFitting.graphError(xdt, ydt, yfit,title)\n title = 'Curve Fit For Sample Out Degree' \n statFitting.graphStat(xdt2, ydt2, yfit2, error,title)\n title = 'Absolute Error For Sample Out Degree' \n statFitting.graphError(xdt2, ydt2, yfit2,title)\n # ---- stat fitting for outdegree distribution ---- \n \n # ---- stat fitting for indegree distribution ----\n S, T = statFitting.drawSample(inKeys, inVals);\n coefs, error = statFitting.polyFit(S[0], S[1], bestDeg);\n xdt, ydt, yfit = statFitting.polyTest(coefs, T[0], T[1]);\n xdt2, ydt2, yfit2 = statFitting.polyTest(coefs, S[0], S[1]);\n title = 'Curve Fit For Test Data In Degree' \n statFitting.graphStat(xdt, ydt, yfit, error, title, test = True)\n title = 'Absolute Error For Test Data Fitting In Degree' \n statFitting.graphError(xdt, ydt, yfit, title);\n title = 'Curve Fit For Sample In Degree' \n statFitting.graphStat(xdt2, ydt2, yfit2, error, title)\n title = 'Absolute Error For Sample In Degree' \n statFitting.graphError(xdt2, ydt2, yfit2, title) \n # ---- stat fitting for indegree distribution ---- \n \n # ---- Calculating NMSE ---- #\n NMSE, deg = calculateNMSE(outdegree, out_degree)\n return NMSE, deg\n\n\n# Computes out_degree distribution, in_degree distribution\n# and clustering coefficient of unsampled graph\ndef graphStatistics(G, inFile, findDeg = False):\n\n out_degree = G.out_degree()\n out_degree_vals = sorted(set(out_degree.values()))\n c = Counter(out_degree.values())\n out_degree_distr = [c[x] for x in out_degree_vals]\n n1 = float(sum(out_degree_distr))\n n1A = np.ones(len(out_degree_distr)) * n1\n norm_out_degree_distr = out_degree_distr / n1A\n in_degree = G.in_degree()\n in_degree_vals = sorted(set(in_degree.values()))\n c2 = Counter(in_degree.values())\n in_degree_distr = [c2[x] for x in in_degree_vals]\n n2 = float(sum(in_degree_distr))\n n2A = np.ones(len(in_degree_distr)) * n2\n norm_in_degree_distr = in_degree_distr / n2A\n \n # ---- Finding the best degree to fit the original data ---- #\n if (findDeg == True): \n bestDeg = statFitting.findDegree(out_degree_vals, norm_out_degree_distr, orig = True); \n print('Best fitting degree suggested = ')\n print(bestDeg)\n else:\n bestDeg = 7; \n \n # ---- stat Fitting for original graph out degree ---- #\n coefs, error = statFitting.polyFit(out_degree_vals, norm_out_degree_distr, bestDeg);\n xdt, ydt, yfit = statFitting.polyTest(coefs, out_degree_vals, norm_in_degree_distr); \n title = 'Curve Fit For Original Data Out Degree' \n statFitting.graphStat(xdt, ydt, yfit, error, title)\n title = 'Absolute Error For Original Data Fitting Out Degree' \n statFitting.graphError(xdt, ydt, yfit, title);\n # ---- stat Fitting for original graph ---- # \n \n # ---- stat Fitting for original graph in degree ---- #\n coefs, error = statFitting.polyFit(in_degree_vals, norm_in_degree_distr, bestDeg);\n xdt, ydt, yfit = statFitting.polyTest(coefs, out_degree_vals, norm_in_degree_distr); \n title = 'Curve Fit For Original Data In Degree' \n statFitting.graphStat(xdt, ydt, yfit, error, title)\n title = 'Absolute Error For Original Data Fitting In Degree' \n statFitting.graphError(xdt, ydt, yfit, title); \n \n return c\n\n\nif __name__ == '__main__':\n # Argument parsing for various options\n parser = argparse.ArgumentParser(description=\"Generate Sampled Graph\")\n parser.add_argument('-f', '--inFile', type=str, required=True,\n help='Input graph file in form .gpickle (.txt support will be added)')\n parser.add_argument('-s', '--sample', type=bool, required=True,\n help='True if Sampled Graph, False if full Graph')\n args = parser.parse_args()\n\n G = nx.read_gpickle(args.inFile)\n if(args.sample):\n graphSampleStatistics(G, args.inFile, 1)\n else:\n graphStatistics(G, args.inFile, 1)\n", "sub_path": "generateStats.py", "file_name": "generateStats.py", "file_ext": "py", "file_size_in_byte": 9156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.ones", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "statFitting.findDegree", "line_number": 149, "usage_type": "call"}, {"api_name": "statFitting.drawSample", "line_number": 158, "usage_type": "call"}, {"api_name": "statFitting.polyFit", "line_number": 159, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 160, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 161, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 163, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 165, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 167, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 169, "usage_type": "call"}, {"api_name": "statFitting.drawSample", "line_number": 173, "usage_type": "call"}, {"api_name": "statFitting.polyFit", "line_number": 174, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 175, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 176, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 178, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 180, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 182, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 184, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 201, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 208, "usage_type": "call"}, {"api_name": "statFitting.findDegree", "line_number": 213, "usage_type": "call"}, {"api_name": "statFitting.polyFit", "line_number": 220, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 221, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 223, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 225, "usage_type": "call"}, {"api_name": "statFitting.polyFit", "line_number": 229, "usage_type": "call"}, {"api_name": "statFitting.polyTest", "line_number": 230, "usage_type": "call"}, {"api_name": "statFitting.graphStat", "line_number": 232, "usage_type": "call"}, {"api_name": "statFitting.graphError", "line_number": 234, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 241, "usage_type": "call"}, {"api_name": "networkx.read_gpickle", "line_number": 248, "usage_type": "call"}]}
+{"seq_id": "616709644", "text": "from click.testing import CliRunner\nfrom mock import patch\n\nfrom examples import misc_examples as misc_ex\nfrom lecli import cli\n\n\n@patch('lecli.cli.user_api.get_owner')\ndef test_get_owner(mocked_get_owner):\n runner = CliRunner()\n runner.invoke(cli.getowner)\n\n mocked_get_owner.assert_called_once_with()\n\n\n@patch('lecli.cli.user_api.delete_user')\ndef test_userdel(mocked_delete_user):\n runner = CliRunner()\n result = runner.invoke(cli.deleteuser, input=None)\n\n assert result.output == \"Example usage: lecli deleteuser -u 12345678-aaaa-bbbb-1234-1234cb123456\\n\"\n\n runner.invoke(cli.deleteuser, ['-u', misc_ex.TEST_USER_KEY])\n mocked_delete_user.assert_called_once_with(misc_ex.TEST_USER_KEY)\n\n\n@patch('lecli.cli.user_api.add_new_user')\ndef test_useradd(mocked_add_new_user):\n first = \"first\"\n last = \"last\"\n email = \"email\"\n\n runner = CliRunner()\n runner.invoke(cli.adduser, ['-f', first, '-l', last, '-e', email], input='y')\n mocked_add_new_user.assert_called_once_with(first, last, email)\n\n\n@patch('lecli.cli.user_api.list_users')\ndef test_userlist(mocked_list_users):\n runner = CliRunner()\n runner.invoke(cli.listusers)\n mocked_list_users.assert_called_once_with()\n\n\n@patch('lecli.cli.query_api.get_recent_events')\ndef test_recentevents(mocked_recent_events):\n runner = CliRunner()\n runner.invoke(cli.recentevents, [str(misc_ex.TEST_LOG_GROUP)])\n\n assert mocked_recent_events.called\n\n\n@patch('lecli.cli.query_api.get_recent_events')\ndef test_recentevents_with_relative_range(mocked_recent_events):\n runner = CliRunner()\n runner.invoke(cli.recentevents, [str(misc_ex.TEST_LOG_GROUP), '-r', misc_ex.RELATIVE_TIME])\n\n assert mocked_recent_events.called\n\n\n@patch('lecli.cli.query_api.get_events')\ndef test_events(mocked_get_events):\n runner = CliRunner()\n runner.invoke(cli.events, [str(misc_ex.TEST_LOG_GROUP), '-f', misc_ex.TIME_FROM, '-t',\n misc_ex.TIME_TO])\n\n assert mocked_get_events.called\n\n\n@patch('lecli.cli.query_api.get_events')\ndef test_events_with_relative_range(mocked_get_events):\n runner = CliRunner()\n runner.invoke(cli.events, [str(misc_ex.TEST_LOG_GROUP), '-r', misc_ex.RELATIVE_TIME])\n\n assert mocked_get_events.called\n\n\n@patch('lecli.cli.query_api.post_query')\ndef test_query(mocked_post_query):\n runner = CliRunner()\n runner.invoke(cli.query, [str(misc_ex.TEST_LOG_GROUP), '-l', misc_ex.TEST_QUERY, '-f',\n misc_ex.TIME_FROM, '-t',\n misc_ex.TIME_TO])\n\n assert mocked_post_query.called\n\n\n@patch('lecli.cli.query_api.post_query')\ndef test_query_with_relative_range(mocked_post_query):\n runner = CliRunner()\n runner.invoke(cli.query, [str(misc_ex.TEST_LOG_GROUP), '-l', misc_ex.TEST_QUERY, '-r',\n misc_ex.RELATIVE_TIME])\n\n assert mocked_post_query.called\n\n\n@patch('lecli.cli.team_api.get_teams')\ndef test_get_teams(mocked_get_teams):\n runner = CliRunner()\n runner.invoke(cli.getteams)\n\n assert mocked_get_teams.called\n\n\n@patch('lecli.cli.team_api.get_team')\ndef test_get_team(mocked_get_team):\n runner = CliRunner()\n runner.invoke(cli.getteam, [str(misc_ex.TEST_TEAM_ID)])\n\n assert mocked_get_team.called\n\n\n@patch('lecli.cli.team_api.create_team')\ndef test_create_team(mocked_create_team):\n runner = CliRunner()\n runner.invoke(cli.createteam, [\"test_team_name\"])\n\n assert mocked_create_team.called\n\n\n@patch('lecli.cli.team_api.delete_team')\ndef test_delete_team(mocked_delete_team):\n runner = CliRunner()\n runner.invoke(cli.deleteteam, [str(misc_ex.TEST_TEAM_ID)])\n\n assert mocked_delete_team.called\n\n\n@patch('lecli.cli.team_api.rename_team')\ndef test_rename_team(mocked_rename_team):\n runner = CliRunner()\n runner.invoke(cli.renameteam, [str(misc_ex.TEST_TEAM_ID), \"new_name\"])\n\n assert mocked_rename_team.called\n\n\n@patch('lecli.cli.team_api.add_user_to_team')\ndef test_add_user_to_team(mocked_add_user):\n runner = CliRunner()\n runner.invoke(cli.addusertoteam, [str(misc_ex.TEST_TEAM_ID), \"test_user_name\"])\n\n assert mocked_add_user.called\n\n\n@patch('lecli.cli.usage_api.get_usage')\ndef test_add_user_to_team(mocked_get_usage):\n runner = CliRunner()\n runner.invoke(cli.usage, ['-s', misc_ex.USAGE_DATE_FROM, '-e', misc_ex.USAGE_DATE_TO])\n\n assert mocked_get_usage.called\n", "sub_path": "tests/test_lecli.py", "file_name": "test_lecli.py", "file_ext": "py", "file_size_in_byte": 4370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "click.testing.CliRunner", "line_number": 10, "usage_type": "call"}, {"api_name": "lecli.cli.getowner", "line_number": 11, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 11, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 8, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 18, "usage_type": "call"}, {"api_name": "lecli.cli.deleteuser", "line_number": 19, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 19, "usage_type": "name"}, {"api_name": "lecli.cli.deleteuser", "line_number": 23, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 23, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_USER_KEY", "line_number": 23, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 23, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_USER_KEY", "line_number": 24, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 24, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 16, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 33, "usage_type": "call"}, {"api_name": "lecli.cli.adduser", "line_number": 34, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 34, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 40, "usage_type": "call"}, {"api_name": "lecli.cli.listusers", "line_number": 41, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 41, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 38, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 47, "usage_type": "call"}, {"api_name": "lecli.cli.recentevents", "line_number": 48, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 48, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 48, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 48, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 45, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 55, "usage_type": "call"}, {"api_name": "lecli.cli.recentevents", "line_number": 56, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 56, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 56, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 56, "usage_type": "name"}, {"api_name": "examples.misc_examples.RELATIVE_TIME", "line_number": 56, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 53, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 63, "usage_type": "call"}, {"api_name": "lecli.cli.events", "line_number": 64, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 64, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 64, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 64, "usage_type": "name"}, {"api_name": "examples.misc_examples.TIME_FROM", "line_number": 64, "usage_type": "attribute"}, {"api_name": "examples.misc_examples.TIME_TO", "line_number": 65, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 65, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 61, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 72, "usage_type": "call"}, {"api_name": "lecli.cli.events", "line_number": 73, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 73, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 73, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 73, "usage_type": "name"}, {"api_name": "examples.misc_examples.RELATIVE_TIME", "line_number": 73, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 70, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 80, "usage_type": "call"}, {"api_name": "lecli.cli.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 81, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 81, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 81, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_QUERY", "line_number": 81, "usage_type": "attribute"}, {"api_name": "examples.misc_examples.TIME_FROM", "line_number": 82, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 82, "usage_type": "name"}, {"api_name": "examples.misc_examples.TIME_TO", "line_number": 83, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 83, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 78, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 90, "usage_type": "call"}, {"api_name": "lecli.cli.query", "line_number": 91, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 91, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_LOG_GROUP", "line_number": 91, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 91, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_QUERY", "line_number": 91, "usage_type": "attribute"}, {"api_name": "examples.misc_examples.RELATIVE_TIME", "line_number": 92, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 92, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 88, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 99, "usage_type": "call"}, {"api_name": "lecli.cli.getteams", "line_number": 100, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 100, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 97, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 107, "usage_type": "call"}, {"api_name": "lecli.cli.getteam", "line_number": 108, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 108, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_TEAM_ID", "line_number": 108, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 108, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 105, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 115, "usage_type": "call"}, {"api_name": "lecli.cli.createteam", "line_number": 116, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 116, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 113, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 123, "usage_type": "call"}, {"api_name": "lecli.cli.deleteteam", "line_number": 124, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 124, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_TEAM_ID", "line_number": 124, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 124, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 121, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 131, "usage_type": "call"}, {"api_name": "lecli.cli.renameteam", "line_number": 132, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 132, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_TEAM_ID", "line_number": 132, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 132, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 129, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 139, "usage_type": "call"}, {"api_name": "lecli.cli.addusertoteam", "line_number": 140, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 140, "usage_type": "name"}, {"api_name": "examples.misc_examples.TEST_TEAM_ID", "line_number": 140, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 140, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 137, "usage_type": "call"}, {"api_name": "click.testing.CliRunner", "line_number": 147, "usage_type": "call"}, {"api_name": "lecli.cli.usage", "line_number": 148, "usage_type": "attribute"}, {"api_name": "lecli.cli", "line_number": 148, "usage_type": "name"}, {"api_name": "examples.misc_examples.USAGE_DATE_FROM", "line_number": 148, "usage_type": "attribute"}, {"api_name": "examples.misc_examples", "line_number": 148, "usage_type": "name"}, {"api_name": "examples.misc_examples.USAGE_DATE_TO", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 145, "usage_type": "call"}]}
+{"seq_id": "574732781", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom django.conf.urls import url, include\nfrom django.contrib.auth.models import User\nfrom rest_framework import routers\nfrom GestionadorApp.views import (ProductList,UserList, posts,\n Ingreso, Egreso,proveedores,log_in,\n log_out,registro,index,productonew,\n proveedornew,ingresonew,egresonew, deleteproducto,\n deleteingreso,deleteegreso,deleteproveedor)\nfrom django.urls import path, include\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom django.conf.urls import include\n\n\n\"\"\"router = routers.DefaultRouter()\nrouter.register('Producto', ProductoViewSet)\"\"\"\n\nrouter = routers.DefaultRouter()\nrouter.register(r'list', ProductList,basename='list')\nrouter.register(r'user', UserList,basename='user')\n\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',index,name='index'),\n path('Login_/',log_in,name='Log_in'),\n path('cerrar-sesion/',log_out, name='sign_out'),\n path('registrar/', registro, name='Register'),\n path('Producto-lista/',posts,name='ProductView',),\n path('ProductoNew',productonew,name='ProductoNew'),\n path('Proveedores-lista/',proveedores,name='ProovedorView',),\n path('ProveedorNew/',proveedornew,name='ProveedorNew'),\n path('Ingreso-lista/',Ingreso,name='IngresoView',),\n path('IngresoNew/',ingresonew,name='IngresoNew'),\n path('Egreso-lista/',Egreso,name='EgresoView',),\n path('EgresoNew',egresonew,name='EgresoNew'),\n path('<pk>[0-9]/producto/',deleteproducto,name='DeleteProduct'),\n path('<pk>[0-9]/proveedor/',deleteproveedor,name='DeleteProveedor'),\n path('<pk>[0-9]/ingreso/', deleteingreso, name='DeleteIngreso'),\n path('<pk>[0-9]/egreso/', deleteegreso, name='DeleteEgreso'),\n\n\n\n path('api/', include(router.urls)),\n path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))\n]\n\n#urlpatterns = format_suffix_patterns(urlpatterns)\n", "sub_path": "Django/app/Gestionador/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 19, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 19, "usage_type": "name"}, {"api_name": "GestionadorApp.views.ProductList", "line_number": 20, "usage_type": "argument"}, {"api_name": "GestionadorApp.views.UserList", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "GestionadorApp.views.index", "line_number": 27, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "GestionadorApp.views.log_in", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "GestionadorApp.views.log_out", "line_number": 29, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "GestionadorApp.views.registro", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "GestionadorApp.views.posts", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "GestionadorApp.views.productonew", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "GestionadorApp.views.proveedores", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "GestionadorApp.views.proveedornew", "line_number": 34, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "GestionadorApp.views.Ingreso", "line_number": 35, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "GestionadorApp.views.ingresonew", "line_number": 36, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "GestionadorApp.views.Egreso", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "GestionadorApp.views.egresonew", "line_number": 38, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "GestionadorApp.views.deleteproducto", "line_number": 39, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "GestionadorApp.views.deleteproveedor", "line_number": 40, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "GestionadorApp.views.deleteingreso", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "GestionadorApp.views.deleteegreso", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 47, "usage_type": "call"}]}
+{"seq_id": "125474685", "text": "#!/usr/bin/env python\n\"\"\"\n\nCopyright (c) 2021 Alex Forencich\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\n\"\"\"\n\nimport itertools\nimport logging\nimport os\nimport re\nimport sys\nfrom contextlib import contextmanager\n\nimport cocotb_test.simulator\nimport pytest\n\nimport cocotb\nfrom cocotb.clock import Clock\nfrom cocotb.triggers import RisingEdge, Timer\nfrom cocotb.regression import TestFactory\n\nfrom cocotbext.pcie.core import RootComplex\nfrom cocotbext.axi import AxiWriteBus, AxiRamWrite\n\n\ntry:\n from pcie_if import PcieIfDevice, PcieIfRxBus\nexcept ImportError:\n # attempt import from current directory\n sys.path.insert(0, os.path.join(os.path.dirname(__file__)))\n try:\n from pcie_if import PcieIfDevice, PcieIfRxBus\n finally:\n del sys.path[0]\n\n\n@contextmanager\ndef assert_raises(exc_type, pattern=None):\n try:\n yield\n except exc_type as e:\n if pattern:\n assert re.match(pattern, str(e)), \\\n \"Correct exception type caught, but message did not match pattern\"\n pass\n else:\n raise AssertionError(\"{} was not raised\".format(exc_type.__name__))\n\n\nclass TB(object):\n def __init__(self, dut):\n self.dut = dut\n\n self.log = logging.getLogger(\"cocotb.tb\")\n self.log.setLevel(logging.DEBUG)\n\n cocotb.start_soon(Clock(dut.clk, 4, units=\"ns\").start())\n\n # PCIe\n self.rc = RootComplex()\n\n self.dev = PcieIfDevice(\n clk=dut.clk,\n rst=dut.rst,\n\n rx_req_tlp_bus=PcieIfRxBus.from_prefix(dut, \"rx_req_tlp\")\n )\n\n self.dev.log.setLevel(logging.DEBUG)\n\n self.dev.functions[0].configure_bar(0, 16*1024*1024)\n self.dev.functions[0].configure_bar(1, 16*1024, io=True)\n\n self.rc.make_port().connect(self.dev)\n\n # AXI\n self.axi_ram = AxiRamWrite(AxiWriteBus.from_prefix(dut, \"m_axi\"), dut.clk, dut.rst, size=2**16)\n\n # monitor error outputs\n self.status_error_uncor_asserted = False\n cocotb.start_soon(self._run_monitor_status_error_uncor())\n\n def set_idle_generator(self, generator=None):\n if generator:\n self.dev.rx_req_tlp_source.set_pause_generator(generator())\n self.axi_ram.b_channel.set_pause_generator(generator())\n\n def set_backpressure_generator(self, generator=None):\n if generator:\n self.axi_ram.aw_channel.set_pause_generator(generator())\n self.axi_ram.w_channel.set_pause_generator(generator())\n\n async def _run_monitor_status_error_uncor(self):\n while True:\n await RisingEdge(self.dut.status_error_uncor)\n self.log.info(\"status_error_uncor (uncorrectable error) was asserted\")\n self.status_error_uncor_asserted = True\n\n async def cycle_reset(self):\n self.dut.rst.setimmediatevalue(0)\n await RisingEdge(self.dut.clk)\n await RisingEdge(self.dut.clk)\n self.dut.rst.value = 1\n await RisingEdge(self.dut.clk)\n await RisingEdge(self.dut.clk)\n self.dut.rst.value = 0\n await RisingEdge(self.dut.clk)\n await RisingEdge(self.dut.clk)\n\n\nasync def run_test_write(dut, idle_inserter=None, backpressure_inserter=None):\n\n tb = TB(dut)\n\n byte_lanes = tb.axi_ram.byte_lanes\n\n pcie_offsets = list(range(byte_lanes))+list(range(4096-byte_lanes, 4096))\n if os.getenv(\"OFFSET_GROUP\") is not None:\n group = int(os.getenv(\"OFFSET_GROUP\"))\n pcie_offsets = pcie_offsets[group::8]\n\n tb.set_idle_generator(idle_inserter)\n tb.set_backpressure_generator(backpressure_inserter)\n\n await tb.cycle_reset()\n\n await tb.rc.enumerate()\n\n dev = tb.rc.find_device(tb.dev.functions[0].pcie_id)\n await dev.enable_device()\n\n dev_bar0 = dev.bar_window[0]\n\n for length in list(range(0, byte_lanes*2))+[1024]:\n for pcie_offset in pcie_offsets:\n tb.log.info(\"length %d, pcie_offset %d\", length, pcie_offset)\n pcie_addr = pcie_offset+0x1000\n test_data = bytearray([x % 256 for x in range(length)])\n\n tb.axi_ram.write(pcie_addr-128, b'\\x55'*(len(test_data)+256))\n\n await dev_bar0.write(pcie_addr, test_data)\n\n await Timer(length*4+150, 'ns')\n\n tb.log.debug(\"%s\", tb.axi_ram.hexdump_str((pcie_addr & ~0xf)-16, (((pcie_addr & 0xf)+length-1) & ~0xf)+48, prefix=\"AXI \"))\n\n assert tb.axi_ram.read(pcie_addr-1, len(test_data)+2) == b'\\x55'+test_data+b'\\x55'\n\n assert not tb.status_error_uncor_asserted\n\n await RisingEdge(dut.clk)\n await RisingEdge(dut.clk)\n\n\nasync def run_test_bad_ops(dut, idle_inserter=None, backpressure_inserter=None):\n\n tb = TB(dut)\n\n tb.set_idle_generator(idle_inserter)\n tb.set_backpressure_generator(backpressure_inserter)\n\n await tb.cycle_reset()\n\n await tb.rc.enumerate()\n\n dev = tb.rc.find_device(tb.dev.functions[0].pcie_id)\n await dev.enable_device()\n\n dev_bar0 = dev.bar_window[0]\n dev_bar1 = dev.bar_window[1]\n\n tb.log.info(\"Test read\")\n\n length = 4\n pcie_addr = 0x1000\n test_data = bytearray([x % 256 for x in range(length)])\n\n tb.axi_ram.write(pcie_addr-128, b'\\x55'*(len(test_data)+256))\n tb.axi_ram.write(pcie_addr, test_data)\n\n tb.log.debug(\"%s\", tb.axi_ram.hexdump_str((pcie_addr & ~0xf)-16, (((pcie_addr & 0xf)+length-1) & ~0xf)+48, prefix=\"AXI \"))\n\n with assert_raises(Exception, \"Timeout\"):\n val = await dev_bar0.read(pcie_addr, len(test_data), timeout=1000, timeout_unit='ns')\n\n assert tb.status_error_uncor_asserted\n\n tb.status_error_uncor_asserted = False\n\n tb.log.info(\"Test IO write\")\n\n length = 4\n pcie_addr = 0x1000\n test_data = bytearray([x % 256 for x in range(length)])\n\n tb.axi_ram.write(pcie_addr-128, b'\\x55'*(len(test_data)+256))\n\n with assert_raises(Exception, \"Timeout\"):\n await dev_bar1.write(pcie_addr, test_data, timeout=1000, timeout_unit='ns')\n\n await Timer(100, 'ns')\n\n tb.log.debug(\"%s\", tb.axi_ram.hexdump_str((pcie_addr & ~0xf)-16, (((pcie_addr & 0xf)+length-1) & ~0xf)+48, prefix=\"AXI \"))\n\n assert tb.axi_ram.read(pcie_addr-1, len(test_data)+2) == b'\\x55'*(len(test_data)+2)\n\n assert tb.status_error_uncor_asserted\n\n tb.status_error_uncor_asserted = False\n\n tb.log.info(\"Test IO read\")\n\n length = 4\n pcie_addr = 0x1000\n test_data = bytearray([x % 256 for x in range(length)])\n\n tb.axi_ram.write(pcie_addr-128, b'\\x55'*(len(test_data)+256))\n tb.axi_ram.write(pcie_addr, test_data)\n\n tb.log.debug(\"%s\", tb.axi_ram.hexdump_str((pcie_addr & ~0xf)-16, (((pcie_addr & 0xf)+length-1) & ~0xf)+48, prefix=\"AXI \"))\n\n with assert_raises(Exception, \"Timeout\"):\n val = await dev_bar1.read(pcie_addr, len(test_data), timeout=1000, timeout_unit='ns')\n\n assert tb.status_error_uncor_asserted\n\n await RisingEdge(dut.clk)\n await RisingEdge(dut.clk)\n\n\ndef cycle_pause():\n return itertools.cycle([1, 1, 1, 0])\n\n\nif cocotb.SIM_NAME:\n\n for test in [\n run_test_write,\n run_test_bad_ops\n ]:\n\n factory = TestFactory(test)\n factory.add_option((\"idle_inserter\", \"backpressure_inserter\"), [(None, None), (cycle_pause, cycle_pause)])\n factory.generate_tests()\n\n\n# cocotb-test\n\ntests_dir = os.path.dirname(__file__)\nrtl_dir = os.path.abspath(os.path.join(tests_dir, '..', '..', 'rtl'))\n\n\n@pytest.mark.parametrize(\"offset_group\", list(range(8)))\n@pytest.mark.parametrize(\"pcie_data_width\", [64, 128])\ndef test_pcie_axi_master_wr(request, pcie_data_width, offset_group):\n dut = \"pcie_axi_master_wr\"\n module = os.path.splitext(os.path.basename(__file__))[0]\n toplevel = dut\n\n verilog_sources = [\n os.path.join(rtl_dir, f\"{dut}.v\"),\n ]\n\n parameters = {}\n\n parameters['TLP_DATA_WIDTH'] = pcie_data_width\n parameters['TLP_HDR_WIDTH'] = 128\n parameters['TLP_SEG_COUNT'] = 1\n parameters['AXI_DATA_WIDTH'] = parameters['TLP_DATA_WIDTH']\n parameters['AXI_ADDR_WIDTH'] = 64\n parameters['AXI_STRB_WIDTH'] = parameters['AXI_DATA_WIDTH'] // 8\n parameters['AXI_ID_WIDTH'] = 8\n parameters['AXI_MAX_BURST_LEN'] = 256\n parameters['TLP_FORCE_64_BIT_ADDR'] = 0\n\n extra_env = {f'PARAM_{k}': str(v) for k, v in parameters.items()}\n\n extra_env['OFFSET_GROUP'] = str(offset_group)\n extra_env['COCOTB_RESOLVE_X'] = 'RANDOM'\n\n sim_build = os.path.join(tests_dir, \"sim_build\",\n request.node.name.replace('[', '-').replace(']', ''))\n\n cocotb_test.simulator.run(\n python_search=[tests_dir],\n verilog_sources=verilog_sources,\n toplevel=toplevel,\n module=module,\n parameters=parameters,\n sim_build=sim_build,\n extra_env=extra_env,\n )\n", "sub_path": "tb/pcie_axi_master_wr/test_pcie_axi_master_wr.py", "file_name": "test_pcie_axi_master_wr.py", "file_ext": "py", "file_size_in_byte": 9672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "sys.path.insert", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 62, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cocotb.start_soon", "line_number": 76, "usage_type": "call"}, {"api_name": "cocotb.clock.Clock", "line_number": 76, "usage_type": "call"}, {"api_name": "cocotbext.pcie.core.RootComplex", "line_number": 79, "usage_type": "call"}, {"api_name": "pcie_if.PcieIfDevice", "line_number": 81, "usage_type": "call"}, {"api_name": "pcie_if.PcieIfRxBus.from_prefix", "line_number": 85, "usage_type": "call"}, {"api_name": "pcie_if.PcieIfRxBus", "line_number": 85, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cocotbext.axi.AxiRamWrite", "line_number": 96, "usage_type": "call"}, {"api_name": "cocotbext.axi.AxiWriteBus.from_prefix", "line_number": 96, "usage_type": "call"}, {"api_name": "cocotbext.axi.AxiWriteBus", "line_number": 96, "usage_type": "name"}, {"api_name": "cocotb.start_soon", "line_number": 100, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 114, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 120, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 121, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 123, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 124, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 126, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 127, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 137, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 138, "usage_type": "call"}, {"api_name": "cocotb.triggers.Timer", "line_number": 163, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 171, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 172, "usage_type": "call"}, {"api_name": "cocotb.triggers.Timer", "line_number": 221, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 247, "usage_type": "call"}, {"api_name": "cocotb.triggers.RisingEdge", "line_number": 248, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 252, "usage_type": "call"}, {"api_name": "cocotb.SIM_NAME", "line_number": 255, "usage_type": "attribute"}, {"api_name": "cocotb.regression.TestFactory", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "attribute"}, {"api_name": "cocotb_test.simulator.simulator.run", "line_number": 304, "usage_type": "call"}, {"api_name": "cocotb_test.simulator.simulator", "line_number": 304, "usage_type": "attribute"}, {"api_name": "cocotb_test.simulator", "line_number": 304, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 273, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 273, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 274, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 274, "usage_type": "attribute"}]}
+{"seq_id": "536503986", "text": "import asyncio\n\nimport client\n\nimport logging\n#logging.basicConfig(level=logging.DEBUG)\n\nloop = asyncio.get_event_loop()\n\nbot = client.Client(loop, 'config')\n\n\n@bot.on('CLIENT_CONNECT')\ndef connect():\n bot.send('NICK', nick=bot.login)\n bot.send('PASS', password=bot.password)\n bot.send('USER', user=bot.login, realname='Bot using bottom.py')\n #bot.send('JOIN', channel=bot.channel)\n\n\n@bot.on('PING')\ndef keepalive(message):\n bot.send('PONG', message=message)\n\n\n#@bot.on('PRIVMSG')\n#def message(nick, target, message):\n# ''' Echo all messages '''\n#\n# # Don't echo ourselves\n# if nick == bot.nick:\n# return\n#\n# (nick, message) = bot.deprefix(nick, message)\n#\n# # Direct message to bot\n# if target == bot.nick:\n# sender = lambda m, **kw: bot.sender(nick, m, **kw)\n# # Message in channel\n# else:\n# sender = lambda m, **kw: bot.sender(target, m, to=nick, **kw)\n#\n# return (yield from bot.modules.reply(nick, message, bot, sender))\n\n\n@bot.on('PRIVMSG')\ndef privmsg(nick, target, message):\n if nick == bot.nick:\n return\n\n (nick, message) = bot.deprefix(nick, message)\n if target == bot.nick:\n sender = lambda m, **kw: bot.sender(nick, m, **kw)\n else:\n sender = lambda m, **kw: bot.sender(target, m, to=nick, **kw)\n\n coros = [f(bot, nick, message, sender) for f in bot.modules.privmsg]\n\n return (yield from asyncio.wait(coros))\n\n\n@asyncio.coroutine\ndef dump(loop):\n while True:\n print('dump lines')\n print(bot.lines)\n yield from asyncio.sleep(1)\n\n#tasks = [bot.run(), dump(loop)]\ntasks = [bot.run()]\n\nloop.run_until_complete(asyncio.wait(tasks))\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "asyncio.get_event_loop", "line_number": 8, "usage_type": "call"}, {"api_name": "client.Client", "line_number": 10, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 59, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 62, "usage_type": "attribute"}, {"api_name": "asyncio.wait", "line_number": 72, "usage_type": "call"}]}
+{"seq_id": "372800981", "text": "import calendar\nfrom hashlib import md5\nfrom datetime import datetime, timedelta\nimport uuid\n\nfrom snuba import settings\nfrom snuba.datasets.factory import enforce_table_writer, get_dataset\nfrom snuba.clickhouse.native import ClickhousePool\nfrom snuba.redis import redis_client\n\n\ndef wrap_raw_event(event):\n \"Wrap a raw event like the Sentry codebase does before sending to Kafka.\"\n\n unique = \"%s:%s\" % (str(event['project']), event['id'])\n primary_hash = md5(unique.encode('utf-8')).hexdigest()\n\n return {\n 'event_id': event['id'],\n 'group_id': int(primary_hash[:16], 16),\n 'primary_hash': primary_hash,\n 'project_id': event['project'],\n 'message': event['message'],\n 'platform': event['platform'],\n 'datetime': event['datetime'],\n 'data': event\n }\n\n\ndef get_event():\n from tests.fixtures import raw_event\n timestamp = datetime.utcnow()\n raw_event['datetime'] = (timestamp - timedelta(seconds=2)).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\")\n raw_event['received'] = int(calendar.timegm((timestamp - timedelta(seconds=1)).timetuple()))\n return wrap_raw_event(raw_event)\n\n\nclass BaseTest(object):\n def setup_method(self, test_method, dataset_name=None):\n assert settings.TESTING, \"settings.TESTING is False, try `SNUBA_SETTINGS=test` or `make test`\"\n\n self.database = 'default'\n self.dataset_name = dataset_name\n\n if self.dataset_name:\n self.dataset = get_dataset(self.dataset_name)\n self.clickhouse = ClickhousePool()\n\n for statement in self.dataset.get_dataset_schemas().get_drop_statements():\n self.clickhouse.execute(statement)\n\n for statement in self.dataset.get_dataset_schemas().get_create_statements():\n self.clickhouse.execute(statement)\n\n redis_client.flushdb()\n\n def teardown_method(self, test_method):\n if self.dataset_name:\n for statement in self.dataset.get_dataset_schemas().get_drop_statements():\n self.clickhouse.execute(statement)\n\n redis_client.flushdb()\n\n\nclass BaseDatasetTest(BaseTest):\n def write_processed_records(self, records):\n if not isinstance(records, (list, tuple)):\n records = [records]\n\n rows = []\n for event in records:\n rows.append(event)\n\n return self.write_rows(rows)\n\n def write_rows(self, rows):\n if not isinstance(rows, (list, tuple)):\n rows = [rows]\n enforce_table_writer(self.dataset).get_writer().write(rows)\n\n\nclass BaseEventsTest(BaseDatasetTest):\n def setup_method(self, test_method, dataset_name='events'):\n super(BaseEventsTest, self).setup_method(test_method, dataset_name)\n self.table = enforce_table_writer(self.dataset).get_schema().get_table_name()\n self.event = get_event()\n\n def create_event_for_date(self, dt, retention_days=settings.DEFAULT_RETENTION_DAYS):\n event = {\n 'event_id': uuid.uuid4().hex,\n 'project_id': 1,\n 'group_id': 1,\n 'deleted': 0,\n }\n event['timestamp'] = dt\n event['retention_days'] = retention_days\n return event\n\n def write_raw_events(self, events):\n if not isinstance(events, (list, tuple)):\n events = [events]\n\n out = []\n for event in events:\n if 'primary_hash' not in event:\n event = wrap_raw_event(event)\n processed = enforce_table_writer(self.dataset) \\\n .get_stream_loader() \\\n .get_processor() \\\n .process_message(event)\n out.extend(processed.data)\n\n return self.write_processed_records(out)\n\n def write_processed_events(self, events):\n if not isinstance(events, (list, tuple)):\n events = [events]\n\n rows = []\n for event in events:\n rows.append(event)\n\n return self.write_rows(rows)\n\n def write_rows(self, rows):\n if not isinstance(rows, (list, tuple)):\n rows = [rows]\n\n enforce_table_writer(self.dataset).get_writer().write(rows)\n\n\nclass BaseApiTest(BaseEventsTest):\n def setup_method(self, test_method, dataset_name='events'):\n super().setup_method(test_method, dataset_name)\n from snuba.views import application\n assert application.testing is True\n application.config['PROPAGATE_EXCEPTIONS'] = False\n self.app = application.test_client()\n", "sub_path": "tests/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 4497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "hashlib.md5", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "tests.fixtures.raw_event", "line_number": 33, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "tests.fixtures.raw_event", "line_number": 34, "usage_type": "name"}, {"api_name": "calendar.timegm", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "tests.fixtures.raw_event", "line_number": 35, "usage_type": "argument"}, {"api_name": "snuba.settings.TESTING", "line_number": 40, "usage_type": "attribute"}, {"api_name": "snuba.settings", "line_number": 40, "usage_type": "name"}, {"api_name": "snuba.datasets.factory.get_dataset", "line_number": 46, "usage_type": "call"}, {"api_name": "snuba.clickhouse.native.ClickhousePool", "line_number": 47, "usage_type": "call"}, {"api_name": "snuba.redis.redis_client.flushdb", "line_number": 55, "usage_type": "call"}, {"api_name": "snuba.redis.redis_client", "line_number": 55, "usage_type": "name"}, {"api_name": "snuba.redis.redis_client.flushdb", "line_number": 62, "usage_type": "call"}, {"api_name": "snuba.redis.redis_client", "line_number": 62, "usage_type": "name"}, {"api_name": "snuba.datasets.factory.enforce_table_writer", "line_number": 79, "usage_type": "call"}, {"api_name": "snuba.datasets.factory.enforce_table_writer", "line_number": 85, "usage_type": "call"}, {"api_name": "snuba.settings.DEFAULT_RETENTION_DAYS", "line_number": 88, "usage_type": "attribute"}, {"api_name": "snuba.settings", "line_number": 88, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 90, "usage_type": "call"}, {"api_name": "snuba.datasets.factory.enforce_table_writer", "line_number": 107, "usage_type": "call"}, {"api_name": "snuba.datasets.factory.enforce_table_writer", "line_number": 129, "usage_type": "call"}, {"api_name": "snuba.views.application.testing", "line_number": 136, "usage_type": "attribute"}, {"api_name": "snuba.views.application", "line_number": 136, "usage_type": "name"}, {"api_name": "snuba.views.application.config", "line_number": 137, "usage_type": "attribute"}, {"api_name": "snuba.views.application", "line_number": 137, "usage_type": "name"}, {"api_name": "snuba.views.application.test_client", "line_number": 138, "usage_type": "call"}, {"api_name": "snuba.views.application", "line_number": 138, "usage_type": "name"}]}
+{"seq_id": "47668256", "text": "import json\n\nfrom django.db import transaction\nfrom django.http import HttpResponse\n\nimport commonware.log\nfrom tastypie import fields, http\nfrom tastypie.authorization import Authorization\nfrom tastypie.resources import ALL_WITH_RELATIONS\nfrom tastypie.serializers import Serializer\n\nimport amo\nfrom amo.decorators import write\nfrom addons.models import Addon, Preview\nfrom files.models import FileUpload\n\nimport mkt.constants\nfrom mkt.api.authentication import (OAuthAuthentication,\n OptionalOAuthAuthentication)\nfrom mkt.api.authorization import AppOwnerAuthorization, OwnerAuthorization\nfrom mkt.api.base import CORSResource, http_error, MarketplaceModelResource\nfrom mkt.api.forms import (NewPackagedForm, PreviewArgsForm, PreviewJSONForm,\n StatusForm)\nfrom mkt.developers import tasks\nfrom mkt.developers.forms import NewManifestForm, PreviewForm\n\nlog = commonware.log.getLogger('z.api')\n\nclass HttpRequestTooBig(HttpResponse):\n status_code = 413\n\n\nclass ValidationResource(CORSResource, MarketplaceModelResource):\n\n class Meta(MarketplaceModelResource.Meta):\n queryset = FileUpload.objects.all()\n fields = ['valid', 'validation']\n list_allowed_methods = ['post']\n detail_allowed_methods = ['get']\n always_return_data = True\n authentication = OptionalOAuthAuthentication()\n authorization = Authorization()\n resource_name = 'validation'\n serializer = Serializer(formats=['json'])\n\n @write\n @transaction.commit_on_success\n def obj_create(self, bundle, request=None, **kwargs):\n packaged = 'upload' in bundle.data\n form = (NewPackagedForm(bundle.data) if packaged\n else NewManifestForm(bundle.data))\n\n if not form.is_valid():\n raise self.form_errors(form)\n\n if not packaged:\n upload = FileUpload.objects.create(\n user=getattr(request, 'amo_user', None))\n # The hosted app validator is pretty fast.\n tasks.fetch_manifest(form.cleaned_data['manifest'], upload.pk)\n else:\n upload = form.file_upload\n # The packaged app validator is much heavier.\n tasks.validator.delay(upload.pk)\n\n # This is a reget of the object, we do this to get the refreshed\n # results if not celery delayed.\n bundle.obj = FileUpload.objects.get(pk=upload.pk)\n log.info('Validation created: %s' % bundle.obj.pk)\n return bundle\n\n @write\n def obj_get(self, request=None, **kwargs):\n # Until the perms branch lands, this is the only way to lock\n # permissions down on gets, since the object doesn't actually\n # get passed through to OwnerAuthorization.\n try:\n obj = FileUpload.objects.get(pk=kwargs['pk'])\n except FileUpload.DoesNotExist:\n raise http_error(http.HttpNotFound, 'No upload with that ID.')\n\n log.info('Validation retreived: %s' % obj.pk)\n return obj\n\n def dehydrate_validation(self, bundle):\n validation = bundle.data['validation']\n return json.loads(validation) if validation else validation\n\n def dehydrate(self, bundle):\n bundle.data['id'] = bundle.obj.pk\n bundle.data['processed'] = (bool(bundle.obj.valid or\n bundle.obj.validation))\n return bundle\n\n\nclass StatusResource(MarketplaceModelResource):\n\n class Meta(MarketplaceModelResource.Meta):\n queryset = Addon.objects.filter(type=amo.ADDON_WEBAPP)\n fields = ['status', 'disabled_by_user']\n list_allowed_methods = []\n allowed_methods = ['patch', 'get']\n always_return_data = True\n authentication = OAuthAuthentication()\n authorization = AppOwnerAuthorization()\n resource_name = 'status'\n serializer = Serializer(formats=['json'])\n\n @write\n @transaction.commit_on_success\n def obj_update(self, bundle, request, **kwargs):\n try:\n obj = self.get_object_list(bundle.request).get(**kwargs)\n except Addon.DoesNotExist:\n raise http_error(http.HttpNotFound, 'No such addon.')\n\n if not AppOwnerAuthorization().is_authorized(request, object=obj):\n raise http_error(http.HttpForbidden,\n 'You are not an author of that app.')\n\n form = StatusForm(bundle.data, instance=obj)\n if not form.is_valid():\n raise self.form_errors(form)\n\n form.save()\n log.info('App status updated: %s' % obj.pk)\n bundle.obj = obj\n return bundle\n\n @write\n def obj_get(self, request=None, **kwargs):\n obj = super(StatusResource, self).obj_get(request=request, **kwargs)\n if not AppOwnerAuthorization().is_authorized(request, object=obj):\n raise http_error(http.HttpForbidden,\n 'You are not an author of that app.')\n\n log.info('App status retreived: %s' % obj.pk)\n return obj\n\n def dehydrate_status(self, bundle):\n return amo.STATUS_CHOICES_API[int(bundle.data['status'])]\n\n def hydrate_status(self, bundle):\n return amo.STATUS_CHOICES_API_LOOKUP[int(bundle.data['status'])]\n\n\nclass PreviewResource(CORSResource, MarketplaceModelResource):\n image_url = fields.CharField(attribute='image_url', readonly=True)\n thumbnail_url = fields.CharField(attribute='thumbnail_url', readonly=True)\n\n class Meta(MarketplaceModelResource.Meta):\n queryset = Preview.objects.all()\n list_allowed_methods = ['post']\n allowed_methods = ['get', 'delete']\n always_return_data = True\n fields = ['id', 'filetype', 'caption']\n authentication = OAuthAuthentication()\n authorization = OwnerAuthorization()\n resource_name = 'preview'\n filtering = {'addon': ALL_WITH_RELATIONS}\n\n def obj_create(self, bundle, request, **kwargs):\n # Ensure that people don't pass strings through.\n args = PreviewArgsForm(request.GET)\n if not args.is_valid():\n raise self.form_errors(args)\n\n addon = self.get_object_or_404(Addon,\n pk=args.cleaned_data['app'],\n type=amo.ADDON_WEBAPP)\n if not AppOwnerAuthorization().is_authorized(request, object=addon):\n raise http_error(http.HttpForbidden,\n 'You are not an author of that app.')\n\n data_form = PreviewJSONForm(bundle.data)\n if not data_form.is_valid():\n raise self.form_errors(data_form)\n\n form = PreviewForm(data_form.cleaned_data)\n if not form.is_valid():\n raise self.form_errors(form)\n\n form.save(addon)\n bundle.obj = form.instance\n log.info('Preview created: %s' % bundle.obj.pk)\n return bundle\n\n def obj_delete(self, request, **kwargs):\n obj = self.get_by_resource_or_404(request, **kwargs)\n if not AppOwnerAuthorization().is_authorized(request,\n object=obj.addon):\n raise http_error(http.HttpForbidden,\n 'You are not an author of that app.')\n\n log.info('Preview deleted: %s' % obj.pk)\n return super(PreviewResource, self).obj_delete(request, **kwargs)\n\n def obj_get(self, request=None, **kwargs):\n obj = super(PreviewResource, self).obj_get(request=request, **kwargs)\n if not AppOwnerAuthorization().is_authorized(request,\n object=obj.addon):\n raise http_error(http.HttpForbidden,\n 'You are not an author of that app.')\n\n log.info('Preview retreived: %s' % obj.pk)\n return obj\n\n def dehydrate(self, bundle):\n # Returning an image back to the user isn't useful, let's stop that.\n if 'file' in bundle.data:\n del bundle.data['file']\n return bundle\n", "sub_path": "mkt/submit/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 8020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "commonware.log.log.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "commonware.log.log", "line_number": 27, "usage_type": "attribute"}, {"api_name": "commonware.log", "line_number": 27, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 29, "usage_type": "name"}, {"api_name": "mkt.api.base.CORSResource", "line_number": 33, "usage_type": "name"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 33, "usage_type": "name"}, {"api_name": "mkt.api.base.MarketplaceModelResource.Meta", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 35, "usage_type": "name"}, {"api_name": "files.models.FileUpload.objects.all", "line_number": 36, "usage_type": "call"}, {"api_name": "files.models.FileUpload.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "files.models.FileUpload", "line_number": 36, "usage_type": "name"}, {"api_name": "tastypie.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "mkt.api.authentication.OptionalOAuthAuthentication", "line_number": 41, "usage_type": "call"}, {"api_name": "tastypie.authorization.Authorization", "line_number": 42, "usage_type": "call"}, {"api_name": "tastypie.serializers.Serializer", "line_number": 44, "usage_type": "call"}, {"api_name": "mkt.api.forms.NewPackagedForm", "line_number": 50, "usage_type": "call"}, {"api_name": "mkt.developers.forms.NewManifestForm", "line_number": 51, "usage_type": "call"}, {"api_name": "files.models.FileUpload.objects.create", "line_number": 57, "usage_type": "call"}, {"api_name": "files.models.FileUpload.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "files.models.FileUpload", "line_number": 57, "usage_type": "name"}, {"api_name": "mkt.developers.tasks.fetch_manifest", "line_number": 60, "usage_type": "call"}, {"api_name": "mkt.developers.tasks", "line_number": 60, "usage_type": "name"}, {"api_name": "mkt.developers.tasks.validator.delay", "line_number": 64, "usage_type": "call"}, {"api_name": "mkt.developers.tasks.validator", "line_number": 64, "usage_type": "attribute"}, {"api_name": "mkt.developers.tasks", "line_number": 64, "usage_type": "name"}, {"api_name": "files.models.FileUpload.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "files.models.FileUpload.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "files.models.FileUpload", "line_number": 68, "usage_type": "name"}, {"api_name": "amo.decorators.write", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 47, "usage_type": "name"}, {"api_name": "files.models.FileUpload.objects.get", "line_number": 78, "usage_type": "call"}, {"api_name": "files.models.FileUpload.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "files.models.FileUpload", "line_number": 78, "usage_type": "name"}, {"api_name": "files.models.FileUpload.DoesNotExist", "line_number": 79, "usage_type": "attribute"}, {"api_name": "files.models.FileUpload", "line_number": 79, "usage_type": "name"}, {"api_name": "mkt.api.base.http_error", "line_number": 80, "usage_type": "call"}, {"api_name": "tastypie.http.HttpNotFound", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 80, "usage_type": "name"}, {"api_name": "amo.decorators.write", "line_number": 72, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 96, "usage_type": "name"}, {"api_name": "mkt.api.base.MarketplaceModelResource.Meta", "line_number": 98, "usage_type": "attribute"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 98, "usage_type": "name"}, {"api_name": "addons.models.Addon.objects.filter", "line_number": 99, "usage_type": "call"}, {"api_name": "addons.models.Addon.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "addons.models.Addon", "line_number": 99, "usage_type": "name"}, {"api_name": "amo.ADDON_WEBAPP", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tastypie.fields", "line_number": 100, "usage_type": "name"}, {"api_name": "mkt.api.authentication.OAuthAuthentication", "line_number": 104, "usage_type": "call"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 105, "usage_type": "call"}, {"api_name": "tastypie.serializers.Serializer", "line_number": 107, "usage_type": "call"}, {"api_name": "addons.models.Addon.DoesNotExist", "line_number": 114, "usage_type": "attribute"}, {"api_name": "addons.models.Addon", "line_number": 114, "usage_type": "name"}, {"api_name": "mkt.api.base.http_error", "line_number": 115, "usage_type": "call"}, {"api_name": "tastypie.http.HttpNotFound", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 115, "usage_type": "name"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 117, "usage_type": "call"}, {"api_name": "mkt.api.base.http_error", "line_number": 118, "usage_type": "call"}, {"api_name": "tastypie.http.HttpForbidden", "line_number": 118, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 118, "usage_type": "name"}, {"api_name": "mkt.api.forms.StatusForm", "line_number": 121, "usage_type": "call"}, {"api_name": "amo.decorators.write", "line_number": 109, "usage_type": "name"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 110, "usage_type": "name"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 133, "usage_type": "call"}, {"api_name": "mkt.api.base.http_error", "line_number": 134, "usage_type": "call"}, {"api_name": "tastypie.http.HttpForbidden", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 134, "usage_type": "name"}, {"api_name": "amo.decorators.write", "line_number": 130, "usage_type": "name"}, {"api_name": "amo.STATUS_CHOICES_API", "line_number": 141, "usage_type": "attribute"}, {"api_name": "amo.STATUS_CHOICES_API_LOOKUP", "line_number": 144, "usage_type": "attribute"}, {"api_name": "mkt.api.base.CORSResource", "line_number": 147, "usage_type": "name"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 147, "usage_type": "name"}, {"api_name": "tastypie.fields.CharField", "line_number": 148, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 148, "usage_type": "name"}, {"api_name": "tastypie.fields.CharField", "line_number": 149, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 149, "usage_type": "name"}, {"api_name": "mkt.api.base.MarketplaceModelResource.Meta", "line_number": 151, "usage_type": "attribute"}, {"api_name": "mkt.api.base.MarketplaceModelResource", "line_number": 151, "usage_type": "name"}, {"api_name": "addons.models.Preview.objects.all", "line_number": 152, "usage_type": "call"}, {"api_name": "addons.models.Preview.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "addons.models.Preview", "line_number": 152, "usage_type": "name"}, {"api_name": "tastypie.fields", "line_number": 156, "usage_type": "name"}, {"api_name": "mkt.api.authentication.OAuthAuthentication", "line_number": 157, "usage_type": "call"}, {"api_name": "mkt.api.authorization.OwnerAuthorization", "line_number": 158, "usage_type": "call"}, {"api_name": "tastypie.resources.ALL_WITH_RELATIONS", "line_number": 160, "usage_type": "name"}, {"api_name": "mkt.api.forms.PreviewArgsForm", "line_number": 164, "usage_type": "call"}, {"api_name": "addons.models.Addon", "line_number": 168, "usage_type": "argument"}, {"api_name": "amo.ADDON_WEBAPP", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 171, "usage_type": "call"}, {"api_name": "mkt.api.base.http_error", "line_number": 172, "usage_type": "call"}, {"api_name": "tastypie.http.HttpForbidden", "line_number": 172, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 172, "usage_type": "name"}, {"api_name": "mkt.api.forms.PreviewJSONForm", "line_number": 175, "usage_type": "call"}, {"api_name": "mkt.developers.forms.PreviewForm", "line_number": 179, "usage_type": "call"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 190, "usage_type": "call"}, {"api_name": "mkt.api.base.http_error", "line_number": 192, "usage_type": "call"}, {"api_name": "tastypie.http.HttpForbidden", "line_number": 192, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 192, "usage_type": "name"}, {"api_name": "mkt.api.authorization.AppOwnerAuthorization", "line_number": 200, "usage_type": "call"}, {"api_name": "mkt.api.base.http_error", "line_number": 202, "usage_type": "call"}, {"api_name": "tastypie.http.HttpForbidden", "line_number": 202, "usage_type": "attribute"}, {"api_name": "tastypie.http", "line_number": 202, "usage_type": "name"}]}
+{"seq_id": "6531507", "text": "import datetime\n\nfrom PyQt5 import QtWidgets\n\nfrom common.parsers import parseDateSoftNoYear\nfrom database.database import DbHandler\nfrom rentdates.rentsetdetail import RateSetType, RentSetDetail\nfrom widgets.widgets import JLabel, JLineEdit, JComboBox\n\n\nclass DateSetInfo(QtWidgets.QVBoxLayout):\n def __init__(self, dbHandler: DbHandler, parent: QtWidgets.QWidget=None):\n super().__init__(parent)\n \n self.dbHandler = dbHandler\n\n self.setSpacing(10)\n self.setContentsMargins(15, 10, 20, 10)\n self.setSizeConstraint(QtWidgets.QLayout.SetMinimumSize)\n\n # DateCode\n self.labelDateCode = JLabel(\"Date code\", self.parentWidget())\n self.labelDateCode.setFixedSizePolicy()\n\n self.leDateCode = JLineEdit(self.parentWidget())\n self.leDateCode.setFixedSizePolicy()\n self.leDateCode.setFixedWidth(100)\n self.leDateCode.setEnabled(False)\n self.leDateCode.setPlaceholderText(\"PENDING\")\n\n self.addWidget(self.labelDateCode)\n self.addWidget(self.leDateCode)\n\n # Date set type\n self.labelDateType, self.comboDateType = self.addDateTypeControls()\n self.addWidget(self.labelDateType)\n self.addWidget(self.comboDateType)\n\n # Dates\n self.labelDate1, self.leDate1, self.labelLongDate1, self.horizDate1 = self.addDateControls(\"Date 1\")\n self.labelDate2, self.leDate2, self.labelLongDate2, self.horizDate2 = self.addDateControls(\"Date 2\")\n self.labelDate3, self.leDate3, self.labelLongDate3, self.horizDate3 = self.addDateControls(\"Date 3\")\n self.labelDate4, self.leDate4, self.labelLongDate4, self.horizDate4 = self.addDateControls(\"Date 4\")\n\n self.addWidget(self.labelDate1)\n self.addLayout(self.horizDate1)\n self.addWidget(self.labelDate2)\n self.addLayout(self.horizDate2)\n self.addWidget(self.labelDate3)\n self.addLayout(self.horizDate3)\n self.addWidget(self.labelDate4)\n self.addLayout(self.horizDate4)\n self.addStretch(12)\n\n self.setReadOnly()\n\n def setUpdateRentCode(self):\n self.leDate1.textChanged.connect(self.updateRentCode)\n self.comboDateType.currentIndexChanged.connect(self.updateRentCode)\n\n def updateRentCode(self):\n date = parseDateSoftNoYear(self.leDate1.text())\n if date is None:\n self.leDateCode.setText(\"\")\n return\n\n self.leDateCode.setText(self.generateDateCode(self.comboDateType.currentText(), date))\n\n def generateDateCode(self, type, date):\n if type == RateSetType.Semi_Annual:\n prefix = \"F2\"\n table = \"dates_f2\"\n else:\n prefix = \"F4\"\n table = \"dates_f4\"\n\n day = date.strftime(\"%d\")\n month = date.strftime(\"%b\")\n\n existing = self.dbHandler.bufferedExecute(\"SELECT DateCode FROM {} WHERE DateCode LIKE %s ORDER BY DateCode DESC\".format(table), (prefix + month + day + \"%\",))\n\n if len(existing) == 0:\n return prefix + month + day + \"A\"\n else:\n endCharacter = chr(ord(existing[0][0][-1:]) + 1)\n return prefix + month + day + endCharacter\n\n def addDateTypeControls(self):\n labelDateType = JLabel(\"Date set type\", self.parentWidget())\n labelDateType.setFixedSizePolicy()\n\n comboDateType = JComboBox(self.parentWidget())\n comboDateType.addItems([\"\", RateSetType.Semi_Annual, RateSetType.Quarterly])\n comboDateType.setFixedWidth(150)\n comboDateType.currentIndexChanged.connect(self.dateTypeChanged)\n\n return labelDateType, comboDateType\n\n def dateTypeChanged(self, arg):\n self.showDates3And4(self.comboDateType.currentText() == RateSetType.Quarterly)\n\n def addDateControls(self, label):\n labelDate = JLabel(label, self.parentWidget())\n labelDate.setFixedSizePolicy()\n\n leDate = JLineEdit(self.parentWidget())\n leDate.setFixedSizePolicy()\n leDate.setFixedWidth(100)\n leDate.setPlaceholderText(\"e.g. 01/01\")\n\n labelLongDate = JLabel(\"\", self.parentWidget())\n labelLongDate.setFixedSizePolicy()\n\n leDate.textChanged.connect(lambda: self.updateDate(leDate, labelLongDate))\n\n horizDate = QtWidgets.QHBoxLayout()\n horizDate.addWidget(leDate)\n horizDate.addWidget(labelLongDate)\n horizDate.addStretch(1)\n\n return labelDate, leDate, labelLongDate, horizDate\n\n @staticmethod\n def updateDate(le, label):\n date = parseDateSoftNoYear(le.text())\n if date is None:\n label.clear()\n else:\n label.setText(date.strftime(\"%d %b\"))\n\n def getRateSetDetail(self):\n dateCode = self.leDateCode.text()\n rentType = self.comboDateType.currentText()\n date1 = self.parseDate(self.leDate1.text())\n date2 = self.parseDate(self.leDate2.text())\n date3 = self.parseDate(self.leDate3.text())\n date4 = self.parseDate(self.leDate4.text())\n\n rentSetDetail = (dateCode, rentType, date1, date2, date3, date4)\n return RentSetDetail(*rentSetDetail)\n\n @staticmethod\n def parseDate(date):\n return parseDateSoftNoYear(date)\n\n def setInfo(self, rateSetDetail):\n\n self.leDateCode.setText(rateSetDetail.dateCode)\n\n self.leDate1.setText(rateSetDetail.date1.toString(\"d/MM\"))\n self.leDate2.setText(rateSetDetail.date2.toString(\"d/MM\"))\n\n self.setReadOnly()\n\n if rateSetDetail.type == RateSetType.Semi_Annual:\n self.comboDateType.setCurrentIndex(1)\n self.showDates3And4(False)\n\n elif rateSetDetail.type == RateSetType.Quarterly:\n self.comboDateType.setCurrentIndex(2)\n self.showDates3And4(True)\n\n self.leDate3.setText(rateSetDetail.date3.toString(\"d/MM\"))\n self.leDate4.setText(rateSetDetail.date4.toString(\"d/MM\"))\n\n else:\n raise Exception(\"Unknown rent type encountered: \" + rateSetDetail.type)\n\n def clearInfo(self):\n self.leDateCode.clear()\n self.comboDateType.setCurrentIndex(0)\n self.leDate1.clear()\n self.leDate2.clear()\n self.leDate3.clear()\n self.leDate4.clear()\n\n def showDates3And4(self, show):\n if show:\n self.labelDate3.show()\n self.leDate3.show()\n self.labelLongDate3.show()\n\n self.labelDate4.show()\n self.leDate4.show()\n self.labelLongDate4.show()\n else:\n self.labelDate3.hide()\n self.leDate3.hide()\n self.labelLongDate3.hide()\n\n self.labelDate4.hide()\n self.leDate4.hide()\n self.labelLongDate4.hide()\n\n def setReadOnly(self):\n self.leDateCode.setEnabled(False)\n self.comboDateType.setEnabled(False)\n self.leDate1.setEnabled(False)\n self.leDate2.setEnabled(False)\n self.leDate3.setEnabled(False)\n self.leDate4.setEnabled(False)\n\n def setEditable(self):\n self.comboDateType.setEnabled(True)\n self.setDatesEditable()\n\n def setDatesEditable(self):\n self.leDate1.setEnabled(True)\n self.leDate2.setEnabled(True)\n self.leDate3.setEnabled(True)\n self.leDate4.setEnabled(True)\n", "sub_path": "rentdates/datesetinfo.py", "file_name": "datesetinfo.py", "file_ext": "py", "file_size_in_byte": 7270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 11, "usage_type": "name"}, {"api_name": "database.database.DbHandler", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLayout", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 19, "usage_type": "name"}, {"api_name": "widgets.widgets.JLabel", "line_number": 22, "usage_type": "call"}, {"api_name": "widgets.widgets.JLineEdit", "line_number": 25, "usage_type": "call"}, {"api_name": "common.parsers.parseDateSoftNoYear", "line_number": 62, "usage_type": "call"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Semi_Annual", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType", "line_number": 70, "usage_type": "name"}, {"api_name": "widgets.widgets.JLabel", "line_number": 89, "usage_type": "call"}, {"api_name": "widgets.widgets.JComboBox", "line_number": 92, "usage_type": "call"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Semi_Annual", "line_number": 93, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType", "line_number": 93, "usage_type": "name"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Quarterly", "line_number": 93, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Quarterly", "line_number": 100, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType", "line_number": 100, "usage_type": "name"}, {"api_name": "widgets.widgets.JLabel", "line_number": 103, "usage_type": "call"}, {"api_name": "widgets.widgets.JLineEdit", "line_number": 106, "usage_type": "call"}, {"api_name": "widgets.widgets.JLabel", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 116, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 116, "usage_type": "name"}, {"api_name": "common.parsers.parseDateSoftNoYear", "line_number": 125, "usage_type": "call"}, {"api_name": "rentdates.rentsetdetail.RentSetDetail", "line_number": 140, "usage_type": "call"}, {"api_name": "common.parsers.parseDateSoftNoYear", "line_number": 144, "usage_type": "call"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Semi_Annual", "line_number": 155, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType", "line_number": 155, "usage_type": "name"}, {"api_name": "rentdates.rentsetdetail.RateSetType.Quarterly", "line_number": 159, "usage_type": "attribute"}, {"api_name": "rentdates.rentsetdetail.RateSetType", "line_number": 159, "usage_type": "name"}]}
+{"seq_id": "247869331", "text": "__author__ = 'andersonpaac'\nimport random\nimport logging\n#@dev\nSENTINEL=random.randint(-100,-1) + 0.0 #Float Sentinel\nSTR_SENT = \"UNSET\" #String Sentinel\n\nclass Stock:\n price=SENTINEL\n symbol=STR_SENT\n chg_value = SENTINEL\n percent_chg = SENTINEL\n volume = SENTINEL\n csvd = STR_SENT\n def makecsv(self):\n csvd=str(self.symbol)+\", \"+str(self.price)+\", \"+str(self.chg_value)+\", \"+str(self.percent_chg)+\", \"\n csvd=csvd+str(self.volume)\n return csvd\n def logsentinal(self):\n logging.debug(\"Sentinel value was set to \"+str(SENTINEL))", "sub_path": "equities.py", "file_name": "equities.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "random.randint", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 20, "usage_type": "call"}]}
+{"seq_id": "343577540", "text": "# gitpushies.py\nimport subprocess\nimport argparse\nimport os\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--remote_ip\")\nparser.add_argument(\"--exp_dir\")\nargs = parser.parse_args()\n\nLINUX_PATH = \"/home/amie/caliper\"\nMAC_PATH = \"/Users/amiecorso/caliper\"\nRESULTS_PATH = \"/home/amie/results_caliper\"\n\nprint(\"gitpullies.py: pushing to github from mac\")\ncommand = \"cd {} && git add . && git commit -m \\\"pushing from mac\\\" && git push\".format(MAC_PATH)\nsubprocess.call(command, shell=True)\n\nprint(\"gitpulles.py: pulling from github on linux\")\n'''\ncommand = \"cd {} && git add . && git commit -m \\'pushing from Linux machine\\' && git push\".format(LINUX_PATH)\ncommand = \"ssh amie@{} \".format(args.remote_ip) + \"\\\"\" + command + \"\\\"\"\nsubprocess.call(command, shell=True)\n'''\n\ncommand = \"cd {} && git pull\".format(LINUX_PATH)\ncommand = \"ssh amie@{} \".format(args.remote_ip) + \"\\\"\" + command + \"\\\"\"\nsubprocess.call(command, shell=True)\n\ncommand = \"cd {} && git pull\".format(MAC_PATH)\nsubprocess.call(command, shell=True)\n\nos.chdir(args.exp_dir)\n", "sub_path": "experiments/poet_intkey_1.0_varyinterval/gitpullies.py", "file_name": "gitpullies.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 17, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 28, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 31, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 33, "usage_type": "call"}]}
+{"seq_id": "643295755", "text": "def tcad_yearly_data(data_path):\n '''Brings in necessary data for all defined years of TCAD Data. Returns dataframes of table values.\n \n data_folder: list of file paths where TCAD data is stored'''\n \n #list of features to bring in from data and their start and stop indexes\n import pandas as pd\n\n PROP_names = [['prop_id', 0, 12], ['prop_val_yr',18,22], ['appraised_val',1915,1930], ['assessed_val', 1945, 1960], \\\n ['situs_street_prefx',1039,1049], ['situs_street',1049,1099], ['situs_street_suffix',1099,1109], \\\n ['situs_city',1109,1139], ['situs_zip',1139,1149], ['situs_num',4459,4474], ['situs_unit',4474,4479], \\\n ['py_owner_name', 608, 678], ['partial_owner', 678, 679], ['hood_cd', 1685, 1695]]\n\n LAND_names = [['prop_id',0,12], ['size_square_feet', 83, 97], ['land_seg_id', 16, 28]]\n\n IMP_INFO_names = [['prop_id',0,12],['imprv_id', 16, 28], ['prop_val_yr', 12, 16], ['imprv_type_cd', 28,38], \\\n ['imprv_type_desc', 38, 63], ['imprv_homesite', 68, 69], ['imprv_state_cd', 63, 68]] \n\n IMP_DET_names = [['prop_id',0,12], ['yr_built', 85, 89], ['imprv_det_area', 93, 108], ['Imprv_det_class_cd', 75, 85], \\\n ['Imprv_det_type_desc',50, 75], ['imprv_det_val', 122, 622], ['Imprb_det_type_cd', 40,50], \\\n ['prop_val_yr', 12, 16], ['imprv_id', 16, 28], ['imprv_det_id', 28, 40]]\n\n #make dictionary for dataframe\n PROP_dict = {i[0]:[] for i in PROP_names}\n LAND_dict = {i[0]:[] for i in LAND_names}\n IMP_INFO_dict = {i[0]:[] for i in IMP_INFO_names}\n IMP_DET_dict = {i[0]:[] for i in IMP_DET_names}\n\n #group file names, dicts, and feature indexes into a list\n files_2017 =[['PROP.TXT', PROP_names, PROP_dict], \n ['LAND_DET.TXT', LAND_names, LAND_dict],\n ['IMP_INFO.TXT', IMP_INFO_names, IMP_INFO_dict], \n ['IMP_DET.TXT', IMP_DET_names, IMP_DET_dict]]\n \n #get needed data from file into dictionaries, consult data dictionary about indexes for more info\n for file in files_2017:\n current_file = open(data_path + file[0], 'r')\n\n for line in current_file:\n for feature in file[1]:\n file[2][feature[0]].append(line[feature[1]:feature[2]].strip())\n \n #create dataframes for exploratory data analysis (EDA)\n df_prop = pd.DataFrame(PROP_dict)\n df_land = pd.DataFrame(LAND_dict)\n df_imp_info = pd.DataFrame(IMP_INFO_dict)\n df_imp_det = pd.DataFrame(IMP_DET_dict)\n\n #return all the newly created DataFrames \n return df_prop, df_land, df_imp_info, df_imp_det\n\n\ndef filter_a1_imprv(df):\n '''Filters data to A1 single family residential homes with only one structure(improvement) on them'''\n import pandas as pd\n\n #gets count of improvements on property\n imprvmnt_count = df[['prop_id', 'imprv_id']].drop_duplicates().groupby(by = 'prop_id')[['imprv_id']] \\\n .count().sort_values(by = 'imprv_id', ascending = False).reset_index().rename(columns = {'imprv_id':'imprv_count'})\n\n #join no. of improvements to main property dataset\n df_prop_counts = df.merge(imprvmnt_count, how = 'left', on = 'prop_id')\n\n #filter to properties that only have one improvement on them and are A1\n prop_wo_a1_imprv = df_prop_counts[(df_prop_counts['imprv_count'] == 1) & (df_prop_counts['imprv_state_cd'] == 'A1')].drop(columns = 'imprv_count')\n\n #filter properties that have more than one land row\n land_rows = prop_wo_a1_imprv[['prop_id', 'land_seg_id']].drop_duplicates().groupby(by = 'prop_id')[['land_seg_id']].count() \\\n .sort_values(by = 'land_seg_id', ascending = False).reset_index().rename(columns = {'land_seg_id':'land_cnt'})\n\n #merge counted land rows back to main filtered dataset\n filtered = prop_wo_a1_imprv.merge(land_rows, how = 'left', on = 'prop_id')\n\n #drop properties where there are more than 1 land segments ENSURE\n filtered.drop(filtered[filtered['land_cnt'] > 1].index, inplace = True)\n\n #reset index and drop column\n filtered_final = filtered.reset_index(drop = True).drop(columns = ['land_cnt'])\n \n return filtered_final\n\ndef geocode_address(df, save_file_path, save_point = 1000, print_address = 0, print_save = 1, return_df = 0):\n '''Geocodes address using ArcGis from geocoder module, must pass dataframe as argument with column 'address',\n Addresses must be saved to a file and pulled in when creating master dataframe because this takes a long time\n using the ArcGis API to geocode the thousands of addresses\n \n df: dataframe, must contain an 'address' column to be geocoded as well as 'lat' and 'lng' columns\n save_file_path: file path to save csv to\n save_point: iterations to save after\n print_address: if 1 print out each line of geocoded information\n print_save: if 1 print out each save instance'''\n \n import pandas as pd\n import geocoder\n\n #number of created csv files\n current_csv = 0\n \n for line in df[df['lat'] ==0].index.tolist():\n #find first empty row\n try:\n if df.loc[line, 'lat'] == 0.0:\n try:\n geo = geocoder.arcgis(df.loc[line, 'address'])\n #while the response is good\n if geo.status_code == 200:\n df.loc[line, 'lat'] = geo.json['lat']\n df.loc[line, 'lng'] = geo.json['lng']\n if print_address == 1:\n print(\"Index Completed:\", line, df.loc[line, 'address'], df.loc[line, 'lat'], \\\n df.loc[line, 'lng'])\n #save to csv\n if line % save_point == 0 and line != 0:\n df.to_csv(save_file_path, columns = ['prop_id', 'address', 'lat', 'lng'], index = False)\n current_csv += 1\n if print_save == 1:\n print(line, 'Saved!')\n except:\n print('Error:',line, df.loc[line, 'address'], df.loc[line, 'lat'], df.loc[line, 'lng'])\n except:\n print(line, \"Error\")\n print(line)\n \n # perform last save \n df.to_csv(save_file_path, columns = ['prop_id', 'address', 'lat', 'lng'], index = False)\n try:\n print(\"Finished Geocoding\", line + 1, \"addresses and saved.\")\n except:\n print(\"Nothing to Geocode,\", save_file_path, \"file was re-saved.\")\n \n if return_df == 1:\n return df\n\ndef austin_only_generate_address_series(df):\n '''Create Address dataframe to be geocoded for AUSTIN, must contain a large amount\n of address information to work, column names came from TCAD files, only going to look at \n Austin addresses(by zipcode) because some zip codes are missing from address'''\n \n import pandas as pd\n\n #Austin zip codes from: http://www.city-data.com/zipmaps/Austin-Texas.html\n austin_zip = ['78610', '78613', '78617', '78641', '78652', '78653', '78660', '78664', '78681', '78701', '78702', '78703', \\\n '78704', '78705', '78712', '78717', '78719', '78721', '78722', '78723', '78724', '78725', '78726', '78727', \\\n '78728', '78729', '78730', '78731', '78732', '78733', '78734', '78735', '78736', '78737', '78738', '78739', \\\n '78741', '78742', '78744', '78745', '78746', '78747', '78748', '78749', '78750', '78751', '78752', '78753', \\\n '78754', '78756', '78757', '78758', '78759']\n \n #fix zip codes, only keep 5 digit version for consistent modeling/geocoding\n df['situs_zip'] = df['situs_zip'].apply(lambda x: x[:5])\n \n df['situs_city'] = df['situs_zip'].apply(lambda x: 'Austin' if x in austin_zip else '')\n \n #drop data that does not have complete address\n df.drop(df[df['situs_zip'] == ''].index, inplace = True)\n df.drop(df[df['situs_street'] == ''].index, inplace = True)\n df.drop(df[df['situs_num'] == ''].index, inplace = True)\n df.drop(df[df['situs_city'] == ''].index, inplace = True)\n \n #create complete address field for geocoding\n add = df[['prop_id', 'situs_num', 'situs_street_prefx','situs_street', \\\n 'situs_street_suffix', 'situs_city', 'situs_zip']].drop_duplicates()\n add['situs_state'] = 'Texas'\n \n add['address'] = add['situs_num'] + ' ' + add['situs_street_prefx'] + ' ' + add['situs_street'] + ' ' + add['situs_street_suffix'] + ' ' +\\\n add['situs_city'] + ', ' + add['situs_state'] + ' ' + add['situs_zip']\n\n add_1 = add[['prop_id', 'address']].reset_index(drop = True).reset_index()\n add_1['lat'] = 0.0\n add_1['lng'] = 0.0\n add_1.drop(columns = ['index'], inplace = True)\n \n return add_1\n\n\ndef match_prop_id(df):\n '''Adds zeroes and makes prop_id a string for dataframes so latlong df can be matched to prop_vals df'''\n import pandas as pd\n\n df['prop_id'] = df['prop_id'].apply(str)\n df['prop_id'] = df['prop_id'].apply(lambda x: '0'*(12 - len(x)) + x)\n\n return df\n\n\ndef create_X_y_data(prop_vals, latlong):\n '''Creates X and y variables for dataframe, does all necessary cleaning'''\n import pandas as pd\n \n #pivot house features so they can be joined to individual improvements\n imprv_det_pivot = prop_vals.pivot_table(values = 'imprv_det_area', index = prop_vals.imprv_id, columns = 'Imprv_det_type_desc', aggfunc = 'first').reset_index()\n \n #need to include year built\n X_int = prop_vals[['prop_id','imprv_id','appraised_val','size_square_feet']].drop_duplicates()\n \n #merge pivoted columns onto the master dataset for modeling\n X = X_int.merge(imprv_det_pivot, how = 'left', on = 'imprv_id')\n \n #convert columns to numeric for modeling\n for col in X.columns[2:]:\n X[col] = pd.to_numeric(X[col])\n\n #Add hood_cd and situs_zip to the resulting dataframe as well, can later be one hot encoded if needed\n X_med = X.merge(prop_vals[['prop_id', 'situs_zip', 'hood_cd']].drop_duplicates(), how = 'left', on = 'prop_id')\n \n #MERGE GEOCODED ADDRESS INFO ONTO MAIN DATAFRAME\n X_final = X_med.merge(latlong, how = 'left', on = 'prop_id')\n \n #create zero-inflated model(fill nans with zero, works for this model) to fill out nans from pivoted data\n X_final.fillna(value = 0, inplace = True)\n \n #define X and y for train test split, added situs_zip and hood_cd\n #y = X[['prop_id', 'imprv_id', 'hood_cd', 'situs_zip', 'appraised_val' ]]\n #X.drop(columns = ['prop_id', 'imprv_id', 'appraised_val', 'address'], inplace = True)\n \n return X_final", "sub_path": "AustinAppraisalsDataCleaning.py", "file_name": "AustinAppraisalsDataCleaning.py", "file_ext": "py", "file_size_in_byte": 10717, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "geocoder.arcgis", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 200, "usage_type": "call"}]}
+{"seq_id": "326175652", "text": "from itertools import islice\nfrom common_functions import read_file, write_file, overlapped, read_data, make_directory\nimport os, os.path\nimport pdb\n\n\n#--------------------------------------------------------------------------------------------------------------------------\n\ndef getData_FromFile(filePath, FIRST_CHR, SECOND_CHR): \n\tfileData = read_file(filePath)\n\tfileData = [item.replace(\"\\n\",\"\").split(\"\\t\") for item in fileData]\n\treturn [item for item in fileData if item[0] == FIRST_CHR or item[0] == SECOND_CHR]\n\n#--------------------------------------------------------------------------------------------------------------------------\n\ndef contact(x, y):\n\n\tif int(x[0]) > int(y[0]):\n\t\treturn 1\n\treturn -1\n#------------------------------------\ndef contactSecond(x, y):\n\n\tif int(x[1]) > int(y[1]):\n\t\treturn 1\n\treturn -1\n\n\n#--------------------------------------------------------------------------------------------------------------------------\n\n\n\n\ndef binary_search_Overlapping(EnhancerData, contactData, CHROMOSOME, EOPstart_Indext, EOPend_Index,contactIndex):\n\toverlapsDetails = []\n\tcontactSize = len(contactData)\n\n\tfor enhancer in EnhancerData:\n\t\tenhancer_start = int(enhancer[EOPstart_Indext])\n\t\tenhancer_end = int(enhancer[EOPend_Index])\n\n\t\t### Binary Search------------------------------------------------------------------------------------------------------------------------------------------------------------\n\t\tfirst = 0\n\t\tlast = (contactSize-1)\n\t\tfound = False\n\t\tmidPoint = None\n\t\tcontact_chrom_start = None\n\n\t\twhile not found and first<= last:\n\t\t\tmidPoint = (first + last)//2\n\t\t\t\n\t\t\tcontact_chrom_start = int(contactData[midPoint][contactIndex])\n\t\t\tcontact_chrom_end = int(contactData[midPoint][contactIndex]) + 5000\n\t\t\t\n\n\t\t\tif (CHROMOSOME == enhancer[0] and overlapped(contact_chrom_start, contact_chrom_end, enhancer_start, enhancer_end)):\n\t\t\t\toverlapsDetails.append(contactData[midPoint] + enhancer)\n\t\t\t\tfound = True\n\n\t\t\t\t### computing for different regions of FIRST CHROMOSOME:\n\t\t\t\tupStream = midPoint - 1\n\t\t\t\tdownStream = midPoint + 1\n\t\t\t\t\n\t\t\t\twhile upStream>=0 and int(contactData[upStream][contactIndex]) == contact_chrom_start:\n\t\t\t\t\toverlapsDetails.append(contactData[upStream] + enhancer)\n\t\t\t\t\tupStream -=1\n\n\t\t\t\t#################################################################################################################################################################\n\t\t\t\t### if an enhancer region is common in different regions. ex: enhancer region: 19000-21000 and two sequential contact regions: 15000-20000, 20001-25000. So, ###\n\t\t\t\t### this enhancer will be picked as overlapping for both contact regionsself. ###\n\t\t\t\t#################################################################################################################################################################\n\t\t\t\t### upstream common region. ex: enhancer: 19000-21000. contact regions: 15000-20000\n\n\t\t\t\tif upStream>=0:\n\t\t\t\t\tsecond_chrom_start_prevRegion = int(contactData[upStream][contactIndex])\n\t\t\t\t\tsecond_chrom_end_prevRegion\t = int(contactData[upStream][contactIndex]) + 5000\n\n\t\t\t\t\tif (second_chrom_start_prevRegion != contact_chrom_start) and overlapped(second_chrom_start_prevRegion, second_chrom_end_prevRegion, enhancer_start, enhancer_end):\n\t\t\t\t\t\t\n\t\t\t\t\t\toverlapsDetails.append(contactData[upStream] + enhancer)\n\n\t\t\t\t\t\tupStream -=1\n\t\t\t\t\t\twhile upStream>=0 and int(contactData[upStream][contactIndex]) == second_chrom_start_prevRegion:\n\t\t\t\t\t\t\toverlapsDetails.append(contactData[upStream] + enhancer)\n\t\t\t\t\t\t\tupStream -=1\n\n\n\t\t\t\t###-----------------\n\n\t\t\t\twhile downStream<contactSize and int(contactData[downStream][contactIndex]) == contact_chrom_start :\n\t\t\t\t\toverlapsDetails.append(contactData[downStream] + enhancer)\n\t\t\t\t\tdownStream +=1\n\n\t\t\t\t###downstream common region. ex : enhancer: 19000-21000. contact regions: 20001-25000\n\t\t\t\tif downStream < contactSize:\n\t\t\t\t\tsecond_chrom_start_nextRegion = int(contactData[downStream][contactIndex])\n\t\t\t\t\tsecond_chrom_end_nextRegion\t = int(contactData[downStream][contactIndex]) + 5000\n\n\t\t\t\t\tif (second_chrom_start_nextRegion != contact_chrom_start) and overlapped(second_chrom_start_nextRegion, second_chrom_end_nextRegion, enhancer_start, enhancer_end):\n\t\t\t\t\t\toverlapsDetails.append(contactData[downStream] + enhancer)\n\n\t\t\t\t\t\tdownStream +=1\n\t\t\t\t\t\twhile downStream<contactSize and int(contactData[downStream][contactIndex]) == second_chrom_start_nextRegion:\n\t\t\t\t\t\t\toverlapsDetails.append(contactData[downStream] + enhancer)\n\t\t\t\t\t\t\tdownStream +=1\n\n\t\t\t\t###---------\n\n\n\t\t\telse:\n\t\t\t\tif enhancer_start < contact_chrom_start:\n\t\t\t\t\tlast = midPoint - 1\n\t\t\t\telse:\n\t\t\t\t\tfirst = midPoint + 1\n\n\treturn overlapsDetails\n\n\n\n\n\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\ndef get_OverlappingData(EnhancerData, contactFilePath, FIRST_CHR, SECOND_CHR):\n\toverlapDetails=[]\n\tfirst_chrom_overlapDetails = []\n\tsecond_chrom_overlapDetails = []\n\tenhancerStart_Index = 1\n\tenhancerEnd_Index = 2\n\n\n\twith open(contactFilePath) as contactFL:\n\t while True:\n\n\t \t### File will read 100000 lines per processing--\n\t contactData = list(islice(contactFL, 100000))\n\t if not contactData:\n\t break\n\n\t contactData = [item.replace(\"\\n\",\"\").split(\"\\t\") for item in contactData]\n\n\t ################################################ SECOND_CHR ############################################################\n\t secondContactIndex = 1\n\t contactData_sorted_secondChrom = sorted(contactData, cmp=contactSecond)\n\t second_chrom_overlapDetails += binary_search_Overlapping(EnhancerData, contactData_sorted_secondChrom, SECOND_CHR, enhancerStart_Index, enhancerEnd_Index, secondContactIndex)\n\t \n\t ################################################ FIRST_CHR##############################################################\n\t firstContactIndex = 0\n\t contactData_sorted_firstChrom = sorted(contactData, cmp=contact)\n\t first_chrom_overlapDetails += binary_search_Overlapping(EnhancerData, contactData_sorted_firstChrom, FIRST_CHR,enhancerStart_Index, enhancerEnd_Index, firstContactIndex)\n\n\t ########################################################################################################################\n\n\n\toverlapDetails = second_chrom_overlapDetails + first_chrom_overlapDetails\n\treturn overlapDetails\n\t\n\t\t\t\t\n\n#----End of get_OverlappingData()-----------------------------------------------------------------------\n\n", "sub_path": "TF_Enhancer_overlapping_with_interchromosomal_contacts.py", "file_name": "TF_Enhancer_overlapping_with_interchromosomal_contacts.py", "file_ext": "py", "file_size_in_byte": 6657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "common_functions.read_file", "line_number": 10, "usage_type": "call"}, {"api_name": "common_functions.overlapped", "line_number": 56, "usage_type": "call"}, {"api_name": "common_functions.overlapped", "line_number": 78, "usage_type": "call"}, {"api_name": "common_functions.overlapped", "line_number": 99, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 137, "usage_type": "call"}]}
+{"seq_id": "411908974", "text": "from Crypto.Cipher import AES\nimport base64\nimport os\nimport random,string\nimport timeit\n\nstart = timeit.default_timer()\n\ndef encryption(message):\n\tBLOCK_SIZE=16\n\tPADDING='{'\n\tpad=lambda s: s+ (BLOCK_SIZE - len(s) % BLOCK_SIZE)*PADDING\n\tEncodeAES=lambda c,s: base64.b64encode(c.encrypt(pad(s)))\n\tkey=os.urandom(BLOCK_SIZE)\n\tprint(f'Encryption Key: {key}')\n\tprint(f'The message is: {message}')\n\n\tcipher=AES.new(key,AES.MODE_ECB)\n\n\tencoded=EncodeAES(cipher,message)\n\tprint(f'Encrypted Message: {encoded}')\n\tdecryption(encoded,key)\n \n\n\ndef decryption(encoded,key):\n\tPADDING='{'\n\tDecodeAES=lambda c, e: c.decrypt(base64.b64decode(e))#.rstrtip(PADDING)\n\t\n\t#key=secret\n\tcipher=AES.new(key,AES.MODE_ECB)\n\tdecoded=DecodeAES(cipher,encoded)\n\tprint(f'Decrypted Message: {decoded}')\n \nencryption('1234567890123456')\n\n\nfor i in range(1001):\n\tx = ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits) for _ in range(16))\n\tencryption(x)\n #decryption(encoded,key)\n\nstop = timeit.default_timer()\n\nprint('Time: ', stop - start) ", "sub_path": "aes.py", "file_name": "aes.py", "file_ext": "py", "file_size_in_byte": 1052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "timeit.default_timer", "line_number": 7, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 13, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 14, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 18, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 18, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_ECB", "line_number": 18, "usage_type": "attribute"}, {"api_name": "base64.b64decode", "line_number": 28, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 31, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 31, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_ECB", "line_number": 31, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 39, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 39, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 39, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 39, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 43, "usage_type": "call"}]}
+{"seq_id": "257026823", "text": "from django import template\nfrom django_components.component import registry\nfrom django.utils.safestring import mark_safe\n\nimport re\n\nregister = template.Library()\n\n@register.simple_tag(name=\"component_dependencies\")\ndef component_dependencies_tag(*args, **kwargs):\n current_component_class = registry._registry[kwargs['component']]\n\n dependencies = (current_component_class.render_dependencies(),)\n\n return mark_safe(\"\\n\".join(dependencies))\n\n@register.simple_tag(name=\"component\")\ndef component_tag(name, *args, **kwargs):\n component_class = registry._registry[name]\n component = component_class()\n return component.render(*args, **kwargs)\n\n\n# Custom parser and compiler for children components\n\n@register.tag(name=\"child_component\")\ndef child_component(parser,token):\n # nodelist now contains exactly what we need to store\n nodelist = parser.parse(('end_child_component',))\n\n parser.delete_first_token() # Skips 'end_child_component' tag\n\n try:\n # Splitting by None == splitting by spaces.\n tag_name, arg = token.contents.split(None, 1)\n\n except ValueError:\n raise template.TemplateSyntaxError(\n \"%r tag requires arguments\" % token.contents.split()[0]\n )\n\n m = re.search(r'as (\\w+)', arg)\n\n if not m:\n raise template.TemplateSyntaxError(\"%r tag had invalid arguments\" % tag_name)\n\n component_name = m.groups() # Gets the matched var name.\n\n return ChildComponentNode(nodelist,component_name)\n\nclass ChildComponentNode(template.Node):\n def __init__(self, nodelist, component_name):\n self.nodelist = nodelist\n self.component_name = component_name[0]\n\n def render(self, context):\n context[self.component_name] = self.nodelist.render(context)\n return ''\n", "sub_path": "django_components/templatetags/component_tags.py", "file_name": "component_tags.py", "file_ext": "py", "file_size_in_byte": 1779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.template.Library", "line_number": 7, "usage_type": "call"}, {"api_name": "django.template", "line_number": 7, "usage_type": "name"}, {"api_name": "django_components.component.registry._registry", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django_components.component.registry", "line_number": 11, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 15, "usage_type": "call"}, {"api_name": "django_components.component.registry._registry", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django_components.component.registry", "line_number": 19, "usage_type": "name"}, {"api_name": "django.template.TemplateSyntaxError", "line_number": 38, "usage_type": "call"}, {"api_name": "django.template", "line_number": 38, "usage_type": "name"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "django.template.TemplateSyntaxError", "line_number": 45, "usage_type": "call"}, {"api_name": "django.template", "line_number": 45, "usage_type": "name"}, {"api_name": "django.template.Node", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.template", "line_number": 51, "usage_type": "name"}]}
+{"seq_id": "67669327", "text": "import logging\n\nfrom pydux.extend import extend\n\nlogger = logging.getLogger(__name__)\n\n\ndef app(state=None, action=None):\n if state is None:\n state = {\"current_screen\": \"WelcomeScreen\", \"screen_history\": [\"WelcomeScreen\"]}\n\n if action[\"type\"] == \"app_set_next_screen\":\n state = set_current_screen(state, action)\n elif action[\"type\"] == \"app_set_jwt_token\":\n state = set_jwt_token(state, action)\n\n return state\n\n\ndef set_jwt_token(state, action):\n return extend(state, {\"jwt_token\": action[\"value\"]})\n\n\ndef set_current_screen(state, action):\n return extend(\n state,\n {\n \"current_screen\": action[\"value\"],\n \"screen_history\": state[\"screen_history\"] + [action[\"value\"]],\n },\n )\n", "sub_path": "client/state/reducers/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "pydux.extend.extend", "line_number": 21, "usage_type": "call"}, {"api_name": "pydux.extend.extend", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "336259540", "text": "from django.conf.urls import patterns, url\n\nfrom .views import SeriesView\n\nurlpatterns = patterns(\n 'series.views',\n url(\n regex=r'^search/(?P<search>[^/]+)/',\n view='search_series',\n name='search_series',\n ),\n url(\n regex=r'^add_series/(?P<series_id>\\d+)/',\n view='add_series',\n name='add_series',\n ),\n url(\n regex=r'^(?P<slug>[-_\\w]+)/$',\n view=SeriesView.as_view(),\n name='view_series',\n ),\n)\n", "sub_path": "jolly_roger/series/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "views.SeriesView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.SeriesView", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "562490161", "text": "from setuptools import setup, find_packages\nimport os\n\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__),fname)).read()\n\nsetup(\n name = 'hellodemo',\n version = '0.0.2',\n keywords= 'demo',\n description = 'a demo',\n long_description = read('README.md'),\n license = 'MIT License',\n url = 'https://github.com/ckcat',\n author='ckcat',\n author_email= 'ckcatck@qq.com',\n classifiers=[\n 'Development Status ::demo',\n 'topics:: demo',\n 'programming language :: Python3:: Only',\n ],\n\n packages = find_packages(exclude=['tests']),\n install_requires = ['requests',],\n)", "sub_path": "Python/python打包/setups/hellodemo/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 24, "usage_type": "call"}]}
+{"seq_id": "86932487", "text": "# Import flask dependencies\nfrom flask import Blueprint, request, render_template, \\\n flash, g, session, redirect, url_for, jsonify\n\n# Import the database object from the main app module\nfrom app import db\n\n# Import login manager login_required decorator - is there a way to avoid having to add this to all functions in a separate blueprint?\nfrom flask.ext.login import login_required\n\nfrom wtforms import SubmitField\n#from webob.multidict import MultiDict\n\n# Import module forms\nfrom app.checklists.forms import ChecklistForm, SectionForm, ChecklistItemForm\n\n# Import module models\nfrom app.checklists.models import Checklist, Section, ChecklistItem, ChecklistEntry\n\n# Define the blueprint: 'checklists', set its url prefix: app.url/chkls\nchecklists = Blueprint('chkls', __name__, url_prefix='/chkls')\n\n# General useful functions - place in general blueprint\ndef get_object(object_id, object_class):\n if object_id is None:\n return None\n else:\n located_object = object_class.query.get(int(object_id))\n if located_object is None:\n return None\n else:\n return located_object\n\n#----Login code for blueprints\n# Check login for all functions\n@checklists.before_request\n@login_required #Comment out when testing\ndef before_request():\n\tpass\n\n@checklists.route('/', methods=['GET', 'POST'])\ndef index():\n\treturn render_template('checklists/index.html')\n\n@checklists.route('/templates/edit', methods=['GET', 'POST'])\ndef viewtemplates():\n\ttemplates \t= \tChecklist.query.filter(Checklist.template == True)\n\t\n\tform\t\t=\tChecklistForm()\n\tdel form.sections\n\t\n\tif form.validate_on_submit():\n\t\tchecklist\t\t\t\t\t=\tChecklist()\n\t\tchecklist.display_title \t=\tform.display_title.data\n\t\tchecklist.template\t\t\t=\tTrue\n\t\tdb.session.add(checklist)\n\t\tdb.session.commit()\n\t\treturn redirect(url_for('chkls.viewtemplates'))\n\t\n\t#Templates are added via table form here\n\treturn render_template('checklists/viewtemplates.html', form=form, templates=templates)\n\n@checklists.route('/templates/select', methods=['GET', 'POST'])\ndef selecttemplates():\n\ttemplates \t= \tChecklist.query.filter(Checklist.template == True)\n\t\n\treturn render_template('checklists/selecttemplates.html', templates=templates)\n\n@checklists.route('/templates/edit/<int:object_id>', methods=['GET', 'POST'])\ndef edit_template(object_id):\n\tchecklist = get_object(object_id, Checklist)\n\t\n\tform = ChecklistForm(obj=checklist)\n\t\n\tif request.method == 'POST':\n\t\t\n\t\tif form.validate_on_submit():\n\t\t\t\n\t\t\tfor section in form.sections:\n\t\t\t\t# Handle Section Data\n\t\t\t\tif \"deleted\" in section.form.order_id.data:\n\t\t\t\t\tcurrent_section = get_object(section.form.id.data, Section)\n\t\t\t\t\tif current_section:\n\t\t\t\t\t\tdb.session.delete(current_section)\n\t\t\t\t\t\tdb.session.commit()\n\t\t\t\telse:\n\t\t\t\t\tif \"new\" in section.form.id.data:\n\t\t\t\t\t\tcurrent_section = Section(checklist.id, increment=False)\n\t\t\t\t\t\tdb.session.add(current_section)\n\t\t\t\t\t\tdb.session.commit()\n\t\t\t\t\t\tsection.form.id.data = current_section.id\n\t\t\t\t\telse:\n\t\t\t\t\t\tcurrent_section = get_object(section.form.id.data, Section)\n\t\t\t\t\tcurrent_section.display_title = section.form.display_title.data\n\t\t\t\t\tcurrent_section.order_id = section.form.order_id.data\n\t\t\t\t\tdb.session.add(current_section)\n\t\t\t\t\tdb.session.commit()\n\t\t\t\t\t\n\t\t\t\t\t#Handle checklist item data\n\t\t\t\t\tfor checklistitem in section.checklistitems:\n\t\t\t\t\t\tif \"deleted\" in checklistitem.form.order_id.data:\n\t\t\t\t\t\t\tcurrent_item = get_object(checklistitem.form.id.data, ChecklistItem)\n\t\t\t\t\t\t\tif current_item:\n\t\t\t\t\t\t\t\tdb.session.delete(current_item)\n\t\t\t\t\t\t\t\tdb.session.commit()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif \"new\" in checklistitem.form.id.data:\n\t\t\t\t\t\t\t\tcurrent_item = ChecklistItem(current_section.id, increment=False)\n\t\t\t\t\t\t\t\tdb.session.add(current_item)\n\t\t\t\t\t\t\t\tdb.session.commit()\n\t\t\t\t\t\t\t\tchecklistitem.form.id.data = current_item.id\n\t\t\t\t\t\t\t\tchecklistitem.form.section_id.data = current_section.id\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tcurrent_item = get_object(checklistitem.form.id.data, ChecklistItem)\n\t\t\t\t\t\t\tchecklistitem.form.populate_obj(current_item)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tdb.session.add(current_item)\n\t\t\t\t\t\t\tdb.session.commit()\n\t\t\treturn redirect(url_for('chkls.viewtemplates'))\n\t\t\n\t\telse:\n\t\t\tflash(form.errors)\n\t\t\tfor field in form:\n\t\t\t\tflash(field.name, field.errors)\n\t\t\tfor field in form.sections:\n\t\t\t\tflash(field.name)\n\t\t\t\tflash(field.errors)\n\t\n\treturn render_template('checklists/editchecklist.html', form=form, checklist=checklist)\n\n\n\n@checklists.route('/render/<int:object_id>', methods=['GET', 'POST'])\ndef renderchecklist(object_id):\n\tchecklist = get_object(object_id, Checklist)\n\treturn render_template('checklists/checklist.html', checklist=checklist)\n\t\n#--- Testcode\n\n@checklists.route('/test/<int:object_id>', methods=['GET', 'POST'])\ndef testforms(object_id):\n\tchecklist = get_object(object_id, Checklist)\n\tif request.method == 'POST':\n\t\treturn jsonify(data=request.form)\n\t#data_in = {'authors': ['Author Guy', 'Other Guy']}\n\t#test_form2 = TestForm(data=MultiDict(data_in))\n\ttest_form = ChecklistForm(obj=checklist)\n\treturn test_form.sections()\n", "sub_path": "app/checklists/controllers.py", "file_name": "controllers.py", "file_ext": "py", "file_size_in_byte": 5021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "flask.Blueprint", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist.query.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist.query", "line_number": 47, "usage_type": "attribute"}, {"api_name": "app.checklists.models.Checklist", "line_number": 47, "usage_type": "name"}, {"api_name": "app.checklists.models.Checklist.template", "line_number": 47, "usage_type": "attribute"}, {"api_name": "app.checklists.forms.ChecklistForm", "line_number": 49, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 56, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 57, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 61, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist.query.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist.query", "line_number": 65, "usage_type": "attribute"}, {"api_name": "app.checklists.models.Checklist", "line_number": 65, "usage_type": "name"}, {"api_name": "app.checklists.models.Checklist.template", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 67, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist", "line_number": 71, "usage_type": "argument"}, {"api_name": "app.checklists.forms.ChecklistForm", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "app.checklists.models.Section", "line_number": 82, "usage_type": "argument"}, {"api_name": "app.db.session.delete", "line_number": 84, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 84, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 84, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 85, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 85, "usage_type": "name"}, {"api_name": "app.checklists.models.Section", "line_number": 88, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 89, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 89, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 90, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 90, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 90, "usage_type": "name"}, {"api_name": "app.checklists.models.Section", "line_number": 93, "usage_type": "argument"}, {"api_name": "app.db.session.add", "line_number": 96, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 96, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 97, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 97, "usage_type": "name"}, {"api_name": "app.checklists.models.ChecklistItem", "line_number": 102, "usage_type": "argument"}, {"api_name": "app.db.session.delete", "line_number": 104, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 104, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 105, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 105, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 105, "usage_type": "name"}, {"api_name": "app.checklists.models.ChecklistItem", "line_number": 108, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 109, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 109, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 109, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 110, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 110, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 110, "usage_type": "name"}, {"api_name": "app.checklists.models.ChecklistItem", "line_number": 114, "usage_type": "argument"}, {"api_name": "app.db.session.add", "line_number": 117, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 117, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 118, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 118, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 129, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist", "line_number": 135, "usage_type": "argument"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "app.checklists.models.Checklist", "line_number": 142, "usage_type": "argument"}, {"api_name": "flask.request.method", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 144, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 144, "usage_type": "name"}, {"api_name": "app.checklists.forms.ChecklistForm", "line_number": 147, "usage_type": "call"}]}
+{"seq_id": "557866861", "text": "# Imports: standard library\nimport os\nimport shutil\nimport argparse\nimport datetime\nfrom timeit import default_timer as timer\nfrom typing import Dict, List, Tuple\nfrom multiprocessing import Pool, cpu_count\n\n# Imports: third party\nimport h5py\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\n# Imports: first party\nfrom ml4c3.datasets import patient_csv_to_set\nfrom definitions.ecg import ECG_PREFIX\nfrom definitions.globals import CSV_EXT, TENSOR_EXT, MRN_COLUMNS\n\n\"\"\"\nTo add a new data source to deidentify:\n1. add the function to get MRNs from the data to _get_mrns()\n2. add the function to deidentify the data to run()\n\"\"\"\n\nphi_keys = {\n \"attendingmdfirstname\",\n \"attendingmdhisid\",\n \"attendingmdlastname\",\n \"consultingmdfirstname\",\n \"consultingmdhisid\",\n \"consultingmdlastname\",\n \"hisorderingmdfirstname\",\n \"hisorderingmdlastname\",\n \"orderingmdfirstname\",\n \"orderingmdhisid\",\n \"orderingmdid\",\n \"orderingmdlastname\",\n \"placersfirstname\",\n \"placershisid\",\n \"placerslastname\",\n \"patientfirstname\",\n \"patientlastname\",\n \"patientid\",\n \"patientid_clean\",\n \"acquisitiontechfirstname\",\n \"acquisitiontechid\",\n \"acquisitiontechlastname\",\n \"admittingmdfirstname\",\n \"admittingmdhisid\",\n \"admittingmdlastname\",\n \"fellowfirstname\",\n \"fellowlastname\",\n \"fellowid\",\n \"hisaccountnumber\",\n \"referringmdid\",\n \"editorfirstname\",\n \"editorlastname\",\n \"editorid\",\n \"overreaderfirstname\",\n \"overreaderlastname\",\n \"overreaderid\",\n}\n\n\ndef _get_hd5_mrns(args):\n mrns = set()\n if args.path_to_hd5 is not None:\n for root, dirs, files in os.walk(args.path_to_hd5):\n for file in files:\n split = os.path.splitext(file)\n if split[-1] != TENSOR_EXT:\n continue\n try:\n mrn = int(split[0])\n mrns.add(mrn)\n except ValueError:\n print(f\"Could not get MRN from ECG HD5: {os.path.join(root, file)}\")\n continue\n return mrns\n\n\npath_of_csv_to_skip = set()\n\n\ndef _get_csv_mrns(args):\n mrns = set()\n if args.path_to_csv_deidentified is not None:\n\n # Get list of full paths to CSV files\n fpaths = []\n if os.path.isdir(args.path_to_csv):\n for root, dirs, fnames in os.walk(args.path_to_csv):\n for fname in fnames:\n split = os.path.splitext(fname)\n if split[-1] != CSV_EXT:\n continue\n fpath = os.path.join(root, fname)\n fpaths.append(fpath)\n\n # If user gave path to single CSV, instead of a directory, use that path\n else:\n fpaths.append(args.path_to_csv)\n\n # Iterate over paths to CSV files\n for fpath in fpaths:\n try:\n _mrns = patient_csv_to_set(patient_csv=fpath)\n except ValueError:\n print(f\"Could not get MRNs from {fpath}, skipping de-identification\")\n global path_of_csv_to_skip\n path_of_csv_to_skip.add(fpath)\n continue\n _mrns = {int(mrn) for mrn in _mrns}\n mrns |= _mrns\n\n return mrns\n\n\ndef _get_mrns(args, skip_mrns=set()):\n \"\"\"\n Get a list of unique MRNs from the data sources that are being remapped.\n \"\"\"\n mrns = set()\n mrns |= _get_hd5_mrns(args)\n mrns |= _get_csv_mrns(args)\n mrns -= skip_mrns\n return mrns\n\n\ndef _remap_mrns(args):\n \"\"\"\n Remap and save the MRNs from the data sources that are being remapped to new random IDs.\n Four scenarios for starting_id:\n 1. if existing map and no starting id given, use last id in existing map\n 2. if existing map and starting id given, use given starting id\n 3. if no existing map and no starting id given, use 1\n 4. if no existing map and starting id given, use given starting id\n \"\"\"\n mrn_map = dict()\n starting_id = args.starting_id\n\n if os.path.isfile(args.mrn_map):\n # call _get_csv_mrns to determine which CSVs to skip for deidentification\n _get_csv_mrns(args)\n mrn_map = pd.read_csv(args.mrn_map, low_memory=False, usecols=[\"mrn\", \"id\"])\n mrn_map = mrn_map.set_index(\"mrn\")\n mrn_map = mrn_map[\"id\"].to_dict()\n\n # Scenario 1: use last ID in map as starting_id\n if starting_id is None:\n starting_id = max(mrn_map.values()) + 1\n print(f\"Existing MRN map loaded from {args.mrn_map}\")\n\n # Scenario 2: user specifies starting_id\n\n # Map file does not exist\n else:\n # Ensure directory housing the map file exists\n os.makedirs(os.path.dirname(args.mrn_map), exist_ok=True)\n\n # Scenario 3: no existing map, so use 1 for starting_id\n if starting_id is None:\n starting_id = 1\n # Scenario 4: user specifies starting_id\n\n # Get new MRNs from HD5 and CSV sources that are not already in mrn_map\n existing_mrns = set(mrn_map.keys())\n new_mrns = _get_mrns(args, skip_mrns=existing_mrns)\n\n # If there are new MRNs in the data sources that were not present in existing\n # MRN map, save new MRN map\n if len(new_mrns) > 0:\n new_ids = list(range(starting_id, len(new_mrns) + starting_id))\n np.random.shuffle(new_ids)\n mrn_map.update(dict(zip(new_mrns, new_ids)))\n print(f\"New MRNs remapped starting at ID {starting_id}\")\n\n # Generate new file name and path\n today = datetime.datetime.now().strftime(\"%Y-%m-%d-%H:%M:%S\")\n path_dir = os.path.dirname(args.mrn_map)\n new_file_name = f\"{args.mrn_map_prefix}-{today}.csv\"\n mrn_map_path_new = os.path.join(path_dir, new_file_name)\n\n # Convert dict of MRN map to dataframe and save to disk\n df = pd.DataFrame(list(mrn_map.items()), columns=[\"mrn\", \"id\"])\n df.sort_values(\"mrn\").to_csv(mrn_map_path_new, index=False)\n print(f\"MRN map saved to {mrn_map_path_new}\")\n\n print(f\"Last ID used in remapping MRNs was {max(mrn_map.values())}\")\n return mrn_map\n\n\ndef _swap_path_prefix(path, prefix, new_prefix):\n \"\"\"\n Given:\n path = /foo/meow/bar.csv\n prefix = /foo\n new_prefix = /baz\n Creates:\n /baz/meow\n Returns:\n /baz/meow/bar.csv\n \"\"\"\n path_relative_root = path.replace(prefix, \"\").lstrip(\"/\")\n new_path = os.path.join(new_prefix, path_relative_root)\n if os.path.isfile(path):\n os.makedirs(os.path.dirname(new_path), exist_ok=True)\n else:\n os.makedirs(new_path, exist_ok=True)\n return new_path\n\n\ndef _deidentify_hd5(old_new_path: List[Tuple]):\n \"\"\"\n Given a path to an existing HD5, copy it to a new path and delete all identifiable\n information. Currently only set up for ECG data.\n \"\"\"\n old_path, new_path = old_new_path\n if os.path.exists(new_path):\n os.remove(new_path)\n shutil.copyfile(old_path, new_path)\n\n with h5py.File(new_path, \"r+\") as hd5:\n # Only delete PHI keys from HD5s that lack 'deidentified' flag\n if ECG_PREFIX in hd5 and \"deidentified\" not in hd5:\n for ecg_date in hd5[ECG_PREFIX]:\n for key in hd5[ECG_PREFIX][ecg_date]:\n if key in phi_keys:\n del hd5[ECG_PREFIX][ecg_date][key]\n\n # Add bool to hd5 indicating this file is de-identified\n hd5.create_dataset(\"deidentified\", data=True, dtype=bool)\n\n\ndef _deidentify_hd5s(\n path_to_hd5_deidentified: str,\n path_to_hd5: str,\n mrn_map: Dict[int, int],\n num_workers: int,\n):\n \"\"\"\n Create de-identified HD5 files in parallel.\n \"\"\"\n if path_to_hd5_deidentified is None:\n return\n\n old_new_paths = []\n for root, dirs, files in os.walk(path_to_hd5):\n new_root = _swap_path_prefix(\n root,\n path_to_hd5,\n path_to_hd5_deidentified,\n )\n for file in files:\n split = os.path.splitext(file)\n if split[-1] != TENSOR_EXT:\n continue\n try:\n mrn = int(split[0])\n new_id = mrn_map[mrn]\n except (ValueError, KeyError):\n print(f\"Bad MRN mapping for ECG HD5: {os.path.join(root, file)}\")\n continue\n old_path = os.path.join(root, file)\n new_path = os.path.join(new_root, f\"{new_id}{TENSOR_EXT}\")\n old_new_paths.append((old_path, new_path))\n\n with Pool(processes=num_workers) as pool:\n pool.map(_deidentify_hd5, old_new_paths)\n\n print(f\"De-identified {len(old_new_paths)} ECGs at {path_to_hd5_deidentified}\")\n\n\ndef _deidentify_csv(path: str, mrn_map: dict, columns_to_remove: list):\n \"\"\"\n Given a path to a CSV, delete all identifiable information.\n \"\"\"\n df = pd.read_csv(path, header=None, low_memory=False)\n\n # Infer csv header\n try:\n # If first cell is an int, it's likely a sample ID and there is no header\n int(df.iloc[0].values[0])\n except ValueError:\n df.columns = df.iloc[0]\n df = df[1:]\n\n # Cast each column name in df to string (in case column name is an int)\n # and check if defined MRN column names match the column names in the df\n matches = {\n col\n for col in df.columns\n for mrn_col in MRN_COLUMNS\n if mrn_col == str(col).lower()\n }\n if len(matches) == 0:\n # If none of the known MRN columns are in the csv, assume it's the first column\n mrn_cols = [df.columns[0]]\n else:\n mrn_cols = list(matches)\n\n # Remap MRNs using the mrn_map dictionary value, where the key is each MRN from the\n # DataFrame from the loaded CSV. Only apply the function to MRNs that are not NaN.\n df[mrn_cols] = df[mrn_cols].applymap(\n lambda mrn: mrn_map[int(float(mrn))] if pd.notnull(mrn) else np.nan,\n )\n\n # Drop PHI columns and other user-specified columns\n cols_to_drop = set(df.columns) & (phi_keys | set(columns_to_remove))\n df = df.drop(cols_to_drop, axis=1)\n df.to_csv(path, index=False)\n\n\ndef _deidentify_csvs(\n path_to_csv_deidentified: str,\n path_to_csv: str,\n mrn_map: dict,\n columns_to_remove: list,\n):\n \"\"\"\n De-identify CSV data.\n \"\"\"\n if path_to_csv_deidentified is None:\n return\n\n count = 0\n new_paths = []\n old_paths = []\n\n if os.path.isfile(path_to_csv):\n dirname = os.path.dirname(path_to_csv)\n new_path = _swap_path_prefix(\n path=path_to_csv,\n prefix=dirname,\n new_prefix=path_to_csv_deidentified,\n )\n new_paths = [new_path]\n old_paths = [path_to_csv]\n else:\n for root, dirs, files in os.walk(path_to_csv):\n for file in files:\n split = os.path.splitext(file)\n if split[-1] != CSV_EXT:\n continue\n\n old_path = os.path.join(root, file)\n old_paths.append(old_path)\n\n new_path = _swap_path_prefix(\n path=os.path.join(root, file),\n prefix=path_to_csv,\n new_prefix=path_to_csv_deidentified,\n )\n new_paths.append(new_path)\n\n # Iterate over all old, new path pairs\n path_pairs = list(zip(old_paths, new_paths))\n for old_path, new_path in tqdm(path_pairs):\n if os.path.exists(new_path):\n os.remove(new_path)\n shutil.copyfile(old_path, new_path)\n\n # if there was no PHI in the original file, copy it without trying to deidentify\n global path_of_csv_to_skip\n if old_path in path_of_csv_to_skip:\n continue\n _deidentify_csv(\n path=new_path,\n mrn_map=mrn_map,\n columns_to_remove=columns_to_remove,\n )\n count += 1\n\n print(f\"De-identified {count} CSV files at {args.path_to_csv_deidentified}\")\n\n\ndef run(args):\n start_time = timer()\n mrn_map = _remap_mrns(args)\n\n _deidentify_csvs(\n path_to_csv_deidentified=args.path_to_csv_deidentified,\n path_to_csv=args.path_to_csv,\n mrn_map=mrn_map,\n columns_to_remove=args.columns_to_remove,\n )\n\n _deidentify_hd5s(\n path_to_hd5_deidentified=args.path_to_hd5_deidentified,\n path_to_hd5=args.path_to_hd5,\n mrn_map=mrn_map,\n num_workers=args.num_workers,\n )\n\n end_time = timer()\n elapsed_time = end_time - start_time\n print(f\"De-identification took {elapsed_time:.2f} seconds.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--mrn_map\",\n default=os.path.expanduser(\"~/dropbox/mrn-deid-maps/mgh.csv\"),\n help=\"Path to CSV with existing map from MRN -> deidentified ID.\",\n )\n parser.add_argument(\n \"--mrn_map_prefix\",\n default=\"mgb\",\n help=\"Prefix before date time in MRN map file name, e.g. $prefix-2021-01-04-14:21:03.csv\",\n )\n parser.add_argument(\n \"--starting_id\",\n type=int,\n help=\"Starting value for new IDs.\",\n )\n parser.add_argument(\n \"--path_to_hd5\",\n default=\"/storage/shared/ecg/mgh\",\n help=\"Path to directory containing hd5 files.\",\n )\n parser.add_argument(\n \"--path_to_hd5_deidentified\",\n help=\"Path to save de-identified ECG HD5s to. \"\n \"Skip this argument to skip de-identification of ECG data.\",\n )\n parser.add_argument(\n \"--path_to_csv\",\n help=\"Path to directory of CSV files, or CSV file.\",\n )\n parser.add_argument(\n \"--path_to_csv_deidentified\",\n help=\"Directory in which de-identified CSVs will be created.\"\n \"Skip this argument to skip de-identification of CSV data.\",\n )\n parser.add_argument(\n \"--num_workers\",\n default=cpu_count() - 1,\n type=int,\n help=\"Number of worker processes to use if processing in parallel.\",\n )\n parser.add_argument(\n \"--columns_to_remove\",\n nargs=\"*\",\n default=[],\n help=\"List of strings defining columns to remove from the final dataframe \"\n \"prior to saving to CSV\",\n )\n args = parser.parse_args()\n if args.path_to_csv is not None and args.path_to_csv_deidentified is None:\n raise ValueError(\n f\"--path_to_csv is given, but no --path_to_csv_deidentified is given.\",\n )\n return args\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n run(args)\n", "sub_path": "scripts/deidentify.py", "file_name": "deidentify.py", "file_ext": "py", "file_size_in_byte": 14514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.walk", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "definitions.globals.TENSOR_EXT", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "definitions.globals.CSV_EXT", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "ml4c3.datasets.patient_csv_to_set", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 147, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 176, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 211, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 215, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 215, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 222, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 223, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 225, "usage_type": "call"}, {"api_name": "definitions.ecg.ECG_PREFIX", "line_number": 227, "usage_type": "name"}, {"api_name": "definitions.ecg.ECG_PREFIX", "line_number": 228, "usage_type": "name"}, {"api_name": "definitions.ecg.ECG_PREFIX", "line_number": 229, "usage_type": "name"}, {"api_name": "definitions.ecg.ECG_PREFIX", "line_number": 231, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 240, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "definitions.globals.TENSOR_EXT", "line_number": 258, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "definitions.globals.TENSOR_EXT", "line_number": 267, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 270, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 280, "usage_type": "call"}, {"api_name": "definitions.globals.MRN_COLUMNS", "line_number": 295, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 307, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "definitions.globals.CSV_EXT", "line_number": 345, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 348, "usage_type": "call"}, {"api_name": "os.path", "line_number": 348, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path", "line_number": 352, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 360, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 361, "usage_type": "call"}, {"api_name": "os.path", "line_number": 361, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 362, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 363, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 380, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 397, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 440, "usage_type": "call"}]}
+{"seq_id": "429645312", "text": "# Docs on session basics\n# https://docs.sqlalchemy.org/en/13/orm/session_basics.html\n\n\n# Now that you have completed your initial analysis, design a Flask API\n# based on the queries that you have just developed.\n\nimport numpy as np\nimport os\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import create_engine, func\nimport datetime as dt\nfrom flask import Flask, jsonify\n\n\n#################################################\n# Database Setup\n#################################################\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n\n# reflect an existing database into a new model\nBase = automap_base()\nBase.prepare(engine, reflect=True)\n\n# Save reference to the table\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n#################################################\n# Flask Routes\n#################################################\n# Use Flask to create your routes.\n\n# Routes\n\n# /\n# Home page. List all routes that are available.\n@app.route(\"/\")\ndef welcome():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"<h3>Available Routes:</h3>\"\n f\"<h4>Precipitation (all dates available in the database):</h4>\"\n f\"/api/v1.0/precipitation<br/><br/>\"\n f\"<h4>Stations:</h4>\"\n f\"/api/v1.0/stations<br/><br/>\"\n f\"<h4>Temperature Observations (last 12 months of dates available in the database):</h4>\"\n f\"/api/v1.0/tobs<br/><br/>\"\n f\"<h4>Minimum, average, and the max temperature for a given start:</h3>\"\n f\"/api/v1.0/start<br/>\"\n f\"<h4>Minimum, average, and the max temperature for a given start or start-end range:</h3>\"\n f\"/api/v1.0/start/end<br/>\"\n )\n\n\n# /api/v1.0/precipitation\n# Convert the query results to a dictionary using date as the key and prcp as the value.\n# Return the JSON representation of your dictionary.\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n \"\"\"Return a list of all precipitation measurments\"\"\"\n\n # open the session to start the communication with the database\n session = Session(engine)\n\n # Retrieve the most recent meas data\n date_last_meas_str = session.query(func.max(Measurement.date))\n\n # Convert the data from string to datetime\n date_last_meas_object = dt.datetime.strptime(\n date_last_meas_str.first()[0], \"%Y-%m-%d\"\n ).date()\n\n # Create a query data interval\n query_date = date_last_meas_object - dt.timedelta(days=365)\n\n # Query the database based on the target date (last 365 days)\n results = (\n session.query(Measurement.date, Measurement.prcp)\n .filter(Measurement.date > query_date)\n .all()\n )\n\n # close the session to end the communication with the database\n session.close()\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_precipitation = []\n for meas in results:\n # meas_dict = {str(meas.date): meas.prcp}\n meas_dict = {(meas.date): meas.prcp}\n all_precipitation.append(meas_dict)\n\n return jsonify(all_precipitation)\n\n\n# /api/v1.0/stations\n# Return a JSON list of stations from the dataset.\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n \"\"\"Return a list of all stations\"\"\"\n\n # open the session to start the communication with the database\n session = Session(engine)\n\n # Query all precipitation measurments\n results = session.query(\n Station.id,\n Station.station,\n Station.name,\n Station.latitude,\n Station.longitude,\n Station.elevation,\n ).all()\n\n # close the session to end the communication with the database\n session.close()\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_stations = []\n for stat in results:\n station_dict = {}\n station_dict[\"Id\"] = stat.id\n station_dict[\"Station\"] = stat.station\n station_dict[\"Name\"] = stat.name\n station_dict[\"Latitude\"] = stat.latitude\n station_dict[\"Longitude\"] = stat.longitude\n station_dict[\"Elevation\"] = stat.elevation\n all_stations.append(station_dict)\n\n return jsonify(all_stations)\n\n\n# /api/v1.0/tobs\n# Query the dates and temperature observations of the most active station for the last year of data.\n# Return a JSON list of temperature observations (TOBS) for the previous year.\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n \"\"\"Return a list of all tobs\"\"\"\n\n # open the session to start the communication with the database\n session = Session(engine)\n\n # Retrieve the most recent meas data\n date_last_meas_str = session.query(func.max(Measurement.date))\n\n # Convert the data from string to datetime\n date_last_meas_object = dt.datetime.strptime(\n date_last_meas_str.first()[0], \"%Y-%m-%d\"\n ).date()\n\n # Create a query data interval\n query_date = date_last_meas_object - dt.timedelta(days=365)\n\n # What are the most active stations? (i.e. what stations have the most rows)?\n # List the stations and the counts in descending order.\n most_active_stations = (\n session.query(Station.station, func.count(Station.station))\n .filter(Measurement.station == Station.station)\n .group_by(Station.station)\n .order_by(func.count(Station.station).desc())\n .all()\n )\n\n # Using the station id from the previous query the temperature recorded\n most_active_station_id = most_active_stations[0][0]\n\n results = (\n session.query(Measurement.date, Measurement.tobs)\n .filter(Measurement.date > query_date)\n .filter(Measurement.station == most_active_station_id)\n .all()\n )\n\n # close the session to end the communication with the database\n session.close()\n\n # Create a dictionary from the row data and append to a list of all_stations\n all_tobs = []\n for tob in results:\n tob_dict = {}\n tob_dict[\"Date\"] = tob.date\n tob_dict[\"Temperature\"] = tob.tobs\n all_tobs.append(tob_dict)\n\n return jsonify(all_tobs)\n\n\n# /api/v1.0/<start> and /api/v1.0/<start>/<end>\n# Return a JSON list of the minimum temperature, the average temperature,\n# and the max temperature for a given start or start-end range.\n# When given the start only, calculate TMIN, TAVG, and TMAX for all dates\n# greater than and equal to the start date.\n@app.route(\"/api/v1.0/<start_date>\")\ndef tobs_from_date(start_date):\n \"\"\"Return a JSON list of the minimum temperature, the average temperature, \n and the max temperature for a given start data on.\"\"\"\n\n # open the session to start the communication with the database\n session = Session(engine)\n\n # This function called `calc_temps_mod` will accept start date and end date in the format '%Y-%m-%d'\n # and return the minimum, average, and maximum temperatures for that range of dates\n # When given the start only, calculate TMIN, TAVG, and TMAX for all dates greater\n # than and equal to the start date.\n def calc_temps_mod(start_date, end_date=0):\n \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n \n Args:\n start_date (string): A date string in the format %Y-%m-%d\n end_date (string): A date string in the format %Y-%m-%d\n \n Returns:\n TMIN, TAVE, and TMAX\n \"\"\"\n if end_date == 0:\n return (\n session.query(\n func.min(Measurement.tobs),\n func.avg(Measurement.tobs),\n func.max(Measurement.tobs),\n )\n .filter(Measurement.date >= start_date)\n .all()\n )\n else:\n return (\n session.query(\n func.min(Measurement.tobs),\n func.avg(Measurement.tobs),\n func.max(Measurement.tobs),\n )\n .filter(Measurement.date >= start_date)\n .filter(Measurement.date <= end_date)\n .all()\n )\n\n temperatures = calc_temps_mod(start_date)\n\n # close the session to end the communication with the database\n session.close()\n\n return jsonify(temperatures)\n\n\n# /api/v1.0/<start> and /api/v1.0/<start>/<end>\n# Return a JSON list of the minimum temperature, the average temperature,\n# and the max temperature for a given start or start-end range.\n# When given the start and the end date, calculate the TMIN, TAVG, and TMAX\n# for dates between the start and end date inclusive.\n@app.route(\"/api/v1.0/<start_date>/<end_date>\")\ndef tobs_from_date_to_date(start_date, end_date):\n \"\"\"Return a JSON list of the minimum temperature, the average temperature, \n and the max temperature for a given start data on.\"\"\"\n\n # open the session to start the communication with the database\n session = Session(engine)\n\n # This function called `calc_temps_mod` will accept start date and end date in the format '%Y-%m-%d'\n # and return the minimum, average, and maximum temperatures for that range of dates\n # When given the start only, calculate TMIN, TAVG, and TMAX for all dates greater\n # than and equal to the start date.\n def calc_temps_mod(start_date, end_date=0):\n \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n \n Args:\n start_date (string): A date string in the format %Y-%m-%d\n end_date (string): A date string in the format %Y-%m-%d\n \n Returns:\n TMIN, TAVE, and TMAX\n \"\"\"\n if end_date == 0:\n return (\n session.query(\n func.min(Measurement.tobs),\n func.avg(Measurement.tobs),\n func.max(Measurement.tobs),\n )\n .filter(Measurement.date >= start_date)\n .all()\n )\n else:\n return (\n session.query(\n func.min(Measurement.tobs),\n func.avg(Measurement.tobs),\n func.max(Measurement.tobs),\n )\n .filter(Measurement.date >= start_date)\n .filter(Measurement.date <= end_date)\n .all()\n )\n\n temperatures = calc_temps_mod(start_date, end_date)\n\n # close the session to end the communication with the database\n session.close()\n\n return jsonify(temperatures)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 10631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 72, "usage_type": "call"}, {"api_name": "sqlalchemy.func.max", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 75, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 139, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 150, "usage_type": "call"}, {"api_name": "sqlalchemy.func.max", "line_number": 153, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 153, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 156, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.func.count", "line_number": 166, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 166, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 169, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 169, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 194, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 208, "usage_type": "call"}, {"api_name": "sqlalchemy.func.min", "line_number": 227, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 227, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 228, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 229, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 229, "usage_type": "name"}, {"api_name": "sqlalchemy.func.min", "line_number": 237, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 237, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 238, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 238, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 239, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 251, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 265, "usage_type": "call"}, {"api_name": "sqlalchemy.func.min", "line_number": 284, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 284, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 285, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 285, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 286, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 286, "usage_type": "name"}, {"api_name": "sqlalchemy.func.min", "line_number": 294, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 294, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 295, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 295, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 296, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 296, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 308, "usage_type": "call"}]}
+{"seq_id": "315167890", "text": "\"\"\"\nРабота с собственными исключениями библиотеки requests\n\"\"\"\nimport requests\nimport sys\n\n\ndef main():\n try:\n url = sys.argv[1]\n response = requests.get(url, timeout=30)\n response.raise_for_status()\n except requests.Timeout:\n print(\"время ожидания истекло \", url)\n except requests.HTTPError as err:\n code = err.response.status_code\n print(\"ошибка url: {0}, {1}\".format(url, code))\n except requests.RequestException:\n print(\"ошибка скачивания url:\", url)\n else:\n print(response.content)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Diving in Python/week3/Exceptions/exc_in_req.py", "file_name": "exc_in_req.py", "file_ext": "py", "file_size_in_byte": 681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.Timeout", "line_number": 13, "usage_type": "attribute"}, {"api_name": "requests.HTTPError", "line_number": 15, "usage_type": "attribute"}, {"api_name": "requests.RequestException", "line_number": 18, "usage_type": "attribute"}]}
+{"seq_id": "543311791", "text": "from logging import getLogger\nfrom logging import NullHandler\n\nimport python_liftbridge.api_pb2\nfrom python_liftbridge.base import BaseClient\nfrom python_liftbridge.errors import handle_rpc_errors\nfrom python_liftbridge.errors import handle_rpc_errors_in_generator\nfrom python_liftbridge.message import Message # noqa: F401\nfrom python_liftbridge.stream import Stream # noqa: F401\n\nlogger = getLogger(__name__)\nlogger.addHandler(NullHandler())\n\n\nclass Lift(BaseClient):\n\n def fetch_metadata(self):\n # TODO\n return self._fetch_metadata(self._fetch_metadata_request())\n\n def subscribe(self, stream):\n \"\"\"\n Subscribe creates an ephemeral subscription for the given stream. It begins\n receiving messages starting at the configured position and waits for new\n messages when it reaches the end of the stream. The default start position\n is the end of the stream. It returns an ErrNoSuchStream if the given stream\n does not exist.\n \"\"\"\n logger.debug('Creating a new subscription to: %s' % stream)\n for message in self._subscribe(self._subscribe_request(stream)):\n yield message\n\n def create_stream(self, stream):\n \"\"\"\n CreateStream creates a new stream attached to a NATS subject. Subject is the\n NATS subject the stream is attached to, and name is the stream identifier,\n unique per subject. It returns ErrStreamExists if a stream with the given\n subject and name already exists.\n \"\"\"\n logger.debug('Creating a new stream: %s' % stream)\n return self._create_stream(self._create_stream_request(stream))\n\n def publish(self, message):\n \"\"\"\n Publish publishes a new message to the NATS subject.\n \"\"\"\n logger.debug('Publishing a new message: %s' % message)\n return self._publish(\n self._create_publish_request(message._build_message()),\n )\n\n @handle_rpc_errors\n def _fetch_metadata(self, metadata_request):\n response = self.stub.FetchMetadata(metadata_request)\n return response\n\n @handle_rpc_errors_in_generator\n def _subscribe(self, subscribe_request):\n for message in self.stub.Subscribe(subscribe_request):\n yield Message(\n message.value,\n message.subject,\n offset=message.offset,\n timestamp=message.timestamp,\n key=message.key,\n )\n\n @handle_rpc_errors\n def _create_stream(self, stream_request):\n response = self.stub.CreateStream(stream_request)\n return response\n\n @handle_rpc_errors\n def _publish(self, publish_request):\n response = self.stub.Publish(publish_request)\n return response\n\n def _fetch_metadata_request(self):\n return python_liftbridge.api_pb2.FetchMetadataRequest()\n\n def _create_stream_request(self, stream):\n response = python_liftbridge.api_pb2.CreateStreamRequest(\n subject=stream.subject,\n name=stream.name,\n group=stream.group,\n replicationFactor=stream.replication_factor,\n )\n return response\n\n def _subscribe_request(self, stream):\n if stream.start_offset:\n return python_liftbridge.api_pb2.SubscribeRequest(\n subject=stream.subject,\n name=stream.name,\n startPosition=stream.start_position,\n startOffset=stream.start_offset,\n )\n elif stream.start_timestamp:\n return python_liftbridge.api_pb2.SubscribeRequest(\n subject=stream.subject,\n name=stream.name,\n startPosition=stream.start_position,\n startTimestamp=stream.start_timestamp,\n )\n else:\n return python_liftbridge.api_pb2.SubscribeRequest(\n subject=stream.subject,\n name=stream.name,\n startPosition=stream.start_position,\n )\n\n def _create_publish_request(self, message):\n return python_liftbridge.api_pb2.PublishRequest(message=message)\n", "sub_path": "python_liftbridge/python_liftbridge.py", "file_name": "python_liftbridge.py", "file_ext": "py", "file_size_in_byte": 4170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 12, "usage_type": "call"}, {"api_name": "python_liftbridge.base.BaseClient", "line_number": 15, "usage_type": "name"}, {"api_name": "python_liftbridge.errors.handle_rpc_errors", "line_number": 52, "usage_type": "name"}, {"api_name": "python_liftbridge.message.Message", "line_number": 60, "usage_type": "call"}, {"api_name": "python_liftbridge.errors.handle_rpc_errors_in_generator", "line_number": 57, "usage_type": "name"}, {"api_name": "python_liftbridge.errors.handle_rpc_errors", "line_number": 68, "usage_type": "name"}, {"api_name": "python_liftbridge.errors.handle_rpc_errors", "line_number": 73, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.FetchMetadataRequest", "line_number": 79, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 79, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 79, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.CreateStreamRequest", "line_number": 82, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 82, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 82, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.SubscribeRequest", "line_number": 92, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 92, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 92, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.SubscribeRequest", "line_number": 99, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 99, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 99, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.SubscribeRequest", "line_number": 106, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 106, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 106, "usage_type": "name"}, {"api_name": "python_liftbridge.api_pb2.api_pb2.PublishRequest", "line_number": 113, "usage_type": "call"}, {"api_name": "python_liftbridge.api_pb2.api_pb2", "line_number": 113, "usage_type": "attribute"}, {"api_name": "python_liftbridge.api_pb2", "line_number": 113, "usage_type": "name"}]}
+{"seq_id": "484827381", "text": "import matplotlib.pyplot as plt \nimport numpy as np\nimport scipy.stats\nimport sys\nimport time\nsys.path.insert(1, '../mlgw_v1') #folder in which every relevant routine is saved\n\nfrom MLGW_generator import *\nfrom GW_helper import * \t#routines for dealing with datasets\n\ngenerator = MLGW_generator(\"../mlgw_v1/TD_model_TEOBResumS\")\n\nN_waves = 10\n\nfrequencies = np.linspace(20,1000, 3500)\ntheta = np.zeros((N_waves,14))\ntrue_amp = np.zeros((N_waves, len(frequencies)))\ntrue_ph = np.zeros((N_waves, len(frequencies)))\nrec_amp = np.zeros((N_waves, len(frequencies)))\nrec_ph = np.zeros((N_waves, len(frequencies)))\n\nfor i in range(N_waves):\n\tq = np.random.uniform(1.,4.9)\n\tm2 = 10#np.random.uniform(1.,10.)\n\tspin1_z = 0.#np.random.uniform(-0.8,0.8)\n\tspin2_z = 0.#np.random.uniform(-0.8,0.8)\n\td = 1#np.random.uniform(.5, 50.)\n\tinclination = 0.#np.random.uniform(0, 3.14)\n\n\ttheta[i,:] = [q*m2, m2, 0,0, spin1_z, 0,0, spin2_z, d, inclination, 0,0,0,0]\n\n\tfull_freq = np.arange(20, 1000, 5e-2)\n\t\t#getting true wave\n\tLALpars = lal.CreateDict()\n\tapprox = lalsim.SimInspiralGetApproximantFromString(\"IMRPhenomPv2\")\n\thptilde, hctilde = lalsim.SimInspiralChooseFDWaveform( #where is its definition and documentation????\n\t\t\tq*m2*lalsim.lal.MSUN_SI, #m1\n\t\t\tm2*lalsim.lal.MSUN_SI, #m2\n\t\t\t0., 0., spin1_z, #spin vector 1\n\t\t\t0., 0., spin2_z, #spin vector 2\n\t\t\td*1e6*lalsim.lal.PC_SI, #distance to source\n\t\t\tinclination, #inclination\n\t\t\t0., #phi ref\n\t\t\t0., #longAscNodes (for precession)\n\t\t\t0., #eccentricity\n\t\t\t0., #meanPerAno (for precession)\n\t\t\t5e-2, # frequency incremental step\n\t\t\t20, # lowest value of frequency\n\t\t\t1000, # highest value of frequency\n\t\t\t20, #some reference value of frequency (??)\n\t\t\tLALpars, #some lal dictionary\n\t\t\tapprox #approx method for the model\n\t)\n\ttrue_h = np.array(hptilde.data.data)+1j*np.array(hctilde.data.data) #complex waveform\n\ttrue_h = true_h[int(20/5e-2):int(1000/5e-2)]\n\ttemp_amp = (np.abs(true_h).real)\n\ttemp_ph = (np.unwrap(np.angle(true_h)).real)\n\n\t\t\t#bringing waves on the chosen grid\n\ttemp_amp = np.interp(frequencies, full_freq, temp_amp)\n\ttemp_ph = np.interp(frequencies, full_freq, temp_ph)\n\ttemp_ph = temp_ph - temp_ph[0] #all frequencies are shifted by a constant to make the wave start at zero phase!!!! IMPORTANT\n\n\t\t\t#removing spourious gaps (if present)\n\t(index,) = np.where(temp_amp/temp_amp[0] < 5e-3) #there should be a way to choose right threshold...\n\tif len(index) >0:\n\t\ttemp_ph[index] = temp_ph[index[0]-1]\n\ttrue_h = np.multiply(temp_amp, np.exp(1j*temp_ph))\n\n\ttrue_amp[i,:] = true_h.real #temp_amp\n\ttrue_ph[i,:] = true_h.imag #temp_ph\n\t\n\t\t#generating surrogate wave\n\tlambd = 1.2\n\tnew_hptilde, new_hctilde = lalsim.SimInspiralChooseFDWaveform( #where is its definition and documentation????\n\t\t\tlambd*q*m2*lalsim.lal.MSUN_SI, #m1\n\t\t\tlambd*m2*lalsim.lal.MSUN_SI, #m2\n\t\t\t0., 0., spin1_z, #spin vector 1\n\t\t\t0., 0., spin2_z, #spin vector 2\n\t\t\td*1e6*lalsim.lal.PC_SI, #distance to source\n\t\t\tinclination, #inclination\n\t\t\t0., #phi ref\n\t\t\t0., #longAscNodes (for precession)\n\t\t\t0., #eccentricity\n\t\t\t0., #meanPerAno (for precession)\n\t\t\t5e-2, # frequency incremental step\n\t\t\t20, # lowest value of frequency\n\t\t\t1000, # highest value of frequency\n\t\t\t20, #some reference value of frequency (??)\n\t\t\tLALpars, #some lal dictionary\n\t\t\tapprox #approx method for the model\n\t)\n\ttrue_h = np.array(new_hptilde.data.data)+1j*np.array(new_hctilde.data.data) #complex waveform\n\ttrue_h = true_h[int(20/5e-2):int(1000/5e-2)]\n\ttemp_amp = true_h.real #(np.abs(true_h).real)\n\ttemp_ph = true_h.imag #(np.unwrap(np.angle(true_h)).real)\n\n\t\t\t#bringing waves on the chosen grid\n\ttemp_amp = np.interp(frequencies, full_freq, temp_amp)\n\ttemp_ph = np.interp(frequencies, full_freq, temp_ph)\n\ttemp_ph = temp_ph - temp_ph[0] #all frequencies are shifted by a constant to make the wave start at zero phase!!!! IMPORTANT\n\n\t\t\t#removing spourious gaps (if present)\n\t(index,) = np.where(temp_amp/temp_amp[0] < 5e-3) #there should be a way to choose right threshold...\n\tif len(index) >0:\n\t\ttemp_ph[index] = temp_ph[index[0]-1]\n\ttrue_h = np.multiply(temp_amp, np.exp(1j*temp_ph))\n\n\trec_amp[i,:] = temp_amp\n\trec_ph[i,:] = temp_ph\n\n\nF = compute_mismatch(true_amp, true_ph, rec_amp, rec_ph)\nprint(\"Avg fit mismatch (avg,max,min,std): \", np.mean(F), np.max(F), np.min(F), np.std(F))\n\n#quit()\n\nN_plots = 5\nindices = np.random.choice(range(N_plots), size=N_plots ,replace = False)\nfor i in range(N_plots):\n\tplt.figure(i+1, figsize=(15,10))\n\th_rec = rec_amp[indices[i]] * np.exp(1j*rec_ph[indices[i]])\n\th_true = true_amp[indices[i]] * np.exp(1j*true_ph[indices[i]])\n\n\n\tplt.title(\"(q,s1,s2) = \"+str(theta[indices[i],:]))\n\n\t#plt.plot(frequencies, (rec_ph[indices[i]] - true_ph[indices[i]]))\n\t#plt.plot(np.divide(h_rec.real, lambd*frequencies))\n\n\tplt.plot(frequencies*lambd*((1+theta[indices[i],0]))*10, \trec_amp[indices[i]], label = \"Rec\")\n\tplt.plot(frequencies*(1+theta[indices[i],0])*10, \t\t\ttrue_amp[indices[i]], label = \"True\")\n\t#plt.plot((1+theta[indices[i],0])*frequencies, true_ph[indices[i]].real, label = \"True\")\n\t#plt.plot(lambd*(1+theta[indices[i],0])*frequencies, 98./68.*rec_ph[indices[i]].real, label = \"rec\")\n\t#plt.xscale(\"log\")\n\tplt.legend()\n\tplt.savefig(\"../pictures/rec_WFs/WF_\"+str(i)+\".jpeg\")\n\nplt.show()\n\n\n\n\n\n\n\n", "sub_path": "dev/tries_checks/checks/play_lal.py", "file_name": "play_lal.py", "file_ext": "py", "file_size_in_byte": 5211, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.unwrap", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}]}
+{"seq_id": "239410616", "text": "\"\"\" URL Configuration\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom . import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nadmin.autodiscover()\n\nurlpatterns = [\n # url(r'^product-details/$', views.product, name='product-details'),\n # url(r'^checkout/$', views.checkout, name='checkout'),\n # url(r'^shop-cart/$', views.cart, name='shop-cart'),\n # url(r'^magazine/$', views.magazine, name='magazine'),\n url(r'^pis/$', views.pis, name='proektirovanie'),\n url(r'^bmk/$', views.bmk, name='bmk'),\n url(r'^kotli/$', views.kotli, name='kotli'),\n url(r'^news/$', views.news, name='news'),\n url(r'^epb/$', views.epb, name='epb'),\n url(r'^asu/$', views.asu, name='asu'),\n url(r'^about_us/$', views.about_us, name='about_us'),\n url(r'^kvartiri/$', views.kvartiri, name='kvartiri'),\n url(r'^$', views.home, name='home'),\n\n] \\\n + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) \\\n + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "sub_path": "landing/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 39, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.conf.urls.static.static", "line_number": 40, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 40, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 40, "usage_type": "attribute"}]}
+{"seq_id": "221769601", "text": "#!/usr/bin/env python\nfrom __future__ import print_function\nimport dxpy\nimport argparse\nimport sys\nimport os\nimport subprocess\nimport json\nimport time\n\nhere = os.path.dirname(sys.argv[0])\ngit_revision = subprocess.check_output([\"git\", \"describe\", \"--always\", \"--dirty\", \"--tags\"]).strip()\n\ndef main():\n argparser = argparse.ArgumentParser(description=\"Build dxvg workflow on DNAnexus.\")\n argparser.add_argument(\"--project\", help=\"DNAnexus project ID\", default=\"project-BgYjfJQ0QpJ5qyBvfbzXY890\")\n argparser.add_argument(\"--folder\", help=\"Folder within project (default: timestamp/git-based)\", default=None)\n argparser.add_argument(\"--vg-bundle\", help=\"ID of vg bundle built by vg_bundle_builder (default: find existing or build new bundle corresponding to current git revision)\")\n argparser.add_argument(\"--run-tests\", help=\"Execute run_tests.py on the new workflow\", action='store_true')\n argparser.add_argument(\"--run-tests-no-wait\", help=\"Execute run_tests.py --no-wait\", action='store_true')\n argparser.add_argument(\"--whole-genome\", help=\"Add --whole-genome to run_tests command if any\", action='store_true')\n args = argparser.parse_args()\n\n if args.folder is None:\n args.folder = time.strftime(\"/builds/%Y-%m-%d/%H%M%S-\") + git_revision\n\n project = dxpy.DXProject(args.project)\n applets_folder = args.folder + \"/applets\"\n print(\"project: {} ({})\".format(project.name, args.project))\n print(\"folder: {}\".format(args.folder))\n\n vg_bundle = get_vg_bundle(project, applets_folder, args.vg_bundle)\n\n build_applets(project, applets_folder, vg_bundle)\n\n def find_applet(applet_name):\n return dxpy.find_one_data_object(classname='applet', name=applet_name,\n project=project.get_id(), folder=applets_folder,\n zero_ok=False, more_ok=False, return_handler=True)\n def find_asset(asset_name,classname=\"file\"):\n return dxpy.find_one_data_object(classname=classname, name=asset_name,\n project=project.get_id(), folder=\"/assets\",\n zero_ok=False, more_ok=False, return_handler=True)\n wf = build_workflow(project, args.folder, find_applet, find_asset)\n\n print(\"workflow: {} ({})\".format(wf.name, wf.get_id()))\n\n if args.run_tests_no_wait is True or args.run_tests is True:\n cmd = \"python {} --project {} --workflow {}\".format(os.path.join(here, \"run_tests.py\"),\n project.get_id(), wf.get_id())\n if args.run_tests_no_wait is True:\n cmd = cmd + \" --no-wait\"\n if args.whole_genome is True:\n cmd = cmd + \" --whole-genome\"\n print(cmd)\n sys.exit(os.system(cmd))\n\ndef get_vg_bundle(project, applets_folder, existing_dxid=None):\n if existing_dxid is not None:\n return dxpy.DXFile(existing_dxid)\n \n # determine desired git revision of vg\n vg_git_revision = subprocess.check_output([\"git\", \"describe\", \"--long\", \"--always\", \"--tags\"],\n cwd=os.path.join(here,\"vg\")).strip()\n # is the exe available already?\n existing = dxpy.find_data_objects(classname=\"file\", typename=\"vg_bundle\",\n project=project.get_id(), folder=\"/vg-bundle\",\n properties={\"git_revision\": vg_git_revision},\n return_handler=True)\n existing = list(existing)\n if len(existing) > 0:\n if len(existing) > 1:\n print(\"Warning: found multiple vg bundles with git_revision={}, picking one\".format(vg_git_revision))\n existing = existing[0]\n print(\"Using vg bundle {} ({})\".format(vg_git_revision,existing.get_id()))\n return existing\n \n # no - build one for this git revision\n project.new_folder(\"/vg-bundle\", parents=True)\n print(\"Building new vg bundle for {}\".format(vg_git_revision))\n build_cmd = [\"dx\",\"build\",\"-f\",\"--destination\",project.get_id()+\":/vg-bundle/\",os.path.join(here,\"vg_bundle_builder\")]\n print(\" \".join(build_cmd))\n build_applet = dxpy.DXApplet(json.loads(subprocess.check_output(build_cmd))[\"id\"])\n build_job = build_applet.run({\"git_commit\": vg_git_revision},\n project=project.get_id(), folder=\"/vg-bundle\",\n name=\"vg_bundle_builder \" + vg_git_revision)\n print(\"Launched {} to build vg bundle, waiting...\".format(build_job.get_id()))\n noise = subprocess.Popen([\"/bin/bash\", \"-c\", \"while true; do sleep 60; date; done\"])\n try:\n build_job.wait_on_done()\n finally:\n noise.kill()\n vg_bundle = dxpy.DXFile(build_job.describe()[\"output\"][\"vg_bundle\"])\n print(\"Using vg bundle {} ({})\".format(vg_git_revision,vg_bundle.get_id()))\n return vg_bundle\n\ndef build_applets(project, applets_folder, vg_bundle):\n here_applets = os.path.join(here, \"applets\")\n applet_dirs = [os.path.join(here_applets,dir) for dir in os.listdir(here_applets)]\n applet_dirs = [dir for dir in applet_dirs if os.path.isdir(dir)]\n\n project.new_folder(applets_folder, parents=True)\n for applet_dir in applet_dirs:\n if os.path.isfile(os.path.join(applet_dir, \"dxapp.json.template\")):\n sed_cmd = \"sed s/VG_BUNDLE_DXID/{}/g {} > {}\"\n sed_cmd = sed_cmd.format(vg_bundle.get_id(),\n os.path.join(applet_dir, \"dxapp.json.template\"),\n os.path.join(applet_dir, \"dxapp.json\"))\n print(sed_cmd)\n subprocess.check_call(sed_cmd, shell=True)\n build_cmd = [\"dx\",\"build\",\"--destination\",project.get_id()+\":\"+applets_folder+\"/\",applet_dir]\n print(\" \".join(build_cmd))\n applet_dxid = json.loads(subprocess.check_output(build_cmd))[\"id\"]\n applet = dxpy.DXApplet(applet_dxid, project=project.get_id())\n applet.set_properties({\"git_revision\": git_revision})\n\ndef build_workflow(project, folder, find_applet, find_asset):\n def build(incl_map):\n nm = \"vg_construct_index_map\" if incl_map else \"vg_construct_index\"\n wf = dxpy.new_dxworkflow(title=nm,\n name=nm,\n description=nm,\n project=project.get_id(),\n folder=folder,\n properties={\"git_revision\": git_revision})\n\n construct_applet = find_applet(\"vg_construct\")\n construct_input = {\n }\n construct_stage_id = wf.add_stage(construct_applet, stage_input=construct_input, name=\"construct\")\n\n index_input = {\n \"vg_tar\": dxpy.dxlink({\"stage\": construct_stage_id, \"outputField\": \"vg_tar\"})\n }\n index_stage_id = wf.add_stage(find_applet(\"vg_index\"), stage_input=index_input, name=\"index\")\n\n if incl_map:\n map_input = {\n \"vg_indexed_tar\": dxpy.dxlink({\"stage\": index_stage_id, \"outputField\": \"vg_indexed_tar\"})\n }\n map_stage_id = wf.add_stage(find_applet(\"vg_map\"), stage_input=map_input, name=\"map\")\n\n return wf\n\n build(False)\n return build(True)\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "build_workflow.py", "file_name": "build_workflow.py", "file_ext": "py", "file_size_in_byte": 7310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "dxpy.DXProject", "line_number": 27, "usage_type": "call"}, {"api_name": "dxpy.find_one_data_object", "line_number": 37, "usage_type": "call"}, {"api_name": "dxpy.find_one_data_object", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "os.system", "line_number": 56, "usage_type": "call"}, {"api_name": "dxpy.DXFile", "line_number": 60, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "dxpy.find_data_objects", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "dxpy.DXApplet", "line_number": 83, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 83, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 83, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 88, "usage_type": "call"}, {"api_name": "dxpy.DXFile", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 110, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 113, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 113, "usage_type": "call"}, {"api_name": "dxpy.DXApplet", "line_number": 114, "usage_type": "call"}, {"api_name": "dxpy.new_dxworkflow", "line_number": 120, "usage_type": "call"}, {"api_name": "dxpy.dxlink", "line_number": 133, "usage_type": "call"}, {"api_name": "dxpy.dxlink", "line_number": 139, "usage_type": "call"}]}
+{"seq_id": "19988887", "text": "#!/usr/bin/env python3\n\nimport sys\nimport math\n\nfrom common import print_solution, read_input\n\n\ndef distance(city1, city2):\n return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)\n\n\ndef is_x_longer(cities):\n x1 = min(map(lambda x: x[0], cities))\n y1 = min(map(lambda x: x[1], cities))\n x2 = max(map(lambda x: x[0], cities))\n y2 = max(map(lambda x: x[1], cities))\n return x2 - x1 > y2 - y1\n\n\ndef divide(cities):\n N = len(cities)\n if N <= 3:\n return cities\n\n middle = int(N/2)\n if is_x_longer(cities):\n cities = sorted(cities, key=lambda x: x[0])\n else:\n cities = sorted(cities, key=lambda x: x[1])\n cities1 = divide(cities[:middle])\n cities2 = divide(cities[middle:])\n\n return merge(cities1, cities2)\n\n\ndef merge(cities1, cities2):\n assert cities1 is not None, \"error cities1 is None\"\n assert cities2 is not None, \"error cities2 is None\"\n\n d1 = distance(cities1[0], cities2[0])\n d2 = distance(cities1[0], cities2[-1])\n d3 = distance(cities1[-1], cities2[0])\n d4 = distance(cities1[-1], cities2[-1])\n\n d = min(d1, d2, d3, d4)\n\n if d1 == d:\n return list(reversed(cities1)) + cities2\n elif d2 == d:\n return cities2 + cities1\n elif d3 == d:\n return cities1 + cities2\n elif d4 == d:\n return cities1 + list(reversed(cities2))\n\n\ndef solve(cities):\n N = len(cities)\n index = [i for i in range(N)]\n index_cities = dict(zip(cities, index))\n sorted_cities = divide(cities)\n\n solution = []\n for city in sorted_cities:\n solution.append(index_cities[city])\n return solution\n\n\nif __name__ == '__main__':\n assert len(sys.argv) > 1\n solution = solve(read_input(sys.argv[1]))\n print_solution(solution)\n", "sub_path": "solver_divide_and_conquer.py", "file_name": "solver_divide_and_conquer.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "math.sqrt", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 71, "usage_type": "attribute"}, {"api_name": "common.read_input", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 72, "usage_type": "attribute"}, {"api_name": "common.print_solution", "line_number": 73, "usage_type": "call"}]}
+{"seq_id": "482296407", "text": "import CalculatorLib\nimport argparse\nimport sys\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-e', '--expression', type=str)\n parser.add_argument('-m', '--measure', type=str, default='deg')\n arguments = parser.parse_args(sys.argv[1:])\n\n line = arguments.expression\n expression = CalculatorLib.parse_variables(line) if line.count(';') != 0 else line\n parsed_line = CalculatorLib.parse_expression(expression)\n n = CalculatorLib.reverse_polish_notation(parsed_line)\n result = CalculatorLib.calculate_rpn(n)\n print(result)\n", "sub_path": "rpn_calculator.py", "file_name": "rpn_calculator.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "CalculatorLib.parse_variables", "line_number": 13, "usage_type": "call"}, {"api_name": "CalculatorLib.parse_expression", "line_number": 14, "usage_type": "call"}, {"api_name": "CalculatorLib.reverse_polish_notation", "line_number": 15, "usage_type": "call"}, {"api_name": "CalculatorLib.calculate_rpn", "line_number": 16, "usage_type": "call"}]}
+{"seq_id": "624993430", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" Read a JSON document and kick out the equivalent output section of install.json \"\"\"\n\nimport json\nimport sys\nfrom warnings import warn\n\n\ndef camel2snake(s):\n \"\"\" Convert a MS Graph camelCase name to snake_case \"\"\"\n o = ''\n if s.upper() == s:\n s = s.lower()\n for letter in s:\n lc = letter.lower()\n if lc != letter:\n o += '_'\n o += lc\n if o.startswith('_'):\n o = o[1:]\n return o\n\n\ndef identify(v, prefix, depth=0):\n \"\"\" Identify all output variables, assigning them a given prefix \"\"\"\n\n outputVariables = []\n\n if depth == 0:\n out = {'name': camel2snake(prefix), 'type': 'String'}\n else:\n out = {'name': camel2snake(prefix), 'type': 'StringArray'}\n if depth > 1:\n warn('List depth exceeds 1, Arrays of Arrays detected with {}'.format(prefix))\n return []\n\n if isinstance(v, (str, int, float)):\n outputVariables.append(out)\n elif isinstance(v, list):\n v = v[0]\n outputVariables.extend(identify(v, prefix, depth + 1))\n elif isinstance(v, dict):\n keys = list(v.keys())\n keys.sort()\n for key in keys:\n s = v[key]\n outputVariables.extend(identify(s, '{}.{}'.format(prefix, key), depth))\n\n return outputVariables\n\n\ndef refold(v, prefix, depth=0, collection=None):\n \"\"\" Refold a dictionary-ish object into a new dictionary, prefixing key\n names with a prefix \"\"\"\n\n if collection is None:\n collection = {}\n\n prefix = camel2snake(prefix)\n\n if depth > 1:\n return collection\n\n if v is None:\n collection[prefix] = None\n elif isinstance(v, (str, int, float)):\n if isinstance(v, bool):\n v = str(v).lower()\n if depth == 0:\n collection[prefix] = str(v)\n else:\n ls = collection.get(prefix, [])\n ls.append(str(v))\n collection[prefix] = ls\n elif isinstance(v, list):\n collection[prefix] = []\n for s in v:\n refold(s, prefix, depth + 1, collection)\n elif isinstance(v, dict) or hasattr(v, 'keys'):\n keys = list(v.keys())\n keys.sort()\n for key in keys:\n s = v[key]\n if prefix:\n newprefix = f'{prefix}.{key}'\n else:\n newprefix = key\n refold(s, newprefix, depth, collection)\n\n return collection\n\n\nif __name__ == '__main__':\n\n def cmdline():\n \"\"\" Put all the variables into a local function so pylint\n doesn't complain \"\"\"\n args = sys.argv[1:]\n filename = args[0]\n prefix = args[1]\n\n f = open(filename, 'r')\n data = json.load(f)\n\n keys = list(data.keys())\n keys.sort()\n\n outputVariables = []\n\n collection = {}\n # for key in keys:\n # v = data[key]\n # outputVariables.extend(identify(v, '{}.{}'.format(prefix, key)))\n # refold(v, '{}.{}'.format(prefix, key), 0, collection)\n\n outputVariables = identify(data, prefix)\n collection = refold(data, prefix)\n\n print(json.dumps(outputVariables, indent=3, ensure_ascii=False))\n\n print(json.dumps(collection, sort_keys=True, indent=3, ensure_ascii=False))\n\n cmdline()\n", "sub_path": "apps/TCPB_-_Expressions/json_util.py", "file_name": "json_util.py", "file_ext": "py", "file_size_in_byte": 3314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "warnings.warn", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 100, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 105, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 123, "usage_type": "call"}]}
+{"seq_id": "224262482", "text": "import mxnet as mx\n\ndef get_symbol(num_classes = 10):\n data = mx.symbol.Variable('data')\n\n conv1_1 = mx.symbol.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=64, name='conv1_1')\n relu1_1 = mx.symbol.Activation(data=conv1_1, act_type=\"relu\", name='relu1_1')\n conv1_2 = mx.symbol.Convolution(data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name='conv1_2')\n relu1_2 = mx.symbol.Activation(data=conv1_2, act_type=\"relu\", name='relu1_2')\n pool1 = mx.symbol.Pooling(data=relu1_2, pool_type=\"max\", kernel=(2, 2), stride=(2, 2), name='pool1')\n\n conv2_1 = mx.symbol.Convolution(data=pool1, kernel=(3, 3), pad=(1, 1), num_filter=128, name='conv2_1')\n relu2_1 = mx.symbol.Activation(data=conv2_1, act_type=\"relu\", name='relu2_1')\n conv2_2 = mx.symbol.Convolution(data=relu2_1, kernel=(3, 3), pad=(1, 1), num_filter=128, name='conv2_2')\n relu2_2 = mx.symbol.Activation(data=conv2_2, act_type=\"relu\", name='relu2_2')\n pool2 = mx.symbol.Pooling(data=relu2_2, pool_type=\"max\", kernel=(2, 2), stride=(2, 2), name='pool2')\n\n conv3_1 = mx.symbol.Convolution(data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=256, name='conv3_1')\n relu3_1 = mx.symbol.Activation(data=conv3_1, act_type=\"relu\", name='relu3_1')\n conv3_2 = mx.symbol.Convolution(data=relu3_1, kernel=(3, 3), pad=(1, 1), num_filter=256, name='conv3_2')\n relu3_2 = mx.symbol.Activation(data=conv3_2, act_type=\"relu\", name='relu3_2')\n conv3_3 = mx.symbol.Convolution(data=relu3_2, kernel=(3, 3), pad=(1, 1), num_filter=256, name='conv3_3')\n relu3_3 = mx.symbol.Activation(data=conv3_3, act_type=\"relu\", name='relu3_3')\n pool3 = mx.symbol.Pooling(data=relu3_3, pool_type=\"max\", kernel=(2, 2), stride=(2, 2), name='pool3')\n\n conv4_1 = mx.symbol.Convolution(data=pool3, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv4_1')\n relu4_1 = mx.symbol.Activation(data=conv4_1, act_type=\"relu\", name='relu4_1')\n conv4_2 = mx.symbol.Convolution(data=relu4_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv4_2')\n relu4_2 = mx.symbol.Activation(data=conv4_2, act_type=\"relu\", name='relu4_2')\n conv4_3 = mx.symbol.Convolution(data=relu4_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv4_3')\n relu4_3 = mx.symbol.Activation(data=conv4_3, act_type=\"relu\", name='relu4_3')\n pool4 = mx.symbol.Pooling(data=relu4_3, pool_type=\"max\", kernel=(2, 2), stride=(2, 2), name='pool4')\n\n conv5_1 = mx.symbol.Convolution(data=pool4, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv5_1')\n relu5_1 = mx.symbol.Activation(data=conv5_1, act_type=\"relu\", name='relu5_1')\n conv5_2 = mx.symbol.Convolution(data=relu5_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv5_2')\n relu5_2 = mx.symbol.Activation(data=conv5_2, act_type=\"relu\", name='relu5_2')\n conv5_3 = mx.symbol.Convolution(data=relu5_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name='conv5_3')\n relu5_3 = mx.symbol.Activation(data=conv5_3, act_type=\"relu\", name='relu5_3')\n pool5 = mx.symbol.Pooling(data=relu5_3, pool_type=\"max\", kernel=(2, 2), stride=(2, 2), name='pool5')\n\n flatten = mx.symbol.Flatten(data=pool5, name='flatten_0')\n fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name='fc6')\n relu6 = mx.symbol.Activation(data=fc6, act_type=\"relu\", name='relu6')\n dropout6 = mx.symbol.Dropout(data=relu6, p=0.5, name='drop6')\n\n fc7 = mx.symbol.FullyConnected(data=dropout6, num_hidden=4096, name='fc7')\n relu7 = mx.symbol.Activation(data=fc7, act_type=\"relu\", name='relu7')\n dropout7 = mx.symbol.Dropout(data=relu7, p=0.5, name='drop7')\n\n fc8 = mx.symbol.FullyConnected(data=dropout7, num_hidden=num_classes, name='fc8')\n prob = mx.symbol.SoftmaxOutput(data=fc8, name='softmax')\n\n return prob", "sub_path": "symbols/vgg16.py", "file_name": "vgg16.py", "file_ext": "py", "file_size_in_byte": 3757, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "mxnet.symbol.Variable", "line_number": 4, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 4, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 6, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 6, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 7, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 7, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 8, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 9, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Pooling", "line_number": 10, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 12, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 13, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 14, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 14, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 15, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 15, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Pooling", "line_number": 16, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 18, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 19, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 20, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 20, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 21, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 22, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 23, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 23, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Pooling", "line_number": 24, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 24, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 26, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 27, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 28, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 29, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 30, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 30, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 31, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Pooling", "line_number": 32, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 34, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 35, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 36, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 37, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 37, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 38, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 38, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 39, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Pooling", "line_number": 40, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Flatten", "line_number": 42, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 43, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 43, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 44, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Dropout", "line_number": 45, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 47, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 48, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Dropout", "line_number": 49, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 49, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 51, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 51, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.SoftmaxOutput", "line_number": 52, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 52, "usage_type": "attribute"}]}
+{"seq_id": "380456406", "text": "\"\"\"\nSERVER\n\"\"\"\nimport socket\nimport select\nimport sys\nimport logging\nimport re\nimport queue\nimport argparse\n\nimport irc_client\nimport patterns\n\nlogging.basicConfig(filename='view.log', level=logging.DEBUG)\nlogger = logging.getLogger()\n\n\nclass IRCServer(patterns.Publisher):\n\n def __init__(self, port):\n super().__init__()\n self.port = port\n self.server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.server_socket.setblocking(0) # server won't block at server_socket.accept()\n host = 'localhost'\n self.server_socket.bind((host, port))\n self.server_socket.listen()\n\n # Select vars\n self.inputs = list()\n self.inputs.append(self.server_socket)\n self.outputs = list()\n self.message_queues = {}\n self.read_size = 2 ** 10\n\n self.client_list = dict()\n\n def run(self):\n print(f\"Running server at localhost:{self.port}\")\n logger.info(f\"Running server at localhost:{self.port}\")\n try:\n while True:\n readable, writable, exceptional = select.select(self.inputs, self.outputs, self.inputs)\n for r in readable:\n # Server socket accepting clients\n # Add client to client_list, add client to global channel?\n if r is self.server_socket:\n client_socket, addr = r.accept()\n client_socket.setblocking(0)\n self.inputs.append(client_socket)\n self.message_queues[r] = queue.Queue()\n\n print(f\"New client @ {addr}\")\n logger.info(f\"New client @ {addr}\")\n # Reading from clients\n else:\n data = r.recv(self.read_size).decode('utf-8')\n print(f\"Received {data} from {r.getpeername()}\")\n self.message_queues[r] = queue.Queue()\n if r not in self.outputs:\n self.outputs.append(r)\n is_registration = re.search(\"^NICK\\s.*;USER\\s.*\\s.*\\s.*\\s.*\", data)\n is_chat_message = re.search(\"^PRIVMSG\\s#Global\\s:.*\", data)\n # Client is sending username and nickname, server must register them\n # Check if another client has the same username\n if is_registration:\n parsed_data = data.split(';')\n nick_data, user_data = parsed_data[0].split(), parsed_data[1].split()\n self.register_new_client(r, nick_data[1], user_data[1], user_data[2],\n user_data[3], user_data[4])\n\n if r not in self.outputs:\n self.outputs.append(r)\n\n # Send message to all clients on #Global channel\n elif is_chat_message:\n msg = data[data.index(':') + 1:]\n for client_socket in self.client_list:\n print(f\"client port: {client_socket} r port {r}\")\n if client_socket == r:\n msg = \":\".join([f\"NICK {self.client_list[client_socket].nickname}\", msg])\n self.message_queues[r].put(msg)\n if r not in self.outputs:\n self.outputs.append(r)\n # Data is empty\n else:\n self.inputs.remove(r)\n del self.client_list[r]\n if r in self.outputs:\n self.outputs.remove(r)\n r.close()\n print(f\"Closed a client socket: {r}\")\n logger.info(f\"Closed a client socket: {r}\")\n del self.message_queues[r]\n for w in writable:\n try:\n msg = self.message_queues[w].get_nowait()\n except KeyError:\n print(f\"Client connection was closed\")\n except queue.Empty:\n print(f\"Output queue for {w.getpeername()} is empty\")\n self.outputs.remove(w)\n else:\n if msg.startswith(\"NOTICE\"):\n print(\"Username already taken, requesting a new one\")\n w.send(msg.encode())\n else:\n print(\"Broadcasting message to #Global\")\n self.send_message_to_channel(w, msg)\n for err in exceptional:\n print(f'Handling exception for {err.getpeername()}')\n self.inputs.remove(err)\n if err in self.outputs:\n self.outputs.remove(err)\n err.close()\n del self.message_queues[err]\n\n except KeyboardInterrupt:\n print(f\"\\nServer interrupted, closing socket connections\")\n self.close()\n\n def close(self):\n \"\"\"\n Close all readable sockets\n (including server socket)\n \"\"\"\n # self.server_socket.close() # might not need but faced problems without it\n for s in self.inputs:\n s.close()\n\n def register_new_client(self, client_socket, nickname, username, host, port, real_name):\n for client in self.client_list.values():\n if client.username == username:\n error_msg = f\"NOTICE The username {username} is already taken, please try a different one.\"\n self.message_queues[client_socket].put(error_msg)\n return False\n new_client = irc_client.IRCClient(nickname=nickname, host=host, port=port)\n new_client.username = username\n logger.info(f\"Client {new_client.username} connected to server\")\n self.client_list[client_socket] = new_client\n reg_msg = f'{new_client.username} joined the #Global channel'\n self.message_queues[client_socket].put(reg_msg)\n return True\n\n def send_message_to_channel(self, client_socket, message):\n for sock in self.inputs:\n if sock != self.server_socket and (message.endswith(\"joined the #Global channel\") or sock != client_socket):\n try:\n sock.send(message.encode())\n except:\n sock.close()\n self.inputs.remove(sock)\n\n\ndef set_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('--port', action=\"store\", dest=\"port\", default=8081, help=\"port to use, default is 8081\")\n return parser\n\n\nif __name__ == \"__main__\":\n parser = set_parser()\n args = parser.parse_args()\n server = IRCServer(args.port)\n server.run()\n", "sub_path": "irc_code/a2/Include/irc_server.py", "file_name": "irc_server.py", "file_ext": "py", "file_size_in_byte": 7067, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "patterns.Publisher", "line_number": 19, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 24, "usage_type": "attribute"}, {"api_name": "select.select", "line_number": 44, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 52, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 60, "usage_type": "call"}, {"api_name": "re.search", "line_number": 63, "usage_type": "call"}, {"api_name": "re.search", "line_number": 64, "usage_type": "call"}, {"api_name": "queue.Empty", "line_number": 101, "usage_type": "attribute"}, {"api_name": "irc_client.IRCClient", "line_number": 138, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 157, "usage_type": "call"}]}
+{"seq_id": "157049319", "text": "import os\n\n__author__ = \"skar.Wei\"\n\nimport xmltodict\nimport json\n\n\ndef py_xml_to_json():\n try:\n filename = \"HMDB00003.xml\"\n if os.path.exists(filename):\n filexml = open(filename)\n filejso = open(\"HMDB00002.json\", \"w\")\n txt_all = filexml.read()\n converted = xmltodict.parse(txt_all)\n # json.dumps(converted, indent=4)\n json.dump(converted, filejso, indent=4)\n filexml.close()\n else:\n print(\"no file exist\")\n except IOError as e:\n print(e)\n\n\n\n stry = \"HMDB\"\n inte = 1\n strint = inte.__str__().zfill(5)\n stry += strint\n print(stry)\n\n\nif __name__ == \"__main__\":\n py_xml_to_json()\n\n\n\n", "sub_path": "infoVisandPy/PyXmlJson/xmltoJson.py", "file_name": "xmltoJson.py", "file_ext": "py", "file_size_in_byte": 719, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.exists", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "xmltodict.parse", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 18, "usage_type": "call"}]}
+{"seq_id": "116798341", "text": "\"\"\"Perform unit test for DB Operations.\"\"\"\nimport unittest\nfrom src.connection import Connector\nfrom src.constants import Role, Game_Role\nfrom src.game_class import Game\nfrom yoyo import read_migrations\nfrom yoyo import get_backend\n\nimport os\n\ntest_db = os.getenv('TEST_SQLITE_DB')\nmigrations = read_migrations('db_migrations')\n\n\nclass DatabaseOperationsTests(unittest.TestCase):\n '''tests operations related to the connector class'''\n def setUp(self):\n # Discard the database if exists \n if os.path.exists(test_db):\n os.remove(test_db)\n backend = get_backend('sqlite:///{}'.format(test_db))\n with backend.lock():\n backend.apply_migrations(backend.to_apply(migrations))\n self.conn = Connector(True, test_db)\n\n def test_user_signup(self):\n res = self.conn.add_user('test@test.test', 'asdaqe2sqwswsqw12esdqw', Role.INSTRUCTOR)\n self.assertEqual(res, 1)\n data = self.conn.get_users()\n emails = [row[1] for row in data]\n self.assertTrue('test@test.test' in emails)\n\n def test_valid_user_session(self):\n # Add a test user\n user_id = self.conn.add_user('test2@test.test', 'asdaqe2sqwswsqw12esdqw', Role.INSTRUCTOR)\n self.assertEqual(user_id, 1)\n\n # Create a user session\n token = self.conn.add_user_session(user_id)\n\n # check the session validity\n session_user_id = self.conn.check_session_validity(token)\n self.assertEqual(session_user_id, user_id)\n\n def test_expired_user_session(self):\n # Add a test user\n user_id = self.conn.add_user('test3@test.test', 'asdaqe2sqwswsqw12esdqw', Role.INSTRUCTOR)\n self.assertEqual(user_id, 1)\n\n # Add a 35 min old timestamp\n token = 'random_string'\n cur = self.conn.conn.cursor()\n cur.execute(\n \"INSERT INTO UserSession (token, user_id, creation_time) VALUES (?, ?, datetime('now', '-35 minutes'))\",\n (token, user_id)\n )\n cur.close()\n\n # verify that there is no session\n session_user_id = self.conn.check_session_validity(token)\n self.assertEqual(session_user_id, None)\n\n def test_game_creation(self):\n #create game by instructor w/ id 1\n g = self.conn.create_game(1)\n self.assertTrue(g is not None,msg=\"game creation returnedNone\")\n\n def test_game_info(self):\n #create game\n g = self.conn.create_game(1)\n self.assertTrue(g is not None, msg=\"none returned for game creation\")\n #use created game id\n d = self.conn.get_game(g)\n self.assertTrue(d is not None, msg=\"game info not properly returned\")\n\n def test_game_creation_custom_params(self):\n d = {\n 'session_length': 22,\n 'active': False,\n 'wholesaler_present': False,\n 'retailer_present': True,\n 'demand_pattern_id': 2,\n 'info_delay': 3,\n 'info_sharing': False,\n 'holding_cost': 6.9,\n 'backlog_cost': 4.20,\n }\n g = self.conn.create_game(1, **d)\n self.assertTrue(g is not None,msg=\"game instance returned None\")\n dict_query = self.conn.get_game(g)\n for i in d:\n self.assertEqual(d[i], dict_query[i], msg=f'error, key {i} differs: {d[i]}, {dict_query[i]}')\n \n def test_password_hash(self):\n p = \"hello, world!\"\n h = self.conn.hash_password(p)\n self.assertTrue(self.conn.check_password(p, h), msg=\"hashes do not match!\")\n\n def test_get_instructor_games(self):\n new_ins = self.conn.add_user('get_instructor_games_test', 'asdaqe2sqwswsqw12esdqw', Role.INSTRUCTOR)\n num_games = 5\n for i in range ( num_games):\n self.conn.create_game(new_ins)\n games = self.conn.get_instructor_games(new_ins)\n self.assertEqual(len(games), num_games, msg=\"the number of games created by the user do not match!\")\n \n def test_join_game(self):\n new_player = self.conn.add_user('join-game-test', '12345', Role.PLAYER)\n new_ins = self.conn.add_user('join-game-ins', '12345', Role.INSTRUCTOR)\n new_game = self.conn.create_game(new_ins)\n res = self.conn.add_player_to_game(new_player, new_game, Game_Role.RETAILER)\n \n def test_get_game_week(self):\n ins_id = self.conn.add_user('test_week_ins', '12345', Role.INSTRUCTOR)\n game_id = self.conn.create_game(ins_id)\n cur = self.conn.conn.cursor()\n week = 0\n orders = {\n 'factory_order':4,\n 'distributor_order':5,\n 'retailer_order':6,\n 'wholesaler_order':7\n }\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (week, game_id, *(orders.values()) )\n )\n get_week = self.conn.get_game_week(game_id, week)\n for i in orders:\n self.assertTrue(orders[i] == get_week[i], msg=f\"values do not match {orders[i]}, {get_week[i]}\")\n \n def test_get_current_game_week(self):\n ins_id = self.conn.add_user('test_current_week_ins', '12345', Role.INSTRUCTOR)\n game_id = self.conn.create_game(ins_id)\n # make instructor create a few more games and populate them to avoid interference\n cur = self.conn.conn.cursor()\n fake_orders = {\n 'factory_order': -1,\n 'distributor_order':-1,\n 'retailer_order': -1,\n 'wholesaler_order': -1\n }\n for fake_week in range (0,5):\n g = self.conn.create_game(ins_id)\n for i in range (0, 13):\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (i, g, *(fake_orders.values()) )\n )\n\n \n for week in range(0,13):\n orders = {\n 'factory_order':4 + week,\n 'distributor_order':5,\n 'retailer_order':6,\n 'wholesaler_order':7 + week\n }\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (week, game_id, *(orders.values()) )\n )\n get_week = self.conn.get_current_game_week(game_id)\n\n for i in orders:\n self.assertTrue(orders[i] == get_week[i], msg=f\"values do not match {orders[i]}, {get_week[i]}\")\n\n def test_update_week_order(self):\n ins_id = self.conn.add_user('test_update_week_order', '12345', Role.INSTRUCTOR)\n g = self.conn.create_game(ins_id)\n cur = self.conn.conn.cursor()\n orders = {\n 'factory_order':4,\n 'distributor_order':5,\n 'retailer_order':6,\n 'wholesaler_order':7\n }\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (0, g, *(orders.values()) )\n )\n self.conn.conn.commit()\n self.conn.update_week_order(g, 0, Game_Role.DISTRIBUTOR, 100)\n w = self.conn.get_game_week(g, 0)\n self.assertTrue(w['distributor_order'] == 100, msg=\"updated order does not match for distributor\")\n\n def test_add_game_week(self):\n ins_id = self.conn.add_user('test_add_game_week12', '12345', Role.INSTRUCTOR)\n g = self.conn.create_game(ins_id)\n cur = self.conn.conn.cursor()\n orders = {\n 'factory_order':4,\n 'distributor_order':5,\n 'retailer_order':6,\n 'wholesaler_order':7\n }\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (0, g, *(orders.values()) )\n )\n new_week = self.conn.get_current_game_week(g)\n i = 1\n for role in Game_Role:\n # add some values \n incoming = 3 +i\n demand = 2 +i\n inventory_brutto = 6 + i\n outgoing = min(inventory_brutto, demand)\n inventory = inventory_brutto - demand\n \n cost = 2 + i\n new_week[f'{role.value}_incoming'] = incoming\n new_week[f'{role.value}_demand'] = demand\n new_week[f'{role.value}_inventory'] = inventory\n new_week[f'{role.value}_cost'] = cost\n new_week[f'{role.value}_outgoing'] = outgoing\n i += 1\n\n self.conn.add_game_week(g, 1, new_week)\n\n retrieved_week = self.conn.get_current_game_week(g)\n # do not use week attribute for newly added week to avoid errors\n new_week.pop('week', None)\n for k in new_week:\n self.assertTrue(new_week[k] == retrieved_week[k], \n msg=f\"expected {new_week[k]} to equal {retrieved_week[k]} on key {k}\")\n\n def test_game_parameter_change(self):\n ins_id = self.conn.add_user('test_add_game_week', '12345', Role.INSTRUCTOR)\n g = self.conn.create_game(ins_id)\n updated_keys = {\n 'holding_cost': 12,\n 'backlog_cost': 1.1,\n 'active': False,\n 'info_sharing': True,\n 'wholesaler_present': False,\n 'retailer_present': False,\n }\n self.assertTrue(self.conn.update_game(g, updated_keys))\n updated_game = self.conn.get_game(g)\n for k in updated_keys:\n self.assertTrue(updated_keys[k] == type(updated_keys[k])(updated_game[k]),\n msg=f\"expected {updated_keys[k]} to equal {updated_game[k]}\")\n\n def test_get_game_weeks(self):\n ins_id = self.conn.add_user('test_get_game_weeks', '12345', Role.INSTRUCTOR)\n g = self.conn.create_game(ins_id)\n cur = self.conn.conn.cursor()\n orders = {\n 'factory_order':4,\n 'distributor_order':5,\n 'retailer_order':6,\n 'wholesaler_order':7\n }\n for i in range(0, 4):\n cur.execute('INSERT INTO GameWeeks \\\n (week, game_id, factory_order, distributor_order, retailer_order, wholesaler_order) \\\n VALUES ( ?, ?, ?, ?, ?, ?)', \n (i, g, *(orders.values()) )\n )\n weeks = self.conn.get_game_weeks(g)\n for elem in weeks:\n for k in orders:\n self.assertTrue(orders[k] == elem[k])\n\n def test_get_demand_patterns(self):\n d = self.conn.get_demand_patterns()\n self.assertTrue(len(d)> 0)\n\n def test_get_demand_pattern(self):\n d = self.conn.get_demand_pattern(1)\n self.assertTrue(d['name'] == 'default')\n\nif __name__ == '__main__':\n unittest.main()", "sub_path": "backend/test/connection_test.py", "file_name": "connection_test.py", "file_ext": "py", "file_size_in_byte": 11063, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "yoyo.read_migrations", "line_number": 12, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 20, "usage_type": "call"}, {"api_name": "yoyo.get_backend", "line_number": 21, "usage_type": "call"}, {"api_name": "src.connection.Connector", "line_number": 24, "usage_type": "call"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 27, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 35, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 35, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 47, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 100, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 100, "usage_type": "name"}, {"api_name": "src.constants.Role.PLAYER", "line_number": 108, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 108, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 109, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 109, "usage_type": "name"}, {"api_name": "src.constants.Game_Role.RETAILER", "line_number": 111, "usage_type": "attribute"}, {"api_name": "src.constants.Game_Role", "line_number": 111, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 114, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 114, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 134, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 134, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 172, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 172, "usage_type": "name"}, {"api_name": "src.constants.Game_Role.DISTRIBUTOR", "line_number": 187, "usage_type": "attribute"}, {"api_name": "src.constants.Game_Role", "line_number": 187, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 192, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 192, "usage_type": "name"}, {"api_name": "src.constants.Game_Role", "line_number": 208, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 234, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 234, "usage_type": "name"}, {"api_name": "src.constants.Role.INSTRUCTOR", "line_number": 251, "usage_type": "attribute"}, {"api_name": "src.constants.Role", "line_number": 251, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 280, "usage_type": "call"}]}
+{"seq_id": "33563162", "text": "# 给定一个整数数组 nums 和一个目标值 target，请你在该数组中找出和为目标值的那 两个 整数，并返回他们的数组下标。 \n# \n# 你可以假设每种输入只会对应一个答案。但是，数组中同一个元素不能使用两遍。 \n# \n# \n# \n# 示例: \n# \n# 给定 nums = [2, 7, 11, 15], target = 9\n# \n# 因为 nums[0] + nums[1] = 2 + 7 = 9\n# 所以返回 [0, 1]\n# \n# Related Topics 数组 哈希表 \n# 👍 9313 👎\n#闫兵，2020年10月13日\n\n# leetcode submit region begin(Prohibit modification and deletion)\nfrom typing import List\nclass Solution:\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n for i in range(len(nums)):\n res = target-nums[i]\n if res in nums and nums.index(res) != i: # 如果存在这样的数，并且该数字的索引不等于i，则是要找的数字\n return [i, nums.index(res)]\n# leetcode submit region end(Prohibit modification and deletion)\nsolution = Solution()\nprint(solution.twoSum([3,2,4], 6))\n", "sub_path": "[1]两数之和.py", "file_name": "[1]两数之和.py", "file_ext": "py", "file_size_in_byte": 1044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "typing.List", "line_number": 21, "usage_type": "name"}]}
+{"seq_id": "207399173", "text": "#coding=utf8\nfrom gensim import corpora, models\nfrom scipy.sparse import csr_matrix\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn import svm\nfrom sklearn.externals import joblib\nimport numpy as np\nimport os, re, time, logging_helper\nimport os, re, time, logging_helper\nimport jieba\nimport pickle as pkl\nimport sys\nimport json\nfrom sklearn import metrics\nBasePath = sys.path[0]\n# logging.basicConfig(level=logging.WARNING,\n# format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\n# datefmt='%a, %d %b %Y %H:%M:%S',\n# )\n\n\n\ndef svm_classify(train_set, train_tag, test_set, test_tag, classify_num):\n clf = svm.LinearSVC()\n clf_res = clf.fit(train_set,train_tag)\n train_pred = clf.predict(train_set)\n test_pred = clf.predict(test_set)\n\n # train_err_num, train_err_ratio = checkPred(train_tag, train_pred)\n # test_err_num, test_err_ratio = checkPred(test_tag, test_pred)\n train_precision = metrics.precision_score(train_tag, train_pred)\n train_recall = metrics.recall_score(train_tag, train_pred)\n train_f1_score = metrics.f1_score(train_tag, train_pred)\n train_hamming_loss = metrics.hamming_loss(train_tag, train_pred)\n\n test_precision = metrics.precision_score(test_tag, test_pred)\n test_recall = metrics.recall_score(test_tag, test_pred)\n test_f1_score = metrics.f1_score(test_tag, test_pred)\n test_hamming_loss = metrics.hamming_loss(test_tag, test_pred)\n print(\"===分类训练完毕， 分类结果如下 ===\")\n print(\"训练集分类的结果指标是: \")\n print(\" samples_precision {:g}, samples_recall {:g}, samples_f1_score {:g}, samples_hamming_loss {:g}\"\n .format(train_precision, train_recall, train_f1_score, train_hamming_loss))\n\n print(\"测试集分类的结果指标是: \")\n print(\" samples_precision {:g}, samples_recall {:g}, samples_f1_score {:g}, samples_hamming_loss {:g}\"\n .format(test_precision, test_recall, test_f1_score, test_hamming_loss))\n\n # print(\"训练集\"+ str(classify_num) +\"误差： {e}\".format(e = train_err_ratio))\n # print(\"测试集\"+ str(classify_num) +\"误差： {e}\".format(e = test_err_ratio))\n return clf\n\ndef checkPred(data_tag, data_pred):\n if data_tag.__len__() != data_pred.__len__():\n raise RuntimeError('The length of data tag and data pred should be the same')\n # err_count = 0\n # print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n # print(data_tag[0:10])\n # print(data_pred[0:10])\n err_count = np.sum(np.equal(data_tag, data_pred))\n err_ratio = np.mean(np.equal(data_tag,data_pred))\n\n # for i in range(data_tag.__len__()):\n # if data_tag[i] != data_pred[i]:\n # err_count += 1\n # err_ratio = err_count / data_tag.__len__()\n return [err_count, err_ratio]\n\ndef read_from_json(json_path):\n with open(json_path, \"rb\") as js_reader:\n data = json.loads(js_reader.read())\n return data\nif __name__ == \"__main__\":\n path_tmp = BasePath + \"/other_data\"\n path_dictionary = os.path.join(path_tmp, 'case.dict')\n path_tmp_tfidf = os.path.join(path_tmp, 'tfidf_corpus')\n path_tmp_lsi = os.path.join(path_tmp, 'lsi_corpus')\n path_tmp_lsimodel = os.path.join(path_tmp, 'lsi_model.pkl')\n path_tmp_predictor = os.path.join(path_tmp, 'predictor')\n\n n = 10 # n 表示抽样率， n抽1\n\n dictionary = None\n corpus_tfidf = None\n corpus_lsi = None\n lsi_model = None\n predictor = None\n if not os.path.exists(path_tmp):\n os.makedirs(path_tmp)\n\n # json_data = read_from_json(path_tmp + \"/json_data.txt\")\n \"\"\"\n 遍历文档，生成字典，并去掉频率较少的项\n \"\"\"\n if not os.path.exists(path_dictionary):\n print(\"=====未检测到字典存在，开始遍历生成字典=====\")\n dictionary = corpora.Dictionary()\n json_data = read_from_json(path_tmp + \"/json_data.txt\")\n for i, line in enumerate(json_data):\n # print(type(line['content']))\n dictionary.add_documents([line['content']])\n if int(i/n)%1000 == 0:\n print('{t} *** {i} \\t docs has been dealed'\n .format(i=i, t=time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())))\n\n # 去掉词典中出现次数过少的\n small_freq_ids = [tokenid for tokenid, docfreq in dictionary.dfs.items() if docfreq < 5 ]\n dictionary.filter_tokens(small_freq_ids)\n dictionary.compactify()\n dictionary.save(path_dictionary)\n print('=== 词典已经生成 ===')\n else:\n print(\"======检测到字典已经存在，跳过该阶段=====\")\n\n \"\"\"\n 第二阶段， 开始将文档转换为tfidf\n \"\"\"\n if not os.path.exists(path_tmp_tfidf):\n print(\"===未检测到有tfidf文件夹的存在，开始生成tfidf向量===\")\n if not dictionary:\n dictionary = corpora.Dictionary.load(path_dictionary)\n os.makedirs(path_tmp_tfidf)\n tfidf_model = models.TfidfModel(dictionary = dictionary)\n corpus_tfidf = []\n json_data = read_from_json(path_tmp + \"/json_data.txt\")\n for i, line in enumerate(json_data):\n file_bow = dictionary.doc2bow(line['content'])\n file_tfidf = tfidf_model[file_bow]\n corpus_tfidf.append(file_tfidf)\n if i % 10000 == 0:\n print('{i} files is dealed'.format(i = i))\n corpora.MmCorpus.serialize('{f}.mm'.format(f = path_tmp_tfidf),\n corpus_tfidf,\n id2word=dictionary)\n print(\"corpus has been transformed into tfidf vector\")\n print(\"==== tfidf向量已经生成 ====\")\n else:\n print('=== 检测到tfidf向量已经生成，跳过该阶段 ===')\n\n\n \"\"\"\n 第三阶段， 开始将tfidf转换成lsi\n \"\"\"\n if not os.path.exists(path_tmp_lsi):\n print(\"==== 未检测到有lsi文件夹存在，开始生成lsi向量 ====\")\n if not dictionary:\n dictionary = corpora.Dictionary.load(path_dictionary)\n if not corpus_tfidf: # 如果跳过了第二阶段， 则从指定位��读取tfidf文档\n print(\"=== 未检测到tfidf文档，开始读取 ===\")\n path = '{f}.mm'.format(f = path_tmp_tfidf)\n corpus_tfidf = corpora.MmCorpus(path)\n print(\"--- tfidf文档读取完毕，开始转化成lsi向量 ---\")\n print(\"the len of corpus_tfidf is : \" + str(len(list(corpus_tfidf))))\n # 生成lsi model\n os.makedirs(path_tmp_lsi)\n lsi_model = models.LsiModel(corpus=list(corpus_tfidf), id2word=dictionary, num_topics=50)\n # 将lsi模型存储到磁盘上\n lsi_file = open(path_tmp_lsimodel, 'wb')\n pkl.dump(lsi_model, lsi_file)\n lsi_file.close()\n # del corpus_tfidf\n print(\"--- lsi模型已经生成 ---\")\n\n # 生成corpus of lsi, 并逐步去掉corpus of tf_idf\n corpus_lsi = []\n # corpu = lsi_model[corpus_tfidf]\n corpu = [lsi_model[doc] for doc in list(corpus_tfidf)]\n corpora.MmCorpus.serialize('{f}.mm'.format(f = path_tmp_lsi),\n corpu,\n id2word=dictionary)\n\n print(\"====== lsi向量已经生成 ======\")\n else:\n print(\"=== 检测到lsi向量已经生成，跳过该阶段 ===\")\n\n # # # ==============================================================\n # # # # # 第四阶段, 分类\n\n if not corpus_lsi: #如果跳过了第三阶段\n print('--- 未检测到lsi文档, 从硬盘中读取 ---')\n path = '{f}.mm'.format(f = path_tmp_lsi)\n corpus = corpora.MmCorpus(path)\n # 将gensim中的mm表示转化成Numpy矩阵表示\n data = []\n rows = []\n cols = []\n line_count = 0\n for line in list(corpus):\n for elem in line:\n rows.append(line_count)\n cols.append(elem[0])\n data.append(elem[1])\n line_count += 1\n lsi_matrix = csr_matrix((data, (rows, cols))).toarray()\n y_data = np.loadtxt(BasePath + \"/other_data/train_dev_data_y_topn70.txt\")\n\n assert len(lsi_matrix) == len(y_data), \"the len of lsi_matrix and y_data is not match\"\n\n percentage = 0.2\n np.random.seed(10)\n shuffle_indices = np.random.permutation(np.arange(len(y_data)))\n x_shuffled = lsi_matrix[shuffle_indices]\n y_shuffled = y_data[shuffle_indices]\n\n dev_sample_index = -1 * int(percentage * float(len(lsi_matrix)))\n x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]\n y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]\n print(\"the total number of samples is : \" + str(len(y_data)))\n one_hot_vocab = read_from_json(BasePath + \"/other_data/one_hot_vocab_70.txt\")\n id2word = {value: key for key, value in one_hot_vocab.items()}\n\n for i in range(0, 70):\n y_tmp_train = y_train[:, i]\n y_tmp_dev = y_dev[:, i]\n predictor_path = path_tmp + \"/predictor/predictor_\" + str(id2word[i]) + \".m\"\n # 生成分类器\n if not os.path.exists(predictor_path):\n # x = open(predictor_path, 'wb')\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n print(\"the positive sample of the key '\" + id2word[i] + \"' is :\" + str(len(y_tmp_train==1)))\n predictor = svm_classify(x_train, y_tmp_train, x_dev, y_tmp_dev, id2word[i])\n joblib.dump(predictor, predictor_path)\n else:\n print(\"=== 分类器\" + str(id2word[i]) + \"已经生成,跳过\")\n\n y_dev_path = BasePath + \"/other_data/y_dev_array.txt\"\n y_pred_path = BasePath + \"/other_data/y_dev_pred.txt\"\n\n if not (os.path.exists(y_dev_path) and os.path.exists(y_pred_path)):\n y_pred = np.zeros(y_dev.shape)\n for i in range(0, 70):\n predictor_path = path_tmp + \"/predictor_\" + str(id2word[i]) + \".m\"\n clf = joblib.load(predictor_path)\n y_tmp_pred = clf.predict(x_dev)\n y_pred[:, i] = y_tmp_pred\n np.savetxt(y_dev_path, y_dev)\n np.savetxt(y_pred_path, y_pred)\n else:\n y_dev = np.loadtxt(y_dev_path)\n y_pred = np.loadtxt(y_pred_path)\n\n\n for topn in [10, 15, 20, 30, 40, 45, 50, 60, 70]:\n dev_precision = metrics.precision_score(y_dev[:, :topn], y_pred[:, :topn], average=\"samples\")\n dev_recall = metrics.recall_score(y_dev[:, :topn], y_pred[:, :topn], average=\"samples\")\n dev_f1_score = metrics.f1_score(y_dev[:, :topn], y_pred[:, :topn], average=\"samples\")\n dev_hamming_loss = metrics.hamming_loss(y_dev[:, :topn], y_pred[:, :topn])\n\n\n macro_dev_precision = metrics.precision_score(y_dev[:, :topn], y_pred[:, :topn], average=\"macro\")\n macro_dev_recall = metrics.recall_score(y_dev[:, :topn], y_pred[:, :topn], average=\"macro\")\n macro_dev_f1_score = metrics.f1_score(y_dev[:, :topn], y_pred[:, :topn], average=\"macro\")\n macro_dev_hamming_loss = metrics.hamming_loss(y_dev[:, :topn], y_pred[:, :topn])\n\n print(\"测试集topn \"+ str(topn) +\" 分类的结果指标是: \")\n print(\" samples_precision {:g}, samples_recall {:g}, samples_f1_score {:g}, samples_hamming_loss {:g}\"\n .format(dev_precision, dev_recall, dev_f1_score, dev_hamming_loss))\n\n print(\" macro_precision {:g}, macro_recall {:g}, macro_f1_score {:g}, macro_hamming_loss {:g}\"\n .format(macro_dev_precision, macro_dev_recall, macro_dev_f1_score, macro_dev_hamming_loss))\n\n # # y_shuffled = np.array(range(0, len(lsi_matrix)))\n # print(\"the total number of samples is : \" + str(len(y_data)))\n # for key in y_data:\n # y_shuffled = np.array([0] * len(lsi_matrix))\n # if key != 'base':\n # y_shuffled[np.array(y_data[key])] = 1\n #\n # predictor_path = path_tmp_predictor + \"/predictor_\" + key + \".pkl\"\n # # 生成分类器\n # if not os.path.exists(predictor_path):\n # x = open(predictor_path, 'wb')\n # # print(len(y_train))\n # # print(len(y_dev))\n # print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\n # print(\"the positive sample of the key '\" + key + \"' is :\" + str(len(y_data[key])))\n # predictor = svm_classify(x_train, y_train, x_dev, y_dev, key)\n # # pkl.dump(predictor, x)\n # # x.close()\n # else:\n # print(\"=== 分类器\" + str(key) + \"已经生成，跳过 ===\")\n\n\n\n\n\n\n\n\n\n", "sub_path": "tf-idf_lsi_svm_classification.py", "file_name": "tf-idf_lsi_svm_classification.py", "file_ext": "py", "file_size_in_byte": 12956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 24, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 31, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 32, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 33, "usage_type": "name"}, {"api_name": "sklearn.metrics.hamming_loss", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 34, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 36, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 37, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 38, "usage_type": "name"}, {"api_name": "sklearn.metrics.hamming_loss", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 97, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 97, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 104, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "gensim.corpora.Dictionary.load", "line_number": 121, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 121, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 121, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 122, "usage_type": "call"}, {"api_name": "gensim.models.TfidfModel", "line_number": 123, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 123, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus.serialize", "line_number": 132, "usage_type": "call"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 132, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 132, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "gensim.corpora.Dictionary.load", "line_number": 147, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 147, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 147, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 151, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 151, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 155, "usage_type": "call"}, {"api_name": "gensim.models.LsiModel", "line_number": 156, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 156, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 159, "usage_type": "call"}, {"api_name": "gensim.corpora.MmCorpus.serialize", "line_number": 168, "usage_type": "call"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 168, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 168, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 182, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 182, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 222, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 222, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 230, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 233, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 233, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 240, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 244, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 244, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 245, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 245, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 246, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 246, "usage_type": "name"}, {"api_name": "sklearn.metrics.hamming_loss", "line_number": 247, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 247, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 250, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 250, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 251, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 251, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 252, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 252, "usage_type": "name"}, {"api_name": "sklearn.metrics.hamming_loss", "line_number": 253, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 253, "usage_type": "name"}]}
+{"seq_id": "388136041", "text": "\"\"\"\r\nUtilities for downloading and unpacking the CIFAR-10 dataset, originally published\r\nby Krizhevsky et al. and hosted here: https://www.cs.toronto.edu/~kriz/cifar.html\r\n\"\"\"\r\n\r\nimport os\r\nimport sys\r\nimport tarfile\r\nfrom six.moves import urllib\r\nimport numpy as np\r\nimport pandas\r\nimport scipy.misc as misc\r\n\r\n'''\r\nimport data.face_data as fd\r\nfd.load('~/data/Cropped_AMFEDPLUS.txt')\r\n'''\r\ndef load(data_dir, subset='train'):\r\n\r\n label_df = pandas.read_csv(data_dir+'labels.txt',sep='\\t')\r\n\r\n im_dim = 48\r\n datax = np.zeros((im_dim, im_dim, len(label_df), 3), dtype=np.float32)\r\n datay = np.zeros((len(label_df)), dtype=np.float32)\r\n X_data = [] \r\n for index, row in label_df.iterrows():\r\n #print '~/data/'+'Cropped_AMFEDPLUS'+row[0]\r\n im = misc.imread(data_dir+row[0])\r\n im2 = misc.imresize(im, [im_dim,im_dim])\r\n #datax = np.dstack((datax, im2))\r\n datax[:,:,index,0] = im2\r\n datax[:,:,index,1] = im2\r\n datax[:,:,index,2] = im2\r\n #datay = np.append(datay, row[1])\r\n datay[index] = row[1]\r\n X_data.append (im2)\r\n datax = np.transpose(datax, (2, 3, 0, 1))\r\n\r\n \r\n if subset=='train':\r\n trainx = datax[:150000,:,:,:]\r\n trainy = datay[:150000]\r\n return trainx, np.array(trainy).astype(np.uint8)\r\n elif subset=='test':\r\n testx = datax[150001:,:,:,:]\r\n testy = datay[150001:]\r\n #testx = datax[150001:,:,:,:]\r\n #testy = datay[150001:]\r\n return testx, np.array(testy).astype(np.uint8)\r\n\r\n\r\n '''\r\n if subset=='train':\r\n train_data = [unpickle(os.path.join(data_dir,'cifar-10-batches-py','data_batch_' + str(i))) for i in range(1,6)]\r\n trainx = np.concatenate([d['x'] for d in train_data],axis=0)\r\n trainy = np.concatenate([d['y'] for d in train_data],axis=0)\r\n return trainx, trainy\r\n elif subset=='test':\r\n test_data = unpickle(os.path.join(data_dir,'cifar-10-batches-py','test_batch'))\r\n testx = test_data['x']\r\n testy = test_data['y']\r\n\tprint testx\r\n\tprint testx.shape\r\n\tprint testy\r\n\tprint testy.shape\r\n return testx, testy\r\n else:\r\n raise NotImplementedError('subset should be either train or test')\r\n '''\r\n\r\nclass DataLoader(object):\r\n \"\"\" an object that generates batches of CIFAR-10 data for training \"\"\"\r\n\r\n def __init__(self, data_dir, subset, batch_size, rng=None, shuffle=False, return_labels=False):\r\n \"\"\" \r\n - data_dir is location where to store files\r\n - subset is train|test \r\n - batch_size is int, of #examples to load at once\r\n - rng is np.random.RandomState object for reproducibility\r\n \"\"\"\r\n\r\n self.data_dir = data_dir\r\n self.batch_size = batch_size\r\n self.shuffle = shuffle\r\n self.return_labels = return_labels\r\n\r\n # create temporary storage for the data, if not yet created\r\n if not os.path.exists(data_dir):\r\n print('creating folder', data_dir)\r\n os.makedirs(data_dir)\r\n\r\n # load CIFAR-10 training data to RAM\r\n self.data, self.labels = load(os.path.join(data_dir), subset=subset)\r\n self.data = np.transpose(self.data, (0,2,3,1)) # (N,3,32,32) -> (N,32,32,3)\r\n \r\n self.p = 0 # pointer to where we are in iteration\r\n self.rng = np.random.RandomState(1) if rng is None else rng\r\n\r\n def get_observation_size(self):\r\n return self.data.shape[1:]\r\n\r\n def get_num_labels(self):\r\n return np.amax(self.labels) + 1\r\n\r\n def reset(self):\r\n self.p = 0\r\n\r\n def __iter__(self):\r\n return self\r\n\r\n def __next__(self, n=None):\r\n \"\"\" n is the number of examples to fetch \"\"\"\r\n if n is None: n = self.batch_size\r\n\r\n # on first iteration lazily permute all data\r\n if self.p == 0 and self.shuffle:\r\n inds = self.rng.permutation(self.data.shape[0])\r\n self.data = self.data[inds]\r\n self.labels = self.labels[inds]\r\n\r\n # on last iteration reset the counter and raise StopIteration\r\n if self.p + n > self.data.shape[0]:\r\n self.reset() # reset for next time we get called\r\n raise StopIteration\r\n\r\n # on intermediate iterations fetch the next batch\r\n x = self.data[self.p : self.p + n]\r\n y = self.labels[self.p : self.p + n]\r\n self.p += self.batch_size\r\n\r\n if self.return_labels:\r\n return x,y\r\n else:\r\n return x\r\n\r\n next = __next__ # Python 2 compatibility (https://stackoverflow.com/questions/29578469/how-to-make-an-object-both-a-python2-and-python3-iterator)\r\n\r\n\r\n", "sub_path": "data/face_data.py", "file_name": "face_data.py", "file_ext": "py", "file_size_in_byte": 4651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "scipy.misc.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 28, "usage_type": "name"}, {"api_name": "scipy.misc.imresize", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.amax", "line_number": 103, "usage_type": "call"}]}
+{"seq_id": "377368236", "text": "import torch\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport pdb\r\n\r\nclass PreferenceNet(nn.Module):\r\n def __init__(self, n_agents, n_items, hidden_dim):\r\n super(PreferenceNet, self).__init__()\r\n size = n_agents * n_items\r\n #size = n_agents * n_items + n_agents * n_items + n_agents\r\n \r\n self.MLP = nn.Sequential(nn.Linear(size, hidden_dim), nn.BatchNorm1d(hidden_dim), nn.ReLU(), \r\n nn.Linear(hidden_dim, hidden_dim), nn.BatchNorm1d(hidden_dim), nn.ReLU(),\r\n nn.Linear(hidden_dim, 1), nn.BatchNorm1d(1), nn.Sigmoid())\r\n\r\n def forward(self, bids, allocs, payments):\r\n bids = bids.reshape(bids.shape[0], -1)\r\n allocs = allocs.reshape(allocs.shape[0], -1)\r\n payments = payments.reshape(payments.shape[0], -1)\r\n \r\n data = allocs\r\n #data = torch.cat([bids, allocs, payments], dim=1)\r\n\r\n return self.MLP(data).squeeze(-1)", "sub_path": "learnable_preferences/preference/network.py", "file_name": "network.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 14, "usage_type": "call"}]}
+{"seq_id": "485180265", "text": "import sys\nimport keyboard\nimport hotkeys\nfrom gui import Ui_MainWindow\nfrom PyQt5 import QtGui, QtWidgets\n\nclass MainWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super(MainWindow, self).__init__()\n\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n\n def aboutAction(self):\n QtWidgets.QMessageBox.about(self, \"About\", \"\\n--Borderlands Helper Alpha--\\n\\nhttps://github.com/Seth-Revz\")\n\n def errorMsg(event=\"\"):\n if event == \"open\":\n error = QtWidgets.QMessageBox()\n error.setWindowTitle(\"Error\")\n error.setText(\"Borderlands2 is not running\")\n error.setIcon(QtWidgets.QMessageBox.Warning)\n error.setStandardButtons(QtWidgets.QMessageBox.Ok)\n error.exec()\n\nif __name__ == \"__main__\":\n \n keyboard.add_hotkey(\"F5\", lambda: keyboard.call_later(hotkeys.execPatch), suppress=True)\n keyboard.add_hotkey(\"F6\", lambda: keyboard.call_later(hotkeys.colorblindFix), suppress=True)\n keyboard.add_hotkey(\"F7\", lambda: keyboard.call_later(hotkeys.reload), suppress=True)\n #keyboard.add_hotkey(\"F8\", lambda: keyboard.call_later(hotkeys.readOnlyReload), suppress=True)\n keyboard.add_hotkey('end', lambda: app.exit(), suppress=True)\n\n app = QtWidgets.QApplication(sys.argv)\n MainWindow = MainWindow()\n MainWindow.show()\n app.exec()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "gui.Ui_MainWindow", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "keyboard.add_hotkey", "line_number": 28, "usage_type": "call"}, {"api_name": "keyboard.call_later", "line_number": 28, "usage_type": "call"}, {"api_name": "hotkeys.execPatch", "line_number": 28, "usage_type": "attribute"}, {"api_name": "keyboard.add_hotkey", "line_number": 29, "usage_type": "call"}, {"api_name": "keyboard.call_later", "line_number": 29, "usage_type": "call"}, {"api_name": "hotkeys.colorblindFix", "line_number": 29, "usage_type": "attribute"}, {"api_name": "keyboard.add_hotkey", "line_number": 30, "usage_type": "call"}, {"api_name": "keyboard.call_later", "line_number": 30, "usage_type": "call"}, {"api_name": "hotkeys.reload", "line_number": 30, "usage_type": "attribute"}, {"api_name": "keyboard.add_hotkey", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 34, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}]}
+{"seq_id": "301243505", "text": "import psycopg2\nimport os\nfrom app.api.v2.auth.auth import User\n\nclass ConnectDB:\n def __init__(self):\n try:\n self.conn = psycopg2.connect(\n os.getenv('DATABASE_URL')\n )\n print(\"connected\")\n self.cursor = self.conn.cursor()\n\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n\nclass CreateTables(ConnectDB):\n\n def __init__(self):\n super().__init__()\n\n def create_tables(self):\n\n queries = [\n \"\"\"\n CREATE TABLE IF NOT EXISTS users(\n id serial PRIMARY KEY,\n username VARCHAR NOT NULL UNIQUE,\n email VARCHAR NOT NULL UNIQUE,\n password VARCHAR NOT NULL,\n is_admin BOOL NOT NULL\n )\n \"\"\",\n\n \"\"\"\n CREATE TABLE IF NOT EXISTS meals(\n id serial PRIMARY KEY,\n name VARCHAR NOT NULL,\n description VARCHAR NOT NULL , \n price INT NOT NULL\n \n )\n \"\"\",\n \"\"\"\n CREATE TABLE IF NOT EXISTS orders(\n id serial PRIMARY KEY,\n username VARCHAR NOT NULL,\n title VARCHAR NOT NULL,\n description VARCHAR NOT NULL,\n price INT NOT NULL,\n status VARCHAR NOT NULL,\n order_date TIMESTAMP\n )\n \"\"\"\n ]\n\n for query in queries:\n self.cursor.execute(query)\n\n self.conn.commit()\n\n def drop(self):\n queries = [\n '''\n DROP TABLE IF EXISTS users\n ''',\n '''\n DROP TABLE IF EXISTS meals\n ''',\n '''\n DROP TABLE IF EXISTS orders\n '''\n ]\n\n for query in queries:\n self.cursor.execute(query)\n\n self.conn.commit()\n \n\n def add_admin(self):\n admin = User(username='Useradmin', email='admin@gmail.com',\n password='Admin123', is_admin=True)\n admin.add()\n", "sub_path": "testdb.py", "file_name": "testdb.py", "file_ext": "py", "file_size_in_byte": 2010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "psycopg2.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "psycopg2.DatabaseError", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.api.v2.auth.auth.User", "line_number": 82, "usage_type": "call"}]}
+{"seq_id": "292703334", "text": "import os\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass MyDataset(Dataset):\n def __init__(self, images_dir, masks_dir, transform=None):\n self.images_dir = images_dir\n self.masks_dir = masks_dir\n self.transform = transform\n self.images = sorted(os.listdir(images_dir))\n self.masks = sorted(os.listdir(masks_dir))\n\n\n def __len__(self):\n return len(self.images)\n\n\n def __getitem__(self, item):\n\n\n\n img_path = os.path.join(self.images_dir, self.images[item])\n mask_path = os.path.join(self.masks_dir, self.masks[item])\n\n\n\n\n image = np.array(Image.open(img_path).convert(\"RGB\"))\n mask = np.array(Image.open(mask_path).convert(\"L\"), dtype=np.float32)\n mask = mask/254.0\n\n\n if self.transform is not None:\n augmentations = self.transform(image=image, mask=mask)\n image = augmentations[\"image\"]\n mask = augmentations[\"mask\"]\n\n return image, mask\n\n", "sub_path": "unet_deploy_flask/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 1042, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 7, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 31, "usage_type": "attribute"}]}
+{"seq_id": "115274925", "text": "import os\n\nimport pytest\n\nfrom substratools import exceptions\nfrom substratools.opener import load_from_module\nfrom substratools.utils import import_module\nfrom substratools.workspace import DEFAULT_INPUT_DATA_FOLDER_PATH\n\n\n@pytest.fixture\ndef tmp_cwd(tmp_path):\n # create a temporary current working directory\n new_dir = tmp_path / \"workspace\"\n new_dir.mkdir()\n\n old_dir = os.getcwd()\n os.chdir(new_dir)\n\n yield new_dir\n\n os.chdir(old_dir)\n\n\ndef test_load_opener_not_found(tmp_cwd):\n with pytest.raises(ImportError):\n load_from_module()\n\n\ndef test_load_invalid_opener(tmp_cwd):\n invalid_script = \"\"\"\ndef get_X():\n raise NotImplementedError\ndef get_y():\n raise NotImplementedError\n\"\"\"\n\n import_module('opener', invalid_script)\n\n with pytest.raises(exceptions.InvalidInterface):\n load_from_module()\n\n\n@pytest.mark.skip(reason=\"not supported\")\ndef test_load_opener_as_module(tmp_cwd):\n script = \"\"\"\ndef _helper():\n pass\ndef get_X(folders):\n return 'X'\ndef get_y(folders):\n return 'y'\ndef fake_X(n_samples):\n return 'fakeX'\ndef fake_y(n_samples):\n return 'fakey'\ndef get_predictions(path):\n return 'pred'\ndef save_predictions(y_pred, path):\n return 'pred'\n\"\"\"\n\n import_module('opener', script)\n\n o = load_from_module()\n assert o.get_X() == 'X'\n\n\ndef test_load_opener_as_class(tmp_cwd):\n script = \"\"\"\nfrom substratools import Opener\nclass MyOpener(Opener):\n def get_X(self, folders):\n return 'Xclass'\n def get_y(self, folders):\n return 'yclass'\n def fake_X(self, n_samples):\n return 'fakeX'\n def fake_y(self, n_samples):\n return 'fakey'\n def get_predictions(self, path):\n return 'pred'\n def save_predictions(self, y_pred, path):\n return 'pred'\n\"\"\"\n\n import_module('opener', script)\n\n o = load_from_module()\n assert o.get_X() == 'Xclass'\n\n\ndef test_load_opener_from_path(tmp_cwd, valid_opener_code):\n dirpath = tmp_cwd / 'myopener'\n dirpath.mkdir()\n path = dirpath / 'my_opener.py'\n path.write_text(valid_opener_code)\n o = load_from_module(path=path)\n assert o.get_X() == 'X'\n\n\ndef test_opener_check_folders(tmp_cwd):\n script = \"\"\"\nfrom substratools import Opener\nclass MyOpener(Opener):\n def get_X(self, folders):\n assert len(folders) == 5\n return 'Xclass'\n def get_y(self, folders):\n return 'yclass'\n def fake_X(self, n_samples):\n return 'fakeX'\n def fake_y(self, n_samples):\n return 'fakey'\n def get_predictions(self, path):\n return 'pred'\n def save_predictions(self, y_pred, path):\n return 'pred'\n\"\"\"\n\n import_module('opener', script)\n\n o = load_from_module()\n\n # create some data folders\n data_root_path = os.path.join(o._workspace._workdir, DEFAULT_INPUT_DATA_FOLDER_PATH)\n data_paths = [os.path.join(data_root_path, str(i)) for i in range(5)]\n [os.makedirs(p) for p in data_paths]\n\n o._workspace.input_data_folder_paths = data_paths\n assert o.get_X() == 'Xclass'\n\n\n@pytest.mark.parametrize('save_predictions_method_body', (\n \"\"\"\n pass\n \"\"\",\n \"\"\"\n with open(path + '.npy', 'w') as f:\n json.dump(pred, f)\n \"\"\",\n))\ndef test_predictions_check(tmp_cwd, save_predictions_method_body):\n script = f\"\"\"\nimport json\nfrom substratools import Opener\n\nclass MyOpener(Opener):\n def get_X(self, folder):\n return 'X'\n\n def get_y(self, folder):\n return list(range(0, 3))\n\n def fake_X(self, n_samples):\n return 'Xfake'\n\n def fake_y(self, n_samples):\n return [0] * 3\n\n def get_predictions(self, path):\n with open(path, 'r') as f:\n return json.load(f)\n\n def save_predictions(self, pred, path):\n {save_predictions_method_body}\n\"\"\"\n import_module('opener', script)\n\n o = load_from_module()\n\n with pytest.raises(exceptions.MissingFileError):\n o.save_predictions({'foo': 'bar'})\n", "sub_path": "tests/test_opener.py", "file_name": "test_opener.py", "file_ext": "py", "file_size_in_byte": 3954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.getcwd", "line_number": 17, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 18, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 26, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 27, "usage_type": "call"}, {"api_name": "substratools.utils.import_module", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "substratools.exceptions.InvalidInterface", "line_number": 40, "usage_type": "attribute"}, {"api_name": "substratools.exceptions", "line_number": 40, "usage_type": "name"}, {"api_name": "substratools.opener.load_from_module", "line_number": 41, "usage_type": "call"}, {"api_name": "substratools.utils.import_module", "line_number": 63, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 65, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 44, "usage_type": "attribute"}, {"api_name": "substratools.utils.import_module", "line_number": 87, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 89, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 98, "usage_type": "call"}, {"api_name": "substratools.utils.import_module", "line_number": 121, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "substratools.workspace.DEFAULT_INPUT_DATA_FOLDER_PATH", "line_number": 126, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 128, "usage_type": "call"}, {"api_name": "substratools.utils.import_module", "line_number": 168, "usage_type": "call"}, {"api_name": "substratools.opener.load_from_module", "line_number": 170, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 172, "usage_type": "call"}, {"api_name": "substratools.exceptions.MissingFileError", "line_number": 172, "usage_type": "attribute"}, {"api_name": "substratools.exceptions", "line_number": 172, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 134, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 134, "usage_type": "attribute"}]}
+{"seq_id": "248772307", "text": "import tldextract\nfrom tqdm import tqdm\nimport pandas as pd\nimport glob\nimport numpy as np\nimport re\nimport argparse\n\nFRAME_COLUMNS = ['age', 'armed', 'attack', 'criminal_record', 'fleeing', 'gender', 'interview', 'legal_language', 'mental_illness', 'official_report', 'race', 'systemic', 'unarmed', 'video']\n\ndef to_ordinal(row, prefix):\n d = {}\n for col_name in row.keys():\n if (len(re.findall(prefix, col_name))>0) \\\n and (type(row[col_name]) in [float, int, np.float64]):\n if row[col_name]>-1:\n d[col_name] = row[col_name]\n return {k:i+1 for i, (k, v) in enumerate(sorted(d.items(), key=lambda item: item[1]))}\n\ndef order_row(row, prefix='found.'):\n orders = np.ones(14)*np.inf\n ordinal = to_ordinal(row, prefix)\n for i, col in enumerate(FRAME_COLUMNS):\n col_name = prefix+col\n if col_name in ordinal:\n orders[i] = ordinal[col_name]\n return orders\n\ndef main():\n \n parser = argparse.ArgumentParser()\n parser.add_argument('--output', type=str, help='path to input file', default='data/prepared/shootings/shooting_frames_.csv')\n args = parser.parse_args()\n \n data = []\n for fn in glob.glob('frames-extracted/frames*.csv'):\n data.append(pd.read_csv(fn))\n shootings = pd.concat(data)\n\n all_ranks = np.stack([order_row(row) for _, row in shootings.iterrows()])\n\n shootings[['found.'+frame for frame in FRAME_COLUMNS]] = all_ranks\n\n bias = []\n urls = []\n media_bias = pd.read_csv('resources/mbfc/media-bias-fc-scrape.csv')\n for i, row in media_bias.iterrows():\n val = row['bias_png']\n if 'center' in val:\n bias.append(1)\n elif 'left' in val:\n bias.append(0)\n elif 'right' in val:\n bias.append(2)\n else:\n bias.append(1)\n subdomain, domain, suffix = tldextract.extract(row['url'])\n urls.append(domain)\n media_bias['bias'] = bias\n media_bias['domain'] = urls\n\n leanings = []\n for i, row in tqdm(shootings.iterrows(), total=len(shootings)):\n bias = media_bias[media_bias['domain']==row['domain']]\n if len(bias)>0:\n leanings.append(bias.iloc[0]['bias'].item())\n else:\n leanings.append(-1)\n\n shootings['leaning'] = leanings\n shootings.to_csv(args.output)\n \nif __name__=='__main__':\n main()", "sub_path": "06_clean_framing_file.py", "file_name": "06_clean_framing_file.py", "file_ext": "py", "file_size_in_byte": 2375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "re.findall", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 21, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 57, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 63, "usage_type": "call"}]}
+{"seq_id": "536737793", "text": "#------------------------------------------------------------------------------\n# Copyright (c) 2011, Enthought, Inc.\n# All rights reserved.\n#------------------------------------------------------------------------------\nimport wx\n\nfrom .wx_constraints_widget import WXConstraintsWidget\n\nfrom ...components.tab_group import AbstractTkTabGroup\n\n\n#: A mapping from TabPosition enum values to qt tab positions.\n_TAB_POSITION_MAP = {\n 'top': wx.NB_TOP,\n 'bottom': wx.NB_BOTTOM,\n 'left': wx.NB_LEFT,\n 'right': wx.NB_RIGHT,\n}\n\n\nclass WXTabGroup(WXConstraintsWidget, AbstractTkTabGroup):\n \"\"\" A wx implementation of the Tabbed container.\n\n \"\"\"\n #--------------------------------------------------------------------------\n # Setup Methods \n #--------------------------------------------------------------------------\n def create(self, parent):\n \"\"\" Create the underlying wxNotebook control.\n\n \"\"\"\n # Changing the tab position of the notebook dynamically is not\n # supported by wx (the rendering gets all messed up). So, the\n # tab position must be set at creation time.\n style = _TAB_POSITION_MAP[self.shell_obj.tab_position]\n self.widget = wx.Notebook(parent, style=style)\n \n def initialize(self):\n \"\"\" Initialize the attributes of the wxNotebook.\n\n \"\"\"\n super(WXTabGroup, self).initialize()\n self.update_tabs()\n self.set_selected_index(self.shell_obj.selected_index)\n\n def bind(self):\n \"\"\" Bind to the events emitted by the underlying control.\n\n \"\"\"\n super(WXTabGroup, self).bind()\n self.widget.Bind(wx.EVT_NOTEBOOK_PAGE_CHANGED, self._on_page_changed)\n\n #--------------------------------------------------------------------------\n # Implementation \n #--------------------------------------------------------------------------\n def shell_tabs_changed(self, tabs):\n \"\"\" The change handler for the 'tabs' attribute of the shell \n object.\n\n \"\"\"\n self.update_tabs()\n\n def shell_tab_position_changed(self, tab_position):\n \"\"\" The change handler for the 'tab_position' attribute of the\n shell object.\n\n \"\"\"\n # Changing tab position dynamically on wx is not supported.\n pass\n\n def shell_selected_index_changed(self, index):\n \"\"\" Update the widget index with the new value from the shell \n object.\n\n \"\"\"\n self.set_selected_index(index)\n\n def size_hint(self):\n \"\"\" Returns a (width, height) tuple of integers which represent\n the suggested size of the widget for its current state. This\n value is used by the layout manager to determine how much\n space to allocate the widget.\n\n \"\"\"\n # XXX - This size hint computation needs some work. Need to \n # look at the source for wxNotebook::GetBestSize and see what\n # is does and compare against QTabWidget::sizeHint. Like always,\n # the Qt implementation does the right thing and wx does not.\n widget = self.widget\n shell = self.shell_obj\n curr_shell = shell.selected_tab\n\n if curr_shell is None:\n # QTabWidget default for no tabs\n return (6, 6)\n \n size_hint = curr_shell.size_hint()\n\n if size_hint == (-1, -1):\n size_hint = curr_shell.min_size()\n \n width_hint, height_hint = size_hint\n\n # Compute the size of the tab so we can offset the size hint.\n # On Windows, the return value of the height function is a hard\n # coded default of 20. This is close for the standard font but\n # will probably break down with different fonts or icons. I've\n # found no other way to measure the height of the tab bar.\n tab_size = wx.RendererNative.Get().GetHeaderButtonHeight(widget)\n\n # Offset the size hint by the tab bar size\n style = widget.GetWindowStyle()\n if (style & wx.NB_TOP) or (style & wx.NB_BOTTOM):\n height_hint += tab_size\n else:\n width_hint += tab_size\n \n width_hint = max(width_hint, 200)\n\n return (width_hint, height_hint)\n\n #--------------------------------------------------------------------------\n # Event Handlers \n #--------------------------------------------------------------------------\n def _on_page_changed(self, event):\n \"\"\" The event handler for the page change event of the underlying \n control. Synchronizes the index of the shell object.\n\n \"\"\"\n event.Skip()\n # Use event.GetSelection since widget.GetSelection returns the\n # wrong value during this event handler.\n self.shell_obj._selected_index = event.GetSelection()\n\n #--------------------------------------------------------------------------\n # Widget Update Methods \n #--------------------------------------------------------------------------\n def set_selected_index(self, index):\n \"\"\" Sets the current index of the tab widget. This is overridden\n from the parent class.\n\n \"\"\"\n if index != -1:\n self.widget.SetSelection(index)\n \n def update_tabs(self):\n \"\"\" Populates the notebook with the child notebook pages. This\n is an overridden parent class method which sets the title of\n of notebook pages properly.\n\n \"\"\"\n # FIXME: there should be a more efficient way to do this, but \n # for now just remove all present widgets and add the current \n # ones. If we use DeleteAllPages(), then the child widgets would\n # be destroyed, which is not the behavior we want.\n widget = self.widget\n shell = self.shell_obj\n \n while widget.GetPageCount():\n widget.RemovePage(0)\n \n selected = shell.selected_tab\n for idx, tab in enumerate(shell.tabs):\n widget.AddPage(tab.toolkit_widget, tab.title)\n if tab is selected:\n widget.SetSelection(idx)\n\n", "sub_path": "enaml/backends/wx/wx_tab_group.py", "file_name": "wx_tab_group.py", "file_ext": "py", "file_size_in_byte": 6042, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "wx.NB_TOP", "line_number": 14, "usage_type": "attribute"}, {"api_name": "wx.NB_BOTTOM", "line_number": 15, "usage_type": "attribute"}, {"api_name": "wx.NB_LEFT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "wx.NB_RIGHT", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wx_constraints_widget.WXConstraintsWidget", "line_number": 21, "usage_type": "name"}, {"api_name": "components.tab_group.AbstractTkTabGroup", "line_number": 21, "usage_type": "name"}, {"api_name": "wx.Notebook", "line_number": 36, "usage_type": "call"}, {"api_name": "wx.EVT_NOTEBOOK_PAGE_CHANGED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.RendererNative.Get", "line_number": 109, "usage_type": "call"}, {"api_name": "wx.RendererNative", "line_number": 109, "usage_type": "attribute"}, {"api_name": "wx.NB_TOP", "line_number": 113, "usage_type": "attribute"}, {"api_name": "wx.NB_BOTTOM", "line_number": 113, "usage_type": "attribute"}]}
+{"seq_id": "627852859", "text": "# for Coverage\nfrom mock import patch, MagicMock\n\n\nclass TestAccount:\n def test_account(self):\n from pyEX import Client\n c = Client('sktest')\n with patch('pyEX.account._getJson'):\n c.account()\n c.metadata()\n\n def test_usage(self):\n from pyEX import Client\n from pyEX import PyEXception\n c = Client('sktest')\n with patch('pyEX.account._getJson'):\n c.usage()\n c.usage('messages')\n try:\n c.usage('test')\n assert False\n except PyEXception:\n pass\n", "sub_path": "pyEX/tests/test_account.py", "file_name": "test_account.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pyEX.Client", "line_number": 8, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 9, "usage_type": "call"}, {"api_name": "pyEX.Client", "line_number": 16, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 17, "usage_type": "call"}, {"api_name": "pyEX.PyEXception", "line_number": 23, "usage_type": "name"}]}
+{"seq_id": "204332735", "text": "import csv\nimport re\nfrom pymongo import MongoClient\n\n# Initial DB\nclient = MongoClient()\ndb = client.data\nusersDB = db.users\nitemsDB = db.items\n\n\nitemData = {}\nwith open(\"./data/raw-data.csv\") as csvFile:\n readCSV = csv.reader(csvFile, delimiter=',')\n next(readCSV, None)\n for row in readCSV:\n tmp = re.sub(r'[^\\w]', ' ', row[4])\n itemData[row[0]] = tmp\n itemsDB.insert({\"_id\": row[0], \"itemDes\": tmp, \"itemValue\": \"-\"})\n\nprev = 1\nitems = []\nwith open(\"./data/user-info.csv\") as csvFile:\n readCSV = csv.reader(csvFile, delimiter=',')\n for row in readCSV:\n if prev != int(row[0]):\n usersDB.insert({\"_id\": str(prev), \"items\": items})\n items = []\n prev = int(row[0])\n items.append({\"_id\": row[1], \"itemDes\": itemData[row[1]], \"itemValue\": \"-\"})\n else:\n items.append({\"_id\": row[1], \"itemDes\": itemData[row[1]], \"itemValue\": \"-\"})\n\n#items = []\n# for itemID in range(10):\n# items.append({\"_id\": str(itemID), \"itemDes\": 'Des'+str(itemID), \"itemValue\": '1,1,1,'+str(itemID)})\n# itemsDB.insert({\"_id\": str(itemID), \"itemDes\": 'Des'+str(itemID), \"itemValue\": '1,1,1,'+str(itemID)})\n#\n# for userID in range(10):\n# usersDB.insert({\"_id\": str(userID), \"items\": items})", "sub_path": "createDB.py", "file_name": "createDB.py", "file_ext": "py", "file_size_in_byte": 1274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pymongo.MongoClient", "line_number": 6, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 17, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 24, "usage_type": "call"}]}
+{"seq_id": "644252793", "text": "import requests\r\nimport sys\r\n\r\n\r\ndef get_today_inf():\r\n from datetime import date\r\n pref = date.today()\r\n return pref.year, pref.month, pref.day\r\n\r\n\r\ndef get_week_of_first_september():\r\n import datetime\r\n return datetime.date(get_today_inf()[0], 9, 1).isocalendar()[1]\r\n\r\n\r\ndef get_today_week():\r\n import datetime\r\n inf = get_today_inf()\r\n return datetime.date(inf[0], inf[1], inf[2]).isocalendar()[1]\r\n\r\n\r\ndef timetable_week():\r\n f_s = get_week_of_first_september() % 2\r\n tod = get_today_week() % 2\r\n if f_s == tod:\r\n return 2\r\n return 1\r\n\r\n\r\ndef download_page(g_num='M3100', evel=1):\r\n evel = str(evel)\r\n url = 'http://www.ifmo.ru/ru/schedule/0/'\r\n url += g_num + '/' + evel + '/' + 'raspisanie_zanyatiy_' + g_num + '.htm'\r\n response = requests.get(url)\r\n return response.text\r\n\r\n\r\ndef return_c_teg(text, pos):\r\n i, n = pos, len(text)\r\n while i < n:\r\n if text[i] == '>':\r\n return i\r\n i += 1\r\n return n\r\n\r\n\r\ndef using_value(text, used, func):\r\n arr = text.split('=')\r\n if len(arr) != 2:\r\n return\r\n ids, name = arr[0], arr[1]\r\n name = name[1: len(name)-1]\r\n if ids in func and func[ids] == name:\r\n used[ids] = True\r\n return\r\n\r\n\r\ndef get_tag_name(text):\r\n arr = text.split()\r\n return arr[0]\r\n\r\n\r\ndef avail_tag(text, tag, func={}):\r\n array = text.split()\r\n m = len(array)\r\n\r\n if not len(func) and array[0] == tag:\r\n return True\r\n elif not len(func) and array[0] != tag:\r\n return False\r\n\r\n used = {i: False for i in func}\r\n for i in range(1, m, 1):\r\n using_value(array[i], used, func)\r\n for i in used:\r\n if not used[i]:\r\n return False\r\n return True\r\n\r\n\r\ndef parsing(text, tag, maps, arrays=False):\r\n i, n = 0, len(text)\r\n stack = []\r\n while i < n:\r\n if text[i: i + 5] == '</tr>' or text[i: i + 4] == '<tr>':\r\n i += 1\r\n continue\r\n if text[i] == '<' and text[i + 1] == '!' and text[i + 2] == '-':\r\n i += 1\r\n continue\r\n if text[i] == '<' and text[i + 1] == '/':\r\n i += 2\r\n pos = return_c_teg(text, i)\r\n b = avail_tag(text[i:pos], tag)\r\n name = get_tag_name(text[i:pos])\r\n pt = len(stack) - 1\r\n if not len(stack):\r\n i = pos + 1\r\n continue\r\n if name == tag and len(stack) == 1:\r\n yield stack[0][1], i - 2\r\n stack.clear()\r\n i = pos + 1\r\n continue\r\n if stack[pt] == name:\r\n stack.pop()\r\n i = pos + 1\r\n continue\r\n\r\n if text[i] == '<' and text[i + 1] != '/':\r\n i += 1\r\n pos = return_c_teg(text, i)\r\n b = avail_tag(text[i:pos], tag, maps)\r\n if b:\r\n stack.append((get_tag_name(text[i:pos]), pos + 1))\r\n elif not b and len(stack) != 0:\r\n stack.append(get_tag_name(text[i:pos]))\r\n i = pos + 1\r\n continue\r\n i += 1\r\n\r\n\r\ndef text_behind(text):\r\n ok = True\r\n p = 0\r\n arr = ['']\r\n for i in text:\r\n if i == '<':\r\n ok = False\r\n if i == '>':\r\n ok = True\r\n arr.append('')\r\n p += 1\r\n continue\r\n if ok:\r\n arr[p] += i\r\n\r\n rezult = set()\r\n for i in arr:\r\n if len(i) > 0 and (not('\\t' or '\\r') in i):\r\n rezult.add(i)\r\n rezult = list(rezult)\r\n rezult.sort()\r\n return rezult\r\n\r\n\r\ndef get_time(array):\r\n for i in array:\r\n if ':' in i and '-' in i:\r\n return i\r\n\r\n\r\ndef get_table_day(texts, n_day):\r\n n_day = str(n_day)\r\n a = parsing(texts, 'table', {'id': n_day + 'day'})\r\n t_day = ''\r\n for i in a:\r\n t_day = texts[i[0]:i[1]]\r\n\r\n b = parsing(t_day, 'td', {'class': 'time'})\r\n c = parsing(t_day, 'td', {'class': 'room'})\r\n d = parsing(t_day, 'td', {'class': 'lesson'})\r\n studies = {}\r\n lesson = 1\r\n while True:\r\n try:\r\n pt1 = next(b)\r\n pt2 = next(c)\r\n pt3 = next(d)\r\n studies[lesson] = [get_time(text_behind(t_day[pt1[0]:pt1[1]])),\r\n text_behind(t_day[pt2[0]:pt2[1]]),\r\n text_behind(t_day[pt3[0]:pt3[1]])]\r\n lesson += 1\r\n except StopIteration:\r\n break\r\n return studies\r\n\r\n\r\ndef time_table(group_number='M3100', evel=1):\r\n b = found(group_number, evel)\r\n texts = ''\r\n if not b:\r\n print(group_number, 'not found')\r\n texts = download_page(group_number, evel)\r\n save(group_number, evel, texts)\r\n else:\r\n texts = load(group_number, evel)\r\n day = {}\r\n for i in range(1, 7, 1):\r\n day[i] = get_table_day(texts, i)\r\n return day\r\n\r\n\r\ndef found(g_num, evel):\r\n try:\r\n file = open(g_num + ('e' if evel == 1 else 'n') + '.txt',\r\n 'r', encoding=\"utf-8\")\r\n file.close()\r\n return True\r\n except FileNotFoundError:\r\n file = open((g_num + ('e' if evel == 1 else 'n') + '.txt'),\r\n 'w', encoding=\"utf-8\")\r\n file.close()\r\n return False\r\n\r\n\r\ndef load(g_num, evel):\r\n file = open(g_num + ('e' if evel == 1 else 'n') + '.txt',\r\n 'r', encoding=\"utf-8\")\r\n txt = ''\r\n for i in file.readlines():\r\n txt += i\r\n file.close()\r\n return txt\r\n\r\n\r\ndef save(g_num, evel, text):\r\n file = open(g_num + ('e' if evel == 1 else 'n') + '.txt',\r\n 'w', encoding=\"utf-8\")\r\n file.write(text)\r\n file.close()\r\n pass\r\n", "sub_path": "lab5/timetable_reader.py", "file_name": "timetable_reader.py", "file_ext": "py", "file_size_in_byte": 5674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "datetime.date.today", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 7, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "575378579", "text": "# -*- encoding: utf-8 -*-\n\nfrom aliyunIoT import Device # iot组件是连接阿里云物联网平台的组件\nimport network # Wi-Fi功能所在库\nimport utime # 延时API所在组件\nimport ujson # json字串解析库\nfrom driver import ADC # ADC类，通过微处理器的ADC模块读取ADC通道输入电压\nimport hcho # 甲醛hcho传感器类\n\nadcObj = 0 # ADC通道对象\nuartObj = 0 # UART通道对象\nhchoDev = 0\n\n# 物联网平台连接标志位\niot_connected = False\nwlan = None\n\n# Wi-Fi SSID和Password设置\nwifiSsid = \"请填写您的路由器名称\"\nwifiPassword = \"请填写您的路由器密码\"\n\n# 三元组信息\nproductKey = \"产品密钥\" #需要填入物联网云平台申请到的productKey信息\ndeviceName = \"设备名称\" #需要填入物联网云平台申请到的deviceName信息\ndeviceSecret = \"设备密钥\" #需要填入物联网云平台申请到的deviceSecret信息\n\n# 物联网设备实例\ndevice = None\n\ndef hcho_init():\n global adcObj,hchoDev\n global uartObj\n\n adcObj = ADC()\n adcObj.open(\"hcho\")\n hchoDev = hcho.HCHO(adcObj)\n\ndef get_hcho_value():\n global hchoDev\n \n return hchoDev.getPPM()\n\n# 等待Wi-Fi成功连接到路由器\ndef get_wifi_status():\n global wlan\n wifi_connected = False\n\n wlan.active(True) #激活界面\n wlan.scan() #扫描接入点\n #print(\"start to connect \", wifiSsid)\n wlan.connect(wifiSsid, wifiPassword) # 连接到指定的路由器（路由器名称为wifiSsid, 密码为：wifiPassword）\n\n while True:\n wifi_connected = wlan.isconnected() # 获取Wi-Fi连接路由器的状态信息\n if wifi_connected: # Wi-Fi连接成功则退出while循环\n break\n else:\n utime.sleep(0.5)\n print(\"wifi_connected:\", wifi_connected)\n\n ifconfig = wlan.ifconfig() #获取接口的IP/netmask/gw/DNS地址\n print(ifconfig)\n utime.sleep(0.5)\n\n# 物联网平台连接成功的回调函数\ndef on_connect(data):\n global iot_connected\n iot_connected = True\n\n# 设置props 事件接收函数（当云平台向设备下发属性时）\ndef on_props(request):\n pass\n\ndef connect_lk(productKey, deviceName, deviceSecret):\n global device, iot_connected\n key_info = {\n 'region': 'cn-shanghai',\n 'productKey': productKey,\n 'deviceName': deviceName,\n 'deviceSecret': deviceSecret,\n 'keepaliveSec': 60\n }\n # 将三元组信息设置到iot组件中\n device = Device()\n\n # 设定连接到物联网平台的回调函数，如果连接物联网平台成功，则调用on_connect函数\n device.on(Device.ON_CONNECT, on_connect)\n\n # 配置收到云端属性控制指令的回调函数，如果收到物联网平台发送的属性控制消息，则调用on_props函数\n device.on(Device.ON_PROPS, on_props)\n\n # 启动连接阿里云物联网平台过程\n device.connect(key_info)\n\n # 等待设备成功连接到物联网平台\n while(True):\n if iot_connected:\n print('物联网平台连接成功')\n break\n else:\n print('sleep for 1 s')\n utime.sleep(1)\n print('sleep for 2s')\n utime.sleep(2)\n\n# 上传甲醛浓度信息到物联网平台\ndef upload_hcho_detector_state():\n global device\n # 无限循环\n while True:\n data = get_hcho_value()\n H_str = \"Hcho : \" + str(round(data,2))+'ppm'\n print('Hcho :' + str(round(data,2)) +'ppm')\n\n # \"HCHO\" - 代表甲醛传感器测量到的浓度值\n upload_data = {'params': ujson.dumps({\n 'HCHO': round(data,2),\n })\n }\n # 上传甲醛浓度信息到物联网平台\n device.postProps(upload_data)\n\n # 每2秒钟上报一次\n utime.sleep(2)\n\nif __name__ == '__main__':\n # 运行此demo之前务必保证模块已经上电5分钟以上\n hcho_init()\n wlan = network.WLAN(network.STA_IF) #创建WLAN对象\n # global productKey, deviceName, deviceSecret ,on_request, on_play\n get_wifi_status()\n\n connect_lk(productKey, deviceName, deviceSecret)\n\n upload_hcho_detector_state()\n", "sub_path": "haas_lib_bundles/python/docs/examples/hcho_detector/ESP-C3-32S-Kit/code/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "driver.ADC", "line_number": 34, "usage_type": "call"}, {"api_name": "hcho.HCHO", "line_number": 36, "usage_type": "call"}, {"api_name": "utime.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "utime.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "aliyunIoT.Device", "line_number": 84, "usage_type": "call"}, {"api_name": "aliyunIoT.Device.ON_CONNECT", "line_number": 87, "usage_type": "attribute"}, {"api_name": "aliyunIoT.Device", "line_number": 87, "usage_type": "name"}, {"api_name": "aliyunIoT.Device.ON_PROPS", "line_number": 90, "usage_type": "attribute"}, {"api_name": "aliyunIoT.Device", "line_number": 90, "usage_type": "name"}, {"api_name": "utime.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "utime.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "ujson.dumps", "line_number": 116, "usage_type": "call"}, {"api_name": "utime.sleep", "line_number": 124, "usage_type": "call"}, {"api_name": "network.WLAN", "line_number": 129, "usage_type": "call"}, {"api_name": "network.STA_IF", "line_number": 129, "usage_type": "attribute"}]}
+{"seq_id": "259854715", "text": "import os\nfrom setuptools import setup, find_packages\nfrom os import path\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(name='DANE',\n version='0.1.1',\n author='Nanne van Noord',\n author_email='n.j.e.vannoord@uva.nl',\n url='https://github.com/CLARIAH/DANE',\n description='Utils for working with the Distributed Annotation and Enrichment system.',\n long_description=long_description,\n long_description_content_type='text/markdown',\n license='Apache License 2.0',\n\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Programming Language :: Python :: 3\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n \"Topic :: Multimedia :: Video\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n ],\n\n packages=find_packages(exclude=('test',)),\n\n install_requires=[\n 'yacs',\n 'pika',\n ])\n", "sub_path": "pypi_install_script/DANE-0.1.1.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "286170451", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n#!pip install dataset\n\n\n# In[19]:\n\n\nimport settings\nimport tweepy\nimport datetime\n\n# In[20]:\n\n\nauth = tweepy.OAuthHandler(settings.API_key, settings.API_Secret_Key)\nauth.set_access_token(settings.Access_token, settings.Access_Token_Secret)\napi = tweepy.API(auth)\n\n\"\"\"\nsearchTerms = [\"corona\", \"covid\"]\nnoOfSearch = 5000\nsearchCountry = \"India\"\n\nplaces = api.geo_search(query=searchCountry, granularity=\"country\")\nplace_id = places[0].id\nprint(place_id)\ntweets = tweepy.Cursor(api.search , q='{} place:{}'.format(searchTerms, place_id)).items(noOfSearch)\n\n\nfor i,tweet in enumerate(tweets):\n print(i, tweet.text)\n\"\"\"\n\n\n\n\n\nclass MyStreamListener(tweepy.StreamListener):\n def __init__(self,api=None):\n super(MyStreamListener,self).__init__()\n self.num_tweets=0 \n \n def on_status(self, status):\n if status.retweeted:\n return\n else: \n location = status.user.location\n if location != None and \"India\" in location:\n print(status.text)\n self.num_tweets+=1\n print(self.num_tweets)\n if self.num_tweets<200:\n return True\n else:\n return False\n #log = open(\"/path/to/my/file.txt\", \"r\")\n #print str(log)\n\n \n \n \n def on_error(self, status_code):\n if status_code == 420:\n #returning False in on_data disconnects the stream\n return False\n\n\n\nstream_listener = MyStreamListener()\nstream = tweepy.Stream(auth=api.auth, listener=stream_listener)\nstream.filter(track=settings.TRACK_TERMS)\n\n\n\"\"\"\nUSE TWINT WITH MAJOR CITY NAME IF TWEEPY DOESNT WORK\nINPSIRATION: https://www.kaggle.com/general/207512\nimport twint\n#configuration\nconfig = twint.Config()\nconfig.Search = [\"corona\", \"covid\"]\nconfig.limit=4000\nconfig.Lang = \"en\"\nconfig.Since = '2021-08-07'\nconfig.Until = '2021-08-8'\nconfig.Geo= \"\"\nconfig.Pandas = True\n\n#running search\ntwint.run.Search(config)\n\ndf = twint.storage.panda.Tweets_df\n\nprint(df.tweet)\n\"\"\"", "sub_path": "apps/Sentiment_Analysis/StreamingTweetsnew.py", "file_name": "StreamingTweetsnew.py", "file_ext": "py", "file_size_in_byte": 2126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "settings.API_key", "line_number": 20, "usage_type": "attribute"}, {"api_name": "settings.API_Secret_Key", "line_number": 20, "usage_type": "attribute"}, {"api_name": "settings.Access_token", "line_number": 21, "usage_type": "attribute"}, {"api_name": "settings.Access_Token_Secret", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 22, "usage_type": "call"}, {"api_name": "tweepy.StreamListener", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tweepy.Stream", "line_number": 75, "usage_type": "call"}, {"api_name": "settings.TRACK_TERMS", "line_number": 76, "usage_type": "attribute"}]}
+{"seq_id": "473934107", "text": "import sys\nfrom . import Visualizer\n\nimport pyglet\nfrom pyglet.window import key\nfrom pyglet.gl import *\n\nimport numpy as np\nimport cv2\n\n\nclass VideoVisualizer(Visualizer):\n\n def __init__(self, file):\n super().__init__(duckietown=False)\n self.file = file\n self.cap = cv2.VideoCapture(file)\n if self.cap.isOpened() is False:\n raise FileNotFoundError(f\"{file}: file not found\")\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.set_size(self.w, self.h)\n self.set_caption(f\"{file} - Visualizer\")\n\n def show(self, obs):\n result = obs\n super().show(result)\n\n def update(self, dt):\n if self.cap.isOpened() is False:\n sys.exit(1)\n ret, frame = self.cap.read()\n if ret is False:\n self.close()\n self.show(frame)\n\n def run(self):\n fps = self.cap.get(cv2.CAP_PROP_FPS)\n pyglet.clock.schedule_interval(self.update, 1.0 / fps)\n super().run()\n\n def __del__(self):\n self.cap.release()\n\n", "sub_path": "app/visualizer/video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 1110, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "cv2.VideoCapture", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 39, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 39, "usage_type": "attribute"}]}
+{"seq_id": "175782638", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('wuqian', '0003_auto_20150725_1816'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='wuqianbusiness',\n options={'verbose_name': '\\u798f\\u68a6\\u4e1a\\u52a1', 'verbose_name_plural': '\\u798f\\u68a6\\u4e1a\\u52a1'},\n ),\n migrations.AlterField(\n model_name='aboutwuqian',\n name='fazhanlicheng',\n field=models.TextField(max_length=10000, verbose_name='\\u53d1\\u5c55\\u5386\\u7a0b'),\n ),\n migrations.AlterField(\n model_name='aboutwuqian',\n name='wuqiangaishu',\n field=models.TextField(max_length=10000, verbose_name='\\u798f\\u68a6\\u6982\\u8ff0'),\n ),\n migrations.AlterField(\n model_name='aboutwuqian',\n name='wuqianzhanlve',\n field=models.TextField(max_length=10000, verbose_name='\\u798f\\u68a6\\u6218\\u7565'),\n ),\n migrations.AlterField(\n model_name='aboutwuqian',\n name='hexinyoushi',\n field=models.TextField(max_length=10000, verbose_name='\\u6838\\u5fc3\\u4f18\\u52bf'),\n ),\n migrations.AlterField(\n model_name='news',\n name='news_type',\n field=models.CharField(max_length=100, verbose_name='\\u65b0\\u95fb\\u7c7b\\u578b', choices=[(b'wuqianxinwen', b'\\xe7\\xa6\\x8f\\xe6\\xa2\\xa6\\xe6\\x96\\xb0\\xe9\\x97\\xbb'), (b'diqudongtai', b'\\xe5\\x9c\\xb0\\xe5\\x8c\\xba\\xe5\\x8a\\xa8\\xe6\\x80\\x81'), (b'meitiguanzhu', b'\\xe5\\xaa\\x92\\xe4\\xbd\\x93\\xe5\\x85\\xb3\\xe6\\xb3\\xa8')]),\n ),\n ]\n", "sub_path": "wuqian/wuqian/migrations/0004_auto_20150726_1127.py", "file_name": "0004_auto_20150726_1127.py", "file_ext": "py", "file_size_in_byte": 1703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterModelOptions", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}]}
+{"seq_id": "511628728", "text": "from tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.initializers import glorot_uniform\n\nfrom tensorflow.keras.layers import deserialize, serialize\nfrom tensorflow.python.keras.saving import saving_utils\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\n\n# All sklearn Transforms must have the `transform` and `fit` methods\nclass DropColumns(BaseEstimator, TransformerMixin):\n def __init__(self, columns):\n self.columns = columns\n\n def fit(self, X, y=None):\n return self\n\n def transform(self, X):\n # Primeiro realizamos a cópia do dataframe 'X' de entrada\n data = X.copy()\n # Retornamos um novo dataframe sem as colunas indesejadas\n return data.drop(labels=self.columns, axis='columns')\n\ndef unpack(model, training_config, weights):\n restored_model = deserialize(model)\n if training_config is not None:\n restored_model.compile(\n **saving_utils.compile_args_from_training_config(\n training_config\n )\n )\n restored_model.set_weights(weights)\n return restored_model\n\n# Hotfix function\ndef make_keras_picklable():\n def __reduce__(self):\n model_metadata = saving_utils.model_metadata(self)\n training_config = model_metadata.get(\"training_config\", None)\n model = serialize(self)\n weights = self.get_weights()\n return (unpack, (model, training_config, weights))\n\n cls = Model\n cls.__reduce__ = __reduce__\n\n# Run the function\nmake_keras_picklable()\n\nclass KerasBuilder():\n def createKerasModel():\n model = Sequential()\n model.add(Dense(100,\n input_dim=12,\n kernel_initializer=glorot_uniform(seed=4444), \n activation='relu'))\n model.add(Dropout(0.2))\n model.add(Dense(100, \n kernel_initializer=glorot_uniform(seed=4444), \n activation='relu'))\n model.add(Dropout(0.2))\n model.add(Dense(6, \n kernel_initializer=glorot_uniform(seed=4444), \n activation='softmax'))\n model.compile(loss='sparse_categorical_crossentropy', \n optimizer='adam', \n metrics=['accuracy'])\n return model\n\n def KerasClassificator(epochs=120, batch_size=32, verbose=1) : \n return KerasClassifier(build_fn=self.createKerasModel, epochs=epochs, batch_size=batch_size, verbose=verbose)\n", "sub_path": "my_custom_sklearn_transforms/sklearn_transformers.py", "file_name": "sklearn_transformers.py", "file_ext": "py", "file_size_in_byte": 2557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sklearn.base.BaseEstimator", "line_number": 12, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.deserialize", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.saving.saving_utils.compile_args_from_training_config", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.saving.saving_utils", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.saving.saving_utils.model_metadata", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.saving.saving_utils", "line_number": 39, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.serialize", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 45, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.glorot_uniform", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.glorot_uniform", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.glorot_uniform", "line_number": 64, "usage_type": "call"}]}
+{"seq_id": "189266405", "text": "import datetime\nfrom collections import defaultdict\nimport pdb\n\nFILE_NAME = \"yuquan.csv\"\nSTART_DATE = '20130801'\nEND_DATE = '20131113'\n\nhour_range = range(5, 22)\n\n# date range\nstart_time = datetime.datetime.strptime(START_DATE, \"%Y%m%d\")\nend_time = datetime.datetime.strptime(END_DATE, \"%Y%m%d\")\ntime_delta = datetime.timedelta(days=1)\n\ndate_range = []\nwhile start_time <= end_time:\n date_range.append(start_time)\n start_time += time_delta\n\nkeys = [d.strftime('%m%d') + '_' + str(h) for h in hour_range for d in date_range]\n\ncount_dict = dict([(key, 0) for key in keys])\n\nwith open(FILE_NAME, 'r') as data:\n line_ = data.readline()\n while True:\n line_ = data.readline()\n line = line_.strip().split(\",\")\n if len(line) <= 1:\n break\n \n date = line[2][4:]\n hour = line[7]\n \n key = date + '_' + hour\n count_dict[key] += 1\n \n \nread_file_name = \"weather_output.csv\"\ntarget_file_name = \"output.csv\"\n\nfw = open(target_file_name, 'w')\n\nwith open(read_file_name, 'r') as data:\n line_ = data.readline()\n fw.write(\"date,weekday,hour,temp,sunny,windy,count\\n\")\n \n while True:\n line_ = data.readline()\n line = line_.strip().split(',')\n if len(line) <= 1:\n break\n \n key = line[0]\n\n if key not in count_dict:\n count = 0\n else:\n count = count_dict[key]\n \n date = '2013' + line[0].split('_')[0]\n weekday = datetime.datetime.strptime(date, '%Y%m%d').strftime(\"%w\")\n hour = line[0].split('_')[1]\n temp = line[1]\n sunny = line[2]\n windy = line[3]\n \n out_list = [date, str(weekday), hour, temp, sunny, windy, str(count)]\n \n \n fw.write(','.join(out_list) + '\\n')\n\nfw.close()\n\n\n", "sub_path": "step4/count_yuquan.py", "file_name": "count_yuquan.py", "file_ext": "py", "file_size_in_byte": 1834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}]}
+{"seq_id": "358011630", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport smart_selects.db_fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('upnextme', '0040_auto_20151225_1148'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Pay_Option',\n fields=[\n ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n ('payment_option', models.CharField(null=True, max_length=50)),\n ('number', models.CharField(null=True, max_length=140)),\n ('project', models.ForeignKey(null=True, to='upnextme.Project')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.RemoveField(\n model_name='level',\n name='project',\n ),\n migrations.RemoveField(\n model_name='transaction',\n name='level',\n ),\n migrations.DeleteModel(\n name='Level',\n ),\n migrations.AddField(\n model_name='transaction',\n name='payment_option',\n field=smart_selects.db_fields.ChainedForeignKey(null=True, auto_choose=True, to='upnextme.Pay_Option', chained_model_field='project', chained_field='project'),\n preserve_default=True,\n ),\n ]\n", "sub_path": "upnextme/migrations/0041_auto_20151225_1202.py", "file_name": "0041_auto_20151225_1202.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.migrations.DeleteModel", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 38, "usage_type": "name"}, {"api_name": "smart_selects.db_fields.db_fields.ChainedForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "smart_selects.db_fields.db_fields", "line_number": 41, "usage_type": "attribute"}, {"api_name": "smart_selects.db_fields", "line_number": 41, "usage_type": "name"}]}
+{"seq_id": "601987442", "text": "# -*- coding: utf-8 -*-\nimport requests, json\nfrom requests.exceptions import ConnectionError\nfrom time import sleep\nimport random\n\n# Метод для корректной обработки строк в кодировке UTF-8 как в Python 3, так и в Python 2\nimport sys\n\nif sys.version_info < (3,):\n def u(x):\n try:\n return x.encode(\"utf8\")\n except UnicodeDecodeError:\n return x\nelse:\n def u(x):\n if type(x) == type(b''):\n return x.decode('utf8')\n else:\n return x\n\n\ndef send(body, url): \n\n CampaignsURL = 'https://api-sandbox.direct.yandex.com/json/v5/' + url\n\n token = 'AgAAAABH84LTAAasZYthWGp4OkBonisZBLrW3Ls'\n clientLogin = 'aslavkovsky'\n headers = {\"Authorization\": \"Bearer \" + token, # OAuth-токен. Использование слова Bearer обязательно\n \"Client-Login\": clientLogin, # Логин клиента рекламного агентства\n \"Accept-Language\": \"ru\", # Язык ответных сообщений\n }\n\n\n jsonBody = json.dumps(body, ensure_ascii=False).encode('utf8')\n\n try:\n result = requests.post(CampaignsURL, jsonBody, headers=headers)\n if result.status_code == 200 or result.json().get(\"error\", False) == False:\n return result\n else: return\n except ConnectionError:\n print(\"Произошла ошибка соединения с сервером API.\")\n return\n except:\n print(\"Произошла непредвиденная ошибка.\")\n return\n\n\n\ndef get_compains(): \n body = {\"method\": \"get\", # Используемый метод.\n \"params\":{\n \"SelectionCriteria\": {}, # Критерий отбора кампаний. Для получения всех кампаний должен быть пустым\n \"FieldNames\": [\"Id\", \"Name\"] # Имена параметров, которые требуется получить.\n }\n }\n res = send(body,\"campaigns\")\n if res != None: \n ids = []\n for campaign in res.json()[\"result\"][\"Campaigns\"]:\n ids.append({'name' : campaign['Name'], 'id': campaign['Id']})\n print(\"Рекламная кампания: {} №{}\".format(u(campaign['Name']), campaign['Id']))\n\n return ids\n else: return\n\ndef get_compain_add(compain): \n body = {\"method\": \"get\", # Используемый метод.\n \"params\": {\n \"SelectionCriteria\":{\"CampaignIds\": [compain['id']]},\n \"FieldNames\" :[\"Id\",\"Status\"],\n \"TextAdFieldNames\" : [\"Title2\"]\n },\n }\n\n res = send(body, \"ads\")\n if res != None: \n ads = [];\n for campaign in res.json()[\"result\"][\"Ads\"]:\n print(\"Компания {}: Объявление #{} {} {}\".format(compain['name'], u(campaign['Id']), campaign['Status'], campaign['TextAd']['Title2']))\n if campaign['Status'] == 'ACCEPTED': ads.append(campaign['Id'])\n\n return ads\n else: return\n\n\ndef update_add(id, title2):\n body = {\"method\": \"update\", # Используемый метод.\n \"params\": {\n \"Ads\": [{\n \"Id\" : id,\n \"TextAd\": {\n \"Title2\" : title2\n }\n }]\n \n }\n }\n res = send(body, \"ads\")\n\n\n\nids = get_compains()\nrand_text = ['Полетаю на дирижабле – на','Зайду в о там','Отправлюсь в тёмную башню ','На арену – люблю помериться ']\n\nif ids != None: \n for i in ids:\n ads = get_compain_add(i)\n if ads != None: \n for ad in ads:\n update_add(ad, rand_text[random.randint(0,3)])\n\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.version_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 43, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 113, "usage_type": "call"}]}
+{"seq_id": "583235033", "text": "import argparse\nfrom .config import Config\nimport os\nimport torch\nimport cv2\n\ndef load_config(mode=None):\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--path', '--checkpoints', type=str, default='./checkpoints/1', help='model checkpoints path (default: ./checkpoints)')\n\n args = parser.parse_args()\n config_path = os.path.join(args.path, 'config.yml')\n\n # load config file\n config = Config(config_path)\n\n # train mode\n if mode == 1:\n config.MODE = 1\n\n # test mode\n elif mode == 2:\n config.MODE = 2\n # config.INPUT_SIZE = 256\n # config.VAL_FLIST = args.input\n # config.RESULTS = args.output\n\n return config\n\n \ndef pred_image(image):\n \n pred_rgb = image[0, :, :, :].permute(1,2,0).cpu().detach().numpy()\n pred_rgb = (pred_rgb+1)/2 *255 # [-1,1] -> [0,1]\n \n return pred_rgb[:,:,::-1]\n \n \n \n \n ", "sub_path": "语义分割/src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 16, "usage_type": "call"}, {"api_name": "config.MODE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.MODE", "line_number": 24, "usage_type": "attribute"}]}
+{"seq_id": "460265364", "text": "from setuptools import setup, find_packages\n\nsetup(name = 'Kuyil',\n version='0.6',\n license='BSD',\n url='https://github.com/4info/Kuyil',\n description='sprint 1 rc',\n author='rohitash',\n author_email='rohitash@sigmoid.com',\n packages=find_packages(),\n package_data={'': ['config.json']},\n data_files=[('.', ['config.json'])],\n include_package_data=True,\n\n install_requires=['SQLAlchemy==1.2.2',\n 'SQLAlchemy-Utils == 0.32.21',\n 'boto3 == 1.5.22',\n 'pytest-cov == 2.5.1',\n 'MySQL-python==1.2.5'\n ],\n)\n\n__author__ = 'rohitash'", "sub_path": "pypi_install_script/Kuyil-0.6.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "setuptools.setup", "line_number": 3, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 10, "usage_type": "call"}]}
+{"seq_id": "49901012", "text": "import os\nimport boto3\n\ndef path(foldername):\n paths = []\n for root, subdirs, files in os.walk(foldername):\n for name in files:\n _e = (os.path.join(root,name)).replace(foldername,'').replace('\\\\','/')\n paths.append(_e[1:])\n return paths\n\ndef s3upload(path_lst,foldername,bucketname):\n for path in path_lst:\n filename = path.split('/')\n s3 = boto3.resource('s3')\n s3.Bucket(bucketname).upload_file((foldername+'/'+path),path)\n print(filename[-1],' ','Uploaded')\n return 'Success'\n \nfoldername = input('Enter Name of the Folder: ')\nbucketname = input('Enter Bucket Name: ')\n\ns3upload(path(foldername),foldername,bucketname)\n", "sub_path": "aws_s3.py", "file_name": "aws_s3.py", "file_ext": "py", "file_size_in_byte": 787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.walk", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 15, "usage_type": "call"}]}
+{"seq_id": "619758092", "text": "from setuptools import setup\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup(\n name=\"whatsappy-py\",\n version=\"2.2\",\n description=\"Whatsappy is a Python library for creating whatsapp bots.\",\n packages=[\"whatsappy\"],\n classifiers=[\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)\",\n \"Operating System :: OS Independent\",\n ],\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n install_requires=[\n \"selenium ~= 3.141.0\",\n \"Send2Trash ~= 1.5.0\",\n \"webdriver-manager ~= 3.2.2\",\n \"rich ~= 10.9.0\",\n \"qrcode ~= 7.3\",\n ],\n extra_requires={\n \"dev\": [\n \"pytest>=3.7\",\n ]\n },\n url=\"https://github.com/italoseara/whatsappy\",\n author=\"Italo Seara\",\n author_email=\"italo.sseara@gmail.com\",\n license=\"MIT\",\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1004, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}]}
+{"seq_id": "267477327", "text": "import datetime\n\nfrom django.test import TestCase, LiveServerTestCase\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom django.contrib.auth.models import User\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nfrom sg_app.models import (\n Sport, Source, Team, FiveThirtyEightTeamStats, Game,\n FiveThirtyEightGameOdds, SportsbookGameOdds, UserSubscription\n)\n\n\nclass SportViewTest(TestCase):\n @classmethod\n def setUpTestData(cls):\n Sport.objects.create(name='NBA')\n\n def test_view_url_exists_at_desired_location(self):\n response = self.client.get('/sg_app/sport/1')\n self.assertEqual(response.status_code, 200)\n\n def test_view_url_accessible_by_id(self):\n response = self.client.get(reverse('sport', args=[1]))\n self.assertEqual(response.status_code, 200)\n\n def test_view_uses_correct_template(self):\n response = self.client.get(reverse('sport', args=[1]))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'sg_app/sport.html')\n\n\nclass TestLogin(LiveServerTestCase):\n\n def setUp(self):\n self.driver = webdriver.Firefox()\n super(TestLogin, self).setUp()\n\n def tearDown(self):\n self.driver.close()\n super(TestLogin, self).tearDown()\n\n def test_login_firefox(self):\n driver = self.driver\n driver.get(\"http://127.0.0.1:8000/accounts/login/\")\n assert 'Sports Grip' in driver.title\n\n", "sub_path": "sg_app/tests/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.test.TestCase", "line_number": 17, "usage_type": "name"}, {"api_name": "sg_app.models.Sport.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "sg_app.models.Sport.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sg_app.models.Sport", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 31, "usage_type": "call"}, {"api_name": "django.test.LiveServerTestCase", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 39, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 39, "usage_type": "name"}]}
+{"seq_id": "482533721", "text": "#!/usr/bin/env python3\n\"\"\"Run word2vec on text and produce semantic spaces.\n\nThis script needs to be refactored\n\"\"\"\nimport logging\nimport re\nimport sys\n\nimport gensim\nfrom gensim.models.word2vec import LineSentence\n\n\ndef export_to_file(model, output_file):\n \"\"\"Save word embeddings in text format.\n\n Each word is saved on a line of the output file, followed by its associated\n vector.\n\n Args\n ----\n model: the model generated with word2vec (word embeddings).\n output_file: the name of the file in which the word embeddings must be\n saved.\n\n \"\"\"\n with open(output_file, 'w') as output_stream:\n vocab = model.wv.vocab\n for mid in vocab:\n vector = [str(dimension) for dimension in model[mid]]\n # print(\"{} {}\".format(mid, \" \".join(vector)), file=output)\n output_stream.writelines(\"{} {}\".format(mid, \" \".join(vector)))\n\n\nif __name__ == \"__main__\":\n SAVE_NAME = re.sub(\".*\\\\/|\\\\.tar\\\\.gz\", \"\", sys.argv[1])\n SENTENCES = LineSentence(sys.argv[1])\n # Move logging.basicConfig to the top (entry point) of the application\n # logging level should be a parameter\n logging.basicConfig(\n format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n W2V_MODEL = gensim.models.Word2Vec(SENTENCES, min_count=50, size=400, sg=1)\n W2V_MODEL.save('./nima/models/' + SAVE_NAME + '.model')\n export_to_file(W2V_MODEL, \"./nima/models/\" + SAVE_NAME + \".dm\")\n", "sub_path": "nima/generation/generators/word2vec.py", "file_name": "word2vec.py", "file_ext": "py", "file_size_in_byte": 1471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "gensim.models.word2vec.LineSentence", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 41, "usage_type": "attribute"}, {"api_name": "gensim.models.Word2Vec", "line_number": 42, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 42, "usage_type": "attribute"}]}
+{"seq_id": "266491097", "text": "import os\nimport numpy as np\nimport pandas as pd\nimport random\nimport cv2\nfrom sklearn.metrics import roc_curve, auc\nimport torch\nimport matplotlib.pyplot as plt\nimport io\nimport tensorflow as tf\n\n\ndef seed_torch(seed):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n\n\ndef plot_roc_curve(y_true, y_score):\n fpr, tpr, _ = roc_curve(y_true, y_score)\n roc_auc = auc(fpr, tpr)\n\n figure = plt.figure(figsize=(8, 8))\n plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.legend(loc=\"lower right\")\n\n return figure\n\n\ndef plot_to_image(figure):\n \"\"\"Converts the matplotlib plot specified by 'figure' to a PNG image and\n returns it. The supplied figure is closed and inaccessible after this call.\"\"\"\n # Save the plot to a PNG in memory.\n buf = io.BytesIO()\n plt.savefig(buf, format='png')\n # Closing the figure prevents it from being displayed directly inside\n # the notebook.\n plt.close(figure)\n buf.seek(0)\n # Convert PNG buffer to TF image\n image = tf.image.decode_png(buf.getvalue(), channels=4)\n\n # tf.Tensor to torch.tensor\n image = torch.from_numpy(image.numpy()).permute(2, 0, 1)\n\n return image\n", "sub_path": "src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1575, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow.image.decode_png", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}]}
+{"seq_id": "147858420", "text": "import tensorflow as tf\nimport glob # 获取路径\nimport tensorflow.keras as keras\nfrom matplotlib import pyplot as plt # 绘图\nAUTOTUNE = tf.data.experimental.AUTOTUNE # prefetch 根据CPU个数创建读取线程\nimport os\nimport math\nimport numpy as np\nimport cv2\nbatch_size = 1\nimg_to_del = None\ndef read_jpg_gt(path):\n img = tf.io.read_file(path)# 2.0里面的读取都在io里面\n img = tf.image.decode_jpeg(img,channels = 1)\n return img\ndef load_img_label(path):\n print(path)\n img = read_jpg_gt(path)\n img = tf.image.resize(img,(64,64))\n img = tf.cast(img, tf.float32)/127.5 - 1#img/127.5-1\n return img\n\n\ndef showImg(name,img):\n cv2.namedWindow(name,cv2.WINDOW_NORMAL|cv2.WINDOW_KEEPRATIO)\n cv2.imshow(name,img)\n cv2.waitKey(20000)\n\n#showImg('name',cv2.imread('./score_map/1.jpg'))\nscore_label = glob.glob('./score_map/*jpg')\nlabel_score_img = tf.data.Dataset.from_tensor_slices(score_label)\nlabel_score_data = label_score_img.map(load_img_label,num_parallel_calls = AUTOTUNE).cache().batch(batch_size)\nfor score in label_score_data:\n\t#print(score)\n\tbreak\ndef read_jpg(path):\n img = tf.io.read_file(path)# 2.0里面的读取都在io里面\n img = tf.image.decode_jpeg(img,channels = 3)\n return img\n\ndef load_img_train(path):\n img = read_jpg(path)\n img = tf.image.resize(img,(512,512)) # resize 会使得在后续显示的时候不是none none\n img = tf.cast(img, tf.float32)/127.5 - 1#img/127.5-1\n return img\n\n# def load_img_train(self):\n# img = tf.image.resize(img_to_del,(512,512)) # resize 会使得在后续显示的时候不是none none\n# img = tf.cast(img, tf.float32)/127.5 - 1 #img/127.5-1\n# return img\ndef jifentu(img):\n\tif len(img.shape) == 3:\n\t\timg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\t\trct, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n\treturn np.sum(img)\ndef to_get_begin_end_time(path):\n\timg_ori = glob.glob(path)#('./1_ch4_training_images/img_1.jpg')#\n\tbatch_size = 1\n\ttrain_img = tf.data.Dataset.from_tensor_slices(img_ori)\n\tbatch_size = 1\n\ttrain_data = train_img.map(load_img_train,num_parallel_calls = AUTOTUNE).cache().batch(batch_size)#.shuffle(buffer_size)\n\t\n\tfor t in train_data:\n\t\tpred_score,angle_map,geo_map = net(t,training=False)\n\t\tprint(geo_map.shape[0])\n\t\tsize_t =64\n\t\tpred_img = np.array(tf.reshape(pred_score,(64,64)))\n\t\tshape = geo_map.shape\n\t\tt = np.array(tf.reshape(geo_map,shape))\n\t\tt = tf.squeeze(t,axis=0)\n\t\tt = tf.squeeze(t,axis=-1)\n\t\tshape = t.shape[1]\n\t\t# print('t.shape',t[1,1,:].shape)\n\t\t# print('shape',shape)\n\t\tlist_cor = []\n\n\t\tfor i in range(shape):\n\t\t\tfor j in range(shape):\n\t\t\t\tif np.sum(np.array(t[i,j,:])) > 0.0:\n\t\t\t\t\t# print('i,j',np.sum(np.array(t[i,j,:])))#np.array(t[i,j,:]))\n\t\t\t\t\tposition = np.array(t[i,j,:])\n\t\t\t\t\t# if len(list_cor) == 0 or (list_cor[-1].all()!=position.all()):\n\t\t\t\t\tlist_cor.append(position)\n\n\t\timg = np.array(tf.reshape(t[:,:,0],(size_t,size_t)))\n\n\t\tlist_cor = list_cor\n\t\t# print(list_cor)\n\t\t\n\t\timg = cv2.imread(path)\n\t\timg = cv2.resize(img, (1280, 720))\n\t\tr1 = 4#*int(img.shape[1]/1280)\n\t\tr2 = 4#*int(img.shape[0]/720) \n\t\tfor position in list_cor:\n\t\t\tcv2.line(img,(int(position[0]* r2),int(position[1]* r1)),(int(position[2]* r2),int(position[3]* r1)),(0,255,0),1)\n\t\t\tcv2.line(img,(int(position[2]* r2),int(position[3]* r1)),(int(position[4]* r2),int(position[5]* r1)),(255,255,0),1)\n\t\t\tcv2.line(img,(int(position[4]* r2),int(position[5]* r1)),(int(position[6]* r2),int(position[7]* r1)),(0,255,255),1)\n\t\t\tcv2.line(img,(int(position[6]* r2),int(position[7]* r1)),(int(position[0]* r2),int(position[1]* r1)),(0,0,255),1)\n\t\t#showImg('ori',img)#cv2.imread('geo_map/1_0.jpg'))\n\t\tcv2.imwrite('./first.jpg',img)\n\t\t# print(pred_img)\n\t\tpred_img = cv2.resize(pred_img, (1280, 720))\n\t\tpred_img = pred_img*255\n\t\tallwhite = jifentu(pred_img)\n\t\t# cv2.imwrite('./second.jpg',pred_img*255)\n\t\treturn allwhite\nnet = tf.keras.models.load_model('net.h5')\npath = './jpg15/danmu2.jpg'#'./1_ch4_training_images/img_3.jpg'#\npath = glob.glob('./192423.mp4')\n\n\n\n\n\ndef getAll(path):\n\tfor p in path:\n\t\tcapture = cv2.VideoCapture(p)\n\t\twhile capture.isOpened():\n\t\t\ttimestamp = int(capture.get(cv2.CAP_PROP_POS_MSEC))\n\t\t\tframe_exists, img_to_del = capture.read()\n\t\t\tif not frame_exists:\n\t\t\t\tprint('because end')\n\t\t\t\tbreak\n\t\t\tcv2.imencode('.jpg', img_to_del)[1].tofile('./now_jpg/1.jpg')\n\t\t\tallwhite = to_get_begin_end_time('./now_jpg/1.jpg')\n\t\t\tprint('allwhite',allwhite)\ngetAll(path)", "sub_path": "testallbk.py", "file_name": "testallbk.py", "file_ext": "py", "file_size_in_byte": 4446, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tensorflow.data", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tensorflow.io.read_file", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.WINDOW_KEEPRATIO", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 27, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.io.read_file", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 55, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 107, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_POS_MSEC", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 124, "usage_type": "call"}]}
+{"seq_id": "388848592", "text": "import argparse\nimport logging\nimport os\nimport subprocess\nimport boto3\nfrom multiprocessing import Pool\nfrom multiprocessing import Process\nfrom datetime import datetime\nfrom botocore.exceptions import ClientError\nimport CustomLogHandler\n\n# Configure AWS credentials before continue\n# http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html#cli-config-files\n\nlogger = logging.getLogger('download')\nformatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\nfileHandler = CustomLogHandler.CustomLogHandler('download.log')\nfileHandler.setFormatter(formatter)\nlogger.setLevel(logging.DEBUG)\nlogger.addHandler(fileHandler)\n\nsummaries_bucket = 'v2.0-summaries'\nactivities_bucket = 'v2.0-activities'\n\nnow = datetime.now()\nmonth = str(now.month)\nyear = str(now.year)\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-p', '--path', help='Path to place the public data files', default='./')\nparser.add_argument('-s', '--summaries', help='Download summaries', action='store_true')\nparser.add_argument('-a', '--activities', help='Download activities', action='store_true')\nparser.add_argument('-t', '--tar', help='Compress the dump', action='store_true')\nparser.add_argument('-r', '--recovery', help='Start recovery process', action='store_true')\nparser.add_argument('-max', '--max_threads', default=60)\nparser.add_argument('-v', '--verbose', help='Print the name of the downloading files.', action='store_true')\nargs = parser.parse_args()\n\npath = args.path if args.path.endswith('/') else (args.path + '/')\npath = path + 'ORCID_public_data_files/'\ndownload_summaries = args.summaries\ndownload_activities = args.activities\nrecovery = args.recovery\ntar_dump = args.tar\nverbose = args.verbose\nMAX_THREADS = int(args.max_threads)\n\n# Create a client\ns3client = boto3.client('s3')\n\n#---------------------------------------------------------\n# Download a single summary file\n#---------------------------------------------------------\ndef download_summary(element):\n\tglobal s3client\n\tglobal summaries_bucket\n\t\n\tcomponents = element.split('/')\t\n\t# Checksum\n\tchecksum = components[0]\n\t# File name \n\tname = components[1]\n\t\n\tif verbose:\n\t\tprint (name)\n\n\tfile_path = path + 'summaries/' + checksum + '/'\n\tlogger.info('Downloading ' + name + ' to ' + file_path)\n\t\n\t# Create the path directory\n\ttry:\n\t\tif not os.path.exists(file_path):\n\t\t\tos.makedirs(file_path)\n\texcept:\t\t\t\n\t\tpass\n\t\t\n\ttry:\n\t\t# Downloading the file\n\t\ts3client.download_file(summaries_bucket, element, file_path + name);\t\n\texcept ClientError as e:\n\t\tlogger.exception('Error fetching ' + element)\n\t\tlogger.exception(e)\n\n#---------------------------------------------------------\n# Download a single activity file\n#---------------------------------------------------------\ndef download_activity(element):\n\tglobal s3client\t\t\n\tglobal activities_bucket\n\t\n\tcomponents = element.split('/')\t\n\t# Checksum\n\tchecksum = components[0]\n\t# ORCID\n\torcid = components[1]\n\t# Activity type\n\ttype = components [2]\n\t# File name \n\tname = components[3]\n\t\n\tfile_path = path + 'activities/' + checksum + '/' + orcid + '/' + type + '/'\n\tlogger.info('Downloading ' + name + ' to ' + file_path)\n\t\n\t# Create the path directory\n\ttry:\n\t\tif not os.path.exists(file_path):\n\t\t\tos.makedirs(file_path)\n\texcept:\t\t\t\n\t\tpass\n\t\t\n\ttry:\n\t\t# Downloading the file\n\t\ts3client.download_file(activities_bucket, element, file_path + name);\t\n\texcept ClientError as e:\n\t\tlogger.exception('Error fetching ' + element)\n\t\tlogger.exception(e)\n\t\t\n#---------------------------------------------------------\n# Compress the given directory\n#---------------------------------------------------------\t\ndef compress(tar_path, directory_name):\t\t\n\tglobal path\n\t# Compress directory\n\tlogger.info('Compressing ' + tar_path + ' -C ' + path + ' directory_name: ' + directory_name)\t\n\tproc = subprocess.Popen(['tar', '-czf', tar_path, '-C', path, directory_name])\n\tproc.communicate()\t\n\tlogger.info(tar_path + ' compressed')\t\n\t\n#---------------------------------------------------------\n# Process summaries\n#---------------------------------------------------------\t\ndef process_summaries():\n\tglobal path\n\tglobal summaries_bucket\n\tglobal month\n\tglobal year\n\tglobal recovery\n\tif download_summaries:\n\t\t# Create the paginator\n\t\tpaginator = s3client.get_paginator('list_objects_v2')\n\t\t\n\t\t# Create a PageIterator from the Paginator\n\t\tpage_iterator = None\n\t\tif recovery:\n\t\t\tf = open('summary_next_continuation_token.config', 'r')\n\t\t\tcontinuation = f.readline()\n\t\t\tpage_iterator = paginator.paginate(Bucket=summaries_bucket, ContinuationToken=continuation, PaginationConfig={'PageSize': 1000})\n\t\telse:\n\t\t\tpage_iterator = paginator.paginate(Bucket=summaries_bucket, PaginationConfig={'PageSize': 1000})\t\t\n\t\t\n\t\tpage_count = 1\n\t\tfor page in page_iterator:\n\t\t\tlogger.info('Summaries page count: ' + str(page_count))\n\t\t\tpage_count += 1\n\t\t\telements = []\n\t\t\tfor element in page['Contents']:\n\t\t\t\telements.append(element['Key'])\n\t\t\tpool = Pool(processes=MAX_THREADS)\t\t\n\t\t\tpool.map(download_summary, elements)\n\t\t\tpool.close()\n\t\t\tpool.join()\n\t\t\tcontinuation_token = None\n\t\t\ttry:\n\t\t\t\tcontinuation_token = page['NextContinuationToken']\n\t\t\texcept:\n\t\t\t\tlogger.info('No more continuation tokens')\t\t\t\n\t\t\tfile = open('summary_next_continuation_token.config','w') \n\t\t\tfile.write(str(continuation_token)) \n\t\t\tfile.close()\n\t\tif tar_dump:\n\t\t\tsummaries_dump_name_xml = 'ORCID-API-2.0_xml_' + month + '_' + year + '.tar.gz'\n\t\t\tcompress(summaries_dump_name_xml, 'summaries')\n\n#---------------------------------------------------------\n# Process activities\n#---------------------------------------------------------\ndef process_activities():\n\tglobal path\n\tglobal activities_bucket\n\tglobal month\n\tglobal year\n\tglobal recovery\n\tif download_activities:\n\t\t# Create the paginator\n\t\tpaginator = s3client.get_paginator('list_objects_v2')\n\t\t# Create a PageIterator from the Paginator\n\t\tpage_iterator = None\n\t\tif recovery:\n\t\t\tf = open('activities_next_continuation_token.config', 'r')\n\t\t\tcontinuation = f.readline()\n\t\t\tpage_iterator = paginator.paginate(Bucket=activities_bucket, ContinuationToken=continuation, PaginationConfig={'PageSize': 1000})\t\t\n\t\telse:\n\t\t\tpage_iterator = paginator.paginate(Bucket=activities_bucket, PaginationConfig={'PageSize': 1000})\n\t\t\n\t\tpage_count = 1\n\t\tfor page in page_iterator:\n\t\t\tlogger.info('Activities page count: ' + str(page_count))\n\t\t\tpage_count += 1\n\t\t\telements = []\n\t\t\tfor element in page['Contents']:\n\t\t\t\telements.append(element['Key'].decode('utf-8'))\n\t\t\tpool = Pool(processes=MAX_THREADS)\t\t\n\t\t\tpool.map(download_activity, elements)\n\t\t\tpool.close()\n\t\t\tpool.join()\t\n\t\t\tcontinuation_token = None\n\t\t\ttry:\n\t\t\t\tcontinuation_token = page['NextContinuationToken']\n\t\t\texcept:\n\t\t\t\tlogger.info('No more continuation tokens')\n\t\t\tfile = open('activities_next_continuation_token.config','w') \n\t\t\tfile.write(str(continuation_token)) \n\t\t\tfile.close()\t\t\t\n\t\tif tar_dump:\n\t\t\tactivities_dump_name_xml = 'ORCID-API-2.0_activities_xml_' + month + '_' + year + '.tar.gz'\n\t\t\tcompress(activities_dump_name_xml, 'activities')\n\t\t\n#---------------------------------------------------------\n# Main process\n#---------------------------------------------------------\nif __name__ == \"__main__\":\n\tif download_summaries is False and download_activities is False:\n\t\tlogger.error('Please specify the elements you want to download using the -s or -a flag')\n\t\traise RuntimeError('Please specify the elements you want to download using the -s or -a flag')\n\n\t# Create the path directory\n\tif not os.path.exists(path):\n\t\tos.makedirs(path)\n\t\n\tstart_time = datetime.now()\n\t\n\tlogger.info('About to start syncing local folder with s3 buckets')\t\n\n\t# Define threads\n\tsummaries_thread = Process(target=process_summaries)\n\tactivities_thread = Process(target=process_activities)\n\n\t# Start threads\n\tsummaries_thread.start()\n\tactivities_thread.start()\n\t\n\t# Join threads\n\tsummaries_thread.join()\n\tactivities_thread.join()\n\t\n\tlogger.info('Download process is done')\t\n\t\n\t# keep track of the last time this process ran\n\tfile = open('last_ran.config','w') \n\tfile.write(str(start_time)) \n\tfile.close()\n", "sub_path": "download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 8073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 16, "usage_type": "call"}, {"api_name": "CustomLogHandler.CustomLogHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 73, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 107, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 114, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 125, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 158, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 228, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 235, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 236, "usage_type": "call"}]}
+{"seq_id": "234470347", "text": "\"\"\"\n.. module:: testnode\n :platform: Darwin, Linux, Unix, Windows\n :synopsis: Module that contains the :class:`TestNode` class which is utilized as the collection point\n which associates a set of tests with their descendant execution scopes.\n\n.. moduleauthor:: Myron Walker <myron.walker@gmail.com>\n\"\"\"\n\n__author__ = \"Myron Walker\"\n__copyright__ = \"Copyright 2020, Myron W Walker\"\n__credits__ = []\n__version__ = \"1.0.0\"\n__maintainer__ = \"Myron Walker\"\n__email__ = \"myron.walker@gmail.com\"\n__status__ = \"Development\" # Prototype, Development or Production\n__license__ = \"MIT\"\n\nfrom typing import List\nfrom akit.exceptions import AKitSemanticError\n\nfrom akit.testing.testplus.registration.resourceregistry import resource_registry\nfrom akit.testing.testplus.testref import TestRef\n\nfrom akit.xlogging.foundations import getAutomatonKitLogger\n\nlogger = getAutomatonKitLogger()\n\nclass TestGroup:\n \"\"\"\n -------------\n | Group A |\n ---------------------------\n | Group AA | Scope AB |\n -------------------------------\n | Scope AAA/ABA |\n -------------------------------\n \"\"\"\n\n def __init__(self, name, package=None):\n self._name = name\n self._package = package\n self._children = {}\n return\n\n def __enter__(self):\n return self\n\n def __exit__(self, ex_type, ex_inst, ex_tb):\n return False\n\n @property\n def children(self):\n return self._children\n\n @property\n def name(self):\n return self._name\n\n @property\n def package(self):\n return self._package\n\n def add_descendent(self, test_ref:TestRef):\n\n if self._package is not None:\n err_msg = \"The 'add_descendent' API can only be called on the root package.\"\n raise AKitSemanticError(err_msg)\n\n testname = test_ref.test_name\n module_name, _ = testname.split(\"#\")\n to_walk_list = module_name.split(\".\")\n path_stack = []\n\n self._add_descendent(test_ref, to_walk_list, path_stack)\n\n return\n\n def get_resource_scope(self):\n scope_name = self._name\n if self._package is not None and len(self._package) > 0:\n scope_name = \"{}.{}\".format(self._package, self._name) \n rscope = resource_registry.lookup_resource_scope(scope_name)\n return rscope\n\n def _add_descendent(self, test_ref:TestRef, to_walk_list: List[str], path_stack: List[str],):\n \n if len(to_walk_list) == 0:\n tbname = test_ref.test_base_name\n self._children[tbname] = test_ref\n else:\n child_leaf = to_walk_list[0]\n\n desc_to_walk_list = []\n if len(to_walk_list) > 1:\n desc_to_walk_list = to_walk_list[1:]\n\n child_leaf_group = None\n if child_leaf in self._children:\n child_leaf_group = self._children[child_leaf]\n else:\n tgname = child_leaf\n tgpkg = \".\".join(path_stack) \n child_leaf_group = TestGroup(tgname, tgpkg)\n self._children[child_leaf] = child_leaf_group\n\n path_stack.append(child_leaf)\n try:\n child_leaf_group._add_descendent(test_ref, desc_to_walk_list, path_stack)\n finally:\n path_stack.pop()\n\n return\n\n def __contains__(self, key):\n has_item = key in self._children\n return has_item\n\n def __getitem__(self, key):\n item = self._children[key]\n return item\n\n def __setitem__(self, key, value):\n self._children[key] = value\n return\n", "sub_path": "packages/akit/testing/testplus/testgroup.py", "file_name": "testgroup.py", "file_ext": "py", "file_size_in_byte": 3665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "akit.xlogging.foundations.getAutomatonKitLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "akit.testing.testplus.testref.TestRef", "line_number": 64, "usage_type": "name"}, {"api_name": "akit.exceptions.AKitSemanticError", "line_number": 68, "usage_type": "call"}, {"api_name": "akit.testing.testplus.registration.resourceregistry.resource_registry.lookup_resource_scope", "line_number": 83, "usage_type": "call"}, {"api_name": "akit.testing.testplus.registration.resourceregistry.resource_registry", "line_number": 83, "usage_type": "name"}, {"api_name": "akit.testing.testplus.testref.TestRef", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}]}
+{"seq_id": "333863205", "text": "\"\"\"The Bocadillo API class.\"\"\"\nimport inspect\nimport os\nfrom contextlib import contextmanager\nfrom http import HTTPStatus\nfrom typing import (\n Optional,\n Tuple,\n Type,\n List,\n Dict,\n Any,\n Union,\n Coroutine,\n Callable,\n)\n\nfrom asgiref.wsgi import WsgiToAsgi\nfrom jinja2 import FileSystemLoader\nfrom starlette.middleware.cors import CORSMiddleware\nfrom starlette.middleware.httpsredirect import HTTPSRedirectMiddleware\nfrom starlette.middleware.trustedhost import TrustedHostMiddleware\nfrom starlette.testclient import TestClient\nfrom uvicorn.main import run, get_logger\nfrom uvicorn.reloaders.statreload import StatReload\n\nfrom .checks import check_route\nfrom .compat import call_all_async\nfrom .constants import ALL_HTTP_METHODS\nfrom .cors import DEFAULT_CORS_CONFIG\nfrom .error_handlers import ErrorHandler, handle_http_error\nfrom .exceptions import HTTPError\nfrom .hooks import HookFunction\nfrom .media import Media\nfrom .middleware import CommonMiddleware, RoutingMiddleware\nfrom .redirection import Redirection\nfrom .request import Request\nfrom .response import Response\nfrom .route import Route\nfrom .static import static\nfrom .templates import Template, get_templates_environment\nfrom .types import ASGIApp, WSGIApp, ASGIAppInstance\n\n\nclass API:\n \"\"\"The all-mighty API class.\n\n This class implements the [ASGI](https://asgi.readthedocs.io) protocol.\n\n # Example\n\n ```python\n >>> import bocadillo\n >>> api = bocadillo.API()\n ```\n\n # Parameters\n\n templates_dir (str):\n The name of the directory where templates are searched for,\n relative to the application entry point.\n Defaults to `'templates'`.\n static_dir (str):\n The name of the directory containing static files, relative to\n the application entry point. Set to `None` to not serve any static\n files.\n Defaults to `'static'`.\n static_root (str):\n The path prefix for static assets.\n Defaults to `'static'`.\n allowed_hosts (list of str, optional):\n A list of hosts which the server is allowed to run at.\n If the list contains `'*'`, any host is allowed.\n Defaults to `['*']`.\n enable_cors (bool):\n If `True`, Cross Origin Resource Sharing will be configured according\n to `cors_config`. Defaults to `False`.\n See also [CORS](../topics/features/cors.md).\n cors_config (dict):\n A dictionary of CORS configuration parameters.\n Defaults to `dict(allow_origins=[], allow_methods=['GET'])`.\n enable_hsts (bool):\n If `True`, enable HSTS (HTTP Strict Transport Security) and automatically\n redirect HTTP traffic to HTTPS.\n Defaults to `False`.\n See also [HSTS](../topics/features/hsts.md).\n media_type (str):\n Determines how values given to `res.media` are serialized.\n Can be one of the supported media types.\n Defaults to `'application/json'`.\n See also [Media](../topics/request-handling/media.md).\n\n # Attributes\n\n media_type (str):\n The currently configured media type.\n When setting it to a value outside of built-in or custom media types,\n an `UnsupportedMediaType` exception is raised.\n media_handlers (dict):\n The dictionary of supported media handlers.\n You can access, edit or replace this at will.\n templates_dir (str):\n The absolute path where templates are searched for (built from the\n `templates_dir` parameter).\n \"\"\"\n\n _error_handlers: List[Tuple[Type[Exception], ErrorHandler]]\n\n def __init__(\n self,\n templates_dir: str = 'templates',\n static_dir: Optional[str] = 'static',\n static_root: Optional[str] = 'static',\n allowed_hosts: List[str] = None,\n enable_cors: bool = False,\n cors_config: dict = None,\n enable_hsts: bool = False,\n media_type: Optional[str] = Media.JSON,\n ):\n self._routes: Dict[str, Route] = {}\n self._named_routes: Dict[str, Route] = {}\n\n self._error_handlers = []\n self.add_error_handler(HTTPError, handle_http_error)\n\n self._templates = get_templates_environment(\n [os.path.abspath(templates_dir)]\n )\n self._templates.globals.update(self._get_template_globals())\n\n self._extra_apps: Dict[str, Any] = {}\n\n self.client = self._build_client()\n\n if static_dir is not None:\n if static_root is None:\n static_root = static_dir\n self.mount(static_root, static(static_dir))\n\n if allowed_hosts is None:\n allowed_hosts = ['*']\n self.allowed_hosts = allowed_hosts\n\n if cors_config is None:\n cors_config = {}\n self.cors_config = {**DEFAULT_CORS_CONFIG, **cors_config}\n\n self._media = Media(media_type=media_type)\n\n # Middleware\n self._routing_middleware = RoutingMiddleware(self)\n self._common_middleware = CommonMiddleware(self._routing_middleware)\n self.add_middleware(\n TrustedHostMiddleware, allowed_hosts=self.allowed_hosts\n )\n if enable_cors:\n self.add_middleware(CORSMiddleware, **self.cors_config)\n if enable_hsts:\n self.add_middleware(HTTPSRedirectMiddleware)\n\n def _build_client(self) -> TestClient:\n return TestClient(self)\n\n def mount(self, prefix: str, app: Union[ASGIApp, WSGIApp]):\n \"\"\"Mount another WSGI or ASGI app at the given prefix.\n\n # Parameters\n prefix (str): A path prefix where the app should be mounted, e.g. `'/myapp'`.\n app: An object implementing [WSGI](https://wsgi.readthedocs.io) or [ASGI](https://asgi.readthedocs.io) protocol.\n \"\"\"\n if not prefix.startswith('/'):\n prefix = '/' + prefix\n self._extra_apps[prefix] = app\n\n @property\n def media_type(self) -> str:\n return self._media.type\n\n @media_type.setter\n def media_type(self, media_type: str):\n self._media.type = media_type\n\n @property\n def media_handlers(self) -> dict:\n return self._media.handlers\n\n @media_handlers.setter\n def media_handlers(self, media_handlers: dict):\n self._media.handlers = media_handlers\n\n def add_error_handler(\n self, exception_cls: Type[Exception], handler: ErrorHandler\n ):\n \"\"\"Register a new error handler.\n\n # Parameters\n exception_cls (Exception class):\n The type of exception that should be handled.\n handler (callable):\n The actual error handler, which is called when an instance of\n `exception_cls` is caught.\n Should accept a `req`, a `res` and an `exc`.\n \"\"\"\n self._error_handlers.insert(0, (exception_cls, handler))\n\n def error_handler(self, exception_cls: Type[Exception]):\n \"\"\"Register a new error handler (decorator syntax).\n\n # Example\n ```python\n >>> import bocadillo\n >>> api = bocadillo.API()\n >>> @api.error_handler(KeyError)\n ... def on_key_error(req, res, exc):\n ... pass # perhaps set res.content and res.status_code\n ```\n \"\"\"\n\n def wrapper(handler):\n self.add_error_handler(exception_cls, handler)\n return handler\n\n return wrapper\n\n def _find_handlers(self, exception):\n return (\n handler\n for err_type, handler in self._error_handlers\n if isinstance(exception, err_type)\n )\n\n def _handle_exception(self, request, response, exception) -> None:\n \"\"\"Handle an exception raised during dispatch.\n\n At most one handler is called for the exception: the first one\n to support it.\n\n If no handler was registered for the exception, it is raised.\n \"\"\"\n for handler in self._find_handlers(exception):\n handler(request, response, exception)\n break\n else:\n raise exception from None\n\n def route(\n self, pattern: str, *, methods: List[str] = None, name: str = None\n ):\n \"\"\"Register a new route by decorating a view.\n\n # Parameters\n pattern (str):\n An URL pattern given as a format string.\n methods (list of str):\n HTTP methods supported by this route.\n Defaults to all HTTP methods.\n Ignored for class-based views.\n name (str):\n A name for this route, which must be unique.\n\n # Raises\n RouteDeclarationError: if the internal call to #checks.check_route() fails.\n\n # Example\n ```python\n >>> import bocadillo\n >>> api = bocadillo.API()\n >>> @api.route('/greet/{person}')\n ... def greet(req, res, person: str):\n ... pass\n ```\n \"\"\"\n if methods is None:\n methods = ALL_HTTP_METHODS\n\n methods = [method.upper() for method in methods]\n\n def wrapper(view):\n nonlocal methods\n if inspect.isclass(view):\n view = view()\n if hasattr(view, 'handle'):\n methods = ALL_HTTP_METHODS\n else:\n methods = [\n method\n for method in ALL_HTTP_METHODS\n if method.lower() in dir(view)\n ]\n check_route(pattern, view, methods)\n route = Route(\n pattern=pattern, view=view, methods=methods, name=name\n )\n\n self._routes[pattern] = route\n if name is not None:\n self._named_routes[name] = route\n\n return route\n\n return wrapper\n\n @staticmethod\n def before(hook_function: HookFunction, *args, **kwargs):\n \"\"\"Register a before hook on a route.\n\n ::: tip NOTE\n `@api.before()` should beplaced **above** `@api.route()`\n when decorating a view.\n :::\n\n # Parameters\n hook_function (callable): A synchronous or asynchronous function with the signature: `(req, res[, params]) -> None`.\n \"\"\"\n return Route.before_hook(hook_function, *args, **kwargs)\n\n @staticmethod\n def after(hook_function: HookFunction, *args, **kwargs):\n \"\"\"Register an after hook on a route.\n\n ::: tip NOTE\n `@api.after()` should be placed **above** `@api.route()`\n when decorating a view.\n :::\n\n # Parameters\n hook_function (callable): A synchronous or asynchronous function with the signature: `(req, res[, params]) -> None`.\n \"\"\"\n return Route.after_hook(hook_function, *args, **kwargs)\n\n def _find_matching_route(self, path: str) -> Tuple[Optional[str], dict]:\n \"\"\"Find a route matching the given path.\"\"\"\n for pattern, route in self._routes.items():\n kwargs = route.match(path)\n if kwargs is not None:\n return pattern, kwargs\n return None, {}\n\n def _get_route_or_404(self, name: str):\n try:\n return self._named_routes[name]\n except KeyError as e:\n raise HTTPError(HTTPStatus.NOT_FOUND.value) from e\n\n def url_for(self, name: str, **kwargs) -> str:\n \"\"\"\n\n # Parameters\n name (str): the name of the route.\n kwargs (dict): route parameters.\n\n # Returns\n url (str): the URL path for a route.\n\n # Raises\n HTTPError(404) : if no route exists for the given `name`.\n \"\"\"\n route = self._get_route_or_404(name)\n return route.url(**kwargs)\n\n def redirect(\n self,\n *,\n name: str = None,\n url: str = None,\n permanent: bool = False,\n **kwargs\n ):\n \"\"\"Redirect to another route.\n\n # Parameters\n name (str): name of the route to redirect to.\n url (str): URL of the route to redirect to, required if `name` is ommitted.\n permanent (bool):\n If `False` (the default), returns a temporary redirection (302).\n If `True`, returns a permanent redirection (301).\n kwargs (dict):\n Route parameters.\n\n # Raises\n Redirection: an exception that will be caught by #API.dispatch().\n \"\"\"\n if name is not None:\n url = self.url_for(name=name, **kwargs)\n else:\n assert url is not None, 'url is expected if no route name is given'\n raise Redirection(url=url, permanent=permanent)\n\n def _get_template_globals(self) -> dict:\n return {'url_for': self.url_for}\n\n @property\n def templates_dir(self) -> str:\n loader: FileSystemLoader = self._templates.loader\n return loader.searchpath[0]\n\n @templates_dir.setter\n def templates_dir(self, templates_dir: str):\n loader: FileSystemLoader = self._templates.loader\n loader.searchpath = [os.path.abspath(templates_dir)]\n\n def _get_template(self, name: str) -> Template:\n return self._templates.get_template(name)\n\n @contextmanager\n def _prevent_async_template_rendering(self):\n \"\"\"If enabled, temporarily disable async template rendering.\n\n Notes\n -----\n Hot fix for a bug with Jinja2's async environment, which always\n renders asynchronously even under `render()`.\n Example error:\n `RuntimeError: There is no current event loop in thread [...]`\n \"\"\"\n if not self._templates.is_async:\n yield\n return\n\n self._templates.is_async = False\n try:\n yield\n finally:\n self._templates.is_async = True\n\n @staticmethod\n def _prepare_context(context: dict = None, **kwargs):\n if context is None:\n context = {}\n context.update(kwargs)\n return context\n\n async def template(\n self, name_: str, context: dict = None, **kwargs\n ) -> Coroutine:\n \"\"\"Render a template asynchronously.\n\n Can only be used within `async` functions.\n\n # Parameters\n\n name (str):\n Name of the template, located inside `templates_dir`.\n The trailing underscore avoids collisions with a potential\n context variable named `name`.\n context (dict):\n Context variables to inject in the template.\n kwargs (dict):\n Context variables to inject in the template.\n \"\"\"\n context = self._prepare_context(context, **kwargs)\n return await self._get_template(name_).render_async(context)\n\n def template_sync(self, name_: str, context: dict = None, **kwargs) -> str:\n \"\"\"Render a template synchronously.\n\n See also: #API.template().\n \"\"\"\n context = self._prepare_context(context, **kwargs)\n with self._prevent_async_template_rendering():\n return self._get_template(name_).render(context)\n\n def template_string(\n self, source: str, context: dict = None, **kwargs\n ) -> str:\n \"\"\"Render a template from a string (synchronous).\n\n # Parameters\n source (str): a template given as a string.\n\n For other parameters, see #API.template().\n \"\"\"\n context = self._prepare_context(context, **kwargs)\n with self._prevent_async_template_rendering():\n template = self._templates.from_string(source=source)\n return template.render(context)\n\n def _is_routing_middleware(self, middleware_cls) -> bool:\n return hasattr(middleware_cls, 'dispatch')\n\n def add_middleware(self, middleware_cls, **kwargs):\n \"\"\"Register a middleware class.\n\n See also [Middleware](../topics/features/middleware.md).\n\n # Parameters\n\n middleware_cls (Middleware class):\n It should be a #~some.middleware.RoutingMiddleware class (not an instance!), or any\n concrete subclass or #~some.middleware.Middleware.\n \"\"\"\n if self._is_routing_middleware(middleware_cls):\n self._routing_middleware.add(middleware_cls, **kwargs)\n else:\n self._common_middleware.add(middleware_cls, **kwargs)\n\n async def dispatch(\n self,\n request: Request,\n before: List[Callable] = None,\n after: List[Callable] = None,\n ) -> Response:\n \"\"\"Dispatch a request and return a response.\n\n For the exact algorithm, see\n [How are requests processed?](../topics/request-handling/routes-url-design.md#how-are-requests-processed).\n\n # Parameters\n request (Request): an inbound HTTP request.\n before (list of callables): a list of middleware `before_dispatch` hooks.\n after (list of callables): a list of middleware `after_dispatch` hooks.\n\n # Returns\n response (Response): an HTTP response.\n \"\"\"\n if before is None:\n before = []\n if after is None:\n after = []\n\n response = Response(request, media=self._media)\n\n try:\n pattern, kwargs = self._find_matching_route(request.url.path)\n route = self._routes.get(pattern)\n if route is None:\n raise HTTPError(status=404)\n route.raise_for_method(request)\n try:\n await call_all_async(before, request)\n await route(request, response, **kwargs)\n await call_all_async(after, request, response)\n except Redirection as redirection:\n response = redirection.response\n except Exception as e:\n self._handle_exception(request, response, e)\n\n return response\n\n def find_app(self, scope: dict) -> ASGIAppInstance:\n \"\"\"Return the ASGI application suited to the given ASGI scope.\n\n This is also what `API.__call__(self)` returns.\n\n # Parameters\n scope (dict):\n An ASGI scope.\n\n # Returns\n app:\n An ASGI application instance\n (either `self` or an instance of a sub-app).\n \"\"\"\n path: str = scope['path']\n\n # Return a sub-mounted extra app, if found\n for prefix, app in self._extra_apps.items():\n if not path.startswith(prefix):\n continue\n # Remove prefix from path so that the request is made according\n # to the mounted app's point of view.\n scope['path'] = path[len(prefix) :]\n try:\n return app(scope)\n except TypeError:\n app = WsgiToAsgi(app)\n return app(scope)\n\n return self._common_middleware(scope)\n\n def run(\n self,\n host: str = None,\n port: int = None,\n debug: bool = False,\n log_level: str = 'info',\n ):\n \"\"\"Serve the application using [uvicorn](https://www.uvicorn.org).\n\n For further details, refer to\n [uvicorn settings](https://www.uvicorn.org/settings/).\n\n # Parameters\n\n host (str):\n The host to bind to.\n Defaults to `'127.0.0.1'` (localhost).\n If not given and `$PORT` is set, `'0.0.0.0'` will be used to\n serve to all known hosts.\n port (int):\n The port to bind to.\n Defaults to `8000` or (if set) the value of the `$PORT` environment\n variable.\n debug (bool):\n Whether to serve the application in debug mode. Defaults to `False`.\n log_level (str):\n A logging level for the debug logger. Must be a logging level\n from the `logging` module. Defaults to `'info'`.\n \"\"\"\n if 'PORT' in os.environ:\n port = int(os.environ['PORT'])\n if host is None:\n host = '0.0.0.0'\n\n if host is None:\n host = '127.0.0.1'\n\n if port is None:\n port = 8000\n\n if debug:\n reloader = StatReload(get_logger(log_level))\n reloader.run(\n run,\n {\n 'app': self,\n 'host': host,\n 'port': port,\n 'log_level': log_level,\n 'debug': debug,\n },\n )\n else:\n run(self, host=host, port=port)\n\n def __call__(self, scope: dict) -> ASGIAppInstance:\n return self.find_app(scope)\n", "sub_path": "bocadillo/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 20362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 107, "usage_type": "name"}, {"api_name": "error_handlers.ErrorHandler", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 114, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 118, "usage_type": "name"}, {"api_name": "media.Media.JSON", "line_number": 118, "usage_type": "attribute"}, {"api_name": "media.Media", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 120, "usage_type": "name"}, {"api_name": "route.Route", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 121, "usage_type": "name"}, {"api_name": "route.Route", "line_number": 121, "usage_type": "name"}, {"api_name": "exceptions.HTTPError", "line_number": 124, "usage_type": "argument"}, {"api_name": "error_handlers.handle_http_error", "line_number": 124, "usage_type": "argument"}, {"api_name": "templates.get_templates_environment", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 131, "usage_type": "name"}, {"api_name": "static.static", "line_number": 138, "usage_type": "call"}, {"api_name": "cors.DEFAULT_CORS_CONFIG", "line_number": 146, "usage_type": "name"}, {"api_name": "media.Media", "line_number": 148, "usage_type": "call"}, {"api_name": "middleware.RoutingMiddleware", "line_number": 151, "usage_type": "call"}, {"api_name": "middleware.CommonMiddleware", "line_number": 152, "usage_type": "call"}, {"api_name": "starlette.middleware.trustedhost.TrustedHostMiddleware", "line_number": 154, "usage_type": "argument"}, {"api_name": "starlette.middleware.cors.CORSMiddleware", "line_number": 157, "usage_type": "argument"}, {"api_name": "starlette.middleware.httpsredirect.HTTPSRedirectMiddleware", "line_number": 159, "usage_type": "argument"}, {"api_name": "starlette.testclient.TestClient", "line_number": 162, "usage_type": "call"}, {"api_name": "starlette.testclient.TestClient", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 164, "usage_type": "name"}, {"api_name": "types.ASGIApp", "line_number": 164, "usage_type": "name"}, {"api_name": "types.WSGIApp", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 192, "usage_type": "name"}, {"api_name": "error_handlers.ErrorHandler", "line_number": 192, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 206, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 247, "usage_type": "name"}, {"api_name": "constants.ALL_HTTP_METHODS", "line_number": 274, "usage_type": "name"}, {"api_name": "inspect.isclass", "line_number": 280, "usage_type": "call"}, {"api_name": "constants.ALL_HTTP_METHODS", "line_number": 283, "usage_type": "name"}, {"api_name": "constants.ALL_HTTP_METHODS", "line_number": 287, "usage_type": "name"}, {"api_name": "checks.check_route", "line_number": 290, "usage_type": "call"}, {"api_name": "route.Route", "line_number": 291, "usage_type": "call"}, {"api_name": "hooks.HookFunction", "line_number": 304, "usage_type": "name"}, {"api_name": "route.Route.before_hook", "line_number": 315, "usage_type": "call"}, {"api_name": "route.Route", "line_number": 315, "usage_type": "name"}, {"api_name": "hooks.HookFunction", "line_number": 318, "usage_type": "name"}, {"api_name": "route.Route.after_hook", "line_number": 329, "usage_type": "call"}, {"api_name": "route.Route", "line_number": 329, "usage_type": "name"}, {"api_name": "route.match", "line_number": 334, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 331, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 331, "usage_type": "name"}, {"api_name": "exceptions.HTTPError", "line_number": 343, "usage_type": "call"}, {"api_name": "http.HTTPStatus.NOT_FOUND", "line_number": 343, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 343, "usage_type": "name"}, {"api_name": "route.url", "line_number": 359, "usage_type": "call"}, {"api_name": "redirection.Redirection", "line_number": 387, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 394, "usage_type": "name"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 399, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 400, "usage_type": "call"}, {"api_name": "os.path", "line_number": 400, "usage_type": "attribute"}, {"api_name": "templates.Template", "line_number": 402, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 405, "usage_type": "name"}, {"api_name": "typing.Coroutine", "line_number": 435, "usage_type": "name"}, {"api_name": "request.Request", "line_number": 499, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 500, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 500, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 501, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 501, "usage_type": "name"}, {"api_name": "response.Response", "line_number": 521, "usage_type": "call"}, {"api_name": "request.url", "line_number": 524, "usage_type": "attribute"}, {"api_name": "exceptions.HTTPError", "line_number": 527, "usage_type": "call"}, {"api_name": "route.raise_for_method", "line_number": 528, "usage_type": "call"}, {"api_name": "compat.call_all_async", "line_number": 530, "usage_type": "call"}, {"api_name": "compat.call_all_async", "line_number": 532, "usage_type": "call"}, {"api_name": "redirection.Redirection", "line_number": 533, "usage_type": "name"}, {"api_name": "redirection.response", "line_number": 534, "usage_type": "attribute"}, {"api_name": "response.Response", "line_number": 502, "usage_type": "name"}, {"api_name": "asgiref.wsgi.WsgiToAsgi", "line_number": 566, "usage_type": "call"}, {"api_name": "types.ASGIAppInstance", "line_number": 540, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 600, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 601, "usage_type": "attribute"}, {"api_name": "uvicorn.reloaders.statreload.StatReload", "line_number": 612, "usage_type": "call"}, {"api_name": "uvicorn.main.get_logger", "line_number": 612, "usage_type": "call"}, {"api_name": "uvicorn.main.run", "line_number": 614, "usage_type": "argument"}, {"api_name": "uvicorn.main.run", "line_number": 624, "usage_type": "call"}, {"api_name": "types.ASGIAppInstance", "line_number": 626, "usage_type": "name"}]}
+{"seq_id": "88564547", "text": "import pandas as pd\n\nfrom sklearn import metrics\n\ndataset = pd.read_csv('../../../../resources/data/cases/dataset_v2/dataset_2.3.csv')\n\nnorm_peaks = dataset['peak_magnitude_norm'].values.reshape(-1, 1)\nfd_jenks_clusters = dataset['fd_jenks_clusters'].to_numpy()\nsturges_jenks_clusters = dataset['sturges_jenks_clusters'].to_numpy()\nscott_jenks_clusters = dataset['scott_jenks_clusters'].to_numpy()\nagg_clusters = dataset['fd_agg_clusters'].to_numpy()\nkm_clusters = dataset['fd_km_clusters'].to_numpy()\nuniform_clusters = dataset['fd_uniform_clusters'].to_numpy()\n\nfd_jenks_ch = metrics.calinski_harabasz_score(norm_peaks, fd_jenks_clusters)\nsturges_ch = metrics.calinski_harabasz_score(norm_peaks, sturges_jenks_clusters)\nscott_ch = metrics.calinski_harabasz_score(norm_peaks, scott_jenks_clusters)\nagg_ch = metrics.calinski_harabasz_score(norm_peaks, agg_clusters)\nkm_ch = metrics.calinski_harabasz_score(norm_peaks, km_clusters)\nuniform_ch = metrics.calinski_harabasz_score(norm_peaks, uniform_clusters)\n\nprint('--Calinski - Harabasz scores--')\n\nprint('FD + JF: ' + str(fd_jenks_ch))\nprint('Sturges + JF: ' + str(sturges_ch))\nprint('Scott + JF: ' + str(scott_ch))\nprint('FD + Agglomerative: ' + str(agg_ch))\nprint('FD + K-means: ' + str(km_ch))\nprint('FD + Uniform binning: ' + str(uniform_ch))\n", "sub_path": "src/experiment_2/cases/metrics/calinski_harabasz.py", "file_name": "calinski_harabasz.py", "file_ext": "py", "file_size_in_byte": 1297, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 15, "usage_type": "name"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 16, "usage_type": "name"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 18, "usage_type": "name"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 19, "usage_type": "name"}, {"api_name": "sklearn.metrics.calinski_harabasz_score", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 20, "usage_type": "name"}]}
+{"seq_id": "521308135", "text": "\r\n\r\nimport numpy as np\r\nimport math\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport gpflow as gp\r\nfrom gpflow.utilities import print_summary\r\n\r\nimport sys\r\nsys.path.append('../bo_cost_budget_cont_domain')\r\nfrom hyperparameter_optimization import logistic_bjt\r\n\r\ndef multi_opt_1d_cost(X):\r\n\r\n\r\n result = np.exp(-(np.sin(np.pi*X)+ np.sin(1.5*np.pi*X) +np.sin(2*np.pi*X)))\r\n\r\n # result = float(result)\r\n\r\n # print('Result = %f' % result)\r\n # time.sleep(np.random.randint(60))\r\n return result\r\n\r\ndef multi_opt_1d_opt_cost():\r\n\r\n\r\n domain = [[-3,3]]\r\n\r\n y_opt= -2.74776185\r\n x_opt= [-0.3]\r\n\r\n\r\n return y_opt, x_opt, domain\r\n\r\ndef multi_opt_1d_cost_plots(disc, plot= False):\r\n\r\n\r\n\r\n domain =[[-3,3]]\r\n x1 = np.linspace(domain[0][0], domain[0][1], disc)\r\n X= x1.reshape(-1,1)\r\n\r\n Y= multi_opt_1d_cost(X)\r\n\r\n if plot== True:\r\n plt.figure()\r\n plt.plot(X[:,0], Y[:,0], color= 'blue', label= 'true target')\r\n plt.show()\r\n return X, Y\r\n\r\ndef multi_opt_1d_cost_find_best_suited_kernel(X, Y, noise=10**(-4)):\r\n\r\n '''constraint values'''\r\n lower= 10**(-5); upper= 10**(6); #lengtscale and variance constarint\r\n lower_noise= 10**(-5); upper_noise= 10**(6); #noise constarint\r\n\r\n logistic = logistic_bjt(lower, upper)\r\n logistic_noise = logistic_bjt(lower_noise, upper_noise)\r\n\r\n D= X.shape[1]\r\n kernel = gp.kernels.RBF(lengthscales=np.array([1] * D))\r\n Y_latent= np.log(Y)\r\n\r\n model = gp.models.GPR((X, Y_latent), kernel=kernel)\r\n '''set hyperparameter constraints'''\r\n model.kernel.lengthscales = gp.Parameter(model.kernel.lengthscales.numpy(), transform=logistic)\r\n model.kernel.variance = gp.Parameter(model.kernel.variance.numpy(), transform=logistic)\r\n # model.likelihood.variance = gp.Parameter(model.likelihood.variance.numpy(), transform=logistic_noise)\r\n\r\n model.likelihood.variance.assign(noise)\r\n\r\n gp.set_trainable(model.likelihood, False)\r\n\r\n opt = gp.optimizers.Scipy()\r\n\r\n opt_logs = opt.minimize(model.training_loss, model.trainable_variables, options=dict(maxiter=100))\r\n print_summary(model)\r\n\r\n return model, kernel", "sub_path": "cost_functions/multi_opt_different_cost_1d_cost.py", "file_name": "multi_opt_different_cost_1d_cost.py", "file_ext": "py", "file_size_in_byte": 2188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "hyperparameter_optimization.logistic_bjt", "line_number": 59, "usage_type": "call"}, {"api_name": "hyperparameter_optimization.logistic_bjt", "line_number": 60, "usage_type": "call"}, {"api_name": "gpflow.kernels.RBF", "line_number": 63, "usage_type": "call"}, {"api_name": "gpflow.kernels", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 64, "usage_type": "call"}, {"api_name": "gpflow.models.GPR", "line_number": 66, "usage_type": "call"}, {"api_name": "gpflow.models", "line_number": 66, "usage_type": "attribute"}, {"api_name": "gpflow.Parameter", "line_number": 68, "usage_type": "call"}, {"api_name": "gpflow.Parameter", "line_number": 69, "usage_type": "call"}, {"api_name": "gpflow.set_trainable", "line_number": 74, "usage_type": "call"}, {"api_name": "gpflow.optimizers.Scipy", "line_number": 76, "usage_type": "call"}, {"api_name": "gpflow.optimizers", "line_number": 76, "usage_type": "attribute"}, {"api_name": "gpflow.utilities.print_summary", "line_number": 79, "usage_type": "call"}]}
+{"seq_id": "69126691", "text": "import os\nimport re\n\nimport numpy as np\nimport pandas as pd\n\nimport datetime\nimport json\n\nfrom coolorange.tools import *\nfrom coolorange.scraper import *\n\n# from tools import *\n# from scraper import *\n\ndef load_excel_sheet(data_path, sheet_name, mapper={'編號':'horse_num'}):\n \"\"\" load an excel sheet\n\n Parameters: data_path (str) - the path to the excel file\n sheet_name (str) - the sheet name\n mapper (dict) - the mapper for renaming column, if empty will do nothing\n \"\"\"\n raw_df = pd.read_excel(data_path, sheet_name=sheet_name)\n\n # extract result\n result = drop_none([transform_str_int(i) for i in list(raw_df)])\n\n col_row_idx = raw_df[list(raw_df)[0]].dropna().index[0]\n # reset column name by detecting the first row with int/float/str\n raw_df.columns = raw_df.loc[col_row_idx].values\n raw_df = raw_df.drop(col_row_idx)\n\n # # drop all\n raw_df = drop_if_whole_na(raw_df, 'row').reset_index(drop=True)\n\n first_col_det = raw_df[list(raw_df)[0]].values[:,0]\n\n # index to cut for second half of table\n cut_idx = np.where(np.array([transform_str_int(i) for i in first_col_det]) == None)[0][0]\n\n # cut the second half\n raw_df = raw_df.iloc[range(cut_idx)]\n\n # drop NA columns and duplicated column\n raw_df = drop_if_whole_na(raw_df.loc[:,~raw_df.columns.duplicated()], 'column')\n # reset column name\n final_df = raw_df.rename(columns=mapper).sort_values('horse_num').reset_index(drop=True)\n\n # add result\n final_df['result'] = encode_result(final_df.shape[0], result)\n\n # add file name\n file_name = re.findall(r'/[\\w|-]+.xls', data_path)[0][1:-4]\n final_df['file_name'] = [file_name]*final_df.shape[0]\n\n # sheet name\n final_df['sheet_name'] = [sheet_name]*final_df.shape[0]\n\n return final_df\n\ndef load_whole_excel(file_path, sheet_hint, mapper={'編號':'horse_num'}, combined=True, verbose=True):\n \"\"\" load every sheet in a excel file\n\n Parameters: file_path (str) - the path to the excel file\n sheet_hint (str) - the hint to eliminate sheet\n eg, hint='Race', then sheet name must contain 'Race'\n mapper (dict) - the mapper for renaming column, if empty will do nothing\n optional, default is {'編號':'horse_num'}\n combined (boo) - if combined, then will combine all sheets\n optional, default is True\n verbose (boo) - print message or not\n optional, default is True\n\n Returns: (DataFrame) or (list)\n \"\"\"\n if verbose:\n print('Loading file: {}'.format(file_path))\n\n all_sheets_name = [i for i in pd.ExcelFile(file_path).sheet_names if sheet_hint in i]\n\n if all_sheets_name:\n all_sheets = list()\n for i in all_sheets_name:\n try:\n data = load_excel_sheet(file_path, i, mapper)\n all_sheets.append(data)\n if verbose:\n print('Loaded: {}'.format(i))\n except:\n if verbose:\n print('Failed to load sheet: {}'.format(i))\n if verbose:\n print('Status: Completed\\n')\n\n if combined:\n return pd.concat(all_sheets, sort=False).reset_index(drop=True)\n else:\n return all_sheets\n\n else:\n if verbose:\n print('Sheet hint error')\n print('Status: Failed\\n')\n return None\n\ndef flow_from_dir(path, file_extension, sheet_hint, mapper={'編號':'horse_num'}, combined=True, verbose=True):\n \"\"\" load all data with the same file extention from a directory\n\n Parameters: path (str) - the directory\n file_extension (str) - the file extension\n sheet_hint (str) - the hint to eliminate sheet\n eg, hint='Race', then sheet name must contain 'Race'\n mapper (dict) - the mapper for renaming column, if empty will do nothing\n optional, default is {'編號':'horse_num'}\n combined (boo) - if combined, then will combine all sheets\n optional, default is True\n verbose (boo) - print message or not\n optional, default is True\n\n Returns: (DataFrame) or (list)\n \"\"\"\n all_files = [i for i in os.listdir(path) if file_extension in i]\n\n all_data = list()\n for j,i in enumerate(all_files):\n if verbose:\n print('File {}'.format(j+1))\n data = load_whole_excel(os.path.join(path, i), sheet_hint, mapper, combined, verbose)\n all_data.append(data)\n\n if verbose:\n print('Loaded {} files'.format(len(all_files)))\n\n if combined and all_data:\n try:\n return pd.concat(all_data, sort=False).reset_index(drop=True)\n except:\n if verbose:\n print('\\nError in loading file\\nGlobal issue, check files')\n elif combined is False and all_data:\n return all_data\n else:\n return None\n\n##########- map data from external source -##########\n\ndef extract_missing_result(data, browser_path, retry=True, verbose=True, save=True):\n \"\"\" load an excel file from load_data module\n and detect which day and race has no result\n then scrape from HKJC to collect the specific data\n\n Parameters: data (df) - the dataframe load from either load_whole_excel() or flow_from_dir()\n browser_path (str) - the browser path\n retry (boo) - retry failed scraping or not, optional, default is True\n verbose (boo) - print message or not, optinal, default is True\n save (boo) - save result or not, optional, default is True\n\n Returns: all_collection (dict) - a collection of results\n \"\"\"\n my_data = data[['result','file_name','sheet_name']]\n\n # detect which day and race has no result\n date_race = my_data[my_data.result.isna()][['file_name','sheet_name']].drop_duplicates().values\n\n # store race result\n all_collection = dict()\n # initiate session to extract result\n hkjc = HKJCRaceResult(browser_path)\n\n # store failed info and retry later\n retry_list = list()\n\n for i,j in date_race:\n year, month, day = format_to_date(i)\n race_num = format_to_race(j)\n # key name for dictionary\n id_ = '_'.join([year, month, day, race_num])\n try:\n # load that specific page\n hkjc.load_race_source(year, month, day, race_num)\n # overall race result\n result = hkjc.extract_race_result()\n # specific rank\n rank = result[result['Pool'] == 'PLACE']['Winning Combination'].values.tolist()\n # append to dictionary\n all_collection[id_] = rank\n if verbose:\n print('Loaded Result for: {}'.format(id_))\n except:\n retry_list.append((year, month, day, race_num))\n if verbose:\n print('Failed to Load Result for: {}'.format(id_))\n\n if retry and retry_list:\n for i in retry_list:\n id_ = '_'.join(i)\n if verbose:\n print('Retrying: {}'.format(id_))\n try:\n hkjc.load_race_source(i[0], i[1], i[2], i[3])\n result = hkjc.extract_race_result()\n rank = result[result['Pool'] == 'PLACE']['Winning Combination'].values.tolist()\n all_collection[id_] = rank\n print('Loaded Result for: {}'.format(id_))\n except:\n if verbose:\n print('Failed to Load Result for: {}'.format(id_))\n\n hkjc.close_browser()\n\n if save:\n json_ = json.dumps(all_collection)\n filename = datetime.datetime.now().strftime('%m_%d_%Y_%H_%M_%S')+'.txt'\n with open(filename, 'w') as outfile:\n json.dump(all_collection, outfile)\n\n return all_collection\n\ndef map_result_to_day_data(in_day_data, in_result, return_type='dataframe'):\n \"\"\" map an external extracted result to data\n serve as a supportive function to map_result_to_data()\n\n Parameters: in_day_data (DataFrame) - a single day (multiple races) race data\n if using internal code, then the data load from load_whole_excel()\n in_result (dict) - result in a dictionary, format: {id_:[rank1, rank2, rank3], ...}\n id_ is the project standard id,\n eg, 10/27/2019 Race 1 -> 2019_10_27_1\n can be extracted using extract_missing_result()\n save option is available\n if using internal code, then the data load from extract_missing_result()\n return_type (str) - return type, optional, defaul is 'dataframe'\n accept values: 'dataframe', 'dict'\n\n Returns: (dict) - mapped result to the data, format: {id_:df, ...}\n \"\"\"\n data_dict = dict()\n separated_data = subset_df(in_day_data, 'sheet_name')\n\n for i in separated_data:\n # mapped id_, eg '2019_10_27_9'\n id_ = '_'.join(format_to_date(i['file_name'].values[0]))+'_'+format_to_race(i['sheet_name'].values[0])\n if id_ in in_result:\n # sort df so to map with result\n sorted_df = i.sort_values('horse_num')\n # result, map it from in_result\n result = encode_result(i.shape[0], [transform_str_int(i) for i in in_result[id_]])\n sorted_df['result'] = result\n sorted_df = sorted_df.reset_index(drop=True)\n data_dict[id_] = sorted_df\n elif i.result.isna().sum() == 0:\n data_dict[id_] = i\n else:\n continue\n if return_type is 'dataframe':\n return pd.concat(data_dict.values()).reset_index(drop=True)\n elif return_type is 'dict':\n return data_dict\n\ndef map_result_to_data(in_df, in_result, verbose=True):\n \"\"\" map an external extracted result to data (directory or single excel file)\n\n in_day_data (DataFrame) - a single day (multiple races) race data\n if using internal code, then the data load from load_whole_excel()\n in_result (dict) - result in a dictionary, format: {id_:[rank1, rank2, rank3], ...}\n id_ is the project standard id,\n eg, 10/27/2019 Race 1 -> 2019_10_27_1\n can be extracted using extract_missing_result()\n save option is available\n if using internal code, then the data load from extract_missing_result()\n verbose (boo) - print error message or not, optional, default is True\n\n \"\"\"\n subset = subset_df(in_df, 'file_name')\n all_data = list()\n for i in subset:\n try:\n appended = map_result_to_day_data(i, in_result)\n all_data.append(appended)\n except:\n day = '_'.join(format_to_date(i['file_name'].values[0]))\n if verbose:\n print('Race day {} has no result'.format(day))\n continue\n\n overall = pd.concat(all_data).reset_index(drop=True)\n return overall\n\n##############- load whole page info from HJC -##############\ndef write_to_txt(path, file_name, content):\n with open(os.path.join(path, file_name), 'w') as f:\n f.writelines(content)\n\ndef load_hkjc_page_info(info, path, retry=True, verbose=True, return_failed=True):\n \"\"\" extract a list of race day info from HKJC\n\n Parameters: info (list) - a list of list containing the info of raceday\n eg. [year, month, day, race] as a sub list\n path (str) - the path that files should write to\n retry (boo) - retry if first time fail or not, optional, default is True\n return_failed (boo) - return failed extraction page info, optional, default is True\n\n Returns: (list) - failed extraction data page info, optional, return only if return_failed is True\n \"\"\"\n hkjc = HKJCRaceResult('../../../geckodriver')\n\n if verbose:\n print('Page to Extract: {}'.format(len(info)))\n print('')\n retry_list = list()\n for year, month, day, race_name in info:\n\n try:\n hkjc.load_race_source(year, month, day, race_name)\n\n race_tab = hkjc.extract_race_tab()\n race_performance = hkjc.extract_race_performance()\n result = hkjc.extract_race_result()\n print('Loaded page data: {}/{}/{}, Race {}'.format(year, month, day, race_name))\n\n complete_name = '{}_{}_{}_{}'.format(year, month, day, race_name)\n\n # race tab write to file , .txt\n write_to_txt(path, complete_name+'_rt.txt', [i+'\\n' for i in race_tab])\n # race perforamnce write to file, .csv\n race_performance.to_csv(os.path.join(path, complete_name+'_rp.csv'), index=False)\n # race result write to file, .csv\n result.to_csv(os.path.join(path, complete_name+'_result.csv'), index=False)\n if verbose:\n print('All data wrote to files')\n print('')\n\n except:\n retry_list.append((year, month, day, race_name))\n if verbose:\n print('Failed to Load page info: {}/{}/{}, Race {}, will retry'.format(year, month, day, race_name))\n print('')\n\n # retry\n if retry:\n failed_report = list()\n for year, month, day, race_name in retry_list:\n try:\n if verbose:\n print('Retrying page info: {}/{}/{}, Race {}'.format(year, month, day, race_name))\n hkjc.load_race_source(year, month, day, race_name)\n\n race_tab = hkjc.extract_race_tab()\n race_performance = hkjc.extract_race_performance()\n result = hkjc.extract_race_result()\n\n complete_name = '{}_{}_{}_{}'.format(year, month, day, race_name)\n\n # race tab write to file , .txt\n write_to_txt(path, complete_name+'_rt.txt', [i+'\\n' for i in race_tab])\n # race perforamnce write to file, .csv\n race_performance.to_csv(os.path.join(path, complete_name+'_rp.csv'), index=False)\n # race result write to file, .csv\n result.to_csv(os.path.join(path, complete_name+'_result.csv'), index=False)\n if verbose:\n print('All data wrote to files')\n print('')\n except:\n failed_report.append('{}_{}_{}_{}'.format(year, month, day, race_name))\n if verbose:\n print('Failed to load page data: {}/{}/{}, Race {}'.format(year, month, day, race_name))\n print('')\n\n hkjc.close_browser()\n\n if return_failed:\n return failed_report\n\n hkjc.close_browser()\n", "sub_path": "ETL/coolorange/load_data.py", "file_name": "load_data.py", "file_ext": "py", "file_size_in_byte": 15388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pandas.read_excel", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.ExcelFile", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 96, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 136, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 215, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 255, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 285, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path", "line_number": 327, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 356, "usage_type": "call"}, {"api_name": "os.path", "line_number": 356, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path", "line_number": 358, "usage_type": "attribute"}]}
+{"seq_id": "654354723", "text": "#!/usr/bin/python3\n\"\"\" New engine for HBNB project \"\"\"\nfrom sqlalchemy.orm import sessionmaker, scoped_session\nfrom sqlalchemy import create_engine\nfrom models.base_model import BaseModel, Base\nfrom models.city import City\nfrom models.state import State\nfrom models.place import Place\nfrom models.amenity import Amenity\nfrom models.review import Review\nfrom models.user import User\nfrom os import getenv\n\nclasses = {\n 'State': State,\n 'City': City,\n 'Place': Place,\n 'Amenity': Amenity,\n 'Review': Review,\n 'User': User\n}\n\n\nclass DBStorage:\n \"\"\"This class manages storage of hbnb models in SQLAlchemy\"\"\"\n __engine = None\n __session = None\n\n def __init__(self):\n user = getenv('HBNB_MYSQL_USER')\n password = getenv('HBNB_MYSQL_PWD')\n host = getenv('HBNB_MYSQL_HOST')\n database = getenv('HBNB_MYSQL_DB')\n environment = getenv('HBNB_ENV')\n self.__engine = create_engine(\n 'mysql+mysqldb://{}:{}@{}/{}'.format(\n user, password, host, database), pool_pre_ping=True)\n if environment == 'test':\n Base.metadata.drop_all(self.__engine)\n\n def all(self, cls=None):\n \"\"\"return all the database storage\"\"\"\n queryList = []\n queryDict = {}\n if cls in classes:\n obj = self.__session.query(classes[cls]).all()\n for obj_ in obj:\n key = \"{}.{}\".format(obj_.__class__.__name__, obj_.id)\n value = obj_\n queryDict[key] = value\n elif cls is None:\n for cls_ in classes:\n obj = self.__session.query(classes[cls]).all()\n for obj_ in obj:\n key = \"{}.{}\".format(obj_.__class__.__name__, obj_.id)\n value = obj_\n queryDict[key] = value\n return queryDict\n\n def new(self, obj):\n \"\"\"Add object to the current database\"\"\"\n if obj:\n self.__session.add(obj)\n\n def save(self):\n \"\"\"save all changes to current session in database\"\"\"\n self.__session.commit()\n\n def delete(self, obj=None):\n \"\"\"Delete changes from the current session to database\"\"\"\n if obj:\n self.__session.delete(obj)\n\n def reload(self):\n \"\"\"Create all tables in the database and current database session\"\"\"\n Base.metadata.create_all(self.__engine)\n Session_ = sessionmaker(bind=self.__engine, expire_on_commit=False)\n Session = scoped_session(Session_)\n self.__session = Session()\n\n def close(self):\n \"\"\"close on the class Session\"\"\"\n self.__session.close()\n", "sub_path": "models/engine/db_storage.py", "file_name": "db_storage.py", "file_ext": "py", "file_size_in_byte": 2641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "models.state.State", "line_number": 15, "usage_type": "name"}, {"api_name": "models.city.City", "line_number": 16, "usage_type": "name"}, {"api_name": "models.place.Place", "line_number": 17, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 18, "usage_type": "name"}, {"api_name": "models.review.Review", "line_number": 19, "usage_type": "name"}, {"api_name": "models.user.User", "line_number": 20, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 31, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 33, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 35, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata.drop_all", "line_number": 39, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 39, "usage_type": "name"}, {"api_name": "models.base_model.Base.metadata.create_all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 76, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 78, "usage_type": "call"}]}
+{"seq_id": "491072111", "text": "import io\nimport boto3\n\n\ndef open_s3_file(bucket, key):\n file = io.BytesIO()\n bucket = boto3.resource('s3').Bucket(bucket)\n bucket.Object(key).download_fileobj(file)\n file.seek(0)\n return file\n\n\ndef write_s3_file(bucket, key, file):\n file.seek(0)\n bucket = boto3.resource('s3').Bucket(bucket)\n bucket.Object(key).upload_fileobj(file)\n\n\ndef write_s3_string(bucket, key, file):\n try:\n file.seek(0)\n buffer = io.BytesIO()\n buffer.write(file.read().encode(\"utf-8\"))\n buffer.seek(0)\n bucket = boto3.resource('s3').Bucket(bucket)\n bucket.Object(key).upload_fileobj(buffer)\n file.close() # might cause problems as I haven't tested this\n except Exception as e:\n print('Exception: ', e)\n return True\n", "sub_path": "src/cloudhelper/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 781, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "io.BytesIO", "line_number": 6, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 7, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 15, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 22, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "148308218", "text": "import requests\nimport json\nimport argparse\nimport random\nfrom random import randint\nimport time\n\nsheetId = None\ntoken = None\nurl = None\nheaders = {}\ncList = []\nrList = []\n\n\ndef setHeaderCreateSheet():\n global headers\n global token\n\n token = \"gt6yaeudn6g2p9beoe2jtkgs9g\"\n\n headers = {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n\ndef setHeaderCreateProof():\n global headers\n global token\n\n token = \"q5i51alif8glq41rihkuw0q1me\"\n\n headers = {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json',\n 'Content-Length': '242779',\n 'Content-Disposition': 'attachment; filename=\"TEST copy 2.png\"'\n }\n\n\ndef createSheet(url, rowNumber):\n global sheetId\n global cList\n global rList\n global headers\n\n url = url\n\n sheetname = \"Test Sheet\"\n payload = {\n 'name': sheetname,\n \"columns\": [{'title': 'Primary Column', 'primary': 'true', 'type': 'TEXT_NUMBER'}]\n }\n\n r = requests.post(url + '/sheets', data=json.dumps(payload), headers=headers)\n\n data = json.loads(r.text)\n\n if (r.status_code == 200):\n sheetId = data['result']['id']\n primaryCol = data['result']['columns'][0]['id']\n cList.append(primaryCol)\n\n # inserting a row depending on number\n for j in range(rowNumber):\n rowPayload = {\n 'toBottom': 'true', 'cells': [{'columnId': str(cList[0]), 'value': 'Test Data ' + str(rowNumber)}]\n }\n\n r = requests.post(url + '/sheets/' + str(sheetId) + '/rows?exclude=nonexistentCells',\n data=json.dumps(rowPayload)\n , headers=headers)\n rowData = json.loads(r.text)\n\n if (r.status_code == 200):\n rowId = rowData['result']['id']\n\n for k in range(len(cList)):\n updatePayload = {\n 'cells': [\n {'columnId': str(cList[k]),\n 'value': \"test\",\n 'strict': False\n }]\n }\n\n r = requests.put(url + '/sheets/' + str(sheetId) + '/rows/' + str(rowId),\n data=json.dumps(updatePayload),\n headers=headers)\n\n updateData = json.loads(r.text)\n\n if (r.status_code == 200):\n print(\"Sucess Row: \" + str(rowId))\n\n data = open('./TEST_copy_2.png', 'rb').read()\n\n r = requests.post(url + '/sheets/' + str(sheetId) + '/rows/' + str(rowId) + 'proofs', data=data,\n headers=setHeaderCreateProof())\n\n updateData = json.loads(r.text)\n\n if (r.status_code == 200):\n print(\"Sucess Proof: \" + str(rowId))\n else:\n print(updateData)\n break\n else:\n print(rowData)\n break\n\n\n else:\n print(data)\n\ndef createProof(url, rowNumber):\n global sheetId\n global rList\n global headers\n\n url = url\n\n data = open('./TEST copy 2.png', 'rb').read()\n\n r = requests.post(url + '/sheets/({{SHEETID}}/rows/', data=data, headers=headers)\n #sheets/{{sheetId}}/rows/4503604730062724/proofs\n\n data = json.loads(r.text)\n\n if (r.status_code == 200):\n sheetId = data['result']['id']\n primaryCol = data['result']['columns'][0]['id']\n cList.append(primaryCol)\n\n # inserting a row depending on number\n for j in range(rowNumber):\n rowPayload = {\n 'toBottom': 'true', 'cells': [{'columnId': str(cList[0]), 'value': 'Test Data ' + str(rowNumber)}]\n }\n\n r = requests.post(url + '/sheets/' + str(sheetId) + '/rows?exclude=nonexistentCells',\n data=json.dumps(rowPayload)\n , headers=headers)\n rowData = json.loads(r.text)\n\n if (r.status_code == 200):\n rowId = rowData['result']['id']\n\n for k in range(len(cList)):\n updatePayload = {\n 'cells': [\n {'columnId': str(cList[k]),\n 'value': \"test\",\n 'strict': False\n }]\n }\n\n r = requests.put(url + '/sheets/' + str(sheetId) + '/rows/' + str(rowId),\n data=json.dumps(updatePayload),\n headers=headers)\n\n updateData = json.loads(r.text)\n\n if (r.status_code == 200):\n print(\"Sucess Row: \" + str(rowId))\n\n else:\n print(updateData)\n break\n else:\n print(rowData)\n break\n\n\n else:\n print(data)\n\n\ndef main():\n # parser = argparse.ArgumentParser()\n # parser.add_argument('url', help='Your url')\n # parser.add_argument('token', help='Your API token')\n # parser.add_argument('rowNumber', help='Number of row')\n # parser.add_argument('columnNumber', help='Number of columns')\n # parser.add_argument('sheetNumber', help=\"Number of sheets\")\n\n # args = parser.parse_args()\n # url = args.url\n # token = args.token\n # rowNumber = args.rowNumber\n # columnNumber = args.columnNumber\n # sheetNumber = args.sheetNumber\n\n setHeaderCreateSheet()\n createSheet(\"https://api.luke1.smart.ninja/2.0\", 55)\n # for i in range(50):\n # createProof()\n\nmain()", "sub_path": "BOMBARDPY.py", "file_name": "BOMBARDPY.py", "file_ext": "py", "file_size_in_byte": 5836, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "requests.post", "line_number": 55, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 70, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 88, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 98, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 101, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 125, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 128, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 142, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 144, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 158, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 159, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 162, "usage_type": "call"}]}
+{"seq_id": "331468375", "text": "import argparse\nimport json\nimport os\n\ndef process_json_files(dirname: str, prefix: str = \"\"):\n workers_arr = []\n autobatching_arr = []\n tensorbatching_arr = []\n workers_autobatching_table_p50 = {}\n workers_autobatching_table_rps = {}\n workers_tensorbatching_table_p50 = {}\n workers_tensorbatching_table_rps = {}\n files_list = os.listdir(dirname)\n for fname in files_list:\n if \".json\" in fname and ((prefix != \"\" and prefix in fname) or (prefix == \"\")):\n with open(\"{}/{}\".format(dirname, fname)) as json_file:\n dd = json.load(json_file)\n workers = dd[\"Workers\"]\n autobatching = dd[\"MetadataAutobatching\"]\n tensorbatching = dd[\"TensorBatchSize\"]\n rps = dd[\"OverallRates\"][\"overallOpsRate\"]\n p50 = dd[\"OverallQuantiles\"][\"AllQueries\"][\"q50\"]\n\n # we fix the tensor batch size to 1 for autobatching\n if tensorbatching == 1:\n process_table_datapoint(autobatching, autobatching_arr, p50, workers, workers_arr,\n workers_autobatching_table_p50)\n process_table_datapoint(autobatching, autobatching_arr, rps, workers, workers_arr,\n workers_autobatching_table_rps)\n # we fix autobatching to 0 when doing tensor batching\n if autobatching == 0:\n process_table_datapoint(tensorbatching, tensorbatching_arr, p50, workers, workers_arr,\n workers_tensorbatching_table_p50)\n process_table_datapoint(tensorbatching, tensorbatching_arr, rps, workers, workers_arr,\n workers_tensorbatching_table_rps)\n\n workers_arr.sort()\n autobatching_arr.sort()\n tensorbatching_arr.sort()\n return workers_arr, autobatching_arr, workers_autobatching_table_rps, workers_autobatching_table_p50, tensorbatching_arr, workers_tensorbatching_table_rps, workers_tensorbatching_table_p50\n\n\ndef process_table_datapoint(metric_key, metric_arr, metric_value, workers, workers_arr, table):\n if workers not in workers_arr:\n workers_arr.append(workers)\n if metric_key not in metric_arr:\n metric_arr.append(metric_key)\n if workers not in table:\n table[workers] = {}\n if metric_key not in table[workers]:\n table[workers][metric_key] = []\n table[workers][metric_key].append(metric_value)\n\n\ndef print_results_table(workers_arr, metric_arr, metric_table, metric_str, functor=min):\n print(\"Workers,{}\".format(\",\".join([\"{} {}\".format(metric_str, x) for x in metric_arr])))\n for workersN in workers_arr:\n line = [\"{} workers\".format(workersN)]\n for metric_key in metric_arr:\n v = \"n/a\"\n if metric_key in metric_table[workersN]:\n v = functor(metric_table[workersN][metric_key])\n v = '{:.3f}'.format(float(v))\n line.append(v)\n print(\",\".join([str(x) for x in line]))\n\n\nparser = argparse.ArgumentParser(\n description=\"Simple script to process RedisAI results JSON and output overall metrics\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n)\nparser.add_argument(\"--dir\", type=str, required=True)\nparser.add_argument(\"--prefix\", type=str, default=\"\", help=\"prefix to filter the result files by\")\nargs = parser.parse_args()\n\nworkers_arr, autobatching_arr, workers_autobatching_table_rps, workers_autobatching_table_p50, tensorbatching_arr, workers_tensorbatching_table_rps, workers_tensorbatching_table_p50 = process_json_files(\n args.dir, args.prefix)\n\nprint(\"-------------------\")\nprint(\"## Auto-batching overall throughput (inferences/sec) ((higher is better))\")\nprint_results_table(workers_arr, autobatching_arr, workers_autobatching_table_rps, \"Auto-batching\",max)\nprint(\"\")\nprint(\"## Auto-batching p50 latency results (latency in ms including RTT) ((lower is better))\")\nprint_results_table(workers_arr, autobatching_arr, workers_autobatching_table_p50, \"Auto-batching\",min)\nprint(\"\")\nprint(\"-------------------\")\nprint(\"## Tensor-batching overall throughput (inferences/sec) ((higher is better))\")\nprint_results_table(workers_arr, tensorbatching_arr, workers_tensorbatching_table_rps, \"Tensor-batching\",max)\nprint(\"\")\nprint(\"## Tensor-batching p50 latency results (latency in ms including RTT) ((lower is better))\")\nprint_results_table(workers_arr, tensorbatching_arr, workers_tensorbatching_table_p50, \"Tensor-batching\",min)\nprint(\"\")", "sub_path": "scripts/redisai_produce_results_table.py", "file_name": "redisai_produce_results_table.py", "file_ext": "py", "file_size_in_byte": 4585, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 68, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 70, "usage_type": "attribute"}]}
+{"seq_id": "248063728", "text": "from scapy.all import *\nimport argparse\nimport socket\n\n\nif __name__ == '__main__':\n\t# parse arguments\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"input\", help=\"name of the file to analyze\")\n\targs = parser.parse_args()\n\tinput_file = args.input\n\n\t# parse input file\n\t# remember inpartb.txt must be the same format as:\n\t# src_ip\n\t# des_ip\n\t# src_port\n\t# dst_port\n\t# GET message\n\twith open(input_file, 'r') as f:\n\t\tsrc = f.readline()\n\t\tsrc, _ = src.split('\\n')\n\t\tdst = f.readline()\n\t\tdst, _ = dst.split('\\n')\n\t\tsport = int(f.readline())\n\t\tdport = int(f.readline())\n\t\tget = f.readline() + '/n'\n\n\t# establish TCP handshank, start from SYN packet\n\tsyn = IP(src=src, dst=dst)/TCP(sport=sport, dport=dport, flags='S')\n\tans = sr1(syn)\n\tprint(ans.show())\n\t# send packet with HTTP request message\n\trequest=IP(src=src, dst=dst)/TCP(dport=dport, sport=ans[TCP].dport, seq=ans[TCP].ack, ack=ans[TCP].seq + 1, flags='A')/Raw(load=get)\n\tans = sr1(syn)\n\tprint(ans.show())", "sub_path": "hw3/problem2_b.py", "file_name": "problem2_b.py", "file_ext": "py", "file_size_in_byte": 967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}]}
+{"seq_id": "147678173", "text": "from django.urls import path\nfrom api import views\n\nurlpatterns = [\n path('login/', views.login),\n path('logout/', views.logout),\n path('products/', views.ProductListsAPIView.as_view()),\n path('products/<int:pk>/', views.ProductListAPIView.as_view()),\n # path('user_products/', views.UserProductListsAPIView.as_view()),\n path('user_products/', views.AddProduct.as_view()),\n path('user_products/<int:pk>', views.UserProductListAPIView.as_view()),\n path('users/create/', views.UserCreateView.as_view()),\n path('users/', views.UserListsAPIView.as_view()),\n]", "sub_path": "Gulim Alkenova/final_back/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "api.views.login", "line_number": 5, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "api.views.logout", "line_number": 6, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "api.views.ProductListsAPIView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "api.views.ProductListsAPIView", "line_number": 7, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "api.views.ProductListAPIView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "api.views.ProductListAPIView", "line_number": 8, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "api.views.AddProduct.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "api.views.AddProduct", "line_number": 10, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "api.views.UserProductListAPIView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "api.views.UserProductListAPIView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "api.views.UserCreateView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "api.views.UserCreateView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "api.views.UserListsAPIView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "api.views.UserListsAPIView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 13, "usage_type": "name"}]}
+{"seq_id": "637384935", "text": "import logging\nimport datetime\nfrom logging.handlers import TimedRotatingFileHandler\n\nclass GetLoggerObj:\n\n def get_logger(self, fname,ctime):\n # Create a custom logger\n logger = logging.getLogger(fname)\n print(fname)\n # Create handlers\n #now = datetime.datetime.now()\n logfilenm = fname + ctime.strftime(\"%Y%m%d%H%M%S\") + '.log'\n print(logfilenm)\n f_handler = logging.FileHandler(logfilenm, mode='a')\n\n # Create formatters and add it to handlers\n f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n f_handler.setFormatter(f_format)\n\n #---Add handlers to the logger, must clear old handlers, otherwise see duplicate entries in logs****\n if (logger.hasHandlers()):\n logger.handlers.clear()\n\n logger.addHandler(f_handler)\n\n #Set log level\n logger.setLevel(logging.INFO)\n\n logger.info(\"This is the logger object\")\n\n return logger\n\n \"\"\"\n Use for bill_generator, and rotate log files every day\n \"\"\"\n def get_timerotatelogger(self, fname):\n # Create a custom logger\n\n # Create handlers\n #now = datetime.datetime.now()\n logfilenm = fname + '.log'\n print(logfilenm)\n f_handler = logging.FileHandler(logfilenm, mode='a')\n\n # Create formatters and add it to handlers\n f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n f_handler = TimedRotatingFileHandler(logfilenm,\n when='midnight',\n #when='M',interval=2,\n backupCount=10)\n\n f_handler.setFormatter(f_format)\n logger = logging.getLogger(fname)\n\n #---Add handlers to the logger, must clear old handlers, otherwise see duplicate entries in logs****\n if (logger.hasHandlers()):\n logger.handlers.clear()\n\n logger.addHandler(f_handler)\n\n #Set log level\n logger.setLevel(logging.INFO)\n\n logger.info(\"This is the logger object\")\n\n return logger", "sub_path": "getlogger.py", "file_name": "getlogger.py", "file_ext": "py", "file_size_in_byte": 2164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 64, "usage_type": "attribute"}]}
+{"seq_id": "200527109", "text": "#\n# Base class for particle cracking models.\n#\nimport pybamm\n\n\nclass BaseCracking(pybamm.BaseSubModel):\n \"\"\"\n Base class for particle cracking models. See [1]_ for mechanical model (thickness\n change) and [2]_ for cracking model.\n\n Parameters\n ----------\n param : parameter class\n The parameters to use for this submodel\n domain : dict, optional\n Dictionary of either the electrode for \"Positive\" or \"Nagative\"\n\n References\n ----------\n .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2019). Electrochemical\n Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch\n Cells. Journal of The Electrochemical Society, 167(1), 013512.\n .. [2] Deshpande, R., Verbrugge, M., Cheng, Y. T., Wang, J., & Liu, P. (2012).\n Battery cycle life prediction with coupled chemical degradation and\n fatigue mechanics. Journal of the Electrochemical Society, 159(10), A1730.\n\n **Extends:** :class:`pybamm.BaseSubModel`\n \"\"\"\n\n def __init__(self, param, domain):\n super().__init__(param, domain)\n\n pybamm.citations.register(\"Ai2019\")\n pybamm.citations.register(\"Deshpande2012\")\n\n def _get_standard_variables(self, l_cr):\n domain = self.domain.lower() + \" particle\"\n if self.domain == \"Positive\":\n l_cr0 = self.param.l_cr_p_0\n else:\n l_cr0 = self.param.l_cr_n_0\n l_cr_av = pybamm.x_average(l_cr)\n variables = {\n self.domain + \" particle crack length [m]\": l_cr * l_cr0,\n self.domain + \" particle crack length\": l_cr,\n f\"X-averaged {domain} crack length\": l_cr_av,\n f\"X-averaged {domain} crack length [m]\": l_cr_av * l_cr0,\n }\n return variables\n\n def _get_mechanical_results(self, variables):\n c_s_rav = variables[\n \"R-averaged \" + self.domain.lower() + \" particle concentration\"\n ]\n c_s_surf = variables[self.domain + \" particle surface concentration\"]\n T_xav = variables[\"X-averaged cell temperature\"]\n eps_s = variables[self.domain + \" electrode active material volume fraction\"]\n\n if \"Cell thickness change [m]\" not in variables:\n cell_thickness_change = (\n T_xav * self.param.Delta_T * self.param.alpha_T_cell_dim\n ) # thermal expansion\n else:\n cell_thickness_change = variables[\"Cell thickness change [m]\"]\n\n if self.domain == \"Negative\":\n x = pybamm.standard_spatial_vars.x_n\n Omega = self.param.Omega_n\n R0 = self.param.R_n(x)\n c_scale = self.param.c_n_max\n c_0 = self.param.c_n_0\n E0 = self.param.E_n\n nu = self.param.nu_n\n L0 = self.param.L_n\n c_init = self.param.c_n_init(1)\n v_change = pybamm.x_average(\n eps_s * self.param.t_n_change(c_s_rav)\n ) - pybamm.x_average(eps_s * self.param.t_n_change(c_init))\n\n elif self.domain == \"Positive\":\n x = pybamm.standard_spatial_vars.x_p\n Omega = self.param.Omega_p\n R0 = self.param.R_p(x)\n c_scale = self.param.c_p_max\n c_0 = self.param.c_p_0\n E0 = self.param.E_p\n nu = self.param.nu_p\n L0 = self.param.L_p\n c_init = self.param.c_p_init(0)\n v_change = pybamm.x_average(\n eps_s * self.param.t_p_change(c_s_rav)\n ) - pybamm.x_average(eps_s * self.param.t_p_change(c_init))\n\n cell_thickness_change += self.param.n_electrodes_parallel * v_change * L0\n disp_surf_dim = Omega * R0 / 3 * (c_s_rav - c_0) * c_scale\n # c0 reference concentration for no deformation\n stress_r_surf_dim = 0 * E0\n stress_t_surf_dim = (\n Omega * E0 / 3.0 / (1.0 - nu) * (c_s_rav - c_s_surf) * c_scale\n )\n disp_surf = disp_surf_dim / R0\n stress_r_surf = stress_r_surf_dim / E0\n stress_t_surf = stress_t_surf_dim / E0\n stress_r_surf_av = pybamm.x_average(stress_r_surf)\n stress_t_surf_av = pybamm.x_average(stress_t_surf)\n\n return {\n self.domain + \" particle surface tangential stress\": stress_t_surf,\n self.domain + \" particle surface radial stress\": stress_r_surf,\n self.domain + \" particle surface displacement\": disp_surf,\n self.domain + \" particle surface tangential stress [Pa]\": stress_t_surf_dim,\n self.domain + \" particle surface radial stress [Pa]\": stress_r_surf_dim,\n self.domain + \" particle surface displacement [m]\": disp_surf_dim,\n \"X-averaged \"\n + self.domain.lower()\n + \" particle surface radial stress\": stress_r_surf_av,\n \"X-averaged \"\n + self.domain.lower()\n + \" particle surface radial stress [Pa]\": stress_r_surf_av * E0,\n \"X-averaged \"\n + self.domain.lower()\n + \" particle surface tangential stress\": stress_t_surf_av,\n \"X-averaged \"\n + self.domain.lower()\n + \" particle surface tangential stress [Pa]\": stress_t_surf_av * E0,\n \"Cell thickness change [m]\": cell_thickness_change,\n }\n\n def _get_standard_surface_variables(self, variables):\n \"\"\"\n A private function to obtain the standard variables which\n can be derived from the local particle crack surfaces.\n\n Parameters\n ----------\n l_cr : :class:`pybamm.Symbol`\n The crack length in electrode particles.\n a0 : :class:`pybamm.Symbol`\n Smooth surface area to volume ratio.\n\n Returns\n -------\n variables : dict\n The variables which can be derived from the crack length.\n \"\"\"\n l_cr = variables[self.domain + \" particle crack length\"]\n a0 = variables[self.domain + \" electrode surface area to volume ratio\"]\n if self.domain == \"Negative\":\n x = pybamm.standard_spatial_vars.x_n\n R0 = self.param.R_n(x)\n rho_cr = self.param.rho_cr_n\n elif self.domain == \"Positive\":\n x = pybamm.standard_spatial_vars.x_p\n R0 = self.param.R_p(x)\n rho_cr = self.param.rho_cr_p\n roughness = l_cr * 2 * rho_cr + 1 # the ratio of cracks to normal surface\n a_cr = (roughness - 1) * a0 # normalised crack surface area\n a_cr_dim = a_cr / R0 # crack surface area to volume ratio [m-1]\n\n roughness_xavg = pybamm.x_average(roughness)\n variables = {\n self.domain + \" crack surface to volume ratio [m-1]\": a_cr_dim,\n self.domain + \" crack surface to volume ratio\": a_cr,\n self.domain + \" electrode roughness ratio\": roughness,\n f\"X-averaged {self.domain.lower()} \"\n \"electrode roughness ratio\": roughness_xavg,\n }\n return variables\n", "sub_path": "pybamm/models/submodels/particle_cracking/base_cracking.py", "file_name": "base_cracking.py", "file_ext": "py", "file_size_in_byte": 6954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pybamm.BaseSubModel", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pybamm.citations.register", "line_number": 34, "usage_type": "call"}, {"api_name": "pybamm.citations", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pybamm.citations.register", "line_number": 35, "usage_type": "call"}, {"api_name": "pybamm.citations", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pybamm.x_average", "line_number": 43, "usage_type": "call"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pybamm.x_average", "line_number": 77, "usage_type": "call"}, {"api_name": "pybamm.x_average", "line_number": 79, "usage_type": "call"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pybamm.x_average", "line_number": 91, "usage_type": "call"}, {"api_name": "pybamm.x_average", "line_number": 93, "usage_type": "call"}, {"api_name": "pybamm.x_average", "line_number": 105, "usage_type": "call"}, {"api_name": "pybamm.x_average", "line_number": 106, "usage_type": "call"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pybamm.standard_spatial_vars", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pybamm.x_average", "line_number": 161, "usage_type": "call"}]}
+{"seq_id": "59662252", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport logging\nimport os\nimport sys\nfrom datetime import datetime\nfrom functools import wraps\nfrom os import path\n\nimport SPARQLWrapper\nfrom cachetools import LFUCache\nfrom rdflib import URIRef\nfrom splendid import make_dirs_for\nfrom splendid import timedelta_to_s\n\nfrom flask import Flask\nfrom flask import abort\nfrom flask import jsonify\nfrom flask import request\nfrom flask_cors import CORS\n\n# noinspection PyUnresolvedReferences\nimport logging_config\n\n# not all import on top due to scoop and init...\n\nlogger = logging.getLogger(__name__)\napp = Flask(__name__)\nCORS(app)\n\n\n@app.route(\"/api/ping\", methods=[\"GET\"])\ndef ping():\n return jsonify({\n 'success': True\n })\n\n\n@app.route(\"/api/graph_patterns\", methods=[\"GET\"])\ndef graph_patterns():\n global GPS_DICT\n if not GPS_DICT:\n GPS_DICT = {\n 'graph_patterns': [\n {\n k: v\n for k, v in gp.to_dict().items()\n if k in (\n 'fitness',\n 'fitness_weighted',\n 'fitness_description',\n 'sparql',\n 'graph_triples',\n # 'matching_node_pairs',\n # 'gtp_precisions',\n 'prefixes',\n )\n }\n for gp in GPS\n ],\n }\n return jsonify(GPS_DICT)\n\n\n@app.route(\"/api/predict\", methods=[\"POST\"])\ndef predict():\n source = request.form.get('source')\n # logger.info(request.data)\n # logger.info(request.args)\n # logger.info(request.form)\n if not source:\n abort(400, 'no source given')\n logger.info('predicting: %s', source)\n source = URIRef(source)\n\n return jsonify(PREDICT_CACHE[source])\n\n\ndef _predict(source):\n from gp_query import calibrate_query_timeout\n from predict import predict\n timeout = TIMEOUT if TIMEOUT > 0 else calibrate_query_timeout(SPARQL)\n return predict(\n SPARQL, timeout, GPS, source,\n FUSION_METHODS, MAX_RESULTS, MAX_TARGET_CANDIDATES_PER_GP)\n\n\n@app.route(\"/api/feedback\", methods=[\"POST\"])\ndef feedback():\n # TODO: add timestamps, ips, log to different file\n fb = {\n 'source': request.form.get('source'),\n 'target': request.form.get('target'),\n 'feedback': request.form.get('feedback') == 'true',\n 'fusion_method': request.form.get('fusion_method'),\n 'rank': int(request.form.get('rank')),\n }\n logger.info('received feedback: %s', json.dumps(fb))\n res = {\n 'success': True,\n 'msg': 'thanks ;)',\n }\n return jsonify(res)\n\n\ndef parse_args():\n import argparse\n\n parser = argparse.ArgumentParser(\n description='gp learner prediction model server',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n # flask settings\n parser.add_argument(\n \"--host\",\n help=\"listen on IP\",\n action=\"store\",\n default=\"0.0.0.0\",\n )\n parser.add_argument(\n \"--port\",\n help=\"port to listen on\",\n action=\"store\",\n default=\"8080\",\n )\n parser.add_argument(\n \"--flask_debug\",\n help=\"flask debug mode\",\n action=\"store_true\",\n default=False,\n )\n\n # gp learner settings\n parser.add_argument(\n \"--resdir\",\n help=\"result directory of the model to serve (overrides --RESDIR)\",\n action=\"store\",\n required=True,\n )\n\n parser.add_argument(\n \"--sparql_endpoint\",\n help=\"the SPARQL endpoint to query\",\n action=\"store\",\n default=config.SPARQL_ENDPOINT,\n )\n\n parser.add_argument(\n \"--associations_filename\",\n help=\"ground truth source target file used for training and evaluation\",\n action=\"store\",\n default=config.GT_ASSOCIATIONS_FILENAME,\n )\n\n parser.add_argument(\n \"--max_queries\",\n help=\"limits the amount of queries per prediction (0: no limit)\",\n action=\"store\",\n type=int,\n default=100,\n )\n\n parser.add_argument(\n \"--clustering_variant\",\n help=\"if specified use this clustering variant for query reduction, \"\n \"otherwise select the best from various.\",\n action=\"store\",\n type=str,\n default=None,\n )\n\n parser.add_argument(\n \"--print_query_patterns\",\n help=\"print the graph patterns which are used to make predictions\",\n action=\"store_true\",\n default=False,\n )\n\n parser.add_argument(\n \"--fusion_methods\",\n help=\"Which fusion methods to train / use. During prediction, each of \"\n \"the learned patterns can generate a list of target candidates. \"\n \"Fusion allows to re-combine these into a single ranked list of \"\n \"predicted targets. By default this will train and use all \"\n \"implemented fusion methods. Any of them, or a ',' delimited list \"\n \"can be used to reduce the output (just make sure you ran \"\n \"--predict=train_set on them before). Also supports 'basic' and \"\n \"'classifier' as shorthands.\",\n action=\"store\",\n type=str,\n default=None,\n )\n\n # serve specific configs\n parser.add_argument(\n \"--timeout\",\n help=\"sets the timeout in seconds for each query (0: auto calibrate)\",\n action=\"store\",\n type=float,\n default=.5,\n )\n parser.add_argument(\n \"--max_results\",\n help=\"limits the result list lengths to save bandwidth (0: no limit)\",\n action=\"store\",\n type=int,\n default=100,\n )\n parser.add_argument(\n \"--max_target_candidates_per_gp\",\n help=\"limits the target candidate list lengths to save bandwidth \"\n \"(0: no limit)\",\n action=\"store\",\n type=int,\n default=100,\n )\n parser.add_argument(\n \"--predict_cache_size\",\n help=\"how many prediction results to cache\",\n action=\"store\",\n type=int,\n default=1000,\n )\n\n cfg_group = parser.add_argument_group(\n 'Advanced config overrides',\n 'The following allow overriding default values from config/defaults.py'\n )\n config.arg_parse_config_vars(cfg_group)\n\n prog_args = vars(parser.parse_args())\n # the following were aliased above, make sure they're updated globally\n prog_args.update({\n 'SPARQL_ENDPOINT': prog_args['sparql_endpoint'],\n 'GT_ASSOCIATIONS_FILENAME': prog_args['associations_filename'],\n 'RESDIR': prog_args['resdir'],\n })\n config.finalize(prog_args)\n\n return prog_args\n\n\ndef init(**kwds):\n from gp_learner import main\n return main(**kwds)\n\n\nif __name__ == \"__main__\":\n logger.info('init run: origin')\n import config\n prog_kwds = parse_args()\n SPARQL, GPS, FUSION_METHODS = init(**prog_kwds)\n\n TIMEOUT = prog_kwds['timeout']\n MAX_RESULTS = prog_kwds['max_results']\n MAX_TARGET_CANDIDATES_PER_GP = prog_kwds['max_target_candidates_per_gp']\n GPS_DICT = None\n PREDICT_CACHE = LFUCache(prog_kwds['predict_cache_size'], _predict)\n if prog_kwds['flask_debug']:\n logger.warning('flask debugging is active, do not use in production!')\n app.run(\n host=prog_kwds['host'],\n port=prog_kwds['port'],\n debug=prog_kwds['flask_debug'],\n )\nelse:\n logger.info('init run: worker')\n", "sub_path": "serve.py", "file_name": "serve.py", "file_ext": "py", "file_size_in_byte": 7570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 32, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 76, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 80, "usage_type": "call"}, {"api_name": "gp_query.calibrate_query_timeout", "line_number": 86, "usage_type": "call"}, {"api_name": "predict.predict", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 113, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 115, "usage_type": "attribute"}, {"api_name": "gp_learner.main", "line_number": 250, "usage_type": "call"}, {"api_name": "cachetools.LFUCache", "line_number": 263, "usage_type": "call"}]}
+{"seq_id": "538639331", "text": "import elasticsearch\nimport csv\nfrom nboost.benchmark.benchmarker import Benchmarker\nfrom nboost.base.helpers import *\nfrom nboost import PKG_PATH\n\nREQUEST_TIMEOUT = 10000\n\n\nclass MsMarco(Benchmarker):\n \"\"\"MSMARCO dataset benchmarker\"\"\"\n DEFAULT_URL = ('https://msmarco.blob.core.windows.net'\n '/msmarcoranking/collectionandqueries.tar.gz')\n BASE_DATASET_DIR = PKG_PATH.joinpath('.cache/datasets/ms_marco')\n\n def __init__(self, args):\n super().__init__(args)\n if not args.url:\n self.url = self.DEFAULT_URL\n else:\n self.url = args.url\n archive_file = self.url.split('/')[-1]\n archive_name = archive_file.split('.')[0]\n self.dataset_dir = self.BASE_DATASET_DIR.joinpath(archive_name)\n self.tar_gz_path = self.dataset_dir.joinpath(archive_file)\n self.qrels_tsv_path = self.dataset_dir.joinpath('qrels.dev.small.tsv')\n self.queries_tsv_path = self.dataset_dir.joinpath('queries.dev.tsv')\n self.collections_tsv_path = self.dataset_dir.joinpath('collection.tsv')\n self.index = 'ms_marco_' + archive_name\n\n # DOWNLOAD MSMARCO\n if not self.dataset_dir.exists():\n self.dataset_dir.mkdir(parents=True, exist_ok=True)\n self.logger.info('Dowloading MSMARCO to %s' % self.tar_gz_path)\n download_file(self.url, self.tar_gz_path)\n self.logger.info('Extracting MSMARCO')\n extract_tar_gz(self.tar_gz_path, self.dataset_dir)\n self.tar_gz_path.unlink()\n\n self.proxy_es = Elasticsearch(\n host=self.args.host,\n port=self.args.port,\n timeout=REQUEST_TIMEOUT)\n self.direct_es = Elasticsearch(\n host=self.args.uhost,\n port=self.args.uport,\n timeout=REQUEST_TIMEOUT)\n\n collection_size = 0\n with open(self.collections_tsv_path) as collection:\n for _ in collection: collection_size += 1\n\n # INDEX MSMARCO\n try:\n if self.direct_es.count(index=self.index)['count'] < collection_size:\n raise elasticsearch.exceptions.NotFoundError\n except elasticsearch.exceptions.NotFoundError:\n try:\n self.direct_es.indices.create(index=self.index, body={\n 'settings': {\n 'index': {\n 'number_of_shards': args.shards\n }\n }\n })\n except: pass\n self.logger.info('Indexing %s' % self.collections_tsv_path)\n es_bulk_index(self.direct_es, self.stream_msmarco_full())\n\n self.logger.info('Reading %s' % self.qrels_tsv_path)\n with self.qrels_tsv_path.open() as file:\n qrels = csv.reader(file, delimiter='\\t')\n for qid, _, doc_id, _ in qrels:\n self.add_qrel(qid, doc_id)\n\n self.logger.info('Reading %s' % self.queries_tsv_path)\n with self.queries_tsv_path.open() as file:\n queries = csv.reader(file, delimiter='\\t')\n for qid, query in queries:\n self.add_query(qid, query)\n\n def stream_msmarco_full(self):\n self.logger.info('Optimizing streamer...')\n num_lines = sum(1 for _ in self.collections_tsv_path.open())\n with self.collections_tsv_path.open() as fh:\n data = csv.reader(fh, delimiter='\\t')\n with tqdm(total=num_lines, desc='INDEXING MSMARCO') as pbar:\n for ident, passage in data:\n body = dict(_index=self.index,\n _id=ident, _source={'passage': passage})\n yield body\n pbar.update()\n\n def proxied_doc_id_producer(self, query: str):\n return self.es_doc_id_producer(self.proxy_es, query)\n\n def direct_doc_id_producer(self, query: str):\n return self.es_doc_id_producer(self.direct_es, query)\n\n def es_doc_id_producer(self, es: Elasticsearch, query: str):\n body = dict(\n size=self.args.topk,\n query={\"match\": {\"passage\": {\"query\": query}}})\n\n res = es.search(\n index=self.index,\n body=body,\n filter_path=['hits.hits._*'])\n\n doc_ids = [hit['_id'] for hit in res['hits']['hits']]\n return doc_ids\n\n\n", "sub_path": "nboost/benchmark/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 4336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "nboost.benchmark.benchmarker.Benchmarker", "line_number": 10, "usage_type": "name"}, {"api_name": "nboost.PKG_PATH.joinpath", "line_number": 14, "usage_type": "call"}, {"api_name": "nboost.PKG_PATH", "line_number": 14, "usage_type": "name"}, {"api_name": "elasticsearch.exceptions", "line_number": 56, "usage_type": "attribute"}, {"api_name": "elasticsearch.exceptions", "line_number": 57, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 72, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 78, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 86, "usage_type": "call"}]}
+{"seq_id": "359020884", "text": "import datetime\n\nfrom dateutil.relativedelta import relativedelta\nfrom django.contrib.auth.models import User, Group\nfrom paypal.standard.ipn.signals import valid_ipn_received\nfrom paypal.standard.models import ST_PP_COMPLETED\n\nfrom .models import UserProfile\n\n\ndef show_me_the_money(sender, **kwargs):\n ipn_obj = sender\n\n if ipn_obj.payment_status == ST_PP_COMPLETED:\n # WARNING !\n # Check that the receiver email is the same we previously\n # set on the `business` field. (The user could tamper with\n # that fields on the payment form before it goes to PayPal)\n if ipn_obj.receiver_email != \"prakhar11509@gmail.com\":\n # Not a valid payment\n return\n\n # ALSO: for the same reason, you need to check the amount\n # received, `custom` etc. are all what you expect or what\n # is allowed.\n\n # add user to Pro group\n pro_group = Group.objects.get(name=\"Pro\")\n\n user = User.objects.get(id=ipn_obj.custom)\n pro_group.user_set.add(user)\n print(pro_group.user_set)\n\n # add a user Profile model (to handle expiry)\n profile = UserProfile(user_id=ipn_obj.custom)\n profile.expiry_date = datetime.datetime.today() + relativedelta(months=+3)\n profile.plan = 1\n profile.save()\n\n\nvalid_ipn_received.connect(show_me_the_money)\n\n", "sub_path": "resumeroot/app_core/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 1362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "paypal.standard.models.ST_PP_COMPLETED", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 28, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 30, "usage_type": "name"}, {"api_name": "models.UserProfile", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "paypal.standard.ipn.signals.valid_ipn_received.connect", "line_number": 41, "usage_type": "call"}, {"api_name": "paypal.standard.ipn.signals.valid_ipn_received", "line_number": 41, "usage_type": "name"}]}
+{"seq_id": "275813205", "text": "\n# A very simple Flask Hello World app for you to get started with...\n\nfrom flask import Flask, redirect, render_template, request, url_for\nfrom gmaps_interface import *\n\napp = Flask(__name__)\napp.config[\"DEBUG\"] = True\n\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef index():\n if request.method == \"GET\":\n # print('newget')\n return render_template(\"index.html\")\n\n if request.method == 'POST':\n print('post')\n input_origin = request.form[\"startingpoint\"]\n input_destination = request.form[\"endingpoint\"]\n input_remaining_miles = request.form[\"remaininggas\"]\n input_optimize = request.form[\"prefer\"]\n input_fuel_type = request.form[\"type\"]\n\n # print(input_origin,input_destination,input_remaining_miles,input_optimize,input_fuel_type)\n optimal_gas_station = calculate_optimal_gas_station(input_origin, input_destination, input_optimize, input_fuel_type, input_remaining_miles)\n embed = get_embed_string(input_origin, input_destination, optimal_gas_station)\n # print('embed',embed)\n return render_template(\"result.html\", embed = embed)\n # return render_template(\"res.html\", input_origin = input_origin)\n\n # db.session.add(comment)\n # db.session.commit()\n return redirect(url_for('index'))\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000, debug=True)\n", "sub_path": "flask_app.py", "file_name": "flask_app.py", "file_ext": "py", "file_size_in_byte": 1378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "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": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "614986743", "text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Post\nfrom .forms import PostForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils import timezone\n\ndef is_in_group(user, group_name):\n return user.groups.filter(name = group_name).exists()\n \ndef user_can_edit_post(request, post):\n wrote_the_post = post.author == request.user\n is_editor = is_in_group(request.user, 'editors')\n superuser = request.user.is_superuser\n return wrote_the_post or superuser or is_editor\n \n\n \n\n\n# Create your views here.\ndef index(request):\n posts = Post.objects.filter(published_date__lte = timezone.now())\n return render(request, 'blog/index.html', {\"posts\":posts})\n \ndef read_post(request, id):\n post = get_object_or_404(Post, pk=id)\n post.views += 1\n post.save()\n can_edit = user_can_edit_post(request, post)\n return render(request, 'blog/read_post.html', {\"post\": post, \"can_edit\" : can_edit})\n\n@login_required \ndef write_post(request):\n if request.method == \"POST\":\n form = PostForm(request.POST, request.FILES)\n post = form.save(commit=False)\n post.author = request.user\n post.save()\n return redirect(\"read_post\", post.id )\n form = PostForm()\n return render(request, 'blog/post_form.html', {\"form\":form})\n \ndef edit_post(request, id):\n post = get_object_or_404(Post, pk=id)\n if request.method == \"POST\":\n form = PostForm(request.POST, request.FILES, instance=post)\n form.save()\n return redirect(\"read_post\", id=post.id )\n form = PostForm(instance=post)\n return render(request, 'blog/post_form.html', {\"post\": post, \"form\":form})\n \ndef get_unpublished_posts(request):\n posts = Post.objects.filter(published_date__isnull = True)\n return render(request, 'blog/index.html', {\"posts\":posts})", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "models.Post.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 44, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}]}
+{"seq_id": "408892307", "text": "from django.core.exceptions import ValidationError\nfrom django.test import TestCase\n\nfrom django_boost.utils.functions import loopfirst, loopfirstlast, looplast\nfrom django_boost.validators import (\n validate_color_code, validate_json, validate_uuid4)\n# Create your tests here.\n\n\nclass UtilFunctionTest(TestCase):\n\n test_list0 = []\n test_list1 = [0]\n test_list2 = [0, 1]\n test_list3 = [0, 1, 2]\n\n def test_loopfirst(self):\n collect = [True, False, False]\n for is_first, v in loopfirst(self.test_list0):\n self.assertEqual(collect[v], is_first)\n for is_first, v in loopfirst(self.test_list1):\n self.assertEqual(collect[v], is_first)\n for is_first, v in loopfirst(self.test_list2):\n self.assertEqual(collect[v], is_first)\n for is_first, v in loopfirst(self.test_list3):\n self.assertEqual(collect[v], is_first)\n\n def test_looplast(self):\n for is_last, v in looplast(self.test_list0):\n self.assertEqual([True][v], is_last)\n for is_last, v in looplast(self.test_list1):\n self.assertEqual([True][v], is_last)\n for is_last, v in looplast(self.test_list2):\n self.assertEqual([False, True][v], is_last)\n for is_last, v in looplast(self.test_list3):\n self.assertEqual([False, False, True][v], is_last)\n\n def test_loopfirstlast(self):\n for is_first_or_last, v in loopfirstlast(self.test_list0):\n self.assertEqual([True][v], is_first_or_last)\n for is_first_or_last, v in loopfirstlast(self.test_list1):\n self.assertEqual([True][v], is_first_or_last)\n for is_first_or_last, v in loopfirstlast(self.test_list2):\n self.assertEqual([True, True][v], is_first_or_last)\n for is_first_or_last, v in loopfirstlast(self.test_list3):\n self.assertEqual([True, False, True][v], is_first_or_last)\n\n\nclass ValidatorTest(TestCase):\n\n def test_validate_color_code(self):\n with self.assertRaises(ValidationError):\n validate_color_code(\"00FF11\")\n\n def test_validate_json(self):\n with self.assertRaises(ValidationError):\n validate_json('{\"a\":\"apple\",}')\n\n def test_validate_uuid4(self):\n with self.assertRaises(ValidationError):\n validate_uuid4(\"59cF05e3-fb29-4be8-af18-da9c94b1964d\")\n", "sub_path": "django_boost/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.test.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "django_boost.utils.functions.loopfirst", "line_number": 19, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirst", "line_number": 21, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirst", "line_number": 23, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirst", "line_number": 25, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.looplast", "line_number": 29, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.looplast", "line_number": 31, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.looplast", "line_number": 33, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.looplast", "line_number": 35, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirstlast", "line_number": 39, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirstlast", "line_number": 41, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirstlast", "line_number": 43, "usage_type": "call"}, {"api_name": "django_boost.utils.functions.loopfirstlast", "line_number": 45, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 49, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 52, "usage_type": "argument"}, {"api_name": "django_boost.validators.validate_color_code", "line_number": 53, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 56, "usage_type": "argument"}, {"api_name": "django_boost.validators.validate_json", "line_number": 57, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 60, "usage_type": "argument"}, {"api_name": "django_boost.validators.validate_uuid4", "line_number": 61, "usage_type": "call"}]}
+{"seq_id": "232699049", "text": "import boto3\r\nimport json\r\nimport os\r\n\r\nregionlist = []\r\n\r\ndef aws_session(role_arn=None, session_name='my_session'):\r\n \"\"\"\r\n If role_arn is given assumes a role and returns boto3 session\r\n otherwise return a regular session with the current IAM user/role\r\n \"\"\"\r\n if role_arn:\r\n client = boto3.client('sts')\r\n response = client.assume_role(RoleArn=role_arn, RoleSessionName=session_name)\r\n session = boto3.Session(\r\n aws_access_key_id=response['Credentials']['AccessKeyId'],\r\n aws_secret_access_key=response['Credentials']['SecretAccessKey'],\r\n aws_session_token=response['Credentials']['SessionToken'])\r\n return session\r\n else:\r\n return boto3.Session()\r\n \r\n\r\ndef lambda_handler(event, context):\r\n regionlist.clear()\r\n eventbusarn = \"\"\r\n secDevopsArn = os.environ['secdevopsid']\r\n if 'accountId' in event:\r\n eventbusarn=\"arn:aws:iam::{}:root\".format(event['accountId'])\r\n else:\r\n raise ValueError(\"No accountID provided\")\r\n if 'secDevopsArn' in event:\r\n secDevopsArn = event['secDevopsArn']\r\n accountId = str(event['accountId'])\r\n \r\n ROLE_ARN = 'arn:aws:iam::' + secDevopsArn +':role/TSI_Base_EventBusHandlerRole'\r\n \r\n session_assumed = aws_session(role_arn=ROLE_ARN, session_name='eventBusLambda')\r\n \r\n ec2client = session_assumed.client('ec2')\r\n ec2regionlist = ec2client.describe_regions()['Regions']\r\n for region in ec2regionlist:\r\n regionlist.append(region['RegionName'])\r\n \r\n print(regionlist)\r\n \r\n for region in regionlist:\r\n found = False\r\n client = session_assumed.client('events',region_name=region)\r\n response = client.describe_rule(Name='S3_DPC_Enforce_Baseline')\r\n eventrule = json.loads(response['EventPattern'])\r\n #print (eventrule['account'])\r\n for account in eventrule['account']:\r\n if(account==accountId):\r\n found = True\r\n print(\"Account found in {}\".format(region))\r\n break\r\n if(found==False):\r\n print(\"Account not found in {}\".format(region))\r\n eventrule['account'].append(accountId)\r\n client.put_rule(Name = 'S3_DPC_Enforce_Baseline',\r\n EventPattern = json.dumps(eventrule),\r\n State='ENABLED',\r\n Description='Enforce s3 bucket policy on customer accounts')\r\n \r\n \r\n", "sub_path": "Virago/src/lambda/addAccountCWDPCRule/lambda_function.py", "file_name": "lambda_function.py", "file_ext": "py", "file_size_in_byte": 2501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "boto3.client", "line_number": 13, "usage_type": "call"}, {"api_name": "boto3.Session", "line_number": 15, "usage_type": "call"}, {"api_name": "boto3.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}]}
+{"seq_id": "205692037", "text": "\"\"\"\n ----------------------------------------------------\n @Author: tsukasa\n @Affiliation: Waseda University\n @Email: rinsa@suou.waseda.jp\n @Date: 2019-05-16 23:10:07\n @Last Modified by: Tsukasa Nozawa\n @Last Modified time: 2019-05-19 04:18:04\n ----------------------------------------------------\n\n \"Chapter 7: Diffuse Materials\"\n --> with gamma correction\n\n\"\"\"\n\nprint(__doc__)\n\nimport sys\nimport math\nimport time\nimport random\n\n## my class\nfrom utils.vector import Vec3\nfrom raytracer.ray import Ray\nfrom utils.progress import Progress\nfrom raytracer.camera import SimpleCamera\nargvs = sys.argv\n\n\n\nclass HitRecord:\n def __init__(self, t = None, p = None, normal = None):\n \"\"\"\n :type t: float\n :type p: Vec3\n :type normal: Vec3\n \"\"\"\n self.t = t\n self.p = p\n self.normal = normal\n\n\n\nclass Hitable:\n def hit(self, r, t_min, t_max):\n \"\"\"\n :type r: Ray\n :type t_min: float\n :type t_max: float\n :rtype: HitRecord\n \"\"\"\n raise NotImplementedError()\n\n\n\nclass HitableList(Hitable):\n def __init__(self, l):\n self.list = l\n\n def hit(self, r, t_min, t_max):\n closest_so_far = t_max\n rec = None\n for h in self.list:\n temp_rec = h.hit(r, t_min, closest_so_far)\n if(temp_rec):\n closest_so_far = temp_rec.t\n rec = temp_rec\n return rec\n\n\n\nclass Sphere(Hitable):\n def __init__(self, center, radius):\n \"\"\"\n :type center: Vec3\n :type radius: float\n \"\"\"\n self.center = center\n self.radius = radius\n\n def hit(self, r, t_min, t_max):\n oc = r.origin - self.center\n a = Vec3.dot(r.direction, r.direction)\n b = Vec3.dot(oc, r.direction)\n c = Vec3.dot(oc, oc) - self.radius * self.radius\n discriminant = b*b - a*c\n\n if(discriminant > 0.0):\n rec = HitRecord()\n\n temp = (-b - math.sqrt(discriminant)) / a\n if(t_min < temp < t_max):\n rec.t = temp\n rec.p = r.point_at_parmeter(rec.t)\n rec.normal = (rec.p - self.center) / self.radius\n return rec\n\n temp = (-b + math.sqrt(discriminant)) / a\n if(t_min < temp < t_max):\n rec.t = temp\n rec.p = r.point_at_parmeter(rec.t)\n rec.normal = (rec.p - self.center) / self.radius\n return rec\n\n return None\n\ndef random_in_unit_sphere():\n \n while True:\n rand_vec = Vec3(random.random(), random.random(), random.random())\n p = rand_vec * 2.0 - Vec3(1.0, 1.0, 1.0)\n \n if(Vec3.squared_length(p) < 1.0):\n return p\n\n\n\ndef color(r, world):\n\n hit_record = world.hit(r, 0.001, sys.float_info.max)\n\n if(hit_record):\n target = hit_record.p + hit_record.normal + random_in_unit_sphere()\n return 0.5 * color(Ray(hit_record.p, target - hit_record.p), world)\n \n else:\n unit_direction = Vec3.unit_vector(r.direction)\n t = 0.5 * (unit_direction.y + 1.0)\n return (1.0 - t)*Vec3(1.0, 1.0, 1.0) + t*Vec3(0.5, 0.7, 1.0)\n\n\n\n\n\n\nif __name__ == '__main__':\n \n ## set image config\n outpath = argvs[1]\n width = int(argvs[2])\n height = int(argvs[3])\n nsample = int(argvs[4])\n start = time.time()\n\n\n ## set parameter\n cam = SimpleCamera()\n\n\n ## set objects\n world = HitableList([Sphere(Vec3(0.0, 0.0, -1.0), 0.5), \n Sphere(Vec3(0.0, -100.5, -1.0), 100)])\n\n\n count = 1\n total = width * height * nsample\n print(\"start rendering.....\")\n\n with open(outpath, mode='w') as f:\n f.write(\"P3\\n\")\n f.write(\"{} {}\\n\".format(width, height))\n f.write(\"255\\n\")\n\n for i in range(height)[::-1]:\n for j in range(width):\n\n rgb = Vec3(0.0, 0.0, 0.0)\n for s in range(nsample):\n Progress(count, total).progress_bar()\n count += 1\n u = float(j + random.random()) / float(width)\n v = float(i + random.random()) / float(height)\n r = cam.get_ray(u, v)\n rgb += color(r, world)\n\n rgb /= nsample\n ir = int(255.99 * math.sqrt(rgb.r))\n ig = int(255.99 * math.sqrt(rgb.g))\n ib = int(255.99 * math.sqrt(rgb.b))\n f.write(\"{} {} {}\\n\".format(ir, ig, ib))\n\n end = time.time()\n print(\"\\ndone!! : {}[sec]\".format(round(end - start, 3)))\n print(\"save rendered image as: {}\".format(outpath))", "sub_path": "ch7-b.py", "file_name": "ch7-b.py", "file_ext": "py", "file_size_in_byte": 4150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.argv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.vector.Vec3.dot", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 84, "usage_type": "name"}, {"api_name": "utils.vector.Vec3.dot", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 85, "usage_type": "name"}, {"api_name": "utils.vector.Vec3.dot", "line_number": 86, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 86, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 92, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 111, "usage_type": "call"}, {"api_name": "random.random", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.vector.Vec3.squared_length", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 114, "usage_type": "name"}, {"api_name": "sys.float_info", "line_number": 121, "usage_type": "attribute"}, {"api_name": "raytracer.ray.Ray", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.vector.Vec3.unit_vector", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 128, "usage_type": "name"}, {"api_name": "utils.vector.Vec3", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "raytracer.camera.SimpleCamera", "line_number": 148, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 153, "usage_type": "call"}, {"api_name": "utils.vector.Vec3", "line_number": 168, "usage_type": "call"}, {"api_name": "utils.progress.Progress", "line_number": 170, "usage_type": "call"}, {"api_name": "random.random", "line_number": 172, "usage_type": "call"}, {"api_name": "random.random", "line_number": 173, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 178, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 179, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 180, "usage_type": "call"}, {"api_name": "time.time", "line_number": 183, "usage_type": "call"}]}
+{"seq_id": "113124421", "text": "\"\"\"\nfrom\nhttps://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_image_display/py_image_display.html\n\nreading and writing an image using matplotlib\n\nNote : opencv uses BGR but matplotlib uses RGB\n\n\"\"\"\n\nimport sys\nimport cv2\nfrom matplotlib import pyplot as plt\n\n\ndef main():\n\n if len(sys.argv) < 2:\n print('one positional arg. required : path to an image')\n quit()\n\n img = cv2.imread(sys.argv[1])\n img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n plt.subplot(2, 1, 1)\n plt.gca().set_title('bgr showed by matplotlib')\n plt.imshow(img)\n\n plt.subplot(2, 1, 2)\n plt.gca().set_title('rgb showed by matplotlib')\n plt.imshow(img_rgb)\n plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "opencv/python/e2.py", "file_name": "e2.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]}
+{"seq_id": "516652709", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport mysql.connector\nimport datetime\n\n#search 5 comment for a moment\n#return 0 for error\ndef searchComment(momentId, startPoint, addPoint, cnx):\n commentQuery = 'SELECT * FROM moment_comment WHERE moment_id = %s ORDER BY comment_id DESC LIMIT %s, 5'\n pin = startPoint * 5 + addPoint\n try:\n commentCursor = cnx.cursor(dictionary=True)\n commentCursor.execute(commentQuery, (momentId, pin))\n return commentCursor.fetchall()\n #return 0 for db error\n except mysql.connector.Error as err:\n print('Something went wrong: {}'.format(err))\n return '0'\n finally:\n commentCursor.close()\n\n#insert one comment\n#return 1 for success\n#return 0 for error\ndef createComment(userId, momentId, content, cnx):\n create = datetime.datetime.now().date()\n insertQuery = (\n 'INSERT INTO moment_comment (comment_content, moment_id, user_id, comment_time) VALUES '\n '(%s, %s, %s, %s)'\n )\n try:\n insertCursor = cnx.cursor()\n insertCursor.execute(insertQuery, (content, momentId, userId, create))\n cnx.commit()\n return '1'\n except mysql.connector.Error as err:\n print('Something went wrong: {}'.format(err))\n cnx.rollback()\n return '0'\n finally:\n insertCursor.close()\n\n#search 20 moment id where user leave a comment\ndef userComments(userId, pin, cnx):\n likeQuery = 'SELECT DISTINCT(moment_id) FROM moment_comment WHERE user_id = %s ORDER by moment_id DESC LIMIT %s, 20'\n try:\n likeCursor = cnx.cursor()\n likeCursor.execute(likeQuery, (userId, pin))\n return likeCursor.fetchall()\n #return 0 for db error\n except mysql.connector.Error as err:\n print('Something went wrong: {}'.format(err))\n return '0'\n finally:\n likeCursor.close()\n", "sub_path": "handlers/comment.py", "file_name": "comment.py", "file_ext": "py", "file_size_in_byte": 1850, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "mysql.connector.connector", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mysql.connector.connector", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 36, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 51, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 51, "usage_type": "name"}]}
+{"seq_id": "628156948", "text": "from odoo import models, fields, api\r\nimport base64\r\nimport logging\r\nimport time\r\n\r\n_logger = logging.getLogger(__name__)\r\n\r\n\r\nclass RecordActives(models.TransientModel):\r\n _name = \"libreria.record_of_actives\"\r\n _description = \"Registro de Activos\"\r\n\r\n date_year = fields.Char(string=\"Año\", size=4)\r\n\r\n state = fields.Selection([('choose', 'choose'), ('get', 'get')], default='choose')\r\n txt_filename = fields.Char('filename', readonly=True)\r\n txt_binary = fields.Binary('file', readonly=True)\r\n\r\n @api.multi\r\n def generate_file(self):\r\n # Data - Jcondori\r\n\r\n lst_account_move_line = self.env['account.asset.asset'].search([('filter_year', 'like', self.date_year)])\r\n content_txt = \"\"\r\n residual = \"\"\r\n res = \"\"\r\n rest = \"\"\r\n _estado_ope = \"\"\r\n value = \"linear\"\r\n estado_ope = \"\"\r\n _depres = \"\"\r\n pxu = \"\"\r\n alv = \"\"\r\n motivobaja = \"\"\r\n\r\n # Iterador - Jcondori\r\n for line in lst_account_move_line:\r\n\r\n # 14\r\n for imp in line.depreciation_line_ids:\r\n if imp.sequence == 1:\r\n _depres = \"%.2f\" % imp.remaining_value\r\n\r\n # 16\r\n for cat2 in line.invoice_line_ids:\r\n pxu = \"%.2f\" % sum(line.price_unit for line in\r\n line.invoice_line_ids) # DATO --- \"%.2f\" % <-- SE UTILIZA PARA REDONDEAR NUMEROS\r\n # if cat2.price_unit:\r\n # cat2.price_unit\r\n # if line.category_id.account_asset_id.company_id.id:\r\n # rest = line.category_id.account_asset_id.company_id.id\r\n\r\n # 17\r\n for alv in line.depreciation_line_ids:\r\n if alv.sequence == 2:\r\n motivobaja = \"%.2f\" % alv.depreciated_value\r\n\r\n # 28\r\n for cat3 in line.depreciation_line_ids:\r\n if cat3.sequence == 2:\r\n amortizacion = \"%.2f\" % cat3.depreciated_value\r\n\r\n # 25\r\n if line.category_id.method == value:\r\n _estado_ope = \"1\"\r\n else:\r\n _estado_ope = \"9\"\r\n\r\n # 36\r\n if line.create_date.strftime(\"%m%Y\") == time.strftime(\"%m%Y\"):\r\n estado_ope = \"1\"\r\n else:\r\n if line.create_date.strftime(\"%Y\") != time.strftime(\"%Y\"):\r\n estado_ope = \"8\"\r\n else:\r\n if int(time.strftime(\"%m\")) == int(time.strftime(\"%m\")) - 1:\r\n estado_ope = \"9\"\r\n else:\r\n estado_ope = \"1\"\r\n\r\n # por cada campo encontrado daran una linea como mostrare\r\n txt_line = \"%s|%s|%s|M%s|%s|%s|%s|%s|%s|%s|%s|%s|%s\" \\\r\n \"|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s\" \\\r\n \"|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s\" % (\r\n\r\n line.date.strftime(\"%Y%m00\") or '', # 1\r\n line.invoice_id.move_id.name or '', # 2\r\n line.seat_code or '', # 3\r\n line.seat_code or '', # 4 cbarraza (crear campo)\r\n line.product_code or '', # 5\r\n line.x_studio_cdigo_de_existencia or '', # 6 cbarraza (crear campo)\r\n line.tipo_de_act or '', # 7\r\n line.category_id.account_asset_id.code or '', # 8\r\n line.active_status or '', # 9\r\n line.category_id.name or '', # 10\r\n line.brand or '-', # 11\r\n line.model or '-', # 12\r\n line.serie or '-', # 13\r\n _depres or '', # 14 (Campo residual)\r\n '-', # 15 null\r\n pxu or '', # 16 ldelacruz (Campo Precio unitario)\r\n motivobaja or '', # 17 ldelacruz (campo motivo de baja)\r\n '0.00', # 18 null\r\n '0.00', # 19 null\r\n '0.00', # 20 null\r\n '0.00', # 21 null\r\n '0.00', # 22 null\r\n line.date.strftime(\"%d/%m/%Y\") or '', # 23\r\n line.date.strftime(\"%d/%m/%Y\") or '', # 24\r\n _estado_ope or '', # 25 jrejas\r\n '', # 26 null\r\n str(\"%.2f\" % line.category_id.method_number \\\r\n if line.category_id.method_number else 0).zfill(6), # 27\r\n amortizacion or '', # 28\r\n '0.00', # 29 null\r\n '0.00', # 30 null\r\n '0.00', # 31 null\r\n '0.00', # 32 null\r\n '0.00', # 33 null\r\n '0.00', # 34 null\r\n '0.00', # 35 null\r\n estado_ope or '' # 36 jrejas (no se encontro)\r\n )\r\n\r\n # Agregamos la linea al TXT\r\n content_txt = content_txt + \"\" + txt_line + \"\\r\\n\"\r\n\r\n self.write({\r\n 'state': 'get',\r\n 'txt_binary': base64.b64encode(content_txt.encode('ISO-8859-1')),\r\n 'txt_filename': \"Registro_Activos.txt\"\r\n })\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Registro de Activos',\r\n 'res_model': 'libreria.record_of_actives',\r\n 'view_mode': 'form',\r\n 'view_type': 'form',\r\n 'res_id': self.id,\r\n 'target': 'new'\r\n }\r\n", "sub_path": "VG/wizard/record_of_actives.py", "file_name": "record_of_actives.py", "file_ext": "py", "file_size_in_byte": 5802, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "odoo.models.TransientModel", "line_number": 9, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 9, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 13, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 15, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 16, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "odoo.fields.Binary", "line_number": 17, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 70, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 73, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 76, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 130, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 19, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "638354253", "text": "import argparse\r\nimport cv2\r\nimport os\r\nfrom pathlib import Path\r\n\r\nfrom lib.directory_processor import DirectoryProcessor\r\nfrom lib.utils import mkdir, extract_aligned_face\r\n\r\n\r\nclass ExtractProcessor(DirectoryProcessor):\r\n\r\n def __init__(self, input_dir, output_dir):\r\n super(ExtractProcessor, self).__init__(input_dir, output_dir)\r\n\r\n def extract_and_save_facial_images(self, size, padding, upper_padding=False,\r\n debug_landmarks=False):\r\n for filename, image, landmarks_and_matrices in self.read_images():\r\n try:\r\n processed = False\r\n\r\n for idx, (landmark, align_mat) in enumerate(landmarks_and_matrices):\r\n # Draws landmarks for debug\r\n if debug_landmarks is True:\r\n for x, y in landmark:\r\n cv2.circle(image, (x, y), 2, (0, 0, 255), -1)\r\n\r\n facial_image = extract_aligned_face(\r\n image, align_mat, size=size, padding=padding, upper_padding=upper_padding)\r\n fname = os.path.join(\r\n self.output_dir,\r\n '{}_{}{}'.format(\r\n Path(filename).stem, idx, Path(filename).suffix))\r\n cv2.imwrite(fname, facial_image)\r\n\r\n processed = True\r\n\r\n if processed is False:\r\n fname = os.path.join(\r\n mkdir(os.path.join(self.output_dir, 'no_face')),\r\n Path(filename).name)\r\n cv2.imwrite(fname, image)\r\n continue\r\n\r\n except Exception as e:\r\n print('Failed to extract facial image: {}. Reason: {}'.format(filename, e))\r\n\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser(description='Extract facial images')\r\n\r\n parser.add_argument('-i', '--input-dir',\r\n dest=\"input_dir\",\r\n default=\"./input/\",\r\n help=\"Input directory. A directory containing the files \\\r\n you wish to process. Defaults to './input/'\")\r\n\r\n parser.add_argument('-o', '--output-dir',\r\n dest=\"output_dir\",\r\n default=\"./output/\",\r\n help=\"Output directory. A destination to save processed \\\r\n images. Defaults to './output/'\")\r\n\r\n parser.add_argument('-s', '--size', \r\n type=int, \r\n default=256,\r\n help=\"Output image size (default: 256)\")\r\n\r\n parser.add_argument('-p', '--padding', \r\n type=int, \r\n default=0,\r\n help=\"Padding around the facial region (default: 0)\")\r\n\r\n parser.add_argument('-up', '--upper-padding',\r\n action=\"store_true\",\r\n default=False,\r\n help=\"Apply additional padding to the upper facial region.\")\r\n\r\n parser.add_argument('-dl', '--debug-landmarks',\r\n action=\"store_true\",\r\n dest=\"debug_landmarks\",\r\n default=False,\r\n help=\"Draw landmarks for debug.\")\r\n\r\n args = parser.parse_args()\r\n processor = ExtractProcessor(\r\n args.input_dir, args.output_dir)\r\n processor_args = dict(\r\n size=args.size,\r\n padding=args.padding,\r\n upper_padding=args.upper_padding,\r\n debug_landmarks=args.debug_landmarks)\r\n processor.extract_and_save_facial_images(**processor_args)\r\n processor.finalize()", "sub_path": "extract.py", "file_name": "extract.py", "file_ext": "py", "file_size_in_byte": 3711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "lib.directory_processor.DirectoryProcessor", "line_number": 10, "usage_type": "name"}, {"api_name": "cv2.circle", "line_number": 25, "usage_type": "call"}, {"api_name": "lib.utils.extract_aligned_face", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "lib.utils.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 41, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 49, "usage_type": "call"}]}
+{"seq_id": "613584795", "text": "#!/usr/bin/env python3\n\"\"\"Functions for testing the Automaton abstract base class.\"\"\"\n\nimport nose.tools as nose\n\nfrom automata.base.automaton import Automaton\n\n\ndef test_abstract_methods_not_implemented():\n \"\"\"Should raise NotImplementedError when calling abstract methods.\"\"\"\n abstract_methods = {\n '__init__': (Automaton,),\n 'validate': (Automaton,),\n 'read_input_stepwise': (Automaton, '')\n }\n for method_name, method_args in abstract_methods.items():\n with nose.assert_raises(NotImplementedError):\n getattr(Automaton, method_name)(*method_args)\n", "sub_path": "tests/test_automaton.py", "file_name": "test_automaton.py", "file_ext": "py", "file_size_in_byte": 601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "automata.base.automaton.Automaton", "line_number": 12, "usage_type": "name"}, {"api_name": "automata.base.automaton.Automaton", "line_number": 13, "usage_type": "name"}, {"api_name": "automata.base.automaton.Automaton", "line_number": 14, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 17, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 17, "usage_type": "name"}, {"api_name": "automata.base.automaton.Automaton", "line_number": 18, "usage_type": "argument"}]}
+{"seq_id": "616931025", "text": "# -*- encoding: utf-8 -*-\nfrom django.contrib import admin\nfrom .models import Entidad\nimport socket\nfrom infos_sistemas.admin import InfoSistemaAdmin\n\n\n@admin.register(Entidad)\nclass EntidadAdmin(InfoSistemaAdmin):\n\tlist_display = (\n\t\t\t\t\t 'clase_entidad', 'tipo_entidad', 'nombre','siglas', 'documento_identificacion', \n\t\t\t\t\t 'numero_documento_identificacion', 'mision', 'vision', \n\t\t\t\t\t 'fecha_creacion', 'fecha_cese', 'descripcion', 'observacion', 'logotipo', \n\t\t\t\t\t 'slug', 'fecha_registro', 'usuario_creador', 'nombre_host', 'direccion_ip',\n\t\t\t\t\t 'fecha_ultima_actualizacion', 'ultimo_usuario_editor', 'ultimo_nombre_host', 'ultimo_direccion_ip')\n\t\n\tlist_instances = True\n\tsearch_fields = ('nombre', 'numero_documento_identificacion')\n\n\tclass Meta:\n\t\tmodel = Entidad", "sub_path": "entidades/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "infos_sistemas.admin.InfoSistemaAdmin", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Entidad", "line_number": 21, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Entidad", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}]}
+{"seq_id": "565293424", "text": "from django.db import models\n\nclass OrderCard(models.Model):\n \n contact_id = models.ForeignKey(\n 'Contact', on_delete = models.CASCADE,\n )\n order_date = models.DateField()\n ORDER_STATES = (\n (0, 'Принят в обработку'),\n (1, 'Выполнен'),\n )\n order_state = models.IntegerField(\n choices = ORDER_STATES,\n )\n", "sub_path": "iskora/shop/models/order_card.py", "file_name": "order_card.py", "file_ext": "py", "file_size_in_byte": 379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.db.models.Model", "line_number": 3, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 3, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 5, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}]}
+{"seq_id": "457278435", "text": "#!/usr/bin/python3\n\"\"\"Create .tgz archive\n\"\"\"\n\n\nfrom fabric.api import *\nfrom datetime import datetime\nimport os.path\n\n\ndef do_pack():\n \"\"\"Do pack\"\"\"\n item = datetime.now()\n time = item.strftime(\"%Y%m%d%H%M%S\")\n local(\"mkdir -p versions\")\n file_name = \"web_static_\" + time + \".tgz\"\n local(\"tar -cvzf versions/\" + file_name + \" web_static\")\n data = \"versions/\" + file_name\n if os.path.exists(data):\n print(\n \"web_static packed: {} -> {}\".format(data,\n os.path.getsize(data)))\n else:\n return None\n", "sub_path": "1-pack_web_static.py", "file_name": "1-pack_web_static.py", "file_ext": "py", "file_size_in_byte": 595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.path.getsize", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}]}
+{"seq_id": "449758499", "text": "# coding: utf-8\n\nimport datetime\nimport json\nimport sys\n\nfrom ..utils import execute\nfrom ..utils import fatal_error\nfrom .resize_im import resize_and_cache as resize_with_im\nfrom .resize_sips import resize_and_cache as resize_with_sips\n\ntry:\n from resize_pil import resize_and_cache as resize_with_pil\nexcept ImportError:\n resize_with_pil = False\n\nEXIFTOOL_AVAILABLE, output = execute(['which', 'exiftool'])\n\nEXIF_TITLE_TAGS = [\n 'ImageDescription',\n 'UserComment',\n 'Caption-Abstract',\n 'Headline',\n]\n\nEXIF_ROTATION_MAP = {\n 1: 0, # Horizontal (normal)\n 2: 0, # Mirrored horizontal\n 3: 180, # Rotated 180\n 4: 0, # Mirrored vertical\n 5: 270, # Mirrored horizontal then rotated 90 CCW\n 6: 90, # Rotated 90 CCW\n 7: 90, # Mirrored horizontal then rotated 90 CW\n 8: 270, # Rotated 90 C\n}\n\n\ndef resize_and_cache(resize_with, *args, **kwargs):\n if not resize_with:\n # Based on performance. IM should only be set specifically\n # success, msg = execute(['convert', '--version'])\n # if success and 'ImageMagick' in msg:\n # preference = 'im'\n if 'darwin' in sys.platform:\n resize_with = 'sips'\n else:\n resize_with = 'pil'\n\n if resize_with == 'im':\n return resize_with_im(*args, **kwargs)\n elif resize_with == 'sips':\n return resize_with_sips(*args, **kwargs)\n else:\n if resize_with_pil:\n return resize_with_pil(*args, **kwargs)\n else:\n fatal_error('The Python Imaging Library cannot be found.')\n\n\ndef image_caption(raw_data, title_tag=False):\n if title_tag and raw_data.get(title_tag, False):\n return raw_data[title_tag].strip()\n for tag in EXIF_TITLE_TAGS:\n if raw_data.get(tag, False):\n return raw_data[tag].strip()\n return None\n\n\ndef image_date(raw_data):\n tstr = raw_data.get('DateTimeOriginal', None)\n if tstr is None:\n return None\n try:\n return datetime.datetime.strptime(tstr, \"%Y:%m:%d %H:%M:%S\")\n except ValueError: # Somehow the format is wrong or all zero's\n return None\n\n\ndef image_geolocation(raw_data):\n if raw_data.get('GPSLongitude', False) and \\\n raw_data.get('GPSLatitude', False):\n return [\n raw_data['GPSLatitude'],\n raw_data['GPSLongitude'],\n ]\n return (None, None)\n\n\ndef image_rotation(raw_data):\n orientation = raw_data.get('Orientation', 1)\n return EXIF_ROTATION_MAP.get(orientation, 0)\n\n\ndef image_exif_data(img_path):\n exif_data = {}\n if EXIFTOOL_AVAILABLE:\n success, raw_json = execute(['exiftool', '-j', '-n', img_path])\n if success:\n try:\n return json.loads(raw_json)[0]\n except ValueError:\n # Invalid json input. Let's pass for now\n pass\n return exif_data\n\n\ndef image_metadata(img_path, title_tag=False):\n raw_data = image_exif_data(img_path)\n return {\n 'caption': image_caption(raw_data, title_tag),\n 'rotation': image_rotation(raw_data),\n 'raw_data': raw_data,\n 'gps': image_geolocation(raw_data),\n 'date': image_date(raw_data)\n }\n", "sub_path": "htg/imaging/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3195, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "resize_pil.resize_and_cache", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.execute", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 44, "usage_type": "attribute"}, {"api_name": "resize_im.resize_and_cache", "line_number": 50, "usage_type": "call"}, {"api_name": "resize_sips.resize_and_cache", "line_number": 52, "usage_type": "call"}, {"api_name": "resize_pil.resize_and_cache", "line_number": 54, "usage_type": "name"}, {"api_name": "resize_pil.resize_and_cache", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.fatal_error", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "attribute"}, {"api_name": "utils.execute", "line_number": 97, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}]}
+{"seq_id": "348025520", "text": "#!/usr/bin/python3\n\nfrom datetime import datetime, timedelta\nfrom json import load\nfrom time import sleep\nfrom os.path import dirname, abspath\nfrom socket import create_connection\n\nfrom sensor import Sensor\nfrom file import File\nfrom mail import Mail\nfrom spreadsheet import Spreadsheet\nfrom logger import Logger\n\n# Функция, для проверки наличия интернет-соединения\ndef is_Connected(site_For_Check):\n try:\n create_connection((str(site_For_Check), 80))\n return True\n except OSError:\n return False\n\n# Path\nPath = f'{dirname(abspath(__file__))}'.rsplit('/', 1)[0]\npath_To_Temperature_Log = '/data/temperature_log.txt'\n\n# Создаем экземпляр класса Logger\nlogger = Logger.get()\n\n# Инициализируем переменные\nlast_Time_Send, last_Arch_Time = datetime.now(), datetime.now()\n# Переменные, указывающие на состояние коннекта к mail/spreadsheet\nis_Connected_To_Mail = False\nis_Connected_To_Spreadsheet = False\n# Первая ли ты, попытка законнектится?\nfirst_Try_To_Connect = True\n\n# Считываем конфигурационный файл\ntry:\n with open(f'{Path}/configs/config.json', 'r') as config_File:\n cfg = load(config_File)\nexcept FileNotFoundError:\n logger.critical('Не найден конфигурационный фа��л, положите его в папку configs и перезапустите программу')\n exit(1)\n\nsleep_Period = cfg['DHT']['Period']\nsite_For_Check = cfg['Main']['Site_for_check'].lower()\nmail_Status = cfg['email']['Mail_status']\nspreadsheet_Status = cfg['Spreadsheet']['Status']\nclear_spreadsheet_on_start = cfg['Spreadsheet']['Clear spreadsheet on start']\nsend_by_str = cfg['Spreadsheet']['Send_by_str']\nperiod_before_send = cfg['Main']['Period_before_send'].split(',')\nperiod_before_archive = cfg['Main']['Period_before_arch'].split(',')\n\n# Создаем экземляры классов\ntemp_logfile = File(\n Path, \n path_To_Temperature_Log, \n logger\n)\nsensor = Sensor(\n logger, \n cfg.get('DHT')\n)\nmail = Mail(\n temp_logfile, logger, \n cfg.get('email')\n)\nspr_sheet = Spreadsheet(\n logger, Path, \n path_To_Temperature_Log, \n cfg.get('Spreadsheet')\n)\n\n# Если есть инет, пробуем законнектится\nif is_Connected(site_For_Check) == True:\n # Инициализруем почту, если включена отправка почты\n if mail_Status is True:\n mail.login()\n is_Connected_To_Mail = True\n\n # Инициализруем гугл докс, если они включены в конфиге\n if spreadsheet_Status is True:\n # Логинимся и открываем таблицу\n spr_sheet.login()\n spr_sheet.open()\n \n # Если нужно, отчищаем гугл таблицу при старте\n if clear_spreadsheet_on_start is True:\n spr_sheet.clear()\n\n # Создаем описание колонок\n spr_sheet.create_Cols_Description()\n\n # Устанавливаем флаг коннекта к Spreadsheet API\n is_Connected_To_Spreadsheet = True\n\n # Записываем время коннекта к серверам\n last_Auth_Refresh_Time = datetime.now()\n \n first_Try_To_Connect = False\n\n# Loop\nwhile True:\n try:\n # Снимаем показания и записываем в файл\n temperature, humidity = sensor.read()\n \n logger.info(f'{datetime.now()} Температура = {temperature}' + '\\u2103 ' + f'Влажность = {humidity} %')\n temp_logfile.write_Data(temperature, humidity)\n\n # Если включена отправка на почту / в таблицу\n if is_Connected(site_For_Check) == False \\\n and (\n mail_Status is True or spreadsheet_Status is True\n ):\n # Ставим переменные в ложь для того, чтобы перелогиниться после того как появится интернет\n is_Connected_To_Mail, is_Connected_To_Spreadsheet = False, False\n\n logger.critical('Connection lost, cannot sell mail/upload data to spreadsheet, retry on next cycle') \n \n # Если не смогли залогиниться на старте или нужно перелогиниться после пропажи интернета\n if is_Connected(site_For_Check) is True:\n if mail_Status is True and is_Connected_To_Mail is False:\n mail.login()\n\n is_Connected_To_Mail, last_Auth_Refresh_Time = True, datetime.now()\n \n if spreadsheet_Status is True and is_Connected_To_Spreadsheet is False:\n spr_sheet.login()\n spr_sheet.open()\n\n # Если это первая попытка логина при неудачном логине на старте\n if clear_spreadsheet_on_start is True and first_Try_To_Connect is True:\n spr_sheet.clear()\n spr_sheet.create_Cols_Description()\n \n is_Connected_To_Spreadsheet, first_Try_To_Connect = True, False\n\n last_Auth_Refresh_Time = datetime.now()\n\n # Перелогиниваемся каждые полчаса, иначе будем получать connection timed out\n if is_Connected(site_For_Check) == True and datetime.now() >= last_Auth_Refresh_Time + timedelta(minutes = 30): \n if mail_Status is True:\n mail.login()\n last_Auth_Refresh_Time = datetime.now()\n\n if spreadsheet_Status is True:\n spr_sheet.refresh_auth()\n last_Auth_Refresh_Time = datetime.now()\n \n # Заливаем в таблицу построчно\n if spreadsheet_Status is True and send_by_str is True:\n spr_sheet.send_str(temperature, humidity)\n logger.info('Str sended to Spreadsheets')\n\n # Проверяем соединение с интернетом и пришло ли время для отправки\n if is_Connected(site_For_Check) == True:\n if datetime.now() >= last_Time_Send \\\n + timedelta(\n hours = int(\n period_before_send[0])\n , \n minutes = int(\n period_before_send[1]\n )\n ):\n\n # Отправляем на почту при необходимости \n if mail_Status is True:\n mail.send_File('Температура')\n logger.info(f'Sended to the e-mail')\n\n # Заливаем в таблицу файл, если построчная отправка выключена \n if spreadsheet_Status is True \\\n and send_by_str is False:\n spr_sheet.send_file()\n logger.info('File sended to Spreadsheets')\n\n # Если включена архивация, то сжимаем файл по прошествию n-ого количества времени\n if cfg['Main']['Archive'] is True and datetime.now() >= last_Arch_Time \\\n + timedelta(\n hours = int(period_before_archive[0])\n , \n minutes = int(period_before_archive[1])\n ):\n # Записываем время архивации\n last_Arch_Time = datetime.now()\n\n # Архивируем с помощью brotli и отчищаем сам файл после\n temp_logfile.zip_File() \n temp_logfile.clear_File()\n\n logger.info('Data compressed')\n\n # Если выбрано удаление, то просто отчищаем файл после каждой отправки\n if cfg['Main']['Delete after sending'] is True \\\n and (spreadsheet_Status is True or mail_Status is True):\n temp_logfile.clear_File()\n\n # Записываем время отправки \n last_Time_Send = datetime.now()\n\n else:\n logger.info('Dont need send anything')\n \n # Ждем сколько-то и начинаем заново\n sleep(sleep_Period)\n\n # Дабы не ловить кучу errorов при выходе\n except KeyboardInterrupt:\n exit()\n", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 9101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "socket.create_connection", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 24, "usage_type": "call"}, {"api_name": "logger.Logger.get", "line_number": 28, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "json.load", "line_number": 41, "usage_type": "call"}, {"api_name": "logger.critical", "line_number": 43, "usage_type": "call"}, {"api_name": "file.File", "line_number": 56, "usage_type": "call"}, {"api_name": "sensor.Sensor", "line_number": 61, "usage_type": "call"}, {"api_name": "mail.Mail", "line_number": 65, "usage_type": "call"}, {"api_name": "spreadsheet.Spreadsheet", "line_number": 69, "usage_type": "call"}, {"api_name": "mail.login", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "name"}, {"api_name": "sensor.read", "line_number": 107, "usage_type": "call"}, {"api_name": "logger.info", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "name"}, {"api_name": "logger.critical", "line_number": 120, "usage_type": "call"}, {"api_name": "mail.login", "line_number": 125, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 140, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 143, "usage_type": "call"}, {"api_name": "mail.login", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 146, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "name"}, {"api_name": "logger.info", "line_number": 155, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 159, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 159, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 160, "usage_type": "call"}, {"api_name": "mail.send_File", "line_number": 171, "usage_type": "call"}, {"api_name": "logger.info", "line_number": 172, "usage_type": "call"}, {"api_name": "logger.info", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 181, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 182, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 188, "usage_type": "name"}, {"api_name": "logger.info", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 202, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 202, "usage_type": "name"}, {"api_name": "logger.info", "line_number": 205, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 208, "usage_type": "call"}]}
+{"seq_id": "435980869", "text": "from datetime import datetime\n\n\nclass Log:\n def __init__(self, log_type=\".log\", log_data=[]):\n self.file_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\") + log_type\n self.log_buffer = log_data\n self.log_add(\"Running Time:\" + datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\n def log_add(self, content):\n with open(self.file_name, 'a') as file:\n file.write(str(content))\n file.write(\"\\n\\n\")\n self.log_buffer = []\n\n def log_rewrite(self, content):\n with open(self.file_name, 'w') as file:\n file.write(str(content))\n file.write(\"\\n\\n\")\n self.log_buffer = []\n", "sub_path": "develop/Detector/utils/log_helper.py", "file_name": "log_helper.py", "file_ext": "py", "file_size_in_byte": 658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "datetime.datetime.now", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}]}
+{"seq_id": "184046504", "text": "#! /usr/bin/env python3\n\nimport cv_bridge\nimport keras_ocr\nimport numpy\nimport rospy\nfrom cv2 import cv2\nfrom sensor_msgs.msg import Image\n\n\nclass ImageProcessing:\n def __init__(self, robot_controller):\n self.robot_controller = robot_controller\n self.image = None\n\n # locations of the dumbbells and blocks as (x, y) tuples.\n self.db_locs = []\n self.block_locs = []\n\n # angle of the dumbbells and blocks with respect to the origin\n self.db_thetas = []\n self.block_thetas = []\n\n # order in which dumbbells and blocks are found\n self.order_db = []\n self.order_blocks = []\n\n self.bridge = cv_bridge.CvBridge()\n self.image_sub = rospy.Subscriber(\n \"camera/rgb/image_raw\", Image, self.image_callback\n )\n\n def image_callback(self, msg):\n self.image = self.bridge.imgmsg_to_cv2(msg, desired_encoding=\"bgr8\")\n\n def find_block_order(self):\n \"\"\"Gets the order and locations in which the blocks appear.\"\"\"\n # use keras-ocr to find block order\n if not self.order_blocks:\n print(\"Finding blocks... \")\n # initialize the keras pipeline\n pipeline = keras_ocr.pipeline.Pipeline()\n\n # assembling images of the 3 blocks individually\n self.images = []\n # turn to face each block and get an image of each block\n for angle in self.block_thetas:\n self.robot_controller.turn(angle * 0.01745329)\n rospy.sleep(2)\n self.images.append(self.image)\n\n # call the recognizer on the list of images\n prediction_groups = pipeline.recognize(self.images)\n\n for i in range(3):\n word = prediction_groups[i][0][0]\n if word == \"l\":\n word = \"1\"\n if word in [\"e\", \"s\"]:\n word = \"3\"\n self.order_blocks.append(int(word))\n\n # reorder theta and dist to reflect order of blocks\n tmp_theta = self.block_thetas.copy()\n tmp_dist = self.block_dist.copy()\n i = 0\n for block in self.order_blocks:\n self.block_thetas[block - 1] = tmp_theta[i]\n self.block_dist[block - 1] = tmp_dist[i]\n i += 1\n\n # calculate x,y values for 3 blocks\n for i in range(3):\n angle = self.block_thetas[i]\n d = self.block_dist[i]\n if angle > 180:\n self.db_thetas[i] -= 360\n angle -= 360\n\n angle_rad = angle * 0.01745329\n\n # x and y coordinates of the blocks\n x = d * numpy.cos(angle_rad)\n y = d * numpy.sin(angle_rad)\n self.block_locs.append((x, y))\n print(\"Block angles: \", self.block_thetas)\n\n def find_block_thetas(self):\n \"\"\"Finds the angles from the origin at which the blocks are located.\"\"\"\n ranges = self.robot_controller.ranges\n block_num = 0\n theta = 90\n self.block_thetas = [-1, -1, -1]\n self.block_dist = [-1, -1, -1]\n while block_num < 3:\n # scan for the 3 blocks\n theta = theta % 360\n if ranges[theta] != numpy.inf:\n self.block_thetas[block_num] = (\n theta + 180 - 12.7\n ) # subtract to get to ~middle of block\n self.block_dist[block_num] = ranges[theta]\n if self.block_thetas[block_num] >= 180:\n self.block_thetas[block_num] -= 360\n block_num += 1\n theta -= 25\n theta -= 1\n\n def find_db_order(self):\n print(\"Finding dumbbell order... \")\n while self.image is None:\n print(\"waiting for image...\")\n rospy.sleep(1)\n\n # Gets centers of red, green, and blue dumbbells, in that order\n for i in range(3):\n self.order_db.append(self.get_center_for_color(i))\n\n # if one of the colors was not found, assume location is at edge of\n # image\n if -1 in self.order_db:\n ind = self.order_db.index(-1)\n avg = (sum(self.order_db) + 1) / 2\n image_width = self.image.shape[2]\n if avg > image_width / 2:\n self.order_db[ind] = 0\n else:\n self.order_db[ind] = image_width\n\n def get_center_for_color(self, color_id: int):\n \"\"\"Gets the center of the image for the given color.\n\n Parameters:\n color: 0 for red, 1 for green, 2 for blue.\n Returns:\n The x coordinate of the center of the color in the current image.\n If no pixels of the color are found, returns -1.\n \"\"\"\n image = self.image\n h, w, d1 = image.shape\n search_top = int(h / 2)\n search_bot = int(h / 2 + 1)\n color = numpy.uint8([[[0, 0, 0]]])\n color[0][0][2 - color_id] = 255\n hsvColor = cv2.cvtColor(color, cv2.COLOR_BGR2HSV)\n lower_color = numpy.array([hsvColor[0][0][0] - 10, 100, 100])\n upper_color = numpy.array([hsvColor[0][0][0] + 10, 255, 255])\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n mask = cv2.inRange(hsv, lower_color, upper_color)\n\n # Erase all pixels that aren't the correct color\n mask[0:search_top, 0:w] = 0\n mask[search_bot:h, 0:w] = 0\n\n # Determine the center of the dumbbell\n M = cv2.moments(mask)\n # Get the center of the dumbbell if color pixels are found\n if M[\"m00\"] > 0:\n # center of the colored pixels in the image\n return int(M[\"m10\"] / M[\"m00\"])\n\n return -1\n\n def find_db_locs(self):\n \"\"\"Find the location of each of the dumbbells.\"\"\"\n ranges = self.robot_controller.ranges\n while not ranges:\n ranges = self.robot_controller.ranges\n # get the initial x and y values of the dumbbells\n if self.order_db and not self.db_locs:\n print(\"getting x y...\")\n # get the order the colors appear in\n front = self.order_db.index(numpy.median(self.order_db))\n right = self.order_db.index(numpy.max(self.order_db))\n left = self.order_db.index(numpy.min(self.order_db))\n\n self.order_db[1] = front\n self.order_db[2] = right\n self.order_db[0] = left\n\n db = 0\n theta = 90\n # getting the angles of the 3 db's\n self.db_thetas = [-1, -1, -1]\n while db < 3:\n theta = theta % 360\n if ranges[theta] != numpy.inf:\n color = self.order_db[db]\n self.db_thetas[color] = theta\n db += 1\n theta -= 25\n theta -= 1\n\n # calculate x,y values for 3 dbs\n for i in range(3):\n angle = self.db_thetas[i]\n d = ranges[angle]\n if angle > 180:\n self.db_thetas[i] -= 360\n angle -= 360\n\n angle_rad = angle * 0.01745329\n\n x = d * numpy.cos(angle_rad)\n y = d * numpy.sin(angle_rad)\n self.db_locs.append((x, y)) # store the three x,y values\n print(\"DB Angles: \", self.db_thetas, \"DB locs:\", self.db_locs)\n\n def analyze_surroundings(self):\n \"\"\"Determines initial locations of blocks and dumbbells.\"\"\"\n self.find_db_order()\n self.find_db_locs()\n self.robot_controller.turn(numpy.pi)\n rospy.sleep(1)\n self.find_block_thetas()\n self.find_block_order()\n print(\"DONE\")\n", "sub_path": "scripts/image_processing.py", "file_name": "image_processing.py", "file_ext": "py", "file_size_in_byte": 7763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "cv_bridge.CvBridge", "line_number": 28, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 29, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 30, "usage_type": "argument"}, {"api_name": "keras_ocr.pipeline.Pipeline", "line_number": 42, "usage_type": "call"}, {"api_name": "keras_ocr.pipeline", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rospy.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 98, "usage_type": "attribute"}, {"api_name": "rospy.sleep", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 145, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_BGR2HSV", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 148, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_BGR2HSV", "line_number": 148, "usage_type": "attribute"}, {"api_name": "cv2.cv2.inRange", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 150, "usage_type": "name"}, {"api_name": "cv2.cv2.moments", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 214, "usage_type": "attribute"}, {"api_name": "rospy.sleep", "line_number": 215, "usage_type": "call"}]}
+{"seq_id": "319489663", "text": "import logging\nimport argparse\nimport getpass\nimport errno\nimport re\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nimport dns.resolver\n\nclass Mail(object):\n\n def __init__(self, recipient=None, sender=None, subject=None, body=None):\n self.recipient = recipient\n self.sender = sender or '{}@example.com'.format(getpass.getuser())\n self.subject = subject or 'Sir! My sir!'\n self.body = body or 'A message from their majesty.'\n self.verbose = False\n\n @property\n def domain(self):\n m = re.match(r'.+@(\\w+\\.\\w+)', self.recipient)\n if m:\n return m.group(1)\n else:\n raise ValueError('Unable to get recipient domain')\n\n @property\n def message(self):\n m = MIMEMultipart('alternative')\n m['Subject'] = self.subject\n m['From'] = self.sender\n m['To'] = self.recipient\n m.attach(MIMEText(self.body, 'plain'))\n return m\n\n def send(self):\n \"\"\"\n Sends an email to a single recipient straight to his MTA.\n Looks up for the MX DNS records of the recipient SMTP server and attempts the delivery through them.\n \"\"\"\n answers = dns.resolver.query(self.domain, 'MX')\n try:\n for answer in answers:\n ex = answer.exchange.to_text()\n server = smtplib.SMTP(ex)\n server.set_debuglevel(self.verbose)\n server.sendmail(self.sender, [self.recipient], self.message.as_string())\n server.quit()\n except OSError as e:\n if e.errno is errno.ENETUNREACH:\n print('Looks like port 25 is blocked')\n raise e\n\n\nclass App(object):\n\n def run(self):\n mail = Mail()\n self.parse(mail)\n mail.send()\n\n @classmethod\n def parse(cls, mail):\n parser = argparse.ArgumentParser(prog='lumpy', description=mail.send.__doc__)\n arg = parser.add_argument\n\n arg('--from', '-f', nargs='?', dest='sender')\n arg('recipient')\n arg('--subject', '-s', nargs='?')\n arg('--body', '-b', nargs='?')\n arg('--verbose', '-v', action='store_true')\n \n parser.parse_args(namespace=mail)\n\n\nif __name__ == \"__main__\":\n App().run()\n", "sub_path": "lumpy.py", "file_name": "lumpy.py", "file_ext": "py", "file_size_in_byte": 2308, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "getpass.getuser", "line_number": 16, "usage_type": "call"}, {"api_name": "re.match", "line_number": 23, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 31, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 35, "usage_type": "call"}, {"api_name": "dns.resolver.resolver.query", "line_number": 43, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 43, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 43, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 47, "usage_type": "call"}, {"api_name": "errno.ENETUNREACH", "line_number": 52, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 66, "usage_type": "call"}]}
+{"seq_id": "316087441", "text": "\"\"\"\"\r\n@ author rudransh \r\n\"\"\"\r\n\r\nfrom keras.layers import Input, Lambda, Dense, Flatten\r\nfrom keras.models import Model\r\nfrom keras.applications.vgg16 import VGG16\r\nfrom keras.applications.vgg16 import preprocess_input\r\nfrom keras.preprocessing import image\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\nfrom keras.models import Sequential\r\nimport numpy as np\r\nfrom glob import glob\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nX_samples = 400\r\nY_samples = 100\r\nepochs = 1\r\nbatch_size = 25\r\n# re-size all the images to this\r\nIMAGE_SIZE = [150, 150]\r\n\r\ntrain_path = 'C:/Users/hp/PlantVillage/train'\r\nvalid_path = 'C:/Users/hp/PlantVillage/test'\r\n\r\n# add preprocessing layer to the front of VGG\r\nvgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\r\n\r\n# don't train existing weights\r\nfor layer in vgg.layers:\r\n layer.trainable = False\r\n \r\n\r\n \r\n # useful for getting number of classes\r\nfolders = glob('C:/Users/hp/PlantVillage/train/*')\r\n \r\n\r\n# our layers - you can add more if you want\r\nx = Flatten()(vgg.output)\r\n# x = Dense(1000, activation='relu')(x)\r\nprediction = Dense(len(folders), activation='softmax')(x)\r\n\r\n# create a model object\r\nmodel = Model(inputs=vgg.input, outputs=prediction)\r\n\r\n# view the structure of the model\r\nmodel.summary()\r\n\r\n# tell the model what cost and optimization method to use\r\nmodel.compile(\r\n loss='categorical_crossentropy', \r\n optimizer='adam',\r\n metrics=['accuracy']\r\n)\r\n\r\n\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\ntrain_datagen = ImageDataGenerator(rescale = 1./255,\r\n shear_range = 0.2,\r\n zoom_range = 0.2,\r\n horizontal_flip = True)\r\n\r\ntest_datagen = ImageDataGenerator(rescale = 1./255)\r\n\r\ntraining_set = train_datagen.flow_from_directory('C:/Users/hp/PlantVillage/train',\r\n target_size = (150, 150),\r\n batch_size = batch_size,\r\n class_mode = 'categorical')\r\n\r\ntest_set = test_datagen.flow_from_directory('C:/Users/hp/PlantVillage/test',\r\n target_size = (150, 150),\r\n batch_size = batch_size,\r\n class_mode = 'categorical')\r\n\r\n\r\n\r\nr = model.fit_generator(\r\n training_set,\r\n steps_per_epoch = X_samples // batch_size,\r\n epochs = epochs,\r\n validation_data = test_set,\r\n validation_steps = Y_samples // batch_size)\r\n\r\n# fit the model\r\n# loss\r\nplt.plot(r.history['loss'], label='train loss')\r\nplt.plot(r.history['val_loss'], label='val loss')\r\nplt.legend()\r\nplt.show()\r\nplt.savefig('LossVal_loss')\r\n\r\n\r\n\r\nmodel_json = model.to_json()\r\nwith open(\"model.json\", \"w\") as json_file:\r\n json_file.write(model_json)\r\n# serialize weights to HDF5\r\nmodel.save_weights(\"disease_detector.h5\")\r\nprint(\"Saved model to disk\")\r\n", "sub_path": "vgg16 trained model.py", "file_name": "vgg16 trained model.py", "file_ext": "py", "file_size_in_byte": 2999, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "keras.applications.vgg16.VGG16", "line_number": 28, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}]}
+{"seq_id": "595895134", "text": "import os\nimport re\nimport glob\nimport math\n\nimport pandas\nimport rasterio\nfrom rasterio import transform\nfrom matplotlib import pyplot as plt\n\n\ndef getDataFiles(data_fps):\n \"\"\"\"\n From list of files in directory structure, return data files by type\n \"\"\"\n data_files = {\n 'centerline': [],\n 'width': [],\n 'curvature': []\n }\n for data_fp in data_fps:\n if re.match(r\".*/.*centerline.csv\", data_fp):\n data_files['centerline'].append(data_fp)\n if re.match(r\".*/.*widths.csv\", data_fp):\n data_files['width'].append(data_fp)\n if re.match(r\".*/.*curvatures.csv\", data_fp):\n data_files['curvature'].append(data_fp)\n\n return data_files\n\n\ndef buildDataFrame(ds, centerline, width, curvature):\n # Save image data to dataframe\n data = {\n 'longitude': [],\n 'latitude': [],\n 'width': [],\n 'curvature': [],\n }\n for idx, row in centerline.iterrows():\n lon, lat = ds.xy(row['row'], row['col'])\n\n data['longitude'].append(lon)\n data['latitude'].append(lat)\n data['width'].append(width.iloc[idx][0] * PIXEL_SIZE)\n data['curvature'].append(curvature.iloc[idx][0] * PIXEL_SIZE)\n\n return pandas.DataFrame(data)\n\n\ndef stackAllIdxs(year_dfs):\n \"\"\"\n Takes the list of dataframes and turns it into a single dataframe\n \"\"\"\n if len(year_dfs) == 0:\n return year_dfs[0]\n else:\n return pandas.concat(year_dfs).reset_index(drop=True)\n\n\nPIXEL_SIZE = 30 # Landsat 30m pixels\nriver = 'ica'\nsave_idx = True\n\nfor year in range(2004, 2018):\n # image_root = f'/home/greenberg/ExtraSpace/PhD/Projects/BarT/riverData/{river}/clipped/{year}/*/*.tif'\n image_root = f'/Users/greenberg/Documents/PHD/Projects/BarT/LinuxFiles/riverData/{river}/clipped/{year}/*/*.tif'\n\n # data_root = f'/home/greenberg/ExtraSpace/PhD/Projects/BarT/riverData/{river}/data/{year}/*/*.csv'\n data_root = f'/Users/greenberg/Documents/PHD/Projects/BarT/LinuxFiles/riverData/{river}/data/{year}/*/*.csv'\n\n # Get image files\n image_fps = glob.glob(image_root)\n\n # Get data files\n data_fps = glob.glob(data_root)\n data_files = getDataFiles(data_fps)\n\n if len(data_fps) == 0:\n continue\n else:\n print(year)\n\n year_dfs = []\n for idx, image_fp in enumerate(image_fps):\n print(idx)\n\n # Load the image\n ds = rasterio.open(image_fp)\n\n # Load the centerline \n centerline = pandas.read_csv(\n data_files['centerline'][idx]\n , names=['col', 'row']\n )\n\n # Load the width\n width = pandas.read_csv(\n data_files['width'][idx], \n names=['width']\n )\n\n # Load the curvature \n curvature = pandas.read_csv(\n data_files['curvature'][idx], \n names=['curvature']\n )\n curvature = curvature.drop([0, 1]).reset_index(drop=True)\n\n # Find Spacing and resample at width spacing\n spacing = int(round((len(centerline) / len(width)), 0))\n centerline = centerline.iloc[::spacing].reset_index(drop=True)\n curvature = curvature.iloc[::spacing].reset_index(drop=True)\n\n # Make sure these two dimensions match\n if len(centerline) > len(width):\n centerline = centerline.iloc[:len(width)]\n if len(centerline) > len(curvature):\n centerline = centerline.iloc[:len(curvature)]\n\n # Build dataframes and append all idxs\n year_df = buildDataFrame(ds, centerline, width, curvature)\n year_dfs.append(year_df)\n\n if save_idx:\n outpath = f'/Users/greenberg/Documents/PHD/Projects/BarT/LinuxFiles/riverData/{river}/data/{year}/idx{idx+1}/{river}_{year}_data.csv'\n year_df.to_csv(outpath)\n\n if not save_idx:\n # Stack the list of dataframes\n data_df = stackAllIdxs(year_dfs)\n\n # outpath = f'/home/greenberg/ExtraSpace/PhD/Projects/BarT/riverData/{river}/data/{year}/{river}_{year}_data.csv'\n outpath = f'/Users/greenberg/Documents/PHD/Projects/BarT/LinuxFiles/riverData/{river}/data/{year}/{river}_{year}_data.csv'\n data_df.to_csv(outpath)\n", "sub_path": "scripts/3Centerlines.py", "file_name": "3Centerlines.py", "file_ext": "py", "file_size_in_byte": 4190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "re.match", "line_number": 22, "usage_type": "call"}, {"api_name": "re.match", "line_number": 24, "usage_type": "call"}, {"api_name": "re.match", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 58, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 73, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 76, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 104, "usage_type": "call"}]}
+{"seq_id": "512622215", "text": "import six\n\n\ndef verify_cph_categories(categories):\n assert len(categories) != 0, \"Response Categories are empty!\"\n iteration = 0\n for entry in categories:\n print(\"Item # \" + str(iteration))\n iteration += 1\n print(\" \" + str(entry))\n assert \"value_type\" in entry, \"'Value_Type' is not present in the entry: \" + str(entry)\n assert len(entry[\"value_type\"]) != 0, \"'Value_Type' value is empty in the entry: \" + str(entry)\n\n assert \"id\" in entry, \"'id' in not present in the entry: \" + str(entry)\n assert len(entry[\"id\"]) != 0, \"'id' value is empty in the entry: \" + str(entry)\n\n assert \"name\" in entry, \"'name' in not present in the entry: \" + str(entry)\n assert len(entry[\"name\"]) != 0, \"'name' value is empty in the entry: \" + str(entry)\n\n if \"options\" in entry:\n assert type(entry[\"options\"]) == dict, \"'options' value is incorrect type in: \" + str(entry)\n if \"hidden\" in entry[\"options\"]:\n assert \"hidden\" in entry[\"options\"], \"'hidden' value is incorrect type in: \" + str(entry)\n assert type(entry[\"options\"][\"hidden\"]) == bool, \"'hidden' value is NOT boolean: \" + str(entry)\n elif \"delta\" in entry[\"options\"]:\n assert \"delta\" in entry[\"options\"], \"'delta' value is incorrect type in: \" + str(entry)\n assert type(entry[\"options\"][\"delta\"]) == bool, \"'delta' value is NOT boolean: \" + str(entry)\n\n # \"id\": \"is_node_alive\",\n # \"value_type\": \"boolean\",\n # \"name\": \"Is Node Alive\",\n # \"options\": {\n # \"hidden\": true\n # }\n\n\ndef verify_cph_items(items):\n assert len(items) != 0, \"Response Items is empty!\"\n iteration = 0\n tag_id = \"category_id\"\n tag_value = \"value\"\n value_type = \"value_type\"\n rule_id = \"id\"\n id_dict = {\n \"id\": \"name\",\n \"value_type\": str,\n }, {\n \"id\": \"model_id\",\n \"value_type\": str\n }, {\n \"id\": \"model_name\",\n \"value_type\": str\n }, {\n \"id\": \"client_id\",\n \"value_type\": str,\n }, {\n \"id\": \"is_alive\",\n \"value_type\": bool,\n }, {\n \"id\": \"is_node_alive\",\n \"value_type\": bool,\n }, {\n \"id\": \"q-interval-holding-values-final-value-last-month\",\n \"value_type\": float,\n \"name\": \"Market Value\"\n }, {\n \"id\": \"q-interval-basic-perf-values-return-last-year\",\n \"value_type\": float,\n \"name\": \"Cumulative Total Return (1Y)\"\n }, {\n \"id\": \"q-interval-basic-perf-values-pn-l-last-year\",\n \"value_type\": int,\n \"name\": \"Total Gain (1Y)\"\n }, {\n \"id\": \"q-spot-relative-perf-contrib-weight-wrt-parent\",\n \"value_type\": float,\n \"name\": \"% of Portfolio\"\n }, {\n \"id\": \"a-entity-account-currency-name\",\n \"value_type\": str,\n \"name\": \"Account Currency\"\n }, {\n \"id\": \"a-entity-account-type-name\",\n \"value_type\": str,\n \"name\": \"Account Type\"\n }, {\n \"id\": \"a-entity-account-firm-provided-key\",\n \"value_type\": str,\n \"name\": \"Account ID\"\n }, {\n \"id\": \"a-entity-account-name\",\n \"value_type\": str,\n \"name\": \"Account Name\"\n }, {\n \"id\": \"q-aggregation-volatility-monthly-last-year\",\n \"value_type\": float,\n \"name\": \"Realized Monthly Volatility Annualized (1Y)\"\n }\n\n for entry in items:\n print(\"Item # \" + str(iteration))\n # print(\" \")\n # print(entry)\n # print(\" \")\n iteration += 1\n sub_iteration = 0\n assert \"id\" in entry, \"'ID' is not present in entry: \" + str(entry)\n assert \"data\" in entry, \"'data' is not present in entry: \" + str(entry)\n print(str(entry[\"id\"]))\n\n for data_entry in entry[\"data\"]:\n print(\" Sub Item # \" + str(sub_iteration))\n print(data_entry)\n for rule in id_dict:\n if data_entry[tag_id] == rule[rule_id]:\n print(\" data_entry = \" + str(data_entry[tag_id]) + \" - id_dict= \" + rule[rule_id])\n if tag_value in data_entry:\n print(\" Value of \" + rule[rule_id] + \" is: \" + str(\n rule[value_type]))\n if rule[value_type] == str:\n assert isinstance(data_entry[tag_value], six.string_types), str(data_entry[tag_id]) + \": \" + str(\n data_entry[tag_value]) + \" is not String\"\n elif rule[value_type] == bool:\n assert isinstance(data_entry[tag_value], bool), str(data_entry[tag_id]) + \": \" + str(\n data_entry[tag_value]) + \" is not Boolean\"\n elif rule[value_type] == float:\n assert isinstance(data_entry[tag_value], float), str(data_entry[tag_id]) + \": \" + str(\n data_entry[tag_value]) + \" is not Float\"\n elif rule[value_type] == int:\n # As per discussion with Cindy Liao about - q-interval-basic-perf-values-pn-l-last-year\n assert isinstance(data_entry[tag_value], int) or isinstance(data_entry[tag_value], float), str(data_entry[tag_id]) + \": \" + str(\n data_entry[tag_value]) + \" is not Integer\"\n else:\n assert False, str(data_entry[tag_id]) + \": \" + str(\n data_entry[tag_value]) + \" is unknown\"\n break\n else:\n print(\"No Value passed in \" + str(rule[rule_id]))\n sub_iteration += 1\n", "sub_path": "d1g1t_api/Resources/CphTables/cph_tables_helper.py", "file_name": "cph_tables_helper.py", "file_ext": "py", "file_size_in_byte": 6346, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "six.string_types", "line_number": 121, "usage_type": "attribute"}]}
+{"seq_id": "442473979", "text": "from __future__ import with_statement\n\nfrom copy import deepcopy\nfrom datetime import timedelta\nimport jsonlib as json\nfrom mock import Mock, patch\n\nfrom proccer.agent import _get_result, raise_for\nfrom proccer.database import Job\nfrom proccer.database import update_proccer_job\nfrom proccer.t.testing import setup_module, assert_eq\nfrom proccer.t.test_mail import ok_result\n\n\ndefault_recipient_patch = patch('proccer.notifications.default_recipient',\n 'devops@example.com')\n\nsend_mail_patch = patch('proccer.notifications.send_mail')\n\ndef test_update_proccer_job_new_error():\n result = deepcopy(ok_result)\n result['result']['ok'] = False\n\n with default_recipient_patch:\n with send_mail_patch as mock:\n update_proccer_job(session, result)\n\n assert mock.call_count == 1, mock.call_count\n msg, recipient = mock.call_args[0]\n assert_eq(msg['Subject'], '[foo@snafu/bar] error')\n\n\ndef test_update_proccer_job():\n with default_recipient_patch:\n with send_mail_patch as mock:\n job = update_proccer_job(session, ok_result)\n assert job\n assert update_proccer_job(session, ok_result) is job\n\n assert mock.call_count == 1, mock.call_count\n msg, recipient = mock.call_args[0]\n assert_eq(msg['Subject'], '[foo@snafu/bar] ok')\n\n\ndef test_update_proccer_job_w_warn_after():\n job = update_proccer_job(session, ok_result)\n assert job.warn_after is None, repr(job.warn_after)\n\n result = deepcopy(ok_result)\n result['config']['warn-after'] = '15 seconds'\n\n update_proccer_job(session, result)\n assert job.warn_after == timedelta(seconds=15), repr(job.warn_after)\n\n\ndef test_update_proccer_job_w_notify():\n job = update_proccer_job(session, ok_result)\n assert job.notify is None, repr(job.notify)\n\n result = deepcopy(ok_result)\n ok_result['config']['notify'] = ['foo@example.com', 'bar@example.com']\n\n job = update_proccer_job(session, ok_result)\n assert job.notify == ['foo@example.com', 'bar@example.com'], job.notify\n\n\ndef test_update_job_state():\n from nose.plugins.skip import SkipTest\n raise SkipTest\n\n with default_recipient_patch:\n with patch('proccer.notifications.smtplib') as smtplib:\n smtp = smtplib.SMTP.return_value = Mock()\n\n job = update_proccer_job(session, ok_result)\n update_job_state(session, job.id, 1, ok_result)\n assert not smtp.sendmail.called\n\n with patch('proccer.notifications.smtplib') as smtplib:\n smtp = smtplib.SMTP.return_value = Mock()\n\n update_job_state(session, job.id, 2, ok_result)\n assert smtp.sendmail.called\n\n with patch('proccer.notifications.smtplib') as smtplib:\n smtp = smtplib.SMTP.return_value = Mock()\n\n update_job_state(session, job.id, 2, ok_result)\n assert not smtp.sendmail.called\n\n with patch('proccer.notifications.smtplib') as smtplib:\n smtp = smtplib.SMTP.return_value = Mock()\n\n update_job_state(session, job.id, 3, None)\n assert smtp.sendmail.called\n", "sub_path": "src/proccer/t/test_database.py", "file_name": "test_database.py", "file_ext": "py", "file_size_in_byte": 3122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "mock.patch", "line_number": 15, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 21, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 21, "usage_type": "argument"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 26, "usage_type": "call"}, {"api_name": "mock.call_count", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mock.call_args", "line_number": 29, "usage_type": "attribute"}, {"api_name": "proccer.t.testing.assert_eq", "line_number": 30, "usage_type": "call"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 36, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 36, "usage_type": "argument"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 38, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 38, "usage_type": "argument"}, {"api_name": "mock.call_count", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mock.call_args", "line_number": 41, "usage_type": "attribute"}, {"api_name": "proccer.t.testing.assert_eq", "line_number": 42, "usage_type": "call"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 46, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 46, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 49, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 49, "usage_type": "argument"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 57, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 57, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 60, "usage_type": "argument"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 61, "usage_type": "name"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 63, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 63, "usage_type": "argument"}, {"api_name": "nose.plugins.skip.SkipTest", "line_number": 69, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 72, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 73, "usage_type": "call"}, {"api_name": "proccer.database.update_proccer_job", "line_number": 75, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 75, "usage_type": "argument"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 76, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 79, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 80, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 82, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 85, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 86, "usage_type": "call"}, {"api_name": "proccer.t.test_mail.ok_result", "line_number": 88, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 91, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 92, "usage_type": "call"}]}
+{"seq_id": "112667128", "text": "import scipy.misc\nimport numpy as np\nfrom skimage.draw import line_aa\nfrom skimage.viewer import ImageViewer\n\"\"\"\n1. \n\"\"\"\nfrom skimage.draw import circle\nimage_pixels = 100\nimg = np.zeros((image_pixels, image_pixels), dtype=np.uint8)\nrr, cc = circle(50, 50, 40)\nimg[rr, cc] = 255\n# viewer = ImageViewer(img)\n# viewer.show()\n\n# Now create sinogram of this. \nimport matplotlib.pyplot as plt\n\nfrom skimage.io import imread\nfrom skimage import data_dir\nfrom skimage.transform import radon, iradon\nfrom scipy.ndimage import zoom\n\nimage = img # imread(data_dir + \"/phantom.png\", as_grey=True)\nimage = zoom(image, 0.4)\n\nplt.figure(figsize=(8, 8.5))\n\nplt.subplot(221)\nplt.title(\"Original\");\nplt.imshow(image, cmap=plt.cm.Greys_r)\n\nplt.subplot(222)\nprojections = radon(image, theta=[0, 45, 90])\nplt.plot(projections);\nplt.title(\"Projections at\\n0, 45 and 90 degrees\")\nplt.xlabel(\"Projection axis\");\nplt.ylabel(\"Intensity\");\n\nprojections = radon(image)\nplt.subplot(223)\nplt.title(\"Radon transform\\n(Sinogram)\");\nplt.xlabel(\"Projection axis\");\nplt.ylabel(\"Intensity\");\nplt.imshow(projections)\n\nreconstruction = iradon(projections)\nplt.subplot(224)\nplt.title(\"Reconstruction\\nfrom sinogram\")\nplt.imshow(reconstruction, cmap=plt.cm.Greys_r)\n\nplt.subplots_adjust(hspace=0.4, wspace=0.5)\nplt.show()", "sub_path": "scratch/MFEIT_Dashboard/reconstruction_tests/ideal_sinogram_big_glass.py", "file_name": "ideal_sinogram_big_glass.py", "file_ext": "py", "file_size_in_byte": 1282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 10, "usage_type": "attribute"}, {"api_name": "skimage.draw.circle", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.ndimage.zoom", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "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.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "skimage.transform.radon", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "skimage.transform.radon", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "skimage.transform.iradon", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]}
+{"seq_id": "95006677", "text": "import sys\n\nsys.path.append('..')\nfrom utils import *\n\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nfrom resnet import resnet18, resnet34, resnet50\n\nclass OthelloNNet(nn.Module):\n def __init__(self, game, args):\n # game params\n self.board_x, self.board_y = game.getBoardSize()\n self.action_size = game.getActionSize()\n self.args = args\n\n super(OthelloNNet, self).__init__()\n self.conv1 = nn.Conv2d(1, args.num_channels, 3, stride=1, padding=1)\n self.conv2 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1, padding=1)\n self.conv3 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)\n self.conv4 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)\n\n self.bn1 = nn.BatchNorm2d(args.num_channels)\n self.bn2 = nn.BatchNorm2d(args.num_channels)\n self.bn3 = nn.BatchNorm2d(args.num_channels)\n self.bn4 = nn.BatchNorm2d(args.num_channels)\n\n self.fc1 = nn.Linear(args.num_channels * (self.board_x - 4) * (self.board_y - 4), 1024)\n self.fc_bn1 = nn.BatchNorm1d(1024)\n\n self.fc2 = nn.Linear(1024, 512)\n self.fc_bn2 = nn.BatchNorm1d(512)\n\n self.fc3 = nn.Linear(512, self.action_size)\n\n self.fc4 = nn.Linear(512, 1)\n\n self.reslayer = Bottleneck(args.num_channels, args.num_channels)\n\n def forward(self, s):\n # s: batch_size x board_x x board_y\n s = s.view(-1, 1, self.board_x, self.board_y) # batch_size x 1 x board_x x board_y\n s = F.relu(self.bn1(self.conv1(s))) # batch_size x num_channels x board_x x board_y\n for _ in range(5):\n s = self.reslayer(s)\n s = F.relu(self.bn2(self.conv2(s))) # batch_size x num_channels x board_x x board_y\n s = F.relu(self.bn3(self.conv3(s))) # batch_size x num_channels x (board_x-2) x (board_y-2)\n s = F.relu(self.bn4(self.conv4(s))) # batch_size x num_channels x (board_x-4) x (board_y-4)\n s = s.view(-1, self.args.num_channels * (self.board_x - 4) * (self.board_y - 4))\n\n s = F.dropout(F.relu(self.fc_bn1(self.fc1(s))), p=self.args.dropout,\n training=self.training) # batch_size x 1024\n s = F.dropout(F.relu(self.fc_bn2(self.fc2(s))), p=self.args.dropout, training=self.training) # batch_size x 512\n\n pi = self.fc3(s) # batch_size x action_size\n v = self.fc4(s) # batch_size x 1\n\n return F.log_softmax(pi, dim=1), torch.tanh(v)\n\n\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=dilation, groups=groups, bias=False, dilation=dilation)\n\n\ndef conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\n\nclass Bottleneck(nn.Module):\n # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)\n # while original implementation places the stride at the first 1x1 convolution(self.conv1)\n # according to \"Deep residual learning for image recognition\"https://arxiv.org/abs/1512.03385.\n # This variant is also known as ResNet V1.5 and improves accuracy according to\n # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.\n\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n base_width=64, dilation=1, norm_layer=None):\n super(Bottleneck, self).__init__()\n if norm_layer is None:\n norm_layer = nn.BatchNorm2d\n width = int(planes * (base_width / 64.)) * groups\n # print(width)\n # print(self.expansion)\n # Both self.conv2 and self.downsample layers downsample the input when stride != 1\n self.conv1 = conv1x1(inplanes, width)\n self.bn1 = norm_layer(width)\n self.conv2 = conv3x3(width, width, stride, groups, dilation)\n self.bn2 = norm_layer(width)\n self.conv3 = conv1x1(width, planes * self.expansion)\n self.bn3 = norm_layer(planes * self.expansion)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n identity = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n # print(out.size(), identity.size())\n\n out += identity\n out = self.relu(out)\n\n return out\n", "sub_path": "othello/pytorch/OthelloNNet.py", "file_name": "OthelloNNet.py", "file_ext": "py", "file_size_in_byte": 4965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional.dropout", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}]}
+{"seq_id": "20651869", "text": "import json\nfrom flask import Flask, jsonify\nfrom os import path\nimport os\n\n\napp = Flask(__name__)\n\n\n@app.route('/mock/<path:mock_name>', methods=['GET', 'POST'])\ndef on_mock(mock_name: str):\n mock_file = f'mock/{mock_name}.json'\n print(f'mock_file: {mock_file}')\n\n if not path.exists(mock_file):\n return '404 Not Found', 404\n\n with open(mock_file, 'r') as f:\n content = f.read()\n mock_content = json.loads(content)\n return jsonify(mock_content)\n\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0')", "sub_path": "mock_server.py", "file_name": "mock_server.py", "file_ext": "py", "file_size_in_byte": 542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}]}
+{"seq_id": "55477450", "text": "# SLIT-Net v2\n# DOI: 10.1167/tvst.10.12.2\n\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\n\nimport os\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport warnings\nimport pickle\nimport skimage.transform\nimport skimage.measure\n\nimport LimbusNet_utils as utils\nimport LimbusNet_model as model\n\n######################################### SETTINGS ####################################################\n\n# Mode:\nIS_TRAINING = False\n\n# Classes:\nNUM_CLASSES = 2\n\n# Threshold:\nTHRESHOLD_RANGE = np.array([0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30,\n 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65,\n 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00])\n\n# Number of thresholds:\nNUM_THRESHOLDS = len(THRESHOLD_RANGE)\n\n# Limbus:\nLIMBUS_ID = 4\n\n########################################## MAIN #######################################################\n\ndef main():\n\n # System inputs:\n MAIN_DIR = sys.argv[1]\n K_TAG = sys.argv[2]\n MODEL_NUM = int(sys.argv[3])\n DATASET_DIR = sys.argv[4]\n\n ####################################################################################################\n\n # Ignore these specific warnings:\n warnings.filterwarnings(\"ignore\", message=\"Matplotlib is currently using agg\")\n warnings.filterwarnings(\"ignore\", message=\"Anti-aliasing will be enabled by default\")\n\n # Config:\n CONFIG_FILENAME = os.path.join(MAIN_DIR, 'config.pickle')\n CONFIG_WRITE_FILENAME = os.path.join(MAIN_DIR, 'config.txt')\n\n if os.path.exists(CONFIG_FILENAME):\n config = pickle.load(open(CONFIG_FILENAME, 'rb', pickle.HIGHEST_PROTOCOL))\n print('\\nA config file exists and will be used.')\n if not os.path.exists(CONFIG_WRITE_FILENAME):\n config.write_to_file(CONFIG_WRITE_FILENAME)\n else:\n print('\\nNo config file exists. Please check.')\n return\n\n # Mean subtraction:\n if config.SUBTRACT_MEAN:\n IMAGE_MEAN = np.array([19.0, 50.9, 67.8])\n\n # Load dataset:\n VAL_FILENAME = os.path.join(DATASET_DIR, 'val_data_{}.mat'.format(K_TAG))\n all_images, all_masks = utils.read_mat_dataset(VAL_FILENAME, LIMBUS_ID)\n VAL_IMAGES = len(all_images)\n print('\\nNumber of validation images: {}'.format(VAL_IMAGES))\n\n # Model directory:\n MODEL_DIR = os.path.join(MAIN_DIR, K_TAG)\n\n # Save directory:\n SAVE_DIR = os.path.join(MODEL_DIR, 'validation', 'model-{}'.format(MODEL_NUM))\n if not os.path.exists(SAVE_DIR):\n os.makedirs(SAVE_DIR)\n\n # Placeholders:\n mode = tf.placeholder(tf.bool)\n image_batch = tf.placeholder(tf.float32, [1, config.IMAGE_HEIGHT, config.IMAGE_WIDTH, 3])\n\n # Model:\n logits, _, _, _ = model.segment(image_batch, mode, config.NUM_FEATURES, config.POOL_SIZE, config.REGULARIZATION_WEIGHT)\n\n # Saver:\n saver = tf.train.Saver(max_to_keep=0)\n\n # Write filename:\n WRITE_FILENAME = os.path.join(SAVE_DIR, 'validation.txt')\n if not os.path.exists(WRITE_FILENAME):\n with open(WRITE_FILENAME, 'a') as wf:\n wf.write('Threshold\\tDSC_Metric')\n\n # Session configuration:\n sessConfig = tf.ConfigProto()\n sessConfig.allow_soft_placement = True\n sessConfig.gpu_options.allow_growth = True\n\n # Session:\n with tf.Session(config=sessConfig) as sess:\n\n # Load model:\n PATH_TO_MODEL = MODEL_DIR + '/model-{}'.format(MODEL_NUM)\n if os.path.exists('{}.meta'.format(PATH_TO_MODEL)):\n saver.restore(sess, PATH_TO_MODEL)\n print('\\nmodel-{} restored'.format(MODEL_NUM))\n else:\n print('\\nmodel-{} does not exist.'.format(MODEL_NUM))\n return\n\n # Keep track:\n accum_dsc = np.zeros([NUM_THRESHOLDS])\n\n for val_step in range(VAL_IMAGES):\n\n print('\\nImage {}'.format(val_step))\n\n # Get image and mask:\n original_image_val = all_images[val_step]\n original_mask_val = all_masks[val_step]\n\n # Resize image:\n image_val = skimage.transform.resize(original_image_val.astype(np.uint8),\n (config.IMAGE_HEIGHT, config.IMAGE_WIDTH),\n order=1, mode=\"constant\", preserve_range=True)\n image_val = image_val.astype(np.float32)\n image_val = image_val[None, :, :, :]\n\n if config.SUBTRACT_MEAN:\n image_val = image_val - IMAGE_MEAN\n\n # Get prediction:\n logits_val = sess.run(logits,\n feed_dict={mode: IS_TRAINING,\n image_batch: image_val})\n\n # Remove dimensions:\n logits_val = logits_val[0,:,:,:]\n original_mask_val = original_mask_val[:,:, 0]\n\n # Convert logits to probability:\n prob_val = utils.softmax(logits_val)[:, :, 1]\n\n # Resize probability:\n original_prob_val = skimage.transform.resize(prob_val,\n (original_mask_val.shape[0], original_mask_val.shape[1]),\n order=1, mode=\"constant\", preserve_range=True)\n\n # Loop through thresholds:\n for threshold_idx, threshold_val in enumerate(THRESHOLD_RANGE):\n\n # Convert probability to binary:\n original_pred_val = (original_prob_val > threshold_val)\n\n # Postprocess:\n original_pred_val = utils.postprocess(original_pred_val)\n\n # Calculate DSC and HD:\n dsc_val = utils.calculate_dsc(original_mask_val, original_pred_val)\n\n # Keep track:\n accum_dsc[threshold_idx] += dsc_val\n\n # Calculate average:\n average_dsc = accum_dsc / VAL_IMAGES\n\n # Write to file:\n for threshold_idx, threshold_val in enumerate(THRESHOLD_RANGE):\n with open(WRITE_FILENAME, 'a') as wf:\n wf.write('\\n{}\\t{}'.format(threshold_val, average_dsc[threshold_idx]))\n\n # Plot curves and get best threshold:\n utils.plot_validation_summary_metrics(SAVE_DIR, THRESHOLD_RANGE, average_dsc, 'max', 'max', 'average_dsc_metric')\n\n print('\\nFinished.')\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Code/Segmentation/LimbusNet_validate_blue.py", "file_name": "LimbusNet_validate_blue.py", "file_ext": "py", "file_size_in_byte": 6311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "warnings.filterwarnings", "line_number": 52, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 60, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "LimbusNet_utils.read_mat_dataset", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "LimbusNet_model.segment", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "skimage.transform.transform.resize", "line_number": 131, "usage_type": "call"}, {"api_name": "skimage.transform.transform", "line_number": 131, "usage_type": "attribute"}, {"api_name": "skimage.transform", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 134, "usage_type": "attribute"}, {"api_name": "LimbusNet_utils.softmax", "line_number": 150, "usage_type": "call"}, {"api_name": "skimage.transform.transform.resize", "line_number": 153, "usage_type": "call"}, {"api_name": "skimage.transform.transform", "line_number": 153, "usage_type": "attribute"}, {"api_name": "skimage.transform", "line_number": 153, "usage_type": "name"}, {"api_name": "LimbusNet_utils.postprocess", "line_number": 164, "usage_type": "call"}, {"api_name": "LimbusNet_utils.calculate_dsc", "line_number": 167, "usage_type": "call"}, {"api_name": "LimbusNet_utils.plot_validation_summary_metrics", "line_number": 181, "usage_type": "call"}]}
+{"seq_id": "13791024", "text": "from django.db import models\nfrom django.utils import timezone\n\n\nclass TaskManager(models.Manager):\n\n def active(self):\n return (\n self.get_queryset()\n .filter(finished_at__isnull=True)\n .order_by(\"created_at\")\n )\n\n\nclass Task(models.Model):\n content = models.CharField(\"タスク\", max_length=100)\n created_at = models.DateTimeField(\"登録日時\", default=timezone.localtime)\n updated_at = models.DateTimeField(\"更新日時\", auto_now=True)\n finished_at = models.DateTimeField(\"完了日時\", null=True)\n\n objects = TaskManager()\n", "sub_path": "source/todolist-back/apps/task/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 598, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.db.models.Manager", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.timezone.localtime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "360897647", "text": "# django import\nfrom django.conf import settings\nfrom django.conf.urls.defaults import include, patterns, url\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.views.generic.simple import direct_to_template\n\n\nurlpatterns = patterns(\n \"\",\n # registration\n url(r'^registration/', include('registration.backends.default.urls')),\n # contrib auth\n url(r'', include('django.contrib.auth.urls')),\n )\n\n\nif hasattr(settings, 'AUTH_ADD_ADMIN_URLS')\\\nand settings.AUTH_ADD_ADMIN_URLS:\n from django.contrib import admin\n admin.autodiscover()\n urlpatterns += patterns(\n \"\",\n url(r'^admin/', include(admin.site.urls)),\n )\n\n\nif hasattr(settings, 'AUTH_ADD_INDEX_URLS')\\\nand settings.AUTH_ADD_INDEX_URLS:\n urlpatterns += patterns(\n \"\",\n url(r\"^$\", direct_to_template, {'template': 'index.html'}, name='index')\n )\n\n\nurlpatterns += staticfiles_urlpatterns()\n", "sub_path": "src/django_toopy_auth/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.conf.settings.AUTH_ADD_ADMIN_URLS", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.admin.autodiscover", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.patterns", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "argument"}, {"api_name": "django.conf.settings.AUTH_ADD_INDEX_URLS", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.patterns", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 31, "usage_type": "call"}, {"api_name": "django.views.generic.simple.direct_to_template", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.contrib.staticfiles.urls.staticfiles_urlpatterns", "line_number": 35, "usage_type": "call"}]}
+{"seq_id": "644863226", "text": "# import MySQLdb\n# ##from functions import fuzzyipk,fuzzyorga,fuzzypot,fuzzypres,fuzzytan\n#\n# db = MySQLdb.connect(\"127.0.0.1\",\"root\",\"\",\"beasiswa\")\n# cursor = db.cursor()\n# # sql = \"\"\"INSERT INTO biodata_mhs(nama_lengkap,fakultas,prodi,nim,alamat,ttl,semester)\n# # VALUES ('fikri khaidir','Komunikasi dan Informatika','Teknik Informatika'\n# # ,'L200130058','jl kebangkitan adik kecil','pontianak 11 juli 1995',6)\"\"\"\n# sql = \"select * from biodata_mhs\"\n# try:\n# cursor.execute(sql)\n# db.commit()\n# except:\n# db.rollback()\n#\n# db.close()\n\nfrom functions.fuzzify import fuzzifyIPK,fuzzifyPOT,fuzzifyTAN,fuzzifyORG,fuzzifyPRE,viewFuzzified\nfrom rules import rulesMin\n\nipk = 4.0\ntan = 8\npot = 900000\npre = 5\norg = 5\n\nlistIPK = []\nlistTAN = []\nlistPOT = []\nlistPRE = []\nlistORG = []\n\n\nfor i in fuzzifyIPK(ipk):\n listIPK.append(i)\nfor i in fuzzifyTAN(tan):\n listTAN.append(i)\nfor i in fuzzifyPOT(pot):\n listPOT.append(i)\nfor i in fuzzifyPRE(pre):\n listPRE.append(i)\nfor i in fuzzifyORG(org):\n listORG.append(i)\n\nviewFuzzified(listIPK,listTAN,listPOT,listPRE,listORG)\n\nrulesMin(listIPK,listTAN,listPOT,listPRE,listORG)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "functions.fuzzify.fuzzifyIPK", "line_number": 34, "usage_type": "call"}, {"api_name": "functions.fuzzify.fuzzifyTAN", "line_number": 36, "usage_type": "call"}, {"api_name": "functions.fuzzify.fuzzifyPOT", "line_number": 38, "usage_type": "call"}, {"api_name": "functions.fuzzify.fuzzifyPRE", "line_number": 40, "usage_type": "call"}, {"api_name": "functions.fuzzify.fuzzifyORG", "line_number": 42, "usage_type": "call"}, {"api_name": "functions.fuzzify.viewFuzzified", "line_number": 45, "usage_type": "call"}, {"api_name": "rules.rulesMin", "line_number": 47, "usage_type": "call"}]}
+{"seq_id": "201506563", "text": "# %%\nimport torch\n\ntorch.manual_seed(0)\ntorch.backends.cudnn.deterministic = False\ntorch.backends.cudnn.benchmark = True\n\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torch.utils.data.dataset import Dataset\n\nimport readData\nfrom readData import train_loader\n\n\n# %%\n# define the model of cnn\nclass SVHN_Model1(nn.Module):\n def __init__(self):\n super(SVHN_Model1, self).__init__()\n\n # featuer fetcher\n self.cnn = nn.Sequential(\n nn.Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2)),\n nn.ReLU(),\n nn.MaxPool2d(2),\n nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2)),\n nn.ReLU(),\n nn.MaxPool2d(2),\n\n )\n\n #\n self.fc1 = nn.Linear(32 * 3 * 7, 11)\n self.fc2 = nn.Linear(32 * 3 * 7, 11)\n self.fc3 = nn.Linear(32 * 3 * 7, 11)\n self.fc4 = nn.Linear(32 * 3 * 7, 11)\n self.fc5 = nn.Linear(32 * 3 * 7, 11)\n self.fc6 = nn.Linear(32 * 3 * 7, 11)\n\n def forward(self, img):\n feat = self.cnn(img)\n feat = feat.view(feat.shape[0], -1)\n c1 = self.fc1(feat)\n c2 = self.fc2(feat)\n c3 = self.fc3(feat)\n c4 = self.fc4(feat)\n c5 = self.fc5(feat)\n c6 = self.fc6(feat)\n\n return c1, c2, c3, c4, c5, c6\n\n\n# %%\nclass SVHN_Model2(nn.Module):\n def __init__(self):\n super(SVHN_Model2, self).__init__()\n model_conv = models.resnet18(pretrained=True)\n model_conv.avgpool = nn.AdaptiveAvgPool2d(1)\n model_conv = nn.Sequential(*list(model_conv.children())[:-1])\n self.cnn = model_conv\n\n self.fc1 = nn.Linear(512, 11)\n self.fc2 = nn.Linear(512, 11)\n self.fc3 = nn.Linear(512, 11)\n self.fc4 = nn.Linear(512, 11)\n self.fc5 = nn.Linear(512, 11)\n\n def forward(self, img):\n feat = self.cnn(img)\n\n # print(feat.shape)\n\n feat = feat.view(feat.shape[0], -1)\n c1 = self.fc1(feat)\n c2 = self.fc2(feat)\n c3 = self.fc3(feat)\n c4 = self.fc4(feat)\n c5 = self.fc5(feat)\n\n return c1, c2, c3, c4, c5\n\n\n# %%\n\n# train\n\nmodel = SVHN_Model2()\n\n# loss function\n\ncriterion = nn.CrossEntropyLoss()\n\n# optimizer\noptimizer = torch.optim.Adam(model.parameters(), 0.005)\n\nloss_plot, c0_plot = [], []\n\n# 10 iterations for epoch\nfor epoch in range(10):\n for data in train_loader:\n # c0, c1, c2, c3, c4, c5 = model(data[0])\n c0, c1, c2, c3, c4 = model(data[0])\n\n loss = criterion(c0, data[1][:, 0]) + \\\n criterion(c1, data[1][:, 1]) + \\\n criterion(c2, data[1][:, 2]) + \\\n criterion(c3, data[1][:, 3]) + \\\n criterion(c4, data[1][:, 4])\n # criterion(c5, data[1][:, 5])\n\n loss /= 6\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n loss_plot.append(loss.item())\n c0_plot.append((c0.argmax(1) == data[1][:, 0]).sum().item() * 1.0 / c0.shape[0])\n print(\"epoch:{},loss_plot:{},c0_plot:{}\".format(epoch, loss_plot, c0_plot))\n\n# %%\n\nx_axis_data = [x for x in range(len(train_loader) * 10)]\nimport matplotlib.pyplot as plt\n\nplt.subplot(121)\nplt.title('loss')\nplt.plot(x_axis_data, loss_plot)\nplt.subplot(122)\nplt.plot(x_axis_data, c0_plot)\nplt.show()\n", "sub_path": "CNN.py", "file_name": "CNN.py", "file_ext": "py", "file_size_in_byte": 3465, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.manual_seed", "line_number": 4, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "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.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.models.resnet18", "line_number": 64, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 101, "usage_type": "attribute"}, {"api_name": "readData.train_loader", "line_number": 107, "usage_type": "name"}, {"api_name": "readData.train_loader", "line_number": 129, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}]}
+{"seq_id": "197963168", "text": "\"\"\"\nThe popularity of the artist: The value will be between 0 and 100, with 100 being the most popular. The artist's popularity is calculated from the popularity of all the artist's tracks.\n\n\"\"\"\nfrom auth import req_token\nimport requests\nimport pprint\n\nClient_id = '1a3a8ea947e0445f88fa68c4056d0e2a'\nClient_secret = '5ed0d9e6382143d2882915ba397271e4'\nToken = req_token()\n\n'''\nProduces the popularity data from Spotify based on an artist search term.\n\n'''\n\ndef search(name):\n headers = {'Authorization':Token}\n url = 'https://api.spotify.com/v1/search'\n name = name.replace(' ', '+')\n params = {'q':name, 'type':'artist'}\n a = requests.get(url, headers = headers, params =params)\n b = a.json()\n if len(b['artists']['items'])<1:\n return False\n else:\n popularity = b['artists']['items'][0]['popularity']\n return popularity\n\n'''\nBased on an artist ID, return the popularity of the artist. \n\n'''\n\ndef popularity(artistID):\n id = artistID\n url = 'https://api.spotify.com/v1/artists/'+id\n headers = {'Authorization':Token}\n a = requests.get(url, headers = headers )\n return a.json()['popularity']\n\ndef history():\n a = open('history.txt', 'r')\n text_split = a.readlines()\n a = []\n for line in text_split:\n a.append(line[:line.find(':')]+', starting popularity:'+ line[line.find(':')+1:line.find(',')])\n \n return a \n \n\n# aid='1vCWHaC5f2uS3yhpwWbIA6'\n\n# print(history())\n# print(popularity(aid))\n# print(search('Katy Perry'))\n\n# print(search('Avicii'))\n# print(search('Loote'))\n# print(search('Led Zeppelin'))\n\n# print(search('The Lone Bellow'))", "sub_path": "artist.py", "file_name": "artist.py", "file_ext": "py", "file_size_in_byte": 1627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "auth.req_token", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "630473288", "text": "from selenium import webdriver\ndriver = webdriver.Chrome(executable_path=\"C:\\driver\\chromedriver.exe\")\ndriver.get('https://www.countries-ofthe-world.com/flags-of-the-world.html')\nprint(driver.title)\ndriver.maximize_window() #maximize window size\n\n#1.Scroll down page by pixels\ndriver.execute_script(\"window.scrollBy(0,1000)\",\" \")\n\n#2.Scroll down page till the element is visible\nflag=driver.find_element_by_xpath(\"//*[@id='content']/div[2]/div[2]/table[1]/tbody/tr[86]/td[2]\")\ndriver.execute_script(\"arguments[0].scrollIntoView();\",flag)\n\n#3.Scroll down page till end\ndriver.execute_script(\"window.scrollBy(0,document.body.scrollHeight)\")\n\ndriver.close()\n\n", "sub_path": "Scrolling.py", "file_name": "Scrolling.py", "file_ext": "py", "file_size_in_byte": 656, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 2, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 2, "usage_type": "name"}]}
+{"seq_id": "125688408", "text": "#!/usr/bin/python3\n\"\"\"\nnew engine\non db->storage\n\"\"\"\nfrom models.base_model import Base, BaseModel\nfrom models.amenity import Amenity\nfrom models.review import Review\nfrom models.state import State\nfrom models.city import City\nfrom models.user import User\nfrom models.place import Place\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, scoped_session\nfrom os import getenv\n\n\nclass DBStorage:\n __engine = None\n __session = None\n\n def __init__(self):\n db_user = getenv('HBNB_MYSQL_USER')\n db_password = getenv('HBNB_MYSQL_PWD')\n db_host = getenv('HBNB_MYSQL_HOST')\n db = getenv('HBNB_MYSQL_DB')\n\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}:3306/{}'\n .format(db_user,\n db_password,\n db_host,\n db), pool_pre_ping=True)\n if getenv('HBNB_ENV') == \"test\":\n Base.metadata.drop_all(self.__engine)\n\n def all(self, cls=None):\n class_list = [State, City, User, Place, Review, Amenity]\n dict_ = {}\n\n if cls is None:\n for clas in class_list:\n objs = self.__session.query(clas).all()\n for obj in objs:\n key = obj.to_dict()['__class__'] + '.' + obj.id\n dict_[key] = obj\n else:\n cls = eval(cls)\n objs = self.__session.query(cls).all()\n for obj in objs:\n key = obj.to_dict()['__class__'] + '.' + obj.id\n dict_[key] = obj\n\n return dict_\n\n def new(self, obj):\n \"\"\" add object\n to currnet\n db session\"\"\"\n self.__session.add(obj)\n\n def save(self):\n \"\"\" stage all changes\n to session db\"\"\"\n self.__session.commit()\n\n def delete(self, obj=None):\n \"\"\" deletes\n object from db session\n \"\"\"\n if obj is not None:\n self.__session.delete(obj)\n\n def reload(self):\n \"\"\" reload from\n db to session \"\"\"\n Base.metadata.create_all(self.__engine)\n session = scoped_session(sessionmaker(bind=self.__engine,\n expire_on_commit=False))\n self.__session = session()\n\n def close(self):\n self.__session.close()\n", "sub_path": "models/engine/db_storage.py", "file_name": "db_storage.py", "file_ext": "py", "file_size_in_byte": 2413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 28, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 33, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata.drop_all", "line_number": 34, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 34, "usage_type": "name"}, {"api_name": "models.state.State", "line_number": 37, "usage_type": "name"}, {"api_name": "models.city.City", "line_number": 37, "usage_type": "name"}, {"api_name": "models.user.User", "line_number": 37, "usage_type": "name"}, {"api_name": "models.place.Place", "line_number": 37, "usage_type": "name"}, {"api_name": "models.review.Review", "line_number": 37, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 37, "usage_type": "name"}, {"api_name": "models.base_model.Base.metadata.create_all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 76, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 77, "usage_type": "call"}]}
+{"seq_id": "439422079", "text": "import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras import datasets, layers, models\nfrom tensorflow.keras.layers import Conv1D\nfrom tensorflow.keras.layers import MaxPooling1D, BatchNormalization\nfrom sklearn.model_selection import KFold\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\nfrom sklearn.metrics import *\n\ndef sep(x):\n if x==0 or x ==2 or x ==6:\n return 0\n else:\n return 1\n\t \n#model structure\nclass MultiHeadSelfAttention(layers.Layer):\n def __init__(self, embed_dim, num_heads=8):\n super(MultiHeadSelfAttention, self).__init__()\n self.embed_dim = embed_dim\n self.num_heads = num_heads\n if embed_dim % num_heads != 0:\n raise ValueError(\n f\"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}\"\n )\n self.projection_dim = embed_dim // num_heads\n self.query_dense = layers.Dense(embed_dim)\n self.key_dense = layers.Dense(embed_dim)\n self.value_dense = layers.Dense(embed_dim)\n self.combine_heads = layers.Dense(embed_dim)\n\n def attention(self, query, key, value):\n score = tf.matmul(query, key, transpose_b=True)\n dim_key = tf.cast(tf.shape(key)[-1], tf.float32)\n scaled_score = score / tf.math.sqrt(dim_key)\n weights = tf.nn.softmax(scaled_score, axis=-1)\n output = tf.matmul(weights, value)\n return output, weights\n\n def separate_heads(self, x, batch_size):\n x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))\n return tf.transpose(x, perm=[0, 2, 1, 3])\n\n def call(self, inputs):\n # x.shape = [batch_size, seq_len, embedding_dim]\n batch_size = tf.shape(inputs)[0]\n query = self.query_dense(inputs) # (batch_size, seq_len, embed_dim)\n key = self.key_dense(inputs) # (batch_size, seq_len, embed_dim)\n value = self.value_dense(inputs) # (batch_size, seq_len, embed_dim)\n query = self.separate_heads(\n query, batch_size\n ) # (batch_size, num_heads, seq_len, projection_dim)\n key = self.separate_heads(\n key, batch_size\n ) # (batch_size, num_heads, seq_len, projection_dim)\n value = self.separate_heads(\n value, batch_size\n ) # (batch_size, num_heads, seq_len, projection_dim)\n attention, weights = self.attention(query, key, value)\n attention = tf.transpose(\n attention, perm=[0, 2, 1, 3]\n ) # (batch_size, seq_len, num_heads, projection_dim)\n concat_attention = tf.reshape(\n attention, (batch_size, -1, self.embed_dim)\n ) # (batch_size, seq_len, embed_dim)\n output = self.combine_heads(\n concat_attention\n ) # (batch_size, seq_len, embed_dim)\n return output\n\nclass TransformerBlock(layers.Layer):\n def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n super(TransformerBlock, self).__init__()\n self.att = MultiHeadSelfAttention(embed_dim, num_heads)\n self.ffn = keras.Sequential(\n [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n )\n self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n self.dropout1 = layers.Dropout(rate)\n self.dropout2 = layers.Dropout(rate)\n\n def call(self, inputs, training):\n attn_output = self.att(inputs)\n attn_output = self.dropout1(attn_output, training=training)\n out1 = self.layernorm1(inputs + attn_output)\n ffn_output = self.ffn(out1)\n ffn_output = self.dropout2(ffn_output, training=training)\n return self.layernorm2(out1 + ffn_output)\n\nclass TokenAndPositionEmbedding(layers.Layer):\n def __init__(self, maxlen, vocab_size, embed_dim):\n super(TokenAndPositionEmbedding, self).__init__()\n self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)\n self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)\n\n def call(self, x):\n maxlen = tf.shape(x)[-1]\n positions = tf.range(start=0, limit=maxlen, delta=1)\n positions = self.pos_emb(positions)\n x = self.token_emb(x)\n return x + positions\n\t\t\n#read data\n\n\nxtrain = r\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/training_sample_3s.csv.gz\"\nytrain = r\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/training_label_3s.csv.gz\"\nxtest = r\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/testing_sample2_3s.csv.gz\"\nytest = r\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/testing_label_3s.csv.gz\"\n\"\"\"\nxtrain = r\"/content/drive/My Drive/estr3108 project/ESTR3108/training_sample_NoSparse.csv.gz\"\nytrain = r\"/content/drive/My Drive/estr3108 project/ESTR3108/training_label_NoSparse.csv.gz\"\nxtest = r\"/content/drive/My Drive/estr3108 project/ESTR3108/testing_sample_NoSparse.csv.gz\"\nytest = r\"/content/drive/My Drive/estr3108 project/ESTR3108/testing_label_NoSparse.csv.gz\"\n\"\"\"\n\n\nsamplesdf = pd.read_csv(xtrain,compression =\"gzip\",delimiter=',')\nx_train = samplesdf.to_numpy()\n\nsamplesdf = pd.read_csv(ytrain,compression =\"gzip\",delimiter=',')\ny_train = samplesdf.to_numpy()\n\nsamplesdf = pd.read_csv(xtest,compression =\"gzip\",delimiter=',')\nx_test = samplesdf.to_numpy()\n\nsamplesdf = pd.read_csv(ytest,compression =\"gzip\",delimiter=',')\ny_test = samplesdf.to_numpy()\nprint(\"done\")\nprint(x_train.shape)\nprint(y_train.shape)\n\nprint(x_test.shape)\nprint(y_test.shape)\n\n#further separate to sepsis(1) and non sepsis(0)\ny_train=np.array(list(map(sep, y_train)))\ny_test=np.array(list(map(sep, y_test)))\n\n#class weight\ncount=[0 for i in range(2)]\ntotal=0\nfor x in y_train:\n x=int(x)\n count[x]=count[x]+1\n total = total + 1\n\ncount = list(map(lambda x: (total/x)/2, count))\nclass_weight={0:count[0],1:count[1]}\nprint(class_weight)\n\n#model structure\ndef gen_model(dim):\n#hyper parameter\n\tvocab_size = 30 #after K-fold , seems the size here will not make a significant different on accuracy\n\tmaxlen = 3271 # length of input\n\tembed_dim = 32+32*dim # Embedding size for each token\n\tnum_heads = 2 # Number of attention heads\n\tff_dim = 128 # Hidden layer size in feed forward network inside transformer\n\n\n \n\tinputs = layers.Input(shape=(maxlen,))\n\tembedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)\n\tx = embedding_layer(inputs)\n\ttransformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)\n\tx = transformer_block(x)\n\tx = layers.BatchNormalization()(x)\n\tx = layers.GlobalMaxPooling1D()(x)\n\n\tx = layers.Reshape((352, 1))(x)\n\n\tx = layers.Conv1D(filters=32, kernel_size=2, activation='relu')(x)\n\tx = layers.BatchNormalization()(x)\n\tx = MaxPooling1D(pool_size=2)(x)\n\t\n\tx = layers.Conv1D(filters=32, kernel_size=2, activation='relu')(x)\n\tx = layers.BatchNormalization()(x)\n\tx = MaxPooling1D(pool_size=2)(x)\n\tx = layers.Flatten()(x)\n\tx = layers.Dropout(0.1)(x)\n\tx = layers.Dense(200, activation=\"relu\")(x)\n\toutputs = layers.Dense(1, activation=\"sigmoid\")(x)\n\n\t\n\t\n\tmodel = keras.Model(inputs=inputs, outputs=outputs)\n\tmodel.compile(\"adam\", \"binary_crossentropy\", metrics=[\"accuracy\"])\n\treturn model\n\nmodel=gen_model(10)\nhistory = model.fit(\n\t\t\t\tx=x_train, \n\t\t\t\ty=y_train, \n\t\t\t\tclass_weight=class_weight,\n\t\t\t\tvalidation_data=(x_test, y_test),\n\t\t\t\tbatch_size=32,\n\t\t\t\tshuffle=True,\n\t\t\t\tepochs=20)\n\t\t\t\t\ntestresult=model.predict(x_test)\n\n\ni = 0\ncorrect = 0\nfor x in testresult:\n if x >=0.5 and y_test[i] == 1:\n correct = correct + 1\n elif x < 0.5 and y_test[i] == 0:\n correct = correct + 1\n i = i + 1\ntestacc = correct/i\ntestacc\n\nfpr, tpr, _ = roc_curve(y_test,testresult)\nroc_auc = auc(fpr,tpr)\nplt.figure()\nlw = 2\nplt.plot(fpr,tpr, color='darkorange',\n lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('AUROC curve for Series traditional Transformer + CNN')\nplt.legend(loc=\"lower right\")\nplt.savefig(\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/4sTransformerAUROC.png\")\n\nplt.figure()\nprecision, recall, _ = precision_recall_curve(y_test,testresult)\nprc_auc = auc(recall,precision)\nplt.plot(recall,precision, color='darkorange',\n lw=lw, label='PRC curve (area = %0.2f)' % prc_auc)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.title('AUPRC curve for Series traditional Transformer + CNN')\nplt.legend(loc=\"lower right\")\nplt.savefig(\"/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/4sTransformerAUPRC.png\")\nprint(\"AUPRC = %.02f\"% prc_auc)\nss = np.zeros((len(testresult)))\ni = 0\nfor x in testresult:\n if x >= 0.5:\n ss[i] = 1\n else:\n ss[i] = 0\n i = i + 1\nf1s = f1_score(y_test,ss)\nprint(\"f1_score = %.02f\"% f1s)\n\nmodel.save('/uac/cprj/cprj2716/borrow_from_ESETR_gpmate_tokaho/seriesTrCNN_4s')", "sub_path": "OTHER_MODELS/4s_series_trTran_CNN/seriesTransformerCNN_4s.py", "file_name": "seriesTransformerCNN_4s.py", "file_ext": "py", "file_size_in_byte": 8991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tensorflow.keras.layers.Layer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 30, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorflow.matmul", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.math.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.softmax", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Layer", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 73, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LayerNormalization", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 80, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.LayerNormalization", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 83, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Layer", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 96, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 97, "usage_type": "name"}, {"api_name": "tensorflow.shape", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 127, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 166, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 171, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 171, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.GlobalMaxPooling1D", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 172, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Reshape", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 174, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 176, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 177, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 180, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 181, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling1D", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 183, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 184, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 185, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 186, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "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": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 246, "usage_type": "call"}]}
+{"seq_id": "393264116", "text": "from itertools import product, permutations\nfrom collections import deque\n\n\nrows = [0, 1, -1, 0]\ncols = [1, 0, 0, -1]\nzs = [-1, 1]\n\n\ndef rotate_list(one_board):\n tmp_board = []\n for _ in range(5):\n tmp_board.append([0] * 5)\n for row in range(5):\n for col in range(5):\n tmp_board[col][4 - row] = one_board[row][col]\n\n return tmp_board\n\n\ndef add_rotated(one_board):\n tmp_list = [one_board]\n for _ in range(3):\n one_board = rotate_list(one_board)\n tmp_list.append(one_board)\n\n return tmp_list\n\n\ndef input_board():\n tmp_board = []\n for _ in range(5):\n tmp_board.append(list(map(int, input().split())))\n return tmp_board\n\n\ndef bfs(one_board):\n global result\n\n visited = []\n for _ in range(5):\n tmp_list = []\n for _ in range(5):\n tmp_list.append([0] * 5)\n visited.append(tmp_list)\n\n d = deque()\n d.append([0, 0, 0, 0])\n visited[0][0][0] = 1\n while d:\n popped = d.popleft()\n distance = popped[3]\n if distance >= result:\n return 0\n channel, row, col = popped[0], popped[1], popped[2]\n if channel == 4 and row == 4 and col == 4:\n if distance < result:\n result = distance\n return 1\n for r, c in zip(rows, cols):\n if 0 <= row + r < 5 and 0 <= col + c < 5 and visited[channel][row + r][col + c] == 0 and one_board[channel][row + r][col + c] == 1:\n d.append([channel, row + r, col + c, distance + 1])\n visited[channel][row + r][col + c] = 1\n for z in zs:\n if 0 <= channel + z < 5 and visited[channel + z][row][col] == 0 and one_board[channel + z][row][col] == 1:\n d.append([channel + z, row, col, distance + 1])\n visited[channel + z][row][col] = 1\n\n return 0\n\n\nboard_0, board_1, board_2, board_3, board_4 = input_board(), input_board(), input_board(), input_board(), input_board()\nlist_0, list_1, list_2, list_3, list_4 = add_rotated(board_0), add_rotated(board_1), add_rotated(board_2), add_rotated(board_3), add_rotated(board_4)\ntotal_dict = {\n 0: list_0,\n 1: list_1,\n 2: list_2,\n 3: list_3,\n 4: list_4\n}\n\nflag = False\ncan_go_flag = False\nresult = 60\nj = 0\nfor stack_perm in permutations([0, 1, 2, 3, 4]):\n if flag:\n break\n for dir_perm in product([0, 1, 2, 3], repeat=5):\n if result == 12:\n flag = True\n break\n board = []\n for total_dict_key, ix in zip(stack_perm, dir_perm):\n board.append(total_dict[total_dict_key][ix])\n if board[0][0][0] == 0 or board[4][4][4] == 0:\n continue\n if bfs(board) == 1:\n can_go_flag = True\n\nif can_go_flag:\n print(result)\nelse:\n print(-1)\n", "sub_path": "study/BJ_16985_Maaaze.py", "file_name": "BJ_16985_Maaaze.py", "file_ext": "py", "file_size_in_byte": 2795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "collections.deque", "line_number": 47, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 86, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 89, "usage_type": "call"}]}
+{"seq_id": "146781441", "text": "from django.shortcuts import render\nfrom django.views.generic import View\n\nfrom .helpers import handle_excel_info\nfrom organization.models import Faculty, Profession, Direction, Klass, Office, Location\n# Create your views here.\n\n\nclass ImportData(View):\n \"\"\"\n 导入信息\n \"\"\"\n def get(self, request):\n return render(request, 'import_data.html')\n\n def post(self, request):\n file = request.FILES.get('file', None)\n type = request.POST.get('type', 0)\n type = int(type)\n info = '导入成功'\n\n if not file:\n info = \"请传入文件\"\n\n type_dict = {\n 0: self.faculty_info,\n 1: self.profession_info,\n 2: self.direction_info,\n 3: self.klass_info,\n 4: self.office_info,\n }\n try:\n file_data = file.read()\n data_list = handle_excel_info(file_data)\n for data in data_list:\n type_dict[type](data)\n\n except Exception as e:\n info = \"文件内容存在异常\"\n print('error info: {}'.format(e))\n\n context = {\n 'info': info,\n 'type': type\n }\n\n return render(request, 'import_data.html', context)\n\n @staticmethod\n def faculty_info(data):\n Faculty.objects.create(name=data[0], number=data[1], monitor=data[2])\n\n @staticmethod\n def profession_info(data):\n Profession.objects.create(name=data[0], number=data[1],\n faculty=Faculty.objects.get(number=data[2])\n )\n\n @staticmethod\n def direction_info(data):\n Direction.objects.create(name=data[0], number=data[1],\n profession=Profession.objects.get(number=data[2])\n )\n\n @staticmethod\n def klass_info(data):\n Klass.objects.create(name=data[0], number=data[1],\n direction=Direction.objects.get(number=data[2])\n )\n\n @staticmethod\n def office_info(data):\n Office.objects.create(name=data[0], number=data[1],\n faculty=Faculty.objects.get(number=data[2])\n )\n\n\nclass ImportLocationInfo(View):\n \"\"\"\n 导入答辩地点信息\n \"\"\"\n\n def get(self, request):\n return render(request, 'import_location_data.html')\n\n def post(self, request):\n file = request.FILES.get('file')\n\n context = {\n 'info': '',\n 'error_info': ''\n }\n error_info = list()\n\n if not file:\n context['info'] = '请传入文件'\n return render(request, 'import_location_data.html', context)\n\n try:\n file_data = file.read()\n data_list = handle_excel_info(file_data)\n for data in data_list[1:]:\n Location.objects.create(location_number=data[0], location_desc=data[1])\n except Exception as e:\n error_info.append(e)\n\n return render(request, 'import_location_data.html', context)\n", "sub_path": "import_data/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.views.generic.View", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "helpers.handle_excel_info", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "organization.models.Faculty.objects.create", "line_number": 51, "usage_type": "call"}, {"api_name": "organization.models.Faculty.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "organization.models.Faculty", "line_number": 51, "usage_type": "name"}, {"api_name": "organization.models.Profession.objects.create", "line_number": 55, "usage_type": "call"}, {"api_name": "organization.models.Profession.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "organization.models.Profession", "line_number": 55, "usage_type": "name"}, {"api_name": "organization.models.Faculty.objects.get", "line_number": 56, "usage_type": "call"}, {"api_name": "organization.models.Faculty.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "organization.models.Faculty", "line_number": 56, "usage_type": "name"}, {"api_name": "organization.models.Direction.objects.create", "line_number": 61, "usage_type": "call"}, {"api_name": "organization.models.Direction.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "organization.models.Direction", "line_number": 61, "usage_type": "name"}, {"api_name": "organization.models.Profession.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "organization.models.Profession.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "organization.models.Profession", "line_number": 62, "usage_type": "name"}, {"api_name": "organization.models.Klass.objects.create", "line_number": 67, "usage_type": "call"}, {"api_name": "organization.models.Klass.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "organization.models.Klass", "line_number": 67, "usage_type": "name"}, {"api_name": "organization.models.Direction.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "organization.models.Direction.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "organization.models.Direction", "line_number": 68, "usage_type": "name"}, {"api_name": "organization.models.Office.objects.create", "line_number": 73, "usage_type": "call"}, {"api_name": "organization.models.Office.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "organization.models.Office", "line_number": 73, "usage_type": "name"}, {"api_name": "organization.models.Faculty.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "organization.models.Faculty.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "organization.models.Faculty", "line_number": 74, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 78, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 97, "usage_type": "call"}, {"api_name": "helpers.handle_excel_info", "line_number": 101, "usage_type": "call"}, {"api_name": "organization.models.Location.objects.create", "line_number": 103, "usage_type": "call"}, {"api_name": "organization.models.Location.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "organization.models.Location", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}]}
+{"seq_id": "479916337", "text": "#\n# Created by djz on 2021/01/21.\n#\n\"\"\"EET transformers gpt2 model. \"\"\"\n\nimport math\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom transformers import GPT2Model,GPT2PreTrainedModel\nfrom transformers.configuration_gpt2 import GPT2Config\nfrom transformers.configuration_utils import PretrainedConfig\n\nfrom EET import MetaDesc as meta_desc\nfrom EET import LayerNorm as eet_layernorm\nfrom EET import FeedForwardNetwork as eet_ffn\nfrom EET import Embedding as eet_embedding\nfrom EET import MaskedMultiHeadAttention as eet_attention\nfrom EET import CrossMultiHeadAttention as eet_cross_attention\n\n\n__all__ = [\n 'EETLayerNorm', 'EETGPT2Embedding', 'EETGPT2Feedforward', 'EETGPT2Attention', 'EETGPT2DecoderLayer', 'EETGPT2Decoder', 'EETGPT2Model'\n]\n\nclass EETLayerNorm():\n def __init__(self,meta_des,layernorm_weights,layernorm_bias,data_type = torch.float32):\n self.layernorm_weights = layernorm_weights.cuda().type(data_type)\n self.layernorm_bias = layernorm_bias.cuda().type(data_type)\n self.layernorm = eet_layernorm(meta_des,self.layernorm_weights,self.layernorm_bias)\n\n def __call__(self,\n input_ids):\n return self.layernorm.layer_norm(input_ids)\n \n @staticmethod\n def from_torch(meta_des,layernorm_weights,layernorm_bias,data_type = torch.float32):\n layernorm = EETLayerNorm(meta_des,layernorm_weights,layernorm_bias,data_type = data_type)\n return layernorm\n\nclass EETGPT2Embedding():\n def __init__(self,meta_des,embedding_dict,data_type = torch.float32):\n self.embedding_weights = embedding_dict['wte.weight'].cuda().type(data_type)\n self.position_weights = embedding_dict['wpe.weight'].cuda().type(data_type)\n # not use token_type\n self.token_type_ids = torch.empty(0).long()\n self.token_type_weights = torch.empty(0)\n self.if_layernorm = False\n # not use layernorm\n self.Layernorm_weights = torch.empty(0) \n self.Layernorm_bias = torch.empty(0)\n self.embedding = eet_embedding(meta_des,self.embedding_weights,self.position_weights,self.token_type_weights,self.Layernorm_weights,self.Layernorm_bias)\n def __call__(self,\n input_ids,\n position_ids,\n token_type_ids):\n if_layernorm = False\n if token_type_ids is None:\n token_type_ids = self.token_type_ids\n return self.embedding.forward_transformers(input_ids,position_ids,token_type_ids,if_layernorm)\n \n @staticmethod\n def from_torch(meta_des,embedding_dict,data_type = torch.float32):\n feedforward = EETGPT2Embedding(meta_des,embedding_dict,data_type = data_type)\n return feedforward\n\nclass EETGPT2Feedforward():\n def __init__(self,meta_des,model_dict,layer_id,data_type = torch.float32):\n self.intermediate_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'mlp.c_fc.weight' in x[0]][0].transpose(0,1)).contiguous()).contiguous().cuda().type(data_type)\n self.intermediate_bias = [x[1] for x in model_dict.items() if 'mlp.c_fc.bias' in x[0]][0].cuda().type(data_type)\n self.output_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'mlp.c_proj.weight' in x[0]][0].transpose(0,1)).contiguous()).contiguous().cuda().type(data_type)\n self.output_bias = [x[1] for x in model_dict.items() if 'mlp.c_proj.bias' in x[0]][0].cuda().type(data_type)\n self.layernorm_weights = [x[1] for x in model_dict.items() if 'ln_2.weight' in x[0]][0].cuda().type(data_type)\n self.layernorm_bias = [x[1] for x in model_dict.items() if 'ln_2.bias' in x[0]][0].cuda().type(data_type)\n self.ffn = eet_ffn(meta_des,self.intermediate_weights,self.intermediate_bias,self.output_weights,self.output_bias,self.layernorm_weights,self.layernorm_bias)\n def __call__(self,\n input_id,\n pre_layernorm = True,\n add_redusial = True):\n return self.ffn.forward(input_id,pre_layernorm,add_redusial)\n \n @staticmethod\n def from_torch(meta_des,model_dict,layer_id,data_type = torch.float32):\n feedforward = EETGPT2Feedforward(meta_des,model_dict,layer_id,data_type = data_type)\n return feedforward\n\nclass EETGPT2Attention():\n def __init__(self,meta_des, model_dict,layer_id,data_type = torch.float32,cross_attn = False):\n self.cross_attn = cross_attn\n if self.cross_attn:\n self.layernorm_weights = [x[1] for x in model_dict.items() if 'ln_1.weight' in x[0]][0].cuda().type(data_type)\n self.layernorm_bias = [x[1] for x in model_dict.items() if 'ln_1.bias' in x[0]][0].cuda().type(data_type)\n emb_size = self.layernorm_bias.size()[0]\n self.q_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[:emb_size]).contiguous()).cuda().type(data_type)\n self.k_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[emb_size:emb_size*2]).contiguous()).cuda().type(data_type)\n self.v_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[emb_size*2:]).contiguous()).cuda().type(data_type)\n self.q_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][:emb_size].cuda().type(data_type)\n self.k_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][emb_size:emb_size*2].cuda().type(data_type)\n self.v_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][emb_size*2:].cuda().type(data_type)\n self.out_weights = [x[1] for x in model_dict.items() if 'attn.c_proj.weight' in x[0]][0].cuda().type(data_type)\n self.out_bias = [x[1] for x in model_dict.items() if 'attn.c_proj.bias' in x[0]][0].cuda().type(data_type)\n self.attention = eet_cross_attention(meta_des,self.q_weights,self.k_weights,self.v_weights,self.q_bias,self.k_bias,self.v_bias,self.out_weights,self.out_bias,self.layernorm_weights,self.layernorm_bias)\n else:\n self.layernorm_weights = [x[1] for x in model_dict.items() if 'ln_1.weight' in x[0]][0].cuda().type(data_type)\n self.layernorm_bias = [x[1] for x in model_dict.items() if 'ln_1.bias' in x[0]][0].cuda().type(data_type)\n emb_size = self.layernorm_bias.size()[0]\n self.q_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[:emb_size]).contiguous()).cuda().type(data_type)\n self.k_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[emb_size:emb_size*2]).contiguous()).cuda().type(data_type)\n self.v_weights = torch.clone(torch.t([x[1] for x in model_dict.items() if 'c_attn.weight' in x[0]][0].transpose(0,1)[emb_size*2:]).contiguous()).cuda().type(data_type)\n self.q_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][:emb_size].cuda().type(data_type)\n self.k_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][emb_size:emb_size*2].cuda().type(data_type)\n self.v_bias = [x[1] for x in model_dict.items() if 'c_attn.bias' in x[0]][0][emb_size*2:].cuda().type(data_type)\n self.out_weights = [x[1] for x in model_dict.items() if 'attn.c_proj.weight' in x[0]][0].cuda().type(data_type)\n self.out_bias = [x[1] for x in model_dict.items() if 'attn.c_proj.bias' in x[0]][0].cuda().type(data_type)\n self.attention = eet_attention(meta_des,self.q_weights,self.k_weights,self.v_weights,self.q_bias,self.k_bias,self.v_bias,self.out_weights,self.out_bias,self.layernorm_weights,self.layernorm_bias)\n\n def __call__(self,\n input_id,\n padding_index,\n encoder_out = None,\n encoder_padding_mask = None,\n pre_layernorm = False,\n add_redusial = True,\n first_pass = False):\n \n if self.cross_attn:\n return self.attention.forward(input_id,encoder_out,padding_index,pre_layernorm,add_redusial,encoder_padding_mask,first_pass)\n else:\n return self.attention.forward(input_id,padding_index,pre_layernorm,add_redusial, first_pass)\n\n @staticmethod\n def from_torch(meta_des,model_dict,layer_id,data_type = torch.float32):\n attention = EETGPT2Attention(meta_des,model_dict,layer_id,data_type = data_type)\n return attention\n\nclass EETGPT2DecoderLayer():\n def __init__(self, config, attention,feedforward,cross_attention = None):\n self.attetion = attention\n self.cross_attention = cross_attention\n self.feedforward = feedforward\n self.normalize_before = True\n self.add_redusial = True\n self.add_cross_attention = config.add_cross_attention\n def __call__(self,\n x,\n encoder_out = None,\n first_pass = True,\n attention_mask = None,\n encoder_attention_mask = None,\n head_mask = None):\n\n ''' gpt2 model struct '''\n ''' layernorm->self_attention-> project->addinputbias->layernorm->ffn->addinputbias'''\n if encoder_out is not None and self.cross_attention is not None:\n self_attn_out = self.attetion(input_id = x,\n padding_index = attention_mask,\n pre_layernorm = self.normalize_before,\n add_redusial = self.add_redusial,\n first_pass = first_pass)\n\n cross_attn_out = self.cross_attention(input_id = self_attn_out,\n padding_index = attention_mask,\n encoder_out = encoder_out,\n encoder_padding_mask = encoder_attention_mask,\n pre_layernorm = self.normalize_before,\n add_redusial = self.add_redusial,\n first_pass = first_pass)\n\n out = self.feedforward(cross_attn_out,\n pre_layernorm = self.normalize_before,\n add_redusial = self.add_redusial)\n else:\n self_attn_out = self.attetion(input_id = x,\n padding_index = attention_mask,\n pre_layernorm = self.normalize_before,\n add_redusial = self.add_redusial,\n first_pass = first_pass)\n\n out = self.feedforward(self_attn_out,\n pre_layernorm = self.normalize_before,\n add_redusial = self.add_redusial) \n\n return out\n\n @staticmethod\n def from_torch(meta_des, config,model_dict,layer_id,data_type = torch.float32):\n attention = EETGPT2Attention.from_torch(meta_des = meta_des, model_dict = model_dict, layer_id = layer_id,data_type = data_type)\n feedforward = EETGPT2Feedforward.from_torch(meta_des = meta_des, model_dict = model_dict, layer_id = layer_id,data_type = data_type)\n\n if config.add_cross_attention:\n cross_attention = EETGPT2Attention.from_torch(meta_des = meta_des, model_dict = model_dict, layer_id = layer_id,data_type = data_type)\n layer = EETGPT2DecoderLayer(config, attention, feedforward,cross_attention)\n else:\n layer = EETGPT2DecoderLayer(config, attention, feedforward)\n \n return layer\n\nclass EETGPT2Decoder():\n def __init__(self,DecoderLayers):\n self.layers = DecoderLayers\n def __call__(\n self,\n x,\n encoder_out = None,\n first_pass = True,\n attention_mask = None,\n encoder_attention_mask = None,\n head_mask = None,\n ):\n for layer in self.layers:\n x = layer(x,\n encoder_out = encoder_out,\n first_pass = first_pass,\n attention_mask = attention_mask,\n encoder_attention_mask = encoder_attention_mask,\n head_mask = None)\n return x\n \n @staticmethod\n def from_torch(layer_model_dict,meta_des,config,data_type = torch.float32):\n \"\"\"from torch.\"\"\"\n DecoderLayers = []\n for i in range(config.n_layer):\n if i < 10:\n DecoderLayers.extend(\n [\n EETGPT2DecoderLayer.from_torch(meta_des,config,layer_model_dict['h.'+str(i)+'.'],i,data_type = data_type)\n ]\n )\n else:\n DecoderLayers.extend(\n [\n EETGPT2DecoderLayer.from_torch(meta_des,config,layer_model_dict['h.'+str(i)],i,data_type = data_type)\n ]\n )\n\n eet_decoder = EETGPT2Decoder(DecoderLayers)\n return eet_decoder\n\nclass EETGPT2Model():\n def __init__(self,config,embedding,decoder, layer_norm):\n self.embedding = embedding\n self.decoder = decoder\n self.layer_norm = layer_norm\n self.position_ids = torch.arange(0,config.n_positions).reshape(1,config.n_positions).cuda()\n self.self_attn_padding_mask = torch.empty(0)\n self.encoder_attention_mask = torch.empty(0)\n\n def __call__(\n self,\n input_ids,\n encoder_out = None,\n first_pass = True,\n position_ids = None,\n token_type_ids = None,\n attention_mask = None,\n ):\n \"\"\" EET suport left padding, ``0`` for tokens that are NOT MASKED, ``1`` for MASKED tokens. The struct like [1,1,1,0,0,0]\"\"\"\n\n input_shape = input_ids.size()\n batch_size = input_shape[0]\n # Attention mask.\n if attention_mask is None:\n attention_mask = self.self_attn_padding_mask\n\n position_ids = self.position_ids[:, :input_shape[1]]\n if token_type_ids is not None:\n token_type_ids = token_type_ids.view(-1, input_shape[-1])\n embedding_out = self.embedding(input_ids,position_ids,token_type_ids)\n\n decoder_out = self.decoder(embedding_out,\n encoder_out = encoder_out,\n first_pass = first_pass,\n attention_mask = attention_mask,\n encoder_attention_mask = self.encoder_attention_mask,\n head_mask = None,)\n \n decoder_out = self.layer_norm(decoder_out)\n return decoder_out\n \n @staticmethod\n def from_pretrained(model_id_or_path: str,max_batch,full_seq_len,data_type = torch.float32):\n \"\"\"from torch.\"\"\"\n torch.set_grad_enabled(False)\n model_dict = {}\n embedding_dict = {}\n layernorm_dict = {}\n\n torch_model = GPT2Model.from_pretrained(model_id_or_path)\n cfg = torch_model.config\n\n for k, v in torch_model.state_dict().items():\n if 'e.' in k:\n embedding_dict[k] = v\n if 'h.' in k:\n model_dict[k] = v\n if 'ln_f' in k:\n layernorm_dict[k] = v\n\n from itertools import groupby\n\n layer_model_dict = {k: dict(v) for k, v in groupby(list(model_dict.items()), lambda item: item[0][:4])}\n\n # data_type = torch.float32\n device = \"cuda:0\"\n activation_fn = cfg.activation_function\n batch_size = max_batch\n full_seq_len = full_seq_len\n meta_des = meta_desc(batch_size, cfg.n_head, cfg.n_embd, cfg.n_layer , cfg.n_positions,full_seq_len, data_type, device, False, activation_fn)\n layer_norm = EETLayerNorm.from_torch(meta_des,layernorm_dict['ln_f.weight'],layernorm_dict['ln_f.bias'],data_type)\n embedding = EETGPT2Embedding.from_torch(meta_des,embedding_dict,data_type)\n # embedding = None\n decoder = EETGPT2Decoder.from_torch(layer_model_dict,meta_des, cfg,data_type)\n eet_model = EETGPT2Model(cfg,embedding, decoder,layer_norm)\n return eet_model\n", "sub_path": "python/eet/transformers/modeling_gpt2.py", "file_name": "modeling_gpt2.py", "file_ext": "py", "file_size_in_byte": 16064, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.float32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "EET.LayerNorm", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.empty", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 53, "usage_type": "call"}, {"api_name": "EET.Embedding", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.clone", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 73, "usage_type": "call"}, {"api_name": "EET.FeedForwardNetwork", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.clone", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 98, "usage_type": "call"}, {"api_name": "EET.CrossMultiHeadAttention", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.clone", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.t", "line_number": 111, "usage_type": "call"}, {"api_name": "EET.MaskedMultiHeadAttention", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 134, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 222, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 284, "usage_type": "attribute"}, {"api_name": "torch.set_grad_enabled", "line_number": 286, "usage_type": "call"}, {"api_name": "transformers.GPT2Model.from_pretrained", "line_number": 291, "usage_type": "call"}, {"api_name": "transformers.GPT2Model", "line_number": 291, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 304, "usage_type": "call"}, {"api_name": "EET.MetaDesc", "line_number": 311, "usage_type": "call"}, {"api_name": "{'groupby': 'itertools.groupby'}", "line_number": 316, "usage_type": "call"}]}
+{"seq_id": "438441294", "text": "# -*- coding: utf-8 -*-\n\nfrom . import data_processor as dp\nimport os\nfrom torch.autograd import Variable\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n#%% get file path\nclass FilePath:\n def __init__(self, filedir='cnn', randn_num='', suffix='.pt', separator='_'):\n \"\"\"Obtain the path of a specific file.\n \n Parameters\n ----------\n filedir : str\n The relative path of a file.\n randn_num : str\n A random number that owned by a file name.\n suffix : str\n The suffix of the file, e.g. '.npy', '.pt'\n separator : str\n Symbol for splitting the random number in the file name.\n \"\"\"\n self.filedir = filedir\n self.randn_num = randn_num\n self.separator = separator\n self.file_suffix = suffix\n \n def filePath(self):\n listdir = os.listdir(self.filedir)\n for File in listdir:\n if File.endswith(self.file_suffix):\n fileName = os.path.splitext(File)[0]\n randn = fileName.split(self.separator)[-1]\n if randn == self.randn_num:\n target_file = self.filedir + '/' + File\n if 'target_file' not in dir():\n raise IOError('No eligible files!')\n return target_file\n\n#%% predict\ndef predict(net, inputs, use_GPU=False, in_type='numpy'):\n \"\"\"Make predictions using a well-trained network.\n \n Parameters\n ----------\n inputs : numpy array or torch tensor\n The inputs of the network.\n use_GPU : bool\n If True, calculate using GPU, otherwise, calculate using CPU.\n in_type : str\n The data type of the inputs, 'numpy' or 'torch'.\n \"\"\"\n if use_GPU:\n net = net.cuda()\n if in_type=='numpy':\n inputs = dp.numpy2cuda(inputs)\n elif in_type=='torch':\n inputs = dp.torch2cuda(inputs)\n else:\n if in_type=='numpy':\n inputs = dp.numpy2torch(inputs)\n net = net.eval() #this works for the batch normalization layers\n pred = net(Variable(inputs))\n if use_GPU:\n pred = dp.cuda2numpy(pred.data)\n else:\n pred = dp.torch2numpy(pred.data)\n return pred\n\n#%% plot loss\ndef plot_loss(loss):\n # print ('The last 5 losses: ', np.array(loss[-5:]))\n print ('The average of last 100 losses: %.8f\\n'%(np.mean(loss[-100:])))\n plt.figure(figsize=(6*2., 4.5*1.))\n plt.subplot(1,2,1)\n plt.semilogx(loss)\n plt.xlabel('Iteration', fontsize=16)\n plt.ylabel('Loss', fontsize=16)\n \n plt.subplot(1,2,2)\n plt.loglog(loss)\n plt.xlabel('Iteration', fontsize=16)\n plt.ylabel('Loss', fontsize=16)\n # plt.show()\n return\n", "sub_path": "ecopann/evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 2698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogx", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}]}
+{"seq_id": "197942769", "text": "# translate.py from tutorial part 15\n# http://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-xv-ajax\nglobal db\ndb = False\nimport sys\nimport inspect\nimport json\nfrom app import app\nfrom flask.ext.babel import gettext\nfrom config import MS_TRANSLATOR_CLIENT_ID, MS_TRANSLATOR_CLIENT_SECRET\n\n\ndef dbfilelineno():\n \"\"\" Returns string containing 'DEBUG', the currently-executing file name and line number in the program.\n Requires import inspect.\n \"\"\"\n return 'DEBUG: ' + __file__ + ', line ' + str(inspect.currentframe().f_back.f_lineno)\n\nif sys.version_info >= (3, 0):\n if db:\n print(dbfilelineno(), 'sys.version_info = ', sys.version_info)\ntry:\n import httplib # Python 2\nexcept ImportError:\n if db:\n print('import http.client as httplib # Python 3')\n import http.client as httplib # Python 3\ntry:\n from urllib import urlencode # Python 2\nexcept ImportError:\n if db:\n print('from urllib.parse import urlencode # Python 3')\n from urllib.parse import urlencode # Python 3\n\n\ndef to_str(bytes_or_str):\n # In Python 3, you’ll need one method that takes a str or bytes and always returns a str.\n if isinstance(bytes_or_str, bytes):\n value = bytes_or_str.decode('utf-8')\n else:\n value = bytes_or_str\n return value # Instance of str\n\n\ndef to_bytes(bytes_or_str):\n # You’ll need another method that takes a str or bytes and always returns a bytes.\n if isinstance(bytes_or_str, str):\n value = bytes_or_str.encode('utf-8')\n else:\n value = bytes_or_str\n return value # Instance of bytes\n\n\n\"\"\"def lineno():\n return inspect.currentframe().f_back.f_lineno\n\"\"\"\n\n\ndef microsoft_translate(text, source_lang, dest_lang):\n # db = False\n if MS_TRANSLATOR_CLIENT_ID == \"\" or MS_TRANSLATOR_CLIENT_SECRET == \"\":\n return gettext('Error: translation service not configured.')\n try:\n # get access token\n params = urlencode({\n 'client_id': MS_TRANSLATOR_CLIENT_ID,\n 'client_secret': MS_TRANSLATOR_CLIENT_SECRET,\n 'scope': 'http://api.microsofttranslator.com',\n 'grant_type': 'client_credentials'\n })\n conn = httplib.HTTPSConnection(\"datamarket.accesscontrol.windows.net\")\n if db:\n print(dbfilelineno(), 'params = ', params)\n conn.request(\"POST\", \"/v2/OAuth2-13\", params)\n # jk fixed the following lines for Python 3\n # str_response = response.readall().decode('utf-8')\n str_response = conn.getresponse().read().decode('utf-8')\n if db:\n print(dbfilelineno(), 'str_response = ', str_response)\n\n obj = json.loads(str_response)\n if db:\n print(dbfilelineno(), 'obj = ', obj)\n response = json.loads(str_response)\n token = response['access_token']\n\n # translate\n conn = httplib.HTTPConnection('api.microsofttranslator.com')\n params = {'appId': 'Bearer ' + token,\n 'from': source_lang,\n 'to': dest_lang,\n 'text': text.encode(\"utf-8\")}\n conn.request(\"GET\",\n '/V2/Ajax.svc/Translate?' + urlencode(params))\n\n resp = conn.getresponse().read().decode('utf-8-sig')\n\n # print(str(dbfilelineno()) + 'resp = [' + str(resp) + ']; resp.type = ' + str(type(resp)))\n resp_json = '{\"response\":' + resp + '}'\n # resp_json = resp_json.strip(' \\t\\n\\r')\n if db:\n print(str(dbfilelineno()) + 'resp_json = [' + resp_json + ']; type(resp_json) = ' + str(type(resp_json)))\n # response = json.loads('{\"response\":' + conn.getresponse().read().decode('utf-8') + '}')\n response = json.loads(resp_json)\n if db:\n print(dbfilelineno(), 'response = ', response)\n # .decode('utf-8)'))\n return response[\"response\"]\n\n except:\n raise\n\n\ndef google_translate(text, source_lang, dest_lang):\n if not app.debug:\n return gettext('Error: translation service not available.')\n try:\n params = urlencode({\n 'client': 't',\n 'text': text.encode(\"utf-8\"),\n 'sl': source_lang,\n 'tl': dest_lang,\n 'ie': 'UTF-8',\n 'oe': 'UTF-8'})\n conn = httplib.HTTPSConnection(\"translate.google.com\")\n conn.request(\"GET\", \"/translate_a/t?\" + params,\n headers={'User-Agent': 'Mozilla/5.0'})\n httpresponse = conn.getresponse().read().replace(\n \",,,\", \",\\\"\\\",\\\"\\\",\").replace(\",,\", \",\\\"\\\",\")\n response = json.loads(\"{\\\"response\\\":\" + httpresponse + \"}\")\n return response[\"response\"][0][0][0]\n except:\n return gettext('Error: Unexpected error.')\n\n\ndef translate_test():\n microsoft_translate('Hi, how are you today?', 'en', 'es')\n # u'¿Hola, cómo estás hoy?'\n\n# test()\n", "sub_path": "app/translate.py", "file_name": "translate.py", "file_ext": "py", "file_size_in_byte": 4860, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "inspect.currentframe", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.MS_TRANSLATOR_CLIENT_ID", "line_number": 61, "usage_type": "name"}, {"api_name": "config.MS_TRANSLATOR_CLIENT_SECRET", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.ext.babel.gettext", "line_number": 62, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 65, "usage_type": "call"}, {"api_name": "config.MS_TRANSLATOR_CLIENT_ID", "line_number": 66, "usage_type": "name"}, {"api_name": "config.MS_TRANSLATOR_CLIENT_SECRET", "line_number": 67, "usage_type": "name"}, {"api_name": "http.client.HTTPSConnection", "line_number": 71, "usage_type": "call"}, {"api_name": "http.client", "line_number": 71, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 81, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "http.client.HTTPConnection", "line_number": 88, "usage_type": "call"}, {"api_name": "http.client", "line_number": 88, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 104, "usage_type": "call"}, {"api_name": "app.app.debug", "line_number": 115, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.ext.babel.gettext", "line_number": 116, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 118, "usage_type": "call"}, {"api_name": "http.client.HTTPSConnection", "line_number": 125, "usage_type": "call"}, {"api_name": "http.client", "line_number": 125, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.ext.babel.gettext", "line_number": 133, "usage_type": "call"}]}
+{"seq_id": "412376285", "text": "\"\"\"solver.py\"\"\"\n\nimport argparse\nfrom utils import str2bool\n\nimport os\nimport visdom\nfrom tqdm import tqdm\n\nimport numpy as np\nimport torch\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom utils import DataGather, mkdirs, grid2gif, BaseFactorVae\nfrom ops import recon_loss, kl_divergence, permute_dims\nfrom model import FactorVAE1, FactorVAE2, Discriminator\nfrom dataset import return_data\n\nimport time\n\n\nclass Solver(BaseFactorVae):\n def __init__(self, args):\n # Misc\n use_cuda = args.cuda and torch.cuda.is_available()\n self.device = 'cuda' if use_cuda else 'cpu'\n self.name = args.name\n self.max_iter = int(args.max_iter)\n self.print_iter = args.print_iter\n self.global_iter = 0\n self.pbar = tqdm(total=self.max_iter)\n\n # Data\n self.dset_dir = args.dset_dir\n self.dataset = args.dataset\n self.batch_size = args.batch_size\n self.data_loader, self.data = return_data(args)\n\n # Networks & Optimizers\n self.z_dim = args.z_dim\n self.gamma = args.gamma\n\n self.lr_VAE = args.lr_VAE\n self.beta1_VAE = args.beta1_VAE\n self.beta2_VAE = args.beta2_VAE\n\n self.lr_D = args.lr_D\n self.beta1_D = args.beta1_D\n self.beta2_D = args.beta2_D\n\n if args.dataset == 'dsprites':\n self.VAE = FactorVAE1(self.z_dim).to(self.device)\n self.nc = 1\n else:\n self.VAE = FactorVAE2(self.z_dim).to(self.device)\n self.nc = 3\n self.optim_VAE = optim.Adam(self.VAE.parameters(), lr=self.lr_VAE,\n betas=(self.beta1_VAE, self.beta2_VAE))\n\n self.D = Discriminator(self.z_dim).to(self.device)\n self.optim_D = optim.Adam(self.D.parameters(), lr=self.lr_D,\n betas=(self.beta1_D, self.beta2_D))\n\n self.nets = [self.VAE, self.D]\n\n # Visdom\n self.viz_on = args.viz_on\n self.win_id = dict(D_z='win_D_z', recon='win_recon', kld='win_kld', acc='win_acc')\n self.line_gather = DataGather('iter', 'soft_D_z', 'soft_D_z_pperm', 'recon', 'kld', 'acc')\n self.image_gather = DataGather('true', 'recon')\n if self.viz_on:\n self.viz_port = args.viz_port\n self.viz = visdom.Visdom(log_to_filename='./logging.log', offline=True)\n self.viz_ll_iter = args.viz_ll_iter\n self.viz_la_iter = args.viz_la_iter\n self.viz_ra_iter = args.viz_ra_iter\n self.viz_ta_iter = args.viz_ta_iter\n if not self.viz.win_exists(env=self.name+'/lines', win=self.win_id['D_z']):\n self.viz_init()\n\n # Checkpoint\n self.ckpt_dir = os.path.join(args.ckpt_dir, args.name)\n self.ckpt_save_iter = args.ckpt_save_iter\n mkdirs(self.ckpt_dir)\n if args.ckpt_load:\n self.load_checkpoint(args.ckpt_load)\n\n # Output(latent traverse GIF)\n self.output_dir = os.path.join(args.output_dir, args.name)\n self.output_save = args.output_save\n mkdirs(self.output_dir)\n\n def train(self):\n self.net_mode(train=True)\n\n ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)\n zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)\n metrics = []\n out = False\n while not out:\n for x_true1, x_true2 in self.data_loader:\n self.global_iter += 1\n self.pbar.update(1)\n\n self.optim_VAE.step()\n self.optim_D.step()\n\n x_true1 = x_true1.to(self.device)\n x_recon, mu, logvar, z = self.VAE(x_true1)\n vae_recon_loss = recon_loss(x_true1, x_recon)\n vae_kld = kl_divergence(mu, logvar)\n\n D_z = self.D(z)\n vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()\n\n vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss\n\n self.optim_VAE.zero_grad()\n vae_loss.backward(retain_graph=True)\n #self.optim_VAE.step()\n\n x_true2 = x_true2.to(self.device)\n z_prime = self.VAE(x_true2, no_dec=True)\n z_pperm = permute_dims(z_prime).detach()\n D_z_pperm = self.D(z_pperm)\n D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones))\n\n self.optim_D.zero_grad()\n D_tc_loss.backward()\n #self.optim_D.step()\n\n\n # Saving the training metrics\n if self.global_iter % 100 == 0:\n metrics.append({'its':self.global_iter,\n 'vae_loss': vae_loss.detach().to(torch.device(\"cpu\")).item(),\n 'D_loss': D_tc_loss.detach().to(torch.device(\"cpu\")).item(),\n 'recon_loss':vae_recon_loss.detach().to(torch.device(\"cpu\")).item(),\n 'tc_loss': vae_tc_loss.detach().to(torch.device(\"cpu\")).item()})\n\n # Saving the disentanglement metrics results\n if self.global_iter % 1500 == 0:\n score = self.disentanglement_metric() \n metrics.append({'its':self.global_iter, 'metric_score': score})\n self.net_mode(train=True) #To continue the training again\n\n if self.global_iter%self.print_iter == 0:\n self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(\n self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))\n\n if self.global_iter%self.ckpt_save_iter == 0:\n self.save_checkpoint(str(self.global_iter)+\".pth\")\n self.save_metrics(metrics)\n metrics = []\n\n if self.global_iter >= self.max_iter:\n out = True\n break\n\n self.pbar.write(\"[Training Finished]\")\n self.pbar.close()\n\n\ndef experiment1(args, seed):\n \"\"\" Ablation study comparing Vanilla FactorVAE\n with AD-FactorVAE\n \"\"\"\n args.max_iter = 150000\n args.ckpt_save_iter = 50000\n\n gammas = [10, 20, 30, 40, 50]\n for ga in gammas:\n args.gamma = ga\n args.name = \"disent_ga_{}_iters_{}_seed_{}/\".format(args.gamma, int(args.max_iter), seed)\n solver = Solver(args)\n solver.train()\n del solver\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Factor-VAE')\n\n parser.add_argument('--name', default='main', type=str, help='name of the experiment')\n parser.add_argument('--cuda', default=True, type=str2bool, help='enable cuda')\n parser.add_argument('--max_iter', default=1e6, type=float, help='maximum training iteration')\n parser.add_argument('--batch_size', default=64, type=int, help='batch size')\n\n parser.add_argument('--z_dim', default=10, type=int, help='dimension of the representation z')\n parser.add_argument('--gamma', default=6.4, type=float, help='gamma hyperparameter')\n parser.add_argument('--lr_VAE', default=1e-4, type=float, help='learning rate of the VAE')\n parser.add_argument('--beta1_VAE', default=0.9, type=float, help='beta1 parameter of the Adam optimizer for the VAE')\n parser.add_argument('--beta2_VAE', default=0.999, type=float, help='beta2 parameter of the Adam optimizer for the VAE')\n parser.add_argument('--lr_D', default=1e-4, type=float, help='learning rate of the discriminator')\n parser.add_argument('--beta1_D', default=0.5, type=float, help='beta1 parameter of the Adam optimizer for the discriminator')\n parser.add_argument('--beta2_D', default=0.9, type=float, help='beta2 parameter of the Adam optimizer for the discriminator')\n\n parser.add_argument('--dset_dir', default='data', type=str, help='dataset directory')\n parser.add_argument('--dataset', default='CelebA', type=str, help='dataset name')\n parser.add_argument('--image_size', default=64, type=int, help='image size. now only (64,64) is supported')\n parser.add_argument('--num_workers', default=2, type=int, help='dataloader num_workers')\n\n parser.add_argument('--viz_on', default=True, type=str2bool, help='enable visdom visualization')\n parser.add_argument('--viz_port', default=8097, type=int, help='visdom port number')\n parser.add_argument('--viz_ll_iter', default=1000, type=int, help='visdom line data logging iter')\n parser.add_argument('--viz_la_iter', default=5000, type=int, help='visdom line data applying iter')\n parser.add_argument('--viz_ra_iter', default=10000, type=int, help='visdom recon image applying iter')\n parser.add_argument('--viz_ta_iter', default=10000, type=int, help='visdom traverse applying iter')\n\n parser.add_argument('--print_iter', default=500, type=int, help='print losses iter')\n\n parser.add_argument('--ckpt_dir', default='checkpoints', type=str, help='checkpoint directory')\n parser.add_argument('--ckpt_load', default=None, type=str, help='checkpoint name to load')\n parser.add_argument('--ckpt_save_iter', default=10000, type=int, help='checkpoint save iter')\n\n parser.add_argument('--output_dir', default='outputs', type=str, help='output directory')\n parser.add_argument('--output_save', default=True, type=str2bool, help='whether to save traverse results')\n\n args = parser.parse_args()\n\n seeds = [1, 2]\n start = time.time()\n for seed in seeds:\n # To achieve reproducible results with sequential runs\n torch.backends.cudnn.enabled = True\n torch.backends.cudnn.benchmark = True\n\n init_seed = seed\n torch.manual_seed(init_seed)\n torch.cuda.manual_seed(init_seed)\n np.random.seed(init_seed)\n\n experiment1(args, seed)\n\n print(\"Finished after {} mins.\".format(str((time.time() - start) // 60)))\n", "sub_path": "disentanglement/vanilla.py", "file_name": "vanilla.py", "file_ext": "py", "file_size_in_byte": 9940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "utils.BaseFactorVae", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 32, "usage_type": "call"}, {"api_name": "dataset.return_data", "line_number": 38, "usage_type": "call"}, {"api_name": "model.FactorVAE1", "line_number": 53, "usage_type": "call"}, {"api_name": "model.FactorVAE2", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 58, "usage_type": "name"}, {"api_name": "model.Discriminator", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 62, "usage_type": "name"}, {"api_name": "utils.DataGather", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.DataGather", "line_number": 71, "usage_type": "call"}, {"api_name": "visdom.Visdom", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "utils.mkdirs", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "utils.mkdirs", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 98, "usage_type": "attribute"}, {"api_name": "ops.recon_loss", "line_number": 111, "usage_type": "call"}, {"api_name": "ops.kl_divergence", "line_number": 112, "usage_type": "call"}, {"api_name": "ops.permute_dims", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 140, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 182, "usage_type": "call"}, {"api_name": "utils.str2bool", "line_number": 185, "usage_type": "name"}, {"api_name": "utils.str2bool", "line_number": 203, "usage_type": "name"}, {"api_name": "utils.str2bool", "line_number": 217, "usage_type": "name"}, {"api_name": "time.time", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 225, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 226, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 231, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 235, "usage_type": "call"}]}
+{"seq_id": "200584477", "text": "#!/usr/bin/env python\n# title : meArmUI\n# description : A really simple wrapper for the python terminal\n# python_version : 3.4.1\n# =============================================================================\n__author__ = \"J. Nolan Hager (NHager)\"\n__email__ = \"jeffrey.hager.dev@gmail.com\"\n__version__ = \"1.0.0\"\n\nimport tkinter as tk\nimport serial\nimport datetime\n\n\nclass serialGUI(tk.Tk):\n def __init__(self):\n super(serialGUI, self).__init__()\n self.title('Serial GUI')\n\n self._connected = False\n self._ard = {}\n self._port = ''\n self._serial_timeout = 1\n self._baud_rate = 9600\n\n self.toolbar = tk.Frame(self)\n self.connect_txt = tk.Entry(self)\n self.connect_btn = tk.Button(self, text='Connect', command=self.connect_to_serial)\n self.quit_btn = tk.Button(self, text='Quit', command=self.exit_program)\n self.input_display = tk.Text(self, wrap='word', bg='black', width=50, height=20, fg='green',\n relief=tk.SUNKEN,\n yscrollcommand='TRUE')\n self.serial_display = tk.Text(self, wrap='word', bg='black', width=50, height=20, fg='green',\n relief=tk.SUNKEN,\n yscrollcommand='TRUE')\n self.focus_set()\n self.cfg_toolbar()\n self.cfg_input_display()\n self.bind_input_display_events()\n self.cfg_serial_display()\n self.after((self._serial_timeout * 1000), self.read_serial) # Registering the read task\n\n def cmd_timestamp(self):\n return datetime.datetime.now().strftime(\"%H:%M:%S\")\n\n def cfg_toolbar(self):\n self.quit_btn = tk.Button(self, text='Quit', command=self.exit_program)\n self.toolbar.pack(side='bottom', fill='x')\n self.connect_txt.pack(in_=self.toolbar, side='left')\n self.connect_btn.pack(in_=self.toolbar, side='left')\n self.quit_btn.pack(in_=self.toolbar, side='right')\n\n def exit_program(self):\n self.destroy()\n\n def connect_to_serial(self, event=None):\n self.serial_display_write('Attempting to connect to serial')\n self._port = self.connect_txt.get()\n if self._port:\n self._ard = serial.Serial(self._port, self._baud_rate, timeout=self._serial_timeout)\n self._connected = True\n self.serial_display_write('Connected to serial port at ' + self._port)\n else:\n self.serial_display_write('No serial port available....')\n\n def read_serial(self):\n if self._connected:\n serial_data = self._ard.readline()\n if serial_data:\n self.serial_display_write(serial_data)\n self.after((self._serial_timeout * 1000), self.read_serial) # Reschedule the method\n\n def write_serial(self, text):\n self._ard.write(str.encode(text))\n\n def input_display_key_event(self, event=None):\n if event.keysym:\n self.input_display_write(event.keysym)\n self.write_serial(event.keysym)\n\n def input_display_write(self, text):\n self.input_display.config(state='normal')\n self.input_display.insert('end', self.cmd_timestamp() + '> ', 'serial_write')\n self.input_display.insert('end', text + '\\n')\n self.input_display.see('end')\n self.input_display.config(state='disabled')\n\n def serial_display_write(self, text):\n if isinstance(text, bytes):\n text = text.decode(\"utf-8\")\n\n output = self.cmd_timestamp() + '> '\n if self._port:\n output = self._port + ' ' + self.cmd_timestamp() + '>'\n\n self.serial_display.config(state='normal')\n self.serial_display.insert('end', output, 'serial_write')\n self.serial_display.insert('end', text + '\\n')\n self.serial_display.see('end')\n self.serial_display.config(state='disabled')\n\n def bind_input_display_events(self):\n self.input_display.bind(\"<Key>\", self.input_display_key_event)\n self.input_display.pack()\n\n def cfg_input_display(self):\n self.input_display.pack(side='left', fill='both', expand=True)\n self.input_display.tag_configure('serial_write_info', foreground='#FF0000')\n self.input_display.tag_configure('serial_write', foreground='#008000')\n\n def cfg_serial_display(self):\n self.serial_display.pack(side='right', fill='both', expand=True)\n self.serial_display.tag_configure('serial_read_info', foreground='#FF0000')\n self.serial_display.tag_configure('serial_read', foreground='#008000')\n self.serial_display.config(state='disabled')\n\n\nif __name__ == \"__main__\":\n gui = serialGUI()\n gui.mainloop()\n", "sub_path": "driver.py", "file_name": "driver.py", "file_ext": "py", "file_size_in_byte": 4734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tkinter.Tk", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 26, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.SUNKEN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tkinter.Text", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.SUNKEN", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 47, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 60, "usage_type": "call"}]}
+{"seq_id": "643003627", "text": "from http.client import RemoteDisconnected\nfrom xmlrpc.client import Fault\n\nfrom fastapi import APIRouter, HTTPException, Path\nfrom XenGarden.Host import Host\nfrom XenGarden.session import create_session\n\nfrom API.v1.Host.serialize import serialize\nfrom app.settings import Settings\n\nrouter = APIRouter()\n\n\n@router.get(\"/{cluster_id}/host/{host_uuid}\")\nasync def host_get_by_uuid(\n cluster_id: str = Path(\n default=None, title=\"cluster_id\", description=\"Cluster ID\"\n ),\n host_uuid: str = Path(\n default=None, title=\"host_uuid\", description=\"Host UUID\"\n ),\n):\n \"\"\" Get Host by UUID \"\"\"\n try:\n # KeyError Handling\n try:\n session = create_session(\n _id=cluster_id, get_xen_clusters=Settings.get_xen_clusters()\n )\n except KeyError as key_error:\n raise HTTPException(\n status_code=400, detail=f\"{key_error} is not a valid path\"\n )\n\n host: Host = Host.get_by_uuid(session=session, uuid=host_uuid)\n\n if host is not None:\n ret = dict(success=True, data=serialize(host))\n else:\n ret = dict(success=False)\n\n session.xenapi.session.logout()\n return ret\n except Fault as xml_rpc_error:\n raise HTTPException(\n status_code=int(xml_rpc_error.faultCode),\n detail=xml_rpc_error.faultString,\n )\n except RemoteDisconnected as rd_error:\n raise HTTPException(status_code=500, detail=rd_error.strerror)\n", "sub_path": "API/v1/Host/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 1514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "fastapi.APIRouter", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "fastapi.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "XenGarden.session.create_session", "line_number": 27, "usage_type": "call"}, {"api_name": "app.settings.Settings.get_xen_clusters", "line_number": 28, "usage_type": "call"}, {"api_name": "app.settings.Settings", "line_number": 28, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 31, "usage_type": "call"}, {"api_name": "XenGarden.Host.Host", "line_number": 35, "usage_type": "name"}, {"api_name": "XenGarden.Host.Host.get_by_uuid", "line_number": 35, "usage_type": "call"}, {"api_name": "API.v1.Host.serialize.serialize", "line_number": 38, "usage_type": "call"}, {"api_name": "xmlrpc.client.Fault", "line_number": 44, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 45, "usage_type": "call"}, {"api_name": "http.client.RemoteDisconnected", "line_number": 49, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 50, "usage_type": "call"}]}
+{"seq_id": "350038277", "text": "__all__ = [\"RdflibDjangoConfig\"]\n\nfrom django.apps import AppConfig\nfrom django.db.models.signals import post_save\n\nextra_namespaces = [\n {\n \"prefix\": \"xml\",\n \"fixed\": True,\n \"uri\": \"http://www.w3.org/XML/1998/namespace\"\n }\n]\n\n\ndef UpdateNamespaces(sender, instance, created, **kwargs):\n if not created:\n return\n from .models import NamespaceModel\n from django.db.models import Q\n from rdflib import namespace\n\n for val in extra_namespaces:\n if not val.get(\"fixed\", False):\n if not NamespaceModel.objects.filter(\n Q(uri=val[\"uri\"]) | Q(prefix=val[\"prefix\"]),\n store=instance\n ):\n NamespaceModel.objects.create(\n store=instance,\n uri=val[\"uri\"],\n prefix=val[\"prefix\"]\n )\n else:\n if not NamespaceModel.objects.filter(\n Q(uri=val[\"uri\"]) & Q(prefix=val[\"prefix\"]),\n store=None\n ):\n # cleanup old namespaces\n NamespaceModel.objects.filter(\n Q(uri=val[\"uri\"]) | Q(prefix=val[\"prefix\"])\n ).delete()\n NamespaceModel.objects.create(\n store=None,\n uri=val[\"uri\"],\n prefix=val[\"prefix\"]\n )\n\n for key in namespace.__all__:\n val = getattr(namespace, key)\n if isinstance(val, namespace.Namespace):\n if not NamespaceModel.objects.filter(\n Q(uri=val.uri) | Q(prefix=key.lower()),\n store=instance\n ):\n NamespaceModel.objects.create(\n prefix=key.lower(),\n uri=val.uri,\n store=instance\n )\n elif isinstance(val, namespace.ClosedNamespace):\n if not NamespaceModel.objects.filter(\n Q(uri=val.uri) & Q(prefix=key.lower()),\n store=None\n ):\n NamespaceModel.objects.filter(\n Q(uri=val.uri) | Q(prefix=key.lower()),\n store=None\n ).delete()\n NamespaceModel.objects.create(\n prefix=key.lower(),\n uri=val.uri,\n store=None\n )\n # cleanup old namespaces\n NamespaceModel.objects.filter(\n Q(uri=val.uri) | Q(prefix=key.lower())\n ).exclude(store__isnull=True).delete()\n\n\nclass RdflibDjangoConfig(AppConfig):\n name = \"rdflib_django\"\n\n def ready(self):\n from .models import Store\n post_save.connect(\n UpdateNamespaces, sender=Store\n )\n", "sub_path": "rdflib_django/apps.py", "file_name": "apps.py", "file_ext": "py", "file_size_in_byte": 2759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "models.NamespaceModel.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 25, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.create", "line_number": 28, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 28, "usage_type": "name"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 35, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 40, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.create", "line_number": 42, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 42, "usage_type": "name"}, {"api_name": "rdflib.namespace.__all__", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rdflib.namespace", "line_number": 48, "usage_type": "name"}, {"api_name": "rdflib.namespace", "line_number": 49, "usage_type": "argument"}, {"api_name": "rdflib.namespace.Namespace", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rdflib.namespace", "line_number": 50, "usage_type": "name"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 52, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.create", "line_number": 55, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 55, "usage_type": "name"}, {"api_name": "rdflib.namespace.ClosedNamespace", "line_number": 60, "usage_type": "attribute"}, {"api_name": "rdflib.namespace", "line_number": 60, "usage_type": "name"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 62, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 66, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects.create", "line_number": 69, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 69, "usage_type": "name"}, {"api_name": "models.NamespaceModel.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "models.NamespaceModel.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.NamespaceModel", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 76, "usage_type": "call"}, {"api_name": "django.apps.AppConfig", "line_number": 80, "usage_type": "name"}, {"api_name": "django.db.models.signals.post_save.connect", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 85, "usage_type": "name"}, {"api_name": "models.Store", "line_number": 86, "usage_type": "name"}]}
+{"seq_id": "68973774", "text": "import caffe\nimport cv2\nimport os\nfrom model_path import get_caffe_model_path\nfrom retinaface_util import RetinaFace\n\ng_obj_threshold = 0.5\ng_nms_threshold = 0.4\ng_img_path = '/workspace/testpics/probe.jpg'\nretinaface_w, retinaface_h = 600, 600\n\ndef inference_from_jpg():\n detector = RetinaFace()\n img = cv2.imread(g_img_path)\n x = detector.preprocess(img, retinaface_w, retinaface_h)\n\n proto, weight, _ = get_caffe_model_path('retinaface_mnet_0.25')\n net = caffe.Net(proto, weight, caffe.TEST)\n net.blobs['data'].reshape(1, 3, x.shape[2], x.shape[3])\n net.blobs['data'].data[...] = x\n y = net.forward()\n faces, landmarks = detector.postprocess(y, retinaface_w, retinaface_h)\n draw_image = detector.draw(img, faces, landmarks, True)\n\n cv2.imshow('face', draw_image)\n cv2.waitKey(0)\n\n\nif __name__ == '__main__':\n caffe.set_mode_gpu()\n inference_from_jpg()\n\n\n", "sub_path": "experiment/widerface/retinaface/inference_demo_retinaface.py", "file_name": "inference_demo_retinaface.py", "file_ext": "py", "file_size_in_byte": 899, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "retinaface_util.RetinaFace", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 14, "usage_type": "call"}, {"api_name": "model_path.get_caffe_model_path", "line_number": 17, "usage_type": "call"}, {"api_name": "caffe.Net", "line_number": 18, "usage_type": "call"}, {"api_name": "caffe.TEST", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 26, "usage_type": "call"}, {"api_name": "caffe.set_mode_gpu", "line_number": 30, "usage_type": "call"}]}
+{"seq_id": "41529924", "text": "import psycopg2\nimport getpass\n\nclass DataSource:\n def __init__(self):\n pass\n\n def connect(self, user, password):\n '''\n Establishes a connection to the database with the following credentials:\n user - username, which is also the name of the database\n password - the password for this database on perlman\n\n Returns: a database connection\n\n Note: exits if a connection cannot be established\n '''\n try:\n connection = psycopg2.connect(host = \"localhost\",database=user, user=user, password=password)\n\n except Exception as e:\n print(\"Connection error: \", e)\n exit()\n return connection\n\n def execute_query(self, connection, query):\n '''\n Returns a string containing the result of the query\n\n PARAMETERS:\n String query entered by user\n\n RETURN:\n String of query result\n '''\n try:\n cursor = connection.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n return result\n\n\n def get_winner(self, connection=\"\", year=0, category=\"\"):\n '''\n Returns a string containing winners of the specified year and category\n\n PARAMETERS:\n int year for the year of release to be examined\n String category for the Oscar awards cateogry to be examined\n\n RETURN:\n String containing name of picture and person that won the specified category in the specified year\n '''\n\n try:\n if category == \"picture\":\n award = \"bestPicture\"\n person = \"\"\n elif category == \"actor\":\n award = \"bestActor\"\n person = \", actor\"\n elif category == \"actress\":\n award = \"bestActress\"\n person = \", actress\"\n elif category == \"director\":\n award = \"bestDirector\"\n person = \", director\"\n\n if year > 1926 and year < 2019:\n get_year = \"\"\n year_query = \" WHERE yearOfRelease = \" + str(year)\n else:\n get_year = \", yearOfRelease\"\n year_query = \"\"\n\n\n query = \"SELECT \" + award + person + get_year + \" FROM winners\" + year_query\n result = self.execute_query(connection, query)\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return result\n\n\n\n def get_by_year(self, connection, year, category):\n '''\n Returns a string containing winning pictures for the specified year and category\n\n PARAMETERS:\n int year for the year of release to be examined\n String category for the Oscar awards cateogry to be examined\n\n RETURN:\n String containing names of pictures that won the specified category in the specified year\n '''\n try:\n if category == \"picture\":\n award = \"bestPicture\"\n elif category == \"actor\":\n award = \"bestActor\"\n person = \", actor\"\n elif category == \"actress\":\n award = \"bestActress\"\n person = \", actress\"\n elif category == \"director\":\n award = \"bestDirector\"\n person = \", director\"\n\n\n query = \"SELECT \" + award + \" FROM winners WHERE yearOfRelease = \" + str(year)\n picture = self.execute_query(connection, query)\n\n item = \"*\"\n result = self.get_by_picture(connection, item, picture[0][0])\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return result\n\n\n\n\n def get_by_picture(self, connection=\"\", item = \"*\", picture=\"\"):\n '''\n Returns a string containing awards won by the specified picture.\n\n PARAMETERS:\n String item\n String picture for the picture to examine\n\n RETURN:\n String containing all the awards won by the specified picture\n '''\n try:\n if item == \"genre\":\n query = \"SELECT subgenre FROM films WHERE picture = '\" + picture + \"'\"\n if self.execute_query(connection, query) != []:\n subgenre = self.execute_query(connection, query)[0][0]\n if subgenre == \"Drama\" or subgenre == \"NA\":\n query = \"SELECT genre FROM films WHERE picture = '\" + picture + \"'\"\n genre = self.execute_query(connection, query)[0][0]\n result = genre\n else:\n result = subgenre\n else:\n query = \"SELECT genre FROM films WHERE picture = '\" + picture + \"'\"\n genre = self.execute_query(connection, query)[0][0]\n result = genre\n\n else:\n query = \"SELECT DISTINCT \" + item + \" FROM films WHERE picture = '\" + picture + \"'\"\n result = self.execute_query(connection, query)\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return result\n\n\n def get_pictures(self, connection, start, end):\n '''\n Returns an array containing all awarded pictures in the year range.\n\n PARAMETERS:\n int the beginning year of the range\n int the ending year of the range\n\n RETURN:\n Array containing all awarded pictures\n '''\n start = start - 1\n end = end - 1\n\n try:\n query = \"SELECT DISTINCT bestPicture, bestActor, bestActress, bestDirector FROM winners WHERE yearOfRelease BETWEEN \" + str(start) + \" AND \" + str(end)\n result = self.execute_query(connection, query)\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return result\n\n def get_Score(self, connection, start, end):\n '''\n Returns an array of integers which include Metacritic scores of Best Picture winners in the year range.\n\n PARAMETERS:\n int the beginning year of the range\n int the ending year of the range\n\n RETURN:\n Array of integers of average scores of all Best Picture winners in the year range.\n '''\n scores = []\n bestPics = []\n category = \"picture\"\n\n\n try:\n for year in range(start, end+1):\n bestPic = self.get_winner(connection, year, category)\n bestPics.append(bestPic)\n for picture in bestPics:\n picture = picture[0][0]\n if \"'\" in picture:\n picture = picture.replace(\"'\", \"''\")\n query = \"SELECT DISTINCT score FROM films WHERE picture = '\" + picture + \"'\"\n score = self.execute_query(connection, query)[0][0]\n scores.append(score)\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return scores\n\n def get_avgScore(self, connection, scores):\n '''\n Returns an integer value of average Metacritic scores of Best Picture winners in the year range.\n\n PARAMETERS:\n scores - array of int metacritic scores\n\n RETURN:\n Integer of average Metacritic score of specific year range.\n '''\n\n total = 0.0\n for score in scores:\n total += score\n print(scores)\n print(\"len\", len(scores))\n avgScore = total/len(scores)\n\n return round(avgScore, 1)\n\n def get_Rating(self, connection, start, end):\n '''\n Returns an array of integers which include IMDb ratings of Best Picture winners in the year range.\n\n PARAMETERS:\n int the beginning year of the range\n int the ending year of the range\n\n RETURN:\n Array of integers of average ratings of all Best Picture winners in the year range.\n '''\n\n\n ratings = []\n bestPics = []\n category = \"picture\"\n\n\n try:\n for year in range(start, end+1):\n bestPic = self.get_winner(connection, year, category)\n bestPics.append(bestPic)\n for picture in bestPics:\n picture = picture[0][0]\n if \"'\" in picture:\n picture = picture.replace(\"'\", \"''\")\n query = \"SELECT rating FROM films WHERE picture = '\" + picture + \"'\"\n rating = self.execute_query(connection, query)[0][0]\n ratings.append(rating)\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return ratings\n\n def get_avgRating(self, connection, ratings):\n '''\n Returns an integer value of average IMDb ratings of Best Picture winners in the year range.\n\n PARAMETERS:\n ratings - array of float IMDB ratings\n\n RETURN:\n Float of average IMDb rating of specific year range.\n '''\n\n total = 0.0\n for rating in ratings:\n total += rating\n\n avgRating = total/len(ratings)\n\n return round(avgRating, 1)\n\n def get_genre(self, connection, pictures):\n '''\n Returns an array of sets which include genre name and corresponding number of films in that genre within a given year.\n\n PARAMETERS:\n Integer the beginning year of the range\n Integer the ending year of the range\n\n RETURN:\n Array of sets which have genre and count\n '''\n genres = []\n try:\n for pictureArray in pictures:\n for picture in pictureArray:\n if \"'\" in picture:\n picture = picture.replace(\"'\", \"''\")\n query = \"SELECT subgenre FROM films WHERE picture = '\" + picture + \"'\"\n if self.execute_query(connection, query) != []:\n subgenre = self.execute_query(connection, query)[0][0]\n if subgenre == \"Drama\" or subgenre == \"NA\":\n query = \"SELECT genre FROM films WHERE picture = '\" + picture + \"'\"\n genre = self.execute_query(connection, query)[0][0]\n genres.append(genre)\n else:\n genres.append(subgenre)\n\n except Exception as e:\n print(\"Connection error: \", e)\n return None\n\n return genres\n\n\n def count_genre(self, connection, genres):\n '''\n Returns an integer which specifies the number of times a specific genre has won in a decade.\n\n PARAMETERS:\n Array of sets which have genre and count\n\n RETURN:\n Integer of count of number of times a specific genre has won in a decade\n '''\n samples = ['Drama', 'Sport', 'History', 'Comedy', 'Biography', 'Crime', 'Adventure', 'Action', 'Western', 'Musical', 'Romance', 'Thriller', 'Mystery', 'Sci-Fi', 'Family']\n counts = []\n for sample in samples:\n counts.append([sample, 0])\n for genre in genres:\n for count in counts:\n if genre == count[0]:\n count[1] += 1\n return counts\n\n\ndef main():\n ds = DataSource()\n user = 'kuritar'\n password = 'lamp977python'\n connection = ds.connect(user, password)\n\n results = []\n\n film = \"Green Book\"\n year = 2000\n category = \"actor\"\n item = \"genre\"\n\n result_pictures = ds.get_pictures(connection, 1928, 2017)\n pictures = result_pictures\n result_genre = ds.get_genre(connection, pictures)\n\n result_testScore = ds.get_Score(connection, 1928, 1930)\n results.append([\"result_testScore\", len(result_testScore)])\n\n categories = [\"picture\",\"actor\",\"actress\",\"director\"]\n infos = []\n\n for year in range(1928, 1930):\n for category in categories:\n result = ds.get_winner(connection, year, category)\n\n for result in results:\n if result is not None:\n print(\"Query results: \" + str(result[0]) + str(result[1]))\n else:\n print(\"The result was None.\")\n\n connection.close()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "backend/datasource.py", "file_name": "datasource.py", "file_ext": "py", "file_size_in_byte": 12537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "psycopg2.connect", "line_number": 19, "usage_type": "call"}]}
+{"seq_id": "600998681", "text": "import scrapy\r\nfrom scrapy.loader import ItemLoader\r\nfrom demo1.items import QuoteItem\r\n\r\n# adding loader to cleaning the data and stored in json correctly\r\n# removing \\n unicodes and more from json formats\r\n\r\nclass GoodReadsSpider(scrapy.Spider):\r\n #identity\r\n name = 'cleanquote'\r\n\r\n # requests\r\n def start_requests(self):\r\n url= 'https://www.goodreads.com/quotes?page=1'\r\n yield scrapy.Request(url=url,callback=self.parse)\r\n \r\n # response\r\n def parse(self,response):\r\n for quote in response.selector.xpath(\"//div[@class='quote']\"):\r\n loader = ItemLoader(item=QuoteItem(), selector=quote,response=response)\r\n loader.add_xpath('text',\".//div[@class='quoteText']/text()[1]\")\r\n loader.add_xpath('author',\"//div[@class='quoteText']/child::span\")\r\n loader.add_xpath('tags',\".//div[@class='greyText smallText left']/a\")\r\n yield loader.load_item()\r\n # /quotes?page=2\r\n next_page= response.selector.xpath(\"//a[@class='next_page']/@href\").extract_first()\r\n if next_page is not None:\r\n next_page_link= response.urljoin(next_page)\r\n yield scrapy.Request(url=next_page_link, callback=self.parse)", "sub_path": "spiders/_3_adding_loader_for_json.py", "file_name": "_3_adding_loader_for_json.py", "file_ext": "py", "file_size_in_byte": 1224, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "scrapy.Spider", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.loader.ItemLoader", "line_number": 20, "usage_type": "call"}, {"api_name": "demo1.items.QuoteItem", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "362010061", "text": "from graph import Graph\nfrom sample import sample\nfrom train import Optimizer\nfrom group import Louvain, groups2inv_index, pure_override_nodes\nimport numpy as np\nimport pandas as pd\nfrom numpy.linalg import norm\nimport time\nfrom multiprocessing import Pool\n\n###################\nK_SIZE = 1000\nDIMENSION = 128\nLAMBDA = 10\nETA = 0.1\nMAX_ITER = 5\n###################\nMERGE = (2000, 8000)\nSAMPLE_METHOD = 'set_cover_undir'\nRANDOM_GROUPING = True\nORDER = 1\nWITHDIAG = True\n###################\nDATASET = 'wiki'\nDATADIR = 'data\\\\'\nFILE_NAME = '_'.join([\n DATASET,\n 'k=%s' % K_SIZE,\n 'd=%s' % DIMENSION,\n 'sample=%s' % SAMPLE_METHOD,\n 'lambda=%.2f' % LAMBDA,\n 'eta=%.2f' % ETA,\n 'max-iter=%02d' % MAX_ITER\n]) + '.vec'\n\n\ndef wrap_train(arg):\n return arg[0].train(arg[1])\n\n\nif __name__ == '__main__':\n ppt = time.time()\n\n f = open(DATADIR + DATASET + '\\\\' + FILE_NAME, 'w')\n\n pt = time.time()\n net = Graph(DATADIR + DATASET + '\\\\links.txt', typ='dir', order=ORDER, withdiag=WITHDIAG)\n print('READ TIME: %.2f' % (time.time() - pt))\n\n f.write('%d %d %d\\n' % (net.nVertices, net.nEdges, DIMENSION))\n\n pt = time.time()\n grouping_model = Louvain(net, rand=RANDOM_GROUPING)\n groups = grouping_model.execute(merge=MERGE)\n print('GROUP TIME: %.2f' % (time.time() - pt))\n\n group_sizes = [len(t) for t in groups]\n print('Grouping Results:')\n print(pd.value_counts(group_sizes))\n inv_index_original = groups2inv_index(groups, net.nVertices)\n # sizes_index = [group_sizes[t - 1] for t in inv_index_original]\n\n pt = time.time()\n # k_set = sample(net, k=K_SIZE, method='deg_deter')\n k_set = sample(net, k=K_SIZE, method=SAMPLE_METHOD) #, vertex_group_sizes=sizes_index)\n print('SAMPLE TIME: %.2f' % (time.time() - pt))\n\n inv_index = groups2inv_index(groups, net.nVertices, k_set)\n pure_override_nodes(groups, inv_index)\n groups = [k_set] + groups\n\n pt = time.time()\n model = Optimizer(net, groups, dim=DIMENSION, lam=LAMBDA, eta=ETA, max_iter=MAX_ITER,\n sample_strategy=SAMPLE_METHOD, verbose=True)\n print('INITIAL OPTIMIZER TIME (SVD): %.2f' % (time.time() - pt))\n\n with Pool(processes=4) as pool:\n grouped_embeddings = pool.map()\n\n f.close()\n\n", "sub_path": "myparallel.py", "file_name": "myparallel.py", "file_ext": "py", "file_size_in_byte": 2254, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "graph.Graph", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "group.Louvain", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.value_counts", "line_number": 59, "usage_type": "call"}, {"api_name": "group.groups2inv_index", "line_number": 60, "usage_type": "call"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "sample.sample", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "group.groups2inv_index", "line_number": 68, "usage_type": "call"}, {"api_name": "group.pure_override_nodes", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "train.Optimizer", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 77, "usage_type": "call"}]}
+{"seq_id": "584403819", "text": "from django import forms\n\nclass PoetrySearchForm(forms.Form):\n \"Search poetry by title/author/keyword\"\n keyword = forms.CharField(required=False)\n title = forms.CharField(required=False)\n author = forms.CharField(required=False)\n\n def clean(self):\n \"\"\"Custom form validation.\"\"\"\n cleaned_data = self.cleaned_data\n\n keyword = cleaned_data.get('keyword')\n title = cleaned_data.get('title')\n author = cleaned_data.get('author')\n \n # raise forms.ValidationError(\"Date invalid\")\n \n #Validate at least one term has been entered\n if not title and not author and not keyword:\n del cleaned_data['title']\n del cleaned_data['author']\n del cleaned_data['keyword']\n\n raise forms.ValidationError(\"Please enter search terms.\")\n\n return cleaned_data\n", "sub_path": "greatwar/poetry/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.forms.Form", "line_number": 3, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 3, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}]}
+{"seq_id": "404453923", "text": "# PyQt5 Video player\n#!/usr/bin/env python\n\nfrom PyQt5.QtCore import QDir, Qt, QUrl, pyqtSignal\nfrom PyQt5.QtMultimedia import QMediaContent, QMediaPlayer\nfrom PyQt5.QtMultimediaWidgets import QVideoWidget\nfrom PyQt5.QtWidgets import (QApplication, QFileDialog, QHBoxLayout, QLabel,\n QPushButton, QSizePolicy, QSlider, QStyle, QVBoxLayout, QWidget)\nfrom PyQt5.QtWidgets import QMainWindow,QWidget, QPushButton, QAction, QLineEdit, QDesktopWidget\nfrom PyQt5.QtGui import QIcon\nimport sys\nfrom tracker import ObjectRectangle, Tracker\n\nclass MainWindow(QWidget):\n def __init__(self):\n super().__init__()\n self.initUI()\n\n def initUI(self):\n self.setWindowTitle('Трекер движения объекта')\n\n layout = QVBoxLayout()\n self.firstFrameBtn = QPushButton(\"1ый кадр\")\n self.firstFrameLine = QLineEdit(self)\n self.secondFrameLine = QLineEdit(self)\n\n self.xObjectLine = QLineEdit(self)\n self.yObjectLine = QLineEdit(self)\n self.xOffsetObjectLine = QLineEdit(self)\n self.yOffsetObjectLine = QLineEdit(self)\n\n self.frameCount = QLineEdit(self)\n\n self.frameCountLabel = QLabel(\"Кол-во кадров\")\n\n self.selectBtn = QPushButton(\"Выбрать объект\")\n self.selectBtn.setEnabled(False)\n self.startBtn = QPushButton(\"Старт!\")\n\n self.firstFrameBtn.clicked.connect(self.selectFile)\n self.selectBtn.clicked.connect(self.selectObject)\n self.startBtn.clicked.connect(self.start)\n\n self.firstFrameLine.textChanged.connect(self.enableSelectBtn)\n self.secondFrameLine.textChanged.connect(self.enableSelectBtn)\n\n h1l = QHBoxLayout()\n h1l.addWidget(self.firstFrameLine, 3)\n h1l.addWidget(self.secondFrameLine, 1)\n h1l.addWidget(self.firstFrameBtn, 1)\n\n h2l = QHBoxLayout()\n h2l.addWidget(self.xObjectLine)\n h2l.addWidget(self.yObjectLine)\n h2l.addWidget(self.xOffsetObjectLine)\n h2l.addWidget(self.yOffsetObjectLine)\n h2l.addWidget(self.selectBtn)\n\n v1l = QVBoxLayout()\n v1l.addWidget(self.frameCountLabel)\n v1l.addWidget(self.frameCount)\n\n h3l = QHBoxLayout()\n h3l.addLayout(v1l)\n\n layout.addLayout(h1l)\n layout.addLayout(h2l)\n layout.addLayout(h3l)\n layout.addWidget(self.startBtn)\n self.setLayout(layout)\n\n self.player = VideoWindow(self)\n \n self.setGeometry(0, 0, 500, 100)\n window = self.frameGeometry()\n centerPoint = QDesktopWidget().availableGeometry().center()\n window.moveCenter(centerPoint)\n self.move(window.topLeft())\n\n self.show()\n\n def selectFile(self):\n self.player.show()\n\n def enableSelectBtn(self):\n if self.firstFrameLine.text() != \"\" and self.secondFrameLine.text() != \"\":\n self.selectBtn.setEnabled(True)\n else:\n self.selectBtn.setEnabled(False)\n\n def selectObject(self):\n obj = ObjectRectangle(self.firstFrameLine.text(), int(self.secondFrameLine.text()))\n obj.showFrame()\n coord = obj.getCoordinates()\n\n self.xObjectLine.setText(str(coord[0]))\n self.yObjectLine.setText(str(coord[1]))\n self.xOffsetObjectLine.setText(str(coord[2]))\n self.yOffsetObjectLine.setText(str(coord[3]))\n\n def start(self):\n self.startBtn.setEnabled(False)\n self.selectBtn.setEnabled(False)\n self.firstFrameBtn.setEnabled(False)\n self.startBtn.setText(\"Пожалуйста, подождите пару минут...\")\n \n coord = [\n int(self.xObjectLine.text()),\n int(self.yObjectLine.text()),\n int(self.xOffsetObjectLine.text()),\n int(self.yOffsetObjectLine.text()),\n ]\n\n t = Tracker(\n self.firstFrameLine.text(),\n int(self.secondFrameLine.text()),\n int(self.frameCount.text()),\n coord)\n t.find_object()\n t.draw_frames()\n \n self.startBtn.setText(\"Старт!\")\n self.startBtn.setEnabled(True)\n self.selectBtn.setEnabled(True)\n self.firstFrameBtn.setEnabled(True)\n\nclass VideoWindow(QMainWindow):\n\n def __init__(self, parent=None):\n super(VideoWindow, self).__init__(parent)\n self.setWindowTitle(\"Выберите первый кадр\")\n self.resize(800, 600)\n \n self.mediaPlayer = QMediaPlayer(None, QMediaPlayer.VideoSurface)\n self.fileName = None\n\n videoWidget = QVideoWidget()\n\n self.playButton = QPushButton()\n self.playButton.setEnabled(False)\n self.playButton.setIcon(self.style().standardIcon(QStyle.SP_MediaPlay))\n self.playButton.clicked.connect(self.play)\n\n self.okBtn = QPushButton(\"OK\")\n self.okBtn.clicked.connect(self.selectFrame)\n\n self.positionSlider = QSlider(Qt.Horizontal)\n self.positionSlider.setRange(0, 0)\n self.positionSlider.sliderMoved.connect(self.setPosition)\n\n self.errorLabel = QLabel()\n self.errorLabel.setSizePolicy(QSizePolicy.Preferred,\n QSizePolicy.Maximum)\n\n # Create new action\n openAction = QAction(QIcon('open.png'), '&Open', self) \n openAction.setShortcut('Ctrl+O')\n openAction.setStatusTip('Open movie')\n openAction.triggered.connect(self.openFile)\n\n # Create menu bar and add action\n menuBar = self.menuBar()\n fileMenu = menuBar.addMenu('&File')\n fileMenu.addAction(openAction)\n\n # Create a widget for window contents\n wid = QWidget(self)\n self.setCentralWidget(wid)\n\n # Create layouts to place inside widget\n controlLayout = QHBoxLayout()\n controlLayout.setContentsMargins(0, 0, 0, 0)\n controlLayout.addWidget(self.playButton)\n controlLayout.addWidget(self.positionSlider)\n\n layout = QVBoxLayout()\n layout.addWidget(videoWidget)\n layout.addLayout(controlLayout)\n layout.addWidget(self.okBtn)\n layout.addWidget(self.errorLabel)\n\n # Set widget to contain window contents\n wid.setLayout(layout)\n\n self.mediaPlayer.setVideoOutput(videoWidget)\n self.mediaPlayer.stateChanged.connect(self.mediaStateChanged)\n self.mediaPlayer.positionChanged.connect(self.positionChanged)\n self.mediaPlayer.durationChanged.connect(self.durationChanged)\n self.mediaPlayer.error.connect(self.handleError)\n\n def openFile(self):\n self.fileName, _ = QFileDialog.getOpenFileName(self, \"Open Movie\")\n\n if self.fileName != '':\n self.mediaPlayer.setMedia(\n QMediaContent(QUrl.fromLocalFile(self.fileName)))\n self.playButton.setEnabled(True)\n\n def play(self):\n if self.mediaPlayer.state() == QMediaPlayer.PlayingState:\n self.mediaPlayer.pause()\n else:\n self.mediaPlayer.play()\n\n def mediaStateChanged(self, state):\n if self.mediaPlayer.state() == QMediaPlayer.PlayingState:\n self.playButton.setIcon(\n self.style().standardIcon(QStyle.SP_MediaPause))\n else:\n self.playButton.setIcon(\n self.style().standardIcon(QStyle.SP_MediaPlay))\n\n def positionChanged(self, position):\n self.positionSlider.setValue(position)\n\n def durationChanged(self, duration):\n self.positionSlider.setRange(0, duration)\n\n def setPosition(self, position):\n self.mediaPlayer.setPosition(position)\n\n def handleError(self):\n self.playButton.setEnabled(False)\n self.errorLabel.setText(\"Error: \" + self.mediaPlayer.errorString())\n\n def selectFrame(self):\n if self.fileName is not None:\n self.parent().firstFrameLine.setText(self.fileName)\n self.parent().secondFrameLine.setText(str(self.positionSlider.value()))\n self.close()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n mw = MainWindow()\n sys.exit(app.exec_())", "sub_path": "lab5/report/code/ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 8100, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 47, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 63, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDesktopWidget", "line_number": 76, "usage_type": "call"}, {"api_name": "tracker.ObjectRectangle", "line_number": 92, "usage_type": "call"}, {"api_name": "tracker.Tracker", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 127, "usage_type": "name"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer.VideoSurface", "line_number": 134, "usage_type": "attribute"}, {"api_name": "PyQt5.QtMultimediaWidgets.QVideoWidget", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_MediaPlay", "line_number": 141, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 141, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 144, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 147, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 147, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 147, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Preferred", "line_number": 152, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 152, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Maximum", "line_number": 153, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 153, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 167, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 171, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 176, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 192, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 192, "usage_type": "name"}, {"api_name": "PyQt5.QtMultimedia.QMediaContent", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl.fromLocalFile", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer.PlayingState", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer", "line_number": 200, "usage_type": "name"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer.PlayingState", "line_number": 206, "usage_type": "attribute"}, {"api_name": "PyQt5.QtMultimedia.QMediaPlayer", "line_number": 206, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_MediaPause", "line_number": 208, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 208, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_MediaPlay", "line_number": 211, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 211, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 234, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 236, "usage_type": "call"}]}
+{"seq_id": "389337461", "text": "import os\nfrom pathlib import Path\nimport requests\nfrom bs4 import BeautifulSoup as bs\nimport re\nimport sqlite3\n\ndef main():\n url = 'https://imas-shinycolors.boom-app.wiki/entry'\n\n # カード用データベース\n file_carddb = '../db/card.sqlite3'\n conn = sqlite3.connect(file_carddb)\n cur = conn.cursor()\n\n cur.execute(\"SELECT COUNT(*) FROM sqlite_master WHERE TYPE='table' AND NAME='card_list';\")\n if cur.fetchone() == (0,):\n cur.execute('CREATE TABLE card_list(ID int, card_name text);')\n cur.execute('CREATE UNIQUE INDEX idindex_card_list ON card_list(ID);')\n\n cur.execute(\"SELECT COUNT(*) FROM sqlite_master WHERE TYPE='table' AND NAME='card_info';\")\n if cur.fetchone() == (0,):\n cur.execute('CREATE TABLE card_info(ID int, idea text, unit text, obtain text, implement text);')\n cur.execute('CREATE UNIQUE INDEX idindex_card_info ON card_info(ID);')\n\n cur.execute(\"SELECT COUNT(*) FROM sqlite_master WHERE TYPE='table' AND NAME='card_possession';\")\n if cur.fetchone() == (0,):\n cur.execute('CREATE TABLE card_possession(ID int, possession int);')\n cur.execute('CREATE UNIQUE INDEX idindex_card_possession ON card_possession(ID);')\n\n p = Path('update/icon')\n dirs = list(p.glob(u'**/'))\n # カレントディレクトリも入るので削除\n dirs.pop(0)\n\n for d in dirs:\n # print(d)\n p = Path(d)\n pics = list(p.glob(u'*.jpg'))\n for pic in pics:\n print(pic)\n filename = os.path.basename(pic)\n id, ext = os.path.splitext(filename)\n card_url = os.path.join(url, 'card-{}'.format(int(id)))\n card_title, dic_info = return_cardinfo(card_url)\n card_name = '{}{}'.format(card_title, dic_info['アイドル'])\n if not 'アイデア' in dic_info:\n dic_info['アイデア'] = '-'\n\n insert_card = 'INSERT INTO card_list VALUES ({}, \"{}\")'.format(id, card_name)\n try:\n cur.execute(insert_card)\n except sqlite3.IntegrityError:\n print('insert error(unique index)')\n\n insert_card_info = 'INSERT INTO card_info VALUES ({}, \"{}\", \"{}\", \"{}\", \"{}\")'.format(id, dic_info['アイデア'], dic_info['ユニット'], dic_info['入手方法'], dic_info['実装日'])\n try:\n cur.execute(insert_card_info)\n except sqlite3.IntegrityError:\n print('insert error(unique index)')\n\n insert_card_possession = 'INSERT INTO card_possession VALUES ({}, {})'.format(id, 0)\n try:\n cur.execute(insert_card_possession)\n except sqlite3.IntegrityError:\n print('insert error(unique index)')\n\n conn.commit()\n cur.close()\n conn.close()\n\ndef return_cardinfo(card_url):\n dic_info = {}\n\n soup = bs(requests.get(card_url).content, 'html.parser')\n title = soup.find('title')\n title = title.text\n\n pattern = r'\\w+【.+】'\n card_title = re.search(pattern, title)[0]\n\n pattern = r'【.+】'\n card_title = re.search(pattern, card_title)[0]\n\n entry_body = soup.find_all('div', class_='entry-body')[0]\n table = entry_body.find('table')\n card_info = table.find_all('tr')\n # print(card_info)\n\n for i in card_info:\n key = i.th.text\n value = i.td.text\n dic_info[key] = value\n\n return card_title, dic_info\n\nif __name__ == '__main__':\n main()", "sub_path": "py_script/collect_info.py", "file_name": "collect_info.py", "file_ext": "py", "file_size_in_byte": 3462, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sqlite3.IntegrityError", "line_number": 65, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "re.search", "line_number": 80, "usage_type": "call"}, {"api_name": "re.search", "line_number": 83, "usage_type": "call"}]}
+{"seq_id": "380032222", "text": "# encoding= utf-8\n# Author: HHB\n# Data: 2022/11/10 11:44\n\nimport requests\n\n'''\n\n需要VPN访问\n\nhttps://udn.com/search/tagging/2/%E5%8D%B0%E5%BA%A6\n\n印度联合新闻报\n\n'''\n\n\nclass Udn(object):\n\n def get_ip(self): # 请求代理IP\n\n url = 'https://www.cloudam.cn/ip/takeip/Xph22JJ8EZbLYbuhhVlzcgHJ7NnBIhxb?protocol=proxy®ionid=us&needpwd=false&duplicate=false&amount=1&type=text'\n\n ip_data = requests.get(url).text\n\n proxies = {'https://': ip_data.replace('\\r\\n', ''),\n 'http://': ip_data.replace('\\r\\n', '')}\n\n return proxies\n\n def get_data(self):\n cookies = {\n '_ga_FAKEIDXXXX': 'GS1.1.1668051745.1.0.1668051745.0.0.0',\n '_ga_4HT5LNXHFC': 'GS1.1.1668051745.1.0.1668051745.60.0.0',\n '_ga_7THXRBK2CK': 'GS1.1.1668051745.1.0.1668051745.60.0.0',\n '__gsas': 'ID=1f3b2c83aab42e31:T=1668051752:S=ALNI_MbQMcMPRw8t8q8uSNjTPJg3cpNuKA',\n 'AMP_TOKEN': '%24NOT_FOUND',\n '_ga': 'GA1.2.125254614.1668051745',\n '_gid': 'GA1.2.826760265.1668051754',\n '_gat_UA-19660006-1': '1',\n 'cto_bundle': 'MxhHu19vNFBGaUc3MjZZU3lsR2FoejBWbDdNZkRPYW9iVDU3YzNjYWJqU1V0amVUYjhDZTBNMyUyQmglMkJPZ3cwdWdJT2tMNktWcUNNSE1nZmx3TEpNTkdCd2UxWUVGeTNUQTdhTGM3czY1TVdOTHFGR29lNlFxREZiQjNkZmltQkdCZk4yUVNCR0FSNXJoVkNZamRXY0V0dGVkWXRBJTNEJTNE',\n }\n\n headers = {\n 'authority': 'udn.com',\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n 'accept-language': 'zh-CN,zh;q=0.9',\n 'cache-control': 'max-age=0',\n # Requests sorts cookies= alphabetically\n # 'cookie': '_ga_FAKEIDXXXX=GS1.1.1668051745.1.0.1668051745.0.0.0; _ga_4HT5LNXHFC=GS1.1.1668051745.1.0.1668051745.60.0.0; _ga_7THXRBK2CK=GS1.1.1668051745.1.0.1668051745.60.0.0; __gsas=ID=1f3b2c83aab42e31:T=1668051752:S=ALNI_MbQMcMPRw8t8q8uSNjTPJg3cpNuKA; AMP_TOKEN=%24NOT_FOUND; _ga=GA1.2.125254614.1668051745; _gid=GA1.2.826760265.1668051754; _gat_UA-19660006-1=1; cto_bundle=MxhHu19vNFBGaUc3MjZZU3lsR2FoejBWbDdNZkRPYW9iVDU3YzNjYWJqU1V0amVUYjhDZTBNMyUyQmglMkJPZ3cwdWdJT2tMNktWcUNNSE1nZmx3TEpNTkdCd2UxWUVGeTNUQTdhTGM3czY1TVdOTHFGR29lNlFxREZiQjNkZmltQkdCZk4yUVNCR0FSNXJoVkNZamRXY0V0dGVkWXRBJTNEJTNE',\n 'sec-ch-ua': '\"Google Chrome\";v=\"107\", \"Chromium\";v=\"107\", \"Not=A?Brand\";v=\"24\"',\n 'sec-ch-ua-mobile': '?0',\n 'sec-ch-ua-platform': '\"Windows\"',\n 'sec-fetch-dest': 'document',\n 'sec-fetch-mode': 'navigate',\n 'sec-fetch-site': 'none',\n 'sec-fetch-user': '?1',\n 'upgrade-insecure-requests': '1',\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36',\n }\n\n response = requests.get('https://udn.com/search/tagging/2/%E5%8D%B0%E5%BA%A6', cookies=cookies, headers=headers,\n proxies=self.get_ip())\n\n print(response.text)\n\n\nif __name__ == '__main__':\n udn = Udn()\n udn.get_data()\n", "sub_path": "PoliceProject/IndiaUnitedNews.py", "file_name": "IndiaUnitedNews.py", "file_ext": "py", "file_size_in_byte": 3157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 62, "usage_type": "call"}]}
+{"seq_id": "377979589", "text": "from maya import cmds\nfrom maya import mel\nfrom maya import OpenMayaAnim\n\nfrom functools import wraps\n\n\ndef undo_able(func):\n \"\"\"\n Decorator - block code into a chunk for easier undo\n \"\"\"\n @wraps(func)\n def wrap(*args, **kwargs):\n try:\n cmds.undoInfo(openChunk=True)\n return func(*args, **kwargs)\n finally:\n cmds.undoInfo(closeChunk=True)\n\n return wrap\n\n\ndef disable_viewport(func):\n \"\"\"\n Decorator - turn off Maya display while func is running.\n if func will fail, the error will be raised after.\n \"\"\"\n\n @wraps(func)\n def wrap(*args, **kwargs):\n\n # Turn $gMainPane Off:\n mel.eval(\"paneLayout -e -manage false $gMainPane\")\n\n # Decorator will try/except running the function.\n # But it will always turn on the viewport at the end.\n # In case the function failed, it will prevent leaving maya viewport off.\n try:\n return func(*args, **kwargs)\n except Exception:\n # will raise original error\n raise\n finally:\n mel.eval(\"paneLayout -e -manage true $gMainPane\")\n\n return wrap\n\n\ndef get_timeline_range():\n \"\"\"Returns integer values of start a and end of maya's timeline\"\"\"\n _min = int(OpenMayaAnim.MAnimControl.minTime().value())\n _max = int(OpenMayaAnim.MAnimControl.maxTime().value())\n return _min, _max\n\n\ndef get_current_frame():\n \"\"\"Returns integer values of start a and end of maya's timeline\"\"\"\n _current = int(OpenMayaAnim.MAnimControl.currentTime().value())\n return _current\n\n\ndef flatten_anim_curve(curve_list, startframe, endframe):\n \"\"\"Flattens a list of animation curves and sets in and out frames to zero.\n (based on the current frame range)\"\"\"\n for curve in curve_list:\n cmds.setKeyframe(curve, time=startframe - 1, insert=True)\n cmds.setKeyframe(curve, time=endframe + 1, insert=True, )\n cmds.cutKey(curve, time=(startframe, endframe))\n cmds.setKeyframe(curve, time=startframe, value=0, outTangentType='linear')\n cmds.setKeyframe(curve, time=endframe, value=0, inTangentType='linear')\n\n\ndef get_namespaces():\n \"\"\" returns a list of namespaces in the scene \"\"\"\n # set to the root \":\" so that we list all namespaces in the root only.\n stored_namespace = cmds.namespaceInfo(currentNamespace=True)\n\n cmds.namespace(setNamespace=':')\n all_namespaces = cmds.namespaceInfo(listOnlyNamespaces=True, recurse=False)\n namespaces = [ns for ns in all_namespaces if ns not in ['UI', 'shared']]\n\n used_namespaces = [':']\n for _namespace in namespaces:\n cmds.namespace(setNamespace=':')\n cmds.namespace(setNamespace=_namespace)\n # if there are any objects in the namespace then return otherwise we don't need it.\n if cmds.namespaceInfo(listOnlyDependencyNodes=True):\n used_namespaces.append(_namespace)\n\n cmds.namespace(setNamespace=stored_namespace)\n return used_namespaces\n", "sub_path": "advanced_lookAt/1.00.00/plotting_utilities.py", "file_name": "plotting_utilities.py", "file_ext": "py", "file_size_in_byte": 2988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "maya.cmds.undoInfo", "line_number": 15, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 15, "usage_type": "name"}, {"api_name": "maya.cmds.undoInfo", "line_number": 18, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 18, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 12, "usage_type": "call"}, {"api_name": "maya.mel.eval", "line_number": 33, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 33, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 44, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 44, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 29, "usage_type": "call"}, {"api_name": "maya.OpenMayaAnim.MAnimControl.minTime", "line_number": 51, "usage_type": "call"}, {"api_name": "maya.OpenMayaAnim.MAnimControl", "line_number": 51, "usage_type": "attribute"}, {"api_name": "maya.OpenMayaAnim", "line_number": 51, "usage_type": "name"}, {"api_name": "maya.OpenMayaAnim.MAnimControl.maxTime", "line_number": 52, "usage_type": "call"}, {"api_name": "maya.OpenMayaAnim.MAnimControl", "line_number": 52, "usage_type": "attribute"}, {"api_name": "maya.OpenMayaAnim", "line_number": 52, "usage_type": "name"}, {"api_name": "maya.OpenMayaAnim.MAnimControl.currentTime", "line_number": 58, "usage_type": "call"}, {"api_name": "maya.OpenMayaAnim.MAnimControl", "line_number": 58, "usage_type": "attribute"}, {"api_name": "maya.OpenMayaAnim", "line_number": 58, "usage_type": "name"}, {"api_name": "maya.cmds.setKeyframe", "line_number": 66, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 66, "usage_type": "name"}, {"api_name": "maya.cmds.setKeyframe", "line_number": 67, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 67, "usage_type": "name"}, {"api_name": "maya.cmds.cutKey", "line_number": 68, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 68, "usage_type": "name"}, {"api_name": "maya.cmds.setKeyframe", "line_number": 69, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 69, "usage_type": "name"}, {"api_name": "maya.cmds.setKeyframe", "line_number": 70, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 70, "usage_type": "name"}, {"api_name": "maya.cmds.namespaceInfo", "line_number": 76, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 76, "usage_type": "name"}, {"api_name": "maya.cmds.namespace", "line_number": 78, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.namespaceInfo", "line_number": 79, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 79, "usage_type": "name"}, {"api_name": "maya.cmds.namespace", "line_number": 84, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 84, "usage_type": "name"}, {"api_name": "maya.cmds.namespace", "line_number": 85, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 85, "usage_type": "name"}, {"api_name": "maya.cmds.namespaceInfo", "line_number": 87, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 87, "usage_type": "name"}, {"api_name": "maya.cmds.namespace", "line_number": 90, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 90, "usage_type": "name"}]}
+{"seq_id": "620976349", "text": "\nfrom aip import AipImageClassify\nimport os,base64\nimport requests as req\nfrom io import BytesIO\nimport json\n# 定义常量\nAPP_ID = '11423030'\nAPI_KEY = 'bfT323NBGt5ke9397FairqMB'\nSECRET_KEY = 'yTDojPMLXhH9M3M37Ud5RGk4oZFc69aL '\n\n# 初始化图像\nclient = AipImageClassify(APP_ID, API_KEY, SECRET_KEY)\n\n\ndef get_path():\n # return \"./data/\"\n return \"/Users/apple/Documents/workspace/java/SE3/Tagx00.MachineLearning/data/\"\n\ndef get_file_content(filePath):\n with open(filePath, 'rb') as fp:\n return fp.read()\n\ndef get_baidu_results(data):\n result = {}\n result['recommendTagItemList'] = []\n for temp in data['recommendTagItemList']:\n img_src = temp['url']\n urldic = {}\n urldic['url'] = img_src\n # skimage可以直接以imread()函数来读取网页图片??这里有点小问题\n response = req.get(img_src)\n ls_f = base64.b64encode(BytesIO(response.content).read())\n imgdata = base64.b64decode(ls_f)\n file = open('1.jpg', 'wb')\n file.write(imgdata)\n image = get_file_content('1.jpg')\n \"\"\" 调用通用物体识别 \"\"\"\n aipgneral = client.advancedGeneral(image)\n apiresult = aipgneral['result']\n urldic['tagConfTuples'] = []\n for a in apiresult:\n keyword = {}\n keyword['tag'] = a['keyword']\n keyword['confidence'] = a['score']\n urldic['tagConfTuples'].append(keyword.copy())\n result['recommendTagItemList'].append(urldic.copy())\n return result\n\ndef write_baidu_results(data):\n with open(get_path() + \"proval/train_baidu.txt\", \"w\") as file:\n result=get_baidu_results(data)\n file.write(\"{'recommendTagItemList':[\")\n for b in result['recommendTagItemList']:\n file.write(\"{'url':'\")\n file.write(b['url'])\n file.write(\"','tagConfTuples':[\")\n for c in b['tagConfTuples']:\n file.write(\"{'tag':'\")\n file.write(c['tag'])\n file.write(\"','confidence':\")\n file.write(str(c['confidence']))\n file.write(\"},\")\n file.write(\"]}\")\n file.write('\\n')\n file.write(\"]}\")\n\n\n\n\n\n", "sub_path": "Tagx00.MachineLearning/baidu_api.py", "file_name": "baidu_api.py", "file_ext": "py", "file_size_in_byte": 2207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "aip.AipImageClassify", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 33, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 33, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "102814143", "text": "from __future__ import absolute_import, unicode_literals\nfrom celery import shared_task\nfrom django.core.management import call_command\nfrom StringIO import StringIO\nimport time\n\n# etl processing\n@shared_task(name='ptop.etl')\ndef etl(step):\n start_time = time.time() \n out = StringIO()\n if step == '1':\n call_command('personVisitETL', stdout=out)\n return out.getvalue() + ' in ' + str(time.time()-start_time) + 's'\n elif step == '2':\n call_command('demographicsETL', stdout=out)\n return out.getvalue() + ' in ' + str(time.time()-start_time) + 's'\n elif step == '3':\n call_command('enrollmentETL', stdout=out)\n return out.getvalue() + ' in ' + str(time.time()-start_time) + 's'\n elif step == '4':\n call_command('deathETL', stdout=out)\n return out.getvalue() + ' in ' + str(time.time()-start_time) + 's'\n elif step == '5':\n call_command('deathCauseETL', stdout=out)\n return out.getvalue() + ' in ' + str(time.time()-start_time) + 's'\n else:\n return 'Invalid step'\n", "sub_path": "v2.8_to_3.1/python_etl/ptop/apps/runit/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1063, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "time.time", "line_number": 10, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 11, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 13, "usage_type": "call"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 8, "usage_type": "call"}]}
+{"seq_id": "147223745", "text": "from __future__ import annotations\nfrom dataclasses import dataclass, field\nfrom xsdata.models.datatype import XmlDate\nfrom travelport.models.type_geo_political_area_type_2 import TypeGeoPoliticalAreaType2\nfrom travelport.models.type_key_element_2 import TypeKeyElement2\nfrom travelport.models.type_other_preference_2 import TypeOtherPreference2\nfrom travelport.models.type_preference_purpose_2 import TypePreferencePurpose2\n\n__NAMESPACE__ = \"http://www.travelport.com/schema/uprofile_v37_0\"\n\n\n@dataclass\nclass TypeOtherPreferenceHistory2(TypeKeyElement2):\n \"\"\"\n History Element for OtherPreference.\n\n Parameters\n ----------\n purpose\n The purpose of the preference.\n priority_order\n Priority order associated with this Preference.\n trip_approval\n inclusive\n Indicates whether this preference is exclusive or inclusive (e.g.\n preference for not having a queen size bed vs preference to HAVE a\n queen size bed).\n other_supplier_type\n The type of the Other Preference.\n booking_start_date\n booking_end_date\n usage_start_date\n usage_end_date\n supplier_name\n geo_political_area_type\n The type of the geographical location.\n geo_political_area_code\n The location code of the geographical location.\n preference_payment_method\n payment_details_ref\n A reference to a payment details element list elsewhere.\n max_cost_amount\n currency\n general_preference\n \"\"\"\n class Meta:\n name = \"typeOtherPreferenceHistory\"\n\n purpose: None | TypePreferencePurpose2 = field(\n default=None,\n metadata={\n \"name\": \"Purpose\",\n \"type\": \"Attribute\",\n }\n )\n priority_order: None | int = field(\n default=None,\n metadata={\n \"name\": \"PriorityOrder\",\n \"type\": \"Attribute\",\n \"min_inclusive\": 1,\n \"max_inclusive\": 99,\n }\n )\n trip_approval: bool = field(\n default=False,\n metadata={\n \"name\": \"TripApproval\",\n \"type\": \"Attribute\",\n }\n )\n inclusive: bool = field(\n default=True,\n metadata={\n \"name\": \"Inclusive\",\n \"type\": \"Attribute\",\n }\n )\n other_supplier_type: None | TypeOtherPreference2 = field(\n default=None,\n metadata={\n \"name\": \"OtherSupplierType\",\n \"type\": \"Attribute\",\n }\n )\n booking_start_date: None | XmlDate = field(\n default=None,\n metadata={\n \"name\": \"BookingStartDate\",\n \"type\": \"Attribute\",\n }\n )\n booking_end_date: None | XmlDate = field(\n default=None,\n metadata={\n \"name\": \"BookingEndDate\",\n \"type\": \"Attribute\",\n }\n )\n usage_start_date: None | XmlDate = field(\n default=None,\n metadata={\n \"name\": \"UsageStartDate\",\n \"type\": \"Attribute\",\n }\n )\n usage_end_date: None | XmlDate = field(\n default=None,\n metadata={\n \"name\": \"UsageEndDate\",\n \"type\": \"Attribute\",\n }\n )\n supplier_name: None | str = field(\n default=None,\n metadata={\n \"name\": \"SupplierName\",\n \"type\": \"Attribute\",\n \"min_length\": 1,\n \"max_length\": 128,\n }\n )\n geo_political_area_type: None | TypeGeoPoliticalAreaType2 = field(\n default=None,\n metadata={\n \"name\": \"GeoPoliticalAreaType\",\n \"type\": \"Attribute\",\n }\n )\n geo_political_area_code: None | str = field(\n default=None,\n metadata={\n \"name\": \"GeoPoliticalAreaCode\",\n \"type\": \"Attribute\",\n \"max_length\": 6,\n }\n )\n preference_payment_method: None | str = field(\n default=None,\n metadata={\n \"name\": \"PreferencePaymentMethod\",\n \"type\": \"Attribute\",\n \"max_length\": 6,\n }\n )\n payment_details_ref: None | str = field(\n default=None,\n metadata={\n \"name\": \"PaymentDetailsRef\",\n \"type\": \"Attribute\",\n }\n )\n max_cost_amount: None | str = field(\n default=None,\n metadata={\n \"name\": \"MaxCostAmount\",\n \"type\": \"Attribute\",\n }\n )\n currency: None | str = field(\n default=None,\n metadata={\n \"name\": \"Currency\",\n \"type\": \"Attribute\",\n \"length\": 3,\n }\n )\n general_preference: None | str = field(\n default=None,\n metadata={\n \"name\": \"GeneralPreference\",\n \"type\": \"Attribute\",\n \"min_length\": 1,\n \"max_length\": 255,\n }\n )\n", "sub_path": "travelport/models/type_other_preference_history_2.py", "file_name": "type_other_preference_history_2.py", "file_ext": "py", "file_size_in_byte": 4765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "travelport.models.type_key_element_2.TypeKeyElement2", "line_number": 13, "usage_type": "name"}, {"api_name": "travelport.models.type_preference_purpose_2.TypePreferencePurpose2", "line_number": 49, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 49, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 56, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 65, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 72, "usage_type": "call"}, {"api_name": "travelport.models.type_other_preference_2.TypeOtherPreference2", "line_number": 79, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 79, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 86, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 86, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 93, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 93, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 100, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 100, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 107, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 107, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 114, "usage_type": "call"}, {"api_name": "travelport.models.type_geo_political_area_type_2.TypeGeoPoliticalAreaType2", "line_number": 123, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 123, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 130, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 138, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 146, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 153, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 160, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 168, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 12, "usage_type": "name"}]}
+{"seq_id": "513112361", "text": "import json\r\nimport os\r\nimport glob \r\n\r\nprint(\"enter folder path\")\r\nfilepath=input('')\r\nprint(\"input file base name\")\r\nipbname=input('')\r\nprint(\"output file name\")\r\nopname=input('')\r\nprint(\"maxfilesize\")\r\nmx=int(input())\r\ns=0\r\nf=0\r\nl=[]\r\nfor filename in glob.glob(os.path.join(filepath , ipbname+'*.json')):\r\n\tf=os.path.getsize(filename)\r\n\ts+=f\r\n\tif s<=mx:\r\n\t\twith open(filename) as f:\t\t\r\n\t\t\ty = json.loads(f.read())\r\n\t\t\tq=list(y.keys())\r\n\t\t\tl.extend(y[q[0]])\r\n\telse:\r\n\t\tprint(\"max size reached....!!!!Skipping current file \"+filename)\r\n\t\tcontinue\r\n\r\nif s!=0:\r\n\tk={q[0]:l}\r\n\tp = json.dumps(k)\r\n\tprint(p)\r\n\twith open(filepath+'\\\\'+opname+'.json','w') as f:\r\n\t\tf.write(p)\r\n\tprint(\"Check Json file : \"+opname)\t", "sub_path": "jsm2.py", "file_name": "jsm2.py", "file_ext": "py", "file_size_in_byte": 707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "glob.glob", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}]}
+{"seq_id": "329388323", "text": "import os\nimport time\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nfrom hbhavens.ui import widgets\n\n\nclass SplittedTab(widgets.AbstractTabWidget):\n \"\"\"\n Widget with the background map\n\n This widget works based on a WMTS, which contains background tiles\n within and around the axes limits. Each action can update the bounding\n box, and thus the tile set.\n \"\"\"\n\n def __init__(self, mainwindow, leftwidget, rightwidget):\n \"\"\"\n Constructor of the tab\n \"\"\"\n # Create child class\n widgets.AbstractTabWidget.__init__(self, mainwindow)\n \n self.splitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n \n self.splitter.addWidget(leftwidget)\n self.splitter.addWidget(rightwidget)\n\n layout = QtWidgets.QHBoxLayout()\n layout.addWidget(self.splitter)\n\n self.splitter.setStretchFactor(0, 1)\n self.splitter.setStretchFactor(1, 0)\n\n self.splitter.splitterMoved.connect(self.on_moved)\n \n handle_layout = QtWidgets.QVBoxLayout()\n handle_layout.setContentsMargins(0, 0, 0, 0)\n self.splitter.setHandleWidth(12)\n\n self.button = QtWidgets.QToolButton()\n self.button.setStyleSheet('background-color: rgba(255, 255, 255, 0)')\n \n\n rightlogo = os.path.join(self.mainmodel.datadir, 'icons', 'iconfinder_icon-chevron-right_211647.png')\n leftlogo = os.path.join(self.mainmodel.datadir, 'icons', 'iconfinder_icon-chevron-left_211647.png')\n self.righticon = QtGui.QIcon(rightlogo)\n self.lefticon = QtGui.QIcon(leftlogo)\n self.icon = self.righticon\n \n self.button.setIcon(self.icon)\n \n self.button.clicked.connect(self.handleSplitterButton)\n\n handle_layout.addWidget(self.button)\n\n handle_layout.addItem(QtWidgets.QSpacerItem(0, 0, QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Expanding))\n\n handle = self.splitter.handle(1)\n handle.setLayout(handle_layout)\n\n self.setLayout(layout)\n\n def handleSplitterButton(self, left=True):\n \n # If one is collapsed\n if not all(self.splitter.sizes()):\n # Open and set right icon\n self.splitter.setSizes([1, 1])\n else:\n self.splitter.setSizes([1, 0])\n \n self.on_moved()\n\n def on_moved(self):\n \"\"\"Method to call when splitter is moved. This method\n updates the icon, but it can be extended to update for example a canvs\n \"\"\"\n\n # If one is collapsed\n if not all(self.splitter.sizes()):\n # Open and set right icon\n icon = self.lefticon\n # If both are open\n else:\n icon = self.righticon\n \n if self.icon is not icon:\n self.icon = icon\n self.button.setIcon(self.icon)\n \n \n\nclass InteractiveLegend:\n\n def __init__(self, widget, elements, loc='upper right', title=''):\n self.handles = {}\n self.labels = {}\n self.lined = {}\n self.widget = widget\n self.elements = elements\n self.loc = loc\n self.title = title\n\n def add_item(self, element, handle, label):\n self.handles[element] = handle\n self.labels[element] = label\n \n def remove(self, element):\n del self.handles[element]\n del self.labels[element]\n\n def _onpick(self, event):\n # on the pick event, find the orig line corresponding to the\n # legend proxy line, and toggle the visibility\n handle = event.artist\n collection = self.lined[handle]\n alpha = collection.get_alpha()\n\n vis = not collection.get_visible()\n collection.set_visible(vis)\n # Change the alpha on the line in the legend so we can see what lines\n # have been toggled\n if vis:\n handle.set_alpha(alpha)\n else:\n handle.set_alpha(alpha / 3.)\n \n self.widget.canvas.draw()\n \n def _update_legend(self):\n \n self.lined.clear()\n\n keys = list(self.elements.keys())\n if not keys:\n self.widget.ax.legend([], [], fancybox=False, edgecolor='0.2', fontsize=8, labelspacing=0.8, borderpad=0.7, loc=self.loc, title=self.title)\n return None\n\n # Collect the handles and labels and construct legend.\n self.legend = self.widget.ax.legend(\n [self.handles[key] for key in keys],\n [self.labels[key] for key in keys],\n fancybox=False, edgecolor='0.2', fontsize=8, labelspacing=0.8, borderpad=0.7, loc=self.loc, title=self.title\n )\n self.legend.get_frame().set_linewidth(0.5)\n\n # Note that the collected handles are not the handles which will be in\n # the legend, so they need to be activated for picking seperately\n for key, handle in zip(keys, self.legend.legendHandles):\n # Enable picking, 5 pts tolerance\n handle.set_picker(5)\n self.lined[handle] = self.elements[key]\n\n\n", "sub_path": "hbhavens/ui/tabs/general.py", "file_name": "general.py", "file_ext": "py", "file_size_in_byte": 5019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "hbhavens.ui.widgets.AbstractTabWidget", "line_number": 9, "usage_type": "attribute"}, {"api_name": "hbhavens.ui.widgets", "line_number": 9, "usage_type": "name"}, {"api_name": "hbhavens.ui.widgets.AbstractTabWidget.__init__", "line_number": 23, "usage_type": "call"}, {"api_name": "hbhavens.ui.widgets.AbstractTabWidget", "line_number": 23, "usage_type": "attribute"}, {"api_name": "hbhavens.ui.widgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QToolButton", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSpacerItem", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 58, "usage_type": "attribute"}]}
+{"seq_id": "324149927", "text": "import random\nfrom game import logic\nfrom tkinter import Frame, Label, CENTER\nimport itertools\nfrom game import constants as c\n\n\nclass Event:\n def __init__(self, char):\n self.char = char\n\n\nclass GameGrid(Frame):\n def __init__(self, param = None, expectimax=False):\n Frame.__init__(self)\n\n self.grid()\n self.master.title('2048')\n self.master.bind(\"<Key>\", self.key_down)\n self.master.bind(\"<Escape>\", lambda q: self.master.destroy())\n self.master.bind(\"<<UP>>\", lambda q: self.key_down(Event(\"w\")))\n self.master.bind(\"<<DOWN>>\", lambda q: self.key_down(Event(\"s\")))\n self.master.bind(\"<<LEFT>>\", lambda q: self.key_down(Event(\"a\")))\n self.master.bind(\"<<RIGHT>>\", lambda q: self.key_down(Event(\"d\")))\n self.master.bind(\"<<QUIT>>\", lambda q: self.master.destroy())\n\n # self.gamelogic = gamelogic\n self.commands = {c.KEY_UP: logic.up, c.KEY_DOWN: logic.down,\n c.KEY_LEFT: logic.left, c.KEY_RIGHT: logic.right,\n c.KEY_UP_ALT: logic.up, c.KEY_DOWN_ALT: logic.down,\n c.KEY_LEFT_ALT: logic.left, c.KEY_RIGHT_ALT: logic.right,\n c.KEY_H: logic.left, c.KEY_L: logic.right,\n c.KEY_K: logic.up, c.KEY_J: logic.down}\n\n self.grid_cells = []\n self.init_score()\n self.init_grid()\n self.is_expectimexpectimaxax = expectimax\n self.init_matrix(param)\n self.update_grid_cells()\n # self.mainloop()\n\n def init_score(self):\n self.score = 0\n\n def init_grid(self):\n background = Frame(self, bg=c.BACKGROUND_COLOR_GAME,\n width=c.SIZE, height=c.SIZE)\n background.grid()\n for i in range(c.GRID_LEN):\n grid_row = []\n for j in range(c.GRID_LEN):\n cell = self.create_cell(i, j, background, \"\")\n grid_row.append(cell)\n self.grid_cells.append(grid_row)\n score_text_cell = self.create_cell(4, 0, background, \"Score\")\n score_cell = self.create_cell(4, 1, background, self.score)\n self.grid_cells.append([score_text_cell, score_cell])\n\n def create_cell(self, x, y, background, text):\n cell = Frame(background, bg=c.BACKGROUND_COLOR_SCORE,\n width=c.SIZE / c.GRID_LEN,\n height=c.SIZE / c.GRID_LEN)\n cell.grid(row=x, column=y, padx=c.GRID_PADDING,\n pady=c.GRID_PADDING)\n t1 = Label(master=cell, text=text,\n bg=c.BACKGROUND_COLOR_SCORE,\n justify=CENTER, font=c.FONT, width=5, height=2)\n t1.grid()\n return t1\n\n def gen(self):\n return random.randint(0, c.GRID_LEN - 1)\n\n def init_matrix(self, mat = None):\n if mat is None:\n self.matrix = logic.new_game(4)\n self.history_matrixs = list()\n self.matrix = logic.add_two(self.matrix)\n self.matrix = logic.add_two(self.matrix)\n elif self.is_expectimax is True:\n self.matrix = [[0] * 4 for _ in range(4)]\n self.history_matrixs = list()\n # Generate 2 tiles when the game begins\n self.generate_tiles(2)\n else:\n self.matrix = mat\n self.history_matrixs = list()\n\n def generate_tiles(self, tiles = 1):\n \"\"\" Default should only spawn 1 slide as the game progress.\"\"\"\n if self.move_exists(self.matrix) or self.move_exists(zip(*self.matrix)):\n rows, cols = list(range(4)), list(range(4))\n random.shuffle(rows)\n random.shuffle(cols)\n distribution = [2] * 9 + [4]\n count = 0\n for i, j in itertools.product(rows, rows):\n if count == tiles: return True\n if self.matrix[i][j] != 0: continue\n\n self.matrix[i][j] = random.sample(distribution, 1)[0]\n count += 1\n return False\n else:\n return False\n\n def update_grid_cells(self):\n for i in range(c.GRID_LEN):\n for j in range(c.GRID_LEN):\n new_number = self.matrix[i][j]\n if new_number == 0:\n self.grid_cells[i][j].configure(\n text=\"\", bg=c.BACKGROUND_COLOR_CELL_EMPTY)\n else:\n self.grid_cells[i][j].configure(text=str(\n new_number), bg=c.BACKGROUND_COLOR_DICT[new_number],\n fg=c.CELL_COLOR_DICT[new_number])\n self.grid_cells[4][0].configure(text=\"Score\", bg=c.BACKGROUND_COLOR_SCORE,\n fg=c.FOREGROUND_COLOR_SCORE)\n self.grid_cells[4][1].configure(text=str(self.score), bg=c.BACKGROUND_COLOR_SCORE,\n fg=c.FOREGROUND_COLOR_SCORE)\n self.update_idletasks()\n\n def key_down(self, event):\n key = repr(event.char)\n if key == c.KEY_BACK and len(self.history_matrixs) > 1:\n self.matrix = self.history_matrixs.pop()\n self.update_grid_cells()\n print('back on step total step:', len(self.history_matrixs))\n elif key in self.commands:\n self.matrix, done, local_score, empty_cells_count, weighted_cell_score = self.commands[repr(event.char)](self.matrix)\n self.score += local_score\n if done:\n self.matrix = logic.add_two(self.matrix)\n # record last move\n self.history_matrixs.append(self.matrix)\n self.update_grid_cells()\n done = False\n if logic.game_state(self.matrix) == 'win':\n self.grid_cells[1][1].configure(\n text=\"You\", bg=c.BACKGROUND_COLOR_CELL_EMPTY)\n self.grid_cells[1][2].configure(\n text=\"Win!\", bg=c.BACKGROUND_COLOR_CELL_EMPTY)\n if logic.game_state(self.matrix) == 'lose':\n self.grid_cells[1][1].configure(\n text=\"You\", bg=c.BACKGROUND_COLOR_CELL_EMPTY)\n self.grid_cells[1][2].configure(\n text=\"Lose!\", bg=c.BACKGROUND_COLOR_CELL_EMPTY)\n\n\n def generate_next(self):\n index = (self.gen(), self.gen())\n while self.matrix[index[0]][index[1]] != 0:\n index = (self.gen(), self.gen())\n self.matrix[index[0]][index[1]] = 2\n\n def play_move(self, move, merge_functions):\n if self.move_exists(self.matrix) or self.move_exists(zip(*self.matrix)):\n self.matrix, _, local_score, _ , _= merge_functions[move](self.matrix)\n self.score += local_score\n return self.generate_tiles()\n\n def move_exists(self, board):\n for row in board:\n for x, y in zip(row[:-1], row[1:]):\n if x == y or x == 0 or y == 0:\n return True\n return False\n", "sub_path": "2048/game/puzzle.py", "file_name": "puzzle.py", "file_ext": "py", "file_size_in_byte": 6976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "tkinter.Frame", "line_number": 13, "usage_type": "name"}, {"api_name": "tkinter.Frame.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 15, "usage_type": "name"}, {"api_name": "game.constants.KEY_UP", "line_number": 28, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 28, "usage_type": "name"}, {"api_name": "game.constants.KEY_DOWN", "line_number": 28, "usage_type": "attribute"}, {"api_name": "game.constants.KEY_LEFT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 29, "usage_type": "name"}, {"api_name": "game.constants.KEY_RIGHT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "game.constants.KEY_UP_ALT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 30, "usage_type": "name"}, {"api_name": "game.constants.KEY_DOWN_ALT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "game.constants.KEY_LEFT_ALT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 31, "usage_type": "name"}, {"api_name": "game.constants.KEY_RIGHT_ALT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "game.constants.KEY_H", "line_number": 32, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 32, "usage_type": "name"}, {"api_name": "game.constants.KEY_L", "line_number": 32, "usage_type": "attribute"}, {"api_name": "game.constants.KEY_K", "line_number": 33, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 33, "usage_type": "name"}, {"api_name": "game.constants.KEY_J", "line_number": 33, "usage_type": "attribute"}, {"api_name": "game.logic.up", "line_number": 28, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 28, "usage_type": "name"}, {"api_name": "game.logic.down", "line_number": 28, "usage_type": "attribute"}, {"api_name": "game.logic.left", "line_number": 29, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 29, "usage_type": "name"}, {"api_name": "game.logic.right", "line_number": 29, "usage_type": "attribute"}, {"api_name": "game.logic.up", "line_number": 30, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 30, "usage_type": "name"}, {"api_name": "game.logic.down", "line_number": 30, "usage_type": "attribute"}, {"api_name": "game.logic.left", "line_number": 31, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 31, "usage_type": "name"}, {"api_name": "game.logic.right", "line_number": 31, "usage_type": "attribute"}, {"api_name": "game.logic.left", "line_number": 32, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 32, "usage_type": "name"}, {"api_name": "game.logic.right", "line_number": 32, "usage_type": "attribute"}, {"api_name": "game.logic.up", "line_number": 33, "usage_type": "attribute"}, {"api_name": "game.logic", "line_number": 33, "usage_type": "name"}, {"api_name": "game.logic.down", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 47, "usage_type": "call"}, {"api_name": "game.constants.BACKGROUND_COLOR_GAME", "line_number": 47, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 47, "usage_type": "name"}, {"api_name": "game.constants.SIZE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 48, "usage_type": "name"}, {"api_name": "game.constants.GRID_LEN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 50, "usage_type": "name"}, {"api_name": "game.constants.GRID_LEN", "line_number": 52, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 52, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 61, "usage_type": "call"}, {"api_name": "game.constants.BACKGROUND_COLOR_SCORE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 61, "usage_type": "name"}, {"api_name": "game.constants.SIZE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 62, "usage_type": "name"}, {"api_name": "game.constants.GRID_LEN", "line_number": 62, "usage_type": "attribute"}, {"api_name": "game.constants.SIZE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 63, "usage_type": "name"}, {"api_name": "game.constants.GRID_LEN", "line_number": 63, "usage_type": "attribute"}, {"api_name": "game.constants.GRID_PADDING", "line_number": 64, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 64, "usage_type": "name"}, {"api_name": "game.constants.GRID_PADDING", "line_number": 65, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 65, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 66, "usage_type": "call"}, {"api_name": "game.constants.BACKGROUND_COLOR_SCORE", "line_number": 67, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 67, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 68, "usage_type": "name"}, {"api_name": "game.constants.FONT", "line_number": 68, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 68, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "game.constants.GRID_LEN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 73, "usage_type": "name"}, {"api_name": "game.logic.new_game", "line_number": 77, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 77, "usage_type": "name"}, {"api_name": "game.logic.add_two", "line_number": 79, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 79, "usage_type": "name"}, {"api_name": "game.logic.add_two", "line_number": 80, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 80, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 94, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 95, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 98, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 102, "usage_type": "call"}, {"api_name": "game.constants.GRID_LEN", "line_number": 109, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 109, "usage_type": "name"}, {"api_name": "game.constants.GRID_LEN", "line_number": 110, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 110, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_CELL_EMPTY", "line_number": 114, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 114, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_DICT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 117, "usage_type": "name"}, {"api_name": "game.constants.CELL_COLOR_DICT", "line_number": 118, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 118, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_SCORE", "line_number": 119, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 119, "usage_type": "name"}, {"api_name": "game.constants.FOREGROUND_COLOR_SCORE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 120, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_SCORE", "line_number": 121, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 121, "usage_type": "name"}, {"api_name": "game.constants.FOREGROUND_COLOR_SCORE", "line_number": 122, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 122, "usage_type": "name"}, {"api_name": "game.constants.KEY_BACK", "line_number": 127, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 127, "usage_type": "name"}, {"api_name": "game.logic.add_two", "line_number": 135, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 135, "usage_type": "name"}, {"api_name": "game.logic.game_state", "line_number": 140, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 140, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_CELL_EMPTY", "line_number": 142, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 142, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_CELL_EMPTY", "line_number": 144, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 144, "usage_type": "name"}, {"api_name": "game.logic.game_state", "line_number": 145, "usage_type": "call"}, {"api_name": "game.logic", "line_number": 145, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_CELL_EMPTY", "line_number": 147, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 147, "usage_type": "name"}, {"api_name": "game.constants.BACKGROUND_COLOR_CELL_EMPTY", "line_number": 149, "usage_type": "attribute"}, {"api_name": "game.constants", "line_number": 149, "usage_type": "name"}]}
+{"seq_id": "501905578", "text": "from DataManipulator import DataManipulator\nimport logging\nimport numpy as np\nimport cv2\nimport pandas as pd\nimport os\n\n\n\n\ndef main():\n logging.basicConfig(level=logging.INFO,\n format=\"%(asctime)s - %(levelname)s - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n filename=\"log.txt\",\n filemode='w')\n\n\n console = logging.StreamHandler()\n console.setLevel(logging.INFO)\n formatter = logging.Formatter('%(message)s')\n console.setFormatter(formatter)\n logging.getLogger('').addHandler(console)\n\n pickle_folder = \"/Users/usi/PycharmProjects/data/16_4_2020/Pitch/\"\n files = os.listdir(pickle_folder)\n\n # train_dir = pickle_folder + \"Train/\"\n # test_dir = pickle_folder + \"Test/\"\n # if not os.path.exists(train_dir):\n # os.makedirs(train_dir)\n # if not os.path.exists(test_dir):\n # os.makedirs(test_dir)\n #\n #\n # train_pickle_list=[]\n # test_pickle_list = []\n #\n # for f in files:\n # if \".pickle\" in f:\n # DataManipulator.DivideDataset(pickle_folder+f, train_dir+f, test_dir+f, 0.7)\n # train_pickle_list.append(train_dir+f)\n # test_pickle_list.append(test_dir + f)\n #\n # DataManipulator.JoinDatasetFromList(train_pickle_list, train_dir + \"160x160HimaxHeightTrain16_4_2020.pickle\")\n # DataManipulator.JoinDatasetFromList(test_pickle_list, test_dir + \"160x160HimaxHeightTest16_4_2020.pickle\")\n\n pickle_list = []\n for f in files:\n if \".pickle\" in f:\n pickle_list.append(pickle_folder + f)\n\n DataManipulator.JoinDatasetFromList(pickle_list, pickle_folder + \"160x160HimaxPitch16_4_2020.pickle\")\n\n\nif __name__ == '__main__':\n main()", "sub_path": "PyTorch/Torture.py", "file_name": "Torture.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 26, "usage_type": "call"}, {"api_name": "DataManipulator.DataManipulator.JoinDatasetFromList", "line_number": 53, "usage_type": "call"}, {"api_name": "DataManipulator.DataManipulator", "line_number": 53, "usage_type": "name"}]}
+{"seq_id": "601978282", "text": "from dice import Pipeline\nfrom dice.evaluation import Tracker\nfrom dice.evaluation import PairEvaluator\nfrom dice.misc import notify\nfrom dice.process import BulkPipeline\nfrom dice.process import BulkGatherer\nfrom dice.constants import Parameters\n\nfrom functools import reduce\nimport hyperopt\nimport pickle\nimport shutil\nimport os\n\nclass BulkTuner:\n\n BULK_TUNER_FOLDER = \"out/bulk/tuner\"\n\n def __init__(self, annotation_file, feature, inputs_folder, partition_file, n_jobs):\n self.annotation_file = annotation_file\n self.inputs_folder = inputs_folder\n self.partition_file = partition_file\n self.n_jobs = n_jobs\n partition = list()\n with open(partition_file) as file:\n for line in file.readlines():\n partition.append(list(map(int, line.strip().split(\"\\t\"))))\n def objective(args):\n pipeline = Pipeline(inputs_folder, args)\n Parameters.process(**args)\n if feature == \"evidence\":\n pipeline.load_detective()\n # pipeline.step_detective()\n if feature == \"confidence\":\n bulk_pipeline = BulkPipeline(inputs_folder, partition)\n if os.path.isdir(BulkTuner.BULK_TUNER_FOLDER):\n shutil.rmtree(BulkTuner.BULK_TUNER_FOLDER)\n bulk_pipeline.process(BulkTuner.BULK_TUNER_FOLDER, int(n_jobs))\n del bulk_pipeline\n assignment = BulkGatherer(BulkTuner.BULK_TUNER_FOLDER).gather(False)\n pipeline.set_assignment(assignment)\n tracker = Tracker()\n tracker.build(pipeline)\n PairEvaluator.FEATURE = feature\n PairEvaluator.CONFIDENCE = .5\n print(PairEvaluator(self.annotation_file, tracker).evaluate(True))\n return PairEvaluator(self.annotation_file, tracker).evaluate()\n self.objective = objective\n self.choices = dict()\n\n def parse_space(self, args):\n space = dict()\n for arg in args.split(\" \"):\n param, rng = arg.split(\"=\")\n if \"-\" in rng:\n low, high = list(map(float, rng.split(\"-\")))\n space[param] = hyperopt.hp.uniform(param, low, high)\n elif \",\" in rng:\n values = list(map(float, rng.split(\",\")))\n space[param] = hyperopt.hp.choice(param, values)\n self.choices[param] = values\n else:\n space[param] = hyperopt.hp.choice(param, (float(rng),))\n self.choices[param] = [float(rng)]\n return space\n\n def optimize(self, args, max_evals=200, trials_file=\"trials.pickle\"):\n space = self.parse_space(args)\n if os.path.isfile(trials_file):\n with open(trials_file, \"rb\") as file:\n trials = pickle.load(file)\n else:\n trials = hyperopt.Trials()\n best = hyperopt.fmin(\n self.objective,\n space,\n algo=hyperopt.tpe.suggest,\n max_evals=max_evals,\n trials=trials,\n )\n pickle.dump(trials, open(trials_file, \"wb\"))\n for param, values in self.choices.items():\n best[param] = values[best[param]]\n notify(\"Finished Processing\", \"Best parameters: {params}\\nLosses: {loss}\".format(\n params=best,\n loss=trials.losses(),\n ))\n return best\n", "sub_path": "dice/process/bulk_tuner.py", "file_name": "bulk_tuner.py", "file_ext": "py", "file_size_in_byte": 3403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "dice.Pipeline", "line_number": 29, "usage_type": "call"}, {"api_name": "dice.constants.Parameters.process", "line_number": 30, "usage_type": "call"}, {"api_name": "dice.constants.Parameters", "line_number": 30, "usage_type": "name"}, {"api_name": "dice.process.BulkPipeline", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 37, "usage_type": "call"}, {"api_name": "dice.process.BulkGatherer", "line_number": 40, "usage_type": "call"}, {"api_name": "dice.evaluation.Tracker", "line_number": 42, "usage_type": "call"}, {"api_name": "dice.evaluation.PairEvaluator.FEATURE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "dice.evaluation.PairEvaluator", "line_number": 44, "usage_type": "name"}, {"api_name": "dice.evaluation.PairEvaluator.CONFIDENCE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "dice.evaluation.PairEvaluator", "line_number": 45, "usage_type": "name"}, {"api_name": "dice.evaluation.PairEvaluator", "line_number": 46, "usage_type": "call"}, {"api_name": "dice.evaluation.PairEvaluator", "line_number": 47, "usage_type": "call"}, {"api_name": "hyperopt.hp.uniform", "line_number": 57, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 57, "usage_type": "attribute"}, {"api_name": "hyperopt.hp.choice", "line_number": 60, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 60, "usage_type": "attribute"}, {"api_name": "hyperopt.hp.choice", "line_number": 63, "usage_type": "call"}, {"api_name": "hyperopt.hp", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 71, "usage_type": "call"}, {"api_name": "hyperopt.Trials", "line_number": 73, "usage_type": "call"}, {"api_name": "hyperopt.fmin", "line_number": 74, "usage_type": "call"}, {"api_name": "hyperopt.tpe", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 81, "usage_type": "call"}, {"api_name": "dice.misc.notify", "line_number": 84, "usage_type": "call"}]}
+{"seq_id": "310955148", "text": "# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nfrom ..decode.viterbi import Viterbi\nfrom keras.utils import to_categorical\nfrom keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n\n__all__ = [\"NER_Callbacks\", \"Accuracy\"]\n\nclass NER_Callbacks(object):\n \n def __init__(self, id2tag, mask_tag=None):\n self.callbacks = [Accuracy(id2tag, mask_tag)]\n \n def best_fit_callbacks(self, callback_configs):\n # add best-fit callbacks\n early_stop_patience = callback_configs.get(\"early_stop_patience\")\n early_stopping = EarlyStopping(monitor=\"val_crf_accuracy\", patience=early_stop_patience)\n self.callbacks.append(early_stopping)\n \n reduce_lr_patience = callback_configs.get(\"reduce_lr_patience\")\n reduce_lr_factor = callback_configs.get(\"reduce_lr_factor\")\n reduce_lr_on_plateau = ReduceLROnPlateau(monitor=\"val_crf_accuracy\", verbose=1, mode=\"max\", factor=reduce_lr_factor, patience=reduce_lr_patience)\n self.callbacks.append(reduce_lr_on_plateau)\n \n save_path = callback_configs.get(\"save_path\")\n checkpoint = ModelCheckpoint(save_path, monitor=\"val_crf_accuracy\", verbose=2, mode=\"max\", save_best_only=True)\n self.callbacks.append(checkpoint)\n return self.callbacks\n\n def callbacks(self):\n return self.callbacks\n \nclass Accuracy(Callback):\n \n def __init__(self, id2tag, mask_tag=None):\n self.id2tag = id2tag\n self.numb_tags = len(self.id2tag)\n self.mask_tag = mask_tag\n self.mask_pos = {self.id2tag[key]:key for key in self.id2tag}.get(self.mask_tag)\n \n def on_epoch_end(self, epoch, logs=None):\n viterbi = Viterbi(self.model, self.numb_tags)\n val_true = np.squeeze(self.validation_data[2], axis=-1)\n mask = np.array(1. - to_categorical(val_true, self.numb_tags)[:, :, self.mask_pos]) if self.mask_pos else None\n val_pred = viterbi.decode([self.validation_data[0], self.validation_data[1]])\n self.get_acc(val_true, val_pred, mask) \n \n def get_acc(self, val_true, val_pred, mask):\n assert isinstance(val_true, np.ndarray), \"expect val_true to be np.ndarray, but got \" + str(type(val_true))\n assert isinstance(val_pred, np.ndarray), \"expect val_pred to be np.ndarray, but got \" + str(type(val_pred))\n assert val_true.shape == val_pred.shape, \"expect val_true and val_pred to have the same shape, but got \" + str(val_true.shape) + \" and \" + str(val_pred.shape)\n all_sents = val_true.shape[0]\n right_sents = 0\n all_tags = {item:0 for item in self.id2tag.values() if item != self.mask_tag}\n right_tags = {item:0 for item in self.id2tag.values() if item != self.mask_tag}\n if self.mask_tag == None:\n for sent_true, sent_pred in zip(val_true, val_pred):\n if all(sent_true == sent_pred):\n right_sents += 1\n for tag_true, tag_pred in zip(sent_true, sent_pred):\n if tag_true == tag_pred:\n right_tags[self.id2tag[tag_pred]] += 1\n all_tags[self.id2tag[tag_true]] += 1\n else:\n for sent_true, sent_pred, sent_mask in zip(val_true, val_pred, mask):\n if all(sent_true*sent_mask == sent_pred*sent_mask):\n right_sents += 1\n for tag_true, tag_pred, tag_mask in zip(sent_true, sent_pred, sent_mask):\n if tag_mask == 0.:\n continue\n if tag_true == tag_pred:\n right_tags[self.id2tag[tag_pred]] += 1\n all_tags[self.id2tag[tag_true]] += 1\n sents_acc = right_sents / all_sents\n tags_acc = {item:right_tags[item]/all_tags[item] for item in right_tags}\n print(\"*\"*25+\" Sentence Accuracy \"+\"*\"*25)\n print(\"\\t\\t{}\\t\\t{}\\t\\t{}\\n\".format(\"Right\",\"All\",\"Accuracy\"))\n print(\"\\t\\t{}\\t\\t{}\\t\\t{}\\n\".format(right_sents,all_sents,sents_acc))\n print(\"*\"*25+\" Tag Accuracy \"+\"*\"*25)\n print(\"\\t\\t{}\\t\\t{}\\t\\t{}\\n\".format(\"Right\",\"All\",\"Accuracy\"))\n for tag in tags_acc:\n print(\"{}\\t\\t{}\\t\\t{}\\t\\t{}\\n\".format(tag,right_tags[tag],all_tags[tag],tags_acc[tag]))", "sub_path": "keras_bert_ner/train/callbacks.py", "file_name": "callbacks.py", "file_ext": "py", "file_size_in_byte": 4375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "keras.callbacks.EarlyStopping", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.callbacks.Callback", "line_number": 38, "usage_type": "name"}, {"api_name": "decode.viterbi.Viterbi", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 55, "usage_type": "attribute"}]}
+{"seq_id": "379512465", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom latexify import latexify\nfrom matplotlib import patches\n\n# Configuro latexify a dos columnas\nlatexify(columns=2, fontawesome=True)\n\n# Grafico\nfig = plt.figure(figsize=(6.9,3))\nax1 = fig.add_subplot(111)\nxmax = 8*np.pi\nymax = xmax/((6.9/3))\nax1.set_xlim(0-1.2,xmax)\nax1.set_ylim(0,ymax)\n\n# EM waves\nx = np.linspace(0-0.85, 19*xmax/20,200)+xmax/30\nax1.plot(x, ymax/4*np.sin(x)+ymax/2, \"C0\")\nax1.plot(x, ymax/4*np.sin(x)*0+ymax/2, \":k\", lw=1, alpha=0.25)\nax1.plot(x+np.sin(-x), -ymax/8*np.sin(x)+ymax/2, color=\"C3\", alpha=0.5)\n\n# Patches\nax1.annotate(r\"\\large$\\hat{z}$\", xy=(xmax/22-1.2, ymax/2), \n xytext=(3*xmax/20-1.2, ymax/2),\n arrowprops=dict(facecolor='black', arrowstyle=\"<-\"),\n va=\"center\", ha=\"center\")\n\nax1.annotate(r\"\\large$\\hat{x}$\", xy=(xmax/20-1.2, ymax/2.02), \n xytext=(xmax/20-1.2, ymax/2+1.1*xmax/10),\n arrowprops=dict(facecolor='black', arrowstyle=\"<-\"),\n va=\"center\", ha=\"center\")\n\nax1.annotate(r\"\\large$\\hat{y}$\", xy=(xmax/19-1.2, ymax/1.98), \n xytext=(xmax/20-0.9*xmax/10/1.41-1.2, ymax/2-0.9*xmax/10/1.41),\n arrowprops=dict(facecolor='black', arrowstyle=\"<-\"),\n va=\"center\", ha=\"center\")\n# Texto\nax1.text(xmax/8,7.25*ymax/9,s=r\"\\large$E(z-\\omega t)$\",horizontalalignment='right', verticalalignment='center_baseline')\nax1.text(xmax/8-1,ymax/3,s=r\"\\large$B(z-\\omega t)$\",horizontalalignment='right', verticalalignment='center_baseline')\nax1.annotate('', xy=(2*np.pi+np.pi/2, 7.25*ymax/9), \n xytext=(4*np.pi+np.pi/2, 7.25*ymax/9),\n arrowprops=dict(facecolor='black', arrowstyle=\"|-|\"))\nax1.text(3*np.pi+np.pi/2,7.65*ymax/9,s=r\"\\large$\\lambda$\",horizontalalignment='center', verticalalignment='center_baseline')\n\nax1.axis('off')\nplt.xticks([])\nplt.yticks([])\nplt.tight_layout()\nplt.savefig(\"curso-ico/figs/fig:t1-wave.pdf\")\nplt.savefig(\"curso-ico/figs/fig:t1-wave.png\", dpi=300)", "sub_path": "curso-ico/fig:t1-wave.py", "file_name": "fig:t1-wave.py", "file_ext": "py", "file_size_in_byte": 2028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "latexify.latexify", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 44, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]}
+{"seq_id": "243282204", "text": "from beer.crop_tools.create_lists import create_train_val_list\nfrom beer.crop_tools.create_lists import create_file_list\nfrom beer.crop_tools.tools import ImageListCropper\n\nimport os\nimport argparse\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Prepare lists txt file for dataset')\n parser.add_argument(\n '--dataset',\n dest='dataset',\n help='dataset to use',\n default='data',\n type=str)\n parser.add_argument(\n '--target',\n dest='target',\n help='output list file',\n default='crop',\n type=str)\n parser.add_argument(\n '--root',\n dest='root_path',\n help='dataset root path',\n default=os.path.join(os.getcwd(), 'data', 'beer'),\n type=str)\n args = parser.parse_args()\n return args\n\n\ndef read_file(root):\n info = []\n file = open(root, 'rt')\n while True:\n string = file.readline()\n if not string:\n break\n info.append(string[:-1])\n return info\n\n\ndef process_all(lists, output_root):\n if not os.path.exists(output_root):\n os.makedirs(output_root)\n for count, paths in enumerate(lists):\n print(paths)\n img_path, xml_path = paths.split('&!&')\n out_root = os.path.join(output_root, '{:04}'.format(count // 1000),\n '{:08}'.format(count))\n if not os.path.exists(out_root):\n os.makedirs(out_root)\n cropper = ImageListCropper(img_path, xml_path, out_root)\n cropper.update(output_root + '/break.txt')\n\n\ndef _make_data(args):\n origin_data = os.path.join(args.root_path, args.dataset)\n output_data = os.path.join(args.root_path, args.target)\n create_train_val_list(origin_data, args.root_path)\n train_list = read_file(os.path.join(args.root_path, 'train_list.txt'))\n train_path = os.path.join(output_data, 'train')\n process_all(train_list, train_path)\n create_file_list(train_path, os.path.join(args.root_path, 'train.txt'))\n val_list = read_file(os.path.join(args.root_path, 'val_list.txt'))\n val_path = os.path.join(output_data, 'val')\n process_all(val_list, val_path)\n create_file_list(val_path, os.path.join(args.root_path, 'val.txt'))\n\n\nif __name__ == '__main__':\n args = parse_args()\n _make_data(args)\n", "sub_path": "research/object_detection/beer/crop_tools/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "beer.crop_tools.tools.ImageListCropper", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "beer.crop_tools.create_lists.create_train_val_list", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "beer.crop_tools.create_lists.create_file_list", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "beer.crop_tools.create_lists.create_file_list", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}]}
+{"seq_id": "83626933", "text": "import sys, os\r\nimport argparse\r\nimport time\r\nimport socket\r\nimport struct\r\nfrom scapy.layers.inet import *\r\nimport threading\r\n\r\n# Author: Josh Messitte (811976008)\r\n# CSCI 6760 Project 4: tcp_traceroute.py\r\n# Usage: sudo python3 tcp_traceroute.py [-m MAX_HOPS] [-p DST_PORT] [-t TARGET]\r\n\r\nTIMEOUT = .025\r\nDST_REACHED = 1\r\nDST_UNREACHABLE = 2\r\nICMP_ECHO_REQUEST = 8\r\nSOCKET_TIMEOUT = 0\r\n\r\n\r\n# Class represents a receiving socket, will either be a ICMP or TCP raw socket\r\nclass RecvSocket:\r\n\r\n def __init__(self, type,ttl):\r\n self.type = type\r\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_RAW, type)\r\n self.sock.setsockopt(socket.IPPROTO_IP, socket.IP_TTL, ttl)\r\n self.sock.settimeout(TIMEOUT)\r\n self.pkt = b''\r\n self.info = None\r\n self.delay = None\r\n self.address = None\r\n self.start_time = None\r\n self.rcv_time = None\r\n self.timeout = False\r\n\r\n # Handler for thread\r\n def run(self):\r\n\r\n self.start_time = time.time()\r\n\r\n while (self.start_time + TIMEOUT - time.time()) > 0:\r\n\r\n try:\r\n self.pkt, self.address = self.sock.recvfrom(1024)\r\n except socket.timeout:\r\n break\r\n \r\n\r\n \r\n self.rcv_time = time.time()\r\n \r\n # object is raw ICMP sock\r\n if self.type == 1:\r\n ip = IP(self.pkt)\r\n icmp = ip[ICMP]\r\n #icmp.show()\r\n imcp_code = ip[ICMP].code\r\n icmp_type = ip[ICMP].type\r\n\r\n # ICMP type 11 code 0 --> TIME EXCEEDED\r\n if icmp_type == 11 and imcp_code == 0:\r\n \r\n self.delay = self.rcv_time - self.start_time\r\n self.info = None\r\n # ICMP type 0 code 0 --> ECHO REPLY (DST Reached)\r\n elif icmp_type == 0 and imcp_code == 0:\r\n \r\n self.delay = self.rcv_time - self.start_time\r\n self.info = DST_REACHED\r\n # ICMP type 3 --> DST UNREACHABLE\r\n elif icmp_type == 3:\r\n \r\n self.delay = None\r\n self.info = DST_UNREACHABLE\r\n\r\n # object is raw TCP socket \r\n elif self.type == 6:\r\n self.delay = self.rcv_time - self.start_time\r\n self.address = None\r\n ip = IP(self.pkt)\r\n tcp = ip[TCP]\r\n if 'A' in tcp.flags:\r\n self.info = DST_REACHED\r\n\r\n # Returns the response address\r\n def get_addr(self):\r\n if self.address:\r\n return self.address\r\n else:\r\n return None\r\n\r\n # Return response delay \r\n def get_delay(self):\r\n return self.delay\r\n\r\n # Return info on response received\r\n def get_info(self):\r\n return self.info\r\n\r\n\r\n# controls flow for performing one ping\r\ndef send_probe(dst_addr, dst_port, ttl):\r\n # sending socket\r\n s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)\r\n s.setsockopt(socket.IPPROTO_IP, socket.IP_TTL, ttl)\r\n\r\n \r\n # receiving sockets\r\n recv_icmp = RecvSocket(socket.IPPROTO_ICMP,ttl)\r\n recv_tcp = RecvSocket(socket.IPPROTO_TCP,ttl)\r\n threads = [threading.Thread(target=recv_icmp.run),threading.Thread(target=recv_tcp.run)]\r\n\r\n\r\n # create and send TCP SYN probe\r\n syn_pkt = IP(dst=dst_addr, ttl=ttl) / TCP(dport=dst_port, sport=54321, flags='S')\r\n s.sendto(bytes(syn_pkt), (dst_addr, dst_port))\r\n\r\n # start all threads\r\n for t in threads:\r\n t.start()\r\n t.join()\r\n\r\n # return based on ICMP or TCP got results\r\n if recv_icmp.get_addr():\r\n return recv_icmp.get_delay(), recv_icmp.get_addr(), recv_icmp.get_info()\r\n elif recv_tcp.get_addr():\r\n return recv_tcp.get_delay(), recv_tcp.get_addr(), recv_tcp.get_info()\r\n else:\r\n return None, None, SOCKET_TIMEOUT\r\n\r\n\r\n# prints results of a ping\r\ndef print_part(delay, address, prev_addr):\r\n if not delay:\r\n print('*', end=' ', flush=True)\r\n return\r\n\r\n # to ms\r\n delay *= 1000\r\n\r\n if not prev_addr == address:\r\n try:\r\n host, _, _ = socket.gethostbyaddr(address)\r\n except:\r\n host = address\r\n\r\n print('{} ({}) {:.3f} ms'.format(host, address, delay),\r\n end=' ', flush=True)\r\n else:\r\n print(' {:.3f} ms'.format(delay),\r\n end=' ', flush=True)\r\n\r\n\r\n# controls main flow of program\r\ndef traceroute(max_hops, dst_port, dst_host, dst_addr):\r\n # iterate over all ttls\r\n for ttl in range(1, max_hops + 1):\r\n\r\n print('{:2} '.format(ttl), end=' ', flush=True)\r\n prev_addr = None\r\n\r\n # compute latency 3 times\r\n for i in range(3):\r\n delay, address, info = send_probe(dst_addr, dst_port, ttl)\r\n print_part(delay, address, prev_addr)\r\n prev_addr = address\r\n\r\n print()\r\n\r\n if info == DST_REACHED:\r\n break\r\n\r\n\r\ndef main():\r\n # Set up argument parsing automation\r\n prog = 'python3 tcp_traceroute.py'\r\n descr = 'TCP Traceroute Program Implemented in Python'\r\n parser = argparse.ArgumentParser(prog=prog, description=descr)\r\n parser.add_argument('-m', '--MAX_HOPS', type=int, default=30, required=False, help='Max hops to probe')\r\n parser.add_argument('-p', '--DST_PORT', type=int, default=80, help='TCP Destination Port')\r\n parser.add_argument('-t', '--TARGET', type=str, required=True, help='Target domain or IP')\r\n\r\n # Parse given arguments\r\n args = parser.parse_args()\r\n max_hops = args.MAX_HOPS\r\n dst_port = args.DST_PORT\r\n target = args.TARGET\r\n\r\n dest = socket.gethostbyname(target)\r\n\r\n print(\"traceroute to %s (%s), %d hops max, TCP SYN to port %d\" % (target, dest, max_hops,dst_port))\r\n\r\n traceroute(max_hops, dst_port, target, dest)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "tcp_traceroute.py", "file_name": "tcp_traceroute.py", "file_ext": "py", "file_size_in_byte": 5999, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "socket.socket", "line_number": 25, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_IP", "line_number": 26, "usage_type": "attribute"}, {"api_name": "socket.IP_TTL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "socket.timeout", "line_number": 45, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 104, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 104, "usage_type": "attribute"}, {"api_name": "socket.SOCK_RAW", "line_number": 104, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_TCP", "line_number": 104, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_IP", "line_number": 105, "usage_type": "attribute"}, {"api_name": "socket.IP_TTL", "line_number": 105, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_ICMP", "line_number": 109, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_TCP", "line_number": 110, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 111, "usage_type": "call"}, {"api_name": "socket.gethostbyaddr", "line_number": 143, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 178, "usage_type": "call"}, {"api_name": "socket.gethostbyname", "line_number": 189, "usage_type": "call"}]}
+{"seq_id": "551669653", "text": "\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef gaussian(x, mu, sig):\n return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))\n\n\nc = 342 # speed of sound\nlx = 342/2 # length in meters\nt = 2 # time in seconds\n\n# TIME\nFs_t = 2000 # samples/second time is dependent of space\n\n# SPACE\nFs_x = 2 # samples/meter\nnum_div_x = int(lx*Fs_x) # divisions of all the space\n\n\n# Simulation steps in Time\nnum_div_t = int(Fs_t*t)\ndelta_t = t / num_div_t\n\nt_axis = np.arange(0, t, delta_t)\n\n\n# number of divisions in x axis\ndelta_x = lx / num_div_x\n\nx_axis = np.arange(0, lx, delta_x)\n\n\n# force signal\n\nt_values = np.arange(0, num_div_t, 1)\nx_values = np.arange(0, num_div_x, 1)\n\nx_n = np.zeros([num_div_t, num_div_x])\n\nk_x = 15\n# x_n[:, 0] = np.cos((np.pi * k_x / num_div_x) * x_values)\n\nk_t = 1 / ((2 * lx) / (k_x*c))\nA = -100\n# pos_x = int(num_div_x/2)\n# pos_x = int(8*num_div_x/20)\npos_x = 0\n\nx_n[:, pos_x] = A * np.sin((2*np.pi * k_t / Fs_t) * t_values)\n# offset = 10\n# x_n[:, pos_x] = A*gaussian(t_values, 38 + offset, 9) - A*gaussian(t_values, 74 + offset, 9)\n# x_n[:, pos_x + 100] = gaussian(t_values, 5, 1) - gaussian(t_values, 10, 1)\n\n# plt.figure()\n# plt.imshow(x_n, cmap='hot')\n\nplt.figure()\nplt.plot(x_n[:, pos_x])\n\n\nprint(\"num_div_t %i \" % num_div_t)\nprint(\"num_div_x %i \" % num_div_x)\n\nprint(\"delta t: %f\" % delta_t)\nprint(\"CFL Condition %f\" % (delta_x/((3**0.5)*c)))\n\n\n# Init Simulation time-stepping scheme----\np_n_minus1 = np.zeros(shape=[num_div_x, 1])\np_n = np.zeros(shape=[num_div_x, 1])\np_n_plus1 = np.zeros(shape=[num_div_x, 1])\n\nk_matrix_global = np.zeros(shape=[num_div_x, num_div_x])\n\nfdtd_kernel_6 = np.array([2, -27, 270, -490, 270, -27, 2])*(1/180)\n# fdtd_kernel_6 = np.array([2, -27, 270, -490, 270, -27, 2])\n\n# Creating K Laplace operator matrix\nk_matrix_temp = np.zeros(shape=[num_div_x, num_div_x + 6])\nfor i in range(num_div_x):\n k_matrix_temp[i, i:i+7] = fdtd_kernel_6.copy()\n\n\nk_matrix_global = k_matrix_temp[:, 3:-3].copy()\n\n# Rigid walls Nuemann boundary condition (partial_p/partial_x)=0 when x=0 and x=l_x\nk_matrix_global[:, 0:3] = k_matrix_global[:, 0:3] + np.fliplr(k_matrix_temp[:, 0:3])\nk_matrix_global[:, -3:num_div_x] = k_matrix_global[:, -3:num_div_x] + np.fliplr(k_matrix_temp[:, -3:num_div_x + 6])\n\n# Two Partitions of equal size\nk_matrix_local = k_matrix_global.copy()\np_1_k_matrix = k_matrix_local[0:int(num_div_x/2), :]\np_2_k_matrix = k_matrix_local[int(num_div_x/2):num_div_x, :]\n\n# Both with boundaries conditions (partial_p/partial_x)=0 when x=0 and x=l_x\np_1_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)] = p_1_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)] \\\n + np.fliplr(p_1_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3])\n\np_1_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3] = p_1_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3] \\\n - p_1_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3]\n\n\np_2_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3] = p_2_k_matrix[:, int(num_div_x/2):int(num_div_x/2)+3] \\\n + np.fliplr(p_2_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)])\n\np_2_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)] = p_2_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)] \\\n - p_2_k_matrix[:, int(num_div_x/2)-3:int(num_div_x/2)]\n\n# Laplace operator Residual = global - local\nk_matrix_res = k_matrix_global - k_matrix_local\n\nk_mini_matrix_res = k_matrix_res[int(num_div_x/2)-3:int(num_div_x/2)+3,int(num_div_x/2)-3:int(num_div_x/2)+3]\n\n\n# Terms\nlambda_2 = (c * delta_t / delta_x) ** 2\n\nk_matrix_local = k_matrix_local*lambda_2\nk_matrix_res = k_matrix_res*lambda_2\nk_mini_matrix_res = k_mini_matrix_res*lambda_2\n\n# Force init update\n# f = (x_n[1, :].reshape([num_div_x, 1])).copy() + k_matrix_res.dot(p_n)\n\np_n_mini = p_n[int(num_div_x/2)-3:int(num_div_x/2)+3, :]\nf = (delta_t ** 2) * (x_n[1, :].reshape([num_div_x, 1])).copy()\nf[int(num_div_x/2)-3:int(num_div_x/2)+3, :] = f[int(num_div_x/2)-3:int(num_div_x/2)+3, :] + k_mini_matrix_res.dot(p_n_mini)\n\nf_bound_list = []\n\nplt.figure()\nfor time_step in range(2, int(num_div_t/10)):\n # Local update\n p_n_plus1 = 2 * p_n - p_n_minus1 + (k_matrix_local.dot(p_n)) + f\n\n # Update Force\n # f = (delta_t ** 2) * ((x_n[time_step, :].reshape([num_div_x, 1])).copy()) + k_matrix_res.dot(p_n_plus1)\n p_n_mini = p_n_plus1[int(num_div_x / 2) - 3:int(num_div_x / 2) + 3, :]\n f = (delta_t ** 2) * (x_n[time_step, :].reshape([num_div_x, 1])).copy()\n f[int(num_div_x / 2) - 3:int(num_div_x / 2) + 3, :] = f[int(num_div_x / 2) - 3:int(num_div_x / 2) + 3,\n :] + k_mini_matrix_res.dot(p_n_mini)\n\n f_boundaries = f[int(num_div_x / 2) - 3:int(num_div_x / 2) + 3, :]\n\n # f_bound_list.append(f_boundaries.copy())\n\n # Update last temporal terms\n p_n_minus1 = p_n.copy()\n p_n = p_n_plus1.copy()\n\n # Plot\n plt.clf()\n\n plt.subplot(2, 1, 1)\n plt.plot(p_n_plus1)\n plt.axis([0, num_div_x, -0.005, +0.005])\n\n plt.subplot(2, 1, 2)\n plt.plot([0, 1, 2, 3, 4, 5], f_boundaries.reshape([6]), 'o', color='r')\n plt.axis([0, 5, -1.3873669866559941e-05, 1.3873669866559924e-05])\n\n plt.pause(0.00001)\n\n\n\n", "sub_path": "Fluids/Easy01_wave/easy-wave-based-master/fdtd/1d/1d_propagation_stencil_2_6_ddm.py", "file_name": "1d_propagation_stencil_2_6_ddm.py", "file_ext": "py", "file_size_in_byte": 5348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.exp", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 51, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}]}
+{"seq_id": "363899076", "text": "import numpy as np\nfrom PIL import Image\n\ndef process_image(image):\n ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n returns an Numpy array\n '''\n im = Image.open(image)\n width, height = im.size\n new_width = 0\n new_height = 0\n if width <= height:\n new_width = 256\n new_height = int((height/width)* new_width)\n elif height < width:\n new_height = 256\n new_width = int((width/height)*new_height)\n size = new_width, new_height\n im = im.resize(size)\n\n left = (new_width - 224)/2\n top = (new_height - 224)/2\n right = (new_width + 224)/2\n bottom = (new_height + 224)/2\n im = im.crop((left, top, right, bottom))\n \n mean = np.array([0.485, 0.456, 0.406])\n std = np.array([0.229, 0.224, 0.225])\n np_image = np.array(im)\n np_image = np_image/255\n np_image = (np_image - mean)/std\n \n return np_image.transpose(2,0,1)", "sub_path": "process_image.py", "file_name": "process_image.py", "file_ext": "py", "file_size_in_byte": 927, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "PIL.Image.open", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "627184648", "text": "from datetime import datetime, timedelta\nfrom adminek.views.generic_views import BaseGenericView\nfrom django.shortcuts import render, redirect\n\nfrom adminek.views.utils import get_all_sundays\nfrom app.models import MassSchemaRows, Intentions, IntentionWeek\n\n\nclass IntentionView(BaseGenericView):\n messes = {}\n\n def __init__(self, **kwargs):\n self.days = ['Niedziela', 'Poniedziałek', 'Wtorek', 'Środa', 'Czwartek', 'Piątek', 'Sobota']\n super().__init__(**kwargs)\n\n def get_for_single(self, model_class, id_object):\n object_context = IntentionWeek.objects.filter(id=id_object).prefetch_related('intentions_set')[0]\n intentions = []\n for intention in object_context.intentions_set.all().order_by('date', 'hour'):\n intentions.append(intention)\n return {\n 'intentionweek': object_context,\n 'intentions': intentions\n }\n\n def get(self, request, *args, **kwargs):\n method = kwargs.get('method', 'get')\n if method == 'edit':\n self.context = {}\n intentions = []\n intention_week = self.get_for_single(self.model_class, kwargs['pk'])\n # intention_week = self.context['object']\n date = intention_week['intentionweek'].week\n self.context['intention_week_start'] = intention_week['intentionweek'].week\n i = 0\n intentions_day = []\n for intention in intention_week['intentions']:\n print(intention.date, date)\n if intention.date != date:\n print('is different', intentions_day)\n intentions.append({\n 'day': self.days[i],\n 'intentions': intentions_day.copy()})\n i += 1\n date = intention.date\n intentions_day = []\n intentions_day.append(intention)\n\n intentions.append({\n 'day': self.days[i],\n 'intentions': intentions_day.copy()})\n self.template_name = 'others/intentionweek_edit.html'\n self.context['intentions'] = intentions\n print('context ', self.context)\n return render(request, self.template_name, self.context)\n else:\n return super().get(request, *args, **kwargs)\n\n # def get_template_name(self, method):\n # if method == 'edit':\n # self.template_name = 'others/intentionweek_edit'\n # else:\n # super().get_template_name(method)\n\n def post(self, request, *args, **kwargs):\n self.context = {}\n params = dict(request.POST)\n if 'stepDay' in params.keys():\n self.context['object'] = {}\n # self.context['object']['date'] = request.POST['date']\n old_week = IntentionWeek.objects.filter(week=request.POST['date'])\n if old_week.exists():\n self.context['intention_week_start'] = old_week[0].week\n return redirect('intentionweek', method='edit', pk=old_week[0].id, object_name='intentionweek')\n self.context['intention_week_start'] = request.POST['date']\n intentions = [self.prepare_intentions(True, self.days[0], 0)]\n for index, day in enumerate(self.days[1:]):\n intentions.append(self.prepare_intentions(False, day, index+1))\n self.context['intentions'] = intentions\n print('context ', self.context)\n return render(request, 'others/intentions_form.html', self.context)\n else:\n return super().post(request, *args, **kwargs)\n\n def prepare_intentions(self, is_sunday, day, index):\n rows_with_coma_sep_hours = MassSchemaRows.objects.filter(is_sunday=is_sunday)\n hours_list_in_day = []\n for mass_hour in rows_with_coma_sep_hours:\n if mass_hour.hours:\n mass_hours = mass_hour.hours.split(', ')\n hours_list_in_day.extend(mass_hours)\n hours_list_in_day.sort()\n hours = []\n # categorize by day\n date = (datetime.strptime(self.context['intention_week_start'], '%Y-%m-%d') + timedelta(days=index)).date()\n for hour in hours_list_in_day:\n intention = Intentions(hour=hour, date=date)\n hours.append(intention)\n return {\n 'day': day,\n 'intentions': hours}\n\n def create(self, request, *args, **kwargs):\n params = dict(request.POST)\n intention_week = IntentionWeek(week=request.POST['date'])\n intention_week.save()\n\n for day in self.days:\n hours_for_day = {key: value for key, value in params.items() if key.startswith(day+'-hour')}\n for hour_key in hours_for_day:\n hour = hour_key.split('-')[-1]\n intention = Intentions(date=params[day+'-date-'+hour][0], hour=hour,\n title=params[day+'-content-'+hour][0], week=intention_week)\n intention.save()\n\n return redirect('detail', method='list', object_name='intentionweek')\n\n def edit(self, request, *args, **kwargs):\n params = dict(request.POST)\n for day in self.days:\n hours_for_day = {key: value[0] for key, value in params.items() if key.startswith(day+'-hour')}\n for hour_key in hours_for_day:\n hour = hour_key.split('-')[-1]\n intention = Intentions.objects.filter(id=params[day+'-id-'+hour][0])[0]\n intention.title = params[day+'-content-'+hour][0]\n intention.save()\n", "sub_path": "adminek/views/intention_view.py", "file_name": "intention_view.py", "file_ext": "py", "file_size_in_byte": 5607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "adminek.views.generic_views.BaseGenericView", "line_number": 9, "usage_type": "name"}, {"api_name": "app.models.IntentionWeek.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "app.models.IntentionWeek.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.models.IntentionWeek", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "app.models.IntentionWeek.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "app.models.IntentionWeek.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "app.models.IntentionWeek", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "app.models.MassSchemaRows.objects.filter", "line_number": 86, "usage_type": "call"}, {"api_name": "app.models.MassSchemaRows.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app.models.MassSchemaRows", "line_number": 86, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 95, "usage_type": "call"}, {"api_name": "app.models.Intentions", "line_number": 97, "usage_type": "call"}, {"api_name": "app.models.IntentionWeek", "line_number": 105, "usage_type": "call"}, {"api_name": "app.models.Intentions", "line_number": 112, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 116, "usage_type": "call"}, {"api_name": "app.models.Intentions.objects.filter", "line_number": 124, "usage_type": "call"}, {"api_name": "app.models.Intentions.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "app.models.Intentions", "line_number": 124, "usage_type": "name"}]}
+{"seq_id": "643374594", "text": "# coding: utf-8\n# # Object Detection Demo\n# License: Apache License 2.0 (https://github.com/tensorflow/models/blob/master/LICENSE)\n# source: https://github.com/tensorflow/models\nimport numpy as np\nimport os\nimport six.moves.urllib as urllib\nimport sys\nimport tarfile\nimport tensorflow as tf\nimport time\nimport zipfile\nfrom PIL import ImageGrab\nfrom directkeys import PressKey, ReleaseKey, W,A,S,D\nfrom PIL import ImageGrab\nfrom sklearn.cluster import KMeans\n\nfrom collections import defaultdict\nfrom io import StringIO\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nfrom grabscreen import grab_screen\nimport cv2\n\n# This is needed since the notebook is stored in the object_detection folder.\nsys.path.append(\"..\")\n\n\n# ## Object detection imports\n# Here are the imports from the object detection module.\n\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\n\n\n# # Model preparation \n# What model to download.\nMODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'\nMODEL_FILE = MODEL_NAME + '.tar.gz'\nDOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n\n# Path to frozen detection graph. This is the actual model that is used for the object detection.\nPATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n\n# List of the strings that is used to add correct label for each box.\nPATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')\n\nNUM_CLASSES = 90\n\n# ## Download Model\nopener = urllib.request.URLopener()\nopener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\ntar_file = tarfile.open(MODEL_FILE)\nfor file in tar_file.getmembers():\n file_name = os.path.basename(file.name)\n if 'frozen_inference_graph.pb' in file_name:\n tar_file.extract(file, os.getcwd())\n\n\n# ## Load a (frozen) Tensorflow model into memory.\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n\n\n# ## Loading label map\n# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\n\n# 무한루프를 돌면서 \n# Region of Interest : 관심영역을 설정하는 함수\ndef roi(img, vertices):\n # img 크기만큼의 영행렬을 mask 변수에 저장하고\n mask = np.zeros_like(img)\n \n # vertices 영역만큼의 Polygon 형상에만 255의 값을 넣습니다\n masked = cv2.fillPoly(mask, vertices, 255)\n \n # img와 mask 변수를 and (비트연산) 해서 나온 값들을 masked에 넣고 반환합니다\n masked = cv2.bitwise_and(img, masked)\n return masked\n\n\n# 이미지에 각종 영상처리를 하는 함수\ndef process_img_1(image):\n original_image = image\n \n # convert to gray\n \n blue_threshold = 160\n green_threshold = 160\n red_threshold = 160\n bgr_threshold = [blue_threshold, green_threshold, red_threshold]\n thresholds = (image[:,:,0] > bgr_threshold[0]) \\\n | (image[:,:,1] > bgr_threshold[1]) \\\n | (image[:,:,2] > bgr_threshold[2])\n mark[thresholds] = [255,255,255]\n processed_img = cv2.cvtColor(mark, cv2.COLOR_BGR2GRAY)\n \n blue_threshold = 50\n green_threshold = 50\n red_threshold = 50\n bgr_threshold = [blue_threshold, green_threshold, red_threshold]\n thresholds = (image[:,:,0] < bgr_threshold[0]) \\\n | (image[:,:,1] < bgr_threshold[1]) \\\n | (image[:,:,2] < bgr_threshold[2])\n mark[thresholds] = [255,255,255]\n processed_img = cv2.cvtColor(mark, cv2.COLOR_BGR2GRAY)\n # edge detection\n #processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)\n #processed_img = cv2.GaussianBlur(processed_img, (5,5), 0)\n \n # 원하는 영역을\n vertices = np.array([[100,500], [100,200], [300,100],[500,100], [700,200], [700,500]], np.int32)\n\n \n # roi()를 사용해 그 영역만큼 영상을 자릅니다\n processed_img = roi(processed_img, [vertices])\n \n \n # BGR 제한 값보다 작으면 검은색으로\n \n \n return processed_img, original_image\n\ndef process_img_2(image):\n original_image = image\n \n # convert to gray\n \n blue_threshold = 160\n green_threshold = 160\n red_threshold = 160\n bgr_threshold = [blue_threshold, green_threshold, red_threshold]\n thresholds = (image[:,:,0] > bgr_threshold[0]) \\\n & (image[:,:,1] > bgr_threshold[1]) \\\n & (image[:,:,2] > bgr_threshold[2])\n mark[thresholds] = [255,255,255]\n processed_img = cv2.cvtColor(mark, cv2.COLOR_BGR2GRAY)\n \n blue_threshold = 50\n green_threshold = 50\n red_threshold = 50\n bgr_threshold = [blue_threshold, green_threshold, red_threshold]\n thresholds = (image[:,:,0] < bgr_threshold[0]) \\\n & (image[:,:,1] < bgr_threshold[1]) \\\n & (image[:,:,2] < bgr_threshold[2])\n mark[thresholds] = [255,255,255]\n processed_img = cv2.cvtColor(mark, cv2.COLOR_BGR2GRAY)\n # edge detection\n #processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)\n #processed_img = cv2.GaussianBlur(processed_img, (5,5), 0)\n\n \n \n # BGR 제한 값보다 작으면 검은색으로\n \n \n return processed_img, original_image\ndef compare_1_2_3(image1_color,image2_color,image3_color):\n #if((image1_color > 200) & (image2_color > 200) & (image3_color > 200)):\n # return 4\n \n \n if(image1_color < image2_color):\n if(image1_color > image3_color):\n return 1\n else:\n return 3\n else:\n if(image2_color < image3_color):\n \n return 3\n else:\n \n return 2\ndef image_avg_color(image):\n shape = image.shape\n shape1 = shape[0]*shape[1]\n image = image.reshape(shape1)\n sum = 0\n for i in range(0,shape1):\n sum += image[i]\n avg = int(sum / shape1)\n return avg\n# ## Helper code\ndef load_image_into_numpy_array(image):\n (im_width, im_height) = image.size\n return np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)\n\ndef roi(img, vertices):\n # img 크기만큼의 영행렬을 mask 변수에 저장하고\n mask = np.zeros_like(img)\n \n # vertices 영역만큼의 Polygon 형상에만 255의 값을 넣습니다\n masked = cv2.fillPoly(mask, vertices, 255)\n \n # img와 mask 변수를 and (비트연산) 해서 나온 값들을 masked에 넣고 반환합니다\n masked = cv2.bitwise_and(img, masked)\n return masked\n \ndef process_img(image):\n original_image = image\n \n # convert to gray\n processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)\n # edge detection\n processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)\n processed_img = cv2.GaussianBlur(processed_img, (5,5), 0)\n \n # 원하는 영역을 만들고\n vertices = np.array([[10,500], [10,300], [300,200],[500,200], [800,300], [800,500]], np.int32)\n \n # roi()를 사용해 그 영역만큼 영상을 자릅니다\n processed_img = roi(processed_img, [vertices])\n return processed_img\n# Size, in inches, of the output images.\nIMAGE_SIZE = (12, 8)\n\n\ndef control_car(num):\n if(num==1):\n PressKey(W)\n time.sleep(1)\n ReleaseKey(W)\n print(\"직진\")\n \n elif(num==2):\n PressKey(A)\n PressKey(W)\n time.sleep(1)\n ReleaseKey(A)\n PressKey(W)\n time.sleep(1)\n ReleaseKey(W)\n print(\"좌회전\")\n \n\n elif(num==3):\n PressKey(D)\n PressKey(W)\n time.sleep(1)\n ReleaseKey(D)\n PressKey(W)\n time.sleep(1)\n ReleaseKey(W)\n print(\"우회전\")\n \n \n else:\n PressKey(S)\n time.sleep(1)\n ReleaseKey(S)\n PressKey(W)\n time.sleep(1)\n ReleaseKey(W)\n \n print(\"후진\")\n\nnum = 0 \nwith detection_graph.as_default():\n with tf.Session(graph=detection_graph) as sess:\n while True:\n image = np.array(ImageGrab.grab(bbox=(0,40,800,600)))\n mark = np.copy(image) # image 복사\n \n new_screen, original_image = process_img_1(image)\n \n image1 = np.array(ImageGrab.grab(bbox=(300,300,500,350)))\n mark = np.copy(image1) # image 복사\n \n new_screen1, original_image1 = process_img_2(image1)\n \n\n image2 = np.array(ImageGrab.grab(bbox=(200,300,300,350)))\n mark = np.copy(image2) # image 복사\n \n new_screen2, original_image2 = process_img_2(image2)\n \n image3 = np.array(ImageGrab.grab(bbox=(500,300,600,350)))\n mark = np.copy(image3) # image 복사\n\n new_screen3, original_image3 = process_img_2(image3)\n\n\n image1_color = image_avg_color(new_screen1)\n image2_color = image_avg_color(new_screen2)\n image3_color = image_avg_color(new_screen3)\n #screen = cv2.resize(grab_screen(region=(0,40,1280,745)), (WIDTH,HEIGHT))\n screen = image = np.array(ImageGrab.grab(bbox=(200,200,600,350)))\n #vertices = np.array([[100,500], [100,200], [300,100],[500,100], [700,200], [700,500]], np.int32)\n #image_np = process_img(screen)\n image_np = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)\n # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n image_np_expanded = np.expand_dims(image_np, axis=0)\n image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n # Each box represents a part of the image where a particular object was detected.\n boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n # Each score represent how level of confidence for each of the objects.\n # Score is shown on the result image, together with the class label.\n scores = detection_graph.get_tensor_by_name('detection_scores:0')\n classes = detection_graph.get_tensor_by_name('detection_classes:0')\n num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n # Actual detection.\n (boxes, scores, classes, num_detections) = sess.run(\n [boxes, scores, classes, num_detections],\n feed_dict={image_tensor: image_np_expanded})\n # Visualization of the results of a detection.\n vis_util.visualize_boxes_and_labels_on_image_array(\n image_np,\n np.squeeze(boxes),\n np.squeeze(classes).astype(np.int32),\n np.squeeze(scores),\n category_index,\n use_normalized_coordinates=True,\n line_thickness=8)\n\n a = []\n min_i = 0\n for i,b in enumerate(boxes[0]):\n # car bus truck\n if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:\n if scores[0][i] >= 0.5:\n apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)\n a.append(apx_distance)\n print(apx_distance)\n \n \n for i in range(0,len(a)):\n if(i==0):\n min = a[i]\n min_i = i\n else:\n if(min > a[i]):\n \n min = a[i]\n min_i = i\n \n if scores[0][min_i] >= 0.5:\n print(len(a)+1,\"대 차가 식별됨, 그 중 대표 차 1대 추출\") \n print(\"score : \", scores[0][min_i])\n mid_x = (boxes[0][min_i][1]+boxes[0][min_i][3])/2\n print(\"mid_x : \", mid_x)\n mid_y = (boxes[0][min_i][0]+boxes[0][min_i][2])/2\n print(\"mid_y\",mid_y)\n apx_distance = round(((1 - (boxes[0][min_i][3] - boxes[0][min_i][1]))**4),1)\n a.append(apx_distance)\n \n print(\"apx_distance : \", apx_distance)\n #cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)\n \n if apx_distance >=0.5:\n if mid_x > 0.2 and mid_x < 0.8:\n control_car(1)\n elif mid_x <= 0.2:\n control_car(2)\n elif mid_x >= 0.8:\n control_car(3)\n else:\n print(\"a\")\n elif apx_distance <= 0.1:\n control_car(4)\n else:\n print(\"b\") \n else:\n print(\"차량이 식별되지 않았습니다. 도로 탐색기반으로 동작합니다.\")\n if(num==0):\n count = 0\n control_num = compare_1_2_3(image1_color,image2_color,image3_color)\n control_car(control_num)\n control_last_num = control_num\n print(control_last_num)\n print(control_num)\n num += 1\n else:\n control_num = compare_1_2_3(image1_color,image2_color,image3_color)\n print(control_last_num)\n print(control_num)\n if(control_num == control_last_num):\n count += 1\n print(count)\n else:\n count = 0\n if(count==3):\n control_car(4)\n else:\n control_car(control_num)\n control_last_num = control_num\n \n \n \n cv2.imshow('main', new_screen) \n \n \n\n cv2.imshow('window',cv2.resize(image_np,(800,450)))\n if cv2.waitKey(25) & 0xFF == ord('q'):\n cv2.destroyAllWindows()\n break \n ", "sub_path": "found_representative_car.py", "file_name": "found_representative_car.py", "file_ext": "py", "file_size_in_byte": 13786, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "six.moves.urllib.request.URLopener", "line_number": 51, "usage_type": "call"}, {"api_name": "six.moves.urllib.request", "line_number": 51, "usage_type": "attribute"}, {"api_name": "six.moves.urllib", "line_number": 51, "usage_type": "name"}, {"api_name": "tarfile.open", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.GraphDef", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.import_graph_def", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.label_map_util.load_labelmap", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 72, "usage_type": "name"}, {"api_name": "utils.label_map_util.convert_label_map_to_categories", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 73, "usage_type": "name"}, {"api_name": "utils.label_map_util.create_category_index", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 104, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 200, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 206, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 213, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 213, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 215, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 219, "usage_type": "attribute"}, {"api_name": "directkeys.PressKey", "line_number": 230, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 230, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 231, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 232, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 232, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 236, "usage_type": "call"}, {"api_name": "directkeys.A", "line_number": 236, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 237, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 237, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 238, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 239, "usage_type": "call"}, {"api_name": "directkeys.A", "line_number": 239, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 240, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 240, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 242, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 242, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 247, "usage_type": "call"}, {"api_name": "directkeys.D", "line_number": 247, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 248, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 248, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 249, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 250, "usage_type": "call"}, {"api_name": "directkeys.D", "line_number": 250, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 251, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 251, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 252, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 253, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 253, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 258, "usage_type": "call"}, {"api_name": "directkeys.S", "line_number": 258, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 259, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 260, "usage_type": "call"}, {"api_name": "directkeys.S", "line_number": 260, "usage_type": "argument"}, {"api_name": "directkeys.PressKey", "line_number": 261, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 261, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 262, "usage_type": "call"}, {"api_name": "directkeys.ReleaseKey", "line_number": 263, "usage_type": "call"}, {"api_name": "directkeys.W", "line_number": 263, "usage_type": "argument"}, {"api_name": "tensorflow.Session", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 271, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 271, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 271, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 276, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 276, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 282, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 282, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 282, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 287, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 287, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 297, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 297, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 297, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 300, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 300, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 302, "usage_type": "call"}, {"api_name": "utils.visualization_utils.visualize_boxes_and_labels_on_image_array", "line_number": 316, "usage_type": "call"}, {"api_name": "utils.visualization_utils", "line_number": 316, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 319, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 320, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 399, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 403, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 403, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 404, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 405, "usage_type": "call"}]}
+{"seq_id": "469983396", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# encoding: utf-8\n#客户端调用，用于查看API返回结果\n\nfrom OkcoinSpotAPI import OKCoinSpot\nfrom OkcoinFutureAPI import OKCoinFuture\nfrom time import *\nfrom conf import *\nfrom itertools import *\nimport logging\nimport json\n\n#CRITICAL > ERROR > WARNING > INFO > DEBUG > NOTSET\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(levelname)s %(message)s',\n datefmt='%a, %d %b %Y %H:%M:%S',\n filename='/root/okex/rest/python/log',\n filemode='a')\n\nSYMBOL = ['ace', 'act', 'amm', 'ark', 'ast', 'avt', 'bnt', 'btm', 'cmt', 'ctr',\n 'cvc', 'dash', 'dat', 'dgb', 'dgd', 'dnt', 'dpy', 'edo', 'elf', 'eng',\n 'eos', 'etc', 'evx', 'fun', 'gas', 'gnt', 'gnx', 'hsr', 'icn', 'icx',\n 'iota', 'itc', 'kcash', 'knc', 'link', 'lrc', 'ltc', 'mana', 'mco',\n 'mda', 'mdt', 'mth', 'nas', 'neo', 'nuls', 'oax', 'omg', 'pay',\n 'ppt', 'pro', 'qtum', 'qvt', 'rcn', 'rdn', 'read', 'req', 'rnt', 'salt',\n 'san', 'sngls', 'snm', 'snt', 'ssc', 'storj', 'sub', 'swftc',\n 'tnb', 'trx', 'ugc', 'ukg', 'vee', 'wrc', 'wtc', 'xem', 'xlm', 'xmr',\n 'xrp', 'xuc', 'yoyo', 'zec', 'zrx', '1st']\n\n\namount = {\n 'eth_btc': 0.015,\n 'eos_btc': 1.8,\n 'eos_eth': 1.8,\n 'etc_btc': 0.5,\n 'etc_eth': 0.5,\n 'mco_btc': 0.8,\n 'mco_eth': 0.8,\n}\n\n\nclass okex():\n def __init__(self):\n ##初始化apikey，secretkey,url\n apikey = config.apikey\n secretkey = config.secretkey\n okcoinRESTURL = 'www.okex.com' #请求注意：国内账号需要 修改为 www.okcoin.cn\n\n #现货API\n self.okcoinSpot = OKCoinSpot(okcoinRESTURL,apikey,secretkey)\n\n # 期货API\n self.okcoinFuture = OKCoinFuture(okcoinRESTURL, apikey, secretkey)\n\n self.depth = {}\n\n\n def getTicker(self, symbol):\n return self.okcoinSpot.ticker(symbol)['ticker']\n\n def getDepth(self, symbol):\n depth = self.okcoinSpot.depth(symbol)\n self.depth[symbol] = {'sell':{'price':depth['asks'][-1][0], 'amount':depth['asks'][-1][1]},\n 'buy':{'price':depth['bids'][0][0], 'amount':depth['bids'][0][1]}}\n return {'sell':{'price':depth['asks'][-1][0], 'amount':depth['asks'][-1][1]},\n 'buy':{'price':depth['bids'][0][0], 'amount':depth['bids'][0][1]}}\n\n def getBalance(self):\n '''\n\n :return: \n '''\n self.balance = {}\n info = json.loads(self.okcoinSpot.userinfo())\n for symbol in info['info']['funds']['free'].keys():\n self.balance[symbol] = float(info['info']['funds']['free'][symbol])\n\n def trade(self, symbol, type, price, amount):\n '''\n\n :param symbol:\n :param type:\n :param price:\n :param amount:\n :return: order_id\n '''\n if price != '':\n logging.info('[order]' + symbol + '|' + type+ '|' + str(price) + '|' + str(amount))\n rsp = json.loads(self.okcoinSpot.trade(symbol, type, price, amount))\n if 'error_code' in rsp:\n if str(rsp['error_code']) != '1003':\n logging.info('[trade error]' + str(rsp['error_code']))\n return False\n if rsp['result']:\n return rsp['order_id']\n\n def getOrderInfo(self, symbol, order_id):\n '''\n\n :param symbol:\n :param order_id:\n :return: order_status: -1:已撤销 0:未成交 1:部分成交 2:完全成交 3:撤单处理中\n '''\n rsp = json.loads(self.okcoinSpot.orderinfo(symbol, order_id))\n if 'error_code' in rsp:\n logging.info('[getOrderInfo error]' + str(rsp['error_code']))\n return False\n if rsp['result']:\n return int(rsp['orders'][0]['status'])\n else:\n return False\n\n def toBtc(self):\n self.getBalance()\n for symbol in self.balance.keys():\n if symbol != 'usdt' and symbol != 'btc' and symbol != 'mtl' and self.balance[symbol] != 0:\n # print(symbol)\n if self.balance[symbol] != 0:\n tradeSymbol = symbol + '_btc'\n self.trade(tradeSymbol, 'sell_market', '', self.balance[symbol])\n\n def cancelOrder(self, symbol, order_id):\n '''\n\n :param symbol:\n :param order_id:\n :return: True or False\n '''\n rsp = json.loads(self.okcoinSpot.cancelOrder(symbol, order_id))\n if 'error_code' in rsp:\n logging.info('[cancelOrder error]' + str(rsp['error_code']))\n return False\n return rsp['result']\n\n def good_trade(self, symbols, Threshold=1.02):\n '''\n\n :param symbols: such as [btc, eth, mco]\n :return:\n '''\n symbol_1 = symbols[1] + '_' + symbols[0]\n symbol_2 = symbols[2] + '_' + symbols[0]\n symbol_3 = symbols[2] + '_' + symbols[1]\n t1 = self.getTicker(symbol_1)\n t2 = self.getTicker(symbol_2)\n t3 = self.getTicker(symbol_3)\n # print ('=======================================')\n # temp = (float(t2['sell']) / float(t3['buy']))\n a1 = (float(t2['sell']) / float(t3['buy'])) / float(t1['buy'])\n a2 = (float(t1['sell']) * float(t3['sell'])) / float(t2['buy'])\n\n if a1 < Threshold:\n traderSymbol = [symbol_2, symbol_3, symbol_1]\n\n logging.debug('=======================================')\n logging.debug(a1)\n logging.debug('[trader] ' + symbols[0] + '--->' + symbols[2] + '--->' + symbols[1] + '--->' + symbols[0])\n logging.debug(t1)\n logging.debug(t2)\n logging.debug(t3)\n elif a2 < Threshold:\n traderSymbol = [symbol_1, symbol_3, symbol_2]\n\n logging.debug('=======================================')\n logging.debug(a2)\n logging.debug('[trader] ' + symbols[0] + '--->' + symbols[1] + '--->' + symbols[2] + '--->' + symbols[0])\n logging.debug(t1)\n logging.debug(t2)\n logging.debug(t3)\n else:\n pass\n\n def tradePolicy(self, symbols, initAmount=0.005, Threshold=1.02):\n '''\n\n :param symbols: such as [btc, eth, mco]\n :return:\n '''\n retry = 3\n symbol_1 = symbols[1] + '_' + symbols[0]\n symbol_2 = symbols[2] + '_' + symbols[0]\n symbol_3 = symbols[2] + '_' + symbols[1]\n t1 = self.getDepth(symbol_1)\n t2 = self.getDepth(symbol_2)\n t3 = self.getDepth(symbol_3)\n a1 = (float(t2['sell']['price']) / float(t3['buy']['price'])) / float(t1['buy']['price'])\n a2 = (float(t1['sell']['price']) * float(t3['sell']['price'])) / float(t2['buy']['price'])\n # logging.debug(t1)\n if a1 < Threshold:\n if float(t2['sell']['amount']) < amount[symbol_2] or float(t3['buy']['amount']) < amount[symbol_3] or\\\n float(t1['buy']['amount']) < amount[symbol_1]:\n return\n logging.info('=========================================================')\n logging.debug(a1)\n traderSymbol = [symbol_2, symbol_3, symbol_1]\n logging.debug('[trader] ' + symbols[0] + '--->' + symbols[2] + '--->' + symbols[1] + '--->' + symbols[0])\n logging.debug(t1)\n logging.debug(t2)\n logging.debug(t3)\n #step1\n logging.info('[step1]')\n self.getBalance()\n self.toBtc()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount1 = round((initAmount * 0.999) / float(t2['sell']['price']), 8)\n for i in range(retry):\n logging.info('[order]' + symbol_2 + '|buy|' + str(float(t2['sell']['price'])) + '|' + str(amount1))\n orderId = self.trade(symbol_2, 'buy', float(t2['sell']['price']), amount1)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_2, orderId)\n if status != 2:\n print(status)\n sleep(0.5)\n status = self.getOrderInfo(symbol_2, orderId)\n if status != 2:\n print(status)\n self.cancelOrder(symbol_2, orderId)\n logging.info('[cancelOrder!]')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n\n #step2\n logging.info('[step2]')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount2 = self.balance[symbols[2]]\n for i in range(retry):\n logging.info('[order]' + symbol_3 + '|sell|' + str(float(t3['buy']['price'])) + '|' + str(amount2))\n orderId = self.trade(symbol_3, 'sell', float(t3['buy']['price']), amount2)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_3, orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbol_3, orderId)\n if status != 2:\n self.cancelOrder(symbol_3, orderId)\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n\n #step3\n logging.info('[step3]')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount3 = self.balance[symbols[1]]\n for i in range(retry):\n logging.info('[order]' + symbol_1 + '|sell|' + str(float(t1['buy']['price'])) + '|' + str(amount3))\n orderId = self.trade(symbol_1, 'sell', float(t1['buy']['price']), amount3)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_1, orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbol_1, orderId)\n if status != 2:\n self.cancelOrder(symbol_1, orderId)\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n\n elif a2 < Threshold:\n if float(t2['buy']['amount']) < amount[symbol_2] or float(t3['sell']['amount']) < amount[symbol_3] or\\\n float(t1['sell']['amount']) < amount[symbol_1]:\n return\n logging.info('=========================================================')\n logging.debug(a2)\n traderSymbol = [symbol_1, symbol_3, symbol_2]\n logging.debug('[trader] ' + symbols[0] + '--->' + symbols[1] + '--->' + symbols[2] + '--->' + symbols[0])\n logging.debug(t1)\n logging.debug(t2)\n logging.debug(t3)\n\n # step1\n logging.info('[step1]')\n self.getBalance()\n self.toBtc()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount1 = round((initAmount * 0.999) / float(t1['sell']['price']), 8)\n for i in range(retry):\n logging.info('[order]' + symbol_1 + '|buy|' + str(float(t1['sell']['price'])) + '|' + str(amount1))\n orderId = self.trade(symbol_1, 'buy', float(t1['sell']['price']), amount1)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_1, orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbol_1, orderId)\n if status != 2:\n self.cancelOrder(symbol_1, orderId)\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n\n # step2\n logging.info('[step2]')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount2 = round((self.balance[symbols[1]] * 0.999) / float(t3['sell']['price']), 8)\n for i in range(retry):\n logging.info('[order]' + symbol_3 + '|buy|' + str(float(t3['sell']['price'])) + '|' + str(amount2))\n orderId = self.trade(symbol_3, 'buy', float(t3['sell']['price']), amount2)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_3, orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbol_3, orderId)\n if status != 2:\n self.cancelOrder(symbol_3, orderId)\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n\n # step3\n logging.info('[step3]')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount3 = self.balance[symbols[2]]\n for i in range(retry):\n logging.info('[order]' + symbol_2 + '|sell|' + str(float(t2['buy']['price'])) + '|' + str(amount3))\n orderId = self.trade(symbol_2, 'sell', float(t2['buy']['price']), amount3)\n if orderId:\n break\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbol_2, orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbol_2, orderId)\n if status != 2:\n self.cancelOrder(symbol_2, orderId)\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order failed!]')\n return\n else:\n pass\n\n def getCoinList(self, symbols):\n coinList = []\n for k in permutations(symbols, 2):\n tmp = ['btc', k[0], 'eth', k[1]]\n coinList.append(tmp)\n return coinList\n\n def getTradeSymbol(self, coinlist):\n ts =[]\n for c in coinlist:\n s = ['_'.join((c[1], c[0])),\n '_'.join((c[1], c[2])),\n '_'.join((c[3], c[2])),\n '_'.join((c[3], c[0]))]\n ts.append(s)\n return ts\n\n def getTradeAmount(self, symbols):\n for symbol in symbols:\n self.getDepth(symbol)\n self.getDepth('eth_btc')\n # print(api.depth[symbol])\n ss = (self.depth[symbols[1]]['buy']['price'] * self.depth[symbols[3]]['buy']['price']) / (self.depth[symbols[0]]['sell']['price'] * self.depth[symbols[2]]['sell']['price'])\n if ss > 1.01:\n logging.debug('profit: %f' % ss)\n logging.debug(symbols)\n logging.debug(self.depth[symbols[0]])\n logging.debug(self.depth[symbols[1]])\n logging.debug(self.depth[symbols[2]])\n logging.debug(self.depth[symbols[3]])\n amount = []\n amount.append(self.depth[symbols[0]]['sell']['price'] * min(self.depth[symbols[0]]['sell']['amount'],self.depth[symbols[1]]['buy']['amount']))\n amount.append(self.depth[symbols[3]]['buy']['price'] * min(self.depth[symbols[3]]['buy']['amount'],self.depth[symbols[2]]['sell']['amount']))\n amount.sort()\n logging.debug('amount: %f' % amount[0])\n return amount[0]\n else:\n return 0\n\n def doTrade(self, symbols, amount):\n if self.balance['btc'] < amount * 0.9:\n initamount = self.balance['btc'] * 0.99\n else:\n initamount = amount * 0.9\n\n logging.debug('step1')\n amount1 = round(initamount / self.depth[symbols[0]]['sell']['price'], 8)\n orderId = self.trade(symbols[0], 'buy', self.depth[symbols[0]]['sell']['price'], amount1)\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbols[0], orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbols[0], orderId)\n if status != 2:\n self.cancelOrder(symbols[0], orderId)\n self.toBtc()\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n self.toBtc()\n logging.info('[order failed!]')\n return\n\n logging.debug('step2')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount2 = self.balance[symbols[1].split('_')[0]]\n orderId = self.trade(symbols[1], 'sell', self.depth[symbols[1]]['buy']['price'], amount2)\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbols[1], orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbols[1], orderId)\n if status != 2:\n self.cancelOrder(symbols[1], orderId)\n self.toBtc()\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n self.toBtc()\n logging.info('[order failed!]')\n return\n\n logging.debug('step3')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount3 = round((self.balance[symbols[2].split('_')[1]] / self.depth[symbols[2]]['sell']['price']) * 0.998, 8)\n orderId = self.trade(symbols[2], 'buy', self.depth[symbols[2]]['sell']['price'], amount3)\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbols[2], orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbols[2], orderId)\n if status != 2:\n self.cancelOrder(symbols[2], orderId)\n self.toBtc()\n logging.info('cancelOrder!')\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n self.toBtc()\n logging.info('[order failed!]')\n return\n\n logging.debug('step4')\n self.getBalance()\n logging.info('[Balance]')\n logging.info(self.balance)\n amount4 = self.balance[symbols[3].split('_')[0]]\n orderId = self.trade(symbols[3], 'sell', self.depth[symbols[3]]['buy']['price'], amount4)\n if orderId:\n logging.info('[orderId]' + str(orderId))\n status = self.getOrderInfo(symbols[3], orderId)\n if status != 2:\n sleep(0.5)\n status = self.getOrderInfo(symbols[3], orderId)\n if status != 2:\n self.cancelOrder(symbols[3], orderId)\n logging.info('cancelOrder!')\n self.toBtc()\n return\n else:\n logging.info('[order succssed!]')\n else:\n logging.info('[order succssed!]')\n else:\n self.toBtc()\n logging.info('[order failed!]')\n return\n\n def policy(self, allsymbol):\n coins = self.getCoinList(allsymbol)\n tradesymbol = self.getTradeSymbol(coins)\n for symbols in tradesymbol:\n # print(symbols)\n # self.toBtc()\n amount = self.getTradeAmount(symbols)\n if amount > 0.00001:\n self.doTrade(symbols, amount)\n\n\nif __name__ == '__main__':\n api = okex()\n symbols = ['eos', 'ltc']\n while(1):\n api.policy(SYMBOL)", "sub_path": "python/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 21714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "OkcoinSpotAPI.OKCoinSpot", "line_number": 51, "usage_type": "call"}, {"api_name": "OkcoinFutureAPI.OKCoinFuture", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 89, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 93, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 107, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 130, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 156, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 157, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 160, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 165, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 166, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 168, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 169, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 170, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 197, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 198, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 199, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 200, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 202, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 206, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 209, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 214, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 223, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 226, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 228, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 230, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 234, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 236, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 237, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 240, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 245, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 252, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 255, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 257, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 259, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 263, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 265, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 266, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 269, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 274, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 281, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 284, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 286, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 295, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 296, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 298, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 299, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 300, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 301, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 304, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 307, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 308, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 311, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 316, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 323, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 326, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 328, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 330, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 334, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 337, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 340, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 345, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 352, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 355, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 357, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 359, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 363, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 365, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 366, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 369, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 374, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 381, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 384, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 386, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 388, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 417, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 418, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 419, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 420, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 421, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 422, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 427, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 438, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 442, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 450, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 453, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 455, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 458, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 461, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 463, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 464, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 468, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 476, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 479, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 481, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 484, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 487, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 489, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 490, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 494, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 502, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 505, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 507, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 510, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 513, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 515, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 516, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 520, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 527, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 531, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 533, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 536, "usage_type": "call"}]}
+{"seq_id": "214489078", "text": "import threading\nimport time\nfrom typing import Optional, Dict, NamedTuple\n\nimport serial\n\nfrom tools.common.esp import detect_port, UartStr\nfrom tools.common.logger import Logger, LoggedError\nfrom tools.common.screen import Colors, scr_print, scr_pause\n\n\ndef decode_device_line(data: bytes) -> str:\n try:\n line = data.decode(\"utf-8\")\n line = line.replace(\"\\r\", \"\")\n line = line.replace(\"\\n\", \"\")\n return line\n except UnicodeDecodeError:\n data = data.replace(b\"\\r\", b\"\")\n data = data.replace(b\"\\n\", b\"\")\n return str(data)\n\n\ndef decode_response(raw: str) -> Dict[str, str]:\n pairs = raw.split(\" \")\n result = {}\n for pair in pairs:\n parts = pair.split(\"=\")\n if len(parts) == 2:\n result[parts[0]] = parts[1]\n return result\n\n\nclass EDeviceInfo(NamedTuple):\n name: str\n version: str\n\n\nclass EdproDevice:\n \"\"\"handles communication with amperia devices (multimeter & powersource)\"\"\"\n\n def __init__(self, tag=\"edpro_device\"):\n self.expect_name = \"noname\"\n self.expect_version = \"0.1\"\n self.log_mode = False\n self.tag = tag\n self.logger = Logger(tag)\n self.trace_commands = True\n self.trace_commands = True\n self.uart_str: UartStr = UartStr.CP210\n self._port: Optional[str] = None\n self._serial: Optional[serial.Serial] = None\n self._rx_thread: Optional[threading.Thread] = None\n self._rx_alive: bool = False\n self._response: Optional[str] = None\n self._lock = threading.Lock()\n self._uart_written = False\n\n def _print_device_line(self, line: str):\n if line == \"\":\n return\n\n color = Colors.GRAY\n\n if self.log_mode:\n if line.startswith('D '):\n line = line[2:]\n color = Colors.GRAY\n if line.startswith('I '):\n line = line[2:]\n color = Colors.LIGHT_BLUE\n if line.startswith('W '):\n line = line[2:]\n color = Colors.YELLOW\n elif line.startswith('E '):\n line = line[2:]\n color = Colors.RED\n print(f\"[{self.tag}] {color}░ {line.strip()}{Colors.RESET}\")\n else:\n if line.startswith('W '):\n color = Colors.YELLOW\n elif line.startswith('E '):\n color = Colors.RED\n print(f\"[{self.tag}] {color}░ {line.strip()}{Colors.RESET}\")\n\n def _reader_proc(self):\n try:\n while self._rx_alive:\n data = self._serial.readline()\n if data and self._rx_alive:\n line = decode_device_line(data)\n self._print_device_line(line)\n if line.startswith(\":\"):\n with self._lock:\n self._response = line\n except Exception as e:\n self.logger.error(e)\n self._rx_alive = False\n raise\n\n def connect(self, reboot: bool = True):\n self.logger.info(\"connect\")\n self._rx_alive = True\n self._port = detect_port(self.uart_str)\n\n self._serial = serial.serial_for_url(self._port, 74880,\n parity=\"N\",\n stopbits=1,\n dsrdtr=False,\n rtscts=False,\n xonxoff=False,\n do_not_open=True,\n timeout=1)\n\n if reboot:\n self._serial.dtr = False\n self._serial.rts = True\n else:\n self._serial.dtr = False\n self._serial.rts = False\n\n # open\n try:\n self._serial.open()\n except Exception as e:\n self.logger.throw(e)\n\n if reboot:\n time.sleep(0.1)\n self._serial.dtr = True\n time.sleep(0.1)\n self._serial.rts = False\n\n # start reading thread\n self._start_reader()\n\n def _start_reader(self):\n # self.logger.trace(\"starting reader\")\n self._rx_alive = True\n self._rx_thread = threading.Thread(target=self._reader_proc, name='rx')\n self._rx_thread.daemon = True\n self._rx_thread.start()\n\n def _stop_reader(self):\n # self.logger.trace(\"stopping reader...\")\n self._rx_alive = False\n if self._rx_thread:\n self._rx_thread.join()\n\n def close(self):\n if self._serial is None:\n return\n self.logger.trace(\"disconnect\")\n self._stop_reader()\n\n # to prevent device being in reset state after serial.Close()\n # self._serial.rts = False\n self._serial.close()\n self._serial = None\n\n def _fix_uart_issue(self):\n if self._uart_written:\n return\n for i in range(0, 8):\n self._serial.write(b\"\\n\")\n self._uart_written = True\n\n def request(self, cmd: str, wait: bool = True, trace: bool = True) -> Dict[str, str]:\n if self.trace_commands and trace:\n self.logger.trace(f\"<- '{cmd}'\")\n\n with self._lock:\n self._response = None\n\n self._fix_uart_issue()\n self._serial.write(f\"{cmd}\\n\".encode())\n self._serial.flush()\n\n if not wait:\n return {}\n\n time_start = time.time()\n timeout = 4\n response = {}\n\n while True:\n time.sleep(0.1)\n if self._response is None:\n elapsed = time.time() - time_start\n if elapsed > timeout:\n self.logger.throw(\"Request timeout!\")\n continue\n with self._lock:\n assert isinstance(self._response, str)\n response = decode_response(self._response)\n break\n\n if self.trace_commands and trace:\n self.logger.trace(f\"-> {str(response)}\")\n return response\n\n def cmd(self, cmd: str):\n r = self.request(cmd)\n if r.get(\"success\") != \"1\":\n self.logger.throw(\"command failed\")\n\n def wait_boot_complete(self):\n self.logger.info(\"waiting for boot complete...\")\n\n time_start = time.time()\n timeout = 4\n\n while True:\n time.sleep(0.1)\n if self._response is None:\n elapsed = time.time() - time_start\n if elapsed > timeout:\n self.logger.throw(\"Waiting timeout!\")\n continue\n with self._lock:\n assert isinstance(self._response, str)\n response = decode_response(self._response)\n\n self.logger.trace(f\"-> {response}\")\n if response.get(\"init\") == \"0\":\n self.logger.throw(\"Device init failed!\")\n if response.get(\"init\") == \"1\":\n break\n\n self.logger.info(\"ready\")\n\n def show_log(self):\n try:\n self.log_mode = True\n self.connect(reboot=False)\n scr_print(\"\\n<?> - help, <q> - exit\", Colors.GREEN)\n while True:\n cmd = input(\"\")\n if cmd == \"q\":\n break\n try:\n self.request(cmd, wait=False, trace=False)\n except LoggedError:\n pass\n self.close()\n except LoggedError:\n self.close()\n scr_pause()\n except KeyboardInterrupt:\n self.close()\n except Exception:\n self.close()\n raise\n\n def save_conf(self):\n self.cmd(\"conf s\")\n\n def get_info(self) -> EDeviceInfo:\n r = self.request(\"i\")\n return EDeviceInfo(name=r[\"name\"],\n version=r[\"version\"])\n\n def validate_firmware(self):\n info = self.get_info()\n if (info.name != self.expect_name):\n self.logger.throw(f\"Device name do not match!\"\n f\"\\n\\texpect: {self.expect_name}\"\n f\"\\n\\tactual: {info.name}\")\n\n def num_ver(ver: str) -> int:\n parts = ver.split(\".\")\n return int(parts[0]) * 1000 + int(parts[1])\n\n expect_v = num_ver(self.expect_version)\n actual_v = num_ver(info.version)\n if (actual_v < expect_v):\n self.logger.throw(f\"Device version do not match!\"\n f\"\\n\\texpect: {self.expect_version}\"\n f\"\\n\\tactual: {info.version}\")\n\n def set_devmode(self):\n self.request(\"devmode\")\n\n\ndef test():\n device = EdproDevice()\n device.connect()\n device.wait_boot_complete()\n device.set_devmode()\n device.get_info()\n device.close()\n\n\nif __name__ == \"__main__\":\n try:\n test()\n except LoggedError:\n pass\n except Exception:\n raise\n", "sub_path": "tools/devices/edpro_base.py", "file_name": "edpro_base.py", "file_ext": "py", "file_size_in_byte": 9003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "typing.Dict", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.NamedTuple", "line_number": 34, "usage_type": "name"}, {"api_name": "tools.common.logger.Logger", "line_number": 47, "usage_type": "call"}, {"api_name": "tools.common.esp.UartStr", "line_number": 50, "usage_type": "name"}, {"api_name": "tools.common.esp.UartStr.CP210", "line_number": 50, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 52, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 52, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 53, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 53, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 56, "usage_type": "call"}, {"api_name": "tools.common.screen.Colors.GRAY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 63, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.GRAY", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 68, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.LIGHT_BLUE", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 71, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.YELLOW", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 74, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.RED", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 77, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.RESET", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 78, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.YELLOW", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 81, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.RED", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 83, "usage_type": "name"}, {"api_name": "tools.common.screen.Colors.RESET", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 84, "usage_type": "name"}, {"api_name": "tools.common.esp.detect_port", "line_number": 104, "usage_type": "call"}, {"api_name": "serial.serial_for_url", "line_number": 106, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 129, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 131, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 140, "usage_type": "call"}, {"api_name": "time.time", "line_number": 182, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 189, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 168, "usage_type": "name"}, {"api_name": "time.time", "line_number": 210, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "time.time", "line_number": 216, "usage_type": "call"}, {"api_name": "tools.common.screen.scr_print", "line_number": 236, "usage_type": "call"}, {"api_name": "tools.common.screen.Colors.GREEN", "line_number": 236, "usage_type": "attribute"}, {"api_name": "tools.common.screen.Colors", "line_number": 236, "usage_type": "name"}, {"api_name": "tools.common.logger.LoggedError", "line_number": 243, "usage_type": "name"}, {"api_name": "tools.common.logger.LoggedError", "line_number": 246, "usage_type": "name"}, {"api_name": "tools.common.screen.scr_pause", "line_number": 248, "usage_type": "call"}, {"api_name": "tools.common.logger.LoggedError", "line_number": 297, "usage_type": "name"}]}
+{"seq_id": "251131270", "text": "\"\"\"\nBased on Amaury's adaption of Eli Golovinsky's post\nhttp://stackoverflow.com/questions/174890/how-to-output-cdata-using-elementtree\n\nGooli's original snippet didn't work in Python 2.7\n\nAdded by LS:\n- support for _serialize_xml having different number of args depending on Python version\n- register_namespace is not in __all__\n\n\"\"\"\nimport six\nimport xml.etree.ElementTree as etree\n\nif hasattr(etree, '_serialize_xml'):\n\n def CDATA(text=None):\n element = etree.Element('![CDATA[')\n element.text = text\n return element\n\n _original_serialize_xml = etree._serialize_xml\n\n def _serialize_xml(write, elem, *args, **kwargs):\n if elem.tag == '![CDATA[':\n write(\"\\n<%s%s]]>\\n\" % (\n elem.tag, elem.text))\n return\n return _original_serialize_xml(write, elem, *args, **kwargs)\n etree._serialize_xml = etree._serialize['xml'] = _serialize_xml\n\n register_namespace = etree.register_namespace\n\nelse:\n def register_namespace(*args, **kw):\n pass\n\nfrom xml.etree.ElementTree import *\n\nif six.PY3:\n _tostring = tostring\n # # change default value for encoding to 'unicode'\n # def tostring(element, encoding=\"us-ascii\", *args, **kwargs):\n # return _tostring(element, encoding, *args, **kwargs)\n def tostring(*args, **kwargs):\n return _tostring(*args, **kwargs).decode()\n", "sub_path": "etgen/etree.py", "file_name": "etree.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "xml.etree.ElementTree", "line_number": 15, "usage_type": "argument"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 18, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 18, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree._serialize_xml", "line_number": 22, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 22, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree._serialize_xml", "line_number": 30, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 30, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree._serialize", "line_number": 30, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.register_namespace", "line_number": 32, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 32, "usage_type": "name"}, {"api_name": "six.PY3", "line_number": 40, "usage_type": "attribute"}]}
+{"seq_id": "133268866", "text": "import copy\n\nimport pytest\n\nfrom otx.mpa.cls.explainer import ClsExplainer\nfrom otx.mpa.cls.stage import ClsStage\nfrom otx.mpa.modules.hooks.recording_forward_hooks import ActivationMapHook\nfrom tests.test_suite.e2e_test_system import e2e_pytest_unit\nfrom tests.unit.algorithms.classification.test_helper import (\n generate_cls_dataset,\n setup_mpa_task_parameters,\n)\n\n\nclass TestOTXClsExplainer:\n @pytest.fixture(autouse=True)\n def setup(self) -> None:\n self.model_cfg, self.data_cfg, recipie_cfg = setup_mpa_task_parameters(\n task_type=\"incremental\", create_test=True\n )\n self.explainer = ClsExplainer(name=\"\", mode=\"train\", config=recipie_cfg, common_cfg=None, index=0)\n\n @e2e_pytest_unit\n def test_run(self, mocker):\n args = {\"explainer\": \"activationmap\"}\n mocker.patch.object(ClsExplainer, \"explain\", return_value=\"fake_output\")\n returned_value = self.explainer.run(self.model_cfg, \"\", self.data_cfg, **args)\n\n assert returned_value == {\"outputs\": \"fake_output\"}\n\n @e2e_pytest_unit\n def test_explain(self, mocker):\n mocker.patch(\"otx.mpa.cls.explainer.build_data_parallel\")\n mock_build_model = mocker.patch.object(ClsStage, \"build_model\")\n mocker.patch.object(ClsStage, \"configure_samples_per_gpu\")\n data_cfg = copy.deepcopy(self.data_cfg)\n dummy_dataset = generate_cls_dataset(number_of_images=1)\n data_cfg.data.test[\"otx_dataset\"] = dummy_dataset\n data_cfg.data.test[\"labels\"] = dummy_dataset.get_labels()\n self.explainer.cfg.merge_from_dict(self.model_cfg)\n self.explainer.cfg.merge_from_dict(data_cfg)\n self.explainer.explainer_hook = ActivationMapHook\n outputs = self.explainer.explain(self.explainer.cfg)\n\n mock_build_model.assert_called_once()\n assert outputs == {\"saliency_maps\": [], \"eval_predictions\": []}\n", "sub_path": "tests/unit/mpa/cls/test_cls_explanier.py", "file_name": "test_cls_explanier.py", "file_ext": "py", "file_size_in_byte": 1895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "tests.unit.algorithms.classification.test_helper.setup_mpa_task_parameters", "line_number": 18, "usage_type": "call"}, {"api_name": "otx.mpa.cls.explainer.ClsExplainer", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 16, "usage_type": "call"}, {"api_name": "otx.mpa.cls.explainer.ClsExplainer", "line_number": 26, "usage_type": "argument"}, {"api_name": "tests.test_suite.e2e_test_system.e2e_pytest_unit", "line_number": 23, "usage_type": "name"}, {"api_name": "otx.mpa.cls.stage.ClsStage", "line_number": 34, "usage_type": "argument"}, {"api_name": "otx.mpa.cls.stage.ClsStage", "line_number": 35, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 36, "usage_type": "call"}, {"api_name": "tests.unit.algorithms.classification.test_helper.generate_cls_dataset", "line_number": 37, "usage_type": "call"}, {"api_name": "otx.mpa.modules.hooks.recording_forward_hooks.ActivationMapHook", "line_number": 42, "usage_type": "name"}, {"api_name": "tests.test_suite.e2e_test_system.e2e_pytest_unit", "line_number": 31, "usage_type": "name"}]}
+{"seq_id": "207075603", "text": "from pyramid.view import view_config\nfrom pyramid.httpexceptions import HTTPFound\nfrom ib.bluelantern.interfaces import IEquipmentCache\n\n@view_config(route_name='home')\ndef home(request):\n return HTTPFound(location='/app/')\n\n@view_config(route_name='stats', renderer='json')\ndef stats(request):\n cache = request.registry.getUtility(IEquipmentCache)\n ac_load = ac_max_load = pv_watt = pv_max_watt = bat_watt = 0.0\n for instance, a in cache.items():\n for id, b in a.items():\n t = b.get('type', None)\n if t == 'pv':\n pv_watt += float(b.get('power', 0))\n pv_max_watt += float(b.get('max_power', 0))\n elif t == 'load':\n ac_load += float(b.get('power', 0))\n ac_max_load += float(b.get('max_power', 0))\n bat_watt = pv_watt - ac_load\n return {\n 'ac_load': ac_load,\n 'ac_max_load': ac_max_load,\n 'pv_watt': pv_watt,\n 'pv_max_watt': pv_max_watt,\n 'bat_watt': bat_watt,\n }\n", "sub_path": "src/ib/bluelantern/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 7, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 5, "usage_type": "call"}, {"api_name": "ib.bluelantern.interfaces.IEquipmentCache", "line_number": 11, "usage_type": "argument"}, {"api_name": "pyramid.view.view_config", "line_number": 9, "usage_type": "call"}]}
+{"seq_id": "607939833", "text": "# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport jieba\n\njieba.add_word(\"XXX\")\n\n\ndef word_tokenize(sent):\n return jieba.lcut(sent)\n\n\ndef _get_word(word, word2idx_dict):\n if word in word2idx_dict:\n return word2idx_dict[word]\n return 1\n\n\ndef preprocess(query, config, word2idx_dict, query_type=None):\n assert isinstance(query, str)\n seg_query = word_tokenize(query)\n # filter via limited length\n if query_type is \"question\":\n ques_limit = config.test_ques_limit\n elif query_type is \"context\":\n ques_limit = config.test_para_limit\n else:\n ques_limit = len(seg_query)\n\n if len(seg_query) > ques_limit:\n return None, None\n # generate indexes data\n ques_idxs = np.zeros([1, ques_limit], dtype=np.int32)\n for i, token in enumerate(seg_query):\n ques_idxs[0, i] = _get_word(token, word2idx_dict)\n return ques_idxs, seg_query\n", "sub_path": "preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "jieba.add_word", "line_number": 6, "usage_type": "call"}, {"api_name": "jieba.lcut", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 33, "usage_type": "attribute"}]}
+{"seq_id": "312874609", "text": "import yfinance as yf\nimport pickle\nimport constants\nimport re\n\n\nstock_list = []\nf = open(constants.filepath_clean_tickerlist, \"r\")\nstock_list = (f.readlines())\n\nfor stock in stock_list:\n print (stock.split()[0])\n stock_obj = yf.Ticker(stock.split()[0])\n\n # get stock info\n stock_obj.info\n\n # get historical market data\n hist = stock_obj.history(period=\"max\")\n\n #datetime we get back from the scraper is super annoying, so convert to simple date in a string string, put that in a python dict\n stock_dict = {}\n\n for dt in hist['Close'].keys():\n date_only_str = dt.strftime(\"%Y-%m-%d\")\n #print (date_only_str)\n #print (hist['Close'][dt])\n stock_dict[str(date_only_str)] = hist['Close'][dt]\n \n with open(constants.filepath_stocks_folder + str(stock.split()[0]) + \".pickle\", 'wb') as handle:\n pickle.dump(stock_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", "sub_path": "stockthingy/get_stock_prices.py", "file_name": "get_stock_prices.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "constants.filepath_clean_tickerlist", "line_number": 8, "usage_type": "attribute"}, {"api_name": "yfinance.Ticker", "line_number": 13, "usage_type": "call"}, {"api_name": "constants.filepath_stocks_folder", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 31, "usage_type": "attribute"}]}
+{"seq_id": "247713757", "text": "import pytest\nfrom contextlib import suppress\nfrom typing import Optional\nfrom tests.base_test import BaseTest\nfrom tests.conftest import env_variables, get_api_client\nfrom assisted_service_client.rest import ApiException\nfrom test_infra import utils\nfrom test_infra.helper_classes.cluster import Cluster\n\nSECOND_OFFLINE_TOKEN = utils.get_env('SECOND_OFFLINE_TOKEN')\nSECOND_PULL_SECRET = utils.get_env('SECOND_PULL_SECRET')\n\n\n@pytest.fixture()\ndef api_client():\n yield get_api_client\n\n\n@pytest.mark.skipif(not env_variables['offline_token'], reason=\"not cloud env\")\nclass TestAuth(BaseTest):\n @pytest.fixture()\n def cluster(self):\n clusters = []\n\n def get_cluster_func(api_client, cluster_name: Optional[str] = None, cluster_id: Optional[str] = None):\n res = Cluster(api_client=api_client, cluster_name=cluster_name, cluster_id=cluster_id)\n clusters.append(res)\n return res\n\n yield get_cluster_func\n\n for cluster in clusters:\n with suppress(ApiException):\n cluster.delete()\n\n def _send_dummy_step_result(self, cluster, host_id):\n cluster.host_post_step_result(\n host_id,\n step_type=\"inventory\",\n step_id=\"inventory-e048e0db\",\n exit_code=0,\n output=\"null\"\n )\n\n def _update_dummy_install_progress(self, cluster, host_id):\n cluster.host_update_install_progress(host_id, \"Failed\")\n\n @pytest.mark.regression\n def test_user_authorization_negative(self, api_client, nodes, cluster):\n client_user1 = api_client()\n client_user2 = api_client(offline_token=SECOND_OFFLINE_TOKEN)\n\n cluster_client_user1 = cluster(client_user1, cluster_name=env_variables['cluster_name'])\n cluster_client_user2 = cluster(client_user2, cluster_id=cluster_client_user1.id)\n\n # user2 cannot get user1's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_details,\n status=404,\n reason=\"Not Found\"\n )\n\n # user2 cannot delete user1's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.delete,\n status=404,\n reason=\"Not Found\",\n )\n\n # user2 cannot generate ISO user1's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.generate_and_download_image,\n status=404,\n reason=\"Not Found\"\n )\n\n cluster_client_user1.prepare_for_install(nodes=nodes)\n\n # user2 cannot patch user1's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.set_network_params,\n status=404,\n reason=\"Not Found\",\n controller=nodes.controller\n )\n\n # user2 cannot list user2's hosts\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_hosts,\n status=404,\n reason=\"Not Found\",\n )\n\n # user2 cannot trigger user2's cluster install\n self.assert_http_error_code(\n api_call=cluster_client_user2.start_install,\n status=404,\n reason=\"Not Found\"\n )\n\n # start cluster install\n cluster_client_user1.start_install()\n cluster_client_user1.wait_for_installing_in_progress()\n\n # user2 cannot download files from user2's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.download_kubeconfig_no_ingress,\n status=404,\n reason=\"Not Found\",\n )\n\n # user2 cannot get user2's cluster install config\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_install_config,\n status=404,\n reason=\"Not Found\"\n )\n\n # user2 cannot cancel user2's cluster install\n self.assert_http_error_code(\n api_call=cluster_client_user2.cancel_install,\n status=404,\n reason=\"Not Found\",\n )\n\n cluster_client_user1.wait_for_hosts_to_install()\n cluster_client_user1.wait_for_install()\n\n # user2 cannot get user2's cluster credentials\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_admin_credentials,\n status=404,\n reason=\"Not Found\"\n )\n\n @pytest.mark.regression\n def test_agent_authorization_negative(self, api_client, nodes, cluster):\n client_user1 = api_client()\n client_user2 = api_client(\n offline_token='',\n pull_secret=SECOND_PULL_SECRET,\n wait_for_api=False\n )\n\n cluster_client_user1 = cluster(client_user1, cluster_name=env_variables['cluster_name'])\n cluster_client_user2 = cluster(client_user2, cluster_id=cluster_client_user1.id)\n\n # agent with user2 pull secret cannot get user1's cluster details\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_details,\n status=404,\n reason=\"Not Found\",\n )\n\n # agent with user2 pull secret cannot register to user1's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.register_dummy_host,\n status=404,\n reason=\"Not Found\",\n )\n\n cluster_client_user1.prepare_for_install(nodes=nodes)\n\n # agent with user2 pull secret cannot list cluster hosts\n self.assert_http_error_code(\n api_call=cluster_client_user2.get_hosts,\n status=404,\n reason=\"Not Found\",\n )\n\n host_ids = cluster_client_user1.get_host_ids()\n test_host_id = host_ids[0]\n\n # agent with user2 pull secret cannot get next step\n self.assert_http_error_code(\n api_call=cluster_client_user2.host_get_next_step,\n status=404,\n reason=\"Not Found\",\n host_id=test_host_id\n )\n\n # agent with user2 pull secret cannot update on next step\n self.assert_http_error_code(\n api_call=self._send_dummy_step_result,\n status=404,\n reason=\"Not Found\",\n cluster=cluster_client_user2,\n host_id=test_host_id\n )\n\n cluster_client_user1.start_install()\n cluster_client_user1.wait_for_installing_in_progress()\n\n # agent with user2 pull secret cannot update install progress\n self.assert_http_error_code(\n api_call=self._update_dummy_install_progress,\n status=404,\n reason=\"Not Found\",\n cluster=cluster_client_user2,\n host_id=test_host_id\n )\n\n # user2 cannot download files from user2's cluster\n self.assert_http_error_code(\n api_call=cluster_client_user2.download_kubeconfig_no_ingress,\n status=404,\n reason=\"Not Found\",\n )\n\n cluster_client_user1.wait_for_hosts_to_install()\n\n # agent with user2 pull secret cannot complete installation\n self.assert_http_error_code(\n api_call=cluster_client_user2.host_complete_install,\n status=404,\n reason=\"Not Found\",\n )\n", "sub_path": "discovery-infra/tests/test_auth.py", "file_name": "test_auth.py", "file_ext": "py", "file_size_in_byte": 7186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "test_infra.utils.get_env", "line_number": 10, "usage_type": "call"}, {"api_name": "test_infra.utils", "line_number": 10, "usage_type": "name"}, {"api_name": "test_infra.utils.get_env", "line_number": 11, "usage_type": "call"}, {"api_name": "test_infra.utils", "line_number": 11, "usage_type": "name"}, {"api_name": "tests.conftest.get_api_client", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "call"}, {"api_name": "tests.base_test.BaseTest", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "test_infra.helper_classes.cluster.Cluster", "line_number": 26, "usage_type": "call"}, {"api_name": "contextlib.suppress", "line_number": 33, "usage_type": "call"}, {"api_name": "assisted_service_client.rest.ApiException", "line_number": 33, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "call"}, {"api_name": "tests.conftest.env_variables", "line_number": 53, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tests.conftest.env_variables", "line_number": 145, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tests.conftest.env_variables", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "322466206", "text": "import rlpy\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport sys\n\ndef main():\n return_sarsa = rl_algorithm('SARSA', 400, 1, 0.5, 0.01)\n eps_sarsa = return_sarsa[0]\n rewards_sarsa = return_sarsa[1]\n max_reward_sarsa = return_sarsa[2]\n path_sarsa = return_sarsa[3]\n best_ep_sarsa = return_sarsa[4]\n\n return_q = rl_algorithm('Qlearning', 400, 1, 0.5, 0.01)\n eps_q = return_q[0]\n rewards_q = return_q[1]\n max_reward_q = return_q[2]\n path_q = return_q[3]\n best_ep_q = return_q[4]\n\n print('-/-/- SARSA -/-/-')\n print('Reward on the last episode: ',rewards_sarsa[-1])\n print('Max reward (at episode #', best_ep_sarsa, '): ', max_reward_sarsa)\n\n print('\\n\\n-/-/- Q LEARNING -/-/-')\n print('Reward on the last episode: ',rewards_q[-1])\n print('Max reward (at episode #', best_ep_q, '): ', max_reward_q)\n\n plt.figure('Episodes vs Rewards')\n plt.subplot(211)\n plt.title('SARSA')\n plt.plot(eps_sarsa, rewards_sarsa)\n plt.ylabel('Reward')\n\n plt.subplot(212)\n plt.title('Q-Learning')\n plt.plot(eps_q, rewards_q)\n plt.xlabel('Episodes')\n plt.ylabel('Reward')\n plt.show()\n\n\n if len(sys.argv) > 1 and sys.argv[1]=='-g':\n import gui\n app = gui.QtGui.QApplication(sys.argv)\n sarsa_grid = gui.Grid('SARSA best path',path_sarsa)\n q_grid = gui.Grid('Q-Learning best path',path_q)\n sys.exit(app.exec_())\n\n\n #Run the algorithm of Reinforcement Learning\n # @name - 'SARSA' / 'Qlearning'\ndef rl_algorithm(name, episodes, gama, alpha, epsilon):\n rewards = []\n eps = list(range(0,episodes))\n board = rlpy.MapState(7, 10)\n max_reward = -10000\n\n\n for i in range(0, episodes):\n path = []\n #Start point\n actual_state = board.states[3][0]\n sum_reward = 0\n path.append(actual_state.pos)\n\n while actual_state.pos != (3,7):\n actual_pos = actual_state.pos\n actual_idx_max = actual_state.max_q_val_idx()\n actual_max = actual_state.q_val[actual_idx_max]\n\n if(random.random() > epsilon):\n next_state = board.get_next_state(actual_pos[0], actual_pos[1])\n else:\n next_state = board.get_next_state(actual_pos[0], actual_pos[1], explore=True)\n\n if name == 'SARSA':\n next_idx_max = next_state.max_q_val_idx()\n next_max = next_state.q_val[next_idx_max]\n\n elif name == 'Qlearning':\n aux_state = board.get_next_state(actual_pos[0], actual_pos[1])\n next_idx_max = aux_state.max_q_val_idx()\n next_max = aux_state.q_val[next_idx_max]\n\n new_reward = actual_max + alpha*(-1 + gama*next_max - actual_max)\n\n sum_reward -= 1\n\n board.change_q_val(actual_pos, actual_idx_max , new_reward)\n path.append(next_state.pos)\n actual_state = next_state\n\n if(sum_reward > max_reward):\n max_reward = sum_reward\n best_path = path\n best_ep = i\n\n rewards.append(sum_reward)\n\n return [eps, rewards, max_reward, best_path, best_ep]\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "reinforcement_learning.py", "file_name": "reinforcement_learning.py", "file_ext": "py", "file_size_in_byte": 3212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "gui.QtGui.QApplication", "line_number": 47, "usage_type": "call"}, {"api_name": "gui.QtGui", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "gui.Grid", "line_number": 48, "usage_type": "call"}, {"api_name": "gui.Grid", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 50, "usage_type": "call"}, {"api_name": "rlpy.MapState", "line_number": 58, "usage_type": "call"}, {"api_name": "random.random", "line_number": 74, "usage_type": "call"}]}
+{"seq_id": "45955033", "text": "\"\"\"Interpretation state changes\n\nRevision ID: 662204b12d07\nRevises: 4629b478b291\nCreate Date: 2018-11-08 12:23:44.840307\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = \"662204b12d07\"\ndown_revision = \"4629b478b291\"\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import JSONB\n\n\nAlleleInterpretation = sa.table(\n \"alleleinterpretation\", sa.column(\"id\", sa.Integer), sa.column(\"state\", JSONB)\n)\n\nAnalysisInterpretation = sa.table(\n \"analysisinterpretation\", sa.column(\"id\", sa.Integer), sa.column(\"state\", JSONB)\n)\n\n\ndef upgrade():\n\n conn = op.get_bind()\n\n def update_interpretation_state(model):\n interpretations = conn.execute(sa.select([c for c in model.c]))\n for i_id, state in interpretations:\n state = dict(state)\n if \"allele\" in state:\n for allele_id in state[\"allele\"]:\n allele_state = state[\"allele\"][allele_id]\n if \"quality\" in allele_state:\n allele_state[\"analysis\"] = allele_state[\"quality\"]\n del allele_state[\"quality\"]\n if \"verification\" in allele_state:\n if \"analysis\" not in allele_state:\n allele_state[\"analysis\"] = dict()\n allele_state[\"analysis\"][\"verification\"] = allele_state[\"verification\"]\n del allele_state[\"verification\"]\n\n conn.execute(model.update().where(model.c.id == i_id).values(state=state))\n\n update_interpretation_state(AlleleInterpretation)\n update_interpretation_state(AnalysisInterpretation)\n\n\ndef downgrade():\n raise NotImplementedError(\"Downgrade not possible!\")\n", "sub_path": "src/vardb/datamodel/migration/alembic/versions/662204b12d07_interpretation_state_changes.py", "file_name": "662204b12d07_interpretation_state_changes.py", "file_ext": "py", "file_size_in_byte": 1767, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sqlalchemy.table", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 21, "usage_type": "argument"}, {"api_name": "sqlalchemy.table", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 25, "usage_type": "argument"}, {"api_name": "alembic.op.get_bind", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "180713106", "text": "from aiogram.dispatcher.filters import Text\nfrom aiogram.types import Message, CallbackQuery\nfrom funcs.all_funcs import is_classroom_teacher\nfrom keyboards.inline import register_canteen_boss_buttons\nfrom loader import dp, bot\nfrom sqlite import cur, con\n\n\n@dp.message_handler(Text(equals=['Главный по столовой']), is_classroom_teacher)\nasync def canteen_bos(msg: Message):\n print(f'{msg.from_user.full_name} нажал на кнопку: Главный по столовой')\n class_id = cur.execute('''SELECT id FROM classes WHERE bos = ?''',\n [msg.from_user.id]).fetchone()[0]\n students = cur.execute('''SELECT u.name, s.user\n FROM students s \n LEFT JOIN users u ON s.user = u.user_id WHERE class = ?''',\n [class_id]).fetchall()\n if not students:\n await msg.answer(text='Учеников нет')\n print(f'{msg.from_user.full_name} нажал на кнопку: Главный по столовой но учеников не было')\n return\n for student, user_id in students:\n await msg.answer(text=f'🧑🎓{student}🧑🎓',\n reply_markup=await register_canteen_boss_buttons(user_id))\n\n\n@dp.callback_query_handler(lambda c: 'register_canteen_boss' in c.data)\nasync def register_canteen_boss(call: CallbackQuery):\n await call.answer(cache_time=60)\n print(call.data)\n student_id = call.data.split('_')[3]\n user_id = call.from_user.id\n cur.execute('''UPDATE classes set canteen = ? WHERE bos = ?''', [student_id, user_id])\n try:\n await call.message.delete()\n except Exception as e:\n print(f'{call.from_user.full_name} не смог зарегестрировать главного по столовой'\n f'\\nОшибка: {e}')\n print(f'{call.from_user.full_name} зарегестрировал главного по столовой в классе с user_id: {student_id} где кл.рук. с user_id: {user_id}')\n await call.message.answer(text='♻️Выполнено♻️')\n await bot.send_message(chat_id=student_id,\n text='Вы стали главным по столовой👨🍳')\n con.commit()", "sub_path": "handlers/users/register_canteen_bos.py", "file_name": "register_canteen_bos.py", "file_ext": "py", "file_size_in_byte": 2309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "aiogram.types.Message", "line_number": 10, "usage_type": "name"}, {"api_name": "sqlite.cur.execute", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlite.cur", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlite.cur.execute", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlite.cur", "line_number": 14, "usage_type": "name"}, {"api_name": "keyboards.inline.register_canteen_boss_buttons", "line_number": 24, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 9, "usage_type": "call"}, {"api_name": "funcs.all_funcs.is_classroom_teacher", "line_number": 9, "usage_type": "argument"}, {"api_name": "loader.dp", "line_number": 9, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 9, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlite.cur.execute", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlite.cur", "line_number": 33, "usage_type": "name"}, {"api_name": "loader.bot.send_message", "line_number": 41, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlite.con.commit", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlite.con", "line_number": 43, "usage_type": "name"}, {"api_name": "loader.dp.callback_query_handler", "line_number": 27, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 27, "usage_type": "name"}]}
+{"seq_id": "520308503", "text": "import urllib.request\nimport io\nimport os\nimport json\nfrom TDhelper.bin.globalvar import *\nfrom TDhelper.network.http.REST_HTTP import GET, POST\n\n\nclass apiCore():\n '''apiCore\n\n Args:\n apiName (str):Open Api platform's Name.\n version (str):Api's version, default value is \"v1\".\n apiRouteFileName (str):Location cache file's name, default value is \"apiRoutePath\"\n basePath (str):Location cache base path, defalut value is os.path.dirname(__file__)\n '''\n\n def __init__(self, apiName: str, version=\"v1\", apiRouteFileName=\"apiRoutePath.arg\", basePath=os.path.dirname(__file__)):\n self._apiName = apiName+\"_\"+version\n self._path = basePath+\"\\\\\"+apiName+\"\\\\\"+version+\"\\\\\"\n # if haven't this directory,create it.\n if not os.path.exists(self._path):\n os.makedirs(self._path)\n # generate the api route path\n self._routePath = os.path.join(self._path, apiRouteFileName)\n if not hasKey(apiName+version):\n # create global route tables\n setGlobalVariable(self._apiName, dict())\n if self._routePath:\n if os.path.exists(self._routePath):\n # read apiRouteFile and init apiroute\n try:\n with open(self._routePath, mode='r', encoding='utf-8', errors=None, newline=None) as f:\n while True:\n line = f.readline().replace(\"\\n\", \"\")\n if line:\n jsondoc = json.loads(line)\n if jsondoc[\"key\"]:\n if jsondoc[\"value\"]:\n self.AddApi(jsondoc[\"key\"], jsondoc[\"value\"][\"uri\"], jsondoc[\"value\"]\n [\"args\"], jsondoc[\"value\"][\"method\"], jsondoc[\"value\"][\"descrition\"])\n else:\n break\n f.close()\n except Exception as e:\n raise e\n\n def AddApi(self, key: str, apiUri: str, argsStr: str, method: str = \"GET\", descrition: str = \"\"):\n '''Add a new api\n\n Args:\n key (str): Api key.\n apiUri (str): Api uri.\n argsStr (str): Api uri args string format, Example: \"gid={0}&name={1}\".\n method (str): Http method, default value is \"GET\".\n descrition (str): api descrition.\n '''\n # if key.lower() not in self._apiRoute:\n if key.lower() not in getGlobalVariable(self._apiName):\n getGlobalVariable(self._apiName)[key.lower()] = {\n \"uri\": apiUri, \"args\": argsStr, \"method\": method.lower(), \"descrition\": descrition}\n return True\n else:\n return False\n\n def GetArgsString(self, key: str):\n '''Get api uri's param string\n\n Args:\n key (str): api key\n '''\n try:\n key = key.lower()\n if key in getGlobalVariable(self._apiName):\n if \"args\" in getGlobalVariable(self._apiName)[key]:\n return getGlobalVariable(self._apiName)[key][\"args\"]\n except Exception as e:\n raise e\n\n def DelApi(self, key: str):\n '''Delete a api\n\n Args:\n key (str): api key\n '''\n key = key.lower()\n if key in getGlobalVariable(self._apiName):\n del getGlobalVariable(self._apiName)[key]\n return True\n else:\n return False\n\n def SaveToFile(self):\n '''Save api route to file\n '''\n wBuffer = \"\" # create file write buffer\n for item in getGlobalVariable(self._apiName):\n wBuffer += json.dumps({\"key\": item,\n \"value\": getGlobalVariable(self._apiName)[item]})+\"\\r\\n\"\n if not wBuffer:\n wBuffer = \"\\r\\n\"\n if wBuffer: # save buffer to file\n with open(self._routePath, mode='w', encoding='utf-8', errors=None, newline=None) as f:\n f.write(wBuffer)\n f.flush()\n f.close()\n\n def ClearCache(self):\n deleteGlobalVariable(self._apiName) # 删除全局变量\n os.remove(self._routePath) # 删除缓存文件\n os.removedirs(self._path) # 删除缓存目录\n\n def Call(self, key: str, data: str):\n '''Call remote api\n\n Args:\n key (str): api key.\n data (str): api uri's params string.\n '''\n key = key.lower()\n if key in getGlobalVariable(self._apiName):\n method = getGlobalVariable(self._apiName)[key][\"method\"]\n uri = getGlobalVariable(self._apiName)[key][\"uri\"]\n if uri:\n if method.lower() == \"get\":\n uri += \"?\"+data\n return self._restfulGET(uri)\n elif method.low() == \"post\":\n return self._restfulPOST(uri, data)\n return False\n return False\n def _restfulGET(self, uri):\n try:\n return GET(uri)\n except Exception as e:\n raise e\n\n def _restfulPOST(self, uri, data):\n try:\n return POST(uri, data)\n except Exception as e:\n raise e\n", "sub_path": "apiCore.py", "file_name": "apiCore.py", "file_ext": "py", "file_size_in_byte": 5366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 99, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 111, "usage_type": "call"}, {"api_name": "os.removedirs", "line_number": 112, "usage_type": "call"}, {"api_name": "TDhelper.network.http.REST_HTTP.GET", "line_number": 135, "usage_type": "call"}, {"api_name": "TDhelper.network.http.REST_HTTP.POST", "line_number": 141, "usage_type": "call"}]}
+{"seq_id": "234479492", "text": "# -*- coding: UTF-8 -*-\n\nimport torch\n\nfrom utils import utils\nfrom models.BaseModel import BaseModel\n\n\nclass BPR(BaseModel):\n @staticmethod\n def parse_model_args(parser, model_name='BPR'):\n parser.add_argument('--emb_size', type=int, default=64,\n help='Size of embedding vectors.')\n return BaseModel.parse_model_args(parser, model_name)\n\n def __init__(self, args, corpus):\n self.emb_size = args.emb_size\n BaseModel.__init__(self, args, corpus)\n\n def _define_params(self):\n self.u_embeddings = torch.nn.Embedding(self.user_num, self.emb_size)\n self.i_embeddings = torch.nn.Embedding(self.item_num, self.emb_size)\n self.user_bias = torch.nn.Embedding(self.user_num, 1)\n self.item_bias = torch.nn.Embedding(self.item_num, 1)\n self.embeddings = ['u_embeddings', 'i_embeddings', 'user_bias', 'item_bias']\n\n def forward(self, feed_dict):\n self.check_list, self.embedding_l2 = [], []\n u_ids = feed_dict['user_id'] # [batch_size]\n i_ids = feed_dict['item_id'] # [batch_size, n_candidates]\n\n cf_u_vectors = self.u_embeddings(u_ids)\n cf_i_vectors = self.i_embeddings(i_ids)\n u_bias = self.user_bias(u_ids)\n i_bias = self.item_bias(i_ids).squeeze(-1)\n self.embedding_l2.extend([cf_u_vectors, cf_i_vectors])\n\n prediction = (cf_u_vectors[:, None, :] * cf_i_vectors).sum(dim=-1)\n prediction = prediction + u_bias + i_bias\n\n out_dict = {'prediction': prediction.view(feed_dict['batch_size'], -1), 'check': self.check_list}\n return out_dict\n\n def get_feed_dict(self, corpus, data, batch_start, batch_size, phase):\n feed_dict = BaseModel.get_feed_dict(self, corpus, data, batch_start, batch_size, phase)\n real_batch_size = feed_dict['batch_size']\n user_ids = data['user_id'][batch_start: batch_start + real_batch_size].values\n feed_dict['user_id'] = utils.numpy_to_torch(user_ids) # [batch_size]\n return feed_dict\n", "sub_path": "src/models/BPR.py", "file_name": "BPR.py", "file_ext": "py", "file_size_in_byte": 2028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "models.BaseModel.BaseModel", "line_number": 9, "usage_type": "name"}, {"api_name": "models.BaseModel.BaseModel.parse_model_args", "line_number": 14, "usage_type": "call"}, {"api_name": "models.BaseModel.BaseModel", "line_number": 14, "usage_type": "name"}, {"api_name": "models.BaseModel.BaseModel.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "models.BaseModel.BaseModel", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.BaseModel.BaseModel.get_feed_dict", "line_number": 45, "usage_type": "call"}, {"api_name": "models.BaseModel.BaseModel", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.utils.numpy_to_torch", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 48, "usage_type": "name"}]}
+{"seq_id": "164636190", "text": "\"\"\"\nProject for Week 2 of \"Python Data Visualization\".\nRead World Bank GDP data and create some basic XY plots.\n\nBe sure to read the project description page for further information\nabout the expected behavior of the program.\n\"\"\"\n\nimport csv\nimport string\nimport pygal\n\ndef read_csv_as_nested_dict(filename, keyfield, separator, quote):\n \"\"\"\n Inputs:\n filename - Name of CSV file\n keyfield - Field to use as key for rows\n separator - Character that separates fields\n quote - Character used to optionally quote fields\n\n Output:\n Returns a dictionary of dictionaries where the outer dictionary\n maps the value in the key_field to the corresponding row in the\n CSV file. The inner dictionaries map the field names to the\n field values for that row.\n \"\"\"\n table ={}\n with open(filename, \"rt\", newline='') as csvfile:\n csvreader = csv.DictReader(csvfile,\n delimiter=separator,\n quotechar=quote)\n for row in csvreader:\n table[row[keyfield]] = row\n return table\n\n\ndef build_plot_values(gdpinfo, gdpdata):\n \"\"\"\n Inputs:\n gdpinfo - GDP data information dictionary\n gdpdata - A single country's GDP stored in a dictionary whose\n keys are strings indicating a year and whose values\n are strings indicating the country's corresponding GDP\n for that year.\n\n Output:\n Returns a list of tuples of the form (year, GDP) for the years\n between \"min_year\" and \"max_year\", inclusive, from gdpinfo that\n exist in gdpdata. The year will be an integer and the GDP will\n be a float.\n \"\"\"\n the_values_list = []\n max_year = int(gdpinfo[\"max_year\"])\n min_year = int(gdpinfo[\"min_year\"])\n #print(gdpdata)\n the_years = gdpdata.keys()\n for item in the_years:\n #print(item)\n if (str.isdigit(item)) and (min_year <= int(item) <= max_year):\n if gdpdata[item] != \"\" :\n tup_of_values = (int(item), float(gdpdata[item]))\n the_values_list.append(tup_of_values)\n #print(the_values_list)\n\n\n return the_values_list\n\n\ndef build_plot_dict(gdpinfo, country_list):\n \"\"\"\n Inputs:\n gdpinfo - GDP data information dictionary\n country_list - List of strings that are country names\n\n Output:\n Returns a dictionary whose keys are the country names in\n country_list and whose values are lists of XY plot values\n computed from the CSV file described by gdpinfo.\n\n Countries from country_list that do not appear in the\n CSV file should still be in the output dictionary, but\n with an empty XY plot value list.\n \"\"\"\n the_country_year_dic = {}\n\n the_dic = read_csv_as_nested_dict( gdpinfo['gdpfile'], gdpinfo['country_name']\n , gdpinfo['separator'], gdpinfo['quote'])\n the_countries_name = the_dic.keys()\n for item in country_list:\n for item2 in the_countries_name:\n if item == item2:\n the_years_dic = {}\n the_valueofcou = the_dic[item2]\n for item3 in the_valueofcou:\n if str.isdigit(item3):\n the_years_dic[item3] = the_valueofcou[item3]\n the_list_of_tup = build_plot_values(gdpinfo,the_years_dic)\n the_list_of_tup.sort(key = lambda pair: pair[0])\n the_country_year_dic[item2] = the_list_of_tup\n if item not in the_countries_name:\n the_country_year_dic[item] = []\n\n return the_country_year_dic\n\n\ndef render_xy_plot(gdpinfo, country_list, plot_file):\n \"\"\"\n Inputs:\n gdpinfo - GDP data information dictionary\n country_list - List of strings that are country names\n plot_file - String that is the output plot file name\n\n Output:\n Returns None.\n\n Action:\n Creates an SVG image of an XY plot for the GDP data\n specified by gdpinfo for the countries in country_list.\n The image will be stored in a file named by plot_file.\n \"\"\"\n the_dic_of_countries = build_plot_dict(gdpinfo, country_list)\n for item in country_list:\n the_list_of_years = the_dic_of_countries[item]\n coords = [(xval, yval) for xval, yval in the_list_of_years]\n xyplot = pygal.XY(height=500)\n xyplot.title = plot_file\n xyplot.add(\"Data\", coords)\n xyplot.render_in_browser()\n\ndef test_render_xy_plot():\n \"\"\"\n Code to exercise render_xy_plot and generate plots from\n actual GDP data.\n \"\"\"\n gdpinfo = {\n \"gdpfile\": \"isp_gdp.csv\",\n \"separator\": \",\",\n \"quote\": '\"',\n \"min_year\": 1960,\n \"max_year\": 2015,\n \"country_name\": \"Country Name\",\n \"country_code\": \"Country Code\"\n }\n\n render_xy_plot(gdpinfo, [], \"isp_gdp_xy_none.svg\")\n render_xy_plot(gdpinfo, [\"China\"], \"isp_gdp_xy_china.svg\")\n render_xy_plot(gdpinfo, [\"United Kingdom\", \"United States\"],\n \"isp_gdp_xy_uk+usa.svg\")\n\n\n", "sub_path": "Course 4/project 1.py", "file_name": "project 1.py", "file_ext": "py", "file_size_in_byte": 5094, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "csv.DictReader", "line_number": 29, "usage_type": "call"}, {"api_name": "pygal.XY", "line_number": 125, "usage_type": "call"}]}
+{"seq_id": "546451568", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('users', '0006_auto_20151113_0842'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='favorite',\n name='mileitem',\n field=models.ForeignKey(to='mile.MileItem', null=True),\n ),\n ]\n", "sub_path": "users/migrations/0007_auto_20151113_0857.py", "file_name": "0007_auto_20151113_0857.py", "file_ext": "py", "file_size_in_byte": 421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]}
+{"seq_id": "332477512", "text": "#!/usr/bin/env python3\n#usage: tira-train-model.py `input_dataset` `run` `outdir` `model1` `model2` ...\n#where model_n is one of 'kim' 'lexical' 'character' 'syntactic'\n#trains each model using the available training data and writes to the appropriate file\nimport sys\nimport tira\nimport features\nimport prepare_documents\nimport regression\nimport svm\nimport config\nif len(sys.argv) < 5:\n\tprint(\"Usage: see \",sys.argv[0])\n\tsys.exit(0)\ntiraInterface = tira.tiraInterface(sys.argv[1],sys.argv[2],sys.argv[3],features.documentFunctionCollection())\ntiraInterface.prepareWorkingDirectory()\ntraining_dataset,unknown_dataset=tiraInterface.loadCorpus()\nprint(\"training authors: \",training_dataset.authors)\nif tuple(unknown_dataset.authors) != (None,):\n\traise Exception(\"Unknown documents should not come labelled.\")\nif None in training_dataset.authors:\n\traise Exception(\"Training data should come labelled.\")\nwith tiraInterface:\n\tfor model in sys.argv[4:]:\n\t\tfilename=None\n\t\tview=None\n\t\tml=None\n\t\tif model == 'kim':\n\t\t\tview = features.kimView()\n\t\t\tml=svm.SVM\n\t\t\tfilename=tiraInterface.model_kim\n\t\telif model == 'lexical':\n\t\t\tview=features.lexicalView()\n\t\t\tfilename=tiraInterface.model_lex\n\t\telif model == 'character':\n\t\t\tview=features.characterView([3])\n\t\t\tfilename=tiraInterface.model_chr\n\t\telif model == 'syntactic':\n\t\t\tview=features.syntacticView([1,2,3], config.min_support, config.num_bins, config.max_embeddable_edges)\n\t\t\tview.functionCollection = training_dataset.functionCollection\n\t\t\tfilename=tiraInterface.model_syn\n\t\t\ttry:\n\t\t\t\twith open(tiraInterface.model_kim,'rb') as f:\n\t\t\t\t\tview.readTreeFeatureFromClassifier(features.loadClassifier(f.read(),training_dataset.functionCollection))\n\t\t\texcept FileNotFoundError:\n\t\t\t\tpass\n\t\telse:\n\t\t\tprint(\"Unknown model: '%s'\" % model)\n\t\t\tsys.exit(1)\n\t\tif ml is None:\n\t\t\tml = regression.multiclassLogit\n\t\tview.functionCollection = training_dataset.functionCollection\n\t\tclassifier=view.createClassifier(training_dataset, ml)\n\t\twith open(filename,'wb') as f:\n\t\t\tf.write(classifier.dumps())\n\t\tprint(\"wrote to \",filename)\n", "sub_path": "tira-train-model.py", "file_name": "tira-train-model.py", "file_ext": "py", "file_size_in_byte": 2053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "tira.tiraInterface", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "features.documentFunctionCollection", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "features.kimView", "line_number": 29, "usage_type": "call"}, {"api_name": "svm.SVM", "line_number": 30, "usage_type": "attribute"}, {"api_name": "features.lexicalView", "line_number": 33, "usage_type": "call"}, {"api_name": "features.characterView", "line_number": 36, "usage_type": "call"}, {"api_name": "features.syntacticView", "line_number": 39, "usage_type": "call"}, {"api_name": "config.min_support", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.num_bins", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.max_embeddable_edges", "line_number": 39, "usage_type": "attribute"}, {"api_name": "features.loadClassifier", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "regression.multiclassLogit", "line_number": 51, "usage_type": "attribute"}]}
+{"seq_id": "484743556", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n-------------------------------------------------\n File Name： redis_operate\n Description :\n Author : 'li'\n date： 2019/10/3\n-------------------------------------------------\n Change Activity:\n 2019/10/3:\n-------------------------------------------------\n\"\"\"\nimport json\nimport redis\n\nfrom db.redis_relevant.connection_pool.redis_connection_pool import REDIS_POOL\n\n\ndef insert_to_redis(key, value):\n \"\"\"\n get from redis\n :param key:\n :param value:\n :return:\n \"\"\"\n conn = redis.Redis(connection_pool=REDIS_POOL,\n decode_responses=True, encoding='utf8')\n conn.set(key, value)\n conn.close()\n\n\ndef get_from_redis(key):\n \"\"\"\n get from redis\n :param key:\n :return:\n \"\"\"\n conn = redis.Redis(connection_pool=REDIS_POOL,\n decode_responses=True, encoding='utf8')\n value = conn.get(key)\n conn.close()\n if value is None:\n return None\n json_obj = json.loads(str(value, encoding='utf8'))\n return json_obj\n\n\ndef get_fuzzy_search_keys(re):\n \"\"\"\n get from redis\n :param re:\n :return:\n \"\"\"\n conn = redis.Redis(connection_pool=REDIS_POOL,\n decode_responses=True, encoding='utf8')\n keys = conn.keys(pattern=re)\n conn.close()\n return keys\n\n\ndef delete_all_data_from_redis():\n conn = redis.Redis(connection_pool=REDIS_POOL, decode_responses=True, encoding='utf8')\n keys = conn.keys()\n for key in keys:\n conn.delete(key)\n\n\ndef delete_all_instance_data_from_redis():\n \"\"\"\n delete all instances data from redis\n :return:\n \"\"\"\n check_instances_key = 'CHECK_INSTANCE_*'\n keys = get_fuzzy_search_keys(check_instances_key)\n delete_keys(keys)\n\n\ndef delete_keys(keys):\n \"\"\"\n delete keys\n :param keys:\n :return:\n \"\"\"\n conn = redis.Redis(connection_pool=REDIS_POOL, decode_responses=True, encoding='utf8')\n for key in keys:\n conn.delete(key)\n\n\ndef get_all_key():\n conn = redis.Redis(connection_pool=REDIS_POOL, decode_responses=True, encoding='utf8')\n return conn.keys()\n\n\ndef main():\n keys = get_fuzzy_search_keys('*10.40.22.27*')\n for key in keys:\n a = get_from_redis(key)\n print(key)\n # a['receive_time'] = TypeChange.date_stamp_to_datetime(a['receive_time'])\n print(a)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "server/network_monitor_web_server/db/redis_relevant/connection_pool/redis_operate_bak.py", "file_name": "redis_operate_bak.py", "file_ext": "py", "file_size_in_byte": 2422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "redis.Redis", "line_number": 26, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 26, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 38, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 38, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 54, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 54, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 62, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 62, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 84, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 84, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 90, "usage_type": "call"}, {"api_name": "db.redis_relevant.connection_pool.redis_connection_pool.REDIS_POOL", "line_number": 90, "usage_type": "name"}]}
+{"seq_id": "408442125", "text": "import sys\nimport os\nimport requests\nfrom functools import wraps\nimport hashlib\nimport zipfile\nimport shutil\nimport contextlib\nimport tarfile\n\nfrom pathlib import Path\n\n\ndef get_data_dir(name=None):\n \"\"\" Returns the directories in which dirty_cat looks for data.\n\n This is typically useful for the end-user to check where the data is\n downloaded and stored.\n\n \"\"\"\n # assuming we are in datasets.utils, this calls the module\n module_path = os.path.dirname(os.path.dirname(__file__))\n data_dir = os.path.join(module_path, 'data')\n if name is not None:\n data_dir = os.path.join(data_dir, name)\n return Path(data_dir)\n\n\n@wraps(requests.get)\ndef request_get(*args, **kwargs):\n return requests.get(*args, **kwargs)\n\n\ndef md5_hash(string):\n m = hashlib.md5()\n m.update(string.encode('utf-8'))\n return m.hexdigest()\n\n\ndef _md5_sum_file(path):\n \"\"\" Calculates the MD5 sum of a file.\n \"\"\"\n with open(path, 'rb') as f:\n m = hashlib.md5()\n while True:\n data = f.read(8192)\n if not data:\n break\n m.update(data)\n return m.hexdigest()\n\n\ndef _check_if_exists(path, remove=False):\n if remove:\n try:\n os.remove(path)\n except OSError:\n pass\n return False\n else:\n return os.path.exists(path)\n\n\ndef _uncompress_file(file_, delete_archive=True):\n \"\"\"Uncompress files contained in a data_set.\n\n\n Parameters\n ----------\n file_: path to file\n delete_archive: whether to delete the compressed file afterwards\n\n\n Returns\n -------\n None if everything worked out fine\n ValueError otherwise\n\n\n Notes\n -----\n only supports zip and gzip\n \"\"\"\n sys.stderr.write('Extracting data from %s...' % file_)\n data_dir = os.path.dirname(file_)\n # We first try to see if it is a zip file\n try:\n filename, ext = os.path.splitext(file_)\n with open(file_, \"rb\") as fd:\n header = fd.read(4)\n processed = False\n if zipfile.is_zipfile(file_):\n z = zipfile.ZipFile(file_)\n z.extractall(path=data_dir)\n z.close()\n if delete_archive:\n os.remove(file_)\n file_ = filename\n processed = True\n elif ext == '.gz' or header.startswith(b'\\x1f\\x8b'):\n import gzip\n gz = gzip.open(file_)\n if ext == '.tgz':\n filename = filename + '.tar'\n out = open(filename, 'wb')\n shutil.copyfileobj(gz, out, 8192)\n gz.close()\n out.close()\n # If file is .tar.gz, this will be handle in the next case\n if delete_archive:\n os.remove(file_)\n file_ = filename\n processed = True\n if os.path.isfile(file_) and tarfile.is_tarfile(file_):\n with contextlib.closing(tarfile.open(file_, \"r\")) as tar:\n tar.extractall(path=data_dir)\n if delete_archive:\n os.remove(file_)\n processed = True\n if not processed:\n raise IOError(\n \"[Uncompress] unknown archive file format: %s\" % file_)\n\n sys.stderr.write('.. done.\\n')\n except Exception as e:\n print('Error uncompressing file: %s' % e)\n raise\n", "sub_path": "templatematching/templatematching/datasets/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 35, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 44, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 84, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "zipfile.is_zipfile", "line_number": 92, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 93, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 97, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 102, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 106, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "tarfile.is_tarfile", "line_number": 114, "usage_type": "call"}, {"api_name": "contextlib.closing", "line_number": 115, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 115, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 118, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 124, "usage_type": "attribute"}]}
+{"seq_id": "37815363", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2006, Mathieu Fenniak\n# Copyright (c) 2013, Jean Schurger <jean@schurger.org>\n#\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are\n# met:\n#\n# * Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n# * The name of the author may not be used to endorse or promote products\n# derived from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\nimport utils\nfrom generic import NameObject, DictionaryObject\n\n\n##\n# A class representing a destination within a PDF file.\n# See section 8.2.1 of the PDF 1.6 reference.\n# Stability: Added in v1.10, will exist for all v1.x releases.\nclass Destination(DictionaryObject):\n def __init__(self, title, page, typ, *args):\n DictionaryObject.__init__(self)\n self[NameObject(\"/Title\")] = title\n self[NameObject(\"/Page\")] = page\n self[NameObject(\"/Type\")] = typ\n\n # from table 8.2 of the PDF 1.6 reference.\n if typ == \"/XYZ\":\n (self[NameObject(\"/Left\")], self[NameObject(\"/Top\")],\n self[NameObject(\"/Zoom\")]) = args\n elif typ == \"/FitR\":\n (self[NameObject(\"/Left\")], self[NameObject(\"/Bottom\")],\n self[NameObject(\"/Right\")], self[NameObject(\"/Top\")]) = args\n elif typ in [\"/FitH\", \"FitBH\"]:\n self[NameObject(\"/Top\")], = args\n elif typ in [\"/FitV\", \"FitBV\"]:\n self[NameObject(\"/Left\")], = args\n elif typ in [\"/Fit\", \"FitB\"]:\n pass\n else:\n raise utils.PdfReadError(\"Unknown Destination Type: %r\" % typ)\n\n ##\n # Read-only property accessing the destination title.\n # @return A string.\n title = property(lambda self: self.get(\"/Title\"))\n\n ##\n # Read-only property accessing the destination page.\n # @return An integer.\n page = property(lambda self: self.get(\"/Page\"))\n\n ##\n # Read-only property accessing the destination type.\n # @return A string.\n typ = property(lambda self: self.get(\"/Type\"))\n\n ##\n # Read-only property accessing the zoom factor.\n # @return A number, or None if not available.\n zoom = property(lambda self: self.get(\"/Zoom\", None))\n\n ##\n # Read-only property accessing the left horizontal coordinate.\n # @return A number, or None if not available.\n left = property(lambda self: self.get(\"/Left\", None))\n\n ##\n # Read-only property accessing the right horizontal coordinate.\n # @return A number, or None if not available.\n right = property(lambda self: self.get(\"/Right\", None))\n\n ##\n # Read-only property accessing the top vertical coordinate.\n # @return A number, or None if not available.\n top = property(lambda self: self.get(\"/Top\", None))\n\n ##\n # Read-only property accessing the bottom vertical coordinate.\n # @return A number, or None if not available.\n bottom = property(lambda self: self.get(\"/Bottom\", None))\n", "sub_path": "PyPDF2/destination.py", "file_name": "destination.py", "file_ext": "py", "file_size_in_byte": 4061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "generic.DictionaryObject", "line_number": 40, "usage_type": "name"}, {"api_name": "generic.DictionaryObject.__init__", "line_number": 42, "usage_type": "call"}, {"api_name": "generic.DictionaryObject", "line_number": 42, "usage_type": "name"}, {"api_name": "generic.NameObject", "line_number": 43, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 44, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 45, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 49, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 50, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 52, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 53, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 55, "usage_type": "call"}, {"api_name": "generic.NameObject", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.PdfReadError", "line_number": 61, "usage_type": "call"}]}
+{"seq_id": "383278749", "text": "import json\n\nclass NSNitroError(Exception):\n\t\"\"\" Custom exception class \"\"\"\n\tdef __init__(self, value):\n\t\tself.message = value\n\tdef __str__(self):\n\t\treturn repr(self.message)\n\nclass NSNitroResponse:\n\t\"\"\" Generic class for accessing LB response dictionary. Can provide string response back and a parsed dictionary \"\"\"\n\t__jresponse = False\n\t__sresponse = False\n\terrorcode = -1\n\tmessage = False\n\tfailed = False\n\n\tdef __init__(self, response):\n\t\t\"\"\" Constructor. reponse - string response \"\"\"\n\t\tself.__sresponse = response\n\t\tself.__jresponse = json.loads(response)\n\t\tself.__parse_response()\n\n\n\tdef get_json_response(self):\n\t\t\"\"\" Returns LB response as parsed dictionary \"\"\"\n\t\treturn self.__jresponse\n\n\tdef get_string_response(self):\n\t\t\"\"\" Returns LB response as a string \"\"\"\n\t\treturn self.__sresponse\n\n\n\tdef __parse_response(self):\n\t\tself.errorcode = self.__jresponse['errorcode']\n\t\tself.message = self.__jresponse['message']\n\t\tif self.errorcode != 0:\n\t\t\tself.failed = True\n\n\tdef get_response_field(self, field_name):\n\t\t\"\"\" Returns field_name of parsed JSON dictionary \"\"\"\n\t\treturn self.__jresponse[field_name]\nclass NSService:\n\n\t__nitro = False\n\n\t# Public variables\n\t__options = {\n\t\t'cachetype' : '',\n\t\t'servername' : '',\n\t\t'downstateflush' : '',\n\t\t'maxreq' : '',\n\t\t'maxbandwidth' : '',\n\t\t'svrtimeout' : '',\n\t\t'port' : '',\n\t\t'clttimeout' : '',\n\t\t'servicetype' : '',\n\t\t'cacheable' : '',\n\t\t'maxclient' : '',\n\t\t'ipaddress' : '',\n\t\t'delay' : '',\n\t\t'usip' : '',\n\t\t'rtspsessionidremap' : '',\n\t\t'cleartextport' : '',\n\t\t'monthreshold' : '',\n\t\t'accessdown' : '',\n\t\t'serverid' : '',\n\t\t'tcpb' : '',\n\t\t'cka' : '',\n\t\t'name' : '',\n\t\t'sp' : '',\n\t\t'dup_weight' : '',\n\t\t'totalfailedprobes' : '',\n\t\t'cip' : '',\n\t\t'useproxyport' : '',\n\t\t'sc' : '',\n\t\t'cmp' : ''\n\t}\n\t# Readonly values\n\t__options_readonly = {\n\t\t'policyname' : '',\n\t\t'monstatparam2' : '',\n\t\t'monstatparam3' : '',\n\t\t'stateupdatereason' : '',\n\t\t'serviceconftype' : '',\n\t\t'gslb' : '',\n\t\t'svrstate' : '',\n\t\t'monstatcode' : '',\n\t\t'timesincelaststatechange' : '',\n\t\t'tickssincelaststatechange' : '',\n\t\t'responsetime' : '',\n\t\t'statechangetimesec' : '',\n\t\t'statechangetimemsec' : '',\n\t\t'failedprobes' : '',\n\t\t'totalprobes' : ''\n\t}\n\t\n\tdef __init__(self, nitro):\n\t\tself.__nitro = nitro\n\n\tdef __get_nitro(self):\n\t\treturn self.__nitro\n\n\n\tdef get(self, service_name):\n\t\turl = self.__nitro.get_url() + \"service/\" + service_name\n\n\t\tnitro = self.__nitro\n\t\tnsresponse = nitro.get(url)\n\t\tif nsresponse.failed:\n\t\t\traise NSNitroError(nsresponse.message)\n\n\t\tfor key in self.__options.iterkeys():\n\t\t\tself.__options[key] = nsresponse.get_response_field(\"service\")[0][key]\n\n\t\tfor key in self.__options_readonly.iterkeys():\n\t\t\tself.__options_readonly[key] = nsresponse.get_response_field(\"service\")[0][key]\n\n\tdef get_resource_type(self):\n\t\treturn \"service\"\n\n\tdef get_name(self):\n\t\treturn self.__options['name']\n\n\tdef json(self):\n\t\treturn json.JSONEncoder().encode(self.__options)\n\nclass NSPayloadFormatter:\n\n\tdef __init__(self, sessionid, options):\n\t\tpass\n", "sub_path": "nsresources.py", "file_name": "nsresources.py", "file_ext": "py", "file_size_in_byte": 2974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 126, "usage_type": "call"}]}
+{"seq_id": "82386752", "text": "\"\"\"\nThis simple animation example shows how to move an item with the joystick\nand game-pad.\n\nIf Python and Arcade are installed, this example can be run from the command line with:\npython -m arcade.examples.move_joystick\n\"\"\"\n\nimport arcade\n\nSCREEN_WIDTH = 640\nSCREEN_HEIGHT = 480\nSCREEN_TITLE = \"Move Joystick Example\"\nMOVEMENT_SPEED = 5\nDEAD_ZONE = 0.02\n\n\nclass Ball:\n def __init__(self, position_x, position_y, change_x, change_y, radius, color):\n\n # Take the parameters of the init function above, and create instance variables out of them.\n self.position_x = position_x\n self.position_y = position_y\n self.change_x = change_x\n self.change_y = change_y\n self.radius = radius\n self.color = color\n\n def draw(self):\n \"\"\" Draw the balls with the instance variables we have. \"\"\"\n arcade.draw_circle_filled(self.position_x, self.position_y, self.radius, self.color)\n\n def update(self):\n # Move the ball\n self.position_y += self.change_y\n self.position_x += self.change_x\n\n # See if the ball hit the edge of the screen. If so, change direction\n if self.position_x < self.radius:\n self.position_x = self.radius\n\n if self.position_x > SCREEN_WIDTH - self.radius:\n self.position_x = SCREEN_WIDTH - self.radius\n\n if self.position_y < self.radius:\n self.position_y = self.radius\n\n if self.position_y > SCREEN_HEIGHT - self.radius:\n self.position_y = SCREEN_HEIGHT - self.radius\n\n\nclass MyGame(arcade.Window):\n\n def __init__(self, width, height, title):\n\n # Call the parent class's init function\n super().__init__(width, height, title)\n\n # Make the mouse disappear when it is over the window.\n # So we just see our object, not the pointer.\n self.set_mouse_visible(False)\n\n arcade.set_background_color(arcade.color.ASH_GREY)\n\n # Create our ball\n self.ball = Ball(50, 50, 0, 0, 15, arcade.color.AUBURN)\n\n # Get a list of all the game controllers that are plugged in\n joysticks = arcade.get_joysticks()\n\n # If we have a game controller plugged in, grab it and\n # make an instance variable out of it.\n if joysticks:\n self.joystick = joysticks[0]\n self.joystick.open()\n else:\n print(\"There are no joysticks.\")\n self.joystick = None\n\n def on_draw(self):\n\n \"\"\" Called whenever we need to draw the window. \"\"\"\n arcade.start_render()\n self.ball.draw()\n\n def on_update(self, delta_time):\n\n # Update the position according to the game controller\n if self.joystick:\n\n # Set a \"dead zone\" to prevent drive from a centered joystick\n if abs(self.joystick.x) < DEAD_ZONE:\n self.ball.change_x = 0\n else:\n self.ball.change_x = self.joystick.x * MOVEMENT_SPEED\n\n # Set a \"dead zone\" to prevent drive from a centered joystick\n if abs(self.joystick.y) < DEAD_ZONE:\n self.ball.change_y = 0\n else:\n self.ball.change_y = -self.joystick.y * MOVEMENT_SPEED\n\n self.ball.update()\n\n\ndef main():\n MyGame(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)\n arcade.run()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "arcade/examples/move_joystick.py", "file_name": "move_joystick.py", "file_ext": "py", "file_size_in_byte": 3358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "arcade.draw_circle_filled", "line_number": 31, "usage_type": "call"}, {"api_name": "arcade.Window", "line_number": 52, "usage_type": "attribute"}, {"api_name": "arcade.set_background_color", "line_number": 63, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 63, "usage_type": "attribute"}, {"api_name": "arcade.color", "line_number": 66, "usage_type": "attribute"}, {"api_name": "arcade.get_joysticks", "line_number": 69, "usage_type": "call"}, {"api_name": "arcade.start_render", "line_number": 83, "usage_type": "call"}, {"api_name": "arcade.run", "line_number": 108, "usage_type": "call"}]}
+{"seq_id": "613175729", "text": "import maya.cmds as cmds\nimport maya.OpenMaya as OpenMaya\nimport deformerData\n\n\nclass LatticeData(deformerData.DeformerData):\n \"\"\"\n LatticeData class object.\n \"\"\"\n\n def __init__(self, deformer=None):\n \"\"\"\n \"\"\"\n # Update Attr Value/Connection List\n # self._data['attrValueList']\n # self._data['attrConnectionList']\n\n # Execute Super Class Initilizer\n super(LatticeData, self).__init__(deformer)\n\n def buildData(self, deformer):\n \"\"\"\n @param deformer:\n \"\"\"\n # ==========\n # - Checks -\n # ==========\n\n # Verify node\n lattice = deformer\n if not cmds.objExists(lattice):\n raise Exception('Lattice deformer ' + lattice + ' does not exists! No influence data recorded!!')\n\n objType = cmds.objectType(lattice)\n if objType == 'transform':\n lattice = cmds.listRelatives(lattice, s=True, ni=True)[0]\n objType = cmds.objectType(lattice)\n if objType == 'lattice':\n lattice = cmds.listConnections(lattice + '.latticeOutput', s=False, d=True, type='ffd')[0]\n objType = cmds.objectType(lattice)\n if objType != 'ffd':\n raise Exception(\n 'Object ' + lattice + ' is not a vaild lattice deformer! Incorrect class for node type ' + objType + '!!')\n\n # =====================\n # - Get Deformer Data -\n # =====================\n\n # FFD Attributes\n self.local = cmds.getAttr(lattice + '.local')\n self.outside = cmds.getAttr(lattice + '.outsideLattice')\n self.falloff = cmds.getAttr(lattice + '.outsideFalloffDist')\n self.resolution = cmds.getAttr(lattice + '.usePartialResolution')\n self.partialResolution = cmds.getAttr(lattice + '.partialResolution')\n self.freeze = cmds.getAttr(lattice + '.freezeGeometry')\n self.localInfluenceS = cmds.getAttr(lattice + '.localInfluenceS')\n self.localInfluenceT = cmds.getAttr(lattice + '.localInfluenceT')\n self.localInfluenceU = cmds.getAttr(lattice + '.localInfluenceU')\n\n # Get Input Lattice and Base\n self.latticeShape = cmds.listConnections(lattice + '.deformedLatticePoints', sh=True)[0]\n self.lattice = cmds.listRelatives(self.latticeShape, p=True)[0]\n self.latticeBaseShape = cmds.listConnections(lattice + '.baseLatticeMatrix', sh=True)[0]\n self.latticeBase = cmds.listRelatives(self.latticeBaseShape, p=True)[0]\n\n # Get Lattice Data\n self.sDivisions = cmds.getAttr(self.latticeShape + '.sDivisions')\n self.tDivisions = cmds.getAttr(self.latticeShape + '.tDivisions')\n self.uDivisions = cmds.getAttr(self.latticeShape + '.uDivisions')\n self.latticeXform = cmds.xform(self.lattice, q=True, ws=True, m=True)\n\n # Get Lattice Base Data\n self.baseXform = cmds.xform(self.latticeBase, q=True, ws=True, m=True)\n\n def rebuild(self):\n \"\"\"\n Rebuild the lattice deformer from the recorded deformerData\n \"\"\"\n # Rebuild deformer\n ffd = cmds.lattice(self.getMemberList(), n=self.deformerName)\n lattice = ffd[0]\n latticeShape = ffd[1]\n latticeBase = ffd[2]\n\n # Set Deformer Attributes\n cmds.setAttr(lattice + '.local', self.local)\n cmds.setAttr(lattice + '.outsideLattice', self.outside)\n cmds.setAttr(lattice + '.outsideFalloffDist', self.falloff)\n cmds.setAttr(lattice + '.usePartialResolution', self.resolution)\n cmds.setAttr(lattice + '.partialResolution', self.partialResolution)\n cmds.setAttr(lattice + '.freezeGeometry', self.freeze)\n cmds.setAttr(lattice + '.localInfluenceS', self.localInfluenceS)\n cmds.setAttr(lattice + '.localInfluenceT', self.localInfluenceT)\n cmds.setAttr(lattice + '.localInfluenceU', self.localInfluenceU)\n\n # Set Lattice Shape Attributes\n cmds.setAttr(latticeShape + '.sDivisions', self.sDivisions)\n cmds.setAttr(latticeShape + '.tDivisions', self.tDivisions)\n cmds.setAttr(latticeShape + '.uDivisions', self.uDivisions)\n\n # Restore World Transform Data\n cmds.xform(lattice, ws=True, m=self.latticeXform)\n cmds.xform(latticeBase, ws=True, m=self.baseXform)\n\n # Return result\n return lattice\n", "sub_path": "data/latticeData.py", "file_name": "latticeData.py", "file_ext": "py", "file_size_in_byte": 4334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "deformerData.DeformerData", "line_number": 6, "usage_type": "attribute"}, {"api_name": "maya.cmds.objExists", "line_number": 31, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 31, "usage_type": "name"}, {"api_name": "maya.cmds.objectType", "line_number": 34, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 34, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 36, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 36, "usage_type": "name"}, {"api_name": "maya.cmds.objectType", "line_number": 37, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 37, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 39, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 39, "usage_type": "name"}, {"api_name": "maya.cmds.objectType", "line_number": 40, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 40, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 50, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 50, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 51, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 51, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 52, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 52, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 53, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 53, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 54, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 54, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 55, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 55, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 56, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 56, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 57, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 57, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 58, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 58, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 61, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 61, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 62, "usage_type": "name"}, {"api_name": "maya.cmds.listConnections", "line_number": 63, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 63, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 64, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 64, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 67, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 67, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 68, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 68, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 69, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 69, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 70, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 70, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 73, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 73, "usage_type": "name"}, {"api_name": "maya.cmds.lattice", "line_number": 80, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 80, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 86, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 86, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 87, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 87, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 88, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 88, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 89, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 89, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 90, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 90, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 91, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 91, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 92, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 92, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 93, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 93, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 94, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 94, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 97, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 97, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 98, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 98, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 99, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 99, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 102, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 102, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 103, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 103, "usage_type": "name"}]}
+{"seq_id": "99400928", "text": "# -*- coding: utf-8 -*-\n#import standard libraries and third-party libraries\nfrom traceback import print_last\nimport pandas, os, pyprind, multiprocessing, numpy\nimport openpyxl, time, scipy, io, timeit, shutil\nfrom copy import copy\nfrom itertools import repeat\nfrom lxml import objectify\nfrom openpyxl import load_workbook\nfrom scipy.sparse import coo_matrix, lil_matrix, csc_matrix, find\nfrom scipy.sparse import linalg\n#import ecoinvent packages\nimport activity_overview, MasterData, dataset_mapping, activityLink_overview\nimport matrix_builder, folder_structure, parameter_overview, simapro_export, RoW_creation\nimport contribution_analysis, utils\nimport spold2_reader as spold2\ndef allocate(sel):\n sel = sel.set_index('name')\n if activityName == 'treatment of tallow to esterquat':\n allocation_factors = ''\n sel = sel.reset_index()\n index = sel[sel['group'] == 'ReferenceProduct'].iloc[0].name\n sel.loc[index, 'group'] = 'FromTechnosphere'\n sel.loc[index, 'amount'] = -sel.loc[index, 'amount']\n index = sel[sel['name'] == 'wastewater, unpolluted'].iloc[0].name\n sel.loc[index, 'group'] = 'FromTechnosphere'\n sel.loc[index, 'amount'] = -sel.loc[index, 'amount']\n index = sel[sel['name'] == 'esterquat'].iloc[0].name\n sel.loc[index, 'group'] = 'ReferenceProduct'\n sel['amount'] = sel['amount'] / sel.loc[index, 'amount']\n else:\n if activityName == 'treatment of waste cooking oil, purified, esterification':\n for product in ['municipal solid waste', 'wastewater, from residence', 'used vegetable cooking oil, purified']:\n sel.loc[product, 'group'] = 'FromTechnosphere'\n sel.loc[product, 'amount'] = -sel.loc[product, 'amount']\n ByProducts = list(sel[sel['group'] == 'ByProduct'].index)\n for ByProduct in ByProducts:\n if chemicals.loc[ByProduct, 'By-product classification'] == 'Waste':\n sel.loc[ByProduct, 'group'] = 'FromTechnosphere'\n sel.loc[ByProduct, 'amount'] = -sel.loc[ByProduct, 'amount']\n allocation_factors = sel[sel['group'].isin(['ReferenceProduct', 'ByProduct'])]\n if len(allocation_factors) > 1:\n allocation_factors = allocation_factors[['amount']]\n allocation_factors = allocation_factors.join(chemicals[['price (EURO/unit)']])\n if 0.1 in list(allocation_factors['price (EURO/unit)']):\n 1/0\n allocation_factors['revenu'] = allocation_factors['amount'] * allocation_factors['price (EURO/unit)']\n allocation_factors['factor'] = allocation_factors['revenu'] / allocation_factors['revenu'].sum()\n if numpy.any(numpy.isnan(list(allocation_factors['factor']))):\n print('missing price')\n print(allocation_factors)\n 1/0\n allocation_factors = allocation_factors[allocation_factors['factor'] != 0.]\n allocated = []\n for name in allocation_factors.index:\n sel_ = sel.copy().reset_index()\n ref = sel_[sel_['group'].isin(['ReferenceProduct', 'ByProduct'])]\n ref = utils.dataframe_to_series(ref[ref['name'] == name])\n c = list(sel_.index)\n c.remove(ref.name)\n sel_.loc[c, 'amount'] = sel_.loc[c, 'amount'] * allocation_factors.loc[name, 'factor']\n sel_['amount'] = sel_['amount'] / ref['amount']\n sel_.loc[ref.name, 'group'] = 'ReferenceProduct'\n c = set(sel_.index)\n to_remove = set(allocation_factors.index)\n to_remove.remove(name)\n index_to_remove = set()\n for name_to_remove in to_remove:\n line_to_remove = sel_[sel_['group'].isin(['ReferenceProduct', 'ByProduct'])]\n line_to_remove = line_to_remove[line_to_remove['name'] == name_to_remove]\n line_to_remove = utils.dataframe_to_series(line_to_remove)\n index_to_remove.add(line_to_remove.name)\n c.difference_update(index_to_remove)\n sel_ = sel_.loc[c]\n allocated.append(sel_)\n if list(sel_['group']).count('ReferenceProduct') != 1:\n 1/0\n else:\n allocated = [sel.reset_index()]\n return allocated, allocation_factors\n\nfolder = os.path.join(utils.db_basepath, r'thinkstep-ecoinvent\\Allocation, cut-off\\excel')\nfilename = 'result_of_mapping_application.xlsx'\ntab = 'mapping'\nredirected = pandas.read_excel(os.path.join(folder, filename), tab)\nredirected = redirected[redirected['mapped']]\nredirected, dummy = utils.accelerate_df(redirected, \n index = ['activityName ecoinvent', 'geography ecoinvent', 'product ecoinvent'])\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\hybrid database\\input'\nfilename = 'GDP_UN_scaling factors_20140312.xlsx'\ntab = 'scaling factors'\nGDP = pandas.read_excel(os.path.join(folder, filename), tab)\nGDP = GDP.set_index('ecoinvent shortcut')\n\n#prepare water regionalization module\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\input'\nfilename = 'chemical_model_input.xlsx'\ntab = 'water_regio_mapping'\nwater_mapping = pandas.read_excel(os.path.join(folder, filename), tab)\nwater_mapping = water_mapping[water_mapping['regionalized name'].notnull()]\nwater_mapping = water_mapping.set_index(['ecoinvent_name', 'ecoinvent_compartment',\n 'ecoinvent_subcompartment']).sortlevel(level=0)\nwater_mapping_ = set(water_mapping.index)\ntab = 'water_weighting'\nwater_weighting = pandas.read_excel(os.path.join(folder, filename), tab)\nwater_weighting = water_weighting.set_index('shortname')\n#water_weighting = water_weighting[water_weighting['ILCD']]\n\nweights = {}\nweighting_set = 'GDP'\nweights['GLO'] = water_weighting.copy()\nweights['RER'] = water_weighting[water_weighting['RER']]\nfor geo in weights:\n weights[geo]['weight'] = weights[geo][weighting_set] / weights[geo][weighting_set].sum()\n unregionalized_share = 1. - weights[geo][weights[geo]['ILCD']]['weight'].sum()\n weights[geo] = (weights[geo][weights[geo]['ILCD']], unregionalized_share)\n\nversion = 'thinkstep-ecoinvent'\nsystem_model = 'Allocation, cut-off'\n#loading matrices and calculating scores for hybrid\nfolder = os.path.join(utils.db_basepath, version, system_model)\nao = utils.pkl_load(os.path.join(folder, 'pkl'), 'ao')\nao = ao.set_index(['activityName', 'name', 'geography']).sortlevel(level=0)\n\n#load the water disaggregation file\nfolder = os.path.join(utils.db_basepath, r'chemicals\\Allocation, cut-off\\excel')\nfilename = 'geo_overlap.xlsx'\ntab = 'Sheet1'\ngeo_replace = pandas.read_excel(os.path.join(folder, filename), tab)\ngeo_replace = geo_replace.set_index('shortname')\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\input'\nfilename = 'chemical_model_input.xlsx'\ntab = 'ee-mapping'\nee_mapping = pandas.read_excel(os.path.join(folder, filename), tab)\nee_water = set()\ngrouped = ee_mapping.groupby(['ecoinvent_name', 'ecoinvent_compartment', 'ecoinvent_subcompartment'])\nfor name, group in grouped:\n if 'Water' in name[0]:\n to_add = set(zip(list(group['ILCD_NAME']), \n list(group['ILCD_CATEGORY_1']), list(group['ILCD_CATEGORY_2'])))\n ee_water = ee_water.union(to_add)\nee_mapping = ee_mapping.set_index(['ecoinvent_name', 'ecoinvent_compartment', \n 'ecoinvent_subcompartment']).sortlevel(level=0)\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\output'\nfilename = 'chemical_output.xlsx'\nmeta = pandas.read_excel(os.path.join(folder, filename), 'meta')\ni = ['activityName', 'geography', 'reference product']\nmeta = meta.set_index(i).sortlevel(level=0)\nexchanges = pandas.read_excel(os.path.join(folder, filename), 'exchanges')\n\nexchanges = exchanges[exchanges['group'].isin(['ReferenceProduct', 'ByProduct'])]\nexchanges = exchanges.set_index(['activityName', 'name']).sortlevel(level=0)\nnew_ie_list = set(exchanges.index)\nexchanges = pandas.read_excel(os.path.join(folder, filename), 'exchanges')\nexchanges = exchanges.set_index(i).sortlevel(level=0)\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\input'\nfilename = 'chemical_model_input.xlsx'\nproduct_to_activity = pandas.read_excel(os.path.join(folder, filename), 'product_to_activity')\nproduct_to_activity = product_to_activity.set_index('product name')\nproduct_to_activity = product_to_activity['activityName'].to_dict()\nchemicals = pandas.read_excel(os.path.join(folder, filename), 'chemicals')\nchemicals = chemicals.set_index('name')\nreaction_characteristic = pandas.read_excel(os.path.join(folder, filename), 'reaction characteristic')\nreaction_characteristic = reaction_characteristic.set_index('reaction name').sortlevel(level=0)\ndataset_id = pandas.read_excel(os.path.join(folder, filename), 'dataset_id')\ndataset_id = dataset_id.set_index('ecoinvent activityName')\nversion = 'chemicals'\nsystem_model = 'Allocation, cut-off'\np = os.path.join(utils.db_basepath, version)\nif not os.path.exists(p):\n folder_structure.create_folder_structure(utils.db_basepath, version, system_models = [system_model])\nversion = 'thinkstep-ecoinvent'\nfolder = os.path.join(utils.db_basepath, version, system_model)\nA, B, C, indexes, Z = utils.load_matrices(folder)\n\nA = A.tolil()\nB = B.tolil()\nd = exchanges[exchanges['group'].isin(['ReferenceProduct', 'ByProduct'])]\nexchanges = exchanges[exchanges['to review'] == False]\nd = d.set_index('name')\ncolumns = {}\nrows = {}\ncoefficients = {}\nfor m, t in [(A, 'ie'), (B, 'ee')]:\n rows[t] = []\n columns[t] = []\n coefficients[t] = []\nalready_treated = set()\nfor i, sel in dataset_id.iterrows():\n ie = (sel.name, sel['ecoinvent geography'], sel['ecoinvent reference product'])\n if ie not in already_treated:\n already_treated.add(ie)\n if ie in indexes.ie:\n ie_number = indexes.toggle['ie'][ie]\n A[:, ie_number] = 0.\n B[:, ie_number] = 0.\n else:\n indexes.add_index('ie', ie, sel['unit'])\n ie_number = indexes.toggle['ie'][ie]\n rows['ie'].append(ie_number)\n columns['ie'].append(ie_number)\n coefficients['ie'].append(1.)\n\n#put coefficients in lists for speed. \nfor m, t in [(A, 'ie'), (B, 'ee')]:\n r, c, cc = find(m)\n rows[t].extend(list(r))\n columns[t].extend(list(c))\n coefficients[t].extend(list(cc))\n\nindexes.to_dfs()\nversion = 'chemicals'\nfolder = os.path.join(utils.db_basepath, version, system_model)\nindexes.to_excel(folder, version, system_model)\nfor_potential_links = indexes.dfs['ie'].reset_index()\nfor_potential_links = for_potential_links[for_potential_links['geography'] != 'ROW']\nfor_potential_links = for_potential_links.set_index(['activityName', 'product', 'geography']).sortlevel(level=0)\nlinking_results = []\nl = list(set(exchanges.index))\n#l.index((activityName, geography, reference_product))\nallocated_datasets = []\nall_allocation_factors = []\ndataset_id = dataset_id.reset_index().set_index(['ecoinvent activityName', \n 'ecoinvent geography', 'ecoinvent reference product']).sortlevel(level=0)\nfor activityName, geography, reference_product in pyprind.prog_bar(\n l, title = 'iterating all datasets'):\n #activityName, geography = 'Electricity grid mix 1kV-60kV', 'GLO'\n sel = exchanges.loc[(activityName, geography, reference_product)]\n sel = sel[sel['amount'] != 0.]\n del sel['needs allocation']\n del sel['price']\n provided_geography = utils.dataframe_to_series(dataset_id.loc[(activityName, geography, \n reference_product)])['provided geography']\n if 'ByProduct' in list(sel['group']):\n allocated, allocation_factors = allocate(sel)\n if len(allocation_factors) > 1:\n allocation_factors['activityName'] = activityName\n allocation_factors['geography'] = geography\n all_allocation_factors.append(allocation_factors)\n else:\n allocated = [sel]\n for sel in allocated:\n ReferenceProduct = utils.dataframe_to_series(sel[sel['group'] == 'ReferenceProduct'])\n ie = (activityName, geography, ReferenceProduct['name'])\n baseline = dict(zip(['activityName', 'geography', 'reference product'], ie))\n baseline['provided geography'] = provided_geography\n to_add = copy(baseline)\n to_add.update({'group': 'ReferenceProduct', 'name': ie[2], \n 'amount': 1., 'unitName': indexes.units[ie]})\n allocated_datasets.append(to_add)\n #print('')\n #print(ie)\n dataset_col_num = indexes.toggle['ie'][ie]\n FromTechnospheres = sel[sel['group'] == 'FromTechnosphere']\n for i, exc in FromTechnospheres.iterrows():\n if utils.is_empty(exc['activityLink_activityName']):\n AL_activityName = product_to_activity[exc['name']]\n else:\n AL_activityName = exc['activityLink_activityName']\n if not utils.is_empty(exc['activityLink_geography']):\n ies = [((AL_activityName, exc['activityLink_geography'], exc['name']), 1.)]\n else:\n potential_links = utils.series_to_dataframe(\n for_potential_links.loc[(AL_activityName, exc['name'])])\n \n if len(potential_links) == 1:\n ies = potential_links.iloc[0]\n ies = [((AL_activityName, ies.name, exc['name']), 1.)]\n elif provided_geography == 'RER' and 'EU-28+3' in list(potential_links.index):\n ies = [((AL_activityName, 'EU-28+3', exc['name']), 1.)]\n else:\n if provided_geography in list(potential_links.index):\n ies = utils.dataframe_to_series(potential_links.loc[provided_geography])\n ies = [((AL_activityName, provided_geography, exc['name']), 1.)]\n elif exc['name'] == 'Electricity':\n if provided_geography == 'RER':\n ies = [((AL_activityName, 'EU-28+3', exc['name']), 1.)]\n else:\n ies = [(('market group for electricity, medium voltage', 'GLO', 'electricity, medium voltage'), 1.)]\n elif provided_geography == 'GLO':\n sel_ao = ao.loc[(AL_activityName, exc['name'])][['productionVolumeAmount']]\n if any(numpy.isnan(list(sel_ao['productionVolumeAmount']))):\n potential_links = potential_links.join(GDP[[2005]])\n potential_links['factor'] = potential_links[2005\n ]/potential_links[2005].sum()\n else:\n potential_links = potential_links.join(sel_ao)\n potential_links['factor'] = potential_links['productionVolumeAmount'\n ]/potential_links['productionVolumeAmount'].sum()\n ies = []\n for j, s in potential_links.iterrows():\n ies.append(((AL_activityName, s.name, exc['name']), s['factor']))\n elif provided_geography == 'RER' and 'Europe without Switzerland' in list(potential_links.index):\n ies = [((AL_activityName, 'Europe without Switzerland', exc['name']), 1.)]\n elif provided_geography == 'RER' and 'CH' in list(potential_links.index):\n ies = [((AL_activityName, 'CH', exc['name']), 1.)]\n elif provided_geography == 'RER' and 'EU-27' in list(potential_links.index):\n ies = [((AL_activityName, 'EU-27', exc['name']), 1.)]\n elif 'GLO' in list(potential_links.index) and 'RoW' in list(potential_links.index):\n ies = [((AL_activityName, 'GLO', exc['name']), 1.)]\n elif provided_geography == 'ZA':\n ies = [((AL_activityName, 'GLO', exc['name']), 1.)]\n elif provided_geography == 'RER' and 'CH' not in list(potential_links.index\n ) and 'Europe without Switzerland' not in list(potential_links.index\n ) and 'RoW' in list(potential_links.index):\n ies = [((AL_activityName, 'RoW', exc['name']), 1.)]\n else:\n print(exc)\n print(exc['name'])\n 1/0\n for ie, proportion_mapped in ies:\n assert exc['unitName'] == indexes.units[ie]\n if ie in redirected:\n sel3 = redirected[ie]\n ie_to_add = list(zip(list(sel3['activityName thinkstep']), \n list(sel3['geography thinkstep']), \n list(sel3['product thinkstep'])))\n proportions = list(sel3['coefficient for A']*proportion_mapped)\n iess = list(zip(ie_to_add, proportions))\n else:\n iess = [(ie, proportion_mapped)]\n for ie , proportion_mapped in iess:\n rows['ie'].append(indexes.toggle['ie'][ie])\n columns['ie'].append(dataset_col_num)\n amount = exc['amount']*proportion_mapped/abs(ReferenceProduct['amount'])\n coefficients['ie'].append(-1.*amount)\n assert not numpy.isnan(coefficients['ie'][-1])\n to_add = {'activityName': activityName, 'geography': geography, \n 'reference product': ReferenceProduct['name'], \n 'original activityLink_activityName': exc['activityLink_activityName'], \n 'original activityLink_geography': exc['activityLink_geography'], \n 'proportion mapped': proportion_mapped}\n to_add.update(dict(zip(['activityLink_activityName', 'activityLink_geography', 'name'], ie)))\n linking_results.append(to_add)\n to_add = copy(baseline)\n to_add.update({'group': 'FromTechnosphere', 'amount': amount, 'unitName': indexes.units[ie]})\n to_add.update(dict(zip(['activityLink_activityName', 'activityLink_geography', 'name'], ie)))\n allocated_datasets.append(to_add)\n \n environment = sel[sel['group'].isin(['FromEnvironment', 'ToEnvironment'])]\n environment = environment[environment['name'].apply(lambda x: 'dropped_emission_of_' not in x)]\n for i, exc in environment.iterrows():\n ee_ecoinvent = tuple(exc[['name', 'compartment', 'subcompartment']])\n ee_ILCD = utils.dataframe_to_series(ee_mapping.loc[ee_ecoinvent])\n if ee_ILCD['ILCD_NAME'] != '(drop)':\n if ee_ecoinvent in water_mapping_:\n sel_water = utils.dataframe_to_series(water_mapping.loc[ee_ecoinvent])\n factor = sel_water['conversion factor']\n if sel_water['level 0'] == 'Resources':\n group = 'FromEnvironment'\n partial_ee = list(sel_water[['level 0', 'level 2']])\n else:\n partial_ee = list(sel_water[['level 1', 'level 2']])\n group = 'ToEnvironment'\n if provided_geography == 'ZA':\n regionalized_shares = []\n unregionalized_share = 1.\n else:\n regionalized_shares, unregionalized_share = weights[provided_geography]\n if len(regionalized_shares) > 0:\n for j, s in regionalized_shares.iterrows():\n ee_ILCD = [sel_water['regionalized name'].replace('XX', s.name)]\n ee_ILCD.extend(partial_ee)\n ee_ILCD = tuple(ee_ILCD)\n rows['ee'].append(indexes.toggle['ee'][ee_ILCD])\n columns['ee'].append(dataset_col_num)\n amount = exc['amount'] * factor * s['weight'] / abs(ReferenceProduct['amount'])\n coefficients['ee'].append(amount)\n assert not numpy.isnan(coefficients['ee'][-1])\n to_add = copy(baseline)\n to_add.update({'group': group, 'amount': amount, 'unitName': indexes.units[ee_ILCD]})\n to_add.update(dict(zip(['name', 'compartment', 'subcompartment'], ee_ILCD)))\n allocated_datasets.append(to_add)\n ee_ILCD = [sel_water['ILCD_NAME']]\n ee_ILCD.extend(partial_ee)\n ee_ILCD = tuple(ee_ILCD)\n rows['ee'].append(indexes.toggle['ee'][ee_ILCD])\n columns['ee'].append(dataset_col_num)\n amount = exc['amount'] * factor * unregionalized_share / abs(ReferenceProduct['amount'])\n coefficients['ee'].append(amount)\n assert not numpy.isnan(coefficients['ee'][-1])\n to_add = copy(baseline)\n to_add.update({'group': group, 'amount': amount, 'unitName': indexes.units[ee_ILCD]})\n to_add.update(dict(zip(['name', 'compartment', 'subcompartment'], ee_ILCD)))\n allocated_datasets.append(to_add)\n else:\n factor = ee_ILCD['CONVERSION_FACTOR']\n if ee_ILCD['ILCD_CATEGORY_0'] == 'Emissions':\n ee_ILCD = tuple(ee_ILCD[['ILCD_NAME', 'ILCD_CATEGORY_1', 'ILCD_CATEGORY_2']])\n elif ee_ILCD['ILCD_CATEGORY_0'] == 'Land use':\n ee_ILCD = (ee_ILCD['ILCD_NAME'], 'Resources', ee_ILCD['ILCD_CATEGORY_1'])\n elif ee_ILCD['ILCD_CATEGORY_0'] == 'Resources':\n ee_ILCD = (ee_ILCD['ILCD_NAME'], 'Resources', ee_ILCD['ILCD_CATEGORY_2'])\n else:\n 1/0\n if numpy.nan in ee_ILCD:\n 1/0\n indexes.add_index('ee', ee_ILCD, exc['unitName'])\n rows['ee'].append(indexes.toggle['ee'][ee_ILCD])\n columns['ee'].append(dataset_col_num)\n coefficients['ee'].append(exc['amount']*factor / abs(ReferenceProduct['amount']))\n assert not numpy.isnan(coefficients['ee'][-1])\n if ee_ILCD[1] == 'Resources':\n group = 'FromEnvironment'\n else:\n group = 'ToEnvironment'\n to_add = copy(baseline)\n to_add.update({'group': group, 'amount': amount, 'unitName': indexes.units[ee_ILCD]})\n to_add.update(dict(zip(['name', 'compartment', 'subcompartment'], ee_ILCD)))\n allocated_datasets.append(to_add)\nindexes.to_dfs()\n#build A\nindex_type = 'ie'\nij = numpy.vstack((rows[index_type], columns[index_type]))\nA = coo_matrix((coefficients[index_type],ij), \n shape = (len(indexes.ie), len(indexes.ie))).tocsc()\n\n#correction for ethoxylated alcohol\nfor geo in ['RoW', 'RER']:\n ie = ('ethoxylated alcohol (AE7) production, palm kernel oil', geo, 'ethoxylated alcohol (AE7)')\n ie = indexes.toggle['ie'][ie]\n link_to_add = ('fatty alcohol production, from palm kernel oil', geo, 'fatty alcohol')\n link_to_add = indexes.toggle['ie'][link_to_add]\n link_to_delete = ('fatty alcohol production, from palm oil', geo, 'fatty alcohol')\n link_to_delete = indexes.toggle['ie'][link_to_delete]\n A[link_to_add, ie] = copy(A[link_to_delete, ie])\n A[link_to_delete, ie] = 0.\n \n#build B\nindex_type = 'ee'\nij = numpy.vstack((rows[index_type], columns[index_type]))\nB = coo_matrix((coefficients[index_type],ij), \n shape = (len(indexes.ee), len(indexes.ie))).tocsc()\nZ = matrix_builder.Z_from_A(A)\n\nfor filename, obj in [('A', A), ('B', B), ('Z', Z), ('indexes', indexes)]:\n utils.pkl_dump(os.path.join(folder, 'pkl'), filename, obj)\nfor i in range(len(indexes.ie)):\n if A[i,i] not in [1., -1.]:\n print(indexes.ie[i], A[i,i])\n 1/0\nlinking_results = utils.list_to_df(linking_results)\ncolumns = ['activityName', 'geography', 'reference product', 'name', \n 'original activityLink_activityName', 'original activityLink_geography', \n 'activityLink_activityName', 'activityLink_geography', 'proportion mapped']\ndfs = [(linking_results, 'linking result', columns)]\nallocated_datasets = utils.list_to_df(allocated_datasets)\ncolumns = ['activityName', 'geography', 'reference product', 'group', 'name', 'compartment', \n 'subcompartment', 'activityLink_activityName', 'activityLink_geography', \n 'unitName', 'amount']\ndfs.append((allocated_datasets, 'allocated datasets', columns))\nall_allocation_factors = pandas.concat(all_allocation_factors).reset_index()\ncolumns = ['activityName', 'geography', 'name', 'amount', 'price (EURO/unit)', \n 'revenu', 'factor']\ndfs.append((all_allocation_factors, 'allocation factors', columns))\ngrouped = all_allocation_factors.groupby(['activityName', 'geography'])\ndf = []\nfor name, group in grouped:\n to_add = dict(zip(['activityName', 'geography'], name))\n comment = ['This dataset was originally multi-functional. Economic allocation was applied.']\n for i, sel in group.iterrows():\n c = 'Product: %s - Amount: %s - Price: %s - Revenu: %s - Allocation factor: %s'\n c = c % tuple(sel)[2:]\n comment.append(c)\n to_add['comment'] = '\\n'.join(comment)\n df.append(to_add)\ndf = utils.list_to_df(df)\ncolumns = ['activityName', 'geography', 'comment']\ndfs.append((df, 'allocation comment', columns))\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\output'\nfilename = 'allocated_datasets.xlsx'\nutils.dataframe_to_excel(folder, filename, dfs)\nutils.dataframe_to_excel(folder, filename, dfs, add_timestamp = True)\nprint('')\nprint('')\nprint('end')", "sub_path": "projects/PEF/2_add_to_matrix.py", "file_name": "2_add_to_matrix.py", "file_ext": "py", "file_size_in_byte": 26202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.any", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "utils.accelerate_df", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 121, "usage_type": "attribute"}, {"api_name": "utils.pkl_load", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "folder_structure.create_folder_structure", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.db_basepath", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 173, "usage_type": "attribute"}, {"api_name": "utils.load_matrices", "line_number": 174, "usage_type": "call"}, {"api_name": "scipy.sparse.find", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "utils.db_basepath", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pyprind.prog_bar", "line_number": 225, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 232, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 243, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 247, "usage_type": "call"}, {"api_name": "utils.is_empty", "line_number": 256, "usage_type": "call"}, {"api_name": "utils.is_empty", "line_number": 260, "usage_type": "call"}, {"api_name": "utils.series_to_dataframe", "line_number": 263, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 327, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 335, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 344, "usage_type": "call"}, {"api_name": "utils.dataframe_to_series", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 369, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 381, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 396, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 402, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 414, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 415, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 426, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 431, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 432, "usage_type": "call"}, {"api_name": "matrix_builder.Z_from_A", "line_number": 434, "usage_type": "call"}, {"api_name": "utils.pkl_dump", "line_number": 437, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 437, "usage_type": "call"}, {"api_name": "os.path", "line_number": 437, "usage_type": "attribute"}, {"api_name": "utils.list_to_df", "line_number": 442, "usage_type": "call"}, {"api_name": "utils.list_to_df", "line_number": 447, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 452, "usage_type": "call"}, {"api_name": "utils.list_to_df", "line_number": 467, "usage_type": "call"}, {"api_name": "utils.dataframe_to_excel", "line_number": 472, "usage_type": "call"}, {"api_name": "utils.dataframe_to_excel", "line_number": 473, "usage_type": "call"}]}
+{"seq_id": "651384245", "text": "import cv2\nfrom pandas import read_csv\nfrom numpy import array, reshape, where, mean, argmax, argsort\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nfrom numpy import max as np_max\n\n\nclass Ui_MainWindow(object):\n def setupUi(self, MainWindow):\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.resize(760, 293)\n MainWindow.setFixedSize(MainWindow.width(), MainWindow.height())\n\n self.path = QtCore.QDir.rootPath()\n self.pos = 0\n self.max_len = 0\n # 1.041 it's a best split value by many times testing\n self.divide = 41\n\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n self.frame = QtWidgets.QLabel(self.centralwidget)\n self.frame.setGeometry(QtCore.QRect(10, 10, 320, 240))\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\n self.frame.setObjectName(\"frame\")\n\n self.verticalSlider = QtWidgets.QSlider(self.centralwidget)\n self.verticalSlider.setGeometry(QtCore.QRect(340, 10, 22, 240))\n self.verticalSlider.setOrientation(QtCore.Qt.Vertical)\n self.verticalSlider.setObjectName(\"verticalSlider\")\n self.verticalSlider.setMaximum(100)\n self.verticalSlider.setValue(self.divide)\n self.verticalSlider.setDisabled(True)\n self.verticalSlider.valueChanged.connect(self.divide_value_changed)\n \n self.horizontalSlider = QtWidgets.QSlider(self.centralwidget)\n self.horizontalSlider.setGeometry(QtCore.QRect(10, 260, 321, 22))\n self.horizontalSlider.setOrientation(QtCore.Qt.Horizontal)\n self.horizontalSlider.setObjectName(\"horizontalSlider\")\n self.horizontalSlider.setMaximum(100)\n self.horizontalSlider.valueChanged.connect(self.value_changed)\n self.horizontalSlider.sliderReleased.connect(self.slider_released)\n self.horizontalSlider.setDisabled(True)\n self.groupBox = QtWidgets.QGroupBox(self.centralwidget)\n self.groupBox.setGeometry(QtCore.QRect(370, 10, 381, 241))\n self.groupBox.setObjectName(\"groupBox\")\n self.fileModel = QtWidgets.QFileSystemModel()\n self.fileModel.setFilter(\n QtCore.QDir.NoDotAndDotDot | QtCore.QDir.Files)\n self.listView = QtWidgets.QListView(self.groupBox)\n self.listView.setGeometry(QtCore.QRect(10, 20, 361, 211))\n self.listView.setModel(self.fileModel)\n self.listView.setObjectName(\"listView\")\n self.listView.setRootIndex(self.fileModel.index(self.path))\n self.listView.clicked.connect(self.on_clicked)\n self.buttonBox = QtWidgets.QDialogButtonBox(self.centralwidget)\n self.buttonBox.setGeometry(QtCore.QRect(596, 260, 156, 23))\n self.buttonBox.setStandardButtons(\n QtWidgets.QDialogButtonBox.Close | QtWidgets.QDialogButtonBox.Open\n )\n self.buttonBox.setObjectName(\"buttonBox\")\n self.buttonBox.accepted.connect(self.select_path)\n self.buttonBox.rejected.connect(self.exit_win)\n self.label = QtWidgets.QLabel(self.centralwidget)\n self.label.setGeometry(QtCore.QRect(370, 262, 41, 16))\n self.label.setObjectName(\"label\")\n self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)\n self.lineEdit.setGeometry(QtCore.QRect(420, 260, 61, 20))\n self.lineEdit.setObjectName(\"lineEdit\")\n self.lineEdit.setText(\"0\")\n self.lineEdit.setDisabled(True)\n self.lineEdit.returnPressed.connect(self.choose_frame)\n MainWindow.setCentralWidget(self.centralwidget)\n\n self.retranslateUi(MainWindow)\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_translate(\n \"MainWindow\", \"Thermal Data View\"))\n self.groupBox.setTitle(_translate(\"MainWindow\", \"Folder\"))\n self.label.setText(_translate(\"MainWindow\", \"Frame\"))\n self.buttonBox.button(QtWidgets.QDialogButtonBox.Close).setText(\"Exit\")\n\n def select_path(self):\n self.path = QtWidgets.QFileDialog.getExistingDirectory()\n self.groupBox.setTitle(self.path)\n self.listView.setRootIndex(self.fileModel.setRootPath(self.path))\n\n def on_clicked(self, index):\n self.path = self.fileModel.fileInfo(index).absoluteFilePath()\n self.show_frame(self.path, 0)\n self.horizontalSlider.setValue(0)\n self.horizontalSlider.setMaximum(self.max_len)\n self.horizontalSlider.setDisabled(False)\n self.verticalSlider.setDisabled(False)\n self.lineEdit.setDisabled(False)\n\n def show_frame(self, file_path, frame_pos):\n data = read_csv(file_path, index_col=None).iloc[:, 2:]\n self.max_len = len(data)-1\n data = data[frame_pos:frame_pos+1]\n frame = self.data_to_frame(data)\n qimage = QtGui.QImage(frame, 320, 240, QtGui.QImage.Format_BGR888)\n self.frame.setPixmap(QtGui.QPixmap(qimage))\n\n def divide_value_changed(self):\n self.divide = self.verticalSlider.value()\n print(\"current divide:\", self.divide)\n pos = self.horizontalSlider.value()\n self.show_frame(self.path, pos)\n\n @staticmethod\n def in_it(x0, y0, x, y, w, h) -> bool:\n if (x0>x and x0<x+w) and (y0>y and y0<y+h):\n return True\n return False\n\n def data_to_frame(self, data):\n def center_point(x, y, w, h):\n x0 = x + w/2\n y0 = y + h/2\n xl = x0 - 8\n yl = y0 - 8\n return int(xl)*10, int(yl)*10, 16*10, 16*10\n\n # out_data = None\n data = array(data).reshape((24, 32))\n\n _mean = mean(data)\n data_mean = where(data > _mean*(1+0.001*self.divide), data, 0)\n\n out_data = None\n out_data = cv2.normalize(data_mean, out_data, 0, 255, cv2.NORM_MINMAX)\n frame = (out_data).astype('uint8')\n frame = cv2.blur(frame, (2,2))\n\n cnts, hierarchy = cv2.findContours(frame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n ### raw mode\n out_data = cv2.normalize(data, out_data, 0, 255, cv2.NORM_MINMAX)\n frame = (out_data).astype('uint8')\n frame = cv2.applyColorMap(frame, cv2.COLORMAP_JET) # COLORMAP_JET\n frame = cv2.resize(frame, (320, 240), interpolation=cv2.INTER_NEAREST)\n\n if cnts:\n cnt = max(cnts, key=cv2.contourArea)\n x, y, w, h = cv2.boundingRect(cnt)\n print(cv2.contourArea(cnt))\n if cv2.contourArea(cnt) >= 4:\n x, y, w, h = center_point(x, y, w, h)\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 3)\n\n return frame\n\n def value_changed(self):\n pos = self.horizontalSlider.value()\n self.lineEdit.setText(str(pos))\n self.show_frame(self.path, pos)\n\n def slider_released(self):\n pos = self.horizontalSlider.value()\n # self.lineEdit.setText(str(pos))\n # self.show_frame(self.path, pos)\n\n def choose_frame(self):\n pos = int(self.lineEdit.text())\n if pos > self.max_len:\n pos = self.max_len\n self.lineEdit.setText(str(pos))\n self.horizontalSlider.setValue(pos)\n self.show_frame(self.path, pos)\n\n def exit_win(self):\n QtCore.QCoreApplication.quit()\n\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n MainWindow = QtWidgets.QMainWindow()\n ui = Ui_MainWindow()\n ui.setupUi(MainWindow)\n MainWindow.show()\n sys.exit(app.exec_())\n", "sub_path": "ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 7627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "PyQt5.QtCore.QDir.rootPath", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDir", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 21, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 47, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileSystemModel", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDir", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QListView", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 61, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 66, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 67, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 69, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 70, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 78, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 78, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 89, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 89, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 107, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 107, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 108, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 137, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.normalize", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 143, "usage_type": "attribute"}, {"api_name": "cv2.applyColorMap", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 146, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 149, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 151, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 152, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 154, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QCoreApplication.quit", "line_number": 177, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 177, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 182, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 182, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 182, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 183, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 183, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 187, "usage_type": "call"}]}
+{"seq_id": "307735608", "text": "import os\nimport sys\nimport subprocess\nimport argparse\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport csv\n\n\ndef remove_sysinfo(csv_file):\n new_csv_file = '{}_wo_sysinfo.csv'.format(os.path.splitext(csv_file)[0])\n\n with open(csv_file, 'r') as original:\n lines = original.read().splitlines(True)\n with open(new_csv_file, 'w') as new:\n counter = 0\n for line in lines:\n if line[0:4] == \"name\":\n new.writelines(lines[counter:])\n break\n counter += 1\n return new_csv_file\n\n\ndef compute_time_charge_corr(csv_file_path, stats_file_path):\n df = pd.read_csv(csv_file_path)\n\n correlation_coef = df['cpu_time'].corr(df['charge'])\n time_charge_factors = df['charge'].divide(df['cpu_time'])\n time_charge_factor_avg = time_charge_factors.mean()\n\n pd.options.display.max_rows = sys.maxsize\n\n with open(stats_file_path, 'w') as out:\n print(correlation_coef)\n out.write(str(correlation_coef)+os.linesep)\n print(time_charge_factor_avg)\n out.write(str(time_charge_factor_avg)+os.linesep)\n print(time_charge_factors)\n out.writelines(str(time_charge_factors))\n\n\ndef plot_time_charge_corr(csv_file_path, output_img_path):\n df = pd.read_csv(csv_file_path)\n\n figure = plt.figure(figsize=(12, 12), dpi=100)\n axe = figure.add_subplot(111)\n\n plt.title('Average correlation factor -- 1 CHARGE_UNIT = {} unit of time'.format(\n df['charge'].divide(df['cpu_time']).mean()))\n\n axe.plot(df['cpu_time'])\n axe.set_ylabel('cpu_time')\n axe.set_yscale(\"log\")\n axe.set_xticklabels(df['name'], rotation='vertical')\n\n axe2 = axe.twinx()\n axe2.plot(df['charge'], '-r')\n axe2.set_ylabel('charge_amount', color='r')\n axe2.set_yscale(\"log\")\n\n plt.tight_layout()\n plt.savefig(output_img_path)\n\n\ndef run_benchmark(bench_binary, bench_name, output_dir):\n\n csvfile = os.path.join(output_dir, 'bm_{}.csv'.format(bench_name))\n pngfile = os.path.join(output_dir, 'bm_{}.png'.format(bench_name))\n statsfile = os.path.join(output_dir, 'bm_{}.stats'.format(bench_name))\n\n cmd = [\n bench_binary,\n '--benchmark_counters_tabular=true',\n '--benchmark_out_format=csv',\n '--benchmark_out={}'.format(csvfile),\n '--benchmark_filter={}'.format(bench_name)\n ]\n\n # run benchmark\n process = subprocess.Popen(cmd)\n process_status = process.wait()\n\n # process csv file\n csvfile = remove_sysinfo(csvfile)\n plot_time_charge_corr(csvfile, pngfile)\n compute_time_charge_corr(csvfile, statsfile)\n\n\ndef verify_file(filename):\n if not os.path.isfile(filename):\n print(\"Couldn't find expected file: {}\".format(filename))\n sys.exit(1)\n\n\ndef verify_dir(dirname):\n if not os.path.isdir(dirname):\n print(\"Couldn't find expected directory: {}\".format(dirname))\n sys.exit(1)\n\n\ndef parse_command_line():\n parser = argparse.ArgumentParser(\n description='Run Google Benchmarks of ML Etch operation charges and execution time, report stats')\n\n parser.add_argument('build_dir', type=str,\n help='Location of ledger build directory')\n parser.add_argument('output_dir', type=str,\n help='Output directory for VM execution Charge/Time statistics')\n\n return parser.parse_args()\n\n\ndef main():\n args = parse_command_line()\n\n # cli args\n build_dir = os.path.abspath(args.build_dir)\n output_dir = os.path.abspath(args.output_dir)\n\n # benchmark config\n bench_binary = os.path.join(\n build_dir, './libs/vm-modules/benchmark/benchmark_vm_modules_model')\n\n verify_file(bench_binary)\n verify_dir(output_dir)\n\n benchmark_functions = [\n 'BM_AddLayer',\n 'BM_Predict',\n 'BM_Fit',\n 'BM_SerializeToString',\n 'BM_DeserializeFromString',\n 'BM_Compile',\n '' # all\n ]\n\n for benchname in benchmark_functions:\n run_benchmark(bench_binary, benchname, output_dir)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/ml_etch_bench/run_ml_etch_time_charge_bench.py", "file_name": "run_ml_etch_time_charge_bench.py", "file_ext": "py", "file_size_in_byte": 4058, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.splitext", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.options", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.linesep", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.linesep", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 99, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}]}
+{"seq_id": "340556946", "text": "import json\n\nclass TelegramMessage:\n def __init__(self):\n self.chatId = ''\n self.firstName = ''\n self.messageText = ''\n self.inlineKeyboard = False\n self.isCallback = False\n self.callbackPayload = False\n self.reminderPayload = False\n \n def setFromWebhook(self,data):\n if 'message' in data:\n self.chatId = data['message']['from']['id']\n self.firstName = data['message']['from']['first_name']\n self.messageText = data['message']['text']\n elif 'callback_query' in data:\n self.isCallback = True\n self.callbackQueryId = data['callback_query']['id']\n self.chatId = data['callback_query']['from']['id']\n self.firstName = data['callback_query']['from']['first_name']\n self.messageText = data['callback_query']['message']['text']\n self.messageId = data['callback_query']['message']['message_id']\n try:\n self.callbackPayload = json.loads(data['callback_query']['data'])\n except TypeError:\n print('Could not serialize callback data, using string instead')\n self.callbackPayload = data['callback_query']['data']\n else:\n print(data)\n raise Exception(\"Webhook payload did not contain message or callback object!\")\n\n def getBody(self):\n data = {\"text\": self.messageText.encode(\"utf8\"), \"chat_id\": self.chatId}\n if self.inlineKeyboard:\n data['reply_markup'] = json.dumps({'inline_keyboard':self.inlineKeyboard})\n return data\n\n def addInlineKeyboard(self,buttons):\n self.inlineKeyboard = [buttons]\n return self.inlineKeyboard\n\n def parseAddReminder(self):\n text = self.messageText\n commands = text.split('--')\n reminder = {}\n hasText = False\n hasStart = False\n if commands[0][:4] == '/add':\n for command in commands:\n if command[:5] == 'start':\n reminder['start'] = command[5:].strip()\n hasStart = True\n elif command[:4] == 'text':\n reminder['text'] = command[4:].strip()\n hasText = True\n else:\n return False\n\n if hasStart and hasText:\n self.reminderPayload = reminder\n else:\n reminder['error'] = \"Not all commands were given\"\n return reminder\n\n\n\n\n\n", "sub_path": "domain/message.py", "file_name": "message.py", "file_ext": "py", "file_size_in_byte": 2474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "json.loads", "line_number": 26, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}]}
+{"seq_id": "125557218", "text": "import rsvp\nimport mongomock\nimport unittest\nimport json\n\nclass BaseTest:\n def setup_method(self):\n print (\"Running testcases\")\n rsvp.client = mongomock.MongoClient()\n rsvp.db = rsvp.client.mock_db_function\n self.client = rsvp.app.test_client()\n \n\n def test_dict(self):\n print (\"Checking variables\")\n doc = rsvp.RSVP(\"test name\", \"test@example.com\", \"1\")\n with rsvp.app.test_request_context():\n assert doc.dict() == {\n \"_id\": \"1\",\n \"name\": \"test name\",\n \"email\": \"test@example.com\",\n \"links\": {\n \"self\": \"http://localhost/api/rsvps/1\"\n }\n }\n\ndef main():\n obj=BaseTest()\n obj.setup_method()\n obj.test_dict()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "testrsvp.py", "file_name": "testrsvp.py", "file_ext": "py", "file_size_in_byte": 825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rsvp.client", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mongomock.MongoClient", "line_number": 9, "usage_type": "call"}, {"api_name": "rsvp.db", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rsvp.client", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rsvp.app.test_client", "line_number": 11, "usage_type": "call"}, {"api_name": "rsvp.app", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rsvp.RSVP", "line_number": 16, "usage_type": "call"}, {"api_name": "rsvp.app.test_request_context", "line_number": 17, "usage_type": "call"}, {"api_name": "rsvp.app", "line_number": 17, "usage_type": "attribute"}]}
+{"seq_id": "538381973", "text": "#compare old IP to current IP\r\n\r\nfrom requests import get\r\nimport io\r\nimport os\r\nimport errno\r\n#\r\n#check if file exists, if not create file named ip.txt\r\n#\r\nif not os.path.exists('ip.txt'):\r\n open('ip.txt', 'w').close()\r\n#\r\n# check currently active IP address\r\ncurrent_ip =get('https://api.ipify.org').text\r\n#\r\ncurrent_ip =str(current_ip)\r\ncurrent_ip =current_ip.strip()\r\n#\r\nprint ('current IP is '+(current_ip))\r\n#\r\nwith open('ip.txt') as pip:\r\n previous_ip = pip.read()\r\n#\r\nprevious_ip =str(previous_ip)\r\nprevious_ip =previous_ip.strip()\r\nprint ('previous ip is '+ previous_ip)\r\n\r\nif current_ip == previous_ip:\r\n print(\"IP's match\")\r\nelse:\r\n print(\"IP's don't match\")\r\n print ('Now updating IP file')\r\n with open('ip.txt', 'w') as ip_file:\r\n ip_file.write(current_ip)\r\n ip_file.close\r\n#\r\n# end\r\n", "sub_path": "check_ip.py", "file_name": "check_ip.py", "file_ext": "py", "file_size_in_byte": 833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.exists", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 14, "usage_type": "call"}]}
+{"seq_id": "319045614", "text": "from django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import render, redirect\nfrom django.utils.timezone import now\nfrom event.models import Event, Registration\nfrom .forms import EventRegistrationForm, EventRegistrationDeleteForm\n\n\n# Create your views here.\ndef skype(request):\n context = {}\n is_user_registered = False\n form = None\n del_form = None\n events = Event.objects.filter(start_time__gt=now(), event_type='skype_session')\n if events.exists():\n # take the first event ordered by start date. It's the first upcoming event\n event = events.first()\n context['event'] = event\n user = request.user\n # if event does not exist, there should be no registration\n # event registration must be available to be able to register\n if request.method == 'POST' and event.is_registration_open:\n form = EventRegistrationForm(request.POST)\n if form.is_valid():\n reg_success = form.save_and_mail(user=user, event=event)\n if reg_success:\n messages.success(request,\n \"\"\"Congratulations! You are registered for the event.\n A confirmation email will be sent to your email address momentarily\"\"\",\n extra_tags='alert-success')\n else:\n messages.error(request, \"Something went wrong. Please try again later\", extra_tags='alert-danger')\n else:\n form = EventRegistrationForm()\n\n # Generate the delete form if user is registered\n if not user.is_anonymous():\n try:\n reg = Registration.objects.get(event=event, attendee=user)\n except ObjectDoesNotExist:\n is_user_registered = False\n else:\n is_user_registered = True\n initialdict = {'reg_id': reg.id}\n del_form = EventRegistrationDeleteForm(initialdict)\n\n context['is_user_registered'] = is_user_registered\n context['form'] = form\n context['del_form'] = del_form\n\n return render(request, 'skype_consultancy/skype_consultancy.html', context)\n\n\n@login_required\ndef delete_skype_registration(request):\n if request.method == 'POST':\n form = EventRegistrationDeleteForm(request.POST)\n user = request.user\n if form.is_valid():\n del_success = form.del_registraion(user)\n if del_success:\n messages.success(request,\n 'You registration is withdrawn from this event',\n extra_tags='alert-warning')\n\n return redirect('/skype/')\n", "sub_path": "skype_consultancy/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2816, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "event.models.Event.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "event.models.Event.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "event.models.Event", "line_number": 16, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 16, "usage_type": "call"}, {"api_name": "event.models", "line_number": 19, "usage_type": "name"}, {"api_name": "event.models", "line_number": 20, "usage_type": "name"}, {"api_name": "event.models.is_registration_open", "line_number": 24, "usage_type": "attribute"}, {"api_name": "event.models", "line_number": 24, "usage_type": "name"}, {"api_name": "forms.EventRegistrationForm", "line_number": 25, "usage_type": "call"}, {"api_name": "event.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 34, "usage_type": "name"}, {"api_name": "forms.EventRegistrationForm", "line_number": 36, "usage_type": "call"}, {"api_name": "event.models.Registration.objects.get", "line_number": 41, "usage_type": "call"}, {"api_name": "event.models.Registration.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "event.models.Registration", "line_number": 41, "usage_type": "name"}, {"api_name": "event.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 42, "usage_type": "name"}, {"api_name": "forms.EventRegistrationDeleteForm", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "forms.EventRegistrationDeleteForm", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 56, "usage_type": "name"}]}
+{"seq_id": "264900064", "text": "from django.urls import path, include\nfrom rest_framework import routers\n\nfrom .views import ItemViewSet, BannerViewSet,ItemFilterListView\n\nrouter = routers.DefaultRouter()\nrouter.register('item', ItemViewSet)\nrouter.register('banner', BannerViewSet)\n\nurlpatterns = [\n path('', include(router.urls)),\n path('item_api',ItemFilterListView.as_view(),name='item_api'),\n]", "sub_path": "home/api_urls.py", "file_name": "api_urls.py", "file_ext": "py", "file_size_in_byte": 372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 6, "usage_type": "name"}, {"api_name": "views.ItemViewSet", "line_number": 7, "usage_type": "argument"}, {"api_name": "views.BannerViewSet", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ItemFilterListView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ItemFilterListView", "line_number": 12, "usage_type": "name"}]}
+{"seq_id": "364744219", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nfrom uuid import uuid4\n\nfrom flask import Flask, jsonify, request\n\nfrom BlockChain import BlockChain\n\n\"\"\"\n区块链节点\n- 区块链浏览器数据展示\n- 挖矿产生新的区块\n- 发送交易\n- 同步节点信息\n- 多个链共识\n\"\"\"\n\nmyBlockChain = BlockChain() # 创建一个网络节点\nnode_id = str(uuid4()).replace(\"-\", \"\") # 生成节点秘钥，即钱包地址\nprint(\"当前节点钱包地址:\", node_id)\n\napp = Flask(__name__) # 初始化flask框架\n\n\n@app.route(\"/\")\ndef index_page():\n return \"Welcome to myBlockChain...\"\n\n\n@app.route(\"/chain\")\ndef index_chain():\n \"\"\"\n 区块链浏览器，查看所有块\n :return:\n \"\"\"\n response = {\n \"chain\": myBlockChain.chain, # 区块链\n \"length\": len(myBlockChain.chain) # 区块链长度\n }\n return jsonify(response), 200\n\n\n@app.route(\"/new_transactions\", methods=[\"POST\"]) # 创建一个新的交易\ndef index_new_transactions():\n values = request.get_json() # 抓取网络传输的信息\n required = [\"sender\", \"recipient\", \"amount\"]\n\n # 判断提交的json数据key值是否合法\n if not all(key in values for key in required):\n return u\"数据不完整或格式错误\", 400\n\n index = myBlockChain.new_transaction(values[\"sender\"],\n values[\"recipient\"],\n float(values[\"amount\"])) # 新增交易\n\n response = {\n \"message\": u\"交易加入到区块\" + str(index)\n }\n\n return jsonify(response), 200\n\n\n@app.route(\"/mine\")\ndef index_mine():\n \"\"\"\n 挖矿\n :return:\n \"\"\"\n last_block = myBlockChain.last_block\n proof = myBlockChain.proof_of_work(last_block)\n\n # 系统奖励比特币\n myBlockChain.new_transaction(\n sender=\"0\", # 0代表系统奖励,即coinBaseTransaction\n recipient=node_id,\n amount=12.5\n )\n\n block = myBlockChain.new_block(proof, myBlockChain.hash(last_block)) # 新增区块\n\n response = {\n \"message\": \"new block created...\",\n \"index\": block[\"index\"],\n \"transactions\": block[\"transactions\"],\n \"proof\": block[\"proof\"],\n \"hash\": myBlockChain.hash(block),\n \"prev_hash\": block[\"prev_hash\"]\n }\n return jsonify(response), 200\n\n\n@app.route(\"/new_nodes\", methods=[\"POST\"]) # 新增节点\ndef index_new_node():\n values = request.get_json()\n nodes = values.get(\"nodes\") # 获取所有节点\n\n if nodes is None:\n return u\"新增的节点为空\"\n\n for node in nodes:\n myBlockChain.register_node(node)\n\n response = {\n \"message\": u\"网络节点加入到区块\",\n \"nodes\": list(myBlockChain.nodes)\n }\n\n return jsonify(response), 200\n\n\n@app.route(\"/node_refresh\") # 刷新节点\ndef index_node_refresh():\n replaced = myBlockChain.resolve_conflicts() # 一致性算法进行最长链选择\n\n print(replaced)\n\n if replaced:\n response = {\n \"message\": u\"区块链被替换为最长有效链\",\n \"new chain\": myBlockChain.chain\n }\n else:\n response = {\n \"message\": u\"当前区块链为最长无需替换\",\n \"chain\": myBlockChain.chain\n }\n return jsonify(response), 200\n\n\nif __name__ == '__main__':\n port = sys.argv[1] if len(sys.argv) > 1 else 5005\n app.run(\"127.0.0.1\", port)\n", "sub_path": "BlockChainNode.py", "file_name": "BlockChainNode.py", "file_ext": "py", "file_size_in_byte": 3399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "BlockChain.BlockChain", "line_number": 19, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 132, "usage_type": "attribute"}]}
+{"seq_id": "8134022", "text": "##Controle de Qualidade=group\n##Verificar quantidade de inconsistencias=name\n##Input=vector\n##Fields=Field Input\n##Frequency=output table\n\nfrom PyQt4 import QtCore\nfrom qgis import core as QgsCore\nfrom qgis.utils import iface\nfrom processing.tools.vector import TableWriter\nfrom collections import defaultdict\nfrom processing.core.GeoAlgorithmExecutionException import GeoAlgorithmExecutionException\n\nlayer = processing.getObject(Input)\ninputFields = layer.pendingFields()\nfieldIdxs = []\nfields = Fields.split(',')\nfor f in fields:\n idx = inputFields.indexFromName(f)\n if idx == -1:\n raise GeoAlgorithmExecutionException('Field not found:' + f)\n fieldIdxs.append(idx)\nwriter = TableWriter(Frequency, None, fields + ['FREQ'])\n\ncounts = {}\nfeats = processing.features(layer)\nnFeats = len(feats)\ncounts = defaultdict(int)\nfor i, feat in enumerate(feats):\n progress.setPercentage(int(100 * i / nFeats))\n attrs = feat.attributes()\n clazz = tuple([attrs[i] for i in fieldIdxs])\n counts[clazz] += 1\n\nfor c in counts:\n writer.addRecord(list(c) + [counts[c]])\n\n\nimport matplotlib.pyplot as plt; plt.rcdefaults()\nimport numpy as np\nimport matplotlib.pyplot as plt\n\t\nlocal = Frequency\n#local='D:/Users/alx/.qgis2/processing/scripts/Frequency.csv'\n\nimport csv\n\nwith open(local) as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n Fields = []\n FREQ = []\n linha = 1\n next(readCSV)\n for row in readCSV:\n temp_1 = row[0][0:30]\n temp_2 = float(row[1])\n Fields.append(temp_1)\n FREQ.append(temp_2) \n\nprint(Fields)\nprint(FREQ)\nobjects = Fields\ny_pos = np.arange(len(objects))\nperformance = FREQ\t\n \nplt.bar(y_pos, performance, align='center')\nplt.xticks(y_pos, objects)\nplt.ylabel('Frequencia')\nplt.title('Grafico de frequencia')\n \nplt.show()\t\n", "sub_path": "Frequency_analysis.py", "file_name": "Frequency_analysis.py", "file_ext": "py", "file_size_in_byte": 1809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "processing.tools.vector.getObject", "line_number": 14, "usage_type": "call"}, {"api_name": "processing.tools.vector", "line_number": 14, "usage_type": "name"}, {"api_name": "processing.core.GeoAlgorithmExecutionException.GeoAlgorithmExecutionException", "line_number": 21, "usage_type": "call"}, {"api_name": "processing.tools.vector.TableWriter", "line_number": 23, "usage_type": "call"}, {"api_name": "processing.tools.vector.features", "line_number": 26, "usage_type": "call"}, {"api_name": "processing.tools.vector", "line_number": 26, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcdefaults", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]}
+{"seq_id": "612710190", "text": "# vim: ts=2:sw=2:tw=80:nowrap\n\nimport copy, time, re\nfrom mmap import mmap, PROT_WRITE, MAP_SHARED\nfrom logging import error, warn, debug, log, DEBUG, INFO, root as rootlog\nimport numpy as np\nimport ctypes\nfrom ctypes import memset, sizeof, byref\nimport Pyro4\n\nfrom physical import unit\n\nimport comedi\n\nfrom .....tools.signal_graphs import nearest_terminal\nfrom .....tools import cached\nfrom ....device import Device as Base\nfrom .. import channels\nfrom . import capabilities\n\n\ncommand_test_errors = {\n 1: 'unsupported trigger in ..._src setting of comedi command, setting zeroed',\n 2: '..._src setting not supported by driver',\n 3: 'TRIG argument of comedi command outside accepted range',\n 4: 'adjusted TRIG argument in comedi command',\n 5: 'chanlist not supported by board',\n}\n\n\n\ndef raiserr(retval, msg='', exception=OSError):\n if retval < 0:\n err = comedi.errno()\n raise exception('comedi, {},{}/{}: {}'.format(msg,retval,err,comedi.strerror(err)))\n\nclass Subdevice(Base):\n \"\"\"\n In the context of Arbwave, a comedi subdevice is actually a represetation of\n what Arbwave considers to be a self-contained device.\n \"\"\"\n\n subdev_type = None # changed by inheriting device types\n units = comedi.UNIT_volt\n default_range_min = 0\n default_range_max = 1\n\n def __init__(self, card, subdevice, name_uses_subdev=False):\n \"\"\"\n parameter: name_uses_subdev\n If there are more than one device on this card that performs the same\n function, such as two analog output devices (which get separately clocked,\n triggered, ...), then we want the name of those devices to also use the\n number of the subdevice. But, when there is only one device of a certain\n type on the card, we simply use the subdev_type as the main part of the\n name. For instance, if it is an analog output device and there is only\n one on the \"0\" card, the name of the device would be \"comedi/0/ao\"\n whereas, if there were two analog output devices on the card, the names\n would be \"comedi/0/ao1\" and \"comedi/0/ao2\".\n \"\"\"\n if name_uses_subdev: devname = '{}{}'.format(self.subdev_type, subdevice)\n else: devname = self.subdev_type\n\n super(Subdevice,self).__init__(name='{}/{}'.format(card, devname))\n self.card = card\n self.subdevice = subdevice\n debug( 'loading comedi subdevice %s', self )\n self.channels = dict()\n self.clocks = dict()\n self.clock_terminal = None\n self.use_case = None\n self.t_max = 0.0 * unit.s\n self.cmd = comedi.cmd()\n self.cmd_chanlist = None\n\n # lets get the src mask to see if we are always required to do internal\n # trigger. We are assuming that if a command cannot be started with\n # TRIG_NOW, it must require some sort of two part setup regardless of\n # whether it uses TRIG_INT or TRIG_EXT. The NI cards are this way\n # explicitly so that DMA transfers get primed--comedi.internal_trigger must\n # be used whether we use TRIG_INT or TRIG_EXT. For the case of TRIG_EXT, it\n # will just wait for the actual trigger.\n comedi.get_cmd_src_mask(card, subdevice, self.cmd)\n self.trig_now_supported = bool( self.cmd.start_src & comedi.TRIG_NOW )\n memset( byref(self.cmd), 0, sizeof(self.cmd) )\n\n sd_flags = self.status()\n self.sampl_t = comedi.sampl_t if sd_flags.sample_16bit else comedi.lsampl_t\n\n assert not sd_flags.flags_per_channel, 'flags per channel!'\n\n size = comedi.get_buffer_size( self.card, self.subdevice )\n # The c version; we can cast directly\n #data = mmap(NULL, size, PROT_WRITE, MAP_SHARED, comedi.fileno(dev), 0)\n\n # Set the current write device so that the mmap will be correct\n comedi.set_write_subdevice( self.card, self.subdevice )\n\n # the python version; we must cast using ctypes/numpy\n self.mapped = mmap( comedi.fileno(self.card), size,\n prot=PROT_WRITE, flags=MAP_SHARED, offset=0 )\n if not self.mapped:\n raise OSError( 'mmap: error!' ) # probably will already be raised\n\n memory = np.ndarray( shape=(size,), dtype=ctypes.c_ubyte,\n buffer=self.mapped, order='C' )\n memory[:] = 0 # zero the buffer to begin\n\n # create and easy lookup from destination to list of sources\n dst_to_src = dict()\n for src,dst in self.card.available_routes:\n D = dst_to_src.setdefault(dst, list())\n D.append(src)\n\n #first find the possible trigger and clock sources\n clk = self.name + '/SampleClock'\n trg = self.name + '/StartTrigger'\n\n self.clock_sources = dst_to_src.get(clk, list())\n self.trig_sources = dst_to_src.get(trg, list())\n self.clock_sources.sort()\n self.trig_sources.sort()\n\n if not self.clock_sources:\n error(\"No clocks found for clock-able device '%s' (%s)\",\n self, self.card.board)\n else:\n self._config_template['clock']['range'] = self.clock_sources\n\n if not self.trig_sources:\n # internal trigger only\n self._config_template = self._config_template.copy()\n self._config_template.pop('trigger')\n else:\n self._config_template['trigger']['source']['range'] = self.trig_sources\n\n self.config = self.get_config_template()\n\n\n def clear(self):\n debug( 'comedi: cancelling commands for comedi subdevice %s', self )\n raiserr( comedi.cancel( self.card, self.subdevice ), 'cancel' )\n self.t_max = 0.0 * unit.s\n memset( byref(self.cmd), 0, sizeof(self.cmd) )\n del self.cmd_chanlist\n self.cmd_chanlist = None\n\n\n def get_config(self, L):\n \"\"\"\n Simple accessor for configuration items. This is primarily used so that\n subclasses can have different names, or even static values, for similar\n config concepts.\n \"\"\"\n if type(L) not in [list, tuple]:\n return L\n\n C = self.config\n for li in L:\n C = C[li]\n return C\n\n @cached.property\n def has_onboardclock(self):\n return self.onboardclock_name in self.clock_sources\n\n @cached.property\n def onboardclock_name(self):\n return self.card.signal_names['onboardclock'].format(dev=self.name)\n\n @property\n def flags(self):\n return comedi.get_subdevice_flags(self.card, self.subdevice)\n\n @property\n def busy(self):\n return bool( self.flags & comedi.SDF_BUSY )\n\n @property\n def running(self):\n return bool( self.flags & comedi.SDF_RUNNING )\n\n @property\n def buf_size(self):\n return comedi.get_buffer_size(self.card, self.subdevice)\n\n #@property\n def status(self):\n return comedi.extensions.subdev_flags.to_dict( self.flags )\n\n @property\n def available_channels(self):\n klass = channels.klasses[self.subdev_type]\n\n return [\n klass('{}{}'.format(self, i), self)\n for i in range(comedi.get_n_channels( self.card, self.subdevice ))\n ]\n\n\n def config_all_channels(self):\n \"\"\"\n Configure all channels and return them.\n\n Actually just calls self._config_all_channels(self.channels)\n \"\"\"\n return self._config_all_channels( dict(self.channels, **self.clocks) )\n\n\n def _config_all_channels(self, channels):\n \"\"\"\n Configure all given channels and return them.\n \"\"\"\n dflt_mn = self.get_config( self.default_range_min )\n dflt_mx = self.get_config( self.default_range_max )\n\n self.ranges = dict()\n self.maxdata = dict()\n C, S = self.card, self.subdevice\n for chname, chinfo in channels.items():\n mx = chinfo.get('max', dflt_mx)\n mn = chinfo.get('min', dflt_mn)\n ch = self.get_channel(chname)\n\n self.ranges[chname]=r=comedi.find_range(C, S, ch, self.units, mn, mx)\n self.maxdata[chname]= comedi.get_maxdata(C, S, ch)\n\n assert r >= 0, 'comedi: Could not identify output range for '+chname\n\n # if self.ranges[chname] >= 0:\n # continue\n\n # simple find did not work, try harder (?)\n #ch_ranges = [\n # ( i, comedi.get_range(C, S, ch, i) )\n # for i in range( comedi.get_n_ranges( C, S, ch ) )\n #]\n\n #ch_ranges.sort(key = lambda ri : ri[1].contents.max - ri[1].contents.min)\n return channels\n\n\n\n def cmd_is_continuous(self):\n \"\"\"\n Tests the comedi.cmd to see if it was configured for continuous mode.\n \"\"\"\n return self.cmd.stop_src == comedi.TRIG_NONE\n\n\n def set_config(self, config=None, channels=None, signal_graph=None):\n debug('comedi[%s].set_config(config=%s, channels=%s, signal_graph=%s)',\n self, config, channels, signal_graph)\n\n if channels is not None and self.channels != channels:\n self.channels = channels\n if config is not None and self.config != config:\n self.config = config\n\n if not self.config['clock']['value']:\n raise UserWarning('comedi.Device({}): please assign clock'.format(self))\n #self.clock_terminal = None\n else:\n if signal_graph:\n if self.has_onboardclock and \\\n self.config['clock']['value'] == self.onboardclock_name:\n # don't have to lookup anymore, since we know it is already the\n # onboard clock\n self.clock_terminal = 'internal'\n else:\n self.clock_terminal = \\\n nearest_terminal(self.config['clock']['value'],\n set(self.clock_sources),\n signal_graph)\n\n if self.clock_terminal == 'internal':\n frequency = self.clocks[ self.onboardclock_name ]['rate']['value']\n clock_args = (comedi.TRIG_TIMER, int(1e9 / frequency))\n else:\n channel = self.card.name_table[self.clock_terminal]\n invert = 0\n if config['clock-edge']['value'] == 'falling':\n invert = comedi.CR_INVERT\n clock_args = (comedi.TRIG_EXT,\n comedi.CR_PACK_FLAGS(channel, 0, 0, comedi.CR_EDGE | invert))\n\n trigger_args = ( comedi.TRIG_INT, 0 )\n if 'trigger' in self.config and self.config['trigger']['enable']['value']:\n channel = self.card.name_table[self.clock_terminal]\n invert = 0\n if self.config['trigger']['edge']['value'] == 'falling':\n invert = comedi.CR_INVERT\n trigger_args = (comedi.TRIG_EXT,\n comedi.CR_PACK_FLAGS(channel, 0, 0, comedi.CR_EDGE | invert))\n\n\n\n # NOW WE ARE READY TO ACTUAL CONFIGURE...\n # start with a clean slate:\n self.clear()\n\n debug('comedi: creating command: %s', self.name)\n\n #### Now set self.cmd ####\n channels = list(self.config_all_channels().items())\n channels.sort( key = lambda i : i[1]['order'] )\n self.cmd_chanlist = create_chanlist(self.cr_pack, channels)\n\n self.cmd.subdev = self.subdevice\n self.cmd.flags = comedi.TRIG_WRITE #bitwise or'd subdevice flags\n self.cmd.chanlist = self.cmd_chanlist\n self.cmd.chanlist_len = len( self.cmd_chanlist )\n self.cmd.start_src = trigger_args[0]\n self.cmd.start_arg = trigger_args[1]\n self.cmd.scan_begin_src = clock_args[0]\n self.cmd.scan_begin_arg = clock_args[1]\n self.cmd.convert_src = comedi.TRIG_NOW # accpets: TRIG_TIMER, TRIG_EXT, TRIG_NOW\n self.cmd.convert_arg = 0\n self.cmd.scan_end_src = comedi.TRIG_COUNT\n self.cmd.scan_end_arg = len( self.cmd_chanlist ) # iterate through all channels\n self.cmd.stop_src = comedi.TRIG_COUNT # accepts: TRIG_COUNT, TRIG_NONE\n self.cmd.stop_arg = 0 # we'll set src/arg at the time of set_waveforms\n #### finished init of self.cmd ####\n\n #### testing cmd ####\n err = comedi.command_test(self.card, self.cmd)\n if err in [1, 2, 3, 5]:\n raise RuntimeError('comedi[{}]: ' + command_test_errors[err])\n if err == 4:\n warn('comedi[%s]: %s', self, command_test_errors[err])\n\n\n def set_clocks(self, clocks):\n \"\"\"\n If this is an analog device, this must be the onboard clock only.\n If this is a digital device, either an Onboard timer for the digital device\n (if supported) or aperiodic clock implemented by a digital line of a digital\n device. If this is a timing device, this must be one of the counters.\n \"\"\"\n if self.clocks != clocks:\n self.clocks = clocks\n\n\n def get_channel(self, name):\n return int( re.search(self.subdev_type + '([0-9]*)$', name).group(1) )\n\n\n def convert_data(self, chname, data):\n \"\"\"\n Takes data in physical units and converts it to (l)sampl_t data that comedi\n needs.\n\n This allows for arrays of data to be converted.\n \"\"\"\n #return comedi.from_phys(data,self.ranges[chname],self.maxdata[chname])\n # implement comedi.from_phys here so that we can use arrays\n rng = comedi.get_range(\n self.card, self.subdevice,\n self.get_channel(chname), self.ranges[chname] ).contents\n maxdata = self.maxdata[chname]\n return np.clip( (data - rng.min) *\n (maxdata / ( rng.max - rng.min )),\n 0, maxdata ).astype( self.sampl_t )\n\n\n def cr_pack(self, chname, chinfo):\n \"\"\"\n Packs data properly whether this is digital or analog\n \"\"\"\n return comedi.CR_PACK(\n self.get_channel(chname),\n self.ranges[chname],\n self.get_config(self.reference_value),\n )\n\n\n def set_output(self, data):\n \"\"\"\n Sets a static value on each output channel of this task.\n \"\"\"\n return self._set_output(data, self.channels)\n\n def _set_output(self, data, channels):\n \"\"\"\n Sets a static value on each output channel of this task.\n\n This version allows the caller to add items to the channel list.\n \"\"\"\n data = list(data.items())\n data.sort( key = lambda i : channels[i[0]]['order'] )\n\n insn_list = comedi.insnlist()\n insn_list.set_length( len(data) )\n # we allocate the data to ensure it does not get garbage collected too soon\n L = [ comedi.lsampl_t() for i in range( len(data) ) ]\n\n for i, (chname, value), di in zip( insn_list, data, L ):\n di.value = self.convert_data( chname, value )\n\n i.insn = comedi.INSN_WRITE\n i.subdev = self.subdevice\n i.chanspec = self.cr_pack(chname, channels[chname])\n i.n = 1\n i.data = ctypes.pointer( di )\n n = comedi.do_insnlist( self.card, insn_list )\n raiserr( n - len(data), 'insnlist not complete' )\n\n\n def get_min_period(self):\n #important function for Arbwave to use clocks\n #below is effective for timing subdevices\n #getting a period of a non-subdevice signal will need a dictionary with their period\n\n if self.subdev_type == 'to':\n chan = 0 #I think this is what we want\n clock = ctypes.c_uint()\n period = ctypes.c_uint()\n ret = comedi.get_clock_source(self.card, self.subdevice, chan, clock,\n period)\n raiserr(ret, 'get_clock_source')\n print(self.subdevice, \"timing device\")\n return int(period.value)*unit.ns\n else:\n # we use the new comedi facility to query async subdevice speeds\n scan_begin_min, convert_min = ctypes.c_uint(), ctypes.c_uint()\n retval = comedi.get_cmd_timing_constraints(self.card, self.subdevice,\n self.cmd.scan_begin_src, byref(scan_begin_min),\n self.cmd.convert_src, byref(convert_min),\n self.cmd.chanlist, self.cmd.chanlist_len)\n if retval < 0:\n raise RuntimeError(\n 'comedi.get_min_period: '\n 'get_cmd_timing_constraints({}, subdev={}, scan_src={}, <addr>, '\n 'convert_src={}, <addr>, <addr>, chlen={}) failed (=={})'\n .format(self.card, self.subdevice, self.cmd.scan_begin_src,\n self.cmd.convert_src, self.cmd.chanlist_len, retval)\n )\n return scan_begin_min.value*unit.ns\n\n @cached.property\n def finite_end_clock(self):\n C = capabilities.get(self.card.kernel, self.card.board, self.subdev_type)\n return C['finite_end_clock']\n\n def set_waveforms(self, waveforms, clock_transitions, t_max, continuous):\n \"\"\"\n Set up the hardware for waveform output. This function does:\n 1. Sets sample clock properly.\n 2. Sets triggering.\n 3. Writes data to hardware buffers without auto_start.\n \"\"\"\n S = self.status()\n assert not S.sample_bitwise, 'Please implement Bitwise cmd data'\n\n if not self.clock_terminal:\n raise UserWarning('cannot start waveform without an output clock defined')\n\n my_clock = clock_transitions[ self.config['clock']['value'] ]\n dt_clk = my_clock['dt']\n transitions = list( my_clock['transitions'] )\n transitions.sort()\n\n if self.finite_end_clock and not continuous:\n # This subdevice requires an additional clock pulse at the end of the\n # sequence in order for the hardware to properly notify the software of\n # completion. It is the responsibility of each driver to ensure that the\n # last clock transitions is ignored if the driver has already indicated to\n # arbwave that an extra clock pulse is required.\n transitions = transitions[:-1]\n\n\n\n # 1. Set (non)continuous mode and number of samples per channel\n\n # Set output to either continuous or one-time\n self.cmd.stop_src = comedi.TRIG_NONE if continuous else comedi.TRIG_COUNT\n\n # should be samples_per_channel\n self.cmd.stop_arg = len(transitions)\n\n\n\n # 3. Data write\n # 3a. Get data array\n # loop through each transition and accumulate a list of scans for each\n # channel for each transition.\n # probably need to do some rounding to the nearest clock pulse to ensure\n # that we only have pulses matched to the correct transition\n\n chlist = [ '{}{}'.format(self, comedi.CR_CHAN(ch_info))\n for ch_info in self.cmd_chanlist\n ]\n\n assert set(chlist).issuperset( waveforms.keys() ), \\\n 'comedi.set_waveforms: mismatched channels'\n\n # get all the waveform data into the scans array. All remaining None values\n # mean that the prior value for the particular channels(s) should be kept\n # for that scan.\n n_channels = len(self.cmd_chanlist)\n\n if n_channels == 0:\n debug('comedi: no channels for waveform output')\n return\n\n scans = dict.fromkeys( transitions )\n nones = [None] * n_channels\n for i in range( n_channels ):\n if chlist[i] not in waveforms:\n continue\n for wf_path, (encoding,group_trans) in waveforms[ chlist[i] ].items():\n assert encoding == 'step', \\\n 'non-step transition encoding for comedi: '+encoding\n for timestamp, value in group_trans:\n if not scans[timestamp]:\n scans[timestamp] = copy.copy( nones )\n scans[timestamp][i] = value\n\n # for now, if a channel does not have any data for t=0, we'll issue\n # an error and set the empty channel value at t=0 to zero.\n def zero_if_none(v, channel):\n if v is None:\n warn('comedi: missing starting value for channel (%s)--using 0',\n chlist[channel])\n return 0\n else:\n return v\n\n S0 = scans[ transitions[0] ]\n if S0 is None:\n # must be sharing a clock with another card. init all channels to zero\n last = scans[ transitions[0] ] = [0] * n_channels\n else:\n scans[ transitions[0] ] = [\n zero_if_none(v,i) for v,i in zip( S0, range(len(S0)) )\n ]\n last = scans[ transitions[0] ]\n\n min_dt = self.get_min_period()\n\n if len(transitions) > 1:\n # NI seems to have problems with only one transition any way, but...\n diff_transitions = np.diff( transitions )\n min_transition = np.argmin( diff_transitions )\n if diff_transitions[min_transition] < round(min_dt/dt_clk):\n raise RuntimeError(\n '{name}: Samples too small for comedi at t={tl}->{t}: {dt}<({m}/{clk})'\n .format(name=self.name,\n tl=transitions[min_transition],\n t=transitions[min_transition+1],\n dt=diff_transitions[min_transition],\n m=min_dt, clk=dt_clk)\n )\n\n for t in transitions:\n t_array = scans[t]\n if t_array is None:\n # must be sharing a clock with another card. keep last values\n scans[t] = last\n else:\n for i in range( n_channels ):\n if t_array[i] is None:\n t_array[i] = last[i]\n last = t_array\n\n # now, we finally build the actual data to send to the hardware\n scans = np.array([ scans[t] for t in transitions ])\n\n # 3b. Send data to hardware\n debug( 'comedi: writing waveform data for channels: %s', chlist )\n debug( 'comedi: len(transitions/in) = %s, len(scans/out) = %s',\n len(transitions), len(scans) )\n if rootlog.getEffectiveLevel() <= (DEBUG-1):\n log(DEBUG-1, 'comedi: mmap.write(<data>)' )\n log(DEBUG-1, '<data>:' )\n for scan in scans:\n log(DEBUG-1, ' %s', np.array(scan).astype(float).tolist())\n\n shape = ( len(transitions), len(self.cmd_chanlist) )\n data = np.ndarray( shape=shape, dtype=self.sampl_t,\n buffer=self.mapped, order='C' )\n\n # this writes it to kernel memory\n # FIXME: this might not be right; might also exist much faster operation\n # for this\n #data[:] = scans\n # scale the data to sampl type.\n for ch_i, ch in zip(range(n_channels), chlist):\n data[:,ch_i] = self.convert_data( ch, scans[:,ch_i] )\n\n self.t_max = t_max\n\n\n @Pyro4.expose\n def start(self):\n if not self.busy and len(self.cmd_chanlist) > 0:\n # 1. Start the command\n err = comedi.command(self.card, self.cmd)\n raiserr(err)\n # 2. Mark the already written buffer as written\n # we have to mark this now, since comedi.command resets all the buffer\n # counters.\n output_size = self.cmd.stop_arg * self.cmd.chanlist_len \\\n * sizeof(self.sampl_t)\n m = comedi.mark_buffer_written( self.card, self.cmd.subdev,\n output_size )\n raiserr(m,'mark_buffer')\n if m != output_size:\n raise OSError('comedi: could not mark entire buffer')\n\n # 3. trigger\n self.trigger()\n\n\n def trigger(self):\n if (not self.trig_now_supported) or self.cmd.start_src == comedi.TRIG_INT:\n debug('comedi: sending internal trigger signal')\n ret = comedi.internal_trigger(self.card, self.subdevice, 0)\n raiserr(ret, 'internal_trigger')\n else:\n debug('comedi: waiting for external trigger')\n\n\n @Pyro4.expose\n def wait(self):\n if self.busy:\n log(DEBUG-1,'comedi: waiting for (%s) to finish...', self)\n if self.cmd_is_continuous():\n raise RuntimeError('Cannot wait for continuous waveform tasks')\n\n timeout = self.t_max.coeff*2\n t0 = time.time()\n while self.running:\n t1 = time.time()\n if (t1 - t0) > timeout:\n raise RuntimeError('timed out waiting for comedi command {}'.format(self))\n time.sleep(0.01)\n\n log(DEBUG-1,'comedi: (%s) finished', self)\n # cancel the command to allow INSN_CONFIG calls (such as\n # get_cmd_timing_constraints) to complete--INSN_CONFIG are not allowed\n # when the subdevice is busy.\n self.stop()\n\n @Pyro4.expose\n def stop(self):\n if self.busy:\n debug('comedi: cancelling cmd for %s', self)\n raiserr( comedi.cancel(self.card, self.subdevice), 'cancel' )\n\n @Pyro4.expose\n def get_config_template(self):\n return self._config_template.copy()\n\n _config_template = {\n 'trigger' : {\n 'enable' : {\n 'value' : False,\n 'type' : bool,\n 'range' : None,\n },\n 'source' : {\n 'value' : '',\n 'type' : str,\n 'range' : None,\n },\n 'edge' : {\n 'value' : 'rising',\n 'type' : str,\n 'range' : [\n ('falling','Trigger on Falling Edge of Trigger'),\n ('rising', 'Trigger on Rising Edge of Trigger'),\n ],\n },\n },\n 'clock' : {\n 'value' : '',\n 'type' : str,\n 'range' : None,\n },\n 'clock-edge' : {\n 'value' : 'rising',\n 'type' : str,\n 'range' : [\n ('falling','Sample on Falling Edge of Trigger'),\n ('rising', 'Sample on Rising Edge of Trigger'),\n ],\n },\n }\n\n\n\n\ndef create_chanlist(cr_pack, channels):\n \"\"\"\n Add all channels to the chanlist array.\n \"\"\"\n cmd_chanlist = ( ctypes.c_uint * len(channels) )()\n\n for i, (chname, chinfo) in zip( range( len(channels) ), channels ):\n cmd_chanlist[i] = cr_pack( chname, chinfo )\n return cmd_chanlist\n", "sub_path": "python/arbwave/backend/drivers/comedi/subdevice/subdevice.py", "file_name": "subdevice.py", "file_ext": "py", "file_size_in_byte": 24158, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "comedi.errno", "line_number": 34, "usage_type": "call"}, {"api_name": "comedi.strerror", "line_number": 35, "usage_type": "call"}, {"api_name": "device.Device", "line_number": 37, "usage_type": "name"}, {"api_name": "comedi.UNIT_volt", "line_number": 44, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 67, "usage_type": "call"}, {"api_name": "physical.unit.s", "line_number": 72, "usage_type": "attribute"}, {"api_name": "physical.unit", "line_number": 72, "usage_type": "name"}, {"api_name": "comedi.cmd", "line_number": 73, "usage_type": "call"}, {"api_name": "comedi.get_cmd_src_mask", "line_number": 83, "usage_type": "call"}, {"api_name": "comedi.TRIG_NOW", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ctypes.memset", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 85, "usage_type": "call"}, {"api_name": "comedi.sampl_t", "line_number": 88, "usage_type": "attribute"}, {"api_name": "comedi.lsampl_t", "line_number": 88, "usage_type": "attribute"}, {"api_name": "comedi.get_buffer_size", "line_number": 92, "usage_type": "call"}, {"api_name": "comedi.set_write_subdevice", "line_number": 97, "usage_type": "call"}, {"api_name": "mmap.mmap", "line_number": 100, "usage_type": "call"}, {"api_name": "comedi.fileno", "line_number": 100, "usage_type": "call"}, {"api_name": "mmap.PROT_WRITE", "line_number": 101, "usage_type": "name"}, {"api_name": "mmap.MAP_SHARED", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 105, "usage_type": "call"}, {"api_name": "ctypes.c_ubyte", "line_number": 105, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 125, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 141, "usage_type": "call"}, {"api_name": "comedi.cancel", "line_number": 142, "usage_type": "call"}, {"api_name": "physical.unit.s", "line_number": 143, "usage_type": "attribute"}, {"api_name": "physical.unit", "line_number": 143, "usage_type": "name"}, {"api_name": "ctypes.memset", "line_number": 144, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 144, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 144, "usage_type": "call"}, {"api_name": "tools.cached.property", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tools.cached", "line_number": 163, "usage_type": "name"}, {"api_name": "tools.cached.property", "line_number": 167, "usage_type": "attribute"}, {"api_name": "tools.cached", "line_number": 167, "usage_type": "name"}, {"api_name": "comedi.get_subdevice_flags", "line_number": 173, "usage_type": "call"}, {"api_name": "comedi.SDF_BUSY", "line_number": 177, "usage_type": "attribute"}, {"api_name": "comedi.SDF_RUNNING", "line_number": 181, "usage_type": "attribute"}, {"api_name": "comedi.get_buffer_size", "line_number": 185, "usage_type": "call"}, {"api_name": "comedi.extensions.subdev_flags.to_dict", "line_number": 189, "usage_type": "call"}, {"api_name": "comedi.extensions", "line_number": 189, "usage_type": "attribute"}, {"api_name": "comedi.get_n_channels", "line_number": 197, "usage_type": "call"}, {"api_name": "comedi.find_range", "line_number": 225, "usage_type": "call"}, {"api_name": "comedi.get_maxdata", "line_number": 226, "usage_type": "call"}, {"api_name": "comedi.TRIG_NONE", "line_number": 248, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 252, "usage_type": "call"}, {"api_name": "tools.signal_graphs.nearest_terminal", "line_number": 272, "usage_type": "call"}, {"api_name": "comedi.TRIG_TIMER", "line_number": 278, "usage_type": "attribute"}, {"api_name": "comedi.CR_INVERT", "line_number": 283, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_EXT", "line_number": 284, "usage_type": "attribute"}, {"api_name": "comedi.CR_PACK_FLAGS", "line_number": 285, "usage_type": "call"}, {"api_name": "comedi.CR_EDGE", "line_number": 285, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_INT", "line_number": 287, "usage_type": "attribute"}, {"api_name": "comedi.CR_INVERT", "line_number": 292, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_EXT", "line_number": 293, "usage_type": "attribute"}, {"api_name": "comedi.CR_PACK_FLAGS", "line_number": 294, "usage_type": "call"}, {"api_name": "comedi.CR_EDGE", "line_number": 294, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 302, "usage_type": "call"}, {"api_name": "comedi.TRIG_WRITE", "line_number": 310, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_NOW", "line_number": 317, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_COUNT", "line_number": 319, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_COUNT", "line_number": 321, "usage_type": "attribute"}, {"api_name": "comedi.command_test", "line_number": 326, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 330, "usage_type": "call"}, {"api_name": "re.search", "line_number": 345, "usage_type": "call"}, {"api_name": "comedi.get_range", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 361, "usage_type": "call"}, {"api_name": "comedi.CR_PACK", "line_number": 370, "usage_type": "call"}, {"api_name": "comedi.insnlist", "line_number": 392, "usage_type": "call"}, {"api_name": "comedi.lsampl_t", "line_number": 395, "usage_type": "call"}, {"api_name": "comedi.INSN_WRITE", "line_number": 400, "usage_type": "attribute"}, {"api_name": "ctypes.pointer", "line_number": 404, "usage_type": "call"}, {"api_name": "comedi.do_insnlist", "line_number": 405, "usage_type": "call"}, {"api_name": "ctypes.c_uint", "line_number": 416, "usage_type": "call"}, {"api_name": "ctypes.c_uint", "line_number": 417, "usage_type": "call"}, {"api_name": "comedi.get_clock_source", "line_number": 418, "usage_type": "call"}, {"api_name": "physical.unit.ns", "line_number": 422, "usage_type": "attribute"}, {"api_name": "physical.unit", "line_number": 422, "usage_type": "name"}, {"api_name": "ctypes.c_uint", "line_number": 425, "usage_type": "call"}, {"api_name": "comedi.get_cmd_timing_constraints", "line_number": 426, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 427, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 428, "usage_type": "call"}, {"api_name": "physical.unit.ns", "line_number": 438, "usage_type": "attribute"}, {"api_name": "physical.unit", "line_number": 438, "usage_type": "name"}, {"api_name": "tools.cached.property", "line_number": 440, "usage_type": "attribute"}, {"api_name": "tools.cached", "line_number": 440, "usage_type": "name"}, {"api_name": "comedi.TRIG_NONE", "line_number": 476, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_COUNT", "line_number": 476, "usage_type": "attribute"}, {"api_name": "comedi.CR_CHAN", "line_number": 490, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 503, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 516, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 523, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 544, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 567, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 570, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 571, "usage_type": "call"}, {"api_name": "logging.root.getEffectiveLevel", "line_number": 573, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 573, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 573, "usage_type": "name"}, {"api_name": "logging.log", "line_number": 574, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 574, "usage_type": "name"}, {"api_name": "logging.log", "line_number": 575, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 575, "usage_type": "name"}, {"api_name": "logging.log", "line_number": 577, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 577, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 577, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 580, "usage_type": "call"}, {"api_name": "comedi.command", "line_number": 598, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 604, "usage_type": "call"}, {"api_name": "comedi.mark_buffer_written", "line_number": 605, "usage_type": "call"}, {"api_name": "Pyro4.expose", "line_number": 594, "usage_type": "attribute"}, {"api_name": "comedi.TRIG_INT", "line_number": 616, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 617, "usage_type": "call"}, {"api_name": "comedi.internal_trigger", "line_number": 618, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 621, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 627, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 627, "usage_type": "name"}, {"api_name": "time.time", "line_number": 632, "usage_type": "call"}, {"api_name": "time.time", "line_number": 634, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 637, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 639, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 639, "usage_type": "name"}, {"api_name": "Pyro4.expose", "line_number": 624, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 648, "usage_type": "call"}, {"api_name": "comedi.cancel", "line_number": 649, "usage_type": "call"}, {"api_name": "Pyro4.expose", "line_number": 645, "usage_type": "attribute"}, {"api_name": "Pyro4.expose", "line_number": 651, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint", "line_number": 698, "usage_type": "attribute"}]}
+{"seq_id": "242283428", "text": "import pandas as pd\r\nimport numpy as np\r\nfrom pandas.tseries.offsets import MonthEnd\r\n\r\nimport statsmodels.api as sm\r\nfrom sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.utils import check_array, check_X_y\r\nfrom sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted\r\n\r\nfrom dateutil.relativedelta import *\r\n\r\nimport fnmatch\r\nimport os\r\n\r\nimport json\r\nimport re\r\n\r\nfrom collections.abc import Iterable\r\n\r\ndef get_start_end_dates(dt, _freq):\r\n '''\r\n Returns a pair of two lists; start and end. A consecutive period starts from start[n] and ends on end[n].\r\n \r\n Parameters:\r\n dt : Series\r\n Its values are dates and type is Timestamp \r\n \r\n _freq : string\r\n either 'M' for monthly or 'W' for weekly\r\n \r\n month_end : Boolean\r\n Change dates in \"end\" list to %Y-%d-LastDay from %Y-%d-01.\r\n \r\n Returns:\r\n The first list contains start dates and the second list contains end dates.\r\n\r\n Examples:\r\n ---------\r\n rec_dt = pd.Series(rec_months[rec_months==1].index.to_timestamp(), name='rec_date')\r\n\r\n >> rec_dt\r\n rec_date\r\n 1926-10-30/1926-11-05 1926-11-05 23:59:59.999999999\r\n 1926-11-06/1926-11-12 1926-11-12 23:59:59.999999999\r\n \r\n rec_starts, rec_ends = get_start_end_dates(rec_dt[rec_dt>='1956'], _freq)\r\n\r\n '''\r\n \r\n if dt.empty:\r\n return [], []\r\n \r\n n = 0\r\n start = []\r\n end = []\r\n start.append(dt.iloc[0])\r\n prev = dt.iloc[0]\r\n \r\n unit_period = relativedelta(months=1) if _freq == 'M' else relativedelta(weeks=1)\r\n done_flag = False\r\n \r\n for d in dt.iloc[1:]:\r\n if done_flag:\r\n # As done_flag is marked, we add a start date into the `start` list.\r\n start.append(prev)\r\n done_flag = False\r\n\r\n if d != (prev + unit_period):\r\n # if dates are not consecutive, than it means it's an end point.\r\n if _freq == 'M':\r\n end.append(prev + MonthEnd(0))\r\n elif _freq == 'W':\r\n end.append(prev)\r\n \r\n # Mark this flag as true so that we can append a start date.\r\n done_flag = True\r\n \r\n prev = d\r\n \r\n end.append(d)\r\n\r\n return start, end\r\n\r\n\r\ndef get_filenames(fname, path='.'):\r\n '''\r\n Returns a list of file names in the path `path`\r\n\r\n Parameters:\r\n fname : str\r\n either a file name or filename-pattern that contains Unix shell-style wildcards.\r\n e.g. 'news*.json' will return news(1).json, news(2).json, news_.json and so forth.\r\n \r\n path : string\r\n path where this function looks for at. Default is a current path where this function is being called.\r\n \r\n Returns:\r\n A list of file names that match a pattern specified in `fname`\r\n '''\r\n filenames = []\r\n for file in os.listdir(path):\r\n if fnmatch.fnmatch(file, fname):\r\n filenames.append(file)\r\n\r\n return filenames\r\n\r\ndef get_df_from_json_chunks(filenames, path='.'):\r\n '''\r\n Returns a DataFrame instance where its contents are extracted from json files specified in `filenames`.\r\n\r\n Parameters:\r\n filenames : list\r\n A list of file names. You may use get_filenames() to get it.\r\n \r\n path: str\r\n path where this function looks for at. Default is a current path where this function is being called.\r\n '''\r\n df = pd.DataFrame()\r\n\r\n for filename in filenames:\r\n with open(path+filename, 'r', encoding='UTF-8') as f:\r\n raw_json_chunks = f.readlines()\r\n assert len(raw_json_chunks) == 1, filename + 'does not consist of a single line'\r\n json_chunks = re.findall('\\[\\{(.*?)\\}\\]', raw_json_chunks[0])\r\n records = ['[{' + json_chunk + '}]' for json_chunk in json_chunks]\r\n print('{:s}: {:d} json chunk(s), each of which has...'.format(filename, len(json_chunks)))\r\n for no, record in enumerate(records):\r\n new_df = pd.read_json(record)\r\n print('\\t#{:d}: {:d} record(s).'.format(no+1, new_df.shape[0]))\r\n if ~df.empty:\r\n df = pd.concat([df, new_df])\r\n else:\r\n df = new_df.copy()\r\n\r\n df = df.reset_index(drop=True)\r\n print('\\nTotal: {:d} records'.format(df.shape[0]))\r\n\r\n return df\r\n\r\ndef get_excel_column_name(i, letter='A'):\r\n \"\"\"Returns the alphabet that is `i`-th apart from `letter`, which is compatible with the column name in Excel.\r\n e.g.: get_letter(26) returns 'AA\r\n \r\n Parameters:\r\n -----------\r\n letter : character\r\n The letter from which `i`-th apart.\r\n \r\n i : int\r\n `i`-th apart from `letter.\r\n \"\"\"\r\n if i <= 25:\r\n return chr(ord(letter)+i)\r\n else:\r\n return get_excel_column_name((i // 26)-1) + get_excel_column_name(i % 26)\r\n\r\n\r\ndef get_last_bday(date_from=None, holidays=[''], weekmask='Mon Tue Wed Thu Fri'):\r\n \"\"\"Returns the last business day from `date_from`.\r\n\r\n Parameters:\r\n -----------\r\n date_from : string ('%Y-%m-%d')\r\n\r\n holidays : list\r\n list/array of dates to exclude from the set of valid business days,\r\n passed to ``numpy.busdaycalendar``\r\n\r\n weekmask : str, Default 'Mon Tue Wed Thu Fri'\r\n weekmask of valid business days, passed to ``numpy.busdaycalendar``\r\n\r\n Returns:\r\n --------\r\n string\r\n The last business day\r\n \"\"\"\r\n \r\n \r\n bday = pd.tseries.offsets.CustomBusinessDay(holidays=holidays, weekmask=weekmask)\r\n if date_from is None:\r\n date_from = pd.Timestamp.today()\r\n\r\n return (date_from - bday).strftime('%Y-%m-%d')\r\n\r\n\r\ndef get_fred_asof(df, col_nm, asof_date, freq='M'):\r\n '''\r\n Returns data in `df` available at a point of time specified by `as_of_date`.\r\n \r\n Parameters:\r\n -----------\r\n df : DataFrame\r\n an object that's returned from fred.get_series_all_releases()\r\n \r\n col_nm: string\r\n A column name.\r\n \r\n asof_date: string or datetime\r\n a point of time\r\n \r\n freq: string\r\n 'M' for a monthly frequency (default)\r\n 'W' for a weekly frequency\r\n\r\n \r\n Returns:\r\n --------\r\n DataFrame\r\n its index is reference date and column name is `col_nm`\r\n \r\n Example:\r\n --------\r\n ip_all = fred.get_series_all_releases('INDPRO')\r\n ip_idx = get_fred_asof(ip_all, 'IP_idx', '2020-05-31')\r\n\r\n '''\r\n\r\n # We extract conditions for 'realtime_start', 'date' so that we can extract data available at a point of time.\r\n cond_inc_rev = df.loc[df.realtime_start <= asof_date, ['realtime_start', 'date']].groupby(by='date').max().reset_index()\r\n \r\n # Filter `df` with `cond_inc_rev`\r\n ef = cond_inc_rev.merge(df, left_on=['date', 'realtime_start'], right_on=['date', 'realtime_start'], how='left')\r\n \r\n if freq == 'M':\r\n # Convert %m-01 format to %m-the last day of that month.\r\n ef['date'] = ef.date + MonthEnd(0)\r\n \r\n ef.set_index('date', drop=True, inplace=True)\r\n ef.drop('realtime_start', axis=1, inplace=True)\r\n \r\n ef.columns = [col_nm]\r\n \r\n return ef\r\n\r\ndef get_fred_asof_history(df_all, start_date, col_nm, freq='M', end_date=pd.Timestamp.today()):\r\n '''\r\n Returns a dictionary where its key is a date to extract a Fred series as of that date.\r\n\r\n Parameters:\r\n -----------\r\n df_all: DataFrame\r\n an object that's returned from fred.get_series_all_releases()\r\n This will be passed to get_fred_asof()\r\n\r\n start_date: string in %Y-%m-%d format.\r\n\r\n freq: string\r\n 'M' for a monthly frequency (default)\r\n 'W' for a weekly frequency\r\n \r\n end_date: string in %Y-%m-%d format.\r\n '''\r\n if freq == 'M':\r\n dt_range = pd.date_range(start=start_date, end=pd.Timestamp.today(), freq='M').strftime('%Y-%m-%d').to_list() + [pd.Timestamp.today().strftime('%Y-%m-%d')]\r\n elif freq == 'W':\r\n dt_range = pd.date_range(start=start_date, end=pd.Timestamp.today(), freq='W-FRI').strftime('%Y-%m-%d').to_list()\r\n\r\n kdf_asof = {}\r\n\r\n for dt in dt_range:\r\n key = dt[:-3] if freq == 'M' else dt\r\n kdf_asof[key] = get_fred_asof(df_all, col_nm, dt, freq)\r\n \r\n if freq == 'M':\r\n kdf_asof_index = pd.to_datetime(kdf_asof[key].index).to_period('M')\r\n elif freq == 'W':\r\n kdf_asof_index = pd.to_datetime(kdf_asof[key].index - pd.Timedelta('1 days')).to_period('W-FRI')\r\n \r\n kdf_asof[key] = kdf_asof[key].set_index(kdf_asof_index, drop=True)\r\n \r\n return kdf_asof\r\n\r\n\r\ndef move_col(df, cols_to_move=[], ref_col='', place='After'):\r\n '''\r\n Moves columns specified in `cols_to_move` (After|Before) a column referred by `ref_col`.\r\n\r\n Parameters:\r\n df: DataFrame\r\n a DataFrame instance to which this function is applied.\r\n \r\n cols_to_move: list\r\n a list of column names to be moved.\r\n \r\n ref_col: string\r\n a column name as a reference column.\r\n \r\n place: string\r\n an either value of 'After' or 'Before'. Default is 'After'\r\n \r\n Source: https://towardsdatascience.com/reordering-pandas-dataframe-columns-thumbs-down-on-standard-solutions-1ff0bc2941d5\r\n '''\r\n \r\n cols = df.columns.tolist()\r\n if place == 'After':\r\n seg1 = cols[:list(cols).index(ref_col) + 1]\r\n seg2 = cols_to_move\r\n if place == 'Before':\r\n seg1 = cols[:list(cols).index(ref_col)]\r\n seg2 = cols_to_move + [ref_col]\r\n \r\n seg1 = [i for i in seg1 if i not in seg2]\r\n seg3 = [i for i in cols if i not in seg1 + seg2]\r\n \r\n return(df[seg1 + seg2 + seg3])\r\n\r\n\r\ndef iterable(obj):\r\n '''\r\n Returns if obj is iterable.\r\n\r\n The isinstance() has been recommended already earlier, but the general consensus has been that using iter() would be better.\r\n If we'd use insinstance(), we wouldn't accidentally consider Faker instances (or any other objects having only __getitem__) to be iterable:\r\n Source: https://stackoverflow.com/questions/1952464/in-python-how-do-i-determine-if-an-object-is-iterable\r\n '''\r\n try:\r\n iter(obj)\r\n except Exception:\r\n return False\r\n else:\r\n return True\r\n\r\n\r\ndef tokenize(s):\r\n '''Splits a string into tokens'''\r\n WHITESPACE = re.compile('\\s+')\r\n return WHITESPACE.split(s)\r\n\r\n\r\ndef untokenize(s):\r\n '''Joins tokens into a string'''\r\n return ' '.join(s)\r\n\r\n\r\ndef get_nonexistant_path(fname_path):\r\n '''\r\n Get the path to a filename which does not exist by incrementing path.\r\n Examples\r\n --------\r\n >>> get_nonexistant_path('/etc/issue')\r\n '/etc/issue-1'\r\n >>> get_nonexistant_path('whatever/1337bla.py')\r\n 'whatever/1337bla.py'\r\n\r\n Reference: https://izziswift.com/create-a-incrementing-filename-in-python/\r\n '''\r\n if not os.path.exists(fname_path):\r\n return fname_path\r\n filename, file_extension = os.path.splitext(fname_path)\r\n i = 1\r\n new_fname = \"{}-{}{}\".format(filename, i, file_extension)\r\n while os.path.exists(new_fname):\r\n i += 1\r\n new_fname = \"{}-{:02d}{}\".format(filename, i, file_extension)\r\n \r\n return new_fname\r\n\r\nclass SMWrapper(BaseEstimator, RegressorMixin):\r\n \"\"\"\r\n A square-root lasso warpper.\r\n A universal sklearn-style wrapper for statsmodels regressors \r\n \r\n Source: https://stackoverflow.com/questions/41045752/using-statsmodel-estimations-with-scikit-learn-cross-validation-is-it-possible\r\n \"\"\"\r\n def __init__(self, model_class, lasso_t, fit_intercept=True, refit=True):\r\n self.model_class = model_class\r\n self.fit_intercept = fit_intercept\r\n self.lasso_t = lasso_t\r\n self.refit = refit\r\n \r\n def fit(self, X, y):\r\n if self.fit_intercept:\r\n X = sm.add_constant(X, has_constant='add')\r\n self.model_ = self.model_class(y, X)\r\n self.results_ = self.model_.fit_regularized(method='sqrt_lasso', refit=self.refit, zero_tol=self.lasso_t)\r\n \r\n def predict(self, X):\r\n if self.fit_intercept:\r\n X = sm.add_constant(X)\r\n return self.results_.predict(X)\r\n\r\n\r\nclass LogWrapper():\r\n def __init__(self, logger):\r\n self.logger = logger\r\n\r\n def info(self, *args, sep=' '):\r\n self.logger.info(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def debug(self, *args, sep=' '):\r\n self.logger.debug(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def warning(self, *args, sep=' '):\r\n self.logger.warning(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def error(self, *args, sep=' '):\r\n self.logger.error(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def critical(self, *args, sep=' '):\r\n self.logger.critical(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def exception(self, *args, sep=' '):\r\n self.logger.exception(sep.join(\"{}\".format(a) for a in args))\r\n\r\n def log(self, *args, sep=' '):\r\n self.logger.log(sep.join(\"{}\".format(a) for a in args))\r\n\r\n\r\n\r\nclass BlockingTimeSeriesSplit():\r\n def __init__(self, n_splits):\r\n self.n_splits = n_splits\r\n \r\n def get_n_splits(self, X, y, groups):\r\n return self.n_splits\r\n \r\n def split(self, X, y=None, groups=None):\r\n n_samples = len(X)\r\n k_fold_size = n_samples // self.n_splits\r\n indices = np.arange(n_samples)\r\n\r\n margin = 0\r\n for i in range(self.n_splits):\r\n start = i * k_fold_size\r\n stop = start + k_fold_size\r\n mid = int(0.8 * (stop - start)) + start\r\n yield indices[start: mid], indices[mid + margin: stop]\r\n\r\n\r\nclass StandardScalerClipper(BaseEstimator, TransformerMixin):\r\n '''\r\n Applies StandardScaler() and then clips the scaled results outside an interval [`zmin`, `zmax`] to the interval edges.\r\n That is, we consider all values ouside any edge as either -3 or 3 if a given interval is [-3, 3]\r\n\r\n Reference: https://ploomber.io/posts/sklearn-custom/\r\n '''\r\n def __init__(self, zmin=-3, zmax=3, scaler_class=StandardScaler, copy=True, with_mean=True, with_std=True, **kwargs):\r\n self.zmin=zmin\r\n self.zmax=zmax\r\n self.scaler_class=scaler_class\r\n self.copy=copy\r\n self.with_mean=with_mean\r\n self.with_std=with_std\r\n \r\n for key, value in kwargs.items():\r\n setattr(self, key, value)\r\n \r\n self._param_names = ['scaler_class'] + list(kwargs.keys())\r\n\r\n def fit(self, X, y=None, **kwargs):\r\n '''\r\n An abstract method that is used to fit the step and to learn by examples\r\n '''\r\n X = check_array(X, copy=self.copy, estimator=self, dtype=FLOAT_DTYPES)\r\n scaler_kwargs = self.get_params()\r\n del scaler_kwargs['scaler_class']\r\n\r\n self.model_ = self.scaler_class(**scaler_kwargs)\r\n self.model_.fit(X, y, **kwargs)\r\n\r\n # self.scaler = StandardScaler(copy=self.copy, with_mean=self.with_mean, with_std=self.with_std)\r\n return self # fit must return self.\r\n\r\n\r\n def transform(self, X):\r\n '''\r\n We simply call the same `transform` method first.\r\n Then we do clipping after that.\r\n '''\r\n\r\n check_is_fitted(self)\r\n X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)\r\n z = self.model_.transform(X=X)\r\n \r\n return np.clip(z, a_min=self.zmin, a_max=self.zmax)\r\n\r\n def get_params(self, deep=True):\r\n '''\r\n Estimators have get_params and set_params functions.\r\n The get_params function takes no arguments and returns a dict of the __init__ parameters of the estimator, together with their values.\r\n It must take one keyword argument, deep, which receives a boolean value that determines whether the method should return the parameters of sub-estimators (for most estimators, this can be ignored). The default value for deep should be true.\r\n \r\n Reference: https://ploomber.io/posts/sklearn-custom/\r\n '''\r\n\r\n return {param: getattr(self, param)\r\n for param in self._param_names}\r\n \r\n def set_params(self, **parameters):\r\n for parameter, value in parameters.items():\r\n setattr(self, parameter, value)\r\n\r\n return self\r\n\r\n def __getattr__(self, key):\r\n if key != 'model_':\r\n if hasattr(self, 'model_'):\r\n return getattr(self.model_, key)\r\n else:\r\n return getattr(self.scaler_class, key)\r\n else:\r\n raise AttributeError(\r\n \"'{}' object has no attribute 'model_'\".format(type(self).__name__))\r\n\r\n", "sub_path": "utils/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 16711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pandas.tseries.offsets.MonthEnd", "line_number": 72, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 102, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 119, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.tseries.offsets.CustomBusinessDay", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.tseries", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pandas.Timestamp.today", "line_number": 182, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pandas.tseries.offsets.MonthEnd", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.Timestamp.today", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 255, "usage_type": "call"}, {"api_name": "pandas.Timestamp.today", "line_number": 255, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 255, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 257, "usage_type": "call"}, {"api_name": "pandas.Timestamp.today", "line_number": 257, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 257, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 266, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 268, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 268, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 348, "usage_type": "call"}, {"api_name": "os.path", "line_number": 348, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 353, "usage_type": "call"}, {"api_name": "os.path", "line_number": 353, "usage_type": "attribute"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 359, "usage_type": "name"}, {"api_name": "sklearn.base.RegressorMixin", "line_number": 359, "usage_type": "name"}, {"api_name": "statsmodels.api.add_constant", "line_number": 374, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 374, "usage_type": "name"}, {"api_name": "statsmodels.api.add_constant", "line_number": 380, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 380, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 421, "usage_type": "call"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 431, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 431, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 438, "usage_type": "name"}, {"api_name": "sklearn.utils.check_array", "line_number": 455, "usage_type": "call"}, {"api_name": "sklearn.utils.validation.FLOAT_DTYPES", "line_number": 455, "usage_type": "name"}, {"api_name": "sklearn.utils.validation.check_is_fitted", "line_number": 472, "usage_type": "call"}, {"api_name": "sklearn.utils.check_array", "line_number": 473, "usage_type": "call"}, {"api_name": "sklearn.utils.validation.FLOAT_DTYPES", "line_number": 473, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 476, "usage_type": "call"}]}
+{"seq_id": "42268028", "text": "#!/usr/bin/python\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n 'status': ['preview'],\n 'supported_by': 'community'}\n\nDOCUMENTATION = r'''\nmodule: ucs_servpoltemp\nshort_description: Adds or removes a named Glencore custom service policy template\ndescription:\n- Adds or removes a named Glencore custom service policy template\n- Examples can be used with the UCS Platform Emulator U(https://communities.cisco.com/ucspe).\nextends_documentation_fragment: ucs\noptions:\n name:\n description:\n - The name of the service policy template\n required: yes\n iqn_pool:\n description:\n - The name of the IQN pool\n required: yes\n vnic_name:\n description:\n - Defines the name of the iSCSI vnic\n required: yes\n init_pool:\n description:\n - Defines the name of the iSCSI initiator pool\n required: yes\n ip_1:\n description:\n - Defines the primary storage ip\n required: yes \n ip_2:\n description:\n - Defines the secondary storage ip\n required: yes\n target:\n description:\n - Defines the iSCSI target\n required: yes \n state:\n description:\n - If C(present), will verify named policy is present and will create if needed.\n - If C(absent), will verify named policy is absent and will delete if needed.\n choices: [present, absent]\n default: present\n\nrequirements:\n- ucsmsdk\nauthor:\n- Matt Thompson\n\n'''\n\nEXAMPLES = r'''\n ucsm_bootpolicy:\n hostname: 192.168.56.107\n username: ucspe\n password: ucspe\n name: ESX-Profile-Template\n iqn_pool: iqn\n vnic_name: iscsi\n init_pool: iscsi-initiator-pool\n ip_1: 10.121.207.72\n ip_2: 10.121.207.73\n target: iqn.1992-08.com.netapp:2804.600a098000f57245000000005cf4f299\n\n ucsm_bootpolicy:\n hostname: 192.168.56.107\n username: ucspe\n password: ucspe\n name: ESX-Profile-Template\n iqn_pool: iqn\n vnic_name: iscsi\n init_pool: iscsi-initiator-pool\n ip_1: 10.121.207.72\n ip_2: 10.121.207.73\n target: iqn.1992-08.com.netapp:2804.600a098000f57245000000005cf4f299\n state: absent\n\n'''\n\nRETURN = r'''\n#\n'''\n\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.remote_management.ucs import UCSModule\n\ndef main():\n argument_spec = {\n 'hostname': {'type': 'str', 'required': True}, \n 'username': {'type': 'str', 'default': 'admin'}, \n 'password': {'type': 'str', 'required': True, 'no_log': True}, \n 'port': {'type': 'int', 'default': None}, \n 'use_ssl': {'type': 'bool', 'default': True}, \n 'use_proxy': {'type': 'bool', 'default': True}, \n 'proxy': {'type': 'str', 'default': None},\n 'name': {'type': 'str', 'required': True},\n 'iqn_pool': {'type': 'str', 'required': True},\n 'vnic_name': {'type': 'str', 'required': True},\n 'init_pool': {'type': 'str', 'required': True},\n 'ip_1': {'type': 'str', 'required': True},\n 'ip_2': {'type': 'str', 'required': True},\n 'target': {'type': 'str', 'required': True},\n 'state': {'type': 'str', 'default': 'present', 'choices': ['present', 'absent']}\n }\n\n module = AnsibleModule(\n argument_spec,\n supports_check_mode=True\n )\n\n ucs = UCSModule(module)\n\n err = False\n changed = False\n try:\n mo_exists = False\n props_match = False\n # dn is org-root/ip-pool-<name>\n dn_base = 'org-root/ls-'\n dn = dn_base + module.params['name']\n \n mo = ucs.login_handle.query_dn(dn)\n \n\n if mo:\n mo_exists = True\n\n if module.params['state'] == 'absent':\n # if mo exists and absent flag is set, remove mo.\n if mo_exists:\n if not module.check_mode:\n ucs.login_handle.remove_mo(mo)\n ucs.login_handle.commit()\n changed = True\n\n if module.params['state'] == 'present':\n # if mo exists already and present flag is set, no change.\n if mo_exists:\n changed = False\n\n if module.params['state'] == 'present':\n # if mo doesn't exist and present flag is set, add it.\n if not mo_exists:\n if not module.check_mode:\n from ucsmsdk.mometa.ls.LsServer import LsServer\n from ucsmsdk.mometa.vnic.VnicIScsiNode import VnicIScsiNode\n from ucsmsdk.mometa.vnic.VnicEther import VnicEther\n from ucsmsdk.mometa.vnic.VnicEtherIf import VnicEtherIf\n from ucsmsdk.mometa.vnic.VnicIScsi import VnicIScsi\n from ucsmsdk.mometa.vnic.VnicVlan import VnicVlan\n from ucsmsdk.mometa.vnic.VnicFcNode import VnicFcNode\n from ucsmsdk.mometa.fabric.FabricVCon import FabricVCon\n from ucsmsdk.mometa.vnic.VnicDefBeh import VnicDefBeh\n from ucsmsdk.mometa.ls.LsPower import LsPower\n from ucsmsdk.mometa.ls.LsVConAssign import LsVConAssign\n\n mo = LsServer(parent_mo_or_dn=\"org-root\", boot_policy_name=\"boot_from_iscsi\", ident_pool_name=\"uuid\", maint_policy_name=\"default\", name=module.params['name'], type=\"updating-template\")\n mo_1 = VnicIScsiNode(parent_mo_or_dn=mo, initiator_name=\"\", initiator_policy_name=\"\", iqn_ident_pool_name=module.params['iqn_pool'])\n mo_2 = VnicEther(parent_mo_or_dn=mo, adaptor_profile_name=\"VMWare\", ident_pool_name=\"fab-a\", name=\"mgmt-a\", order=\"3\", switch_id=\"A-B\")\n mo_2_1 = VnicEtherIf(parent_mo_or_dn=mo_2, default_net=\"no\", name=\"default\")\n mo_2_2 = VnicEtherIf(parent_mo_or_dn=mo_2, default_net=\"no\", name=\"vlan2500_mgmt\")\n mo_3 = VnicEther(parent_mo_or_dn=mo, adaptor_profile_name=\"VMWare\", ident_pool_name=\"fab-b\", name=\"mgmt-b\", order=\"4\", switch_id=\"B-A\")\n mo_3_1 = VnicEtherIf(parent_mo_or_dn=mo_3, default_net=\"no\", name=\"default\")\n mo_3_2 = VnicEtherIf(parent_mo_or_dn=mo_3, default_net=\"no\", name=\"vlan2500_mgmt\")\n mo_4 = VnicEther(parent_mo_or_dn=mo, adaptor_profile_name=\"VMWare\", ident_pool_name=\"fab-a\", name=\"data-a\", order=\"5\", switch_id=\"A-B\")\n mo_4_1 = VnicEtherIf(parent_mo_or_dn=mo_4, default_net=\"no\", name=\"vlan2200_servers\")\n mo_4_2 = VnicEtherIf(parent_mo_or_dn=mo_4, default_net=\"no\", name=\"vlan2700_serversbase\")\n mo_5 = VnicEther(parent_mo_or_dn=mo, adaptor_profile_name=\"VMWare\", ident_pool_name=\"fab-b\", name=\"data-b\", order=\"6\", switch_id=\"B-A\")\n mo_5_1 = VnicEtherIf(parent_mo_or_dn=mo_5, default_net=\"no\", name=\"vlan2200_servers\")\n mo_5_2 = VnicEtherIf(parent_mo_or_dn=mo_5, default_net=\"no\", name=\"vlan2700_serversbase\")\n mo_6 = VnicEther(parent_mo_or_dn=mo, adaptor_profile_name=\"VMWare\", ident_pool_name=\"default\", mtu=\"9000\", name=\"iscsi\", order=\"7\", switch_id=\"A-B\")\n mo_6_1 = VnicEtherIf(parent_mo_or_dn=mo_6, default_net=\"no\", name=\"default\")\n mo_6_2 = VnicEtherIf(parent_mo_or_dn=mo_6, default_net=\"yes\", name=\"vlan60_iscsi\")\n mo_7 = VnicIScsi(parent_mo_or_dn=mo, name=\"iscsi\", order=\"6\", vnic_name=\"iscsi\")\n mo_7_1 = VnicVlan(parent_mo_or_dn=mo_7, name=\"\")\n mo_8 = VnicFcNode(parent_mo_or_dn=mo, addr=\"pool-derived\", ident_pool_name=\"node-default\")\n mo_9 = FabricVCon(parent_mo_or_dn=mo, fabric=\"NONE\", id=\"1\", inst_type=\"manual\", placement=\"physical\", select=\"all\", share=\"shared\", transport=\"ethernet,fc\")\n mo_10 = FabricVCon(parent_mo_or_dn=mo, fabric=\"NONE\", id=\"2\", inst_type=\"manual\", placement=\"physical\", select=\"all\", share=\"shared\", transport=\"ethernet,fc\")\n mo_11 = FabricVCon(parent_mo_or_dn=mo, fabric=\"NONE\", id=\"3\", inst_type=\"manual\", placement=\"physical\", select=\"all\", share=\"shared\", transport=\"ethernet,fc\")\n mo_12 = FabricVCon(parent_mo_or_dn=mo, fabric=\"NONE\", id=\"4\", inst_type=\"manual\", placement=\"physical\", select=\"all\", share=\"shared\", transport=\"ethernet,fc\")\n mo_13 = VnicDefBeh(parent_mo_or_dn=mo, action=\"none\", descr=\"\", name=\"\", nw_templ_name=\"\", policy_owner=\"local\", type=\"vhba\")\n mo_14 = LsPower(parent_mo_or_dn=mo, state=\"admin-up\")\n mo_15 = LsVConAssign(parent_mo_or_dn=mo, admin_host_port=\"ANY\", admin_vcon=\"any\", order=\"3\", transport=\"ethernet\", vnic_name=\"mgmt-a\")\n mo_16 = LsVConAssign(parent_mo_or_dn=mo, admin_host_port=\"ANY\", admin_vcon=\"any\", order=\"4\", transport=\"ethernet\", vnic_name=\"mgmt-b\")\n mo_17 = LsVConAssign(parent_mo_or_dn=mo, admin_host_port=\"ANY\", admin_vcon=\"any\", order=\"5\", transport=\"ethernet\", vnic_name=\"data-a\")\n mo_18 = LsVConAssign(parent_mo_or_dn=mo, admin_host_port=\"ANY\", admin_vcon=\"any\", order=\"6\", transport=\"ethernet\", vnic_name=\"data-b\")\n mo_19 = LsVConAssign(parent_mo_or_dn=mo, admin_host_port=\"ANY\", admin_vcon=\"any\", order=\"7\", transport=\"ethernet\", vnic_name=module.params['vnic_name'])\n ucs.login_handle.add_mo(mo)\n ucs.login_handle.commit()\n\n from ucsmsdk.mometa.vnic.VnicIScsiBootParams import VnicIScsiBootParams\n from ucsmsdk.mometa.vnic.VnicIScsiBootVnic import VnicIScsiBootVnic\n from ucsmsdk.mometa.vnic.VnicIPv4If import VnicIPv4If\n from ucsmsdk.mometa.vnic.VnicIPv4PooledIscsiAddr import VnicIPv4PooledIscsiAddr\n from ucsmsdk.mometa.vnic.VnicIScsiStaticTargetIf import VnicIScsiStaticTargetIf\n from ucsmsdk.mometa.vnic.VnicLun import VnicLun\n\n mo = VnicIScsiBootParams(parent_mo_or_dn=dn, descr=\"\", policy_owner=\"local\")\n mo_1 = VnicIScsiBootVnic(parent_mo_or_dn=mo, descr=\"\", initiator_name=\"\", iqn_ident_pool_name=module.params['iqn_pool'], name=module.params['vnic_name'], policy_owner=\"local\")\n mo_1_1 = VnicIPv4If(parent_mo_or_dn=mo_1, name=\"\")\n mo_1_1_1 = VnicIPv4PooledIscsiAddr(parent_mo_or_dn=mo_1_1, ident_pool_name=module.params['init_pool'])\n mo_1_2 = VnicIScsiStaticTargetIf(parent_mo_or_dn=mo_1, ip_address=module.params['ip_1'], name=module.params['target'], priority=\"1\")\n mo_1_2_1 = VnicLun(parent_mo_or_dn=mo_1_2, bootable=\"no\", id=\"0\")\n mo_1_3 = VnicIScsiStaticTargetIf(parent_mo_or_dn=mo_1, ip_address=module.params['ip_2'], name=module.params['target'], priority=\"2\")\n mo_1_3_1 = VnicLun(parent_mo_or_dn=mo_1_3, bootable=\"no\", id=\"0\")\n ucs.login_handle.add_mo(mo, True)\n ucs.login_handle.commit()\n\n changed = True\n\n except Exception as e:\n err = True\n ucs.result['msg'] = \"setup error: %s \" % str(e)\n\n ucs.result['changed'] = changed\n if err:\n module.fail_json(**ucs.result)\n module.exit_json(**ucs.result)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Glencore/ucsm_servpoltemplate2.py", "file_name": "ucsm_servpoltemplate2.py", "file_ext": "py", "file_size_in_byte": 11237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "ansible.module_utils.basic.AnsibleModule", "line_number": 115, "usage_type": "call"}, {"api_name": "ansible.module_utils.remote_management.ucs.UCSModule", "line_number": 120, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsServer.LsServer", "line_number": 166, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsiNode.VnicIScsiNode", "line_number": 167, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEther.VnicEther", "line_number": 168, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 169, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 170, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEther.VnicEther", "line_number": 171, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 172, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 173, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEther.VnicEther", "line_number": 174, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 175, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 176, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEther.VnicEther", "line_number": 177, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 178, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 179, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEther.VnicEther", "line_number": 180, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 181, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicEtherIf.VnicEtherIf", "line_number": 182, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsi.VnicIScsi", "line_number": 183, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicVlan.VnicVlan", "line_number": 184, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicFcNode.VnicFcNode", "line_number": 185, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.fabric.FabricVCon.FabricVCon", "line_number": 186, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.fabric.FabricVCon.FabricVCon", "line_number": 187, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.fabric.FabricVCon.FabricVCon", "line_number": 188, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.fabric.FabricVCon.FabricVCon", "line_number": 189, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicDefBeh.VnicDefBeh", "line_number": 190, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsPower.LsPower", "line_number": 191, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsVConAssign.LsVConAssign", "line_number": 192, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsVConAssign.LsVConAssign", "line_number": 193, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsVConAssign.LsVConAssign", "line_number": 194, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsVConAssign.LsVConAssign", "line_number": 195, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.ls.LsVConAssign.LsVConAssign", "line_number": 196, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsiBootParams.VnicIScsiBootParams", "line_number": 207, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsiBootVnic.VnicIScsiBootVnic", "line_number": 208, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIPv4If.VnicIPv4If", "line_number": 209, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIPv4PooledIscsiAddr.VnicIPv4PooledIscsiAddr", "line_number": 210, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsiStaticTargetIf.VnicIScsiStaticTargetIf", "line_number": 211, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicLun.VnicLun", "line_number": 212, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicIScsiStaticTargetIf.VnicIScsiStaticTargetIf", "line_number": 213, "usage_type": "call"}, {"api_name": "ucsmsdk.mometa.vnic.VnicLun.VnicLun", "line_number": 214, "usage_type": "call"}]}
+{"seq_id": "602895377", "text": "from django.forms import ModelForm, TextInput, Textarea, Select, SelectMultiple, CheckboxSelectMultiple\nfrom .models import Post, Category, Comment\nclass PostForm(ModelForm):\n class Meta:\n model = Post\n\n fields = ['headline', 'text', 'article_default_news', 'categories']\n widgets = {\n 'text': Textarea(attrs={\n 'class': 'form-control',\n 'placeholder': 'Введите текст...'\n }),\n\n 'article_default_news': Select(attrs={\n 'class': 'custom-select',\n 'option selected': 'Выбрать...'\n }),\n 'categories': CheckboxSelectMultiple(attrs={\n 'multiple class': 'form-control',\n 'class': 'special',\n 'size': '100',\n }),\n }\n\nclass CategoryForm(ModelForm):\n class Meta:\n model = Category\n fields = []\nclass AddCommentForm(ModelForm):\n class Meta:\n model = Comment\n fields = ['com_text']\n widgets = {\n 'com_text': Textarea(attrs={\n 'class': 'form-control',\n 'placeholder': 'Введите текст...'\n }),}", "sub_path": "apps/news/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.forms.ModelForm", "line_number": 3, "usage_type": "name"}, {"api_name": "models.Post", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms.Select", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Category", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Comment", "line_number": 31, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "26043138", "text": "\"\"\"\n RexExportModule: Export to tundra format\n\"\"\"\nimport math\nimport os\nimport uuid\n\nfrom .base import SyncModule\n\nimport b2rexpkg.tools.rexio.export\nfrom b2rexpkg.tools import rexio\nfrom b2rexpkg.tools.rexio.library import library\nfrom b2rexpkg.tools import runexternal\n\nfrom b2rexpkg.b25 import logic\nfrom b2rexpkg.b25.material import RexMaterialIO\n\n#from .props.rexlogic import RexLogicProps\n\nimport bpy\nimport subprocess\n\nclass RexExportModule(SyncModule):\n \"\"\"\n b2rex module taking care of exporting to tundra\n \"\"\"\n def export(self, context):\n \"\"\"\n Export and pack the scene to rex logic format.\n \"\"\"\n editor = self._parent\n editor.exportSettings = context.scene.b2rex_props\n editor.exportSettings.loc = [0,0,0]\n dest = editor.ensureDestinationDir(delete=True)\n\n # export materials\n for ob in bpy.context.scene.objects:\n if ob.type == 'MESH':\n self.export_materials(ob, dest)\n\n # export ogre data\n editor.onExport(context, delete=False)\n\n # export rex data\n dest_tundra = os.path.join(dest, editor.exportSettings.pack + '.txml')\n e = rexio.export.RexSceneExporter()\n e.export(context.scene, dest_tundra)\n return dest_tundra\n\n def run(self, context):\n \"\"\"\n Export to tundra format and run the server on the given file.\n \"\"\"\n dest_tundra = self.export(context)\n editor = self._parent\n\n # run tundra\n props = editor.exportSettings\n paths = []\n if props.tundra_path:\n paths.append(props.tundra_path)\n app_path = runexternal.find_application('server', paths)\n prevdir = os.curdir\n os.chdir(os.path.dirname(app_path))\n\n subprocess.call([app_path,\n '--file',\n dest_tundra])\n os.chdir(prevdir)\n\n\n def export_materials(self, obj, dest):\n \"\"\"\n Export materials for the given file.\n \"\"\"\n editor = self._parent\n mesh = obj.data\n faces = editor._getFaceRepresentatives(mesh)\n f = open(os.path.join(dest, mesh.name + '.material'), 'w')\n for face in faces:\n bmat = editor._getFaceMaterial(mesh, face)\n if not bmat.opensim.uuid:\n bmat.opensim.uuid = str(uuid.uuid4())\n bmat.name = bmat.opensim.uuid\n matio = RexMaterialIO(editor, mesh, face, bmat)\n matio.write(f)\n f.write('\\n\\n')\n f.close()\n\n def draw(self, layout, session, props):\n if not self.expand(layout, title='Rex logic'):\n return False\n col = layout.column_flow(0)\n col.operator(\"b2rex.rexexport\", text=\"Export\").action = 'export'\n if runexternal.find_application('server', [props.tundra_path]):\n col.operator(\"b2rex.rexexport\", text=\"Export and run\").action = 'run'\n components = library.get_components('jsscript')\n box = layout.box()\n for component_name in components:\n component = components[component_name]\n box.label(component.name)\n if component.dependencies:\n deps = map(lambda s: s.replace('EC_', ''), component.dependencies)\n box.label(\" \"+\", \".join(deps), icon='RNA')\n if component.attributes:\n box.label(\" \"+\", \".join(component.attributes), icon='SETTINGS')\n\n\n", "sub_path": "All_In_One/addons/b2rexpkg/editsync/handlers/rexexport.py", "file_name": "rexexport.py", "file_ext": "py", "file_size_in_byte": 3448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "base.SyncModule", "line_number": 23, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "b2rexpkg.tools.rexio.export.RexSceneExporter", "line_number": 46, "usage_type": "call"}, {"api_name": "b2rexpkg.tools.rexio.export", "line_number": 46, "usage_type": "attribute"}, {"api_name": "b2rexpkg.tools.rexio", "line_number": 46, "usage_type": "name"}, {"api_name": "b2rexpkg.tools.runexternal.find_application", "line_number": 62, "usage_type": "call"}, {"api_name": "b2rexpkg.tools.runexternal", "line_number": 62, "usage_type": "name"}, {"api_name": "os.curdir", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 66, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 83, "usage_type": "call"}, {"api_name": "b2rexpkg.b25.material.RexMaterialIO", "line_number": 85, "usage_type": "call"}, {"api_name": "b2rexpkg.tools.runexternal.find_application", "line_number": 95, "usage_type": "call"}, {"api_name": "b2rexpkg.tools.runexternal", "line_number": 95, "usage_type": "name"}, {"api_name": "b2rexpkg.tools.rexio.library.library.get_components", "line_number": 97, "usage_type": "call"}, {"api_name": "b2rexpkg.tools.rexio.library.library", "line_number": 97, "usage_type": "name"}]}
+{"seq_id": "348588562", "text": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom func1 import *\n\n\nclass SEG(nn.Module):\n def __init__(self, pic_class = 9):\n super(SEG, self).__init__()\n self.pic_class = pic_class\n self.convY = nn.Conv2d(1, 10, 7, padding=3)\n self.convUV = nn.Conv2d(2, 6, 7, padding=3)\n self.conv2 = nn.Conv2d(16, 64, 7, padding=3, groups=2)\n self.conv3 = nn.Conv2d(64, 256, 7, padding=3, groups=2)\n self.pool1 = nn.MaxPool2d((2, 2), padding=1)\n self.pool2 = nn.MaxPool2d((2, 2), padding=1)\n self.fc1 = nn.Linear(768, 1024)\n self.fc2 = nn.Linear(1024, self.pic_class)\n self.loss = nn.CrossEntropyLoss()\n self._initialize_weights()\n\n def forward(self, pic, gt, rows, cols):\n # conv\n pic[0] = self.seg_hierarchy(pic[0])\n pic[1] = self.seg_hierarchy(pic[1])\n pic[2] = self.seg_hierarchy(pic[2])\n # upsample to size0\n patch0, channels0, rows0, cols0 = pic[0].size()\n upSamp = nn.Upsample((rows0, cols0), mode='bilinear')\n pic[1] = upSamp(pic[1])\n pic[2] = upSamp(pic[2])\n # feature vector, same size as pic0\n featureVec = torch.cat((pic[0], pic[1], pic[2]), 1)\n featureVec = torch.transpose(featureVec[0], 0, 2)\n featureVec = self.fc1(featureVec)\n featureVec = self.fc2(F.tanh(featureVec))\n featureVec = torch.transpose(featureVec, 0, 2)\n # feature vector, same size as pic\n upSamp2 = nn.Upsample((rows, cols), mode='bilinear')\n pp = torch.unsqueeze(featureVec, 0)\n pp = upSamp2(pp)\n pp = torch.squeeze(pp, 0)\n # loss\n loss = torch.transpose(pp, 0, 2)\n loss = torch.transpose(loss, 0, 1)\n loss = loss.resize(rows*cols, self.pic_class)\n gt = gt.resize(rows*cols)\n loss = self.loss(loss, gt)\n loss = loss.mean()\n m = nn.Softmax2d()\n featureVec = torch.unsqueeze(featureVec, 0)\n featureVec = m(featureVec)\n featureVec = torch.squeeze(featureVec, 0)\n pp = torch.unsqueeze(pp, 0)\n pp = m(pp)\n pp = torch.squeeze(pp, 0)\n return featureVec, pp, loss\n\n def seg_hierarchy(self, pic):\n picY = pic[0]\n picUV = pic[1]\n #\n # first stage\n pic = torch.cat((self.convY(picY), self.convUV(picUV)), 1)\n pic = self.pool1(F.tanh(pic))\n #\n # second stage\n pic = self.conv2(pic)\n pic = self.pool2(F.tanh(pic))\n #\n # third stage\n pic = self.conv3(pic)\n\n return pic\n\n def _initialize_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.zero_()\n # m.bias.data.zero_()\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.weight.data.zero_()\n # m.bias.data.zero_()\n # n = m.__sizeof__()\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n\n\nclass DIS(nn.Module):\n def __init__(self, pic_class = 8):\n super(DIS, self).__init__()\n self.pic_class = pic_class\n self.convL = nn.Conv2d(self.pic_class * 3, 64, 5, padding=2)\n self.convI_1 = nn.Conv2d(3, 16, 5, padding=2)\n self.convI_2 = nn.Conv2d(16, 64, 5, padding=2)\n self.conv1 = nn.Conv2d(128, 128, 3, padding=1)\n self.conv2 = nn.Conv2d(128, 256, 3, padding=1)\n self.conv3 = nn.Conv2d(256, 512, 3, padding=1)\n self.conv4 = nn.Conv2d(512, 2, 3, padding=1)\n self.pool = nn.MaxPool2d((2, 2), padding=1)\n self._initialize_weights()\n\n def forward(self, label, pic, mul_pic):\n ch1, r1, c1 = label.size()\n ch2, r2, c2 = mul_pic.size()\n r = min(r1, r2)\n c = min(c1, c2)\n label = label[:, 0:r, 0:c]\n mul_pic = mul_pic[:, 0:r, 0:c]\n input1 = torch.mul(label[0], mul_pic)\n for i in range(1, self.pic_class):\n temp = torch.mul(label[i], mul_pic)\n input1 = torch.cat((input1, temp), 0)\n input1 = torch.unsqueeze(input1, 0)\n input2 = torch.unsqueeze(pic, 0)\n input2 = self.pool(F.relu(self.convI_1(input2)))\n input2 = self.pool(F.relu(self.convI_2(input2)))\n b1, ch1, r1, c1 = input1.size()\n b2, ch2, r2, c2 = input2.size()\n r = min(r1, r2)\n c = min(c1, c2)\n input1 = input1[:, :, 0:r, 0:c]\n input2 = input2[:, :, 0:r, 0:c]\n input1 = F.relu(self.convL(input1))\n output = torch.cat((input1, input2), 1)\n output = self.pool(F.relu(self.conv1(output)))\n output = self.pool(F.relu(self.conv2(output)))\n output = F.relu(self.conv3(output))\n output = self.conv4(output)\n output = torch.mean(output, 2)\n output = torch.mean(output, 2)\n output = F.sigmoid(output)\n return output\n\n def _initialize_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n m.weight.data.zero_()\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\n\n\n", "sub_path": "module1.py", "file_name": "module1.py", "file_ext": "py", "file_size_in_byte": 5600, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Upsample", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.transpose", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.Upsample", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.Softmax2d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional.tanh", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}]}
+{"seq_id": "197601466", "text": "import sys\nimport Pyro4\nimport Pyro4.util\nfrom person import Person\n\nsys.excepthook = Pyro4.util.excepthook\n\nneuron_parameters = {\n 'v_thresh': -35.0, \n 'tau_m': 20.,\n 'tau_syn_E': 10.0, \n 'e_rev_E': 0., \n 'tau_refrac': 0.1 , \n 'v_reset': -50.0, #hdbrgs\n 'tau_syn_I': 5., \n 'i_offset': 0.0,\n #ESS - BrainScaleS\n 'cm': 0.2,\n 'v_rest': -50.0,\n 'e_rev_I': -100.,\n} \n\ndescription = {\n 'populations': {\n 'source': {\n 'type': 'SpikeSourceArray',\n 'size': 1,\n 'params': { \n 'spike_times': [10, 20, 30],\n },\n 'record': ['spikes'],\n },\n 'destination': {\n 'type': 'IF_cond_exp',\n 'size': 1,\n 'params': neuron_parameters,\n 'record': ['spikes']\n }\n },\n 'projections':{\n 'source': {\n 'destination': {\n 'conn': 'OneToOneConnector',\n 'weights': 0.01,\n 'delays': 1.0,\n }\n }\n }\n}\n\npynn_server = Pyro4.Proxy(\"PYRONAME:spikevo.pynn_server\")\n\npynn_server.set_net('nest', description)\npynn_server.run(100)\nrecs = pynn_server.get_records()\npynn_server.end()\n\nfrom pprint import pprint\n\npprint(recs)\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nfor pop in recs:\n fig = plt.figure()\n for i, train in enumerate(recs[pop]['spikes']):\n plt.plot(train, i*np.ones_like(train), '.')\n plt.savefig('{}_spikes.pdf'.format(pop))", "sub_path": "codebase/crosstalk/run_network.py", "file_name": "run_network.py", "file_ext": "py", "file_size_in_byte": 1569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.excepthook", "line_number": 6, "usage_type": "attribute"}, {"api_name": "Pyro4.util", "line_number": 6, "usage_type": "attribute"}, {"api_name": "Pyro4.Proxy", "line_number": 51, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.use", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.ones_like", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]}
+{"seq_id": "32651878", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/dobg/commands/listdroplets.py\n# Compiled at: 2019-08-25 16:23:30\n# Size of source mod 2**32: 1161 bytes\nimport sys\nsys.path.append('..')\nimport digitalocean\nfrom digitalocean import DataReadError, TokenError\nfrom dobg.helper.confighandler import ConfigHandler\nfrom dobg.exceptions.configexceptions import InvalidTokenException\n\ndef list_droplets(args):\n \"\"\" Lists all of Droplets \"\"\"\n token = ConfigHandler.get_config_setting('token')\n manager = digitalocean.Manager(token=token)\n try:\n droplets = manager.get_all_droplets()\n except (DataReadError, TokenError):\n raise InvalidTokenException\n\n if len(droplets) == 0:\n print('No droplets.')\n return\n result = '{:15}{:40}{:10}{:18}{:37}{}'.format('ID:', 'Name:', 'Region:', 'Size:', 'Image:', 'Status:\\n')\n for droplet in droplets:\n image = droplet.image['slug'] if droplet.image['slug'] else 'None'\n result += '{:<15}{:40}{:10}{:18}{:37}{}\\n'.format(droplet.id, droplet.name, droplet.region['slug'], droplet.size_slug, image, droplet.status)\n\n print(result)", "sub_path": "pycfiles/dobg-0.0.23-py3.7/listdroplets.cpython-37.py", "file_name": "listdroplets.cpython-37.py", "file_ext": "py", "file_size_in_byte": 1261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "dobg.helper.confighandler.ConfigHandler.get_config_setting", "line_number": 17, "usage_type": "call"}, {"api_name": "dobg.helper.confighandler.ConfigHandler", "line_number": 17, "usage_type": "name"}, {"api_name": "digitalocean.Manager", "line_number": 18, "usage_type": "call"}, {"api_name": "digitalocean.DataReadError", "line_number": 21, "usage_type": "name"}, {"api_name": "digitalocean.TokenError", "line_number": 21, "usage_type": "name"}, {"api_name": "dobg.exceptions.configexceptions.InvalidTokenException", "line_number": 22, "usage_type": "name"}]}
+{"seq_id": "213351655", "text": "\n\nimport requests\nimport json\nimport re\nimport time\n\nheader = {\n'Cookie': 'uuid_tt_dd=-6992902498996105809_20170209; __utma=17226283.1648016979.1486950242.1486964688.1486964688.1; __utmz=17226283.1486964688.1.1.utmcsr=yiibai.com|utmccn=(referral)|utmcmd=referral|utmcct=/python/python3-webbug-series1.html; _ga=GA1.2.1648016979.1486950242; __message_district_code=310000; _message_m=iihvphhau1inlxoz02t2zd1f; UserName=zhz459880251; UserInfo=%2F1ipRJ88JEjQ3UVVFN%2FqJgTvMKI6Hg10pio9Dv%2Fkd%2Bx43xIyYqx%2FO4iFCwWbOSmSSmHtwzCCJiV2q9AzBkBm3vSP8yPnT3QeKp78DvnX0Sz%2FSUArjudFBlFftImIR66H; UserNick=zhz459880251; AU=676; UN=zhz459880251; UE=\"\"; BT=1497318187766; access-token=f718a0e0-8f38-4feb-88fb-e8c5cc6c0626; __message_sys_msg_id=0; __message_gu_msg_id=0; __message_cnel_msg_id=0; __message_in_school=0; dc_tos=orgq44; dc_session_id=1497318186579; Hm_lvt_6bcd52f51e9b3dce32bec4a3997715ac=1497268230,1497269318,1497271488,1497318189; Hm_lpvt_6bcd52f51e9b3dce32bec4a3997715ac=1497318201',\n'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Mobile Safari/537.36'\n}\n\ndef get_single_article(art_id):\n response = requests.get('http://write.blog.csdn.net/mdeditor/getArticle?id={}&username=zhz459880251'.format(art_id), headers=header)\n json_response = json.loads(response.text)\n data = json_response['data']\n title = data['title']\n markdowncontent = data['markdowncontent']\n create = data['create']\n url = data['url']\n categories = data['categories'] # list\n tags = data['tags'] # list\n con = data['content'] # html\n description = data['description']\n head = '''\n---\ntitle: {c_title}\ndate: {c_create}\ncategories : {c_cats}\n---\n \n'''.format(\n c_title = title.replace(\"/\", ' '),\n c_create = create,\n c_cats = categories,\n c_desc = re.sub('( )|(\\n)', '', description),)\n\n content = '''\n{c_content}\n \n原文链接: {c_url}\n\n'''.format(\n c_url = url,\n c_content = markdowncontent,\n )\n content = head + re.sub(r'(#*)(.*)', r'\\1 \\2', content)\n write_file(content, title)\n\ndef write_file(content, title):\n with open('/Users/zhz/Blogs/HexoBlog/source/_posts/' + title.replace(\"/\", ' ') + '.md', 'w') as f:\n f.write(content)\n\ndef main():\n pages = ['http://write.blog.csdn.net/postlist/0/0/enabled/' + str(i) for i in range(1, 6)]\n for page in pages:\n response = requests.get(page, headers=header)\n match = re.findall('/article/details/(\\d+)', response.text)\n for p in match:\n get_single_article(p)\n time.sleep(1)\n print('正在生成 {}'.format(p))\n\nif __name__ == '__main__':\n main()", "sub_path": "other/LLDB.py", "file_name": "LLDB.py", "file_ext": "py", "file_size_in_byte": 2716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 57, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}]}
+{"seq_id": "79923737", "text": "from flask import Blueprint, render_template, request, json, jsonify\nfrom flaskblog import db\nfrom flaskblog.models import Post\nfrom flask_sqlalchemy import SQLAlchemy\nimport sqlite3\n\nmain = Blueprint('main', __name__)\n\n#Sets route for home page and html template\n#Lists 5 most recent blog posts on home page\n@main.route(\"/\")\n@main.route(\"/home\")\ndef home():\n page = request.args.get('page', 1, type=int)\n posts = Post.query.order_by(Post.date_posted.desc()).paginate(page=page, per_page=5)\n return render_template('home.html', posts=posts)\n\n#Sets route for about page and html template\n@main.route(\"/about\")\ndef about():\n return render_template('about.html', title='About')\n\n#Sets route for an api endpoint returning JSON \n@main.route('/api/health')\ndef health():\n health = {\n \"status\": \"ok\"\n}\n\n return jsonify(health)\n\n#Sets route to see all blog posts from api endpoint\n@main.route(\"/api/posts\") \ndef view(): \n con = sqlite3.connect(\"site.db\") \n con.row_factory = sqlite3.Row \n cur = con.cursor() \n cur.execute(\"SELECT * FROM post\") \n rows = cur.fetchall() \n return render_template(\"table_posts.html\",rows = rows)\n\n#Sets route to see all users from api endpoint\n@main.route(\"/api/users\") \ndef table_users(): \n con = sqlite3.connect(\"site.db\") \n con.row_factory = sqlite3.Row \n cur = con.cursor() \n cur.execute(\"SELECT * FROM user\") \n rows = cur.fetchall() \n return render_template(\"table_users.html\",rows = rows)\n\n\n", "sub_path": "flaskblog/main/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 1503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flaskblog.models.Post.query.order_by", "line_number": 15, "usage_type": "call"}, {"api_name": "flaskblog.models.Post.query", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flaskblog.models.Post", "line_number": 15, "usage_type": "name"}, {"api_name": "flaskblog.models.Post.date_posted.desc", "line_number": 15, "usage_type": "call"}, {"api_name": "flaskblog.models.Post.date_posted", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}]}
+{"seq_id": "191426247", "text": "from functools import wraps\n\n\ndef singleton(cls):\n instances = {}\n\n @wraps(cls)\n def get_instance(*args, **kwargs):\n if cls not in instances:\n instances[cls] = cls(*args, **kwargs)\n return instances[cls]\n\n return get_instance\n\n\n@singleton\nclass MyClass(object):\n def __init__(self, a, b):\n self.a = a\n self.b = b\n\n\nc1 = MyClass(1, 2)\nc2 = MyClass(3, 4)\nprint(c1 == c2)\nprint(c1 is c2)\n", "sub_path": "python/standard_library/Design_patterns/singleton_test/singleton_test002.py", "file_name": "singleton_test002.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "functools.wraps", "line_number": 7, "usage_type": "call"}]}
+{"seq_id": "424228757", "text": "#!/usr/bin/env python\n\"\"\"multipage-ocr.py\n\nUsage:\n multipage-ocr.py <input_path> <output_path> [options]\n\nOptions:\n --density N dpi density for ImageMagick convert [default: 300]\n --depth N bit depth for ImageMagick convert [default: 8]\n --imageformat FORMAT image format (e.g., jpg, png, tif) [default: jpg]\n --psm N tesseract layout analysis mode [default: 3]\n\"\"\"\nfrom docopt import docopt\nimport os\nimport os.path\nimport tempfile\nfrom PyPDF2 import PdfFileReader\nfrom tqdm import trange\nfrom glob import glob\nfrom io import StringIO\nfrom joblib import Parallel, delayed\nimport time\n\n\ndef convert_one_page(args, page_number, tmp_dir):\n tmp_file_path = os.path.join(\n tmp_dir,\n '{page:010d}.{imageformat}'.format(\n page=page_number,\n imageformat=args['--imageformat']\n )\n )\n\n cmd = '''convert \\\n -density {density}\\\n -depth {depth}\\\n {input_path}[{page}]\\\n -background white\\\n {tmp_file_path}'''.format(\n density=args['--density'],\n depth=args['--depth'],\n input_path=args['<input_path>'],\n page=page_number,\n tmp_file_path=tmp_file_path\n )\n os.system(cmd)\n\n text_file_path = os.path.join(tmp_dir, '{page:010d}'.format(\n page=page_number)\n )\n\n cmd = '''tesseract \\\n -psm {psm} \\\n {tmp_file_path} \\\n {text_file_path} \\\n > /dev/null 2>&1\n '''.format(\n psm=args['--psm'],\n tmp_file_path=tmp_file_path,\n text_file_path=text_file_path)\n os.system(cmd)\n\n\ndef main(args):\n args['<input_path>'] = os.path.realpath(args['<input_path>'])\n args['<output_path>'] = os.path.realpath(args['<output_path>'])\n args['--density'] = int(args['--density'])\n args['--depth'] = int(args['--depth'])\n args['--psm'] = int(args['--psm'])\n assert os.path.exists(args['<input_path>'])\n assert args['<input_path>'].endswith(\".pdf\")\n\n num_pages = PdfFileReader(open(args['<input_path>'], 'rb')).getNumPages()\n\n tmp_dir = tempfile.mkdtemp(\n prefix='{timestamp}_{inputfilebase}_mocr_'.format(\n timestamp=time.strftime('%Y%m%d%H%M'),\n inputfilebase=os.path.split(args['<input_path>'])[1],\n ),\n dir='.'\n )\n\n Parallel(n_jobs=6)(\n delayed(convert_one_page)(args, page_number, tmp_dir) for page_number in range(num_pages))\n\n with open(args['<output_path>'], 'w') as outfile:\n for path in sorted(glob(os.path.join(tmp_dir, '*.txt'))):\n outfile.write(open(path).read())\n\nif __name__ == '__main__':\n args = docopt(__doc__, version='multipage-ocr.py 1.0')\n main(args)\n", "sub_path": "multipage-ocr.py", "file_name": "multipage-ocr.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 73, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 75, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "joblib.Parallel", "line_number": 83, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 84, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "docopt.docopt", "line_number": 91, "usage_type": "call"}]}
+{"seq_id": "245135234", "text": "\nimport wrds\nimport pandas as pd\nimport os\nimport argparse\n\n\n# os.getcwd()\n# # list tables in the crsp dataset\n# db.list_tables(library=\"compd\")\n\n# # get values from a single table\n# db.get_table(library=\"compd\",table=\"names_ix\",obs=10)\n# print(db.get_table(library=\"compd\",table=\"co_hgic\",obs=10).to_string())\n# print(db.get_table(library=\"compd\",table=\"r_giccd\",obs=10).to_string())\n\n# # select top 10 records from SP500 daily table to test connection\n# db.raw_sql(\"select caldt, spindx, sprtrn, totval from crsp.dsp500 limit 10\")\n# db.raw_sql(\"select caldt, spindx, sprtrn, totval from crsp.dsp500 where caldt >= '1989-01-01' limit 10\")\n\n# db.raw_sql(\"select * from compd.secd where gvkey='001487' and datadate='2018-01-31'\")\n# db.raw_sql(\"select * from compd.idxcst_his h where gvkeyx='174041'\")\" and h.from <='2018-01-31' and case when h.thru is null then '9999-12-31' else h.thru end >= '2018-01-31'\")\n# db.raw_sql(\"select distinct indextype from compd.names_ix\")\n# print(\n# db.raw_sql(f\"\"\"select distinct n.conm, n.gvkeyx, n.indextype\n# from compd.names_ix n \n# join compd.idxcst_his c \n# on n.gvkeyx = c.gvkeyx\n# join compd.secd s\n# on c.gvkey = s.gvkey\n# and s.iid = c.iid\n# and s.datadate >= c.from\n# and s.datadate <= case when c.thru is null then '9999-12-31' else c.thru end\n\n# where n.indextype not in ('GIND', 'GSUBIND', 'SECTOR', 'SPECIALTY','INDUSTRY','HOME PRICE','SELECT IND', 'GGROUP', 'GSECTOR')\n# and s.datadate = '2018-01-31' LIMIT 2000\"\"\").to_string())\n\n\ndef wrds_connect():\n # set up connection to WRDS\n db = wrds.Connection(wrds_username=\"rskowron\")\n # db.create_pgpass_file() # create pgpass file to no longer require pwd\n return db \n\ndef get_sp500_idx_return_data(db, startdate, enddate):\n \"\"\"retrieves return information for the S&P500 index between the specified dates. Uses CRSP\"\"\"\n data = db.raw_sql(f\"select caldt, spindx, sprtrn, totval from crsp.dsp500 where caldt >= '{startdate}' and caldt <= '{enddate}'\")\n return data \n\ndef get_sp500_const_return_data(db, startdate, enddate):\n \"\"\"retrieves return information for S&P500 constituents between the specified dates. Uses CRSP\"\"\"\n data = db.raw_sql(f\"\"\"select s.date, sn.ticker, s.shrout, s.prc, s.ret, sn.siccd, abs(s.prc)*s.shrout as mtkcap\n from crsp.dsp500list as l join crsp.dsf as s \n on l.permno=s.permno and s.date >= l.start and s.date <= l.ending\n join crsp.stocknames sn\n on s.permno = sn.permno and sn.namedt <= s.date and sn.nameenddt >= s.date\n where s.date >= '{startdate}' and s.date <= '{enddate}'\n order by s.date, sn.ticker\"\"\")\n return data \n\ndef get_idx_return_data(db, startdate, enddate, idx_list):\n data = db.raw_sql(f\"\"\"select d.datadate, n.gvkeyx, n.conm, d.prccd as close, coalesce(d.prccddiv, d.prccd) as totalreturn \n from compd.names_ix n join compd.idx_daily d \n on n.gvkeyx = d.gvkeyx \n where n.gvkeyx in ('{\"','\".join(idx_list)}') \n and datadate >= '{startdate}' and datadate <= '{enddate}'\"\"\")\n return data\n\ndef get_idx_const_return_data(db, startdate, enddate, idx_list):\n data = db.raw_sql(f\"\"\"select s.datadate, n.gvkeyx, s.gvkey, n.conm, s.tic, s.prccd as close, s.cshoc*s.prccd as mktcap, \n rgsector.gicdesc as gsector, rggroup.gicdesc as ggroup, rgind.gicdesc as gind, rgsubind.gicdesc as gsubind\n from compd.names_ix n \n join compd.idxcst_his c \n on n.gvkeyx = c.gvkeyx\n join compd.secd s\n on c.gvkey = s.gvkey\n and s.iid = c.iid\n and s.datadate >= c.from\n and s.datadate <= case when c.thru is null then '9999-12-31' else c.thru end\n\n join compd.co_hgic g\n on s.gvkey = g.gvkey\n and s.datadate >= g.indfrom\n and s.datadate <= case when g.indthru is null then '9999-12-31' else g.indthru end\n\n join compd.r_giccd rgsector\n on g.gsector = rgsector.giccd and rgsector.gictype = 'GSECTOR'\n\n join compd.r_giccd rggroup\n on g.ggroup = rggroup.giccd and rggroup.gictype = 'GGROUP'\n\n join compd.r_giccd rgind\n on g.gind = rgind.giccd and rgind.gictype = 'GIND'\n\n join compd.r_giccd rgsubind\n on g.gsubind = rgsubind.giccd and rgsubind.gictype = 'GSUBIND'\n\n where n.gvkeyx in ('{\"','\".join(idx_list)}') \n and s.datadate >= '{startdate}' and s.datadate <= '{enddate}'\"\"\")\n return data\n\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Source data from WRDS')\n parser.add_argument('startdate', type=str, help='starting date for retrieval')\n parser.add_argument('enddate', type=str, help='ending date for retrieval')\n parser.add_argument('folder', type=str, help='folder to store the data in')\n\n args = parser.parse_args()\n\n # connect to wrds\n db = wrds_connect()\n startdate = args.startdate\n enddate = args.enddate\n\n # create folder for raw output\n if not os.path.exists(args.folder):\n os.mkdir(args.folder)\n\n # get S&P500 data and write to file\n idx_data = get_sp500_idx_return_data(db, startdate, enddate)\n idx_data.to_csv(f\"{args.folder}/spx-returns.csv\", index=False)\n\n # get const data and write to file\n const_data = get_sp500_const_return_data(db, startdate, enddate)\n const_data.to_csv(f\"{args.folder}/spx-const-returns.csv\", index=False)\n\n # get more index data from compustat\n # S&P500, S&P1500 (composite), S&P600 (smallcap), S&P 100 (large cap)\n idx_list = [\"000003\", \"031855\", \"030824\", \"000664\"]\n for i in idx_list:\n more_idx_data = get_idx_return_data(db, startdate, enddate, [i])\n more_idx_const = get_idx_const_return_data(db, startdate, enddate, [i])\n\n more_idx_data.to_csv(f\"{folder}/{i}-returns.csv\", index=False)\n more_idx_const.to_csv(f\"{folder}/{i}-const-returns.csv\", index=False)\n\n # close the connection\n db.close()", "sub_path": "data/source-data.py", "file_name": "source-data.py", "file_ext": "py", "file_size_in_byte": 5867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "wrds.Connection", "line_number": 41, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 119, "usage_type": "call"}]}
+{"seq_id": "36371473", "text": "print('Loading Modules!')\nimport discord\nimport asyncio\nimport lastpull\nimport random\nimport imp\nimport atexit\nprint('------')\n\nprint('Loading Client')\nclient = discord.Client()\nprint('Waiting for Login...')\n\n@client.event\nasync def on_ready():\n\tprint('Logged in as:')\n\tprint(client.user.name)\n\tprint(client.user.id)\n\tprint('------')\n\n@client.event\nasync def on_message(message):\n\tauth = message.author\n\ts_auth = str(auth)\n\ttext = message.content\n\tcmd = text.lower().split(' ')\n\tchnl = message.channel\n\t\n\tif cmd[0].startswith('!help'):\n\t\tprint('Help command from %s' % s_auth)\n\t\thelpmsg = '''Hi %s, here are my commands! All commands start with an exclamation point (!)\n```python\nreload = \"Reloads all active dynamic modules\"\nexit = \"Closes me down\"\nroll = \"Rolls 1-100 for all online members on this server\"\nfm = \"Scrobbling from Last.FM. Type '!fm help' for more help\"\n```''' % (s_auth[:s_auth.find('#')])\n\t\tthismessage = await client.send_message(chnl, 'Help is on the way %s!' % (s_auth[:s_auth.find('#')]))\n\t\tawait client.send_message(auth, helpmsg)\n\t\tawait asyncio.sleep(3)\n\t\tawait client.delete_message(thismessage)\n\n\telif cmd[0].startswith('!reload'):\n\t\tprint('Scrobbler: Cleaning Up')\n\t\tawait lastpull.cleanup(client)\n\t\tprint('Scrobbler: Reloading Modules')\n\t\timp.reload(lastpull)\n\t\tprint('Scrobbler: Done!')\n\n\telif cmd[0].startswith('!exit'):\n\t\tprint('Exit Command from %s' % s_auth)\n\t\tawait client.send_message(chnl, 'Bye %s!' % s_auth)\n\t\tawait clientexit()\n\n\telif cmd[0].startswith('!roll'):\n\t\trandom.seed()\n\t\tplayers = [(x.name,str(random.randint(1,100))) for x in chnl.server.members if x.status == discord.Status.online and not x.bot]\n\t\tmsg = '\\n'.join([' = '.join(x) for x in players])\n\t\tawait client.send_message(chnl, '```python\\n' + msg + '\\n```')\n\n\telif cmd[0].startswith('!fm'):\n\t\tprint('Last.fm Command from %s' % s_auth)\n\t\tawait lastpull.process(client,message)\n\n\tif message.type == discord.MessageType.pins_add and auth == client.user:\n\t\tawait client.delete_message(message)\n\n@atexit.register\nasync def clientexit():\n\tawait lastpull.cleanup(client)\n\tawait client.logout()\n\nclient.run('MjYwOTYxOTE1MDk2OTI0MTYw.Czt_ig.-6CB98v-JDNgLbNByphd1nHHzv4')", "sub_path": "archive/scrobblerbot.py", "file_name": "scrobblerbot.py", "file_ext": "py", "file_size_in_byte": 2170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "discord.Client", "line_number": 11, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "lastpull.cleanup", "line_number": 45, "usage_type": "call"}, {"api_name": "imp.reload", "line_number": 47, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "discord.Status", "line_number": 57, "usage_type": "attribute"}, {"api_name": "lastpull.process", "line_number": 63, "usage_type": "call"}, {"api_name": "discord.MessageType", "line_number": 65, "usage_type": "attribute"}, {"api_name": "lastpull.cleanup", "line_number": 70, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 68, "usage_type": "attribute"}]}
+{"seq_id": "646943717", "text": "#!/usr/bin/env python\r\n'''\r\nDeveloper: Christian Nazili - Universite de Namur, Namur, Belgium\r\n'''\r\n\r\nfrom collections import defaultdict\r\nfrom itertools import combinations, product, izip\r\n\r\ndef freq_words_with_mismatches(seq, k, d):\r\n \"\"\"Returns all most frequent k-mers with up to d mismatches in the dna sequence seq.\"\"\"\r\n # Frequency analysis so we don't generate mismatches for the same k-mer more than once.\r\n kmer_freq = defaultdict(int)\r\n for i in xrange(len(seq)-k+1):\r\n kmer_freq[seq[i:i+k]] += 1\r\n\r\n # Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.\r\n mismatch_count = defaultdict(int)\r\n for kmer, freq in kmer_freq.iteritems():\r\n for mismatch in kmer_mismatches(kmer, d):\r\n mismatch_count[mismatch] += freq\r\n\r\n # Computing the maximum value is somewhat time consuming to repeat, so only do it once!\r\n max_count = max(mismatch_count.values())\r\n return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])\r\n\r\n\r\ndef kmer_mismatches(kmer, d):\r\n \"\"\"Returns all k-mers that are within d mismatches of the given k-mer.\"\"\"\r\n mismatches = [kmer] # Initialize mismatches with the k-mer itself (i.e. d=0).\r\n alt_bases = {'A':'CGT', 'C':'AGT', 'G':'ACT', 'T':'ACG'}\r\n for dist in xrange(1, d+1):\r\n for change_indices in combinations(xrange(len(kmer)), dist):\r\n for substitutions in product(*[alt_bases[kmer[i]] for i in change_indices]):\r\n new_mistmatch = list(kmer)\r\n for idx, sub in izip(change_indices, substitutions):\r\n new_mistmatch[idx] = sub\r\n mismatches.append(''.join(new_mistmatch))\r\n return mismatches\r\n", "sub_path": "Python_Scripts/Finding Hidden Messages in DNA (Bioinformatics I)/Semaine 3/MisMatchList.py", "file_name": "MisMatchList.py", "file_ext": "py", "file_size_in_byte": 1736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 17, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 32, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 33, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 35, "usage_type": "call"}]}
+{"seq_id": "539292574", "text": "\"\"\"\n6.12.2014\nby real\n\nTesting the idea of routing in a mesh using a Virtual DHT. Inspired by\n\"Pushing Chord into the Underlay\".\n\"\"\"\nimport random\nimport heapq\nimport bisect\nfrom collections import namedtuple\n\n# Number of bits in ident number:\nIDENT_BITS = 40\n\n# Maximum possible identity value.\n# Note that this value isn't really the maximum. It is maximum + 1.\nMAX_IDENT = 2**IDENT_BITS\n\n# Fingers we are interested in:\nSUCC_FINGERS = [0]\nPRED_FINGERS = [0]\n\n# SUCC_FINGNERS = list(range(IDENT_BITS))\n# PRED_FINGERS = list(range(IDENT_BITS))\n\n# A named tuple for Known node:\n# path_len is the path length source node,\n# ident is the identity value of the Known node.\n# lindex is the list index of the Known node.\nKnode = namedtuple('Knode', ['path_len', 'ident','lindex'])\n\n\ndef rand_ident():\n \"\"\"\n Generate random identity in the range [0,MAX_IDENT)\n \"\"\"\n return random.randrange(MAX_IDENT)\n\ndef dist_ident(x,y):\n \"\"\"\n Distance between two nodes (According to ident):\n \"\"\"\n return (y - x) % MAX_IDENT\n\ndef remove_knodes_duplicates(knodes):\n \"\"\"\n Go over a list of knodes, and remove knodes that show up more than once.\n In case of node ident showing more than once, we pick the shorter path.\n \"\"\"\n if len(knodes) == 0:\n return knodes\n\n knodes.sort(key=lambda kn:(kn.ident,kn.path_len))\n\n # Resulting array\n cur_ident = knodes[0].ident\n res = [knodes[0]]\n for kn in knodes[1:]:\n if kn.ident != cur_ident:\n cur_ident = kn.ident\n res.append(kn)\n\n return res\n\n\n\n# A node:\nclass Node():\n def __init__(self,fk,ident=None):\n \"\"\"\n Initialize a node.\n \"\"\"\n # If ident value is not specified, we randomize one:\n if ident is None:\n self.ident = rand_ident()\n else:\n self.ident = ident\n\n # Argument related to amount of known best finger candidates.\n self.fk = fk\n\n # Initialize list of known nodes:\n self.neighbours = []\n self.best_finger_succ = [list() for f in range(IDENT_BITS)]\n self.best_finger_pred = [list() for f in range(IDENT_BITS)]\n\n def get_finger_succ_loc(self,f):\n \"\"\"\n Get the exact location of successor finger f.\n \"\"\"\n return (self.ident + 2**f) % MAX_IDENT\n\n def get_finger_pred_loc(self,f):\n \"\"\"\n Get the exact location of predecessor finger f.\n \"\"\"\n return (self.ident - 2**f) % MAX_IDENT\n\n def set_neighbours(self,knodes):\n \"\"\"\n set knodes to be the neighbours of this Node.\n \"\"\"\n self.neighbours = []\n for kn in knodes:\n # Make sure we don't have ourselves as a neighbour:\n if kn.ident == self.ident:\n continue\n # A neighbour has a path length 1:\n self.neighbours.append(\\\n kn._replace(path_len=1))\n\n\n # Update known nodes:\n self.add_known_nodes(0,self.neighbours)\n\n def add_known_best_finger_succ(self,f,knodes):\n \"\"\"\n If any of the nodes in knodes is a better candidate for the f's\n successor finger, we replace.\n \"\"\"\n pool = remove_knodes_duplicates(self.best_finger_succ[f] + knodes)\n self.best_finger_succ[f] = heapq.nsmallest(self.fk,pool,key=lambda kn:\\\n (dist_ident(self.get_finger_succ_loc(f),kn.ident),kn.path_len))\n\n def add_known_best_finger_pred(self,f,knodes):\n \"\"\"\n If any of the nodes in knodes is a better candidate for the f's\n predecessor finger, we replace.\n \"\"\"\n pool = remove_knodes_duplicates(self.best_finger_pred[f] + knodes)\n self.best_finger_pred[f] = heapq.nsmallest(self.fk,pool,key=lambda kn:\\\n (dist_ident(kn.ident,self.get_finger_pred_loc(f)),kn.path_len))\n\n def add_known_nodes(self,source_path_len,knodes):\n \"\"\"\n Add a set of known nodes to self.known .\n Take the change of path_len into acount.\n \"\"\"\n # Update the path lengths:\n updated_knodes = [kn._replace(path_len=kn.path_len+source_path_len)\\\n for kn in knodes]\n\n # Make sure the node self.ident is not inside:\n updated_knodes = list(filter(lambda kn:kn.ident != self.ident,\\\n updated_knodes))\n\n for f in SUCC_FINGERS:\n self.add_known_best_finger_succ(f,updated_knodes)\n for f in PRED_FINGERS:\n self.add_known_best_finger_pred(f,updated_knodes)\n\n\n def get_known(self):\n \"\"\"\n Return a list of all known nodes.\n Items in the list are unique.\n \"\"\"\n pool = set()\n\n # Add neighbours:\n pool.update(self.neighbours)\n\n # Add fingers:\n for f in SUCC_FINGERS:\n pool.update(self.best_finger_succ[f])\n for f in PRED_FINGERS:\n pool.update(self.best_finger_pred[f])\n return list(pool)\n\n def get_close(self):\n \"\"\"\n Return a list of the closest known nodes.\n Close in the virtual sense, to self.ident,\n and to the possible fingers on the Chord DHT.\n \"\"\"\n pool = set()\n\n for f in SUCC_FINGERS:\n pool.update(self.best_finger_succ[f])\n for f in PRED_FINGERS:\n pool.update(self.best_finger_pred[f])\n\n return list(pool)\n\n def get_best_succ_finger(self,f):\n \"\"\"\n Get the best successor for finger f.\n \"\"\"\n return min(self.best_finger_succ[f],\\\n key=lambda kn:dist_ident(self.get_finger_succ_loc(f),kn.ident))\n\n\n def get_best_pred_finger(self,f):\n \"\"\"\n Get the best predecessor for finger f.\n \"\"\"\n return min(self.best_finger_pred[f],\\\n key=lambda kn:dist_ident(kn.ident,self.get_finger_pred_loc(f)))\n\n\n# Simulation for a mesh network with Virtual DHT abilities:\nclass VirtualDHT():\n def __init__(self,n,fk,nei):\n\n # Amount of nodes:\n self.num_nodes = n\n # Half amount of neighbours per node:\n self.nei = nei\n # Known finger nodes parameter:\n self.fk = fk\n\n # Generate nodes and neighbours links:\n self.gen_nodes()\n self.rand_neighbours()\n\n\n def gen_nodes(self):\n \"\"\"\n Generate n nodes with random identity numbers.\n \"\"\"\n self.nodes = []\n for i in range(self.num_nodes):\n self.nodes.append(Node(self.fk))\n\n def make_knode(self,i,path_len=0):\n \"\"\"\n Given an index i of a node in self.nodes,\n create a Knode tuple. Optionally set path_len.\n \"\"\"\n return Knode(path_len=path_len,\\\n ident=self.nodes[i].ident,\\\n lindex=i)\n\n def rand_neighbours(self):\n \"\"\"\n Randomize immediate neighbours links between the nodes.\n \"\"\"\n # Initialize neighbours sets as empty sets:\n nodes_nei = [set() for _ in range(self.num_nodes)]\n\n for i,nd in enumerate(self.nodes):\n # Sample a set of indices (Which represent a set of nodes).\n # Those nodes will be nd's neighbours:\n nodes_nei[i].update(\\\n random.sample(range(self.num_nodes),self.nei))\n\n # Remove myself:\n nodes_nei[i].discard(i)\n\n # To make the graph undirected, we add i to be neighbour of all\n # i's neighbours:\n for j in nodes_nei[i]:\n nodes_nei[j].add(i)\n\n for i,nd in enumerate(self.nodes):\n # Initialize a list of neighbours:\n nd.set_neighbours(map(self.make_knode,list(nodes_nei[i])))\n\n def iter_node(self,i):\n \"\"\"\n Ask all known nodes for better known nodes.\n i is the index of the node in self.nodes.\n \"\"\"\n nd = self.nodes[i]\n for kn in nd.get_close():\n # for kn in nd.get_known():\n # for kn in nd.neighbours:\n kn_node = self.nodes[kn.lindex]\n nd.add_known_nodes(kn.path_len,kn_node.get_close())\n\n def iter_all(self):\n \"\"\"\n Perform a full iteration, where all nodes ask other nodes for better\n nodes.\n \"\"\"\n for i in range(self.num_nodes):\n self.iter_node(i)\n\n def converge(self,max_iters=0x10):\n \"\"\"\n \"converge\" the DHT by iterating until nothing changes.\n \"\"\"\n for i in range(max_iters):\n self.iter_all()\n print(\".\",end=\"\",flush=True)\n if self.verify():\n print(\"\\nReached correct succ and pred + fingers.\")\n return\n\n print(\"\\nmax_iters acheived.\")\n\n def verify_succ_pred_fingers(self):\n \"\"\"\n Verify the succ and pred fingers found for all nodes.\n \"\"\"\n # Get all nodes (as Knodes), and sort them according to ident:\n lnodes = list(map(self.make_knode,range(self.num_nodes)))\n lnodes.sort(key=lambda ln:ln.ident)\n idents = [ln.ident for ln in lnodes]\n\n for i,ln in enumerate(lnodes):\n nd = self.nodes[ln.lindex]\n \n for f in SUCC_FINGERS:\n ind = bisect.bisect_left(\\\n idents,nd.get_finger_succ_loc(f))\n f_succ = lnodes[(ind) % self.num_nodes]\n\n if nd.get_best_succ_finger(f).ident != f_succ.ident:\n return False\n\n for f in PRED_FINGERS:\n ind = bisect.bisect_right(\\\n idents,nd.get_finger_pred_loc(f))\n f_pred = lnodes[(ind-1) % self.num_nodes]\n\n if nd.get_best_pred_finger(f).ident != f_pred.ident:\n return False\n\n\n return True\n\n def verify(self):\n \"\"\"\n Verify all the found nodes.\n \"\"\"\n if not self.verify_succ_pred_fingers():\n return False\n\n return True\n\n def sample_path_len(self,num_samp=0x200):\n \"\"\"\n Find an approximated average to the path_len to successor and\n predecessor.\n \"\"\"\n sum_finger_path = 0.0\n\n # We don't want to sample more than the total amount of nodes:\n num_samp = min([num_samp,self.num_nodes])\n\n snodes = random.sample(self.nodes,num_samp)\n for sn in snodes:\n for f in SUCC_FINGERS:\n sum_finger_path += sn.get_best_succ_finger(f).path_len\n for f in PRED_FINGERS:\n sum_finger_path += sn.get_best_pred_finger(f).path_len\n\n num_fingers = len(SUCC_FINGERS) + len(PRED_FINGERS)\n return sum_finger_path/(num_samp * num_fingers)\n\ndef go():\n print(\"SUCC_FINGERS: \",SUCC_FINGERS)\n print(\"PRED_FINGERS: \",PRED_FINGERS)\n for i in range(7,16):\n print(\"i =\",i)\n nei = i # amount of neighbours\n fk = i\n # fk = i\n # fk = 1\n n = 2**i\n vd = VirtualDHT(n,fk=fk,nei=nei)\n vd.converge(max_iters=0x80)\n print(vd.sample_path_len())\n \n\nif __name__ == \"__main__\":\n go()\n\n\n", "sub_path": "virtual_dht/custom_fingers.py", "file_name": "custom_fingers.py", "file_ext": "py", "file_size_in_byte": 10969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "collections.namedtuple", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 38, "usage_type": "call"}, {"api_name": "heapq.nsmallest", "line_number": 123, "usage_type": "call"}, {"api_name": "heapq.nsmallest", "line_number": 132, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 246, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 306, "usage_type": "call"}, {"api_name": "bisect.bisect_right", "line_number": 314, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 343, "usage_type": "call"}]}
+{"seq_id": "491140772", "text": "import argparse\nimport json\nimport logging\nimport os\nimport sys\nimport warnings\n\nfrom lxml import etree\n\nfile_name = os.path.basename(__file__).replace('.py', '')\nproj_dir = os.path.normpath(os.path.join(os.path.dirname(__file__), '..'))\ndata_dir = os.path.join(proj_dir, 'data')\n\n# configure logging\nlog = logging.getLogger(file_name)\nlog.setLevel(logging.ERROR)\n\nch = logging.StreamHandler(sys.stdout)\nch.setLevel(logging.ERROR)\n\nformatter = logging.Formatter(\n u'[%(name)s:%(levelname)s]: %(message)s')\nch.setFormatter(formatter)\n\nlog.addHandler(ch)\n\n# ignore useless warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n# script globals\nSETTINGS = dict()\nSETTINGS['xml_dir'] = data_dir\n\n\nclass ExistingDir(argparse.Action):\n def __call__(self, parser, namespace, values, option_string = None):\n possible_dir = os.path.abspath(values)\n if not os.path.isdir(possible_dir):\n raise argparse.ArgumentTypeError(\n \"readable_dir:{0} is not a valid path\".format(possible_dir))\n if os.access(possible_dir, os.R_OK):\n setattr(namespace, self.dest, possible_dir)\n else:\n raise argparse.ArgumentTypeError(\n \"readable_dir:{0} is not a readable dir\".format(\n possible_dir))\n\n\ndef set_settings(options):\n # process debug\n if options.debug:\n SETTINGS['debug'] = True\n log.setLevel(logging.DEBUG)\n ch.setLevel(logging.DEBUG)\n log.debug('Args: %s', options)\n else:\n SETTINGS['debug'] = False\n\n # process debug\n if options.force:\n SETTINGS['force'] = True\n else:\n SETTINGS['force'] = False\n\n # initialize data path dictionary\n SETTINGS['data_paths'] = dict()\n\n # process rome 2 files\n if options.rome2:\n r2_path = os.path.abspath(options.rome2)\n SETTINGS.get('data_paths')['rome2'] = r2_path\n\n # process attila files\n if options.attila:\n attila_path = os.path.abspath(options.attila)\n SETTINGS.get('data_paths')['attila'] = attila_path\n\n\ndef set_options(arguments):\n parser = argparse.ArgumentParser(prog=__doc__,\n description='')\n\n # process command line args\n parser.add_argument('-d',\n '--debug',\n help='Display debug messages.',\n action=\"store_true\")\n\n parser.add_argument('-f',\n '--force',\n help='Force overwrite of existing data files.',\n action=\"store_true\")\n\n parser.add_argument('-r',\n '--rome2',\n action=ExistingDir,\n help='Path to \"Total War: Rome 2\" XML files to '\n 'convert into JSON data files.')\n\n parser.add_argument('-a',\n '--attila',\n action=ExistingDir,\n help='Path to \"Total War: Attila\" XML files to '\n 'convert into JSON data files.')\n\n set_settings(parser.parse_args(arguments))\n\n\ndef get_column_value(element, name, field):\n # initialize return value\n return_value = None\n\n # get column data\n column = element.find(field['column'])\n # if column is not optional or optional and not none\n if 'optional' not in field or \\\n (field.get('optional') and column is not None):\n # add field\n log.debug('column %s: %s', name, column.text)\n return_value = column.text\n\n # return\n return return_value\n\n\ndef get_joined_column(joins, element, select_column):\n # if no joins, return none\n if len(joins) == 0:\n return None\n\n # pop first join on list\n join = joins.pop(0)\n\n # parse join file as xml\n join_file = os.path.join(SETTINGS['xml_dir'], join['join_file'])\n log.debug('reading xml file: %s', join_file)\n join_tree = etree.parse(join_file)\n\n # get source column\n source_column = element.find(join['source_column']).text\n\n # set xpath query for elements\n xpath_query = './' + os.path.splitext(join['join_file'])[0]\n xpath_query += '[{0}=\\\"{1}\\\"]'.format(join.get('join_column'),\n source_column)\n if 'where' in join:\n xpath_query += join.get('where')\n log.debug('using xpath query: %s', xpath_query)\n\n # get elements from xpath query on tree\n elements = join_tree.xpath(xpath_query)\n\n # if no elements, return none\n if len(elements) == 0:\n return None\n\n # get first element from elements\n element = elements[0]\n\n if len(joins) > 0:\n return get_joined_column(joins, element, select_column)\n\n # return value\n if element.find(select_column) is not None:\n return element.find(select_column).text\n else:\n return None\n\n\ndef get_joined_array(joins, source_elements, select_columns):\n # if no joins, return none\n if len(joins) == 0:\n return None\n\n # pop first join on list\n join = joins.pop(0)\n\n # parse join file as xml\n join_file = os.path.join(SETTINGS['xml_dir'], join['join_file'])\n log.debug('reading xml file: %s', join_file)\n join_tree = etree.parse(join_file)\n\n # initialize elements list\n elements = list()\n\n # get elements list\n for e in source_elements:\n # select column tag\n source_columns = e.xpath('./{0}'.format(join['source_column']))\n\n # for each source column\n for sc in source_columns:\n # get source column value\n source_column = sc.text\n\n # set xpath query for elements\n xpath_query = './' + os.path.splitext(join['join_file'])[0]\n xpath_query += '[{0}=\\\"{1}\\\"]'.format(join.get('join_column'),\n source_column)\n if 'where_column' in join and 'where_value' in join:\n xpath_query += '[{0}=\\\"{1}\\\"]'.format(join.get('where_column'),\n join.get('where_value'))\n log.debug('using xpath query: %s', xpath_query)\n\n # get elements from xpath query on tree\n elements.extend(join_tree.xpath(xpath_query))\n\n # if no elements, return none\n if len(elements) == 0:\n return None\n\n if len(joins) > 0:\n return get_joined_array(joins, elements, select_columns)\n\n # initialize values list\n values_list = list()\n\n # for each element\n for e in elements:\n\n # create dictionary of values\n values = dict()\n\n # for each select column\n for sc in select_columns.keys():\n log.debug('adding select column field \"%s\" to select column'\n ' dictionary', sc)\n\n # get select column\n select_column = select_columns.get(sc)\n\n # get column value\n column_value = get_column(e, sc, select_column)\n\n # add column value to values\n values[sc] = column_value\n\n # add values to values_list\n if len(select_columns) == 1:\n values_list.append(next(iter(values.values())))\n else:\n values_list.append(values)\n\n # for select columns\n return values_list\n\n\ndef get_column(element, field_name, field):\n # initialize column value\n column_value = None\n\n # read column data\n if field.get('field_type') == 'column':\n log.debug('adding column field \"%s\" to dictionary', field_name)\n\n # get column value\n column_value = get_column_value(element, field_name, field)\n\n # check for data type\n if field.get('data_type') == 'int':\n column_value = int(column_value)\n elif field.get('data_type') == 'float':\n column_value = float(column_value)\n elif field.get('data_type') == 'bool':\n if column_value == '0':\n column_value = False\n else:\n column_value = bool(column_value)\n\n # read joined column data\n elif field.get('field_type') == 'joined_column':\n log.debug('adding joined column field \"%s\" to dictionary', field_name)\n\n # get column value\n column_value = get_joined_column(list(field.get('joins')),\n element,\n field.get('select_column'))\n\n # read joined column data\n elif field.get('field_type') == 'joined_array':\n log.debug('adding joined array \"%s\" to dictionary', field_name)\n\n # get column value\n column_value = get_joined_array(list(field.get('joins')),\n [element],\n field.get('select_columns'))\n\n return column_value\n\n\ndef create_data_file(metadata, new_file_path):\n # initialize data list\n data = dict()\n\n # get xml data from file\n xml_file = os.path.join(SETTINGS['xml_dir'], metadata['file'])\n log.debug('reading xml file: %s', xml_file)\n tree = etree.parse(xml_file)\n\n # set xpath query for elements\n xpath_query = './' + os.path.splitext(metadata['file'])[0]\n if 'filter' in metadata:\n xpath_query += metadata['filter']\n log.debug('using xpath query: %s', xpath_query)\n\n # execute xpath query on tree\n elements = tree.xpath(xpath_query)\n\n # get fields dictionary from metadata\n fields = metadata['fields']\n\n # for each element\n for e in elements:\n # initialize entry dictionary\n entry = dict()\n\n # initialize primary key list\n key_list = list()\n\n # initialize filter flag\n filter_entry = False\n\n # for all fields\n for f in fields.keys():\n # get field as field\n field = fields.get(f)\n\n # get column value\n column_value = get_column(e, f, field)\n\n # add field\n log.debug('%s: %s', f, column_value)\n entry[f] = column_value\n\n # if field is a key\n if 'is_key' in field and field.get('is_key'):\n key_list.append(column_value)\n\n # if column value is none and field is a filter field\n if column_value is None and \\\n 'filter' in field and field.get('filter'):\n # set filter flag\n filter_entry = True\n\n # if not filtering\n if not filter_entry:\n # add entry to data list\n assert len(key_list) > 0\n key = '|'.join(key_list)\n data[key] = entry\n\n # dump data to new file\n with open(new_file_path, 'w') as data_file:\n if SETTINGS['debug']:\n json.dump(data, data_file, indent=2)\n else:\n json.dump(data, data_file)\n\n\ndef create_json_files():\n # cache data paths in variable\n data_paths = SETTINGS['data_paths']\n\n # for each data path key\n for game in data_paths.keys():\n print('Processing data files for {0}'.format(game))\n\n # set xml dir\n SETTINGS['xml_dir'] = data_paths.get(game)\n\n # get game data dir\n game_data_dir = os.path.join(data_dir, game)\n log.debug('%s data path: %s', game, game_data_dir)\n\n # get projection files\n projections_dir = os.path.join(game_data_dir, 'projections')\n projection_files = os.listdir(projections_dir)\n if not SETTINGS['force']:\n projection_files = filter(\n lambda x: x not in os.listdir(game_data_dir),\n projection_files)\n\n # for all projection files\n for projection_file in projection_files:\n # if valid import file\n proj_file_path = os.path.join(projections_dir, projection_file)\n if os.path.isfile(proj_file_path) and \\\n os.path.splitext(projection_file)[1] == '.json':\n print('Importing data for {0} file'.format(projection_file))\n\n # open import file\n with open(proj_file_path, 'r') as file:\n # load json file\n projection = json.load(file)\n\n # create json data file from metadata\n log.debug('creating new file: %s', projection_file)\n create_data_file(projection, os.path.join(data_dir,\n game,\n projection_file))\n\n\ndef main(arguments):\n # set settings from arguments\n set_options(arguments)\n\n # create json files\n create_json_files()\n\n\nif __name__ == '__main__':\n main(sys.argv[1:])\n", "sub_path": "scripts/convert_xml_data_to_json.py", "file_name": "convert_xml_data_to_json.py", "file_ext": "py", "file_size_in_byte": 12626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.basename", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 21, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 28, "usage_type": "call"}, {"api_name": "argparse.Action", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 39, "usage_type": "call"}, {"api_name": "os.access", "line_number": 41, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 41, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 137, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 137, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 181, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 181, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 296, "usage_type": "call"}, {"api_name": "os.path", "line_number": 296, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 298, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 298, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 355, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 377, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path", "line_number": 386, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 387, "usage_type": "call"}, {"api_name": "os.path", "line_number": 387, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 398, "usage_type": "call"}, {"api_name": "os.path", "line_number": 398, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 412, "usage_type": "attribute"}]}
+{"seq_id": "252985523", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 6 17:52:11 2019\n\n@author: Admin\n\"\"\"\nimport numpy as np\n#from student import Student\nfrom collections import deque\nimport random\nimport hashlib\nimport numpy as np\nimport os\nimport random as _random\nfrom six import integer_types\nimport struct\nimport sys\n\nfrom gym import error\n\ndef np_random(seed=None):\n if seed is not None and not (isinstance(seed, integer_types) and 0 <= seed):\n raise error.Error('Seed must be a non-negative integer or omitted, not {}'.format(seed))\n\n seed = create_seed(seed)\n\n rng = np.random.RandomState()\n rng.seed(_int_list_from_bigint(hash_seed(seed)))\n return rng, seed\n\ndef hash_seed(seed=None, max_bytes=8):\n \"\"\"Any given evaluation is likely to have many PRNG's active at\n once. (Most commonly, because the environment is running in\n multiple processes.) There's literature indicating that having\n linear correlations between seeds of multiple PRNG's can correlate\n the outputs:\n http://blogs.unity3d.com/2015/01/07/a-primer-on-repeatable-random-numbers/\n http://stackoverflow.com/questions/1554958/how-different-do-random-seeds-need-to-be\n http://dl.acm.org/citation.cfm?id=1276928\n Thus, for sanity we hash the seeds before using them. (This scheme\n is likely not crypto-strength, but it should be good enough to get\n rid of simple correlations.)\n Args:\n seed (Optional[int]): None seeds from an operating system specific randomness source.\n max_bytes: Maximum number of bytes to use in the hashed seed.\n \"\"\"\n if seed is None:\n seed = create_seed(max_bytes=max_bytes)\n hash = hashlib.sha512(str(seed).encode('utf8')).digest()\n return _bigint_from_bytes(hash[:max_bytes])\n\ndef create_seed(a=None, max_bytes=8):\n \"\"\"Create a strong random seed. Otherwise, Python 2 would seed using\n the system time, which might be non-robust especially in the\n presence of concurrency.\n Args:\n a (Optional[int, str]): None seeds from an operating system specific randomness source.\n max_bytes: Maximum number of bytes to use in the seed.\n \"\"\"\n # Adapted from https://svn.python.org/projects/python/tags/r32/Lib/random.py\n if a is None:\n a = _bigint_from_bytes(os.urandom(max_bytes))\n elif isinstance(a, str):\n a = a.encode('utf8')\n a += hashlib.sha512(a).digest()\n a = _bigint_from_bytes(a[:max_bytes])\n elif isinstance(a, integer_types):\n a = a % 2**(8 * max_bytes)\n else:\n raise error.Error('Invalid type for seed: {} ({})'.format(type(a), a))\n\n return a\n\n# TODO: don't hardcode sizeof_int here\ndef _bigint_from_bytes(bytes):\n sizeof_int = 4\n padding = sizeof_int - len(bytes) % sizeof_int\n bytes += b'\\0' * padding\n int_count = int(len(bytes) / sizeof_int)\n unpacked = struct.unpack(\"{}I\".format(int_count), bytes)\n accum = 0\n for i, val in enumerate(unpacked):\n accum += 2 ** (sizeof_int * 8 * i) * val\n return accum\n\ndef _int_list_from_bigint(bigint):\n # Special case 0\n if bigint < 0:\n raise error.Error('Seed must be non-negative, not {}'.format(bigint))\n elif bigint == 0:\n return [0]\n\n ints = []\n while bigint > 0:\n bigint, mod = divmod(bigint, 2 ** 32)\n ints.append(mod)\n return ints# -*- coding: utf-8 -*-\n\n################################################################\n\ndef categorical_sample(prob_n, np_random):\n \"\"\"\n Each row specifies class probabilities\n \"\"\"\n prob_n = np.asarray(prob_n)\n csprob_n = np.cumsum(prob_n)\n return (csprob_n > np_random.rand()).argmax()\n\n \nclass CampusEnv():\n #Create students\n #Create road capacity\n #Create parking decks\n def _calculate_transition_prob(self, current, assigned_lot):\n current_state_as_list = current.split(\",\")\n \n is_done = int(current_state_as_list[0]) <= 0 and int(current_state_as_list[1]) <= 0 and int(current_state_as_list[2]) <= 0 and int(current_state_as_list[3]) <= 0 and int(current_state_as_list[4]) <= 0 and int(current_state_as_list[5]) <= 0\n #Case 1: not assignable\n temp = []\n if is_done:\n temp.append((1.0, current, 0, is_done))\n elif int(current_state_as_list[assigned_lot]) == 0:\n temp.append((1.0, current, -100, is_done))\n else:\n current_state_as_list[assigned_lot] = str(int(current_state_as_list[assigned_lot]) - 1)\n for i in range(3):\n for k in range(3):\n if i !=k:\n reward = 0\n \n #Students preferences reward\n if assigned_lot == int(current_state_as_list[6]):\n reward = reward + 200\n elif assigned_lot == int(current_state_as_list[7]):\n reward = reward + 100\n else:\n reward = reward + 20\n \n #Work on capacity reward\n if int(current_state_as_list[assigned_lot]) < (self.lots[assigned_lot] * 0.1):\n reward = reward - 10\n \n #if int(current_state_as_list[0]) <= 1:\n #reward = reward - 2\n \n #if int(current_state_as_list[1]) <= 1:\n # reward = reward - 2\n \n #if int(current_state_as_list[2]) <= 1:\n # reward = reward - 2\n \n #if int(current_state_as_list[3]) <= 1:\n # reward = reward - 2\n \n #if int(current_state_as_list[4]) <= 1:\n # reward = reward - 2\n \n # if int(current_state_as_list[5]) <= 1:\n # reward = reward - 2\n \n current_state_as_list[6] = str(i)\n current_state_as_list[7] = str(k)\n \n str_2 = \",\"\n\n is_done = int(current_state_as_list[0]) <= 0 and int(current_state_as_list[1]) <= 0 and int(current_state_as_list[2]) <= 0 and int(current_state_as_list[3]) <= 0 and int(current_state_as_list[4]) <= 0 and int(current_state_as_list[5]) <= 0\n #If out of cars\n temp.append((1/30,str_2.join(current_state_as_list), reward, is_done))\n\n return temp\n \n def seed(self, seed=None):\n self.np_random, seed = np_random(seed)\n return [seed]\n \n def step(self, a):\n transitions = self.P[self.s][a]\n i = categorical_sample([t[0] for t in transitions], self.np_random)\n #i = random.randint(0,len(transitions)-1)\n p, s, r, d= transitions[i]\n self.s = s\n self.lastaction = a\n return (s, r, d, {\"prob\" : p})\n\n def reset(self):\n self.s = self.states_list[categorical_sample(self.isd, self.np_random)]\n self.lastaction = None\n return self.s\n\n def __init__(self):\n self.nS = 4*5*6*6*5*4*6*5\n self.lots = [4,5,6,6,5,4]\n self.nA = 6\n self.states_list = []\n #create a list of all states\n for a in range(6):\n for b in range(6):\n if a != b:\n for d in range(4):\n for e in range(5):\n for f in range(6):\n for g in range(6):\n for h in range(5):\n for i in range(4):\n temp = str(d)+\",\"+str(e)+\",\"+str(f)+\",\"+str(g)+\",\"+str(h)+\",\"+str(i)+\",\"+str(a)+\",\"+str(b)\n self.states_list.append(temp)\n \n #print(len(self.states_list))\n #print(temp)\n print(\"getting states list\")\n P = {}\n i = 0\n for s in self.states_list:\n current = s\n P[s] = { a : [] for a in range(self.nA) }\n P[s][0] = self._calculate_transition_prob(current, 0)\n P[s][1] = self._calculate_transition_prob(current, 1)\n P[s][2] = self._calculate_transition_prob(current, 2)\n P[s][3] = self._calculate_transition_prob(current, 3)\n P[s][4] = self._calculate_transition_prob(current, 4)\n P[s][5] = self._calculate_transition_prob(current, 5)\n \n \n \n self.P = P\n isd = np.full((1, self.nS), 1/self.nS).ravel()\n #isd = np.zeros(self.nS)\n #isd[] = 1.0 #Selecting the state at last and the first state\n self.isd = isd\n self.lastaction = None # for rendering\n self.seed()\n self.s = self.states_list[categorical_sample(self.isd, self.np_random)]\n print(\"finish init\")\n \n \n \nif __name__ == \"__main__\":\n campus = CampusEnv()", "sub_path": "lib/envs/campus_env_compact.py", "file_name": "campus_env_compact.py", "file_ext": "py", "file_size_in_byte": 9446, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "six.integer_types", "line_number": 22, "usage_type": "argument"}, {"api_name": "gym.error.Error", "line_number": 23, "usage_type": "call"}, {"api_name": "gym.error", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "hashlib.sha512", "line_number": 49, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 62, "usage_type": "call"}, {"api_name": "hashlib.sha512", "line_number": 65, "usage_type": "call"}, {"api_name": "six.integer_types", "line_number": 67, "usage_type": "argument"}, {"api_name": "gym.error.Error", "line_number": 70, "usage_type": "call"}, {"api_name": "gym.error", "line_number": 70, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 80, "usage_type": "call"}, {"api_name": "gym.error.Error", "line_number": 89, "usage_type": "call"}, {"api_name": "gym.error", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 226, "usage_type": "call"}]}
+{"seq_id": "462686025", "text": "from tastypie import fields\nfrom tastypie.authorization import DjangoAuthorization\nfrom tastypie.constants import ALL, ALL_WITH_RELATIONS\nfrom tastypie.resources import ModelResource\n\nfrom auth.authentication.authentication import SocialAuthentication\n\nfrom .models import Customer, Dog, TemporaryCredit\nfrom api_apps.common.resources import AddressResource\nfrom auth.accounts.resources import UserResource\n\n\nclass CustomerResource(ModelResource):\n\n user = fields.ForeignKey(UserResource, 'user', full=True, blank=True, null=True)\n billing_addresses = fields.ManyToManyField(AddressResource, 'billing_addresses', full=True)\n shipping_addresses = fields.ManyToManyField(AddressResource, 'shipping_addresses', full=True)\n temporary_credits = fields.ToManyField(\n 'api_apps.customers.resources.TemporaryCreditResource', 'temporary_credits', 'temporary_credits')\n created_on = fields.DateTimeField(attribute='created_on', blank=True, null=True)\n modified_on = fields.DateTimeField(attribute='modified_on', blank=True, null=True)\n comments = fields.ToManyField('api_apps.common.resources.CommentResource', 'comments', blank=True, null=True)\n sub_campaign = fields.ForeignKey('api_apps.affiliates.resources.SubCampaignResource', 'sub_campaign', blank=True, null=True)\n\n class Meta:\n resource_name = 'customers'\n queryset = Customer.objects.all()\n filtering = {\n 'user': ALL_WITH_RELATIONS,\n 'billing_addresses': ALL_WITH_RELATIONS,\n 'shipping_addresses': ALL_WITH_RELATIONS,\n 'sub_campaign': ALL_WITH_RELATIONS,\n }\n ordering = ('id', )\n for field in Customer.__dict__['_meta'].fields:\n if field.name not in filtering:\n filtering.update({field.name: ALL})\n always_return_data = True\n authentication = SocialAuthentication()\n authorization = DjangoAuthorization()\n list_allowed_methods = ('get', 'post')\n detail_allowed_methods = ('get', 'put', 'patch', 'delete')\n\n\nclass DogResource(ModelResource):\n\n owner = fields.ForeignKey(CustomerResource, 'owner', full=True)\n created_on = fields.DateTimeField(attribute='created_on', blank=True, null=True)\n modified_on = fields.DateTimeField(attribute='modified_on', blank=True, null=True)\n\n class Meta:\n resource_name = 'dogs'\n queryset = Dog.objects.all()\n filtering = {\n 'owner': ALL_WITH_RELATIONS,\n }\n ordering = ('id', )\n for field in Dog.__dict__['_meta'].fields:\n if field.name not in filtering:\n filtering.update({field.name: ALL})\n always_return_data = True\n authentication = SocialAuthentication()\n authorization = DjangoAuthorization()\n list_allowed_methods = ('get', 'post')\n detail_allowed_methods = ('get', 'put', 'patch', 'delete')\n\n\nclass TemporaryCreditResource(ModelResource):\n\n customer = fields.ForeignKey(CustomerResource, 'customer', full=True)\n created_on = fields.DateTimeField(attribute='created_on', blank=True, null=True)\n modified_on = fields.DateTimeField(attribute='modified_on', blank=True, null=True)\n comments = fields.ToManyField('api_apps.common.resources.CommentResource', 'comments', blank=True, null=True)\n time_to_live = fields.IntegerField(attribute='time_to_live', readonly=True)\n\n class Meta:\n resource_name = 'temporary-credits'\n queryset = TemporaryCredit.objects.all()\n filtering = {\n 'customer': ALL_WITH_RELATIONS,\n }\n ordering = ('id', )\n for field in TemporaryCredit.__dict__['_meta'].fields:\n if field.name not in filtering:\n filtering.update({field.name: ALL})\n always_return_data = True\n authentication = SocialAuthentication()\n authorization = DjangoAuthorization()\n list_allowed_methods = ('get', 'post')\n detail_allowed_methods = ('get', 'put', 'patch', 'delete')\n", "sub_path": "api_apps/customers/resources.py", "file_name": "resources.py", "file_ext": "py", "file_size_in_byte": 3993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tastypie.resources.ModelResource", "line_number": 13, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "auth.accounts.resources.UserResource", "line_number": 15, "usage_type": "argument"}, {"api_name": "tastypie.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "tastypie.fields.ManyToManyField", "line_number": 16, "usage_type": "call"}, {"api_name": "api_apps.common.resources.AddressResource", "line_number": 16, "usage_type": "argument"}, {"api_name": "tastypie.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "tastypie.fields.ManyToManyField", "line_number": 17, "usage_type": "call"}, {"api_name": "api_apps.common.resources.AddressResource", "line_number": 17, "usage_type": "argument"}, {"api_name": "tastypie.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "tastypie.fields.ToManyField", "line_number": 18, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "tastypie.fields.ToManyField", "line_number": 22, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Customer.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Customer.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Customer", "line_number": 27, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 29, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 30, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 31, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Customer.__dict__", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Customer", "line_number": 35, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 37, "usage_type": "name"}, {"api_name": "auth.authentication.authentication.SocialAuthentication", "line_number": 39, "usage_type": "call"}, {"api_name": "tastypie.authorization.DjangoAuthorization", "line_number": 40, "usage_type": "call"}, {"api_name": "tastypie.resources.ModelResource", "line_number": 45, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 47, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 47, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 48, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 49, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 49, "usage_type": "name"}, {"api_name": "models.Dog.objects.all", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Dog.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Dog", "line_number": 53, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Dog.__dict__", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Dog", "line_number": 58, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 60, "usage_type": "name"}, {"api_name": "auth.authentication.authentication.SocialAuthentication", "line_number": 62, "usage_type": "call"}, {"api_name": "tastypie.authorization.DjangoAuthorization", "line_number": 63, "usage_type": "call"}, {"api_name": "tastypie.resources.ModelResource", "line_number": 68, "usage_type": "name"}, {"api_name": "tastypie.fields.ForeignKey", "line_number": 70, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 70, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 71, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "tastypie.fields.DateTimeField", "line_number": 72, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 72, "usage_type": "name"}, {"api_name": "tastypie.fields.ToManyField", "line_number": 73, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 73, "usage_type": "name"}, {"api_name": "tastypie.fields.IntegerField", "line_number": 74, "usage_type": "call"}, {"api_name": "tastypie.fields", "line_number": 74, "usage_type": "name"}, {"api_name": "models.TemporaryCredit.objects.all", "line_number": 78, "usage_type": "call"}, {"api_name": "models.TemporaryCredit.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "models.TemporaryCredit", "line_number": 78, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL_WITH_RELATIONS", "line_number": 80, "usage_type": "name"}, {"api_name": "models.TemporaryCredit.__dict__", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.TemporaryCredit", "line_number": 83, "usage_type": "name"}, {"api_name": "tastypie.constants.ALL", "line_number": 85, "usage_type": "name"}, {"api_name": "auth.authentication.authentication.SocialAuthentication", "line_number": 87, "usage_type": "call"}, {"api_name": "tastypie.authorization.DjangoAuthorization", "line_number": 88, "usage_type": "call"}]}
+{"seq_id": "90138054", "text": "\"\"\"Alter dnv_forecast table\n\nRevision ID: a01724e06851\nRevises: a18f4e9a8cf7\nCreate Date: 2021-03-25 23:10:39.360173\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = 'a01724e06851'\ndown_revision = 'a18f4e9a8cf7'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('dnv_forecast', sa.Column('utc_computed', sa.DateTime(), nullable=True))\n op.add_column('dnv_forecast', sa.Column('utc_forecast', sa.DateTime(), nullable=True))\n op.drop_column('dnv_forecast', 'time_forecasted')\n op.drop_column('dnv_forecast', 'time_of_forecast')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('dnv_forecast', sa.Column('time_of_forecast', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n op.add_column('dnv_forecast', sa.Column('time_forecasted', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n op.drop_column('dnv_forecast', 'utc_forecast')\n op.drop_column('dnv_forecast', 'utc_computed')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/a01724e06851_alter_dnv_forecast_table.py", "file_name": "a01724e06851_alter_dnv_forecast_table.py", "file_ext": "py", "file_size_in_byte": 1230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.TIMESTAMP", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.TIMESTAMP", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]}
+{"seq_id": "241811864", "text": "\"\"\" conftest.py \"\"\"\nimport pytest\n\n\ndef verify_output(capsys: pytest.CaptureFixture[str], filename: str) -> None:\n \"\"\" Utility function to ensure output matches file. \"\"\"\n captured, _ = capsys.readouterr()\n with capsys.disabled(), open(filename, encoding=\"utf-8\") as output_file:\n expected = output_file.read()\n assert captured == expected\n", "sub_path": "conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pytest.CaptureFixture", "line_number": 5, "usage_type": "attribute"}]}
+{"seq_id": "34419518", "text": "h,w= map(int, input().split())\na = input()\n\nans = 0\nfor i in range(h-1):\n a = a+ input()\n\nfrom collections import Counter\n\nc = Counter(a)\n\ncm = c.most_common()\n\n\nif h*w%2==1:\n cnt_odd = 0\n cnt_2 =0\n limit_2 = h//2 + w//2\n\n for k,v in cm:\n if v%2==1:\n cnt_odd+=1\n if cnt_odd >1:\n print('No')\n exit()\n elif v%4!=0:\n cnt_2+=1\n if cnt_2 >limit_2:\n print('No')\n exit()\n if cnt_odd == 0:\n print('No')\n else:\n print('Yes')\n\nelse:\n cnt_odd = 0\n cnt_2 =0\n if h%2!=0:\n limit_2 = w//2\n elif w%2!=0:\n limit_2 = h//2\n else:\n limit_2 = 0\n\n for k,v in cm:\n if v%2==1:\n cnt_odd+=1\n if cnt_odd >0:\n print('No')\n exit()\n elif v%4!=0:\n cnt_2+=1\n if cnt_2 >limit_2:\n print('No')\n exit()\n print('Yes')\n", "sub_path": "Python_codes/p03593/s827243540.py", "file_name": "s827243540.py", "file_ext": "py", "file_size_in_byte": 994, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}]}
+{"seq_id": "289249428", "text": "import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\n# --------------------------------------------------------\n# Transform numpy or tensorflow array to tf.data.Dataset\n# --------------------------------------------------------\n# numpy variable\nX_numpy = np.array([2013, 2014, 2015, 2016, 2017])\nY_numpy = np.array([12000, 14000, 15000, 16500, 17500])\n\n# tensorflow variable\nX_tf = tf.constant([2013, 2014, 2015, 2016, 2017])\nY_tf = tf.constant([12000, 14000, 15000, 16500, 17500])\n\ndataset = tf.data.Dataset.from_tensor_slices((X_numpy, Y_numpy))\nfor x, y in dataset:\n print(x.numpy(), y.numpy())\n\n\n\n# --------------------------------------------------------\n# Load MNIST data to tf.data.Dataset\n# --------------------------------------------------------\n(train_data, train_label), (_, _) = tf.keras.datasets.mnist.load_data()\ntrain_data = np.expand_dims(train_data.astype(np.float32) / 255.0, axis = -1)\nmnist_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_label))\n# for image, label in mnist_dataset:\n# plt.title(label.numpy())\n# plt.imshow(image.numpy()[:, :, 0])\n# plt.show()\n\n\n# --------------------------------------------------------\n# tf.data.Dataset 数据预处理\n# --------------------------------------------------------\n# Dataset.map(f)\n# Dataset.shuffle(buffer_size)\n# Dataset.batch(batch_size)\n# Dataset.repeat()\n# Dataset.reduce()\n# Dataset.take()\n# Dataset.prefetch()\n\n\ndef rot90(image, label):\n image = tf.image.rot90(image)\n return image, label\n\nmnist_dataset = mnist_dataset.map(rot90)\n\nfor image, label in mnist_dataset:\n plt.title(label.numpy())\n plt.imshow(image.numpy()[:, :, 0])\n plt.show()\n\n\n", "sub_path": "src/src_tensorflow/tensorflow_keras_src/tf_data_Dataset.py", "file_name": "tf_data_Dataset.py", "file_ext": "py", "file_size_in_byte": 1711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.image.rot90", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 49, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]}
+{"seq_id": "345454232", "text": "import asyncio\nimport logging\nfrom .signals import create_future_for_signal\n\nlog = logging.getLogger(\"unitd.processpool\")\n\n\nclass ProcessPool:\n def __init__(self, loop=None):\n self.loop = loop if loop is not None else asyncio.get_event_loop()\n # Future that will get a result if the quit signal is received\n self.quit_signal = None\n # Set to False when a process fails to start\n self.success = True\n # List of processes that we manage\n self.processes = []\n\n def set_quit_signal(self, sig):\n log.debug(\"Installing handler for signal %d\", sig)\n self.quit_signal = create_future_for_signal(sig)\n\n @asyncio.coroutine\n def start_sync(self, process):\n if not self.success:\n log.debug(\"A previous process failed to start, %s will not be started\", process.logger.log_tag)\n return False\n\n if self.quit_signal.done():\n log.debug(\"Quit signal received, %s will not be started\", process.logger.log_tag)\n return False\n\n log.debug(\"Starting process %s synchronously\", process.logger.log_tag)\n res = yield from process.start()\n if not res:\n self.success = False\n self.processes.append(process)\n return self.success\n\n @asyncio.coroutine\n def run(self):\n try:\n if not self.success:\n return\n\n wait_for = [p.terminated for p in self.processes]\n if self.quit_signal:\n wait_for.append(self.quit_signal)\n\n done, pending = yield from asyncio.wait(\n wait_for,\n return_when=asyncio.FIRST_COMPLETED,\n loop=self.loop)\n finally:\n yield from asyncio.wait(\n [p.stop() for p in self.processes],\n return_when=asyncio.ALL_COMPLETED)\n", "sub_path": "unitd/processpool.py", "file_name": "processpool.py", "file_ext": "py", "file_size_in_byte": 1859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 10, "usage_type": "call"}, {"api_name": "signals.create_future_for_signal", "line_number": 20, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 22, "usage_type": "attribute"}, {"api_name": "asyncio.wait", "line_number": 49, "usage_type": "call"}, {"api_name": "asyncio.FIRST_COMPLETED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "asyncio.wait", "line_number": 54, "usage_type": "call"}, {"api_name": "asyncio.ALL_COMPLETED", "line_number": 56, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 39, "usage_type": "attribute"}]}
+{"seq_id": "200119067", "text": "import requests\nfrom requests.utils import requote_uri\nimport json\nimport datetime\n\n\nclass Timetable(object):\n def __init__(self, start_date=None, end_date=None):\n self.start_date = start_date\n self.end_date = end_date\n def get_timetable(self, group=None, lecturer=None):\n url = 'http://calc.nuwm.edu.ua:3002/api/sched?'\n now = datetime.datetime.now()\n today = now.strftime('%d.%m.%Y')\n\n if self.start_date == None:\n self.start_date = today\n\n if self.end_date == None:\n self.end_date = today\n\n if group is None:\n lecturer = requote_uri(lecturer)\n url_add = 'name={}'.format(lecturer)\n\n if lecturer is None:\n group = requote_uri(group)\n url_add = 'group={}'.format(group)\n\n r = requests.get(url + '{}&sdate={}&edate={}&type=weeks'.format(url_add, self.start_date, self.end_date))\n\n return self.parse_timetable(r.content)\n\n def parse_timetable(self, timetable_json):\n timetable_json = timetable_json.decode('utf-8')\n j = json.loads(timetable_json)\n\n if j['code'] is not 100:\n return 'Помилка. Немає інформації. 😟'\n\n j = j['response']['schedule'][-1]\n\n week_number = j['weeknum']\n week_start = j['weekstart']\n week_end = j['weekend']\n days = j['days']\n\n answer = ('Номер тижня: {}\\n'.format(week_number) +\n 'Початок тижня: {}\\n'.format(week_start) + 'Кінець тижня: {}\\n\\n'.format(week_end))\n\n for day in days:\n current_day = day['day']\n current_day_name = day['dayname']\n subjects = day['subjects']\n\n answer += 'Дата пари: {} {}\\n'.format(current_day_name, current_day)\n\n for subject in subjects:\n lecturer = subject['lecturer']\n subgroup = subject['subgroup']\n streams_type = subject['streams_type']\n lesson_num = subject['lessonNum']\n time = subject['time']\n classroom = subject['classroom']\n subject_name = subject['subject']\n type = subject['type']\n\n answer += ('Предмет: {}\\n'.format(subject_name) + 'Викладач: {}\\n'.format(lecturer) +\n 'Підгрупа: {} {}\\n'.format(streams_type, subgroup) + 'Номер пари: {}\\n'.format(lesson_num) +\n 'Час пари: {}\\n'.format(time) + 'Тип пари: {}\\n'.format(type) + 'Аудиторія: {}\\n\\n'.format(classroom))\n\n return answer[:-2]\n\nclass GroupTimetable(Timetable):\n def get_timetable(self, group):\n if not group:\n return 'Інформації по групі не знайдено 😟'\n\n answer = super().get_timetable(group=group)\n return answer\n\nclass LecturerTimetable(Timetable):\n def get_timetable(self, lecturer):\n if not lecturer:\n return 'Інформації по викладачу не знайдено 😟'\n\n answer = super().get_timetable(lecturer=lecturer)\n return answer\n", "sub_path": "timetable.py", "file_name": "timetable.py", "file_ext": "py", "file_size_in_byte": 3204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "requests.utils.requote_uri", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.utils.requote_uri", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}]}
+{"seq_id": "459377278", "text": "import numpy as np\nimport pandas as pd\n\nfrom PyQuantum.Common.Matrix import *\nfrom PyQuantum.TC.FullBase import *\nfrom PyQuantum.TC.Hamiltonian import Hamiltonian as H_Full\nfrom scipy.sparse import kron, identity\n\n\nclass Hamiltonian:\n def set_base(self, base):\n self.base = base\n\n def __init__(self, capacity, cavity, RWA=True, reduced=True):\n self.capacity = capacity\n self.cavity = cavity\n\n print(\"Hamiltonian naive\", capacity)\n\n H_field = get_Hfield(capacity, cavity.n_atoms, cavity.n_levels,\n cavity.wc, cavity.wa, cavity.g)\n print(\"get_Hfield\", capacity)\n\n H_atoms = get_Hatoms(capacity, cavity.n_atoms, cavity.n_levels,\n cavity.wc, cavity.wa, cavity.g)\n print(\"get_Hatoms\", capacity)\n\n if RWA:\n H_int = get_Hint_RWA(\n capacity, cavity.n_atoms, cavity.n_levels, cavity.wc, cavity.wa, cavity.g)\n else:\n H_int = get_Hint_EXACT(\n capacity, cavity.n_atoms, cavity.n_levels, cavity.wc, cavity.wa, cavity.g)\n print(\"get_Hint\", capacity)\n\n Assert(np.shape(H_field) == np.shape(H_atoms), \"size mismatch\", FILE(), LINE())\n Assert(np.shape(H_atoms) == np.shape(H_int), \"size mismatch\", FILE(), LINE())\n\n H = H_field + H_atoms + H_int\n # H = lil_matrix(H_field + H_atoms + H_int)\n\n self.size = np.shape(H)[0]\n\n self.matrix = Matrix(self.size, self.size, dtype=np.double)\n self.matrix.data = H\n\n at = AtomicBasis(count=cavity.n_atoms, n_levels=cavity.n_levels)\n base = Base(capacity, at)\n print(\"at, base\", capacity)\n\n self.set_base(base)\n print(\"self.set_base(base)\", capacity)\n\n if reduced:\n self.reduce()\n print(\"self.reduce()\", capacity)\n\n self.set_states()\n print(\"self.set_states()\", capacity)\n # print(self.states)\n # print(self.states)\n # self.to_html(\"H.html\")\n\n H = H_Full(capacity, cavity)\n\n # H.matrix.data = np.abs(H.matrix.data)\n # print(H.matrix.data)\n # print(self.matrix.data)\n\n # for i in range(self.size):\n # for j in range(self.size):\n # if H.matrix.data[i, j] != self.matrix.data[i, j]:\n # print(H.matrix.data[i, j], '!= ', self.matrix.data[i, j])\n # return\n\n # if np.any(H.matrix.data != self.matrix.data):\n # print(\"TC: Not equal\")\n\n # exit(0)\n\n # ---------------------------------------------------------------------------------------------\n\n def print(self):\n for i in range(self.size):\n for j in range(self.size):\n print(wc_str(self.matrix.data[i, j]), end='\\t')\n\n print()\n # -------------------------------------------------------------------------------------------------\n\n def to_csv(self, filename):\n self.matrix.to_csv(filename)\n\n return\n # -------------------------------------------------------------------------------------------------\n\n def iprint(self):\n df = pd.DataFrame()\n\n for i in range(self.size):\n for j in range(self.size):\n df.loc[i, j] = wc_str(abs(self.matrix.data[i, j]))\n\n # df.index = df.columns = self.states_str\n df.index = df.columns = self.states\n print(self.states)\n # df.index = df.columns = self.base.base_str\n\n self.df = df\n\n # ---------------------------------------------------------------------------------------------\n\n def reduce(self):\n for k, v in list(enumerate(self.base.base))[::-1]:\n if v[0] + np.sum(v[1]) != self.capacity:\n self.matrix.data = np.delete(self.matrix.data, k, axis=0)\n self.matrix.data = np.delete(self.matrix.data, k, axis=1)\n self.base.base.remove(v)\n self.base.base_str.remove(str(v))\n\n self.size = np.shape(self.matrix.data)[0]\n self.matrix.m = self.matrix.n = self.size\n\n def set_states(self):\n self.states = {}\n\n for k, v in enumerate(self.base.base):\n self.states[k] = v\n\n\ndef get_Hfield(capacity, at_count, n_levels, wc, wa, g):\n # ------------------------------------------------------------------------------------------------------------------\n adiag = np.sqrt(np.arange(1, capacity+1))\n\n across = np.diagflat(adiag, -1)\n a = np.diagflat(adiag, 1)\n acrossa = np.dot(across, a)\n # ------------------------------------------------------------------------------------------------------------------\n H_dim = (capacity+1) * pow(n_levels, at_count)\n # ------------------------------------------------------------------------------------------------------------------\n # at_dim = pow(2, at_count)\n at_dim = pow(n_levels, at_count)\n\n I_at = identity(at_dim)\n # ------------------------------------------------------------------------------------------------------------------\n H_field = wc * kron(acrossa, I_at)\n # ------------------------------------------------------------------------------------------------------------------\n return H_field\n\n\ndef get_Hatoms(capacity, at_count, n_levels, wc, wa, g):\n # ------------------------------------------------------------------------------------------------------------------\n sigmadiag = range(1, n_levels)\n sigmacross = np.diagflat(sigmadiag, -1)\n sigma = np.diagflat(sigmadiag, 1)\n sigmacrosssigma = np.dot(sigmacross, sigma)\n # ------------------------------------------------------------------------------------------------------------------\n ph_dim = capacity+1\n\n I_ph = np.identity(ph_dim)\n # ------------------------------------------------------------------------------------------------------------------\n H_dim = (capacity+1) * pow(n_levels, at_count)\n\n H_atoms = np.zeros([H_dim, H_dim])\n # ------------------------------------------------------------------------------------------------------------------\n for i in range(1, at_count+1):\n elem = sigmacrosssigma\n\n at_prev = identity(pow(n_levels, i-1))\n elem = kron(at_prev, elem)\n\n at_next = identity(pow(n_levels, at_count-i))\n elem = kron(elem, at_next)\n\n H_atoms += wa * kron(I_ph, elem)\n # ------------------------------------------------------------------------------------------------------------------\n return H_atoms\n\n\ndef get_Hint_RWA(capacity, at_count, n_levels, wc, wa, g):\n # ------------------------------------------------------------------------------------------------------------------\n adiag = np.sqrt(np.arange(1, capacity+1))\n\n across = np.diagflat(adiag, -1)\n a = np.diagflat(adiag, 1)\n acrossa = np.dot(across, a)\n # ------------------------------------------------------------------------------------------------------------------\n sigmadiag = range(1, n_levels)\n\n sigmacross = np.diagflat(sigmadiag, -1)\n sigma = np.diagflat(sigmadiag, 1)\n sigmacrosssigma = np.dot(sigmacross, sigma)\n # ------------------------------------------------------------------------------------------------------------------\n H_dim = (capacity+1) * pow(n_levels, at_count)\n\n H_int = np.zeros([H_dim, H_dim])\n # ------------------------------------------------------------------------------------------------------------------\n for i in range(1, at_count+1):\n # ------------------------------------------------\n elem = across\n\n before = identity(pow(n_levels, i-1))\n elem = kron(elem, before)\n\n elem = kron(elem, sigma)\n\n after = identity(pow(n_levels, at_count-i))\n elem = kron(elem, after)\n\n H_int += g * elem\n # ------------------------------------------------\n elem = a\n\n before = identity(pow(n_levels, i-1))\n elem = kron(elem, before)\n\n elem = kron(elem, sigmacross)\n\n after = identity(pow(n_levels, at_count-i))\n elem = kron(elem, after)\n\n H_int += g * elem\n # ------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n return H_int\n\n\ndef get_Hint_EXACT(capacity, at_count, n_levels, wc, wa, g):\n # ------------------------------------------------------------------------------------------------------------------\n adiag = np.sqrt(np.arange(1, capacity+1))\n\n across = np.diagflat(adiag, -1)\n a = np.diagflat(adiag, 1)\n acrossa = np.dot(across, a)\n # ------------------------------------------------------------------------------------------------------------------\n sigmadiag = range(1, n_levels)\n\n sigmacross = np.diagflat(sigmadiag, -1)\n sigma = np.diagflat(sigmadiag, 1)\n sigmacrosssigma = np.dot(sigmacross, sigma)\n # ------------------------------------------------------------------------------------------------------------------\n H_dim = (capacity+1) * pow(n_levels, at_count)\n\n H_int = np.zeros([H_dim, H_dim], dtype=np.double)\n # ------------------------------------------------------------------------------------------------------------------\n for i in range(1, at_count+1):\n # ------------------------------------------------\n elem = (across + a)\n\n before = np.identity(pow(n_levels, i-1))\n elem = kron(elem, before)\n\n elem = kron(elem, sigmacross + sigma)\n\n after = identity(pow(n_levels, at_count-i))\n elem = kron(elem, after)\n\n H_int += g * elem\n # ------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n return H_int\n\n# =====================================================================================================================\n# class HamiltonianL:\n# def set_base(self, base):\n# self.base = base\n\n# def __init__(self, capacity, cavity, RWA=True, reduced=True):\n# self.capacity = capacity\n# self.cavity = cavity\n\n# self.size = 0\n\n# HC = {}\n\n# size_start = 0\n\n# self.states = {}\n\n# for c in range(capacity, -1, -1):\n# Hc = Hamiltonian(capacity=c, cavity=cavity,\n# RWA=RWA, reduced=reduced)\n# HC[c] = Hc\n\n# # for i in Hc.base.base:\n# # # for i in Hc.states.values():\n# # print(i)\n# # print()\n# for k, v in Hc.states.items():\n# self.states[size_start + k] = v\n\n# size_start += HC[c].size\n# # self.states += Hc.states\n# # print(Hc.states)\n# self.size += Hc.size\n\n# # for i in self.states.values():\n# # print(i)\n\n# I = np.zeros([self.size, self.size], dtype=np.complex128)\n# # print(self.states)\n\n# size_start = 0\n\n# for c in range(capacity, -1, -1):\n# # print(\"c=\", c)\n# # print(HC[c])\n# # print(HC[c].size)\n# # print(HC[c].states)\n# # print(HC[c].matrix.data)\n# I[size_start:size_start+HC[c].size,\n# size_start:size_start+HC[c].size] = HC[c].matrix.data\n# size_start += HC[c].size\n\n# self.matrix = Matrix(self.size, self.size, dtype=np.complex128)\n# self.matrix.data = I\n\n# def iprint(self):\n# df = pd.DataFrame()\n\n# for i in range(self.size):\n# for j in range(self.size):\n# df.loc[i, j] = wc_str(abs(self.matrix.data[i, j]))\n\n# # df.index = df.columns = self.states_str\n# df.index = df.columns = [str(v) for v in self.states.values()]\n\n# self.df = df\n# # print(I)\n# # exit(0)\n# # for c in range(capacity):\n# # I[0:size_1, 0:size_1] = H0\n\n# # I[size_1:size_1+size_2, size_1:size_1+size_2] = H1\n\n# # I[size_1+size_2:size, size_1+size_2:size] = H2\n\n# # self.matrix = Matrix(self.size, self.size, dtype=np.complex128)\n\n# # H2 = Hamiltonian(capacity-1, cavity, RWA, reduced).matrix.data\n# # H3 = Hamiltonian(capacity-2, cavity, RWA, reduced).matrix.data\n\n# # print(self.size)\n# # H_field = get_Hfield(capacity, cavity.n_atoms, cavity.n_levels,\n# # cavity.wc, cavity.wa, cavity.g)\n\n# # H_atoms = get_Hatoms(capacity, cavity.n_atoms, cavity.n_levels,\n# # cavity.wc, cavity.wa, cavity.g)\n\n# # if RWA:\n# # H_int = get_Hint_RWA(\n# # capacity, cavity.n_atoms, cavity.n_levels, cavity.wc, cavity.wa, cavity.g)\n# # else:\n# # H_int = get_Hint_EXACT(\n# # capacity, cavity.n_atoms, cavity.n_levels, cavity.wc, cavity.wa, cavity.g)\n\n# # Assert(np.shape(H_field) == np.shape(H_atoms), \"size mismatch\", FILE(), LINE())\n# # Assert(np.shape(H_atoms) == np.shape(H_int), \"size mismatch\", FILE(), LINE())\n\n# # H = np.matrix(H_field + H_atoms + H_int)\n\n# # self.size = np.shape(H)[0]\n\n# # self.matrix = Matrix(self.size, self.size, dtype=np.complex128)\n# # self.matrix.data = H\n\n# # at = AtomicBasis(count=cavity.n_atoms, n_levels=cavity.n_levels)\n# # base = Base(capacity, at)\n\n# # self.set_base(base)\n\n# # if reduced:\n# # self.reduce()\n\n# # print(self.matrix.data)\n\n# # exit(1)\n\n# # self.set_states()\n# =====================================================================================================================\n# def to_html(self, filename):\n# self.matrix.states = self.states\n# self.matrix.to_html(filename)\n# =====================================================================================================================\n# =====================================================================================================================\n", "sub_path": "PyQuantum/TC_sink/old/Hamiltonian_naive.py", "file_name": "Hamiltonian_naive.py", "file_ext": "py", "file_size_in_byte": 14097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.shape", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PyQuantum.TC.Hamiltonian.Hamiltonian", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 137, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 164, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 170, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 172, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 203, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 207, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 208, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 214, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 217, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 219, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.diagflat", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 244, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 250, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 251, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 253, "usage_type": "call"}, {"api_name": "scipy.sparse.identity", "line_number": 255, "usage_type": "call"}, {"api_name": "scipy.sparse.kron", "line_number": 256, "usage_type": "call"}]}
+{"seq_id": "593907323", "text": "import numpy as np\nfrom astropy.io import fits\nimport matplotlib.pyplot as plt\nimport glob \nimport os\nimport scipy.optimize as scipy\n\ndef counts(files,bias_b_1,bias_b_2,bias_r):\n datalist = []\n countlist=[]\n pixellist=[]\n exptimelist=[]\n versionlist=[]\n for i, file in enumerate(files):\n \n hduFITS = fits.open(file)\n \n data=hduFITS[0].data\n head=hduFITS[0].header\n exptime = head['EXPTIME']\n version = head['VERSION']\n exptimelist.append(exptime)\n versionlist.append(version)\n \n if version == 'kastb':\n new_data=data[:,0:-64]-bias_b_1\n new_data[:,1024:]=new_data[:,1024:]+bias_b_1-bias_b_2\n datalist.append(new_data)\n \n if version =='kastr':\n data=data[:,0:-32]-bias_r \n datalist.append(data)\n \n for i, data in enumerate(datalist):\n midCCD = len(data[:,0])/2\n dataMedian = np.median(data)\n counts = data[midCCD,:]\n countlist.append(counts)\n \n pixels = np.arange(len(data[0,:]))+1\n pixellist.append(pixels)\n \n plt.figure()\n plt.clf()\n plt.imshow(data,cmap = 'gray', origin='lower')\n plt.colorbar()\n plt.clim(dataMedian-20,dataMedian+20)\n plt.title(versionlist[i]+'_'+str(exptimelist[i]))\n plt.show()\n \n plt.figure()\n plt.clf()\n plt.plot(pixels,counts)\n plt.xlim(np.min(pixels),np.max(pixels))\n plt.title(versionlist[i]+'_'+str(exptimelist[i]))\n plt.show()\n\n return countlist#,pixellist\n \ndef gaussianfunc(x,amp,centroid,error,floor):\n return floor + amp*np.exp(-((x-centroid)**2)/(2*error**2))\n \ndef centroid(counts,peak,guess):\n pixels = peak + np.arange(-5,5,1)#count_range[0]\n counts = counts[pixels]\n \n stuff = scipy.curve_fit(gaussianfunc,pixels,counts, p0=guess)\n param = stuff[0]\n\n \n\n #plt.figure()\n plt.clf()\n plt.scatter(pixels, counts, marker='o')\n xval = np.linspace(param[1]-15, param[1]+15, 100)\n yval = gaussianfunc(xval, param[0], param[1], param[2], param[3])\n plt.plot(xval,yval, 'r')\n \n #don't need to see the plots right now \n #plt.show()\n \n return param[1]\n\ndef bestline(columns,wavelength,filt):\n\n # calculate polynomial\n z = np.polyfit(columns,wavelength,1)\n f = np.poly1d(z)\n slope = z[0]\n print(slope)\n \n slope1 = format(z[0], '0.2f')\n intercept1 = format(z[1], '0.2f')\n # calculate new x's and y's\n x_new = np.linspace(0, 2048, 100)\n y_new = f(x_new)\n\n path = '/Users/martinlopezjr/Library/Mobile Documents/com~apple~CloudDocs/LaTeX/Experiment_3'\n filename= filt + 'wavelengthvspixel.pdf' \n fullpath=os.path.join(path,filename)\n \n plt.figure(figsize=(9,7),dpi=100)\n plt.clf()\n plt.plot(columns,wavelength,'o',label = 'Data from arclamp')\n plt.plot(x_new, y_new,label = 'Best Fit Line: ' + r'$\\lambda =$' +str(slope1) +r'$x+$' + str(intercept1) )\n plt.xlim(np.min(x_new),np.max(x_new))\n plt.title(filt+' '+r'$\\lambda$' + ' vs. Pixel' + r'$(x)$')\n plt.ylabel(r'$\\lambda \\ [\\AA{}]$')\n plt.xlabel('Pixel '+r'$(x)$')\n plt.legend(loc = 'best')\n plt.savefig(fullpath) \n plt.show()\n \n return z[0],z[1]\n", "sub_path": "SPECANmodule.py", "file_name": "SPECANmodule.py", "file_ext": "py", "file_size_in_byte": 3330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "astropy.io.fits.open", "line_number": 16, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clim", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}]}
+{"seq_id": "326469477", "text": "import discord\nfrom discord import Activity, ActivityType\nfrom discord.ext import commands\nfrom discord.ext.commands import Cog\n\n\nclass App(Cog):\n def __init__(self, bot):\n self.bot = bot\n \n self._message = \"listening Cool Music\"\n \n @property\n def message(self):\n return self._message\n \n @message.setter\n def message(self, value):\n if value.split(\" \")[0] not in (\"playing\", \"watching\", \"listening\"):\n raise ValueError(\"Invalit Activity Type\")\n \n self._message = value\n \n async def set(self):\n _type, _name = self.message.split(\" \", maxsplit=1)\n \n await self.bot.change_presence(activity=Activity(name=_name, type=getattr(ActivityType, _type, ActivityType.playing)))\n\ndef setup(bot):\n bot.add_cog(App(bot))", "sub_path": "main/cogs/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.Activity", "line_number": 27, "usage_type": "call"}, {"api_name": "discord.ActivityType", "line_number": 27, "usage_type": "argument"}, {"api_name": "discord.ActivityType.playing", "line_number": 27, "usage_type": "attribute"}]}
+{"seq_id": "213712038", "text": "from fastapi import APIRouter, status\nfrom logger import logger\nfrom fastapi.responses import JSONResponse\nfrom config.redis.redis_base_client import redis_cli\nfrom util.email_util import *\nfrom set_path_config import *\nfrom common.common import *\nfrom account.service import register_service\nimport random\n\nrouter = APIRouter()\n\n#注册\n@router.post(\"/\",tags=[\"send_email\"])\nasync def send_email(telephone:str,email_mess:str):\n # 判断传入手机格式是否正确,不正确返回True\n if telephone_verification(telephone):\n return JSONResponse(content=\"手机号格式错误\", status_code=status.HTTP_400_BAD_REQUEST)\n if email_verification(email_mess):\n return JSONResponse(content=\"邮箱格式错误\", status_code=status.HTTP_400_BAD_REQUEST)\n #查询账号\n account = register_service.get_account(telephone)\n if account is not None:\n # 用户是重置密码发送邮件，需判断账号和邮箱是否一致\n if account.email != email_mess:\n return JSONResponse(content=\"您的邮箱与注册手机号绑定邮箱不一致，无法重置密码\", status_code=status.HTTP_417_EXPECTATION_FAILED)\n #用户是注册发送邮件\n try:\n email = MailManager(from_addr= os.environ.get(\"EMAIL_ADDR\"),\n password=os.environ.get(\"EMAIL_PASSWORD\"),\n to_addr=email_mess,\n name=\"orange\",\n type=\"html\")\n context = \"\"\n for i in range(4):\n ch = chr(random.randrange(ord('0'), ord('9') + 1))\n context += ch\n email.SendMail(\"<p>您的验证码是:\"+context+\"</p>\")\n redis_cli.setex(telephone, 6000, context)\n except Exception as e:\n logger.error(e)\n return JSONResponse(content=str(e), status_code=status.HTTP_417_EXPECTATION_FAILED)\n return JSONResponse(content=\"验证码发送成功\", status_code=status.HTTP_200_OK)", "sub_path": "backend/send_email/send_email.py", "file_name": "send_email.py", "file_ext": "py", "file_size_in_byte": 1956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "fastapi.APIRouter", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 18, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 18, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 20, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 20, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 20, "usage_type": "name"}, {"api_name": "account.service", "line_number": 22, "usage_type": "name"}, {"api_name": "account.service.register_service.get_account", "line_number": 22, "usage_type": "call"}, {"api_name": "account.service.register_service", "line_number": 22, "usage_type": "name"}, {"api_name": "account.service", "line_number": 23, "usage_type": "name"}, {"api_name": "account.service.email", "line_number": 25, "usage_type": "attribute"}, {"api_name": "account.service", "line_number": 25, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_417_EXPECTATION_FAILED", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 26, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 36, "usage_type": "call"}, {"api_name": "config.redis.redis_base_client.redis_cli.setex", "line_number": 39, "usage_type": "call"}, {"api_name": "config.redis.redis_base_client.redis_cli", "line_number": 39, "usage_type": "name"}, {"api_name": "logger.logger.error", "line_number": 41, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 41, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 42, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_417_EXPECTATION_FAILED", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 42, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_200_OK", "line_number": 43, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 43, "usage_type": "name"}]}
+{"seq_id": "354719301", "text": "import theano\nimport numpy\nimport time\n\n\nclass DataMNIST(object):\n def __init__(self, path, mbs, bs, rng, unlabled):\n self.path = path\n self.mbs = mbs\n self.bs = bs\n self.rng = rng\n self.unlabled = unlabled\n self.data = numpy.load(path)\n # print(self.data.files)\n # with open('mnist2.npz', 'wb') as f:\n # import numpy as np\n # np.savez(f, train_x=self.data['train'], train_y=self.data['train_labels'], test_x=self.data['test'], test_y=self.data['test_labels'])\n # with open('mnist3.npz', 'wb') as f:\n # import numpy as np\n # train_x = self.data['train_x'].T\n # train_y = self.data['train_y'].T.flatten()\n # test_x = self.data['test_x'].T\n # test_y = self.data['test_y'].T.flatten()\n # print('IMP -1', train_x.shape, train_y.shape, test_x.shape, test_y.shape, np.unique(train_y), np.unique(test_y))\n # valid_size = int(len(train_x) * .1)\n # valid_x, train_x = train_x[:valid_size], train_x[valid_size:]\n # valid_y, train_y = train_y[:valid_size], train_y[valid_size:]\n # train_x, valid_x, test_x = [x.astype(np.float64) / 255. for x in [train_x, valid_x, test_x]]\n # train_y, valid_y, test_y = [y.astype(np.int32) for y in [train_y, valid_y, test_y]]\n # np.savez(f, train_x=train_x, train_y=train_y, valid_x=valid_x, valid_y=valid_y, test_x=test_x, test_y=test_y)\n self.xdim = self.data['train_x'].shape[1]\n self.ydim = numpy.max(self.data['train_y'])+1\n\n self.offset = theano.shared(numpy.int32(0))\n self.begin = self.offset * self.mbs\n self.end = self.offset*self.mbs + self.mbs\n self._train_x = theano.shared(self.data['train_x'], name='train_x')\n self._train_y = theano.shared(self.data['train_y'], name='train_y')\n self._valid_x = theano.shared(self.data['valid_x'], name='valid_x')\n self._valid_y = theano.shared(self.data['valid_y'], name='valid_y')\n self._test_x = theano.shared(self.data['test_x'], name='test_x')\n self._test_y = theano.shared(self.data['test_y'], name='test_y')\n # Codes:\n # 0 -> same minibatch\n # 1 -> different minibatch\n # 2 -> validation set\n if unlabled == 0:\n self._natgrad = self._train_x[self.begin:self.end]\n self._natgrady = self._train_y[self.begin:self.end]\n elif unlabled == 1:\n self._natgrad = self._train_x[self.begin:self.end]\n self._natgrady = self._train_y[self.begin:self.end]\n elif unlabled == 2:\n self._natgrad = self._valid_x\n self._natgrady = self.valid_y\n\n self.eval_variables = [self._train_x,\n self._train_y]\n self.n_valid_samples = self.data['valid_x'].shape[0]\n self.n_test_samples = self.data['test_x'].shape[0]\n\n self.n_batches = 50000 // self.bs\n self.nat_batches = self.n_batches\n\n if self.unlabled ==2:\n self.nat_batches = 10000 // self.mbs\n self.grad_perm = self.rng.permutation(self.n_batches)\n self.nat_perm = self.rng.permutation(self.nat_batches)\n self.variables = [self.train_x, self.train_y]\n self.train_variables = [\n self._train_x[self.offset*self.bs:\n self.offset*self.bs+self.bs],\n self._train_y[self.offset*self.bs:\n self.offset*self.bs+self.bs]]\n self.pos = -1\n self.nat_pos = -1\n\n def train_x(self, start, end):\n return self._train_x[\n self.offset*self.bs+start:self.offset*self.bs+end]\n\n def train_y(self, start, end):\n return self._train_y[\n self.offset*self.bs+start:self.offset*self.bs+end]\n\n def valid_x(self, start, end):\n return self._valid_x[start:end]\n\n def valid_y(self, start, end):\n return self._valid_y[start:end]\n\n def test_x(self, start, end):\n return self._test_x[start:end]\n\n def test_y(self, start, end):\n return self._test_y[start:end]\n\n def update_before_computing_gradients(self):\n self.pos = (self.pos + 1) % self.n_batches\n if self.pos % self.n_batches == 0:\n self.grad_perm = self.rng.permutation(self.n_batches)\n self.offset.set_value(self.grad_perm[self.pos])\n\n\n\n def update_before_computing_natural_gradients(self):\n if self.unlabled == 1:\n self.nat_pos = (self.nat_pos + 1) % self.nat_batches\n if self.nat_pos % self.nat_batches == 0:\n self.nat_perm = self.rng.permutation(self.nat_batches)\n self.offset.set_value(self.nat_perm[self.nat_pos])\n\n def update_before_evaluation(self):\n if self.unlabled == 1:\n self.offset.set_value(self.grad_perm[self.pos])\n", "sub_path": "dataMNIST_standard.py", "file_name": "dataMNIST_standard.py", "file_ext": "py", "file_size_in_byte": 4860, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.load", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 32, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 34, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 37, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 38, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 39, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 40, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 41, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 42, "usage_type": "call"}]}
+{"seq_id": "346355817", "text": "import tabulate\nimport csv\nimport copy\n\nitems = [\n {'name': 'coffee', 'quantity': 10.0, 'unit': 'pkg.', 'unit_price': 65.99},\n {'name': 'soy sauce', 'quantity': 7.0, 'unit': 'btl.', 'unit_price': 14.45},\n {'name': 'sugar', 'quantity': 50.0, 'unit': 'kg', 'unit_price': 9.5}\n ]\nsold_items = []\n\n\ndef sort_key(element):\n return element['name']\n\n\ndef sorted_items_list(items_list):\n \"\"\"Sorts alphabetically the names of the products in the list\"\"\"\n return sorted(items_list, key=sort_key)\n\n\ndef get_items(_items):\n _items = sorted_items_list(_items)\n header = 'name', 'quantity', 'unit', 'unit_price (PLN)'\n row = [item.values() for item in _items]\n print(tabulate.tabulate(row, header, tablefmt='github'))\n\n\ndef menu():\n print('MENU'.center(91, '_'))\n print('|{:<89}|'.format('->Q/q: exit<-|->W/w: show<-|->A/a: add<-|->S/s: sell<-|->E/e: show_revenue<-|->R/r save<-'))\n print(''.center(91, '-'))\n\n\ndef is_in_list(item_list, name, unit, unit_price):\n for item in item_list:\n if item['name'] == name and item['unit'] == unit and item['unit_price'] == unit_price:\n return True\n\n\ndef add_item(items_list, **item_info):\n if is_in_list(items_list, item_info['name'], item_info['unit'], item_info['unit_price']) is True:\n for item in items_list:\n if item['name'] == item_info['name']:\n item['quantity'] += item_info['quantity']\n else:\n items_list.append(item_info)\n return items_list\n\n\ndef add_to_sold_items(sold_items_list, **product_info):\n if is_in_list(sold_items_list, product_info['name'], product_info['unit'], product_info['unit_price']) is True:\n for item in sold_items_list:\n if item['name'] == product_info['name']:\n item['quantity'] += product_info['quantity']\n else:\n sold_items_list.append(product_info)\n\n\ndef is_number(value):\n try:\n float(value)\n return True\n except ValueError:\n return False\n\n\ndef sell_item(items_list, sold_items_list, **item_info):\n for item in items_list:\n if item['name'] == item_info['name']:\n item['quantity'] -= item_info['quantity']\n _name = item['name']\n _quantity = item_info['quantity']\n _unit = item['unit']\n _unit_price = item['unit_price']\n add_to_sold_items(sold_items_list, name=_name, quantity=_quantity, unit=_unit, unit_price=_unit_price)\n\n\ndef get_cost(items_list):\n total_cost_list = [item['quantity']*item['unit_price'] for item in items_list]\n return sum(total_cost_list)\n\n\ndef show_revenue(items_list, sold_items_list):\n print()\n print('-'*20)\n cost = get_cost(items_list)\n income = get_cost(sold_items_list)\n revenue = income - cost\n cost = round(cost, 2)\n income = round(income, 2)\n revenue = round(revenue, 2)\n print('Income: {}zł'.format(income))\n print('Costs: {}zł'.format(cost))\n print('-'*20)\n print('Revenue: {}zł'.format(revenue))\n print('-' * 20)\n print()\n\n\ndef export_item_to_csv(items_list):\n with open('magazyn.csv', 'w') as magazyn_csv:\n fieldnames = ['name', 'quantity', 'unit', 'unit_price']\n csv_writer = csv.DictWriter(magazyn_csv, fieldnames=fieldnames, delimiter='\\t')\n csv_writer.writeheader()\n for item in items_list:\n csv_writer.writerow(item)\n\n\ndef export_sales_to_csv(sold_items_list):\n with open('sales.csv', 'w') as sales_csv:\n fieldnames = ['name', 'quantity', 'unit', 'unit_price']\n csv_writer = csv.DictWriter(sales_csv, fieldnames=fieldnames, delimiter='\\t')\n csv_writer.writeheader()\n for item in sold_items_list:\n csv_writer.writerow(item)\n\n\ndef change_quantity_and_unit_price_from_str_to_float(items_list):\n for item in items_list:\n for key, value in item.items():\n if key == 'quantity' or key == 'unit_price':\n item[key] = float(value)\n\n\ndef import_items_from_csv(items_list):\n with open('magazyn.csv', 'r') as magazyn_csv:\n csv_reader = csv.DictReader(magazyn_csv, delimiter='\\t')\n items_list.clear()\n for line in csv_reader:\n items_list.append(line)\n change_quantity_and_unit_price_from_str_to_float(items_list)\n\n\nif __name__ == '__main__':\n import_items_from_csv(items)\n print(items)\n while True:\n menu()\n operation_name = input(\"What would you like to do?: \")\n if operation_name.lower() == 'exit' or operation_name.lower() == 'q':\n print('see you later alligator...')\n break\n elif operation_name.lower() == 'show' or operation_name.lower() =='w':\n get_items(items)\n print(get_cost(items))\n elif operation_name.lower() == 'add' or operation_name.lower() == 'a':\n name = input('Item name: ')\n while True:\n quantity = input('Item quantity: ')\n quantity = quantity.replace(',', '.')\n if is_number(quantity) is True:\n quantity = float(quantity)\n quantity = round(quantity, 2)\n break\n else:\n print('-' * 51)\n print('the value entered is not a number, please try again')\n print('-' * 51)\n unit = input('Item unit of measure (kg, l, pkg., btl.): ')\n while True:\n unit_price = input('Item price in PLN: ')\n unit_price = unit_price.replace(',', '.')\n if is_number(unit_price) is True:\n unit_price = float(unit_price)\n unit_price = round(unit_price, 2)\n break\n else:\n print('-' * 51)\n print('the value entered is not a number, please try again')\n print('-' * 51)\n add_item(items, name=name, quantity=quantity, unit=unit, unit_price=unit_price)\n get_items(items)\n elif operation_name.lower() == 'sell' or operation_name.lower() == 's':\n name = input('Item name: ')\n while True:\n quantity = input('Item quantity: ')\n quantity = quantity.replace(',', '.')\n if is_number(quantity) is True:\n quantity = float(quantity)\n quantity = round(quantity, 2)\n break\n else:\n print('-' * 51)\n print('the value entered is not a number, please try again')\n print('-' * 51)\n sell_item(items, sold_items, name=name, quantity=quantity)\n get_items(items)\n get_items(sold_items)\n elif operation_name.lower() == 'show_revenue' or operation_name.lower() == 'e':\n show_revenue(items, sold_items)\n elif operation_name.lower() == 'save' or operation_name.lower() == 'r':\n export_item_to_csv(items)\n export_sales_to_csv(sold_items)\n elif operation_name.lower() == 'load' or operation_name.lower() == 'l':\n import_items_from_csv(items)\n", "sub_path": "warehouse.py", "file_name": "warehouse.py", "file_ext": "py", "file_size_in_byte": 7167, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tabulate.tabulate", "line_number": 26, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 104, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 113, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 128, "usage_type": "call"}]}
+{"seq_id": "587054261", "text": "# -*- coding: utf-8 -*-\nfrom flask_mongoengine import Document\nfrom mongoengine import CASCADE, signals\nfrom mongoengine.fields import (\n LazyReferenceField,\n StringField,\n ListField,\n DictField,\n BooleanField,\n)\nfrom mpcontribs.api.contributions.document import Contributions\nfrom mpcontribs.api.notebooks.document import Notebooks\n\n\nclass Tables(Document):\n contribution = LazyReferenceField(\n Contributions,\n passthrough=True,\n reverse_delete_rule=CASCADE,\n required=True,\n help_text=\"contribution this table belongs to\",\n )\n is_public = BooleanField(\n required=True, default=False, help_text=\"public/private table\"\n )\n name = StringField(required=True, help_text=\"table name\")\n columns = ListField(StringField(), required=True, help_text=\"column names\")\n data = ListField(ListField(StringField()), required=True, help_text=\"table rows\")\n config = DictField(help_text=\"graph config\")\n meta = {\n \"collection\": \"tables\",\n \"indexes\": [\n \"contribution\",\n \"is_public\",\n \"name\",\n \"columns\",\n {\"fields\": (\"contribution\", \"name\"), \"unique\": True},\n ],\n }\n\n @classmethod\n def post_save(cls, sender, document, **kwargs):\n Notebooks.objects(pk=document.contribution.id).delete()\n\n\nsignals.post_save.connect(Tables.post_save, sender=Tables)\n", "sub_path": "mpcontribs-api/mpcontribs/api/tables/document.py", "file_name": "document.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask_mongoengine.Document", "line_number": 15, "usage_type": "name"}, {"api_name": "mongoengine.fields.LazyReferenceField", "line_number": 16, "usage_type": "call"}, {"api_name": "mpcontribs.api.contributions.document.Contributions", "line_number": 17, "usage_type": "argument"}, {"api_name": "mongoengine.CASCADE", "line_number": 19, "usage_type": "name"}, {"api_name": "mongoengine.fields.BooleanField", "line_number": 23, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 26, "usage_type": "call"}, {"api_name": "mongoengine.fields.ListField", "line_number": 27, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 27, "usage_type": "call"}, {"api_name": "mongoengine.fields.ListField", "line_number": 28, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 28, "usage_type": "call"}, {"api_name": "mongoengine.fields.DictField", "line_number": 29, "usage_type": "call"}, {"api_name": "mpcontribs.api.notebooks.document.Notebooks.objects", "line_number": 43, "usage_type": "call"}, {"api_name": "mpcontribs.api.notebooks.document.Notebooks", "line_number": 43, "usage_type": "name"}, {"api_name": "mongoengine.signals.post_save.connect", "line_number": 46, "usage_type": "call"}, {"api_name": "mongoengine.signals.post_save", "line_number": 46, "usage_type": "attribute"}, {"api_name": "mongoengine.signals", "line_number": 46, "usage_type": "name"}]}
+{"seq_id": "542079729", "text": "import random\nimport torch\nimport numpy as np\n\n\ndef set_seed(state):\n seed = state[\"seed\"]\n if seed >= 0:\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n else:\n torch.backends.cudnn.benchmark = True\n\n\ndef rng_save(state):\n state[\"rng_state_gpu_torch\"] = torch.cuda.get_rng_state_all()\n state[\"rng_state_cpu_torch\"] = torch.get_rng_state()\n state[\"rng_state_cpu_numpy\"] = np.random.get_state()\n state[\"rng_state_cpu_random\"] = random.getstate()\n\n\ndef rng_restore(state):\n torch.cuda.set_rng_state_all(state[\"rng_state_gpu_torch\"])\n torch.set_rng_state(state[\"rng_state_cpu_torch\"])\n np.random.set_state(state[\"rng_state_cpu_numpy\"])\n random.setstate(state[\"rng_state_cpu_random\"])\n\n\ndef register(mf):\n mf.register_defaults({\n \"seed\": -1 # not deterministic\n })\n mf.register_helpers({\n \"rng_state_gpu_torch\": None,\n \"rng_state_cpu_torch\": None,\n \"rng_state_cpu_numpy\": None,\n \"rng_state_cpu_random\": None\n })\n mf.register_event('init', set_seed)\n mf.register_event('set_seed', set_seed)\n mf.register_event('rng_save', rng_save)\n mf.register_event('rng_restore', rng_restore)\n", "sub_path": "grp_modules/util/seed/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "torch.backends", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.cuda.get_rng_state_all", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.get_rng_state", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.get_state", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "random.getstate", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda.set_rng_state_all", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.set_rng_state", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.set_state", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "random.setstate", "line_number": 30, "usage_type": "call"}]}
+{"seq_id": "150989955", "text": "from __future__ import absolute_import, with_statement\n\nimport datetime\nimport logbook\ntry:\n import threading\nexcept ImportError:\n threading = None\n\nfrom flask_debugtoolbar.panels import DebugPanel\nfrom flask_debugtoolbar.utils import format_fname\n\n\n_ = lambda x: x\n\n\nclass ThreadTrackingHandler(logbook.Handler):\n def __init__(self):\n if threading is None:\n raise NotImplementedError(\"threading module is not available, \\\n the logbook panel cannot be used without it\")\n super(ThreadTrackingHandler, self).__init__()\n self.records = {} # a dictionary that maps threads to log records\n\n def emit(self, record):\n record.pull_information()\n thread = threading.currentThread()\n self.records.setdefault(thread, [])\n self.records[thread].append(record)\n\n def get_records(self, thread=None):\n if thread is None:\n thread = threading.currentThread()\n self.records.setdefault(thread, [])\n return self.records[thread]\n\n def clear_records(self, thread=None):\n if thread is None:\n thread = threading.currentThread()\n if thread in self.records:\n del self.records[thread]\n\n\nhandler = None\n_init_lock = threading.Lock()\n\n\ndef _init_once():\n # Initialize the logbook handler once.\n global handler\n if handler is not None:\n return\n with _init_lock:\n if handler is not None:\n return\n handler = ThreadTrackingHandler()\n handler.push_application()\n\n\nclass LogbookPanel(DebugPanel):\n name = 'Logbook'\n has_content = True\n\n def process_request(self, request):\n _init_once()\n handler.clear_records()\n\n def get_and_delete(self):\n records = handler.get_records()\n handler.clear_records()\n return records\n\n def nav_title(self):\n return _(\"Logbook\")\n\n def nav_subtitle(self):\n # FIXME l10n: use ngettext\n return \"%s message%s\" % (\n (len(handler.get_records()),\n (len(handler.get_records()) == 1) and '' or 's'))\n\n def title(self):\n return _('Log Messages')\n\n def url(self):\n return ''\n\n def content(self):\n records = []\n for record in self.get_and_delete():\n records.append({\n 'level': record.level_name,\n 'channel': record.channel,\n 'message': record.message,\n 'time': record.time.strftime('%Y-%m-%d %H:%M:%S'),\n 'file': format_fname(record.filename),\n 'file_long': record.filename,\n 'line': record.lineno\n })\n\n context = self.context.copy()\n context.update({'records': records})\n\n return self.render('panels/logbook.html', context)\n\n\n", "sub_path": "flask_debugtoolbar/panels/logbook.py", "file_name": "logbook.py", "file_ext": "py", "file_size_in_byte": 2771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logbook.Handler", "line_number": 17, "usage_type": "attribute"}, {"api_name": "threading.currentThread", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.currentThread", "line_number": 33, "usage_type": "call"}, {"api_name": "threading.currentThread", "line_number": 39, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 45, "usage_type": "call"}, {"api_name": "flask_debugtoolbar.panels.DebugPanel", "line_number": 60, "usage_type": "name"}, {"api_name": "flask_debugtoolbar.utils.format_fname", "line_number": 96, "usage_type": "call"}]}
+{"seq_id": "575916676", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\ndataset = pd.read_csv('G:\\Simple_Linear_Regression\\Simple_Linear_Regression\\Salary_Data.csv')\n\n\nX= dataset.iloc[:,:-1].values\nY=dataset.iloc[:,1].values\nprint(len(Y))\n\nfrom sklearn.model_selection import train_test_split\n\nX_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2, random_state=0)\n\nprint(len(X_train))\n\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train,Y_train)\n\ny_pred = regressor.predict(X_test)\nprint(y_pred)\nprint(Y_test)\n\n\nplt.scatter(X_train,Y_train,color='red')\nplt.plot(X_train,regressor.predict(X_train),color='blue')\nplt.show()\n", "sub_path": "LinearModel.py", "file_name": "LinearModel.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]}
+{"seq_id": "512866824", "text": "import sqlite3\nimport json\nimport pickle\nimport jieba\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.cluster import MiniBatchKMeans\nimport numpy as np\n\ndef newsInsert(data, db):\n conn = sqlite3.connect(\"./data/\"+db)\n c = conn.cursor()\n c.executemany(\"INSERT INTO news VALUES (?,?,?)\", data)\n conn.commit()\n conn.close()\n\nconn = sqlite3.connect(\"./data/news.db\")\nc = conn.cursor()\nc.execute(\"SELECT * FROM news\")\nsrcdata = c.fetchall()\n\nlength = len(srcdata)\njieba.load_userdict(\"./data/dict.txt\")\n\nwith open(\"./data/vec.obj\", \"rb\") as f:\n vec = pickle.load(f)\n\nfor i in range(10):\n filename = \"Kmeans_{}_obj.pickle\".format(i+5)\n with open(\"./data/\"+filename, \"rb\") as f:\n kmean = pickle.load(f)\n cnt = 0\n dstdata = []\n for url, title, txt in srcdata:\n cnt += 1\n print(\"{}:{}/{}\".format(i, cnt, length))\n seg = jieba.cut(txt, cut_all=True)\n cluster = kmean.predict(vec.transform((\" \".join(seg), )).toarray())[0]\n dstdata.append((url, title, str(cluster)))\n if cnt%10000 == 0:\n newsInsert(dstdata, \"news{}.db\".format(i+5))\n dstdata = [] \n \n if len(dstdata) > 0:\n newsInsert(dstdata, \"news{}.db\".format(i+5))\n \n", "sub_path": "cluster.py", "file_name": "cluster.py", "file_ext": "py", "file_size_in_byte": 1320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sqlite3.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "jieba.load_userdict", "line_number": 23, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 31, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 37, "usage_type": "call"}]}
+{"seq_id": "434532808", "text": "\nimport getpass as gp\nname = gp.getuser()\n\nimport os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\nsys.path.append('/Users/%s/OneDrive/Master Thesis/Data/Analysis_Skripts/Library/' %name)\nfrom Functions import *\nfrom sklearn.covariance import ledoit_wolf as LW, oas, shrunk_covariance\n\nfreq = 'M'\nyears = 5\n\nreturns, rf_rate, market, estLength, nAssets = get_Data(freq, years) \n\n\n#Index dates for dataframe\ndatesPF = returns.index.values[(estLength-1):(len(returns.index)-1)] \n\n#Index dates for dataframe\ndatesImpl = returns.index.values[(estLength):(len(returns.index))]\n\n #Column names for dataframe\nindices = returns.columns.values \n\n#initialize monthly return matrix with each asset after implementation\nretAssets = np.empty((len(returns.index)-estLength,nAssets)) \n\n#initialize monthly return vector of Portfolio after implementation\nretPF = np.empty((len(returns.index)-estLength,1)) \n\n#initialize matrix for Garlappi Wang portfolio weights\nPFRPdyn = np.empty((len(returns.index)-estLength,nAssets)) \n\n#initialize historical return vector for month after implementation of strategy\nhistRet = np.empty((1,nAssets))\n\n\n\n\n\n'''Calculate the return of a Risk Parity Portfolio'''\nfrom scipy.optimize import minimize\n\n\n'''set constraints for optimization'''\ncons = ({'type' : 'eq', 'fun' : weight_constraint },\n {'type': 'ineq', 'fun': long_only_constraint})\n\n\n'''set input parameters for optimization'''\nx_t = np.ones(nAssets) / nAssets #equal risk contribution target vector\nw_0 = rand_weights(nAssets) #initial weights from which to start opimization\n \n\nfor n in range(0,(len(returns.index)-estLength)):\n '''loop in order to calculate the risk parity portfolio in each period'''\n df_estimation = returns[n:estLength+n]\n varCov = varCovar(df_estimation) #variance covariance matrix\n res = minimize(risk_objective,\n w_0,\n args = [varCov, x_t],\n method = 'SLSQP',\n constraints = cons,\n options = {'ftol': 1e-12, 'maxiter' : 45, 'disp' : False})\n w_RP = np.array(res.x)\n PFRPdyn[n,:] = w_RP.T\n histRet = returns.iloc[(estLength + n)] \n retAssets[n,:] = w_RP.T * (np.exp(histRet)-1)\n retPF[n,:] = retAssets[n,:].sum()\n \ndf_retPF = pd.DataFrame(retPF, index = datesImpl) \nRPPortfolios = pd.DataFrame(PFRPdyn, index = datesPF, columns = indices) \n\nos.chdir('/Users/%s/OneDrive/Master Thesis/Data/Portfolios/' %name)\nRPPortfolios.to_csv('RPPortfolios%s.csv' %freq)\n\n\n\n\n\n\n\n\n\n", "sub_path": "scripts/Portfolios/RiskParity.py", "file_name": "RiskParity.py", "file_ext": "py", "file_size_in_byte": 2517, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "getpass.getuser", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 56, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 79, "usage_type": "call"}]}
+{"seq_id": "44829147", "text": "import argparse\nimport os\nimport sys\nimport subprocess\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--wheel_file\", help=\"wheel filename used to test. skip build wheel and install\")\n return parser.parse_args()\n\ndef run_subprocess(args, cwd=None):\n if isinstance(args, str):\n raise ValueError(\"args should be a sequence of strings, not a string\")\n\n return subprocess.run(args, cwd=cwd, shell=False, check=True)\n\ndef run_ort_module_tests(cwd, source_dir):\n args = [sys.executable, os.path.join(source_dir, 'tests/bert_for_sequence_classification.py')]\n run_subprocess(args, cwd)\n\ndef build_wheel(cwd, source_dir):\n args = [sys.executable, os.path.join(source_dir, 'setup.py'), 'bdist_wheel']\n run_subprocess(args, cwd)\n\ndef main():\n cmd_line_args = parse_arguments()\n\n source_dir = os.path.realpath(os.path.dirname(__file__))\n cwd = os.path.normpath(os.path.join(source_dir, \"..\"))\n\n if not cmd_line_args.wheel_file:\n build_wheel(source_dir, source_dir)\n\n # installing torch-ort wheel\n dist_path = os.path.join(source_dir, 'dist')\n wheel_file = os.listdir(dist_path)[0]\n run_subprocess([sys.executable, \"-m\", \"pip\", \"install\", \"--upgrade\", os.path.join(dist_path, wheel_file)], cwd)\n else:\n print(\"cmd_line_args.wheel_file: \", cmd_line_args.wheel_file)\n print(\"With Devops pipeline, please confirm that the wheel file matches the one being built from a previous step.\")\n run_subprocess([sys.executable, \"-m\", \"pip\", \"install\", \"--upgrade\", cmd_line_args.wheel_file], cwd)\n\n # installing requirements-test.txt\n requirements_path = os.path.join(source_dir, 'tests', 'requirements-test.txt')\n run_subprocess([sys.executable, \"-m\", \"pip\", \"install\", \"-r\", requirements_path], cwd)\n\n # testing torch-ort\n run_ort_module_tests(source_dir, source_dir)\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n", "sub_path": "build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 1959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}]}
+{"seq_id": "644728972", "text": "# Heavily based on https://github.com/Prodicode/ann-visualizer\nimport torch\n\nimport os\nimport h5py\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport seaborn as sns\nfrom pylab import rcParams\nimport matplotlib.pylab as pylab\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom math import cos, sin, atan\nfrom torch import nn, optim\nimport copy\nimport torch.nn.functional as F\nimport torchvision.transforms as transform\n\n\n\"\"\"\nParam for the graphics\n\"\"\"\nplt.rc('text', usetex=True)\nplt.rc('font', family='serif')\ntitle_font = {'size':'19', 'color':'black', 'weight':'normal',\n 'verticalalignment':'bottom'}\naxis_font = {'size':'19'}\nlegend_font={'size':'15'}\nparams = {'legend.fontsize': 'x-large',\n 'figure.figsize': (8,6.5),\n 'axes.labelsize': 'x-large',\n 'axes.titlesize':'x-large',\n 'xtick.labelsize':'x-large',\n 'ytick.labelsize':'x-large'}\npylab.rcParams.update(params)\n\"\"\"\nfunction to write the NN parameter to FPGA readable format\n+converterd back to do\n\"\"\"\n\ndef twosCom_binDec(bin1, digit):\n while len(bin1)<digit :\n bin1 = '0'+bin1\n if bin1[0] == '0':\n return int(bin1, 2)\n else:\n return -1 * (int(''.join('1' if x == '0' else '0' for x in bin1), 2) + 1)\n\ndef twosCom_decBin(dec, digit):\n dec = int(dec*(2**(digit-1)))\n if abs(dec)>2**(digit-1):\n dec = int(2**(digit-1)*dec/abs(dec))\n if dec>=0:\n bin1 = bin(dec).split(\"0b\")[1]\n while len(bin1)<digit:\n bin1 = '0'+bin1\n else:\n bin1 = -1*dec\n bin1 = bin(bin1-pow(2,digit)).split(\"0b\")[1]\n return bin1,dec\ndef mult_model(dec, digit):\n dec = int(dec*(2**(digit-1)))\n if abs(dec)>2**(digit-1):\n dec = int(2**(digit-1)*dec/abs(dec))\n if dec>=0:\n bin1 = bin(dec).split(\"0b\")[1]\n while len(bin1)<digit:\n bin1 = '0'+bin1\n else:\n bin1 = -1*dec\n bin1 = bin(bin1-pow(2,digit)).split(\"0b\")[1]\n return bin1,dec\n\ndef dic_to_txt(a_dict,filename,digit):\n f= open(filename,\"w+\")\n a_dicti = copy.deepcopy(a_dict)\n for key in a_dict:\n f.write(\"******************************************************************\"+'\\n')\n f.write(\"************\"+key+\"**********\"+'\\n')\n f.write(\"******************************************************************\"+'\\n')\n tensor = a_dict[key]\n #print(tensor)\n np_tensor = tensor.numpy()\n for idx, x in np.ndenumerate(tensor):\n b,dec= twosCom_decBin(x, digit)\n val2=twosCom_binDec(b, digit)\n a_dicti[key][idx] = val2\n if val2 != dec:\n print(val2)\n print(dec)\n a=input(\"error conversion for binary \"+str(digit))\n f.write(key+'_'+str(idx)+' = '+ str(b)+' ;'+'\\n')\n return a_dicti\n \ndef dic_to_txt_int(a_dict,filename,digit):\n f= open(filename,\"w+\")\n a_dicti = copy.deepcopy(a_dict)\n for key in a_dict:\n f.write(\"******************************************************************\"+'\\n')\n f.write(\"************\"+key+\"**********\"+'\\n')\n f.write(\"******************************************************************\"+'\\n')\n tensor = a_dict[key]\n #print(tensor)\n np_tensor = tensor.numpy()\n for idx, x in np.ndenumerate(tensor):\n a_dicti[key][idx] = int(a_dict[key][idx]*(2**(digit-1)))\n f.write(key+'_'+str(idx)+' = '+ str(int(a_dict[key][idx]*(2**(digit-1))))+' ;'+'\\n')\n return a_dicti\n \ndef dic_to_txt2(a_dict,filename,digit):\n f= open(filename,\"w+\")\n a_dicti = copy.deepcopy(a_dict)\n\n for key in a_dict:\n f.write(\"******************************************************************\"+'\\n')\n f.write(\"************\"+key+\"**********\"+'\\n')\n f.write(\"******************************************************************\"+'\\n')\n tensor = a_dict[key]\n #print(tensor)\n np_tensor = tensor.numpy()\n for idx, x in np.ndenumerate(tensor):\n val2=x*digit\n a_dicti[key][idx] = val2\n f.write(key+'_'+str(idx)+' = '+ str(val2)+' ;'+'\\n')\n return a_dicti\n\n\nclass Net(nn.Module):\n\n def __init__(self):\n super(Net, self).__init__()\n self.fc1 = nn.Linear(20, 20)\n self.fc2 = nn.Linear(20, 2)\n\n def forward(self, x):\n x = F.relu(self.fc1(x))\n x = F.softmax(self.fc2(x),dim=1)\n return x\n\nnet = Net().float()\nprint(net)\n#network = DrawNN( [20,20,2] )\n#network.draw()\nimport torch\nimport torchvision\nimport torchvision.transforms as transform\n\nf = h5py.File('data/dark_uni.h5', 'r')\n\ntrain_set =torch.from_numpy(np.array(f['train_data'],dtype=float)).float()\ntrain_label=torch.from_numpy(np.array(f['train_label'],dtype=float)).type(torch.LongTensor)\nsize_data_set= int(len(train_set))\nn_epoch = 100\ncriterion = nn.CrossEntropyLoss()\ntrainloader=[]\nk=0\n \n\nfor i in range(size_data_set):\n trainloader.append([train_set[i],train_label[i,1]])\n \nbatch_size = 500\n\n\"\"\"\nloading data test\n\"\"\"\ntest_set = torch.from_numpy(np.array(f['test_data'],dtype=float)).float()\ntest_label = torch.from_numpy(np.array(f['test_label'],dtype=float)).type(torch.LongTensor)\ntestloader=[]\n\nfor i in range(20000):\n testloader.append([test_set[i],test_label[i,1]]) \n\ntestloader2 = torch.utils.data.DataLoader(testloader, batch_size=batch_size,\n shuffle=False, num_workers=0)\n\n\nls_batch=[]\nls_loss=[]\nls_epoch=[]\nlstest=[]\nls_lr=[]\nnum_steps = 5\nn_lr = 5\nsep_lr = int(n_epoch/n_lr)\nlearning_rate = 0.00001#exp_decay(epoch)\noptimizer = optim.Adam(net.parameters(), lr=learning_rate)\n#scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30,80], gamma=0.1) # optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)\nsteps = int(size_data_set/batch_size/num_steps)\n\nfor epoch in range(n_epoch): # loop over the dataset multiple times\n running_loss = 0.0\n trainloader2 = torch.utils.data.DataLoader(trainloader, batch_size=batch_size,\n shuffle=True, num_workers=0)\n for i, data in enumerate(trainloader2, 0):\n # get the inputs; data is a list of [inputs, labels]\n inputs,labels = data\n # zero the parameter gradients\n optimizer.zero_grad()\n # forward + backward + optimize\n outputs = net(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n \n #scheduler.step()\n # print statistics\n running_loss += loss.item()\n if i % steps == steps-1: # print every 2000 mini-batches\n print('[%d, %5d] loss: %.3f' %\n (epoch + 1, i + 1, running_loss / steps))\n ls_loss.append(running_loss/steps)\n ls_batch.append(k)\n running_loss = 0.0\n k=k+1\n ls_lr.append(np.log(learning_rate))\n\n \"\"\"\n testing of my neural network\n \"\"\"\n correct = 0.0\n total = 0.0\n\n with torch.no_grad():\n for data in testloader2:\n images, labels = data\n outputs = net(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n lstest.append(100.0 * correct / total)\n ls_epoch.append(epoch)\n print('Accuracy of the network on the 20000 test events: %d %%' % (\n 100.0 * correct / total))\n\n\n\"\"\"\ngraphic for the loss \n\"\"\"\nfig = plt.figure()\nplt.plot(ls_batch,ls_loss)\nplt.xlabel(r\"Number of mini-batches\", **axis_font)\nplt.ylabel(\"Loss\", **axis_font)\nplt.title(r\"Loss in function of the number of mini-batches used for training\", **title_font)\nplt.legend(loc=2,prop =legend_font)\nplt.grid(True)\nplt.savefig('loss_uni.png', bbox_inches='tight')\nplt.close(fig)\n\n\"\"\"\ngraphic for the prediction \n\"\"\"\nfig = plt.figure()\nplt.plot(ls_epoch,lstest)\nplt.xlabel(r\"epoch\", **axis_font)\nplt.ylabel(\"validated prediction\", **axis_font)\nplt.title(r\"testing results after each epoch\", **title_font)\nplt.legend(loc=2,prop =legend_font)\nplt.grid(True)\nplt.savefig('prediction_uni.png', bbox_inches='tight')\nplt.close(fig)\n\na_dict_net = net.state_dict()\ntorch.save(a_dict_net,\"cnn_noformat_uni.pt\")\nmodel_None= Net().float()\nmodel_None.load_state_dict(torch.load('cnn_noformat_uni.pt'))\n\nls_error=[]\nls_batches=[]\nls_accuray=[]\n\n\nls_digit = [2,3,4,5,6,7,8,12,16,20,32]\nfor i in range(len(ls_digit)):\n correct=0.0\n total=0.0\n digit=ls_digit[i]\n filename = \"To_FPGA_uni_\"+str(digit)+\".txt\"\n filename3 = \"To_FPGA_uni_\"+str(digit)+\"dec.txt\"\n filename2 = \"cnn_uni\"+str(digit)+\".pt\"\n a_dict = model_None.state_dict()\n a_dicti= dic_to_txt(a_dict,filename,digit)\n a_dictii= dic_to_txt_int(a_dict,filename3,digit)\n torch.save(a_dictii,filename2)\n model_dicti = Net().float()\n model_dicti.load_state_dict(torch.load(filename2))\n a_dictiii = model_dicti.state_dict()\n print(\"coucou\")\n for key in a_dicti:\n for idx, x in np.ndenumerate(a_dicti[key]):\n val = a_dictiii[key][idx]\n val_2 = a_dicti[key][idx]#int(a_dicti[key][idx]*(2**digit-1))\n if val_2 != val :\n print(val)\n print(val_2)\n\n a=input(\"error conversion \"+str(digit))\n\n ls_i=[]\n ls_res=[]\n with torch.no_grad():\n for i,data in enumerate(testloader2, 0):\n images, labels = data\n outputs = model_dicti(images)\n #outputs=[float, float]\n #labels = [0,1] or [1,0]\n ls_i.append(i)\n results, prediction = torch.max(outputs.data, 1) #if the value predicted is a event return 1 into prediction varibale.\n total += labels.size(0)\n correct += (prediction == labels).sum().item()\n print('Accuracy of the network on the 20000 test events: %d %%' % (100.0 * correct / total))\n ls_accuray.append(100.0 * correct / total)\n\n ls_error.append(ls_res)\n ls_batches.append(ls_i)\n a_dicti = None\n model_dicti = None\n\nls_error2=[]\nls_batches2=[]\nls_accuray2 =[]\nls_digit2 = [100,1000,10000,100000,1000000]\n\nfor i in range(len(ls_digit)):\n correct=0.0\n total=0.0\n digit=ls_digit[i]\n\n filename = \"To_FPGA_uni_\"+str(digit)+\".txt\"\n filename3 = \"To_FPGA_uni_\"+str(digit)+\"dec.txt\"\n filename2 = \"cnn_uni\"+str(digit)+\".pt\"\n a_dict = model_None.state_dict()\n a_dicti= dic_to_txt2(a_dict,filename,digit)\n torch.save(a_dicti,filename2)\n model_dicti = Net().float()\n model_dicti.load_state_dict(torch.load(filename2))\n a_dictiii = model_dicti.state_dict()\n for key in a_dicti:\n for idx, x in np.ndenumerate(a_dicti[key]):\n val = a_dictiii[key][idx]\n val_2 = a_dict[key][idx]*digit\n if val_2 != val :\n print(val)\n print(val_2)\n\n a=input(\"error conversion \"+str(digit))\n ls_i=[]\n ls_res=[]\n with torch.no_grad():\n for i,data in enumerate(testloader2, 0):\n images, labels = data\n outputs = model_dicti(images)\n #outputs=[float, float]\n #labels = [0,1] or [1,0]\n ls_i.append(i)\n results, prediction = torch.max(outputs.data, 1) #if the value predicted is a event return 1 into prediction varibale.\n total += labels.size(0)\n correct += (prediction == labels).sum().item()\n print('Accuracy of the network on the 20000 test events: %d %%' % (100.0 * correct / total))\n ls_accuray2.append(100.0 * correct / total)\n\n ls_error2.append(ls_res)\n ls_batches2.append(ls_i)\n a_dicti = None\n model_dicti = None\n\n\n\n\n\nwith torch.no_grad():\n ls_i=[]\n ls_res=[]\n correct=0.0\n total=0.0\n for i,data in enumerate(testloader2, 0):\n images, labels = data\n outputs = model_None(images)\n results, prediction = torch.max(outputs.data, 1) #if the value predicted is a event return 1 into prediction varibale.\n total += labels.size(0)\n correct += (prediction == labels).sum().item()\n print('Accuracy of the network on the 20000 test events: %d %%' % (100.0 * correct / total)) \n ls_accuray2.append(100.0 * correct / total)\n ls_accuray.append(100.0 * correct / total)\nls_error.append(ls_res)\nls_error2.append(ls_res)\nls_digit.append(\"None\")\nls_digit2.append(\"None\")\n\n\n\nfig = plt.figure()\npositions=[]\nfor i in range(len(ls_digit)):\n positions.append(i)\n plt.plot(i,ls_accuray[i],'o',label = str(ls_digit[i]))\nplt.xticks(positions, ls_digit)\nplt.xlabel(r\"BITS\", **axis_font)\nplt.ylabel(\"Precision in \\%\", **axis_font)\nplt.title(r\"testing results for different data format uni\", **title_font)\n#plt.legend(loc=2,prop =legend_font)\nplt.grid(True)\nplt.savefig('comparaison_data_formats_otherform_uni.pdf', bbox_inches='tight')\nplt.close(fig)\n\nfig = plt.figure()\npositions=[]\nfor i in range(len(ls_digit2)):\n positions.append(i)\n plt.plot(i,ls_accuray2[i],'o',label = str(ls_digit[i]))\nplt.xticks(positions, ls_digit2)\nplt.xlabel(r\"mutlifly factor\", **axis_font)\nplt.ylabel(\"Precision in \\%\", **axis_font)\nplt.title(r\"testing results for different data format uni\", **title_font)\n#plt.legend(loc=2,prop =legend_font)\nplt.grid(True)\nplt.savefig('comparaison_multiply_uni.pdf', bbox_inches='tight')\nplt.close(fig)\n\n", "sub_path": "NN_V5.1test.py", "file_name": "NN_V5.1test.py", "file_ext": "py", "file_size_in_byte": 13617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pylab.rcParams.update", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pylab.rcParams", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab", "line_number": 38, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 87, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 108, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 140, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 177, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 337, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 342, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 352, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 359, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 374, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 395, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 395, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 399, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 399, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 400, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 401, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 401, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 402, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 402, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 403, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 403, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 405, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 405, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 406, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 406, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 407, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 407, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 409, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 409, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 413, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 413, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 414, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 414, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 415, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 416, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 416, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 417, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 417, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 419, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 419, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 420, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 420, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 421, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 421, "usage_type": "name"}]}
+{"seq_id": "39027426", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport xml.etree.cElementTree as ET\nimport pprint\nimport re\nimport codecs\nimport json\nfrom collections import defaultdict\nimport audit \n\n\nlower = re.compile(r'^([a-z]|_)*$')\nlower_digit = re.compile(r'^([a-z0-9]|_)*$')\nlower_colon = re.compile(r'^([a-z]|_)*:([a-z]|_)*$')\nproblemchars = re.compile(r'[=\\+/&<>;\\'\"\\?%#$@\\,\\. \\t\\r\\n]')\naddr = re.compile(r'^addr:([a-z]+)$')\nCREATED = [ \"version\", \"changeset\", \"timestamp\", \"user\", \"uid\"]\nduplist = [\"colour\", \"name\", \"atm\", \"building\", \"source\", \"destination\", \"lanes\", \n \"railway\",\"maxspeed\",\"opening_hours\",\"hov\",\"internet_access\",\"phone\",\n \"aerialway\",\"capacity\"]\nweird_set = set()\ndef shape_element(element):\n node = {}\n if element.tag == \"node\" or element.tag == \"way\" :\n node['type'] = element.tag\n node['created'] = {}\n\n for attrib in element.attrib.keys():\n if attrib in CREATED:\n node['created'][attrib] = element.attrib[attrib]\n elif attrib not in ['lat', 'lon']:\n node[attrib] = element.attrib[attrib]\n try:\n node['pos'] = [float(element.attrib['lat']), float(element.attrib['lon'])]\n #print node['pos']\n except:\n pass\n # address\n for tag in element.iter(\"tag\"):\n key = tag.attrib['k']\n value = tag.attrib['v']\n if not problemchars.search(key):\n m1 = addr.search(key)\n m2 = lower_colon.search(key)\n m3 = lower.search(key)\n m4 = lower_digit.search(key)\n if m1:\n if 'address' not in node.keys():\n node['address'] = {}\n if key == \"addr:street\":\n value = audit.update_name(value)\n if key == \"addr:postcode\":\n value = update_postcode(value)\n if len(value)!= 7:\n value = None\n k = m1.group(1)\n if value:\n node['address'][k] = value\n #print \"address:%s : %s\"%(k, value)\n elif m2:\n k1, k2= m2.group().split(':')\n if k1 not in duplist:\n if k1 not in node.keys():\n node[k1] = {}\n #print \"%s:%s : %s\"%(k1, k2, value)\n node[k1][k2] = value\n elif m3:\n node[key] = value\n elif m4: \n k = m4.group()\n k = k.translate(None, \"_1\")\n node[k] = value\n elif key.startswith(\"geobase:\") or key.startswith(\"addr:\"):\n k1, k2 = key.split(\":\")\n if k1 not in node.keys():\n node[k1] = {}\n if lower_digit.search(k2):\n k2 = k2.translate(None, \"_1\")\n node[k1][k2] = value \n else: \n weird_set.add(key)\n if element.tag == \"way\" and element.iter(\"nd\"):\n key = \"node_refs\"\n node[key] = []\n for tag in element.iter(\"nd\"):\n value = tag.attrib['ref']\n node[key].append(value)\n # print '\\n'\n \n return node\n else:\n return None\n\ndef update_postcode(postcode):\n if postcode.startswith(\"L\") and (len(postcode) == 6):\n postcode = postcode[:3]+\" \"+postcode[3:]\n return postcode\n\ndef process_map(file_in, pretty = False):\n # You do not need to change this file\n file_out = \"{0}.json\".format(file_in)\n data = []\n with codecs.open(file_out, \"w\") as fo:\n for _, element in ET.iterparse(file_in):\n el = shape_element(element)\n if el:\n data.append(el)\n if pretty:\n fo.write(json.dumps(el, indent=2)+\"\\n\")\n else:\n fo.write(json.dumps(el) + \"\\n\")\n return data\n\ndef test():\n # with open('example.osm.xml', 'r') as f:\n # pprint.pprint(f.read())\n data = process_map('../richmondhill.osm', False)\n \n # # UNCOMMENT to see what tags are ignored\n # print \"==========ignored tags===========\"\n # print weird_set\n\n # pprint.pprint(data)\n \n\nif __name__ == \"__main__\":\n test()", "sub_path": "P3_wrangle_openstreetmap_data/project/case_study/preparing_for_database.py", "file_name": "preparing_for_database.py", "file_ext": "py", "file_size_in_byte": 4443, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 16, "usage_type": "call"}, {"api_name": "audit.update_name", "line_number": 51, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 103, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree.iterparse", "line_number": 104, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 104, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 109, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 111, "usage_type": "call"}]}
+{"seq_id": "17909271", "text": "\"\"\"\nRole of the indirect pathway of the basal ganglia in perceptual decision making.\nW Wei, JE Rubin, & X-J Wang, JNS 2015.\nhttp://dx.doi.org/10.1523/JNEUROSCI.3611-14.2015\nThis example also demonstrates how to use Python's cPickle module to save and load\ncomplex data.\n\nAdapted from (Brian, Python2) to (Brian2, Python3) by Lethe Field\n\"\"\"\nfrom __future__ import division\n\nimport pickle\nimport os\nimport cython\nimport numpy as np\nimport random # Import before Brian floods the namespace\nimport argparse\nimport multiprocessing\nfrom collections import OrderedDict\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n\nfrom scipy.sparse import csr_matrix\nfrom brian2 import *\nprefs.codegen.target=\"numpy\"\n# Once code is working, turn off unit-checking for speed\n# import brian_no_units\n\n# Make Brian faster\n# set_global_preferences(\n# useweave=True,\n# usecodegen=True,\n# usecodegenweave=True,\n# usecodegenstateupdate=True,\n# usenewpropagate=True,\n# usecodegenthreshold=True,\n# gcc_options=['-ffast-math', '-march=native']\n# )\n#=========================================================================================\n# Input arguments\n#=========================================================================================\nparser = argparse.ArgumentParser()\nparser.add_argument('--taskID', type=int, required=True, help='Task number')\nparser.add_argument('--OUTDIR', required=True, help='Output directory')\nparser.add_argument('--plot', action='store_true', help='Plot with existent data')\nparser.add_argument('--sim', action='store_true', help='Run simulation')\nparser.add_argument('--count', action='store_true', help='Count decisions')\nparser.add_argument('--coh', type=float, default=0, help='Coherence level')\nparser.add_argument('--coh_c', action='store_true',\n help='Whether coherence level will change')\nparser.add_argument('--coh_s', type=float, default=2.5,\n help='Step for coherence change')\nparser.add_argument('--time', type=float, default=1.1,\n help='Simulation time')\nparser.add_argument('--ntrial', type=int, default=48, help='Trials for each condition')\nparser.add_argument('--Iconst', type=float, default=0.38,\n help='Constant current input to ACC')\nparser.add_argument('--Iconst_c', action='store_true')\nparser.add_argument('--Iconst_s', default=0.005)\nparser.add_argument('--pA_ACC', default=0.09)\nparser.add_argument('--pA_ACC_c', action='store_true')\nparser.add_argument('--pA_ACC_s', default=0.01)\nparser.add_argument('--ACC_STN_a', default=0.47)\nparser.add_argument('--ACC_STN_a_c', action='store_true')\nparser.add_argument('--ACC_STN_a_s', default=0.05)\nparser.add_argument('--ACC_STN_n', default=0.00)\nparser.add_argument('--ACC_STN_n_c', action='store_true')\nparser.add_argument('--ACC_STN_n_s', default=0.01)\nparser.add_argument('--Cx_pA', default=0.42)\nparser.add_argument('--Cx-pA_c', action='store_true')\nparser.add_argument('--Cx_pA_s', default=0.03)\nparser.add_argument('--G_STN', default=0.45)\nparser.add_argument('--G_STN_c', action='store_true')\nparser.add_argument('--G_STN_s', default=0.05)\nparser.add_argument('--STN_SNr', default=0.1)\nparser.add_argument('--STN_SNr_c', action='store_true')\nparser.add_argument('--STN_SNr_s', default=0.005)\nparser.add_argument('--Str_SNr', default=1.5)\nparser.add_argument('--Str_SNr_c', action='store_true')\nparser.add_argument('--Str_SNr_s', default=-0.3)\nparser.add_argument('--G_SNr', default=0.065)\nparser.add_argument('--G_SNr_c', action='store_true')\nparser.add_argument('--G_SNr_s', default=-0.006)\nparser.add_argument('--nSTN', default=2.6)\nparser.add_argument('--nSTN_c', action='store_true')\nparser.add_argument('--nSTN_s', default=-0.2)\nparser.add_argument('--nSNr', default=0.5)\nparser.add_argument('--nSNr_c', action='store_true')\nparser.add_argument('--nSNr_s', default=-0.03)\nparser.add_argument('--gSNr', default=8)\nparser.add_argument('--gSNr_c', action='store_true')\nparser.add_argument('--gSNr_s', default=-1.0)\nopt = parser.parse_args()\n\n#=========================================================================================\n# Equations\n#=========================================================================================\n\n# sAMPA, x, sNMDA, sGABA are synaptic conductances stored pre-synaptically\n# S_AMPA, S_NMDA, S_GABA are synaptic conductances stored post-synaptically\n\nequations = dict(\n # Excitatory neurons in cerebral cortex\n E=''' \n dV/dt = (-(V - V_L) - Isyn/gE) / tau_m_E : volt\n Isyn = I_AMPA_ext + I_AMPA + I_NMDA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_E*sAMPA_ext*(V - V_E) : amp\n I_AMPA = gAMPA_E*S_AMPA*(V - V_E) : amp\n I_NMDA = gNMDA_E*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n I_GABA = gGABA_E*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsAMPA/dt = -sAMPA/tauAMPA : 1\n dsNMDA/dt = -sNMDA/tauNMDA : 1\n dD/dt = (1 - D)/tauD : 1\n S_AMPA : 1\n S_NMDA : 1\n S_GABA : 1\n ''',\n\n # Inhibitory neurons in cerebral cortex\n # Note that sAMPA and sNMDA does not decay in this group\n I='''\n dV/dt = (-(V - V_L) - Isyn/gI) / tau_m_I : volt\n Isyn = I_AMPA_ext + I_AMPA + I_NMDA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_AMPA = gAMPA_I*S_AMPA*(V - V_E) : amp\n I_NMDA = gNMDA_I*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n I_GABA = gGABA_I*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n S_AMPA: 1\n S_NMDA: 1\n S_GABA: 1\n ''',\n\n\n Str='''\n dV/dt = (-(V - V_L) - Isyn/gI) / tau_m_I : volt\n Isyn = I_AMPA_ext + I_AMPA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_AMPA = gAMPA_I*S_AMPA*(V - V_E) : amp\n I_GABA = gGABA_I*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n S_AMPA: 1\n S_GABA: 1\n ''',\n\n GPe='''\n dV/dt = (-(V - V_L) - I_T/gI - Isyn/gI) / tau_m_I : volt\n Isyn = I_AMPA_ext + I_GABA_ext + I_AMPA + I_NMDA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_GABA_ext = gGABA_ext_I*sGABA_ext*(V - V_I) : amp\n I_AMPA = gAMPA_I*S_AMPA*(V - V_E) : amp\n I_NMDA = gNMDA_I*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n I_GABA = gGABA_I*S_GABA*(V - V_I) : amp\n I_T = gT*h*(V>V_h)*(V-V_T) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA_ext/dt = -sGABA_ext/tauGABA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n dh/dt = -h/tauhminus*(V>=V_h) + (1-h)/tauhplus*(V<V_h) : 1\n S_AMPA: 1\n S_NMDA: 1\n S_GABA: 1\n ''',\n\n STN='''\n dV/dt = (-(V - V_L) - I_T/gE - Isyn/gE) / tau_m_E : volt\n Isyn = I_AMPA_ext + I_GABA + I_NMDA + I_AMPA : amp\n I_AMPA_ext = gAMPA_ext_E*sAMPA_ext*(V - V_E) : amp\n I_GABA = gGABA_E*S_GABA*(V - V_I) : amp\n I_AMPA = gAMPA_E*S_AMPA*(V-V_E) : amp\n I_NMDA = gNMDA_E*S_NMDA*(V-V_E) : amp\n I_T = gT*h*(V>V_h)*(V-V_T) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsAMPA/dt = -sAMPA/tauAMPA : 1\n dsNMDA/dt = -sNMDA/tauNMDA : 1\n dh/dt = -h/tauhminus*(V>=V_h) + (1-h)/tauhplus*(V<V_h) :1\n S_GABA : 1\n S_NMDA : 1\n S_AMPA : 1\n ''',\n\n SNr='''\n dV/dt = (-(V - V_L) - Isyn/gI) / tau_m_I : volt\n Isyn = I_AMPA_ext + I_NMDA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_NMDA = gNMDA_I*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n I_GABA = gGABA_I*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n S_NMDA: 1\n S_GABA: 1\n ''',\n\n preA='''\n dV/dt = (-(V - V_L) - Isyn/gI) / tau_m_I : volt (unless refractory)\n Isyn = I_AMPA_ext + I_AMPA + I_NMDA : amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_AMPA = gAMPA_I*S_AMPA*(V - V_E) : amp\n I_NMDA = gNMDA_I*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n S_AMPA : 1\n S_NMDA : 1\n ''',\n\n ACC='''\n dV/dt = (-(V - V_L) - Isyn/gE) / tau_m_E : volt (unless refractory)\n Isyn = I_AMPA_ext - I_const + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_E*sAMPA_ext*(V - V_E) : amp\n I_GABA = gGABA_E*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsAMPA/dt = -sAMPA/tauAMPA : 1\n dsNMDA/dt = -sNMDA/tauNMDA : 1\n S_GABA : 1\n ''',\n\n SCE='''\n dV/dt = (-(V - V_L) - Isyn/gE) / tau_m_E : volt\n Isyn = I_AMPA_ext + I_AMPA + I_NMDA + I_GABA : amp\n I_AMPA_ext = gAMPA_ext_E*sAMPA_ext*(V - V_E) : amp\n I_AMPA = gAMPA_E*S_AMPA*(V - V_E) : amp\n I_NMDA = gNMDA_E*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n I_GABA = gGABA_E*S_GABA*(V - V_I) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsNMDA/dt = -sNMDA/tauNMDA : 1\n dF/dt = -F/tauF :1\n S_AMPA : 1\n S_NMDA : 1\n S_GABA : 1\n ''',\n\n SCI='''\n dV/dt = (-(V - V_L) - Isyn/gI) / tau_m_I : volt\n Isyn = I_AMPA_ext + I_NMDA: amp\n I_AMPA_ext = gAMPA_ext_I*sAMPA_ext*(V - V_E) : amp\n I_NMDA = gNMDA_I*S_NMDA*(V - V_E)/(1 + exp(-a*V)/b) : amp\n dsAMPA_ext/dt = -sAMPA_ext/tauAMPA : 1\n dsGABA/dt = -sGABA/tauGABA : 1\n S_NMDA: 1\n '''\n)\n\n#=========================================================================================\n# Parameters\n#=========================================================================================\n\nmodelparams = {}\n\nmodelparams['Cx'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Excitatory LIF\n gE=25*nS,\n tau_m_E=20*ms,\n tau_ref_E=2*ms,\n\n # Inhibitory LIF\n gI=20*nS,\n tau_m_I=10*ms,\n tau_ref_I=1*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauNMDA=100*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_E=2.1*nS, # This will be reduced to 2.0 to make the Cx recurrent connection not enough to support persistent activity\n gAMPA_ext_I=1.62*nS,\n\n # Unscaled recurrent synaptic conductances (onto excitatory)\n gAMPA_E=80.0*nS,\n gNMDA_E=264.0*nS,\n gGABA_E=520.0*nS,\n\n # Unscaled recurrent synaptic conductances (onto inhibitory)\n gAMPA_I=64*nS,\n gNMDA_I=208*nS,\n gGABA_I=400*nS,\n\n # Background noise\n nu_ext=2.4*kHz,\n\n # Number of neurons\n N_E=1600,\n N_I=400,\n\n # Fraction of selective neurons\n fsel=0.15,\n\n # Hebb-strengthened weight\n wp=1.7,\n\n # STD\n tauD=600*ms,\n\n gNMDA_SCE_CxE=0.05*nS, # From SCE to CxE\n gNMDA_SCE_CxI=0.11*nS # From SCE to CxE\n)\n\nmodelparams['Str'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gI=25*nS,\n tau_m_I=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_I=4.0*nS,\n # Background Possion rate\n nu_ext=0.8*kHz,\n\n # Scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_I=3.0*nS, # From Cx, this varies from 1.0 to 4.5\n gNMDA_I=0.0*nS, # From Cx\n gGABA_I=1.0*nS, # From within Str\n\n # Number of neurons\n N_PJ=250*2\n)\n\nmodelparams['SNr'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gI=25*nS,\n tau_m_I=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_I=(opt.gSNr+(opt.taskID-1)*float(opt.gSNr_c)\n * opt.gSNr_s)*nS,\n\n # Background Possion rate\n nu_ext=(opt.nSNr+(opt.taskID-1)*float(opt.nSNr_c)\n * opt.nSNr_s)*kHz,\n\n # scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_I=0.0*nS, # From STN\n gNMDA_I=(opt.STN_SNr+(opt.taskID-1)*float(opt.STN_SNr_c)\n * opt.STN_SNr_s)*nS, # From STN\n gGABA_I=(opt.Str_SNr+(opt.taskID-1)*float(opt.Str_SNr_c)\n * opt.Str_SNr_s)*nS, # From Str\n\n gGABA_GPe_SNr=(opt.G_SNr+(opt.taskID-1)*float(opt.G_SNr_c)\n * opt.G_SNr_s)*nS, # from GPe\n\n # Number of neurons\n N_PJ=250*2\n)\n\nmodelparams['GPe'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gI=25*nS,\n tau_m_I=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # IFB model parameters\n V_T=120*mV,\n V_h=-60*mV,\n gT=60*nS,\n tauhminus=20*ms,\n tauhplus=100*ms,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_I=3.0*nS,\n\n # Background Possion rate\n nu_ext_AMPA=3.2*kHz,\n\n # External synaptic conductances\n gGABA_ext_I=2.0*nS,\n\n # Background Possion rate\n nu_ext_GABA=2.0*kHz,\n\n # scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_I=0.05*nS, # From STN\n gNMDA_I=2.0*nS, # From STN\n gGABA_I=4.0*nS, # From Str, this varies from 0 to 8\n\n gGABA_GPe_GPe=1.5*nS,\n\n # Number of neurons\n N_PJ=2500*2\n)\n\nmodelparams['STN'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gE=25*nS,\n tau_m_E=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # IFB model parameters\n V_T=120*mV,\n V_h=-60*mV,\n gT=60*nS,\n tauhminus=20*ms,\n tauhplus=100*ms,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauNMDA=100*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_E=1.6*nS,\n\n # Background Possion rate\n nu_ext=(opt.nSTN+(opt.taskID-1)*float(opt.nSTN_c)\n * opt.nSTN_s)*kHz,\n\n # Scaled recurrent synaptic conductances (onto projection neurons)\n gGABA_E=(opt.G_STN+(opt.taskID-1)*float(opt.G_STN_c)\n * opt.G_STN_s)*nS, # From GPe\n gAMPA_E=(opt.ACC_STN_a+(opt.taskID-1)*float(opt.ACC_STN_a_c)\n * opt.ACC_STN_a_s)*nS, # From ACC\n gNMDA_E=(opt.ACC_STN_n+(opt.taskID-1)*float(opt.ACC_STN_n_c)\n * opt.ACC_STN_n_s)*nS, # From ACC\n\n # Number of neurons\n N_PJ=2500*2\n)\n\nmodelparams['preA'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gI=25*nS,\n tau_m_I=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_I=4.0*nS,\n # Background Possion rate\n nu_ext=0.8*kHz,\n\n # Scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_I=(opt.Cx_pA+(opt.taskID-1)*float(opt.Cx_pA_c)*opt.Cx_pA_s) * \\\n nS, # From Cx, this varies from 1.0 to 4.5\n gNMDA_I=0.0*nS, # From Cx\n gGABA_I=1.0*nS, # From within preA\n\n # Number of neurons\n N_PJ=250\n)\n\nmodelparams['ACC'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gE=25*nS,\n tau_m_E=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauNMDA=100*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_E=0.6*nS,\n\n # Background Possion rate\n nu_ext=2.28*kHz,\n\n # Constant bias\n I_const=(opt.Iconst+(opt.taskID-1)*float(opt.Iconst_c)*opt.Iconst_s)*namp,\n\n # Scaled recurrent synaptic conductances (onto projection neurons)\n gGABA_E=(opt.pA_ACC+(opt.taskID-1)*float(opt.pA_ACC_c)\n * opt.pA_ACC_s)*nS, # From preA\n\n # STF parameter\n tauF=1000*ms,\n\n # Number of neurons\n N_PJ=250\n)\n\n\nmodelparams['SCE'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gE=25*nS,\n tau_m_E=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauNMDA=100*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_E=0.19*nS,\n\n # Background Possion rate\n nu_ext=1.28*kHz,\n\n # Scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_E=3.3*nS, # From Cx\n gGABA_E=2.5*nS, # From SNr\n gNMDA_E=1.3*nS, # From SCE to SCE\n gGABA_SCI_SCE=2.5*nS, # From SCI to SCE\n\n # STF parameter\n tauF=1000*ms,\n\n # Number of neurons\n N_PJ=250*2\n)\n\nmodelparams['SCI'] = dict(\n # Common LIF\n V_L=-70*mV,\n Vth=-50*mV,\n Vreset=-55*mV,\n\n # Projection LIF\n gI=25*nS,\n tau_m_I=20*ms,\n tau_ref_PJ=0*ms,\n\n # Reversal potentials\n V_E=0*mV,\n V_I=-70*mV,\n\n # NMDA nonlinearity\n a=0.062*mV**-1,\n b=3.57,\n\n # Synaptic time constants\n tauAMPA=2*ms,\n tauGABA=5*ms,\n delay=0.2*ms,\n\n # External synaptic conductances\n gAMPA_ext_I=2.0*nS,\n\n # Background Possion rate\n nu_ext=1.28*kHz,\n\n # scaled recurrent synaptic conductances (onto projection neurons)\n gAMPA_I=0.0*nS, # From SCE\n gNMDA_I=0.7*nS, # From SCE\n gGABA_I=0.0*nS, # No recurrent SCI -> SCI\n\n # Number of neurons\n N_PJ=250\n)\n\n#=========================================================================================\n# Model\n#=========================================================================================\n\n# Stimulus input to Cxe\n\n\nclass Stimulus(object):\n def __init__(self, Ton, Toff, mu0, muA, muB, coh):\n self.Ton = Ton\n self.Toff = Toff\n self.mu0 = mu0\n self.muA = muA\n self.muB = muB\n\n #set coherence\n self.set_coh(coh)\n\n def s1(self, T):\n t_array = np.arange(0, T + defaultclock.dt, defaultclock.dt)\n vals = np.zeros_like(t_array) * Hz\n vals[np.logical_and(self.Ton <= t_array, t_array <\n self.Toff)] = self.pos\n return TimedArray(vals, defaultclock.dt)\n\n def s2(self, T):\n t_array = np.arange(0, T + defaultclock.dt, defaultclock.dt)\n vals = np.zeros_like(t_array) * Hz\n vals[np.logical_and(self.Ton <= t_array, t_array <\n self.Toff)] = self.neg\n return TimedArray(vals, defaultclock.dt)\n\n def set_coh(self, coh):\n self.pos = self.mu0 + self.muA*coh/100\n self.neg = self.mu0 - self.muB*coh/100\n\n\nclass Model(object):\n def __init__(self, modelparams, stimulus, T):\n #---------------------------------------------------------------------------------\n # Complete the model specification\n #---------------------------------------------------------------------------------\n\n # Model parameters\n params = modelparams.copy()\n\n # Rescale conductances by number of neurons\n for x in ['gAMPA_E', 'gAMPA_I', 'gNMDA_E', 'gNMDA_I']:\n params['Cx'][x] /= params['Cx']['N_E']\n for x in ['gGABA_E', 'gGABA_I']:\n params['Cx'][x] /= params['Cx']['N_I']\n\n # Make local variables for convenience\n N_E = params['Cx']['N_E']\n fsel = params['Cx']['fsel']\n wp = params['Cx']['wp']\n delay = params['Cx']['delay']\n\n # Subpopulation size\n N1 = int(fsel*N_E)\n N2 = N1\n N0 = N_E - (N1 + N2)\n params['Cx']['N0'] = N0\n params['Cx']['N1'] = N1\n params['Cx']['N2'] = N2\n\n # Hebb-weakened weight\n wm = (1 - wp*fsel)/(1 - fsel)\n params['Cx']['wm'] = wm\n\n # Synaptic weights between populations\n self.W = np.asarray([\n [1, 1, 1],\n [wm, wp, wm],\n [wm, wm, wp]\n ])\n\n #---------------------------------------------------------------------------------\n # Neuron populations\n #---------------------------------------------------------------------------------\n\n net = OrderedDict() # Network objects\n netPJsub = OrderedDict() # Projection neuron subpopulations\n\n for x in ['E', 'I']:\n net['Cx'+x] = NeuronGroup(params['Cx']['N_'+x],\n equations[x],\n threshold='V > Vth',\n reset='V=Vreset',\n refractory=params['Cx']['tau_ref_'+x],\n method='rk2',\n namespace=params['Cx'])\n # Excitatory subpopulations\n netPJsub['Cx0'] = net['CxE'][:params['Cx']['N0']]\n netPJsub['Cx1'] = net['CxE'][params['Cx']['N0']:params['Cx']['N0'] + params['Cx']['N1']]\n netPJsub['Cx2'] = net['CxE'][params['Cx']['N0'] + params['Cx']['N1']:]\n\n for x in ['Str', 'SNr', 'GPe', 'STN', 'SCE', 'SCI', 'preA', 'ACC']:\n net[x] = NeuronGroup(params[x]['N_PJ'],\n equations[x],\n threshold='V > Vth',\n reset='V = Vreset',\n refractory=params[x]['tau_ref_PJ'],\n method='rk2',\n namespace=params[x])\n if x != 'SCI' and x != 'preA' and x != 'ACC':\n netPJsub[x+'1'] = net[x][: params[x]['N_PJ']//2]\n netPJsub[x+'2'] = net[x][params[x]['N_PJ']//2:]\n netPJsub['preA'] = net['preA']\n netPJsub['ACC'] = net['ACC']\n #---------------------------------------------------------------------------------\n # Background input (post-synaptic)\n #---------------------------------------------------------------------------------\n\n for x in ['E', 'I']:\n net['pg'+x] = PoissonGroup(params['Cx']\n ['N_'+x], params['Cx']['nu_ext'])\n net['ic'+x] = Synapses(net['pg'+x], net['Cx'+x],\n on_pre='sAMPA_ext += 1', delay=delay)\n net['ic'+x].connect(condition='i == j')\n\n for x in ['Str', 'SNr', 'STN', 'SCE', 'SCI', 'preA', 'ACC']:\n net['pg'+x] = PoissonGroup(params[x]['N_PJ'], params[x]['nu_ext'])\n net['ic'+x] = Synapses(net['pg'+x], net[x],\n on_pre='sAMPA_ext += 1', delay=delay)\n net['ic'+x].connect(condition='i == j')\n\n net['pg'+'GPe_AMPA'] = PoissonGroup(params['GPe']\n ['N_PJ'], params['GPe']['nu_ext_AMPA'])\n net['ic'+'GPe_AMPA'] = Synapses(net['pg'+'GPe_AMPA'],\n net['GPe'], on_pre='sAMPA_ext += 1', delay=delay)\n net['ic'+'GPe_AMPA'].connect(condition='i == j')\n\n net['pg'+'GPe_GABA'] = PoissonGroup(params['GPe']\n ['N_PJ'], params['GPe']['nu_ext_GABA'])\n net['ic'+'GPe_GABA'] = Synapses(net['pg'+'GPe_GABA'],\n net['GPe'], on_pre='sGABA_ext += 1', delay=delay)\n net['ic'+'GPe_GABA'].connect(condition='i == j')\n\n #---------------------------------------------------------------------------------\n # Recurrent input\n #---------------------------------------------------------------------------------\n\n # Change pre-synaptic variables\n for x in ['CxI', 'SNr', 'GPe', 'SCI', 'Str', 'preA']:\n net['icGABA_'+x] = Synapses(net[x], net[x],\n on_pre='sGABA += 1', delay=delay)\n net['icGABA_'+x].connect(condition='i==j')\n\n # CxE\n net['icAMPA_NMDA_CxE'] = Synapses(net['CxE'], net['CxE'], on_pre='''sAMPA += 1\n sNMDA += 0.63*(1-sNMDA)\n ''', delay=delay) # D += -0.45*D\n net['icAMPA_NMDA_CxE'].connect(condition='i==j')\n\n # ACC\n net['icAMPA_NMDA_ACC'] = Synapses(net['ACC'], net['ACC'], on_pre='''sAMPA += 1\n sNMDA += 0.63*(1-sNMDA)\n ''', delay=delay) # D += -0.45*D\n net['icAMPA_NMDA_ACC'].connect(condition='i==j')\n\n # STN\n net['icAMPA_NMDA_STN'] = Synapses(net['STN'], net['STN'], on_pre='''sAMPA += 1\n sNMDA += 0.63*(1-sNMDA)''', delay=delay) # alpha*(1-sNMDA)\n net['icAMPA_NMDA_STN'].connect(condition='i==j')\n\n # SCE\n net['icNMDA_SCE'] = Synapses(net['SCE'], net['SCE'], on_pre='''sNMDA += 0.63*(1-sNMDA)\n F += 0.15*(1-F)''', delay=delay)\n net['icNMDA_SCE'].connect(condition='i==j')\n\n # sparse recurrent connection\n prob_GPe_GPe = 0.05\n prob_GPe_STN = 0.02 # GPe to STN\n prob_STN_GPe = 0.05 # STN to GPe\n\n N_PJ1 = params['GPe']['N_PJ']//2\n\n # here the seed 100 is choosed arbitarily\n rns = np.random.RandomState(100)\n\n conn_GPe_GPe = 1*(rns.random_sample((N_PJ1, N_PJ1)) < prob_GPe_GPe)\n conn_GPe_STN = 1*(rns.random_sample((N_PJ1, N_PJ1)) < prob_GPe_STN)\n conn_STN_GPe = 1*(rns.random_sample((N_PJ1, N_PJ1)) < prob_STN_GPe)\n\n self.sconn_GPe_GPe = csr_matrix(conn_GPe_GPe)\n self.sconn_GPe_STN = csr_matrix(conn_GPe_STN)\n self.sconn_STN_GPe = csr_matrix(conn_STN_GPe)\n\n self.wGABA_GPe_SNr = params['SNr']['gGABA_GPe_SNr'] / \\\n params['SNr']['gGABA_I']\n self.wGABA_GPe_GPe = params['GPe']['gGABA_GPe_GPe'] / \\\n params['GPe']['gGABA_I']\n self.wNMDA_SCE_CxE = params['Cx']['gNMDA_SCE_CxE'] / \\\n params['Cx']['gNMDA_E']\n self.wNMDA_SCE_CxI = params['Cx']['gNMDA_SCE_CxI'] / \\\n params['Cx']['gNMDA_I']\n self.wGABA_SCI_SCE = params['SCE']['gGABA_SCI_SCE'] / \\\n params['SCE']['gGABA_E']\n\n # Link pre-synaptic variables to post-synaptic variables\n @network_operation(when='start')\n def recurrent_input():\n SAMPA = {i: {} for i in ['1', '2']}\n SNMDA = {i: {} for i in ['1', '2']}\n SGABA = {i: {} for i in ['1', '2']}\n\n for x in ['Cx', 'STN']:\n for i in ['1', '2']:\n # Sum for all neurons in the group\n SAMPA[i][x] = sum(self.netPJsub[x+i].sAMPA)\n SNMDA[i][x] = sum(self.netPJsub[x+i].sNMDA)\n\n for x in ['Str', 'SNr', 'GPe']:\n for i in ['1', '2']:\n SGABA[i][x] = sum(self.netPJsub[x+i].sGABA)\n\n for i in ['1', '2']:\n SNMDA[i]['SCE'] = sum(self.netPJsub['SCE'+i].sNMDA)\n\n SGABA['SCI'] = sum(self.net['SCI'].sGABA)\n SAMPA['ACC'] = sum(self.net['ACC'].sAMPA)\n SNMDA['ACC'] = sum(self.net['ACC'].sNMDA)\n SGABA['preA'] = sum(self.net['preA'].sGABA)\n\n SCx0_AMPA = sum(self.netPJsub['Cx0'].sAMPA)\n SCx0_NMDA = sum(self.netPJsub['Cx0'].sNMDA)\n\n # Calculating post-synaptic variables in Cx\n # AMPA\n S = self.W.dot([SCx0_AMPA, SAMPA['1']['Cx'], SAMPA['2']['Cx']])\n for i in range(3):\n self.netPJsub['Cx'+str(i)].S_AMPA = S[i]\n self.net['CxI'].S_AMPA = S[0]\n\n # NMDA\n S = self.W.dot([SCx0_NMDA, SNMDA['1']['Cx'], SNMDA['2']['Cx']])\n for i in range(3):\n self.netPJsub['Cx'+str(i)].S_NMDA = S[i] + \\\n self.wNMDA_SCE_CxE*(SNMDA['1']['SCE']+SNMDA['2']['SCE'])\n self.net['CxI'].S_NMDA = S[0] + self.wNMDA_SCE_CxI * \\\n (SNMDA['1']['SCE']+SNMDA['2']['SCE'])\n\n # GABA\n S = sum(self.net['CxI'].sGABA)\n self.net['CxE'].S_GABA = S\n self.net['CxI'].S_GABA = S\n\n for i in ['1', '2']:\n # For SCE -> SCI\n SNMDA[i]['SCE_F'] = dot(\n self.netPJsub['SCE'+i].F, self.netPJsub['SCE'+i].sNMDA)\n\n # Str\n self.netPJsub['Str'+i].S_AMPA = dot(\n self.netPJsub['Cx'+i].D, self.netPJsub['Cx'+i].sAMPA)\n self.netPJsub['Str'+i].S_GABA = SGABA[i]['Str']\n\n # SNr\n self.netPJsub['SNr'+i].S_NMDA = SNMDA[i]['STN']\n self.netPJsub['SNr'+i].S_GABA = SGABA[i]['Str'] + \\\n self.wGABA_GPe_SNr*SGABA[i]['GPe']\n\n # GPe\n self.netPJsub['GPe'+i].S_AMPA = self.sconn_STN_GPe.dot(\n array(self.netPJsub['STN'+i].sAMPA))\n self.netPJsub['GPe'+i].S_NMDA = self.sconn_STN_GPe.dot(\n array(self.netPJsub['STN'+i].sNMDA))\n self.netPJsub['GPe'+i].S_GABA = self.sconn_GPe_GPe.dot(\n array(self.netPJsub['GPe'+i].sGABA))*self.wGABA_GPe_GPe + SGABA[i]['Str']\n # STN:\n self.netPJsub['STN'+i].S_GABA = self.sconn_GPe_STN.dot(\n array(self.netPJsub['GPe'+i].sGABA))\n self.netPJsub['STN'+i].S_AMPA = SAMPA['ACC']\n self.netPJsub['STN'+i].S_NMDA = SNMDA['ACC']\n\n # SC\n self.netPJsub['SCE'+i].S_AMPA = SAMPA[i]['Cx']\n self.netPJsub['SCE'+i].S_NMDA = SNMDA[i]['SCE']\n self.netPJsub['SCE'+i].S_GABA = SGABA[i]['SNr'] + \\\n self.wGABA_SCI_SCE*SGABA['SCI']\n\n self.net['SCI'].S_NMDA = SNMDA['1']['SCE_F']+SNMDA['2']['SCE_F']\n self.net['preA'].S_AMPA = SAMPA['1']['Cx']+SAMPA['2']['Cx']\n self.net['preA'].S_NMDA = SNMDA['1']['Cx']+SNMDA['2']['Cx']\n self.net['ACC'].S_GABA = SGABA['preA']\n #---------------------------------------------------------------------------------\n # External input (post-synaptic)\n #---------------------------------------------------------------------------------\n global s1\n s1 = stimulus.s1(T)\n global s2\n s2 = stimulus.s2(T)\n for ind, sname in zip([1, 2], ['s1', 's2']):\n net['pg'+str(ind)] = PoissonGroup(params['Cx']\n ['N'+str(ind)], '%s(t)' % sname)\n net['ic'+str(ind)] = Synapses(net['pg'+str(ind)],\n netPJsub['Cx'+str(ind)], on_pre='sAMPA_ext += 1', delay=delay)\n net['ic'+str(ind)].connect(condition='i == j')\n\n #---------------------------------------------------------------------------------\n # Record rates\n #---------------------------------------------------------------------------------\n\n rates = OrderedDict()\n for x in netPJsub:\n rates[x] = PopulationRateMonitor(\n netPJsub[x]) # bin have to be changed\n\n #---------------------------------------------------------------------------------\n # Setup\n #---------------------------------------------------------------------------------\n\n self.params = params\n self.net = net\n self.netPJsub = netPJsub\n self.rates = rates\n\n # Add network objects and monitors to NetworkOperation's contained_objects\n self.contained_objects = list()\n self.contained_objects.extend(self.net.values())\n self.contained_objects.extend(self.rates.values())\n self.contained_objects.extend([recurrent_input])\n\n def reinit(self):\n # Randomly initialize membrane potentials\n for x in ['E', 'I']:\n self.net['Cx'+x].V = np.random.uniform(self.params['Cx']['Vreset'],\n self.params['Cx']['Vth'],\n size=self.params['Cx']['N_'+x]) * volt\n for x in ['Str', 'SNr', 'GPe', 'STN', 'SCE', 'SCI', 'preA', 'ACC']:\n self.net[x].V = np.random.uniform(self.params[x]['Vreset'],\n self.params[x]['Vth'],\n size=self.params[x]['N_PJ']) * volt\n\n # Set synaptic variables to zero\n for i in ['CxE', 'STN', 'ACC']:\n for x in ['sAMPA_ext', 'sAMPA', 'sNMDA']:\n setattr(self.net[i], x, 0)\n for i in ['CxI', 'Str', 'SNr', 'SCI', 'preA']:\n for x in ['sAMPA_ext', 'sGABA']:\n setattr(self.net[i], x, 0)\n for x in ['sAMPA_ext', 'sNMDA']:\n setattr(self.net['SCE'], x, 0)\n for x in ['sAMPA_ext', 'sGABA_ext', 'sGABA']:\n setattr(self.net['GPe'], x, 0)\n setattr(self.net['SCE'], 'F', 0)\n setattr(self.net['GPe'], 'h', 1)\n setattr(self.net['STN'], 'h', 1)\n setattr(self.net['CxE'], 'D', 1)\n\n#=========================================================================================\n# Simulation\n#=========================================================================================\n\n\nclass Simulation(object):\n def __init__(self, modelparams, stimparams, sim_dt, T):\n defaultclock.dt = sim_dt\n self.stimulus = Stimulus(stimparams['Ton'], stimparams['Toff'],\n stimparams['mu0'], stimparams['muA'],\n stimparams['muB'], stimparams['coh'])\n self.model = Model(modelparams, self.stimulus, T)\n self.network = Network(self.model.contained_objects)\n\n def run(self, T, randseed=1):\n # Initialize random number generators\n seed(randseed)\n\n # Initialize and run\n self.model.reinit()\n if randseed==5:\n self.network.run(T, report='text')\n else:\n self.network.run(T)\n\n def saverates(self, filename):\n time = self.model.rates['Cx1'].t/ms\n rates = {}\n for name in ['Cx', 'Str', 'SNr', 'GPe', 'STN', 'SCE']:\n rates[name+'1'] = self.model.rates[name +\n '1'].smooth_rate(width=5*ms)/Hz\n rates[name+'2'] = self.model.rates[name +\n '2'].smooth_rate(width=5*ms)/Hz\n rates['preA'] = self.model.rates['preA'].smooth_rate(width=5*ms)/Hz\n rates['ACC'] = self.model.rates['ACC'].smooth_rate(width=5*ms)/Hz\n\n with open(filename, 'wb') as f:\n pickle.dump((time, rates), f)\n\n\n#/////////////////////////////////////////////////////////////////////////////////////////\nstimparams = dict(\n Ton=0.5*second, # Stimulus onset\n Toff=1.5*second, # Stimulus offset\n mu0=30*Hz, # Input rate\n muA=60*Hz,\n muB=20*Hz,\n coh=opt.coh+(float(opt.coh_c)*(opt.taskID-1) * \\\n opt.coh_s) # Percent coherence\n)\nsim_dt = 0.05*ms\nT = opt.time*second\nsubdir = 'data{}'.format(opt.taskID)\n##############################################################################################\n\n\ndef run_sim(seedi):\n sim = Simulation(modelparams, stimparams, sim_dt, T)\n sim.run(T, randseed=seedi)\n\n dataname = 'rates{}.pkl'.format(seedi)\n datapath = os.path.join(subdir, dataname)\n sim.saverates(datapath)\n\ndef plot_rate(time, rates, filepath):\n w = 0.23\n h = 0.20\n dx = 0.08\n dy = 0.12\n x1 = 0.1\n x2 = x1+w+dx\n x3 = x2+w+dx\n y1 = 0.1\n y2 = y1+h+dy\n y3 = y2+h+dy\n # Figure setup\n fig = plt.figure()\n plots = {\n 'SNr': fig.add_axes([x1, y1, w, h]),\n 'STN': fig.add_axes([x2, y1, w, h]),\n 'SCE': fig.add_axes([x3, y1, w, h]),\n 'Cx': fig.add_axes([x1, y2, w, h]),\n 'GPe': fig.add_axes([x2, y2, w, h]),\n 'Str': fig.add_axes([x3, y2, w, h]),\n 'preA': fig.add_axes([x1, y3, w, h]),\n 'ACC': fig.add_axes([x2, y3, w, h])\n }\n for name, plot in plots.items():\n plot.set_title(name)\n plots['SNr'].set_xlabel('Time from stimulus (ms)')\n plots['SNr'].set_ylabel('Firing rate (Hz)')\n\n for name, plot in plots.items():\n if name != 'preA' and name != 'ACC':\n plot.plot(time, rates[name+'1'], 'g', zorder=5)\n plot.plot(time, rates[name+'2'], 'b', zorder=5)\n else:\n plot.plot(time, rates[name], 'r', zorder=5)\n plot.set_xlim(-100, 600)\n plot.set_xticks([0, 200, 400, 600])\n\n plots['Cx'].set_ylim(0, 20)\n plots['STN'].set_ylim(0, 100)\n plots['SCE'].set_ylim(0, 300)\n plots['GPe'].set_ylim(0, 120)\n plots['Str'].set_ylim(0, 45)\n plots['SNr'].set_ylim(0,200)\n\n plt.savefig(filepath)\n\n\nif __name__ == '__main__':\n os.chdir('{}'.format(opt.OUTDIR))\n if not os.path.exists(subdir):\n os.mkdir(subdir)\n \n ntrial = opt.ntrial\n if opt.sim:\n pool = multiprocessing.Pool(ntrial)\n seedlist = []\n for i in range(ntrial):\n seedlist.append(i+1)\n pool.map(run_sim, seedlist)\n # run_sim(1)\n #-------------------------------------------------------------------------------------\n # Plot firing rates in different areas\n #-------------------------------------------------------------------------------------\n rates_load = {}\n # Load firing rates\n for idx in range(ntrial):\n dataname = 'rates{}.pkl'.format(idx+1)\n datapath = os.path.join(subdir, dataname)\n with open(datapath, 'rb') as f:\n time, rates_load[idx] = pickle.load(f)\n\n # Average across trials\n # rates = {}\n # for idx in range(ntrial):\n # for name in ['Cx', 'Str', 'SNr', 'GPe', 'STN', 'SCE']:\n # if idx == 0:\n # rates[name+'1'] = rates_load[idx][name+'1']/ntrial\n # rates[name+'2'] = rates_load[idx][name+'2']/ntrial\n # else:\n # rates[name+'1'] += rates_load[idx][name+'1']/ntrial\n # rates[name+'2'] += rates_load[idx][name+'2']/ntrial\n # if idx == 0:\n # rates['preA'] = rates_load[idx]['preA']/ntrial\n # rates['ACC'] = rates_load[idx]['ACC']/ntrial\n # else:\n # rates['preA'] += rates_load[idx]['preA']/ntrial\n # rates['ACC'] += rates_load[idx]['ACC']/ntrial\n\n # # Align time to stimulus onset\n # time -= stimparams['Ton']/ms\n # filename = 'figure_avg.pdf'\n # filepath = os.path.join(subdir, filename)\n # plot_rate(time, rates, filepath)\n\n if opt.plot:\n for idx in range(ntrial):\n dataname = 'rates{}.pkl'.format(idx+1)\n datapath = os.path.join(subdir, dataname)\n with open(datapath, 'rb') as f:\n time, rates = pickle.load(f)\n time -= stimparams['Ton']/ms\n filename = 'figure_{}.pdf'.format(idx+1)\n filepath = os.path.join(subdir, filename)\n plot_rate(time, rates, filepath)\n\n if opt.count:\n spike_count = 0 \n spike_time = []\n spike_thre = []\n for idx in range(ntrial):\n dataname = 'rates{}.pkl'.format(idx+1)\n datapath = os.path.join(subdir, dataname)\n with open(datapath, 'rb') as f:\n time, rates = pickle.load(f)\n time -= stimparams['Ton']/ms\n\n t = 0\n while time[t]<599:\n if rates['SCE1'][t]>30 or rates['SCE2'][t]>30:\n spike_count += 1\n spike_time.append(time[t])\n spike_thre.append(max(rates['Cx1'][t], rates['Cx2'][t]))\n break\n t += 1\n \n if spike_count>0: \n time_mean = np.mean(spike_time)\n time_sigma = np.sqrt(np.var(spike_time))\n thre_mean = np.mean(spike_thre)\n thre_sigma = np.sqrt(np.var(spike_thre))\n else:\n time_mean = -1\n time_sigma = -1\n thre_mean = -1\n thre_sigma = -1\n dataname = os.path.join(subdir, 'thre.txt')\n np.savetxt(dataname, [time_mean, time_sigma, thre_mean, thre_sigma])\n\n\n \n", "sub_path": "acc.py", "file_name": "acc.py", "file_ext": "py", "file_size_in_byte": 40586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "matplotlib.use", "line_number": 21, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 692, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 693, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 694, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 699, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 701, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 744, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 754, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 755, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 852, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 852, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 858, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 859, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 860, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 977, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 1000, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 1004, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1004, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 1061, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1085, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1085, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 1100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 1132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1132, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 1136, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 1137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1137, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 1138, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 1142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1155, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 1157, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1185, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 1187, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1190, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 1201, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1214, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1215, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 1215, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1216, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1217, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 1217, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1223, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 1224, "usage_type": "call"}]}
+{"seq_id": "104130882", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nA permutation is an ordered arrangement of objects. For example, 3124 is one \r\npossible permutation of the digits 1, 2, 3 and 4. If all of the permutations are \r\nlisted numerically or alphabetically, we call it lexicographic order. The lexicographic \r\npermutations of 0, 1 and 2 are:\r\n\r\n012 021 102 120 201 210\r\n\r\nWhat is the millionth lexicographic permutation of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9?\r\n\r\n\r\n\"\"\"\r\nfrom itertools import permutations\r\nimport time\r\nimport math\r\n\r\nstart_time = time.time()\r\n\r\nd = [0,1,2,3,4,5,6,7,8,9]\r\nindex = 1\r\nfor p in permutations(d):\r\n if index == 1000000 :\r\n print (index, p)\r\n s = map(str,p)\r\n print (''.join(s))\r\n index += 1\r\n \r\nelapsed_time = time.time() - start_time\r\nprint (\"Elapse time: {:.2f} sec\".format(elapsed_time))\r\n\r\n\r\n", "sub_path": "Euler project no 24.py", "file_name": "Euler project no 24.py", "file_ext": "py", "file_size_in_byte": 842, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "252798639", "text": "import json\nimport requests\n\nfrom django.views.generic import ListView\nfrom django.contrib.postgres.search import TrigramSimilarity, SearchVector, SearchQuery\n\nfrom artsumers.artwork.views import ArtworkListView\n\n\nclass SearchView(ArtworkListView):\n template_name = 'search/main_search.html'\n\n def get_queryset(self):\n search_word = self.request.GET.get('search_word', '')\n search_query = SearchQuery(self.request.GET.get('search_word', ''))\n # from IPython import embed; embed()\n if (search_word != ''):\n # vector = SearchVector(\n # 'title',\n # weight='A',\n # ) + SearchVector(\n # 'artist__name_kor',\n # weight='A',\n # ) + SearchVector(\n # 'artist__name_eng',\n # weight='A',\n # )\n\n # queryset = self.model.objects.annotate(\n # similarity=TrigramSimilarity('title', search_query),\n # ).filter(similarity__gt=0.3).order_by('-similarity')\n\n queryset = self.model.objects.annotate(\n similarity=TrigramSimilarity(\n 'title', search_word\n ) + TrigramSimilarity(\n 'artist__name_kor', search_word\n ) + TrigramSimilarity(\n 'artist__name_eng', search_word\n )\n ).filter(similarity__gt=0.3).order_by('-similarity')\n # queryset = self.model.objects.filter(title__trigram_similar=search_query)\n else:\n queryset = self.model.objects.all()\n\n return queryset\n\n\n# class SearchView(TemplateView):\n# template_name = 'search/main_search.html'\n\n# def get_context_data(self, **kwargs):\n# style = self.request.GET.get('style', 'all')\n# subject = self.request.GET.get('subject', 'all')\n# color = self.request.GET.get('color', 'all')\n\n# url = 'http://localhost:8000/api/search/?' + 'style=' + style + '&subject=' + subject + '&color=' + color\n# response = requests.get(url)\n# search_dict_li = json.loads(response.text)\n# context = super(SearchView, self).get_context_data(**kwargs)\n# context['search_dict_li'] = search_dict_li\n# return context\n", "sub_path": "artsumers/search/views/main_search.py", "file_name": "main_search.py", "file_ext": "py", "file_size_in_byte": 2278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "artsumers.artwork.views.ArtworkListView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.postgres.search.SearchQuery", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.postgres.search.TrigramSimilarity", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.postgres.search.TrigramSimilarity", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.postgres.search.TrigramSimilarity", "line_number": 38, "usage_type": "call"}]}
+{"seq_id": "458463564", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom students.views import *\n\nurlpatterns = patterns('',\n url(r'^$', home, name='home'),\n\n url(r'^groups/create/$', create_group, name='create_group'),\n url(r'^students/create/$', create_student, name='create_student'),\n\n url(r'^groups/edit/(\\d+)$', edit_group, name='edit_group'),\n url(r'^students/edit/(\\d+)$', edit_student, name='edit_student'),\n\n url(r'groups/delete/(\\d+)$', delete_entry, {'item_model': 'Group'}),\n url(r'students/delete/(\\d+)$', delete_entry, {'item_model': 'Student'}),\n url(r'^groups/(.+)/$', students_in_group, name='students_in_group'),\n\n url(r'^track/$', view_db_changes, name='view_db_changes'),\n url(r'^login/$', sign_in, name='sign_in'),\n url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "student_tracker/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "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": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}]}
+{"seq_id": "547645555", "text": "import falcon\n\nfrom .request import Request\nfrom .response import Response\nfrom . import middlewares\nfrom .routing import Router\n\n\nclass API(falcon.API):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._router.register_route(self)\n\n def register_resource(self, resource):\n self._router.register_resource(resource)\n\n def endpoint(self, path='', verb=None, suffix=None):\n \"\"\"Override default endpoint path and verb.\"\"\"\n\n def wrapped(func):\n _, autoverb, *extra = func.__name__.split('_')\n func._path = path\n func._suffix = suffix if suffix is not None else '-'.join(extra)\n func._verb = verb or autoverb\n return func\n\n return wrapped\n\n def _get_responder(self, req):\n responder, params, resource = super()._get_responder(req)\n if responder == falcon.responders.path_not_found:\n # See https://github.com/falconry/falcon/issues/668\n responder = self._router.on_get\n elif req.query_string == 'help':\n params['responder'] = responder\n params['resource'] = resource\n responder = self._router.on_get_endpoint_help\n return responder, params, resource\n\n\napplication = app = API(\n middleware=[\n middlewares.CorsMiddleware(),\n middlewares.SessionMiddleware(),\n ],\n response_type=Response,\n request_type=Request,\n router=Router(),\n)\n", "sub_path": "ban/http/wsgi.py", "file_name": "wsgi.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "falcon.API", "line_number": 9, "usage_type": "attribute"}, {"api_name": "falcon.responders", "line_number": 32, "usage_type": "attribute"}, {"api_name": "response.Response", "line_number": 47, "usage_type": "name"}, {"api_name": "request.Request", "line_number": 48, "usage_type": "name"}, {"api_name": "routing.Router", "line_number": 49, "usage_type": "call"}]}
+{"seq_id": "67621471", "text": "#!/usr/bin/env python\nfrom setuptools import setup, find_packages\nfrom codecs import open\nfrom os import path\nimport sys\nfrom setuptools.command.test import test as TestCommand\n\nclass PyTest(TestCommand):\n user_options = [('pytest-args=', 'a', \"Arguments to pass to py.test\")]\n\n def initialize_options(self):\n TestCommand.initialize_options(self)\n self.pytest_args = []\n\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n #import here, cause outside the eggs aren't loaded\n import pytest\n errno = pytest.main(self.pytest_args)\n sys.exit(errno)\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(\n name='authprox',\n version='1.0',\n description='SSH Proxy',\n long_description=long_description,\n url=\"https://github.com/GooseYArd/authprox\",\n author='Andy Bailey',\n author_email='gooseyard@gmail.com',\n license='MIT',\n packages=find_packages(exclude=['venv']),\n include_package_data = True,\n package_data = {\n '' : ['etc/*']\n },\n tests_require=['pytest'],\n cmdclass = {'test' : PyTest },\n entry_points = {\n 'console_scripts':\n [\n 'apsh=authprox.apsh:main',\n 'authprox_force_command=authprox.force_command:main',\n 'authprox_sshd_config=authprox.sshd_config:main',\n 'authprox_expire_sessions=authprox.expire_sessions:main',\n 'authprox_user_config=authprox.user_config:main',\n 'authprox_agent_init=authprox.agent_init:main',\n 'authprox_add_key=authprox.add_key:main',\n 'authprox_gen_grant=authprox.gen_grant:main',\n 'authprox_gen_auth_keys=authprox.gen_auth_keys:main',\n 'authprox_agent_proxy=authprox.agent_proxy:main'\n ],\n }\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "setuptools.command.test.test", "line_number": 8, "usage_type": "name"}, {"api_name": "setuptools.command.test.test.initialize_options", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 12, "usage_type": "name"}, {"api_name": "setuptools.command.test.test.finalize_options", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 31, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "362774934", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('training', '0014_auto_20160801_1714'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='GpxTrackPoint',\n fields=[\n ('id', models.AutoField(serialize=False, verbose_name='ID', primary_key=True, auto_created=True)),\n ('lat', models.DecimalField(decimal_places=8, max_digits=10)),\n ('log', models.DecimalField(decimal_places=8, max_digits=11)),\n ('time', models.DateTimeField()),\n ('gpx', models.ForeignKey(to='training.Gpx')),\n ],\n ),\n ]\n", "sub_path": "training/migrations/0015_gpxtrackpoint.py", "file_name": "0015_gpxtrackpoint.py", "file_ext": "py", "file_size_in_byte": 749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]}
+{"seq_id": "386468137", "text": "import json\nfrom datetime import datetime\nfrom http.cookies import SimpleCookie\nfrom typing import Any, Dict, Iterable, Mapping, Optional, Union\n\nimport attr\nfrom aiohttp import ClientResponse, ClientResponseError\nfrom aiohttp.client_reqrep import ContentDisposition\nfrom aiohttp.typedefs import StrOrURL\n\n# CachedResponse attributes to not copy directly from ClientResponse\nEXCLUDE_ATTRS = {\n '_body',\n 'created_at',\n 'encoding',\n 'history',\n 'is_expired',\n 'request_info',\n}\nJsonResponse = Optional[Dict[str, Any]]\n\n\n@attr.s(auto_attribs=True, slots=True)\nclass RequestInfo:\n \"\"\"A picklable version of aiohttp.client_reqrep.RequestInfo\"\"\"\n\n url: str\n method: str\n headers: dict\n real_url: str\n\n @classmethod\n def from_object(cls, request_info):\n return cls(\n url=str(request_info.url),\n method=request_info.method,\n headers=dict(request_info.headers),\n real_url=str(request_info.real_url),\n )\n\n\n@attr.s(slots=True)\nclass CachedResponse:\n \"\"\"A dataclass containing cached response information. Will mostly behave the same as a\n :py:class:`aiohttp.ClientResponse` that has been read.\n \"\"\"\n\n method: str = attr.ib()\n reason: str = attr.ib()\n status: int = attr.ib()\n url: StrOrURL = attr.ib()\n version: str = attr.ib()\n _body: Any = attr.ib(default=None)\n content_disposition: ContentDisposition = attr.ib(default=None)\n cookies: SimpleCookie = attr.ib(default=None)\n created_at: datetime = attr.ib(factory=datetime.utcnow)\n encoding: str = attr.ib(default=None)\n headers: Mapping = attr.ib(factory=dict)\n history: Iterable = attr.ib(factory=tuple)\n is_expired: bool = attr.ib(default=False)\n request_info: RequestInfo = attr.ib(default=None)\n\n @classmethod\n async def from_client_response(cls, client_response: ClientResponse):\n # Response may not have been read yet, if fetched by something other than CachedSession\n if not client_response._released:\n await client_response.read()\n\n # Copy most attributes over as is\n copy_attrs = set(attr.fields_dict(cls).keys()) - EXCLUDE_ATTRS\n response = cls(**{k: getattr(client_response, k) for k in copy_attrs})\n\n # Set some remaining attributes individually\n response._body = client_response._body\n response.headers = dict(client_response.headers)\n\n # The encoding may be unset even if the response has been read\n try:\n response.encoding = client_response.get_encoding()\n except RuntimeError:\n pass\n\n response.request_info = RequestInfo.from_object(client_response.request_info)\n response.url = str(client_response.url)\n if client_response.history:\n response.history = (\n *[await cls.from_client_response(r) for r in client_response.history],\n )\n return response\n\n @property\n def ok(self) -> bool:\n \"\"\"Returns ``True`` if ``status`` is less than ``400``, ``False`` if not\"\"\"\n try:\n self.raise_for_status()\n return True\n except ClientResponseError:\n return False\n\n def get_encoding(self):\n return self.encoding\n\n async def json(self, encoding: Optional[str] = None, **kwargs) -> Optional[Dict[str, Any]]:\n \"\"\"Read and decode JSON response\"\"\"\n\n stripped = self._body.strip()\n if not stripped:\n return None\n return json.loads(stripped.decode(encoding or self.encoding))\n\n def raise_for_status(self) -> None:\n if self.status >= 400:\n raise ClientResponseError(\n self.request_info, # type: ignore # These types are interchangeable\n tuple(),\n status=self.status,\n message=self.reason,\n headers=self.headers,\n )\n\n def read(self):\n \"\"\"No-op function for compatibility with ClientResponse\"\"\"\n\n def release(self):\n \"\"\"No-op function for compatibility with ClientResponse\"\"\"\n\n async def text(self, encoding: Optional[str] = None, errors: str = \"strict\") -> str:\n \"\"\"Read response payload and decode\"\"\"\n return self._body.decode(encoding or self.encoding, errors=errors)\n\n\nAnyResponse = Union[ClientResponse, CachedResponse]\n", "sub_path": "aiohttp_client_cache/response.py", "file_name": "response.py", "file_ext": "py", "file_size_in_byte": 4343, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 20, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 23, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 48, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 49, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 50, "usage_type": "call"}, {"api_name": "aiohttp.typedefs.StrOrURL", "line_number": 51, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 51, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 52, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 53, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 53, "usage_type": "call"}, {"api_name": "aiohttp.client_reqrep.ContentDisposition", "line_number": 54, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 54, "usage_type": "call"}, {"api_name": "http.cookies.SimpleCookie", "line_number": 55, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 56, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Mapping", "line_number": 58, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 59, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 59, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 60, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 61, "usage_type": "call"}, {"api_name": "aiohttp.ClientResponse", "line_number": 64, "usage_type": "name"}, {"api_name": "attr.fields_dict", "line_number": 70, "usage_type": "call"}, {"api_name": "aiohttp.ClientResponseError", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 103, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 109, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 103, "usage_type": "name"}, {"api_name": "aiohttp.ClientResponseError", "line_number": 113, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 42, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 132, "usage_type": "name"}, {"api_name": "aiohttp.ClientResponse", "line_number": 132, "usage_type": "name"}]}
+{"seq_id": "10545290", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils import timezone\nfrom .models import Product\ndef home(request):\n context = {\n 'products': Product.objects.order_by('-publication_date')\n }\n return render(request, 'home.html', context)\n\n@login_required\ndef create(request):\n if request.method == 'POST':\n if (request.POST['title']\n and request.POST['body']\n and request.POST['url']\n and request.FILES['icon']\n and request.FILES['image']):\n product = Product()\n product.title = request.POST['title']\n product.body = request.POST['body']\n product.icon = request.FILES['icon']\n product.image = request.FILES['image']\n product.publication_date = timezone.datetime.now()\n product.hunter = request.user\n product.url = request.POST['url']\n product.save()\n return redirect('/products/'+str(product.id))\n else:\n return render(request, 'create.html', {'error':'All Fields Required'})\n else:\n return render(request, 'create.html')\n\n@login_required\ndef detail(request, product_id):\n product = get_object_or_404(Product, pk=product_id)\n return render(request, 'detail.html', {'product': product})\n\n@login_required\ndef upvote(request, product_id):\n if request.method == 'POST':\n product = get_object_or_404(Product, pk=product_id)\n product.votes_total += 1\n product.save()\n return redirect('/products/'+str(product.id))\n\ndef home_upvote(request, product_id):\n if request.method == 'POST':\n product = get_object_or_404(Product, pk=product_id)\n product.votes_total += 1\n product.save()\n return redirect('home')\n\n@login_required\ndef delete(request, product_id):\n if request.method == 'POST':\n product = get_object_or_404(Product, pk=product_id)\n product.delete()\n return redirect('home')\n\n@login_required\ndef update_page(request, product_id):\n product = get_object_or_404(Product, pk=product_id)\n return render(request, 'update.html', {'product':product})\n\n@login_required\ndef update(request, product_id):\n product = Product.objects.get(pk=product_id)\n if request.method == 'POST':\n product.title = request.POST['title']\n product.url = request.POST['url']\n product.body = request.POST['body']\n product.icon = request.FILES['icon']\n product.image = request.FILES['image']\n product.save()\n return redirect('/products/'+str(product.id))\n", "sub_path": "advanced_projects/product_hunt_website/products/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "models.Product.objects.order_by", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 19, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 36, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 49, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 57, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 63, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Product.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 68, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 66, "usage_type": "name"}]}
+{"seq_id": "210501080", "text": "import networkx as nx\r\nimport matplotlib.pyplot as plt\r\n\r\n#G = nx.DiGraph()\r\ndef PLOTTING(Node_Name, x, y, From, To, weight, total_cost):\r\n G = nx.DiGraph()\r\n\r\n for i in range(len(Node_Name)):\r\n G.add_node(Node_Name[i], pos=(x[i], y[i]))\r\n \r\n for i in range(len(From)):\r\n G.add_edge(From[i], To[i], weight=weight[i])\r\n\r\n pos = nx.get_node_attributes(G, 'pos')\r\n\r\n fig, ax = plt.subplots()\r\n nx.draw_networkx(G, nx.get_node_attributes(G, 'pos'), with_labels=True, node_size=400, ax = ax)\r\n labels = nx.get_edge_attributes(G,'weight')\r\n nx.draw_networkx_edge_labels(G, pos, edge_labels = labels)\r\n \r\n# ax.draw_networkx_nodes(..., ax=ax)\r\n ax.tick_params(left=True, bottom=True, labelleft=True, labelbottom=True)\r\n figManager = plt.get_current_fig_manager()\r\n figManager.window.showMaximized()\r\n\r\n plt.xlabel(\"Total Cost: %.2f\" %(total_cost))\r\n plt.show()", "sub_path": "graph_plot.py", "file_name": "graph_plot.py", "file_ext": "py", "file_size_in_byte": 918, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "networkx.DiGraph", "line_number": 6, "usage_type": "call"}, {"api_name": "networkx.get_node_attributes", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "networkx.draw_networkx", "line_number": 17, "usage_type": "call"}, {"api_name": "networkx.get_node_attributes", "line_number": 17, "usage_type": "call"}, {"api_name": "networkx.get_edge_attributes", "line_number": 18, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_edge_labels", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_current_fig_manager", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]}
+{"seq_id": "622975560", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..common.blocks import Conv2dReLU, SCSEModule\nfrom pixel_shuffle import PixelShuffle_ICNR\nfrom ..base.model import Model\n\nclass DecoderBlock(nn.Module):\n def __init__(self, in_channels, out_channels, upsample_channels=[], use_batchnorm=True,\n attention_type=None, upsample='transpose', shuffle_blur=True):\n super().__init__()\n if attention_type is None:\n self.attention1 = nn.Identity()\n self.attention2 = nn.Identity()\n elif attention_type == 'scse':\n self.attention1 = SCSEModule(in_channels)\n self.attention2 = SCSEModule(out_channels)\n upsample_dict = {'shuffle': PixelShuffle_ICNR(upsample_channels, upsample_channels, scale=2, blur=shuffle_blur),\n 'transpose': nn.ConvTranspose2d(upsample_channels, upsample_channels, kernel_size=2, stride=2)}\n self.up = upsample_dict[upsample]\n # self.shuffle = PixelShuffle_ICNR(upsample_channels, upsample_channels, scale=2, blur=shuffle_blur)\n\n\n self.block = nn.Sequential(\n Conv2dReLU(in_channels, out_channels, kernel_size=3, padding=1, use_batchnorm=use_batchnorm),\n Conv2dReLU(out_channels, out_channels, kernel_size=3, padding=1, use_batchnorm=use_batchnorm),\n )\n\n def forward(self, x):\n x, skip = x\n\n x = self.up(x)\n\n # x = F.pixel_shuffle(x,2)\n # x = self.conv1(x) \n\n if skip is not None:\n x = torch.cat([x, skip], dim=1)\n x = self.attention1(x)\n\n x = self.block(x)\n x = self.attention2(x)\n return x\n\n # x, skip = x\n # print(f'before: {x.shape}')\n # x = F.interpolate(x, scale_factor=2, mode='nearest')\n # if skip is not None:\n # # print(f'skip: {skip.shape}')\n # x = torch.cat([x, skip], dim=1)\n # x = self.attention1(x)\n # # print(f'after concat: {x.shape}')\n # x = self.block(x)\n # x = self.attention2(x)\n # # print(f'after block: {x.shape}')\n # return x\n\n\nclass CenterBlock(DecoderBlock):\n\n def forward(self, x):\n return self.block(x)\n\n\nclass UnetDecoder(Model):\n\n def __init__(\n self,\n encoder_channels,\n decoder_channels=(256, 128, 64, 32, 16),\n final_channels=1,\n use_batchnorm=True,\n center=False,\n attention_type=None,\n upsample='transpose',\n shuffle_blur=True\n ):\n super().__init__()\n\n if center:\n channels = encoder_channels[0]\n self.center = CenterBlock(channels, channels, use_batchnorm=use_batchnorm)\n else:\n self.center = None\n\n in_channels = self.compute_channels(encoder_channels, decoder_channels)\n out_channels = decoder_channels\n upsample_channels = encoder_channels[:1]+decoder_channels[:-1]\n self.layer1 = DecoderBlock(in_channels[0], out_channels[0], upsample_channels[0],\n upsample=upsample, shuffle_blur=shuffle_blur,\n use_batchnorm=use_batchnorm, attention_type=attention_type)\n self.layer2 = DecoderBlock(in_channels[1], out_channels[1], upsample_channels[1],\n upsample=upsample, shuffle_blur=shuffle_blur,\n use_batchnorm=use_batchnorm, attention_type=attention_type)\n self.layer3 = DecoderBlock(in_channels[2], out_channels[2], upsample_channels[2],\n upsample=upsample, shuffle_blur=shuffle_blur,\n use_batchnorm=use_batchnorm, attention_type=attention_type)\n self.layer4 = DecoderBlock(in_channels[3], out_channels[3], upsample_channels[3],\n upsample=upsample, shuffle_blur=shuffle_blur,\n use_batchnorm=use_batchnorm, attention_type=attention_type)\n self.layer5 = DecoderBlock(in_channels[4], out_channels[4], upsample_channels[4],\n upsample=upsample, shuffle_blur=shuffle_blur,\n use_batchnorm=use_batchnorm, attention_type=attention_type)\n self.final_conv = nn.Conv2d(out_channels[4], final_channels, kernel_size=(1, 1))\n\n self.initialize()\n\n def compute_channels(self, encoder_channels, decoder_channels):\n channels = [\n encoder_channels[0] + encoder_channels[1],\n encoder_channels[2] + decoder_channels[0],\n encoder_channels[3] + decoder_channels[1],\n encoder_channels[4] + decoder_channels[2],\n 0 + decoder_channels[3],\n ]\n return channels\n\n def forward(self, x):\n encoder_head = x[0]\n skips = x[1:]\n\n if self.center:\n encoder_head = self.center(encoder_head)\n\n x = self.layer1([encoder_head, skips[0]])\n x = self.layer2([x, skips[1]])\n x = self.layer3([x, skips[2]])\n x = self.layer4([x, skips[3]])\n x = self.layer5([x, None])\n x = self.final_conv(x)\n\n return x\n", "sub_path": "segmentation_models_pytorch/unet/decoder.py", "file_name": "decoder.py", "file_ext": "py", "file_size_in_byte": 5210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "common.blocks.SCSEModule", "line_number": 17, "usage_type": "call"}, {"api_name": "common.blocks.SCSEModule", "line_number": 18, "usage_type": "call"}, {"api_name": "pixel_shuffle.PixelShuffle_ICNR", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "common.blocks.Conv2dReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "common.blocks.Conv2dReLU", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 39, "usage_type": "call"}, {"api_name": "base.model.Model", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}]}
+{"seq_id": "214046407", "text": "import os\nimport subprocess\nimport sys\nimport re\nimport pytest\nimport tempfile\n\n\ndef test_list_subcommands_has_all_scripts():\n \"\"\"Tests if the output from running `fontbakery --list-subcommands` matches\n the fontbakery scripts within the bin folder and the promoted profiles.\"\"\"\n import fontbakery.commands\n from fontbakery.cli import CLI_PROFILES\n commands_dir = os.path.dirname(fontbakery.commands.__file__)\n\n scripts = [\n f.rstrip(\".py\").replace(\"_\", \"-\")\n for f in os.listdir(commands_dir)\n if (f.endswith(\".py\") and not f.startswith('_'))\n ]\n scripts = scripts + [ (\"check-\"+i).replace(\"_\", \"-\") for i in CLI_PROFILES ]\n subcommands = subprocess.check_output(\n ['fontbakery', '--list-subcommands']).decode().split()\n assert sorted(scripts) == sorted(subcommands)\n\n\ndef test_command_check_googlefonts():\n \"\"\"Test if `fontbakery check-googlefonts` can run successfully`.\"\"\"\n subprocess.check_output([\"fontbakery\", \"check-googlefonts\", \"-h\"])\n\n test_font = os.path.join(\"data\", \"test\", \"nunito\", \"Nunito-Regular.ttf\")\n\n subprocess.check_output([\n \"fontbakery\", \"check-googlefonts\", \"-c\", \"com.google.fonts/check/canonical_filename\",\n test_font\n ])\n\n with pytest.raises(subprocess.CalledProcessError):\n subprocess.check_output([\"fontbakery\", \"check-googlefonts\"])\n\n\n#@pytest.mark.skipif(sys.version_info < (3, 7), reason=\"requires python3.7 or higher\")\n@pytest.mark.xfail(strict=True) # This test is too much prone to failing whenever we update\n # the text-output formatting or the actual log messages in that fontbakery check\n # I would like to have this test refactored to be in a good state for much longer.\n # Please, only remove the xfail mark once the test is more robust / future proof.\ndef test_status_log_is_indented():\n \"\"\"Test if statuses are printed in a limited boundary.\"\"\"\n test_font = os.path.join(\"data\", \"test\", \"nunito\", \"Nunito-Regular.ttf\")\n\n result = subprocess.run([\"fontbakery\", \"check-googlefonts\",\n \"-c\", \"old_ttfautohint\", \"-c\", \"font_copyright\",\n test_font], capture_output=True)\n\n p = re.compile('([^][a-zA-Z0-9@:,.()\\'\"\\n ])', re.I | re.M)\n stdout = p.sub('#', result.stdout.decode()).split('\\n')\n assert '\\n'.join(stdout[24:30]) == '\\n'.join([\n ' #[0#31#40mFAIL#[0m Name Table entry: Copyright notices should match a ',\n ' pattern similar to: \"Copyright 2019 The Familyname Project Authors ',\n ' (git url)\" ',\n ' But instead we have got: ',\n ' \"Copyright 2014 The Nunito Project Authors (contact@sansoxygen.com)\" ',\n ' [code: bad#notice#format] '\n ])\n assert '\\n'.join(stdout[10:15]) == '\\n'.join([\n ' #[0#36#40mINFO#[0m Could not detect which version of ttfautohint was used ',\n ' in this font. It is typically specified as a comment in the font ',\n \" version entries of the 'name' table. Such font version strings are \",\n \" currently: ['Version 3.000', 'Version 3.000'] [code: \",\n ' version#not#detected] '\n ])\n\n\ndef test_command_check_profile():\n \"\"\"Test if `fontbakery check-profile` can run successfully`.\"\"\"\n subprocess.check_output([\"fontbakery\", \"check-profile\", \"-h\"])\n\n with pytest.raises(subprocess.CalledProcessError):\n subprocess.check_output([\"fontbakery\", \"check-profile\"])\n\n\ndef test_command_check_opentype():\n \"\"\"Test if `fontbakery check-opentype` can run successfully`.\"\"\"\n subprocess.check_output([\"fontbakery\", \"check-opentype\", \"-h\"])\n\n with pytest.raises(subprocess.CalledProcessError):\n subprocess.check_output([\"fontbakery\", \"check-opentype\"])\n\n\ndef test_command_check_ufo_sources():\n \"\"\"Test if `fontbakery check-ufo-sources` can run successfully`.\"\"\"\n subprocess.check_output([\"fontbakery\", \"check-ufo-sources\", \"-h\"])\n\n with pytest.raises(subprocess.CalledProcessError):\n subprocess.check_output([\"fontbakery\", \"check-ufo-sources\"])\n\ndef test_command_config_file():\n \"\"\"Test if we can set checks using a config file.\"\"\"\n config = tempfile.NamedTemporaryFile(delete=False)\n config.write(b\"explicit_checks = ['com.adobe.fonts/check/name/empty_records']\")\n config.close()\n test_font = os.path.join(\"data\", \"test\", \"nunito\", \"Nunito-Regular.ttf\")\n result = subprocess.run([\"fontbakery\", \"check-googlefonts\",\n \"--config\", config.name,\n test_font], stdout=subprocess.PIPE)\n stdout = result.stdout.decode()\n assert \"running 1 individual check\" in stdout\n os.unlink(config.name)\n\n\ndef test_command_config_file_injection():\n \"\"\"Test if we can inject a config variable into a check.\"\"\"\n config = tempfile.NamedTemporaryFile(delete=False)\n config.write(b\"\"\"\n[a_test_profile]\nOK = 123\n\"\"\")\n config.close()\n test_font = os.path.join(\"data\", \"test\", \"nunito\", \"Nunito-Regular.ttf\")\n test_profile = os.path.join(\"tests\", \"profiles\", \"a_test_profile.py\")\n result = subprocess.run([\"fontbakery\", \"check-profile\",\n \"-C\",\n \"--config\", config.name,\n test_profile,\n test_font], stdout=subprocess.PIPE)\n stdout = result.stdout.decode()\n assert \"FAIL: 0\" in stdout\n os.unlink(config.name)\n\n\ndef test_config_override():\n \"\"\"Test we can override check statuses in the configuration file\"\"\"\n config = tempfile.NamedTemporaryFile(delete=False)\n config.write(b\"\"\"\noverrides:\n com.google.fonts/check/file_size:\n large-font: FAIL\nexplicit_checks:\n - com.google.fonts/check/file_size\n\"\"\")\n config.close()\n test_font = os.path.join(\"data\", \"test\", \"varfont\", \"inter\", \"Inter[slnt,wght].ttf\")\n result = subprocess.run([\"fontbakery\", \"check-googlefonts\",\n \"-C\",\n \"--config\", config.name,\n test_font], stdout=subprocess.PIPE)\n stdout = result.stdout.decode()\n # This font has a WARN here, so should now FAIL\n assert \"FAIL: 1\" in stdout\n os.unlink(config.name)\n", "sub_path": "tests/commands/test_usage.py", "file_name": "test_usage.py", "file_ext": "py", "file_size_in_byte": 6274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "fontbakery.commands.commands", "line_number": 14, "usage_type": "attribute"}, {"api_name": "fontbakery.commands", "line_number": 14, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 18, "usage_type": "call"}, {"api_name": "fontbakery.cli.CLI_PROFILES", "line_number": 21, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 38, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 38, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 51, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 55, "usage_type": "call"}, {"api_name": "re.I", "line_number": 55, "usage_type": "attribute"}, {"api_name": "re.M", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 43, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 76, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 78, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 78, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 79, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 86, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 86, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 87, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 94, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 94, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 95, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 108, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 121, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 128, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 143, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 150, "usage_type": "call"}]}
+{"seq_id": "131666062", "text": "#coding:utf8\nimport os\nimport datetime\nimport pymysql\nimport uuid\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import Flask, render_template, redirect, url_for,flash, session, Response,request\nfrom forms import LoginForm, RegisterForm, ArtForms, ArtEditForm\nfrom models import User,db, Art\nfrom werkzeug.security import generate_password_hash\nfrom functools import wraps\nfrom werkzeug.utils import secure_filename\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"]= \"12345678\"\napp.config[\"UP\"]=os.path.join(os.path.dirname(__file__),'static/uploads')\n\ndef user_login_req(f):\n @wraps(f)\n def login_req(*args, **kwargs):\n if \"user\" not in session:\n return redirect(url_for('login',next=request.url))\n return f(*args, **kwargs)\n return login_req\n\n@app.route(\"/login/\",methods = [\"GET\",\"POST\"])\ndef login():\n form = LoginForm()\n if form.validate_on_submit():\n data = form.data\n session[\"user\"] = data[\"name\"]\n flash(\"登录成功\",\"ok\")\n return redirect(\"/art/list/1/\")\n return render_template('login.html',title=\"登陆\", form=form)\n\n@app.route(\"/register/\",methods = [\"GET\",\"POST\"])\ndef register():\n form = RegisterForm()\n if form.validate_on_submit():\n data = form.data\n user = User(\n name=data[\"name\"],\n pwd=generate_password_hash(data[\"pwd\"]),\n addtime=datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n )\n db.session.add(user)\n db.session.commit()\n flash(u\"注册成功，请登录\", \"ok\")\n return redirect(\"/login/\")\n return render_template(\"register.html\",title=\"注册\",form=form)\n\n@app.route(\"/logout/\",methods = [\"GET\"])\n@user_login_req\ndef logout():\n session.pop(\"user\",None)\n return redirect(\"/login/\")\n\ndef change_name(name):\n info = os.path.splitext(name)\n name = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\") + str(uuid.uuid4().hex)+info[-1]\n return name\n\n@app.route(\"/art/add/\",methods = [\"GET\",\"POST\"])\n@user_login_req\ndef art_add():\n form = ArtForms()\n if form.validate_on_submit():\n data = form.data\n file = secure_filename(form.logo.data.filename)\n logo = change_name(file)\n if not os.path.exists(app.config[\"UP\"]):\n os.makedirs(app.config[\"UP\"])\n form.logo.data.save(app.config[\"UP\"] + '/' + logo)\n user = User.query.filter_by(name = session[\"user\"]).first()\n user_id = user.id\n art = Art(\n title = data[\"title\"],\n cate = data[\"cate\"],\n user_id = user_id,\n logo = logo,\n content = data[\"content\"],\n addtime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n )\n db.session.add(art)\n db.session.commit()\n flash(\"发布成功\",\"ok\")\n\n return render_template(\"art_add.html\",title=\"发布文章\",form=form)\n\n@app.route(\"/art/edit/<int:id>\",methods = [\"GET\",\"POST\"])\n@user_login_req\ndef art_edit(id):\n art = Art.query.get_or_404(int(id))\n form = ArtEditForm()\n if request.method == \"GET\":\n form.content.data = art.content\n form.cate.data = art.cate\n form.logo.data = art.logo\n if form.validate_on_submit():\n data = form.data\n # 上传logo\n file = secure_filename(form.logo.data.filename)\n logo = change_name(file)\n if not os.path.exists(app.config[\"UP\"]):\n os.makedirs(app.config[\"UP\"])\n # 保存文件\n form.logo.data.save(app.config[\"UP\"] + \"/\" + logo)\n art.logo = logo\n art.title = data[\"title\"]\n art.content = data[\"content\"]\n art.cate = data[\"cate\"]\n db.session.add(art)\n db.session.commit()\n flash(u\"编辑文章成功！\", \"ok\")\n return render_template(\"art_edit.html\",form=form, title=u\"编辑文章\", art=art)\n\n@app.route(\"/art/del/<int:id>\",methods = [\"GET\"])\n@user_login_req\ndef art_del(id):\n art=Art.query.get_or_404(int(id))\n db.session.delete(art)\n db.session.commit()\n flash(\"删除成功\",\"ok\")\n return redirect(\"/art/list/1\")\n\n@app.route(\"/art/list/<int:page>/\",methods=[\"GET\"])\n@user_login_req\ndef art_list(page = None):\n if page is None:\n page =1\n user = User.query.filter_by(name = session[\"user\"]).first()\n page_data = Art.query.filter_by(\n user_id = user.id\n ).order_by(Art.addtime.desc()\n ).paginate(page = 1, per_page=3)\n cate = [(1,u\"科技\"), (2,u\"搞笑\"),(3,u\"军事\")]\n return render_template(\"art_list.html\",title=\"文章列表\",page_data = page_data, cate = cate)\n\n@app.route(\"/codes/\",methods=[\"GET\"])\ndef codes():\n from codes import Codes\n c = Codes()\n info = c.create_code()\n image = os.path.join(os.path.dirname(__file__),\"static/Codes\") + \"/\" + info[\"img_name\"]\n with open(image,'rb') as f:\n image = f.read()\n session[\"code\"] = info[\"codes\"]\n return Response(image,mimetype=\"jpeg\")\n\n\nif __name__ == '__main__':\n app.run(debug=True,host='127.0.0.1',port=8080)", "sub_path": "artcms_project/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 19, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 38, "usage_type": "call"}, {"api_name": "models.User", "line_number": 41, "usage_type": "call"}, {"api_name": "werkzeug.security.generate_password_hash", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.db.session.add", "line_number": 46, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 46, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 47, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 60, "usage_type": "call"}, {"api_name": "forms.ArtForms", "line_number": 66, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 72, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 74, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "models.Art", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.db.session.add", "line_number": 84, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 84, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 85, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Art.query.get_or_404", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Art.query", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Art", "line_number": 93, "usage_type": "name"}, {"api_name": "forms.ArtEditForm", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 105, "usage_type": "call"}, {"api_name": "models.db.session.add", "line_number": 112, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 112, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 112, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 113, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 115, "usage_type": "call"}, {"api_name": "models.Art.query.get_or_404", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Art.query", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.Art", "line_number": 120, "usage_type": "name"}, {"api_name": "models.db.session.delete", "line_number": 121, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 121, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 122, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 122, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 131, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 131, "usage_type": "name"}, {"api_name": "models.Art.query.filter_by", "line_number": 132, "usage_type": "call"}, {"api_name": "models.Art.query", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.Art", "line_number": 132, "usage_type": "name"}, {"api_name": "models.Art.addtime.desc", "line_number": 134, "usage_type": "call"}, {"api_name": "models.Art.addtime", "line_number": 134, "usage_type": "attribute"}, {"api_name": "models.Art", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "codes.Codes", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 148, "usage_type": "call"}]}
+{"seq_id": "206075924", "text": "from django.shortcuts import render\nfrom .models import RegistrationData\n\ndef reg(request):\n if request.method == 'POST':\n first_name = request.POST.get('first_name')\n last_name = request.POST.get('last_name')\n user_name = request.POST.get('user_name')\n mobile_number = request.POST.get('mobile_number')\n email = request.POST.get('email')\n data = RegistrationData(first_name=first_name, last_name=last_name,\n user_name=user_name, mobile_number=mobile_number, email=email)\n data.save()\n return render(request, 'reg.html')\n else:\n return render(request, 'reg.html')\ndef hello(request):\n return render(request,'hello.html')", "sub_path": "DjangoAndAngularjs/FirstPro/FirstApp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "models.RegistrationData", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}]}
+{"seq_id": "433873079", "text": "#!/usr/bin/env python3\n# Retrieves National Bank of Poland (NBP) currency rates\n\nimport os\nimport re\nimport csv\nimport argparse\nimport requests\nfrom datetime import datetime, timedelta\n\nCACHE_FILE = '/tmp/nbp_rates_{type}_{year}.csv'\nRATES = {}\n\ndef nbp_rate_last(currency, dt=datetime.utcnow()):\n \"\"\"Returns NBP currency rate for last workday\n\n Arguments:\n dt -- date as datetime object\n currency -- 3-letter currency symbol, e.g. EUR\n\n Returns a tuple (date, price)\n \"\"\"\n\n for i in range(1,10):\n lastday = dt - timedelta(days=i)\n year = int(lastday.strftime('%Y'))\n __init_rates(year)\n try:\n date = lastday.strftime('%Y%m%d')\n return date, RATES[year][currency][date]\n except KeyError:\n continue\n\n\ndef nbp_rate(currency, dt=datetime.utcnow()):\n \"\"\"Returns NBP currency rate for date\n\n Arguments:\n dt -- date as datetime object\n currency -- 3-letter currency symbol, e.g. EUR\n\n Returns a tuple (date, price)\n \"\"\"\n\n year = int(dt.strftime('%Y'))\n __init_rates(year)\n\n try:\n date = dt.strftime('%Y%m%d')\n return date, RATES[year][currency][date]\n except KeyError as e:\n raise KeyError('NBP rate not found for date {}'.format(date)) from e\n\n\ndef nbp_rates(currency, year):\n \"\"\"Returns yearly rates for a currency\n\n Arguments:\n year -- year string, e.g. 2018\n currency -- 3-letter currency symbol, e.g. EUR\n\n Returns a generator of tuples (date, price)\n \"\"\"\n\n year = int(year)\n __init_rates(year)\n for date in sorted(RATES[year][currency]):\n yield (date, RATES[year][currency][date])\n\n\ndef __init_rates(year):\n current_year = int(datetime.utcnow().strftime('%Y'))\n if year < 2002 or year > current_year:\n raise ValueError('Rates not found for year {}'.format(year))\n\n if year in RATES:\n return\n\n for rates_type in ('a', 'b'):\n __download_rates(year, rates_type)\n cache_file = CACHE_FILE.format(type=rates_type, year=year)\n with open(cache_file, encoding='iso8859-2') as f:\n data = csv.reader(f, delimiter=';')\n if year not in RATES:\n RATES[year] = {}\n RATES[year].update(__parse_rates(data))\n\ndef __download_rates(year, rates_type):\n nbp_url = 'https://www.nbp.pl/kursy/Archiwum/archiwum_tab_{type}_{year}.csv'.format(type=rates_type, year=year)\n cache_file = CACHE_FILE.format(type=rates_type, year=year)\n\n if os.path.exists(cache_file):\n curtime = int(datetime.utcnow().strftime('%s'))\n mtime = int(os.path.getmtime(cache_file))\n if curtime - mtime <= 3600:\n return\n\n r = requests.get(nbp_url)\n with open(cache_file, 'wb') as f:\n f.write(r.content)\n\ndef __parse_rates(data):\n rates = {}\n currencies = []\n for row in data:\n # Get currencies names\n if not currencies:\n for field in row:\n currency_match = re.match('^\\d+(\\w+)$', field)\n if currency_match:\n currency = currency_match[1]\n rates[currency] = {}\n currencies.append(currency)\n continue\n\n if not row:\n break\n\n # Get currency price by date\n date = row[0]\n date_match = re.match('^\\d{8}$', date)\n if date_match:\n for i, currency in enumerate(currencies, 1):\n price = row[i].replace(',', '.')\n if not price:\n continue\n rates[currency][date] = float(price)\n\n return rates\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-y', '--year', metavar='YEAR', action='store', default=datetime.utcnow().strftime('%Y'))\n parser.add_argument('currency', metavar='CURRENCY', type=str)\n args = parser.parse_args()\n\n for date, price in nbp_rates(args.currency, args.year):\n print(\"{date}\\t{price}\".format(date=date, price=price))\n", "sub_path": "nbp_rates.py", "file_name": "nbp_rates.py", "file_ext": "py", "file_size_in_byte": 4022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "os.path.getmtime", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 98, "usage_type": "call"}, {"api_name": "re.match", "line_number": 109, "usage_type": "call"}, {"api_name": "re.match", "line_number": 121, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 133, "usage_type": "name"}]}
+{"seq_id": "430709643", "text": "from bs4 import BeautifulSoup\n\nhtmlDoc = open('source.html', 'r')\nsoup = BeautifulSoup(htmlDoc, 'html.parser')\n\n\nchampName = open('champions.txt', 'w')\nchampName.close()\n\nchampInfo = open('championsInfo.txt', 'w')\nchampInfo.close()\n\n\nfor tr in soup.find_all('tr'):\n tds = tr.find_all('td')\n try:\n champName = tds[0].get('data-sort-value')\n except IndexError:\n champName = None\n if(champName is not None):\n primary = tds[1].text\n secondary = tds[2].text\n attack = tds[3].text\n defense = tds[4].text\n ability = tds[5].text\n difficulty = tds[6].text\n date = tds[7].text\n ip = tds[8].text\n rp = tds[9].text\n with open('champions.txt','a') as textFile:\n textFile.write(champName + '\\n')\n with open('championsInfo.txt','a') as textFile:\n textFile.write('Champion: ' + champName + '\\n')\n textFile.write('Primary Role: ' + primary)\n textFile.write('Secondary Role: ' + secondary)\n textFile.write('Attack: ' + attack)\n textFile.write('Defense: ' + defense)\n textFile.write('Ability: ' + ability)\n textFile.write('Difficulty: ' + difficulty)\n textFile.write('Date Released: ' + date)\n textFile.write('IP: ' + ip )\n textFile.write('RP: ' + rp + '\\n')\n\n", "sub_path": "beautifulScraping/soup.py", "file_name": "soup.py", "file_ext": "py", "file_size_in_byte": 1401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 4, "usage_type": "call"}]}
+{"seq_id": "541077552", "text": "# 导入日志工具\n\n# 在日志打印之前,设置日志的输出文件\n# logging.basicConfig(level=logging.DEBUG)\nimport logging\n\nfmt = \"%(asc_time)s %(level_name)s [%(name)s] [%(filename)s(%(funcName)s:%(line_no)d)] - %(message)s\"\n\nlogging.basicConfig(\n format=fmt,\n filename='.log'.encode('utf-8'),\n level=logging.INFO)\n\n# 打印五个级别的日志\nlogging.debug(\"这是个调试信息\")\nlogging.info(\"这是个info信息\")\nlogging.warning(\"这是个警告信息\")\nlogging.error(\"这是个错误信息\")\nlogging.critical(\"这是个重大错误信息\")\n", "sub_path": "TestAutoProject/WebAutoDrive/day8_web/day8/lianxi_04_logging_file.py", "file_name": "lianxi_04_logging_file.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 19, "usage_type": "call"}]}
+{"seq_id": "532778690", "text": "import oauth2 as oauth\nimport cgi\nfrom sauth.config import *\nimport twython\n\n''' \nTwitter using oauth authentication so here using python oauth2 library \nand Twython package. python oauth2 : pip install oauth2 , Twython : pip install twython\noauth2 : https://github.com/NateFerrero/oauth2lib , https://python-oauth2.readthedocs.org/en/latest/grant.html\n'''\n\ndef get_twitter_consumer(app_id,secret_key):\n\t''' \n\tcreating consumer with twitter oauth using twitter app id and secret key \n\t'''\n\tconsumer = oauth.Consumer(app_id,secret_key)\n\treturn consumer\n\n\ndef create_twitter_client(twitter_app_id,twitter_secret_key):\n\t'''\n\tcreating client with twitter oauth using consumer ( get_twitter_consumer )\n\t'''\n\tconsumer = get_twitter_consumer(twitter_app_id, twitter_secret_key)\n\tclient = oauth.Client(consumer)\n\treturn client\n\ndef get_twitter_auth_code_url(redirect_uri=None,app_id=None,backend=None,request=None):\n\t'''\n\tGenarating authorization url with app id and secret key . Store the \n\toauth token and oauth secret in to the session for further work\n\t''' \n\tclient = create_twitter_client(app_id,twitter_secret_key)\n\tresp, content = client.request(REQUEST_TOKEN_URL, \"GET\")\n\tif resp['status'] != '200':\n\t\traise Exception(\"Invalid response from Twitter.\")\n\trequest.session['request_token'] = dict(cgi.parse_qsl(content))\n\turl = \"%s?oauth_token=%s\" % (AUTHETICATE_URL,request.session['request_token']['oauth_token'])\n\treturn url\n\ndef get_twitter_user_details(access_token=None,request=None,backend=None):\n\n\t'''\n\tGet the user details from the twitter using Twython package and \n\tthier access token and owner twitter app id and twitter secret key \n\t'''\n\tresponse_data = {}\n\targs = {'screen_name': access_token['screen_name']}\n\ttemp_data = twython.Twython(app_key=twitter_app_id,app_secret=twitter_secret_key,oauth_token=access_token['oauth_token'],oauth_token_secret=access_token['oauth_token_secret'])\n\tresp = temp_data.request(API_URL,params=args)\n\tresponse_data['user_id'] = resp['id']\n\tresponse_data['first_name'] = resp['name']\n\tresponse_data['username'] = resp['screen_name']\n\tresponse_data['last_name'] = ''\n\tresponse_data['email'] = ''\n\tresponse_data['profile-image-url'] = resp['profile_image_url']\n\tresponse_data['provider'] = backend\n\tresponse_data['extra_data'] = {\"profile-image\":resp['profile_image_url'],'id':resp['id'],'screen name':resp['screen_name'],\"email\":\"twitter is not provideing email\"}\n\treturn response_data", "sub_path": "sauth/twitter.py", "file_name": "twitter.py", "file_ext": "py", "file_size_in_byte": 2427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "oauth2.Consumer", "line_number": 16, "usage_type": "call"}, {"api_name": "oauth2.Client", "line_number": 25, "usage_type": "call"}, {"api_name": "cgi.parse_qsl", "line_number": 37, "usage_type": "call"}, {"api_name": "twython.Twython", "line_number": 49, "usage_type": "call"}]}
+{"seq_id": "59480621", "text": "from business_rules.fields import FIELD_NO_INPUT\nimport business_rules.API as API\nimport xml.etree.ElementTree as ET\nimport threading\n\ndef run_all(rule_list,\n defined_variables,\n defined_actions,\n stop_on_first_trigger=False):\n\n rule_was_triggered = False\n for rule in rule_list:\n result = run(rule, defined_variables, defined_actions)\n if result:\n rule_was_triggered = True\n if stop_on_first_trigger:\n return True\n return rule_was_triggered\n\ndef run(rule, defined_variables, defined_actions):\n #added function action for when rule is false\n conditions, actions_true, actions_false = rule['conditions'], rule['actions_true'], rule['actions_false']\n rule_triggered = check_conditions_recursively(conditions, defined_variables)\n if rule_triggered:\n do_actions(actions_true, defined_actions)\n return True\n else :\n do_actions(actions_false, defined_actions)\n return False\n\n\n\ndef check_conditions_recursively(conditions, defined_variables):\n keys = list(conditions.keys())\n if keys == ['all']:\n assert len(conditions['all']) >= 1\n for condition in conditions['all']:\n if not check_conditions_recursively(condition, defined_variables):\n return False\n return True\n\n elif keys == ['any']:\n assert len(conditions['any']) >= 1\n for condition in conditions['any']:\n if check_conditions_recursively(condition, defined_variables):\n return True\n return False\n\n else:\n # help prevent errors - any and all can only be in the condition dict\n # if they're the only item\n assert not ('any' in keys or 'all' in keys)\n return check_condition(conditions, defined_variables)\n\ndef check_condition(condition, defined_variables):\n \"\"\" Checks a single rule condition - the condition will be made up of\n variables, values, and the comparison operator. The defined_variables\n object must have a variable defined for any variables in this condition.\n \"\"\"\n name, op, value = condition['name'], condition['operator'], condition['value']\n operator_type = [] #allowing for multiple variable....only 2 allowed as of now\n for var in name :\n operator_type.append(_get_variable_value(defined_variables, var))\n if value == None : #We assume doing operation on similiar data_types\n def fallback(*args, **kwargs):\n raise AssertionError(\"Variable {0} is not defined in class {1}\".format(\n name, defined_variables.__class__.__name__))\n method = getattr(defined_variables, name[1], fallback)\n val = method()\n return _do_operator_comparison(operator_type[0], op, val)\n else :\n return _do_operator_comparison(operator_type[0], op, value)\n\ndef _get_variable_value(defined_variables, name):\n \"\"\" Call the function provided on the defined_variables object with the\n given name (raise exception if that doesn't exist) and casts it to the\n specified type.\n\n Returns an instance of operators.BaseType\n \"\"\"\n def fallback(*args, **kwargs):\n raise AssertionError(\"Variable {0} is not defined in class {1}\".format(\n name, defined_variables.__class__.__name__))\n method = getattr(defined_variables, name, fallback)\n val = method()\n return method.field_type(val)\n\ndef _do_operator_comparison(operator_type, operator_name, comparison_value):\n \"\"\" Finds the method on the given operator_type and compares it to the\n given comparison_value.\n\n operator_type should be an instance of operators.BaseType\n comparison_value is whatever python type to compare to\n returns a bool\n \"\"\"\n def fallback(*args, **kwargs):\n raise AssertionError(\"Operator {0} does not exist for type {1}\".format(\n operator_name, operator_type.__class__.__name__))\n method = getattr(operator_type, operator_name, fallback)\n if getattr(method, 'input_type', '') == FIELD_NO_INPUT:\n return method()\n return method(comparison_value)\n\n\ndef do_actions(actions, defined_actions):\n threads = []\n for action in actions:\n API.logger.info(\"Starting action: {}\".format(action['name']))\n if action['multi_thread'] :\n thread = threading.Thread(target = _run_action, args = (action,defined_actions,))\n thread.start()\n threads.append(thread)\n else :\n _run_action(action,defined_actions)\n for thread in threads:\n thread.join()\n \n\ndef _run_action(action, defined_actions) :\n try :\n method_name = action['name']\n def fallback(*args, **kwargs):\n raise AssertionError(\"Action {0} is not defined in class {1}\"\\\n .format(method_name, defined_actions.__class__.__name__))\n params = action.get('params') or {}\n method = getattr(defined_actions, method_name, fallback)\n API.output.append(method(**params))\n except Exception as e:\n API.logger.info(\"Action: \" + action['name'] + \" Cannot be run on engine!Errror: {}\".format(e))\n ET.SubElement(API.root[0],\"RuntimeError\").text = \"Action: \" + action['name'] + \" Cannot be run on engine!Errror: {}\".format(e)\n", "sub_path": "web-services/business_rules/engine.py", "file_name": "engine.py", "file_ext": "py", "file_size_in_byte": 5306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "business_rules.fields.FIELD_NO_INPUT", "line_number": 100, "usage_type": "name"}, {"api_name": "business_rules.API.logger.info", "line_number": 108, "usage_type": "call"}, {"api_name": "business_rules.API.logger", "line_number": 108, "usage_type": "attribute"}, {"api_name": "business_rules.API", "line_number": 108, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 110, "usage_type": "call"}, {"api_name": "business_rules.API.output.append", "line_number": 127, "usage_type": "call"}, {"api_name": "business_rules.API.output", "line_number": 127, "usage_type": "attribute"}, {"api_name": "business_rules.API", "line_number": 127, "usage_type": "name"}, {"api_name": "business_rules.API.logger.info", "line_number": 129, "usage_type": "call"}, {"api_name": "business_rules.API.logger", "line_number": 129, "usage_type": "attribute"}, {"api_name": "business_rules.API", "line_number": 129, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 130, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 130, "usage_type": "name"}, {"api_name": "business_rules.API.root", "line_number": 130, "usage_type": "attribute"}, {"api_name": "business_rules.API", "line_number": 130, "usage_type": "name"}]}
+{"seq_id": "430123733", "text": "#-*- coding=utf-8 -*-\n#@Time: \n#@Author: zjh\n#@File: RelationNetwork.py\n#@Software: PyCharm\n\n# 有向图的顺序可能有点问题,但貌似是最初的文件就有问题\n# 对于没有关系的实体暂时无法可视化\n\nimport json\nimport torch\nimport numpy as np\nimport networkx as nx\nfrom GraphVis import NetworkVis\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\n\nclass RelationNetwork:\n def __init__(self,path_data_r,path_data_e,text):\n self.__rpath = path_data_r\n self.__epath = path_data_e\n self.__text = text\n self.__rel = \"demo_visualization/data/relation_label_tag.txt\"\n #self.__savepth = \"\"\n\n\n def __show_edge_info(self,edges):\n '''\n\n :param edges: [['Frank\\nG.\\nZarb', 'NASD', 19, '/business/person/company']]\n :return: [['Frank G. Zarb', 'NASD', 19, '/business/person/company']]\n '''\n info = []\n for rel in edges:\n rel[0] = rel[0].replace(\"\\n\",\" \")\n info.append(rel)\n return info\n # def save_path(self,save_path):\n # self.__savepth = save_path\n\n def __append_dict(self,dict,key,value):\n if value not in dict.values():\n dict[key] = value\n\n def __search_rel_name(self,num):\n for r in self.__rel:\n if int(r.split()[1]) == num:\n break\n return r.split()[0][:-2]\n\n def __search_another_part(self,array,num):\n rtn = []\n for word in array:\n if word[1] == num:\n #print(word)\n rtn.append(word)\n\n return rtn\n\n def __add_one_edge(self,e1,e2):\n '''\n\n :param e1: the first entity-rel\n :param e2: the second entity-rel\n :return: edges consist of e1 and e2\n '''\n c1 = e1[0] # 第一个实体\n c2 = e2[0] # 第二个实体\n c3 = e1[1] # 关系代号\n c4 = self.__search_rel_name(c3) # 关系名称\n return list([c1,c2,c3,c4])\n\n\n def __node_process(self,triple,text):\n '''\n 将单词进行合并\n 合并说明：只有两种结构会被作为正确实体: 1 或者 (24*3)\n 将关系三元组按照relation的顺序排序,从上往下遍历\n - 若1开头,则是单独的实体,放入array,游标word_id推进1\n - 若2开头,则是复合实体,游标word_id推进1 [2]\n - 循环到不是4结束,游标不断推进,此时应做边界处理,如果游标推导头后,两次break跳出两重循环 [4*]\n - 判断是不是3,如果是3的话组成正确的结构,将整体放入array[3],游标word_id推进1\n - 若0 3 4开头,则不是合理的实体结构,直接跳过本次循环\n 注: 01234分别对应 O S B E I\n\n :param d_tuple: 原始的二维关系标签组\n :param text: 原文列表\n :return: (字符串标签,关系代号)二维数组,标签列表\n '''\n idex = np.lexsort([triple[:,1]]) # 按照关系代号排序\n #print(idex)\n triple = triple[idex, :]\n print(\"triple_sort*****\\n\",triple)\n array = []\n\n\n word_id = 0\n while word_id < len(triple):\n #print(triple[word_id])\n if triple[word_id][2] == 0 or \\\n triple[word_id][2] == 3 or \\\n triple[word_id][2] == 4:\n # 非实体和不合理标记跳过(predict中出现)\n word_id += 1\n continue\n\n elif triple[word_id][2] == 1:\n if [ text[triple[word_id][0]],triple[word_id][1] ] not in array: # 防止一个实体重复出现\n array.append([\n text[triple[word_id][0]],\n triple[word_id][1]\n ])\n word_id += 1\n\n elif triple[word_id][2] == 2:\n print(\"here\",triple[word_id])\n #print(\"here\",text)\n tempcat = [text[triple[word_id][0]]]\n word_id += 1\n while triple[word_id][2] == 4: # 中间词\n tempcat.append(text[triple[word_id][0]])\n word_id += 1\n if (word_id >= len(triple)): break # 当识别末尾有问题时退出\n if (word_id >= len(triple)): break\n if triple[word_id][2] == 3: # 末尾词\n print(\"here\",triple[word_id])\n tempcat.append(text[triple[word_id][0]])\n\n # append代码缩进到if中,可以保证当上述过程中有任何一处情况不符合时不保存该实体\n if [ \"\\n\".join(tempcat),triple[word_id][1] ] not in array:\n array.append([\n \"\\n\".join(tempcat), # 换行是为了展示好看,vis.js不会给实体换行\n triple[word_id][1]\n ])\n word_id += 1\n\n\n # 自己训练的结果234不一定按照这个结构,当不符合结构时应当设置抛出机制（等待设置）\n\n return array\n\n def __relation_process(self,array):\n '''\n 抽取关系,转化为绘图所需的结构\n :param d_tuple: (字符串标签,关系代号)二维数组[按照标签序号排好序]\n :param rel 完整的关系,不同关系列表,一个关系一个字符串\n :return: (字符串1,字符串2,关系代号)\n '''\n edges = []\n\n for i in range(len(array)): # 遍历array数组由奇找偶\n #print(\"now\",array[i],end='')\n temp = array[i][1]\n #print(\"temp\",temp)\n if temp % 2 == 1:\n another_part = self.__search_another_part(array, temp + 1)\n #print(another_part)\n for word in another_part:\n edges.append(self.__add_one_edge(array[i],word))\n\n return edges\n\n\n def __eredges_process(self,edges,sen): #解开耦合,用不同句子区分关系\n er_edges = []\n for edge in edges:\n edge[3] = edge[3][1:].replace('/','/\\n') + ':' + str(sen)\n #er_edges.append(edge)\n er_edges.append([edge[0],edge[3]])\n er_edges.append([edge[3],edge[1]])\n return er_edges\n\n\n def __getkey(self,d,value):\n return [k for k, v in d.items() if v == value][0]\n # 此处可以保证一个单词只对应一个node\n\n\n #@profile\n def lazy_vis(self):\n with open(self.__rpath, 'r') as f:\n relation_data = json.load(f)\n with open(self.__epath, 'r') as f:\n entity_data = json.load(f)\n with open(self.__rel, 'r') as f:\n self.__rel = f.read().splitlines()\n\n def vis(*args,label_show = True,save_pth = \"../images/1.png\"):\n #G = nx.DiGraph()\n # plot the networkx\n G = nx.MultiDiGraph()\n bar = tqdm(list(args))\n events = [] # Vis边可视化\n #rel_id_dict = {} # 用于的添加\n\n for sen in bar:\n bar.set_description(\"Now get sen \" + str(sen))\n entity_label = entity_data[sen]\n d_tuple = torch.tensor(relation_data[sen]).nonzero()\n d_tuple = d_tuple.numpy()\n triple = np.empty((0,3)) # 创建0行空数组\n for i in range(len(d_tuple)):\n #print(d_tuple[i][0],d_tuple[i][1],entity_label[d_tuple[i][0]])\n temp_entity = np.array([\n int(d_tuple[i][0]),\n int(d_tuple[i][1]),\n int(entity_label[d_tuple[i][0]])\n ]).reshape(1,3)\n #print(temp_entity.shape,triple.shape)\n triple = np.insert(temp_entity,0,values=triple,axis=0)\n #print(\"triple*********:\\n\",triple)\n text = self.__text[sen].strip().split() # 成功访问到self？\n #gh_11.19 19: print(\"text:**********:\\n\",text)\n array= self.__node_process(triple,text)\n #gh_11.19 print(\"array****\\n\",array)\n ret=[]\n edges = self.__relation_process(array)\n ret.append(edges)\n #gh_11.19 19: print(\"edges****\\n\",edges)\n edges = self.__eredges_process(edges,sen)\n #gh_11.19 19: print(\"er_edges****\\n\", edges)\n\n for e in edges:\n G.add_edge(e[0],e[1])\n #if list(e[0:2]) not in events: # 只添加一次\n # events.append(list(e[0:2])) # 添加事件\n events.append(list(e[0:2]))\n ret.append(events)\n return ret\n\n # #pos = nx.spring_layout(G, seed=3113794652) # positions for all nodes\n # #pos = nx.spring_layout(G, scale=3) #标准布局\n # pos = nx.circular_layout(G)\n # nx.draw(G,pos,node_color = '#31ECA8')\n # for p in pos: # raise text positions\n # pos[p][1] += 0.07\n # nx.draw_networkx_labels(G, pos,font_size=10)\n\n\n #edge_labels = {} # 用来整合edge_dict信息\n # if label_show == True:\n # edge_dict = nx.get_edge_attributes(G, 'name')\n # else:\n # edge_dict = nx.get_edge_attributes(G, 'ind')\n #\n # for k,v in edge_dict.items():\n # if k[-1] == 0:\n # edge_labels[k[:2]] = str(v)\n # else:\n # edge_labels[k[:2]] = edge_labels[k[:2]] + '\\n' + str(v)\n #print(edge_dict,edge_labels)\n\n\n # nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)\n # plt.savefig(save_pth)\n #plt.show()\n return vis\n\n\n# C = RelationNetwork('test_relation_part.json',\n# 'test_label_part.json',\n# 'test_text.json',\n# 'relation_label_tag.txt')\n# f = C.lazy_vis()\n# # temp = list(range(10))\n# # events = f(*temp)\n# events = f(267)\n# print(\"events****\\n\",events)\n#\n# G = NetworkVis.GraphShow()\n# G.create_page(events)\n", "sub_path": "mysite/demo_visualization/GraphVis/BuildERGraph.py", "file_name": "BuildERGraph.py", "file_ext": "py", "file_size_in_byte": 10176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.lexsort", "line_number": 91, "usage_type": "call"}, {"api_name": "json.load", "line_number": 183, "usage_type": "call"}, {"api_name": "json.load", "line_number": 185, "usage_type": "call"}, {"api_name": "networkx.MultiDiGraph", "line_number": 192, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 211, "usage_type": "call"}]}
+{"seq_id": "356936729", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n# 自動擷取每小時PM2.5的值\n#開啟 While true 便可以自動開爬\n\n\n# In[3]:\n\n\nimport requests\nimport csv\n\n\ndef downloadPMCSV(time):\n CSV_URL = 'https://opendata.epa.gov.tw/ws/Data/ATM00625/?$format=csv'\n download = requests.get(CSV_URL,verify=False)\n download = download.content.decode(\"utf-8\")\n reader = csv.reader(download.split('\\n'), delimiter=',')\n\n name = time + 'PM25.CSV'\n with open(name,'a',encoding = 'utf8') as f:\n w = csv.writer(f)\n for row in reader:\n w.writerow(row)\n\n\n# In[53]:\n\n\nimport time\nfrom datetime import datetime\n#while True:\ntime = str(datetime.now().year) + str(datetime.now().month) + str(datetime.now().day) + str(datetime.now().hour)\ndownloadPMCSV(time)\n#time.sleep(3600) # s\n\n\n# In[ ]:\n\n\n\n\n", "sub_path": "scrapPM25.py", "file_name": "scrapPM25.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 22, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}]}
+{"seq_id": "510381855", "text": "'''\nFIFO 니까 q를 이용해서 중요도를 필터링 하면서 진행 하면 될 듯\n'''\nfrom collections import deque\n\n\ndef max_ch(r,printer):\n for i in printer:\n if r < i[1]:\n return False\n return True\n\n\nT=int(input())\nfor _ in range(T):\n cnt=0\n N,M = map(int,input().split())\n ranks = list(map(int,input().split()))\n printer = deque([])\n for i,rank in enumerate(ranks):\n printer.append([i,rank])\n while True:\n # print('printer:',printer)\n r = printer[0][1]\n if max_ch(r,printer):\n temp = printer.popleft()[0]\n cnt+=1\n if temp == M:\n break\n else:\n printer.append(printer.popleft())\n print(cnt)\n\n\n #뽑을때마나 cnt+=1\n #뽑힌 i 가 M 일때 종료", "sub_path": "백준/Python/카테고리/큐(Queue),덱(Dequeue)/1966(프린터 큐).py", "file_name": "1966(프린터 큐).py", "file_ext": "py", "file_size_in_byte": 810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "collections.deque", "line_number": 19, "usage_type": "call"}]}
+{"seq_id": "262976162", "text": "# -*- encoding: utf-8 -*-\n\nfrom openerp.osv import osv\nimport base64\nfrom openerp import models, fields, api\nimport codecs\nimport pprint\n\nclass timport_caja_cabecera_v1(models.Model):\n\t_name = 'timport.caja.cabecera.v1'\n\n\tcampo1 = fields.Char('Campo1')\n\tcampo2 = fields.Char('Campo2')\n\tcampo3 = fields.Char('Campo3')\n\tcampo4 = fields.Char('Campo4')\n\tcampo5 = fields.Char('Campo5')\n\tcampo6 = fields.Char('Campo6')\n\tcampo7 = fields.Char('Campo7')\n\tcampo8 = fields.Char('Campo8')\n\tcampo9 = fields.Char('Campo9')\n\tcampo10 = fields.Char('Campo10')\n\tcamporelleno = fields.Char('Campo9')\n\n\nclass timport_caja_detalle_v1(models.Model):\n\t_name = 'timport.caja.detalle.v1'\n\n\tcampo1 = fields.Char('Campo1')\n\tcampo2 = fields.Char('Campo2')\n\tcampo3 = fields.Char('Campo3')\n\tcampo4 = fields.Char('Campo4')\n\tcampo5 = fields.Char('Campo5')\n\tcampo6 = fields.Char('Campo6')\n\tcampo7 = fields.Char('Campo7')\n\tcampo8 = fields.Char('Campo8')\n\tcampo9 = fields.Char('Campo9')\n\tcampo10 = fields.Char('Campo10')\n\tcampo11 = fields.Char('Campo11')\n\tcampo12 = fields.Char('Campo12')\n\tcamporelleno = fields.Char('Campo9')\n\n\nclass account_move(models.Model):\n\t_inherit = 'account.move'\n\n\tcod_tienda = fields.Char('Codigo Tienda')\n\tcod_caja = fields.Char('Codigo Caja')\n\timport_caja_id = fields.Integer('IC_id')\n\n\nclass importacion_caja(models.Model):\n\t_name='importacion.caja'\n\n\tfecha = fields.Date('Fecha')\n\n\tfile_imp = fields.Binary('Archivo Importacion',required=True)\n\tfile_head_imp = fields.Binary('Archivo importación cabecera', required=True)\n\t\n\tfile_sal_primer = fields.Binary('Archivo Paso 1',readonly=True)\n\tfile_sal_error = fields.Binary('Archivo Paso 1 Errores',readonly=True,copy=False)\n\n\tfile_sal_primer_head = fields.Binary('Archivo Paso 1 Cabecera',readonly=True)\n\tfile_sal_error_head = fields.Binary('Archivo Paso 1 Errores Cabecera',readonly=True,copy=False)\n\t\n\t\n\tdelimitador = fields.Char('Delimitador', size=1, default=',')\n\n\tperiod_id =fields.Many2one('account.period','Periodo',required=True)\n\tdiario =fields.Many2one('account.journal','Diario',required=True)\n\t\n\tsal_name1 = fields.Char('Name 1',default='Observacion_cabecera.csv')\n\tsal_name2 = fields.Char('Name 2',default='Observacion_detalle.csv')\n\n\tn_cabecera = fields.Char('Name 1')\n\tn_detalle = fields.Char('Name 2')\n\t\n\tglosa = fields.Char('Glosa')\n\t\n\tstate = fields.Selection([('1','Borrador'),('2','Por Importar'),('3','Importado'),('4','Anulado')],'Estado',readonly=True,default=\"1\",copy=False)\n\n\tname = fields.Char('Nombre',compute=\"get_name_caja\")\n\n\t@api.one\n\tdef get_name_caja(self):\n\t\tif self.id:\n\t\t\tself.name = 'Importacion C-' + str(self.id)\n\t\telse:\n\t\t\tself.name = 'Importacion Borrador'\n\n\n\t@api.model\n\tdef create(self,vals):\t\n\t\tif len( self.env['importacion.caja'].search([('state','in',('1','2'))]) ) >0:\n\t\t\traise osv.except_osv('Alerta!','Existe otra importacion pendiente en estado Borrador o Por Importar.')\n\t\tt = super(importacion_caja,self).create(vals)\n\t\tt.refresh()\n\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',t.period_id.id),('journal_id','=',t.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\t\t#otros = self.env['importacion.caja'].search([('id','!=',t.id),('fecha','=',t.fecha)])\n\t\t#if len(otros)>0:\n\t\t#\traise osv.except_osv('Alerta!','Ya existe una importación para esa fecha.')\n\t\treturn t\n\n\t@api.one\n\tdef write(self,vals):\n\t\tif len( self.env['importacion.caja'].search([('state','in',('1','2')),('id','!=',self.id)]) )\t>0:\n\t\t\traise osv.except_osv('Alerta!','Existe otra importacion pendiente en estado Borrador o Por Importar.')\n\n\t\tt = super(importacion_caja,self).write(vals)\n\t\tself.refresh()\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\t\t#otros = self.env['importacion.caja'].search([('id','!=',self.id),('fecha','=',self.fecha)])\n\t\t#if len(otros)>0:\n\t\t#\traise osv.except_osv('Alerta!','Ya existe una importación para esa fecha.')\n\t\treturn t\n\n\t@api.one\n\tdef unlink(self):\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\t\tif self.state != '1':\n\t\t\traise osv.except_osv('Alerta!','No se puede eliminar una importación en proceso.')\n\t\treturn super(importacion_caja,self).unlink()\n\n\t@api.multi\n\tdef eliminarImportacion(self):\n\t\t\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\t\t# if self.period_id.state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo esta cerrado.')\n\n\t\tself.env.cr.execute(\"\"\" \n\t\t\tdelete from account_move_line where move_id in (select id from account_move where import_caja_id = \"\"\"+str(self.id)+\"\"\")\n\t\t\t\"\"\")\n\n\t\tself.env.cr.execute(\"\"\" \n\t\t\tdelete from account_move where id in (select id from account_move where import_caja_id = \"\"\"+str(self.id)+\"\"\")\n\t\t\t\"\"\")\n\t\tself.state = '4'\n\n\n\n\t@api.multi\n\tdef primerpaso(self):\t\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\n\t\t######################################### CABECERA #########################################\n\t\timport time\t\t\n\t\timport base64\n\t\timport codecs\n\n\n\t\tparametros = self.env['main.parameter'].search([])[0]\n\n\t\tcabe_v1 = base64.b64decode(self.file_head_imp)\n\t\tfile_cv1 = open(parametros.dir_create_file + 'icv1p.csv','wb')\n\t\tfile_cv1.write(cabe_v1)\n\t\tfile_cv1.close()\n\n\n\t\tflag = False\n\t\ttry:\n\t\t\tf = codecs.open(parametros.dir_create_file + 'icv1p.csv',encoding='utf-8',errors='strict')\n\t\t\tf.read()\n\t\texcept UnicodeDecodeError:\n\t\t\tflag= True\n\n\t\tif flag:\n\t\t\timport codecs\n\t\t\tBLOCKSIZE = 1048576 # or some other, desired size in bytes\n\t\t\twith codecs.open(parametros.dir_create_file + 'icv1p.csv', \"r\", \"iso-8859-1\") as sourceFile:\n\t\t\t\twith codecs.open(parametros.dir_create_file + 'icv1.csv', \"w\", \"utf-8\") as targetFile:\n\t\t\t\t\twhile True:\n\t\t\t\t\t\tcontents = sourceFile.read(BLOCKSIZE)\n\t\t\t\t\t\tif not contents:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\ttargetFile.write(contents)\n\t\telse:\t\t\t\n\t\t\tfile_cv1 = open(parametros.dir_create_file + 'icv1.csv','wb')\n\t\t\tfile_cv1.write(cabe_v1)\n\t\t\tfile_cv1.close()\n\n\n\t\tself.env.cr.execute(\"\"\"\n\t\tdelete from timport_caja_cabecera_v1;\n\t\t \"\"\")\n\n\t\ttry:\n\t\t\tself.env.cr.execute(\"\"\"\n\t\t\tcopy timport_caja_cabecera_v1 (campo1,campo2,campo3,campo4,campo5,campo6,campo7,campo8,campo9,campo10) from '\"\"\" +parametros.dir_create_file + 'icv1.csv'+ \"\"\"' with delimiter '\"\"\"+self.delimitador+\"\"\"' CSV ;\n\t\t\t \"\"\")\n\t\texcept Exception as e:\n\t\t\traise osv.except_osv(\"Alerta!\",\"El Archivo ha importar posiblemente no es el correcto o contiene en alguno de sus campos como parte de su información el separador: '\"+ self.delimitador + \"'.\"+ \"\\n\\n\"+ str(e))\n\n\t\tself.env.cr.execute(\"\"\"\n\t\t\tdrop table if exists timport_caja_cabecera_v2;\n\n\t\t\tcreate table timport_caja_cabecera_v2 AS (\n\t\t\t\tselect campo1,campo2,campo3,campo4,campo5,campo6,campo7,campo8,campo9, rp.id as campo10, itd.id as campo11, cv1.campo10 as campo12, imp.id as campo13 from timport_caja_cabecera_v1 cv1\n\t\t\t\tleft join res_partner rp on rp.nro_documento = cv1.campo8\n\t\t\t\tleft join einvoice_catalog_01 itd on itd.code = cv1.campo7 \n\t\t\t\tleft join einvoice_means_payment imp on imp.code = cv1.campo10\n\t\t\t);\n\t\t \"\"\")\n\n\t\tself.env.cr.execute(\"\"\"\n\t\t\tcopy (\n\t\t\tselect distinct\n\t\t\t\tcase when campo10 is null then 'No existe el Partner: ' || coalesce(campo8,'') else '-' end as ver_partner\n\t\t\t\t--case when campo11 is null then 'No existe el Tipo Documento: ' || coalesce(campo7,'') else '-' end as ver_tipo_doc\n\t\t\tfrom timport_caja_cabecera_v2 where campo10 is null\n\t\t\t\t)\t\n\t\t\tTO '\"\"\"+ str( parametros.dir_create_file + 'icv2.csv' )+ \"\"\"'\n\t\t\twith delimiter '|' CSV \n\n\t\t \"\"\")\n\n\t\texp = open(str( parametros.dir_create_file + 'icv2.csv' ),'r' ).read()\n\n\t\t#\ttmp += lineas[0] +\"|\"+ id_periodo+\"|\"+lineas[2]+\"|\"+lineas[3]+\"|\"+lineas[4]+\"|\"+lineas[5]+\"|\"+id_tipo_doc+\"|\"+id_partner+\"\\n\"\n\t\tself.file_sal_error_head = base64.encodestring(exp)\n\n\n\t\t##################################### detalle ###########################################\n\n\n\t\timport codecs\n\n\t\tdet_v1 = base64.b64decode(self.file_imp)\n\t\tfile_dv1 = open(parametros.dir_create_file + 'idv1p.csv','wb')\n\t\tfile_dv1.write(det_v1)\n\t\tfile_dv1.close()\n\n\n\t\tflag = False\n\t\ttry:\n\t\t\tf = codecs.open(parametros.dir_create_file + 'idv1p.csv',encoding='utf-8',errors='strict')\n\t\t\tf.read()\n\t\texcept UnicodeDecodeError:\n\t\t\tflag= True\n\n\t\tif flag:\n\t\t\timport codecs\n\t\t\tBLOCKSIZE = 1048576 # or some other, desired size in bytes\n\t\t\twith codecs.open(parametros.dir_create_file + 'idv1p.csv', \"r\", \"iso-8859-1\") as sourceFile:\n\t\t\t\twith codecs.open(parametros.dir_create_file + 'idv1.csv', \"w\", \"utf-8\") as targetFile:\n\t\t\t\t\twhile True:\n\t\t\t\t\t\tcontents = sourceFile.read(BLOCKSIZE)\n\t\t\t\t\t\tif not contents:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\ttargetFile.write(contents)\n\t\telse:\t\t\t\n\t\t\tfile_cv1 = open(parametros.dir_create_file + 'idv1.csv','wb')\n\t\t\tfile_cv1.write(det_v1)\n\t\t\tfile_cv1.close()\n\n\n\t\tself.env.cr.execute(\"\"\"\n\t\tdelete from timport_caja_detalle_v1;\n\t\t \"\"\")\n\n\t\ttry:\n\t\t\tself.env.cr.execute(\"\"\"\n\t\t\tcopy timport_caja_detalle_v1 (campo1,campo2,campo3,campo4,campo5,campo6,campo7,campo8,campo9,campo10,campo11,campo12,camporelleno) from '\"\"\" +parametros.dir_create_file + 'idv1.csv'+ \"\"\"' with delimiter '\"\"\"+self.delimitador+\"\"\"' CSV ;\n\t\t\t \"\"\")\n\t\texcept Exception as e:\t\t\t\n\t\t\traise osv.except_osv(\"Alerta!\",\"El Archivo ha importar posiblemente no es el correcto o contiene en alguno de sus campos como parte de su información el separador: '\"+ self.delimitador + \"'.\"+ \"\\n\\n\"+ str(e))\n\n\n\t\tself.env.cr.execute(\"\"\"\n\t\t\tdrop table if exists timport_caja_detalle_v2;\n\n\t\t\tcreate table timport_caja_detalle_v2 AS (\n\t\t\t\tselect campo1,campo2,campo3,campo4,campo5,campo6,campo7,campo8,campo9,campo10,campo11,campo12, rp.id as campo13,\n\t\t\t\titd.id as campo14, imp.id as campo15, rc.id as campo16, aa.id as campo17, aaa.id as campo18 from timport_caja_detalle_v1 dv1\n\t\t\t\tleft join res_partner rp on rp.nro_documento = dv1.campo3\n\t\t\t\tleft join einvoice_catalog_01 itd on itd.code = dv1.campo4\n\t\t\t\tleft join einvoice_means_payment imp on imp.code = dv1.campo12\n\t\t\t\tleft join res_currency rc on rc.name = dv1.campo11 and rc.name != 'PEN'\n\t\t\t\tleft join account_account aa on aa.code = dv1.campo6\n\t\t\t\tleft join account_analytic_account aaa on aaa.code = dv1.campo9\n\t\t\t);\n\t\t \"\"\")\n\n\t\tself.env.cr.execute(\"\"\"\n\t\t\tcopy (\n\t\t\tselect distinct\n\t\t\t\tcase when campo13 is null then 'No existe el Partner: ' || coalesce(campo3,'') else '-' end as ver_partner,\n\t\t\t\tcase when campo14 is null then 'No existe el Tipo Documento: ' || coalesce(campo4,'') else '-' end as ver_tipo_doc,\n\t\t\t\t--case when campo16 is null then 'No existe el Moneda: ' || coalesce(campo11,'') else '-' end as ver_moneda,\n\t\t\t\tcase when campo17 is null then 'No existe el Cuenta Contable: ' || coalesce(campo6,'') else '-' end as ver_cuenta_contable\n\t\t\t\t--case when campo18 is null then 'No existe el Cuenta Analitica: ' || coalesce(campo9,'') else '-' end as ver_cuenta_analitica\n\t\t\tfrom timport_caja_detalle_v2 where campo13 is null or campo14 is null or campo17 is null \n\t\t\t\t)\t\n\t\t\tTO '\"\"\"+ str( parametros.dir_create_file + 'idv2.csv' )+ \"\"\"'\n\t\t\twith delimiter '|' CSV \n\t\t \"\"\")\n\n\t\texp = open(str( parametros.dir_create_file + 'idv2.csv' ),'r' ).read()\n\n\t\tself.file_sal_error = base64.encodestring(exp)\n\n\t\t\n\t\t#################################################################################################\n\n\t\tif self.file_sal_error_head or self.file_sal_error:\n\t\t\tpass\n\t\telse:\n\t\t\tself.state = '2'\n\n\t@api.one\n\tdef regresar_borrador(self):\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\t\tself.state = '1'\n\n\t@api.one\n\tdef segundopaso(self):\n\t\t# permiso = self.env['account.journal.period'].search([('period_id','=',self.period_id.id),('journal_id','=',self.diario.id)])\n\t\t# if len(permiso)>0 and permiso[0].state != 'draft':\n\t\t# \traise osv.except_osv('Alerta!','El Periodo para el diario esta cerrado.')\n\n\t\tif self.period_id.state == 'done':\n\t\t\traise osv.except_osv(\"Alerta!\",\"El periodo \"+self.period_id.code+u\" está cerrado.\") \n\n\t\t######################################### Cabecera #########################################\n\t\timport time\t\t\n\t\timport base64\n\n\n\t\tself.env.cr.execute(\"\"\" \n\t\t\tINSERT INTO account_move(fecha_contable,partner_id,date,name,state,journal_id,ref,company_id,cod_tienda,cod_caja,ple_diariomayor,import_caja_id, means_payment_it) \n\t\t\tselect campo3::date,campo10,campo3::date,campo1,'posted',\"\"\"+str(self.diario.id)+\"\"\",'Importacion-c\"\"\"+str(self.id)+\"\"\"',1,campo4,campo5,'1',\"\"\"+str(self.id)+\"\"\" , campo13 from timport_caja_cabecera_v2;\t\t\n\t\t\"\"\")\n\n\t\t\n\n\t\tself.env.cr.execute(\"\"\" \n\t\t\tINSERT INTO account_move_line(name,partner_id,nro_comprobante,account_id,debit,credit,analytic_account_id,amount_currency,currency_id,type_document_it,journal_id,date, move_id,company_id,date_maturity) \n\t\t\tselect campo1,campo13,campo5,campo17,campo7::numeric,campo8::numeric,campo18,campo10::numeric,campo16,campo14,am.journal_id,am.date,am.id,1,am.date from timport_caja_detalle_v2\n\t\t\tinner join account_move am on am.name = timport_caja_detalle_v2.campo1 and am.import_caja_id = \"\"\" +str(self.id)+ \"\"\";\t\t\n\t\t\"\"\")\n\n\n\n\t\tself.env.cr.execute(\"\"\" \n\t\t\tUPDATE ACCOUNT_MOVE SET amount = (select sum(debit) from account_move_line where move_id = ACCOUNT_MOVE.id )\n\t\t\twhere import_caja_id = \"\"\" +str(self.id)+ \"\"\";\t\t\n\t\t\"\"\")\n\n\t\tself.state = '3'\n", "sub_path": "importacion_caja_it/wizard/importacion.py", "file_name": "importacion.py", "file_ext": "py", "file_size_in_byte": 14121, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "openerp.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 9, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 12, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 13, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 14, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 15, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 16, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 17, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 18, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 19, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 19, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 20, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 21, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 22, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "openerp.models.Model", "line_number": 25, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 25, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 28, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 29, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 30, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 31, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 32, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 33, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 34, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 35, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 36, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 37, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 38, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 39, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 40, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 40, "usage_type": "name"}, {"api_name": "openerp.models.Model", "line_number": 43, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 43, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 46, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 46, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 47, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 47, "usage_type": "name"}, {"api_name": "openerp.fields.Integer", "line_number": 48, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "openerp.models.Model", "line_number": 51, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 51, "usage_type": "name"}, {"api_name": "openerp.fields.Date", "line_number": 54, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 54, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 56, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 56, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 57, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 57, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 59, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 59, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 60, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 60, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 62, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 62, "usage_type": "name"}, {"api_name": "openerp.fields.Binary", "line_number": 63, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 63, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 66, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 66, "usage_type": "name"}, {"api_name": "openerp.fields.Many2one", "line_number": 68, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 68, "usage_type": "name"}, {"api_name": "openerp.fields.Many2one", "line_number": 69, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 69, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 71, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 72, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 72, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 74, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 74, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 75, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 75, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 77, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 77, "usage_type": "name"}, {"api_name": "openerp.fields.Selection", "line_number": 79, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 79, "usage_type": "name"}, {"api_name": "openerp.fields.Char", "line_number": 81, "usage_type": "call"}, {"api_name": "openerp.fields", "line_number": 81, "usage_type": "name"}, {"api_name": "openerp.api.one", "line_number": 83, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 83, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 94, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 94, "usage_type": "name"}, {"api_name": "openerp.api.model", "line_number": 91, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 91, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 109, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 109, "usage_type": "name"}, {"api_name": "openerp.api.one", "line_number": 106, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 106, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 129, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 129, "usage_type": "name"}, {"api_name": "openerp.api.one", "line_number": 122, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 122, "usage_type": "name"}, {"api_name": "openerp.api.multi", "line_number": 132, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 132, "usage_type": "name"}, {"api_name": "base64.b64decode", "line_number": 168, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 176, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 184, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 185, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 206, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 206, "usage_type": "name"}, {"api_name": "base64.encodestring", "line_number": 234, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 242, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 250, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 258, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 259, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 280, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 280, "usage_type": "name"}, {"api_name": "base64.encodestring", "line_number": 314, "usage_type": "call"}, {"api_name": "openerp.api.multi", "line_number": 153, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 153, "usage_type": "name"}, {"api_name": "openerp.api.one", "line_number": 324, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 324, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 339, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 339, "usage_type": "name"}, {"api_name": "openerp.api.one", "line_number": 332, "usage_type": "attribute"}, {"api_name": "openerp.api", "line_number": 332, "usage_type": "name"}]}
+{"seq_id": "618051880", "text": "from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.template import loader\nfrom .models import Expenses\nimport csv\nimport codecs\nimport re\nfrom datetime import datetime\n\n# View to upload the CSV\ndef index(request):\n if request.POST and request.FILES:\n # Open the CSV, and parse it into a dictionary\n csvfile = request.FILES['csv_file']\n csvfile.open()\n reader = csv.DictReader(csvfile)\n # Using the dictionary, organise the data into\n # month-by-month summations\n monthlyTaxes = {}\n for row in reader:\n # Create and save this expense to the database\n expense = Expenses()\n expense.date = datetime.strptime(row['date'], '%m/%d/%Y').isoformat()[:10]\n expense.employee_name = row['employee name']\n expense.employee_address = row['employee address']\n expense.category = row['category']\n expense.description = row['expense description']\n expense.pretax = row['pre-tax amount'].replace(',','')\n expense.tax_name = row['tax name']\n expense.tax = row['tax amount'].replace(',','')\n expense.save()\n \n # This is the part where we fill up the monthly taxes\n # dictionary.\n \n # Pattern to match first 1-2 numbers, and last 4\n month_match = re.match('^(\\d{1,2}\\/).*(\\d{4})$', row['date'])\n month = month_match.group(1) + month_match.group(2)\n # Add the pre-tax amount to the entry in the dictionary\n if month in monthlyTaxes:\n monthlyTaxes[month] += float(row['pre-tax amount'].replace(',','')) + float(row['tax amount'].replace(',',''))\n else: # ... or, add the month to the dictionary\n monthlyTaxes[month] = float(row['pre-tax amount'].replace(',','')) + float(row['tax amount'].replace(',',''))\n\n # Set context for the view\n context = {\n 'reader': reader,\n 'monthlyTaxes': monthlyTaxes,\n }\n return render(request, 'index.html', context)\n else:\n return render(request, 'index.html')", "sub_path": "codechallenge/waveapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2194, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "csv.DictReader", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Expenses", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "re.match", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}]}
+{"seq_id": "622999742", "text": "# Module imports, done to reduce compiled file size\r\nfrom os import chdir, path, getcwd\r\nfrom socketserver import TCPServer\r\nfrom http.server import SimpleHTTPRequestHandler\r\nfrom webbrowser import get, register, BackgroundBrowser\r\nfrom datetime import datetime\r\nfrom traceback import format_exc\r\nfrom sys import exit as sys_exit\r\n\r\n# Done for error logging\r\ntry:\r\n # Getting the original directory that this file is placed in\r\n orig_dir = getcwd()\r\n \r\n ''' Config file reading and creation '''\r\n # Creating the config file if it doesn't exist\r\n if not path.exists(\"config.ini\"):\r\n with open(\"config.ini\", \"w+\") as file:\r\n file.write(\"# Edit this config file if you need to change where your browser is and what url you want the webserver to use.\\n\")\r\n file.write(\"# If the program doesn't run, delete this file and rerun it. If it doesn't work after that, make a github issue on the repo.\\n\")\r\n file.write(\"[Options]\\n\")\r\n file.write(\"browser=C:\\\\Program Files\\\\Firefox Developer Edition\\\\firefox.exe\\n\")\r\n file.write(\"url=127.0.0.1\")\r\n file.close()\r\n del file\r\n\r\n # Reading from the config file\r\n with open(\"config.ini\", \"r\") as file:\r\n config = file.readlines()\r\n file.close()\r\n del file\r\n\r\n # Getting the browser dir from the config file\r\n browser_dir = config[3].strip()\r\n browser_dir = browser_dir.split(\"=\")\r\n browser_dir = browser_dir[1]\r\n if not path.exists(browser_dir):\r\n raise FileNotFoundError(\"Chosen browser does not exist, edit the chosen browser in config.ini\")\r\n\r\n # Getting the url from the config file\r\n url = config[4].strip()\r\n url = url.split(\"=\")\r\n url = url[1]\r\n\r\n # Setting the directory to the fnf directory\r\n chdir(\"fnf7\")\r\n \r\n # Setting up the webserver (because browsers don't let you load images from a file:// directory\r\n Handler = SimpleHTTPRequestHandler\r\n with TCPServer((url, 80), Handler) as httpd:\r\n \r\n # Opening the page in firefox (because it doesn't work in my chromium browser\r\n register(\"user-browser\", None, BackgroundBrowser(browser_dir))\r\n get(\"user-browser\").open(f\"http://{url}:80/index.html\", new=2)\r\n \r\n # Starting the webserver\r\n httpd.serve_forever()\r\n\r\n # Successful exit\r\n sys_exit(0)\r\nexcept:\r\n # Logging errors to a traceback file\r\n chdir(orig_dir)\r\n print(\"Error occured, check the traceback text file\")\r\n current_time = datetime.now()\r\n current_time = current_time.strftime(\"%a, %d %b %Y %H;%M;%S\")\r\n with open(f\"Traceback {current_time}.txt\", \"w+\") as traceback_log:\r\n traceback_log.write(format_exc())\r\n traceback_log.close()\r\n \r\n # Unsuccessful exit\r\n sys_exit(1)\r\n", "sub_path": "funkin_with_gamefiles.py", "file_name": "funkin_with_gamefiles.py", "file_ext": "py", "file_size_in_byte": 2803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 46, "usage_type": "call"}, {"api_name": "http.server.SimpleHTTPRequestHandler", "line_number": 49, "usage_type": "name"}, {"api_name": "socketserver.TCPServer", "line_number": 50, "usage_type": "call"}, {"api_name": "webbrowser.register", "line_number": 53, "usage_type": "call"}, {"api_name": "webbrowser.BackgroundBrowser", "line_number": 53, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 72, "usage_type": "call"}]}
+{"seq_id": "405525693", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n__author__ = 'MFC'\n__time__ = '2020-04-16 19:20'\n\n\n\"\"\"\n优化版ftp上传\n\n即便是视频文件，也是可以按行来读取的，也可以readline，也可以for循环，但是读取出来的数据大小就不固定了，影响效率，有可能读的比较小，\n也可能很大，像视频文件一般都是一行的二进制字节流。\n所有我们可以用read，设定一个一次读取内容的大小，一边读一边发，一边收一边写。\n\"\"\"\n\nimport os\nimport json\nimport struct\nimport socket\n\nclient = socket.socket()\nip_port = (\"127.0.0.1\", 8001)\nclient.connect(ip_port)\nbuffer = 1024\nheader = { # 报头为dict类型\n \"filename\": \"test.jpg\",\n \"filepath\": \"\",\n \"filesize\": 0,\n}\nfile_path = os.path.join(header['filepath'], header['filename'])\nfile_size = os.path.getsize(file_path)\nheader['filesize'] = file_size\nheader_json = json.dumps(header) # 报头序列化为json字符串\nheader_bytes = header_json.encode(\"utf-8\") # 报头编码为bytes类型\nclient.send(struct.pack('i', len(header_bytes))) # 发送4个字节的报头大小\nclient.send(header_bytes) # 发送报头\nprint(file_size, buffer)\nwith open(file_path, \"rb\") as f:\n while file_size:\n if file_size >= buffer:\n client.send(f.read(buffer))\n file_size -= buffer\n print(file_size, buffer, \"第一次或中间的\")\n else:\n client.send(f.read(buffer))\n print(file_size, buffer, \"最后一次\")\n break\nclient.close()", "sub_path": "otherdemo/ftp_demo/client2.py", "file_name": "client2.py", "file_ext": "py", "file_size_in_byte": 1539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "socket.socket", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "307398104", "text": "#!/usr/bin/env python\n\n\"\"\"\nCompare Oracle ReqMgr tables to data stored in CouchDB database.\n\nNeeds to have credentials for accessing CMS web ready in\n$X509_USER_CERT $X509_USER_KEY, or proxy stored in /tmp/x509up_u<ID>\n\nAssumes ReqMgr's CouchDB database consistent with Oracle reqmgr_request\n table, so that both of these contains the same and mutually\n corresponding requests.\n \nThis tools checks consistency on the request level, that is data\nfields in CouchDB request documents with data stored in Oracle.\n\nThis script should also correct any inconsistencies (correcting values\nin CouchDB and later removing dispensable CouchDB request fields).\nIt will have to be run repeatedly like Oracle/CouchDB database level\nconsistency check (which is done here at the beginning too).\n\nRun with -c (commit) to perform updates in CouchDB.\n\nAll checks and updates correspond to progress recoreded on\nhttps://github.com/dmwm/WMCore/issues/4388\n\n\"\"\"\nfrom __future__ import print_function\n\ncouch_url = \"https://cmsweb.cern.ch/couchdb\"\ncouchdb_name = \"reqmgr_workload_cache\"\n \n\nimport sys\nimport re\n\nimport cx_Oracle\n\nfrom WMCore.Database.CMSCouch import CouchServer, Database, Document\n\n\n# do not consider consistency of these, to be removed from Couch later\nCOUCH_TO_IGNORE = (\n \"ReqMgrGroupID\",\n \"ReqMgrRequestID\",\n \"ReqMgrRequestBasePriority\",\n \"ReqMgrRequestorID\",\n \"Workflowspec\",\n \"RequestSizeEvents\",\n \"RequestEventSize\",\n )\n\n# columns in the Oracle SQL statement,\n# the order and number of items here have to agree with select columns ...\nMAPPING=(\n {\"oracle\": \"REQUEST_NAME\", \"couch\": \"RequestName\"},\n {\"oracle\": \"TYPE_NAME\", \"couch\": \"RequestType\"},\n {\"oracle\": \"STATUS_NAME\", \"couch\": \"RequestStatus\"},\n {\"oracle\": \"REQUEST_PRIORITY\", \"couch\": \"RequestPriority\"},\n {\"oracle\": \"REQUESTOR_GROUP_ID\", \"couch\": \"ReqMgrGroupID\"},\n {\"oracle\": \"WORKFLOW\", \"couch\": \"RequestWorkflow\"},\n {\"oracle\": \"REQUEST_EVENT_SIZE\", \"couch\": \"RequestEventSize\"},\n {\"oracle\": \"REQUEST_SIZE_FILES\", \"couch\": \"RequestSizeFiles\"},\n {\"oracle\": \"PREP_ID\", \"couch\": \"PrepID\"},\n {\"oracle\": \"REQUEST_NUM_EVENTS\", \"couch\": \"RequestNumEvents\"},\n {\"oracle\": \"GROUP_NAME\", \"couch\": \"Group\"},\n {\"oracle\": \"REQUESTOR_HN_NAME\", \"couch\": \"Requestor\"},\n {\"oracle\": \"REQUESTOR_DN_NAME\", \"couch\": \"RequestorDN\"},\n \n # team\n # applies only to requests which reached assignment status\n # WARNING:\n # by including teams, not all requests will be returned in\n # the query to compare/update the above data fields\n # {\"oracle\": \"TEAM_NAME\", \"couch\": \"Team\"},\n \n # reqmgr_input_dataset\n # WARNING:\n # only some requests have InputDataset assigned, so not\n # all requests are returned\n #{\"oracle\": \"DATASET_NAME\", \"couch\": \"InputDataset\"},\n #{\"oracle\": \"DATASET_TYPE\", \"couch\": \"InputDatasetTypes\"},\n \n # reqmgr_output_dataset\n #{\"oracle\": \"DATASET_NAME\", \"couch\": \"OutputDatasets\"},\n #{\"oracle\": \"SIZE_PER_EVENT\", \"couch\": \"SizePerEvent\"},\n \n # reqmgr_software reqmgr_software_dependency\n {\"oracle\": \"SOFTWARE_NAME\", \"couch\": \"CMSSWVersion\"},\n {\"oracle\": \"SCRAM_ARCH\", \"couch\": \"ScramArch\"}, \n )\n\nORACLE_FIELDS = [item[\"oracle\"] for item in MAPPING]\nCOUCH_FIELDS = [item[\"couch\"] for item in MAPPING]\n\n\nSQL=(\"select \"\n \"reqmgr_request.REQUEST_NAME, \"\n \"reqmgr_request_type.TYPE_NAME, \"\n \"reqmgr_request_status.STATUS_NAME, \"\n \"reqmgr_request.REQUEST_PRIORITY, \"\n \"reqmgr_request.REQUESTOR_GROUP_ID, \"\n \"reqmgr_request.WORKFLOW, \" \n \"reqmgr_request.REQUEST_EVENT_SIZE, \"\n \"reqmgr_request.REQUEST_SIZE_FILES, \"\n \"reqmgr_request.PREP_ID, \"\n \"reqmgr_request.REQUEST_NUM_EVENTS, \"\n \"reqmgr_group.GROUP_NAME, \"\n \"reqmgr_requestor.REQUESTOR_HN_NAME, \"\n \"reqmgr_requestor.REQUESTOR_DN_NAME, \"\n \n # team\n #\"reqmgr_teams.TEAM_NAME \"\n \n # reqmgr_input_dataset\n #\"reqmgr_input_dataset.DATASET_NAME, \"\n #\"reqmgr_input_dataset.DATASET_TYPE \"\n \n # reqmgr_output_dataset\n #\"reqmgr_output_dataset.DATASET_NAME, \"\n #\"reqmgr_output_dataset.SIZE_PER_EVENT \"\n \n # reqmgr_software reqmgr_software_dependency\n \"reqmgr_software.SOFTWARE_NAME, \"\n \"reqmgr_software.SCRAM_ARCH \"\n \n \"from reqmgr_request, reqmgr_request_type, reqmgr_request_status, \"\n \"reqmgr_group, reqmgr_group_association, reqmgr_requestor, \"\n \n # team\n #\"reqmgr_teams, reqmgr_assignment \"\n \n # reqmgr_input_dataset\n # \"reqmgr_input_dataset \"\n \n # reqmgr_output_dataset\n # \"reqmgr_output_dataset \"\n \n # reqmgr_software reqmgr_software_dependency\n \"reqmgr_software, reqmgr_software_dependency \"\n \n \"where reqmgr_request_type.TYPE_ID=reqmgr_request.REQUEST_TYPE \"\n \"and reqmgr_request_status.STATUS_ID=reqmgr_request.REQUEST_STATUS \"\n \"and reqmgr_group.GROUP_ID=reqmgr_group_association.GROUP_ID \"\n \"and reqmgr_group_association.ASSOCIATION_ID=reqmgr_request.REQUESTOR_GROUP_ID \"\n \"and reqmgr_request.REQUESTOR_GROUP_ID=reqmgr_group_association.ASSOCIATION_ID \"\n \"and reqmgr_group_association.REQUESTOR_ID=reqmgr_requestor.REQUESTOR_ID \"\n # team\n #\"and reqmgr_request.REQUEST_ID=reqmgr_assignment.REQUEST_ID \"\n #\"and reqmgr_assignment.TEAM_ID=reqmgr_teams.TEAM_ID \"\n \n # reqmgr_input_dataset\n #\"and reqmgr_request.REQUEST_ID=reqmgr_input_dataset.REQUEST_ID\"\n \n # reqmgr_output_dataset\n #\"and reqmgr_request.REQUEST_ID=reqmgr_output_dataset.REQUEST_ID\"\n \n # reqmgr_software reqmgr_software_dependency\n \"and reqmgr_request.REQUEST_ID=reqmgr_software_dependency.REQUEST_ID \"\n \"and reqmgr_software_dependency.SOFTWARE_ID=reqmgr_software.SOFTWARE_ID\"\n \n# limit the number of rows returned by oracle\n# \"and rownum < 5\"\n )\n# END OF SQL\n\n# ; semi-colon at the end nicely yields\n# cx_Oracle.DatabaseError: ORA-00911: invalid character\n# without any other description\n\n\n\ndef oracle_query(oradb, sql_cmd):\n print(\"Retrieving data from Oracle ...\")\n ora_cursor = cx_Oracle.Cursor(oradb)\n print(\"# SQL: '%s'\" % sql_cmd)\n ora_cursor.prepare(sql_cmd)\n ora_cursor.execute(sql_cmd)\n return ora_cursor\n\n\ndef get_oracle_row_count(oradb, table_name):\n cmd = \"select * from %s\" % table_name\n ora_cursor = oracle_query(oradb, cmd)\n # accessing just .rowcount integer was returning 0 on non-empty result\n num_requests = len(ora_cursor.fetchall())\n ora_cursor.close()\n return num_requests\n\n\ndef get_oracle_data(oradb):\n ora_cursor = oracle_query(oradb, SQL)\n # result is a list of tuples, make a dict from it\n request = {}\n for row in ora_cursor.fetchall():\n for k, v in zip(ORACLE_FIELDS, row):\n request[k] = v\n yield request\n ora_cursor.close()\n \n\ndef get_couchdb_row_count(db):\n \"\"\"\n Returns number of request documents excluding design documents.\n \n \"\"\"\n print(\"Retrieving data from CouchDB ...\")\n doc_count = 0 \n for row in db.allDocs()[\"rows\"]:\n if row[\"id\"].startswith(\"_design\"): continue\n doc_count += 1\n return doc_count\n \n\ndef main():\n if len(sys.argv) < 2:\n print(\"Missing the connect Oracle TNS argument (user/password@server).\")\n sys.exit(1)\n tns = sys.argv[1]\n \n print(\"Creating CouchDB database connection ...\")\n couchdb = Database(couchdb_name, couch_url)\n print(\"Creating Oracle database connection ...\")\n oradb = cx_Oracle.Connection(tns)\n \n num_couch_requests = get_couchdb_row_count(couchdb)\n print(\"Total CouchDB request documents in ReqMgr: %s\" % num_couch_requests)\n num_oracle_requests = get_oracle_row_count(oradb, \"reqmgr_request\") \n print(\"Total Oracle requests entries in ReqMgr: %s\" % num_oracle_requests)\n \n if num_couch_requests != num_oracle_requests:\n print(\"Number of requests in Oracle, CouchDB don't agree, fix that first.\")\n sys.exit(1)\n else:\n print(\"Database cross-check (Oracle request names vs CouchDB): DONE, THE SAME.\")\n \n \n def get_couch_value(couch_req, mapping):\n try:\n c = couch_req[mapping[\"couch\"]]\n couch_missing = False\n except KeyError: \n # comparison will not happen due to missing flag, the value\n # will be stored in couch\n c = \"N/A\"\n couch_missing = False\n return str(c), couch_missing\n \n \n def check_oracle_worflow_value(oracle_value, mapping, req_name):\n # check Oracle WORKFLOW value\n if mapping[\"oracle\"] == \"WORKFLOW\":\n # https://cmsweb.cern.ch/couchdb/reqmgr_workload_cache/linacre_2011A_442p2_DataReprocessingMuOnia_111119_005717/spec\n from_wf_url_req_name = oracle_value.rsplit('/', 2)[-2]\n if req_name != from_wf_url_req_name:\n print(\"Workflow URL mismatch: %s\" % o)\n sys.exit(1) \n\n\n counter = 0\n for oracle_req in get_oracle_data(oradb):\n req_name = oracle_req[\"REQUEST_NAME\"]\n\n # FILTER\n # check only requests injected approx. after last deployment (a lot of\n # stuff should have already been fixed in ReqMgr)\n # _13041._*$ (ending of request name with date/time)\n #if not re.match(\".*_1304[0-3][0-9]_.*$\", req_name): # all April 2013\n # continue\n \n counter += 1\n print(\"\\n\\n%s (%s)\" % (req_name, counter)) \n \n couch_req = couchdb.document(req_name)\n couch_fields_to_correct = {}\n for mapping in MAPPING:\n if mapping[\"couch\"] in COUCH_TO_IGNORE:\n continue\n o = str(oracle_req[mapping[\"oracle\"]])\n c, couch_missing = get_couch_value(couch_req, mapping)\n check_oracle_worflow_value(o, mapping, req_name)\n \n # compare oracle and couch values\n # don't update value in couch if it exists and is non-empty\n if (couch_missing or o != c) and c in ('None', '0', '', \"N/A\"):\n print(\"%s %s != %s\" % (mapping, o, c))\n # correct couch request by oracle value\n couch_fields_to_correct[mapping[\"couch\"]] = o\n \n if couch_fields_to_correct:\n print(\"Couch corrected fields:\")\n print(couch_fields_to_correct)\n if sys.argv[-1] == \"-c\":\n couchdb.updateDocument(req_name, \"ReqMgr\", \"updaterequest\",\n fields=couch_fields_to_correct, useBody=True)\n print(\"Couch updated\")\n else:\n print(\"OK\")\n \n # fields that should be removed from couch\n \"\"\"\n print \"Couch fields to remove, values: ...\"\n for removable in COUCH_TO_IGNORE:\n try:\n val = couch_req[removable]\n except KeyError:\n continue\n print \"%s: %s: %s\" % (req_name, removable, val)\n \"\"\"\n \n # // for for oracle_req in ...\n \n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "src/python/WMCore/ReqMgr/database_cleanup/oracle_couchdb_consistency_checker.py", "file_name": "oracle_couchdb_consistency_checker.py", "file_ext": "py", "file_size_in_byte": 11418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "cx_Oracle.Cursor", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 215, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 217, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 218, "usage_type": "attribute"}, {"api_name": "WMCore.Database.CMSCouch.Database", "line_number": 221, "usage_type": "call"}, {"api_name": "cx_Oracle.Connection", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 232, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 256, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 292, "usage_type": "attribute"}]}
+{"seq_id": "345414498", "text": "# coding=UTF-8\n# **********************************************************************\n# Copyright (c) 2013-2020 Cisco Systems, Inc. All rights reserved\n# written by zen warriors, do not modify!\n# **********************************************************************\n\n\nfrom cobra.mit.meta import ClassMeta\nfrom cobra.mit.meta import StatsClassMeta\nfrom cobra.mit.meta import CounterMeta\nfrom cobra.mit.meta import PropMeta\nfrom cobra.mit.meta import Category\nfrom cobra.mit.meta import SourceRelationMeta\nfrom cobra.mit.meta import NamedSourceRelationMeta\nfrom cobra.mit.meta import TargetRelationMeta\nfrom cobra.mit.meta import DeploymentPathMeta, DeploymentCategory\nfrom cobra.model.category import MoCategory, PropCategory, CounterCategory\nfrom cobra.mit.mo import Mo\n\n\n# ##################################################\nclass LDevOperInfo(Mo):\n \"\"\"\n The operational status information from the device gathered during service graph deployment.\n\n \"\"\"\n\n meta = ClassMeta(\"cobra.model.vns.LDevOperInfo\")\n\n meta.moClassName = \"vnsLDevOperInfo\"\n meta.rnFormat = \"LDevOpInf-%(name)s\"\n meta.category = MoCategory.REGULAR\n meta.label = \"Logical Device Operational Information\"\n meta.writeAccessMask = 0x4000000000000001\n meta.readAccessMask = 0x6000000000000001\n meta.isDomainable = False\n meta.isReadOnly = False\n meta.isConfigurable = True\n meta.isDeletable = False\n meta.isContextRoot = False\n\n meta.childClasses.add(\"cobra.model.tag.Tag\")\n meta.childClasses.add(\"cobra.model.fault.Counts\")\n meta.childClasses.add(\"cobra.model.health.Inst\")\n meta.childClasses.add(\"cobra.model.vns.Consump\")\n meta.childClasses.add(\"cobra.model.fault.Inst\")\n meta.childClasses.add(\"cobra.model.vns.DevConfIssue\")\n meta.childClasses.add(\"cobra.model.aaa.RbacAnnotation\")\n meta.childClasses.add(\"cobra.model.vns.TxId\")\n meta.childClasses.add(\"cobra.model.vns.DevHealth\")\n meta.childClasses.add(\"cobra.model.vns.CDevOperInfo\")\n meta.childClasses.add(\"cobra.model.vns.RsLDevOperInfoToALDev\")\n meta.childClasses.add(\"cobra.model.vns.Capct\")\n meta.childClasses.add(\"cobra.model.tag.Annotation\")\n\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.RsLDevOperInfoToALDev\", \"rsLDevOperInfoToALDev\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.tag.Annotation\", \"annotationKey-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.DevConfIssue\", \"devConfIssue-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.DevHealth\", \"devHealth-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.CDevOperInfo\", \"CDevOpInf-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.Consump\", \"consump-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.aaa.RbacAnnotation\", \"rbacDom-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.tag.Tag\", \"tagKey-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.fault.Counts\", \"fltCnts\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.health.Inst\", \"health\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.fault.Inst\", \"fault-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.Capct\", \"capct-\"))\n meta.childNamesAndRnPrefix.append((\"cobra.model.vns.TxId\", \"txId\"))\n\n meta.parentClasses.add(\"cobra.model.vns.ScriptHandlerState\")\n\n meta.superClasses.add(\"cobra.model.naming.NamedObject\")\n\n meta.rnPrefixes = [\n ('LDevOpInf-', True),\n ]\n\n prop = PropMeta(\"str\", \"annotation\", \"annotation\", 38005, PropCategory.REGULAR)\n prop.label = \"Annotation. Suggested format orchestrator:value\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.range = [(0, 128)]\n prop.regex = ['[a-zA-Z0-9_.:-]+']\n meta.props.add(\"annotation\", prop)\n\n prop = PropMeta(\"str\", \"backoff\", \"backoff\", 23601, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = 1\n prop.defaultValueStr = \"1\"\n meta.props.add(\"backoff\", prop)\n\n prop = PropMeta(\"str\", \"childAction\", \"childAction\", 4, PropCategory.CHILD_ACTION)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop._addConstant(\"deleteAll\", \"deleteall\", 16384)\n prop._addConstant(\"deleteNonPresent\", \"deletenonpresent\", 8192)\n prop._addConstant(\"ignore\", \"ignore\", 4096)\n meta.props.add(\"childAction\", prop)\n\n prop = PropMeta(\"str\", \"configured\", \"configured\", 5184, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = False\n prop.defaultValueStr = \"no\"\n prop._addConstant(\"no\", None, False)\n prop._addConstant(\"yes\", None, True)\n meta.props.add(\"configured\", prop)\n\n prop = PropMeta(\"str\", \"devState\", \"devState\", 15941, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"init\"\n prop._addConstant(\"auditfailed\", \"auditfailed\", 6)\n prop._addConstant(\"auditpending\", \"auditpending\", 5)\n prop._addConstant(\"auditrequested\", \"auditrequested\", 4)\n prop._addConstant(\"cleanrequested\", \"cleanrequested\", 8)\n prop._addConstant(\"init\", \"init\", 0)\n prop._addConstant(\"modifyfailed\", \"modifyfailed\", 3)\n prop._addConstant(\"modifypending\", \"modifypending\", 2)\n prop._addConstant(\"modifyrequested\", \"modifyrequested\", 1)\n prop._addConstant(\"stable\", \"stable\", 7)\n prop._addConstant(\"unmanaged\", \"unmanaged\", 8)\n meta.props.add(\"devState\", prop)\n\n prop = PropMeta(\"str\", \"dn\", \"dn\", 1, PropCategory.DN)\n prop.label = \"None\"\n prop.isDn = True\n prop.isImplicit = True\n prop.isAdmin = True\n prop.isCreateOnly = True\n meta.props.add(\"dn\", prop)\n\n prop = PropMeta(\"str\", \"extMngdBy\", \"extMngdBy\", 40144, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"undefined\"\n prop._addConstant(\"msc\", \"msc\", 1)\n prop._addConstant(\"undefined\", \"undefined\", 0)\n meta.props.add(\"extMngdBy\", prop)\n\n prop = PropMeta(\"str\", \"lastTransitionTime\", \"lastTransitionTime\", 16361, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"0\"\n meta.props.add(\"lastTransitionTime\", prop)\n\n prop = PropMeta(\"str\", \"lcOwn\", \"lcOwn\", 9, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"local\"\n prop._addConstant(\"implicit\", \"implicit\", 4)\n prop._addConstant(\"local\", \"local\", 0)\n prop._addConstant(\"policy\", \"policy\", 1)\n prop._addConstant(\"replica\", \"replica\", 2)\n prop._addConstant(\"resolveOnBehalf\", \"resolvedonbehalf\", 3)\n meta.props.add(\"lcOwn\", prop)\n\n prop = PropMeta(\"str\", \"modResp\", \"modResp\", 15343, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop._addConstant(\"audit\", \"audit\", 3)\n prop._addConstant(\"permanent\", \"permanent\", 2)\n prop._addConstant(\"success\", \"success\", 0)\n prop._addConstant(\"transient\", \"transient\", 1)\n meta.props.add(\"modResp\", prop)\n\n prop = PropMeta(\"str\", \"modTs\", \"modTs\", 7, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"never\"\n prop._addConstant(\"never\", \"never\", 0)\n meta.props.add(\"modTs\", prop)\n\n prop = PropMeta(\"str\", \"monPolDn\", \"monPolDn\", 14907, PropCategory.REGULAR)\n prop.label = \"Monitoring policy attached to this observable object\"\n prop.isImplicit = True\n prop.isAdmin = True\n meta.props.add(\"monPolDn\", prop)\n\n prop = PropMeta(\"str\", \"name\", \"name\", 7346, PropCategory.REGULAR)\n prop.label = \"Name\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.isCreateOnly = True\n prop.isNaming = True\n prop.range = [(1, 16)]\n prop.regex = ['[a-zA-Z0-9_.:-]+']\n meta.props.add(\"name\", prop)\n\n prop = PropMeta(\"str\", \"nameAlias\", \"nameAlias\", 28417, PropCategory.REGULAR)\n prop.label = \"Name alias\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.range = [(0, 63)]\n prop.regex = ['[a-zA-Z0-9_.-]+']\n meta.props.add(\"nameAlias\", prop)\n\n prop = PropMeta(\"str\", \"priKey\", \"priKey\", 28588, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n meta.props.add(\"priKey\", prop)\n\n prop = PropMeta(\"str\", \"rn\", \"rn\", 2, PropCategory.RN)\n prop.label = \"None\"\n prop.isRn = True\n prop.isImplicit = True\n prop.isAdmin = True\n prop.isCreateOnly = True\n meta.props.add(\"rn\", prop)\n\n prop = PropMeta(\"str\", \"status\", \"status\", 3, PropCategory.STATUS)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n prop._addConstant(\"created\", \"created\", 2)\n prop._addConstant(\"deleted\", \"deleted\", 8)\n prop._addConstant(\"modified\", \"modified\", 4)\n meta.props.add(\"status\", prop)\n\n prop = PropMeta(\"str\", \"trigReSync\", \"trigReSync\", 16561, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"off\"\n prop._addConstant(\"off\", \"off\", 0)\n prop._addConstant(\"on\", \"on\", 1)\n meta.props.add(\"trigReSync\", prop)\n\n prop = PropMeta(\"str\", \"trigReSyncResp\", \"trigReSyncResp\", 18102, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isOper = True\n prop.defaultValue = 2\n prop.defaultValueStr = \"NotTrig\"\n prop._addConstant(\"NotTrig\", \"nottrig\", 2)\n prop._addConstant(\"failure\", \"failure\", 1)\n prop._addConstant(\"success\", \"success\", 0)\n meta.props.add(\"trigReSyncResp\", prop)\n\n prop = PropMeta(\"str\", \"trigSvcCnt\", \"trigSvcCnt\", 16563, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"off\"\n prop._addConstant(\"off\", \"off\", 0)\n prop._addConstant(\"on\", \"on\", 1)\n meta.props.add(\"trigSvcCnt\", prop)\n\n prop = PropMeta(\"str\", \"trigSvcCntResp\", \"trigSvcCntResp\", 18104, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isOper = True\n prop.defaultValue = 2\n prop.defaultValueStr = \"NotTrig\"\n prop._addConstant(\"NotTrig\", \"nottrig\", 2)\n prop._addConstant(\"failure\", \"failure\", 1)\n prop._addConstant(\"success\", \"success\", 0)\n meta.props.add(\"trigSvcCntResp\", prop)\n\n prop = PropMeta(\"str\", \"trigSvcHlth\", \"trigSvcHlth\", 16562, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isConfig = True\n prop.isAdmin = True\n prop.defaultValue = 0\n prop.defaultValueStr = \"off\"\n prop._addConstant(\"off\", \"off\", 0)\n prop._addConstant(\"on\", \"on\", 1)\n meta.props.add(\"trigSvcHlth\", prop)\n\n prop = PropMeta(\"str\", \"trigSvcHlthResp\", \"trigSvcHlthResp\", 18103, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isOper = True\n prop.defaultValue = 2\n prop.defaultValueStr = \"NotTrig\"\n prop._addConstant(\"NotTrig\", \"nottrig\", 2)\n prop._addConstant(\"failure\", \"failure\", 1)\n prop._addConstant(\"success\", \"success\", 0)\n meta.props.add(\"trigSvcHlthResp\", prop)\n\n prop = PropMeta(\"str\", \"uid\", \"uid\", 8, PropCategory.REGULAR)\n prop.label = \"None\"\n prop.isImplicit = True\n prop.isAdmin = True\n meta.props.add(\"uid\", prop)\n\n meta.namingProps.append(getattr(meta.props, \"name\"))\n\n def __init__(self, parentMoOrDn, name, markDirty=True, **creationProps):\n namingVals = [name]\n Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps)\n\n\n\n# End of package file\n# ##################################################\n", "sub_path": "venv/Lib/site-packages/cobra/modelimpl/vns/ldevoperinfo.py", "file_name": "ldevoperinfo.py", "file_ext": "py", "file_size_in_byte": 11679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "cobra.mit.mo.Mo", "line_number": 22, "usage_type": "name"}, {"api_name": "cobra.mit.meta.ClassMeta", "line_number": 28, "usage_type": "call"}, {"api_name": "cobra.model.category.MoCategory.REGULAR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cobra.model.category.MoCategory", "line_number": 32, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 78, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 78, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 86, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 86, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 86, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 94, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.CHILD_ACTION", "line_number": 94, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 94, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 103, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 103, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 113, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 113, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 113, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 131, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.DN", "line_number": 131, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 131, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 139, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 139, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 139, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 149, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 149, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 149, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 157, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 157, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 157, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 170, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 170, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 180, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 180, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 180, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 189, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 189, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 189, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 195, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 195, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 195, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 205, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 205, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 205, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 213, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 213, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 213, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 219, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.RN", "line_number": 219, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 219, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 227, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.STATUS", "line_number": 227, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 227, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 236, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 236, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 236, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 246, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 246, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 246, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 256, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 256, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 256, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 266, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 266, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 266, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 276, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 276, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 276, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 286, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 286, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 286, "usage_type": "name"}, {"api_name": "cobra.mit.meta.PropMeta", "line_number": 296, "usage_type": "call"}, {"api_name": "cobra.model.category.PropCategory.REGULAR", "line_number": 296, "usage_type": "attribute"}, {"api_name": "cobra.model.category.PropCategory", "line_number": 296, "usage_type": "name"}, {"api_name": "cobra.mit.mo.Mo.__init__", "line_number": 306, "usage_type": "call"}, {"api_name": "cobra.mit.mo.Mo", "line_number": 306, "usage_type": "name"}]}
+{"seq_id": "16515219", "text": "from __future__ import print_function\nimport os\nfrom biothings.utils.mongo import doc_feeder\nimport jsonpatch\nimport time\nimport os.path\nfrom utils.common import dump, timesofar, get_timestamp\nfrom utils.backend import GeneDocMongoDBBackend, GeneDocESBackend\nfrom utils.mongo import get_src_db\nfrom utils.es import ESIndexer\nfrom utils import jsondiff\n\n\ndef apply_patch(doc, patch):\n return jsonpatch.apply_patch(doc, patch)\n\n\ndef diff_doc(doc_1, doc_2, exclude_attrs=['_timestamp']):\n diff_d = {'update': {},\n 'delete': [],\n 'add': {}}\n for attr in set(doc_1) | set(doc_2):\n if exclude_attrs and attr in exclude_attrs:\n continue\n if attr in doc_1 and attr in doc_2:\n _v1 = doc_1[attr]\n _v2 = doc_2[attr]\n if _v1 != _v2:\n diff_d['update'][attr] = _v2\n elif attr in doc_1 and attr not in doc_2:\n diff_d['delete'].append(attr)\n else:\n diff_d['add'][attr] = doc_2[attr]\n if diff_d['update'] or diff_d['delete'] or diff_d['add']:\n return diff_d\n\n\ndef two_docs_iterator(b1, b2, id_list, step=10000, verbose=False):\n t0 = time.time()\n n = len(id_list)\n for i in range(0, n, step):\n t1 = time.time()\n if verbose:\n print(\"Processing %d-%d documents...\" % (i + 1, min(i + step, n)))\n _ids = id_list[i:i+step]\n iter1 = b1.mget_from_ids(_ids, asiter=True)\n iter2 = b2.mget_from_ids(_ids, asiter=True)\n for doc1, doc2 in zip(iter1, iter2):\n yield doc1, doc2\n if verbose:\n print('Done.[%.1f%%,%s]' % (i*100./n, timesofar(t1)))\n if verbose:\n print(\"=\"*20)\n print('Finished.[total time: %s]' % timesofar(t0))\n\n\ndef _diff_doc_worker(args):\n # b1_target_collection, b2_es_index, ids, _path = args\n _b1, _b2, ids, _path = args\n import sys\n if _path not in sys.path:\n sys.path.append(_path)\n import utils.diff\n reload(utils.diff)\n from utils.diff import _diff_doc_inner_worker, get_backend\n\n b1 = get_backend(*_b1)\n b2 = get_backend(*_b2)\n _updates = _diff_doc_inner_worker(b1, b2, ids)\n return _updates\n\n\ndef _diff_doc_inner_worker(b1, b2, ids, fastdiff=False):\n '''if fastdiff is True, only compare the whole doc,\n do not traverse into each attributes.\n '''\n _updates = []\n for doc1, doc2 in two_docs_iterator(b1, b2, ids):\n assert doc1['_id'] == doc2['_id'], repr((ids, len(ids)))\n if fastdiff:\n if doc1 != doc2:\n _updates.append({'_id': doc1['_id']})\n else:\n _diff = diff_doc(doc1, doc2)\n if _diff:\n _diff['_id'] = doc1['_id']\n _updates.append(_diff)\n return _updates\n\n\ndef _diff_doc_inner_worker2(b1, b2, ids, fastdiff=False):\n '''if fastdiff is True, only compare the whole doc,\n do not traverse into each attributes.\n '''\n _updates = []\n for doc1, doc2 in two_docs_iterator(b1, b2, ids):\n assert doc1['_id'] == doc2['_id'], repr((ids, len(ids)))\n if fastdiff:\n if doc1 != doc2:\n _updates.append(doc1['_id'])\n else:\n _patch = jsondiff.make(doc1, doc2)\n if _patch:\n _diff = {}\n _diff['patch'] = _patch\n _diff['_id'] = doc1['_id']\n _updates.append(_diff)\n return _updates\n\n\ndef diff_collections(b1, b2, use_parallel=True, step=10000):\n \"\"\"\n b1, b2 are one of supported backend class in databuild.backend.\n e.g.,\n b1 = GeneDocMongoDBBackend(c1)\n b2 = GeneDocMongoDBBackend(c2)\n \"\"\"\n\n id_s1 = set(b1.get_id_list())\n id_s2 = set(b2.get_id_list())\n print(\"Size of collection 1:\\t\", len(id_s1))\n print(\"Size of collection 2:\\t\", len(id_s2))\n\n id_in_1 = id_s1 - id_s2\n id_in_2 = id_s2 - id_s1\n id_common = id_s1 & id_s2\n print(\"# of docs found only in collection 1:\\t\", len(id_in_1))\n print(\"# of docs found only in collection 2:\\t\", len(id_in_2))\n print(\"# of docs found in both collections:\\t\", len(id_common))\n\n print(\"Comparing matching docs...\")\n _updates = []\n if len(id_common) > 0:\n if not use_parallel:\n _updates = _diff_doc_inner_worker2(b1, b2, list(id_common))\n else:\n from utils.parallel import run_jobs_on_ipythoncluster\n _path = os.path.split(os.path.split(os.path.abspath(__file__))[0])[0]\n id_common = list(id_common)\n # b1_target_collection = b1.target_collection.name\n # b2_es_index = b2.target_esidxer.ES_INDEX_NAME\n _b1 = (b1.target_name, b1.name)\n _b2 = (b2.target_name, b2.name)\n task_li = [(_b1, _b2, id_common[i: i + step], _path) for i in range(0, len(id_common), step)]\n job_results = run_jobs_on_ipythoncluster(_diff_doc_inner_worker2, task_li)\n _updates = []\n if job_results:\n for res in job_results:\n _updates.extend(res)\n else:\n print(\"Parallel jobs failed or were interrupted.\")\n return None\n\n print(\"Done. [{} docs changed]\".format(len(_updates)))\n\n _deletes = []\n if len(id_in_1) > 0:\n _deletes = sorted(id_in_1)\n\n _adds = []\n if len(id_in_2) > 0:\n _adds = sorted(id_in_2)\n\n changes = {'update': _updates,\n 'delete': _deletes,\n 'add': _adds,\n 'source': b2.target_collection.name,\n 'timestamp': get_timestamp()}\n return changes\n\n\ndef get_backend(target_name, bk_type, **kwargs):\n '''Return a backend instance for given target_name and backend type.\n currently support MongoDB and ES backend.\n '''\n if bk_type == 'mongodb':\n return GeneDocMongoDBBackend(target_name)\n elif bk_type == 'es':\n esi = ESIndexer(target_name, **kwargs)\n return GeneDocESBackend(esi)\n\n\ndef diff_collections2(b1, b2, result_dir, step=10000):\n '''\n b2 is new collection, b1 is old collection\n '''\n DIFFFILE_PATH = '/home/kevinxin/diff_result/'\n DATA_FOLDER = os.path.join(DIFFFILE_PATH, result_dir)\n if not os.path.exists(DATA_FOLDER):\n os.mkdir(DATA_FOLDER)\n data_new = doc_feeder(b2.target_collection, step=step, inbatch=True, fields=[])\n data_old = doc_feeder(b1.target_collection, step=step, inbatch=True, fields=[])\n cnt = 0\n cnt_update = 0\n cnt_add = 0\n cnt_delete = 0\n\n for _batch in data_new:\n cnt += 1\n id_list_new = [_doc['_id'] for _doc in _batch]\n docs_common = b1.target_collection.find({'_id': {'$in': id_list_new}}, projection=[])\n ids_common = [_doc['_id'] for _doc in docs_common]\n id_in_new = list(set(id_list_new) - set(ids_common))\n _updates = []\n if len(ids_common) > 0:\n _updates = _diff_doc_inner_worker2(b1, b2, list(ids_common), fastdiff=True)\n file_name = DATA_FOLDER + '/' + str(cnt) + '.pyobj'\n _result = {'add': id_in_new,\n 'update': _updates,\n 'delete': [],\n 'source': b2.target_collection.name,\n 'timestamp': get_timestamp()}\n if len(_updates) != 0 or len(id_in_new) != 0:\n dump(_result, file_name)\n print(\"(Updated: {}, Added: {})\".format(len(_updates), len(id_in_new)), end='')\n cnt_update += len(_updates)\n cnt_add += len(id_in_new)\n print(\"Finished calculating diff for the new collection. Total number of docs updated: {}, added: {}\".format(cnt_update, cnt_add))\n print(\"=\"*100)\n for _batch in data_old:\n cnt += 1\n id_list_old = [_doc['_id'] for _doc in _batch]\n docs_common = b2.target_collection.find({'_id': {'$in': id_list_old}}, projection=[])\n ids_common = [_doc['_id'] for _doc in docs_common]\n id_in_old = list(set(id_list_old)-set(ids_common))\n file_name = DATA_FOLDER + '/' + str(cnt) + '.pyobj'\n _result = {'delete': id_in_old,\n 'add': [],\n 'update': [],\n 'source': b2.target_collection.name,\n 'timestamp': get_timestamp()}\n if len(id_in_old) != 0:\n dump(_result, file_name)\n print(\"(Deleted: {})\".format(len(id_in_old)), end='')\n cnt_delete += len(id_in_old)\n print(\"Finished calculating diff for the old collection. Total number of docs deleted: {}\".format(cnt_delete))\n print(\"=\"*100)\n print(\"Summary: (Updated: {}, Added: {}, Deleted: {})\".format(cnt_update, cnt_add, cnt_delete))\n", "sub_path": "src/utils/diff.py", "file_name": "diff.py", "file_ext": "py", "file_size_in_byte": 8632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "jsonpatch.apply_patch", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.common.timesofar", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.common.timesofar", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "utils.common.diff", "line_number": 64, "usage_type": "attribute"}, {"api_name": "utils.common", "line_number": 64, "usage_type": "name"}, {"api_name": "utils.diff.get_backend", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.diff.get_backend", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.diff._diff_doc_inner_worker", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.jsondiff.make", "line_number": 102, "usage_type": "call"}, {"api_name": "utils.jsondiff", "line_number": 102, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.parallel.run_jobs_on_ipythoncluster", "line_number": 145, "usage_type": "call"}, {"api_name": "utils.common.get_timestamp", "line_number": 168, "usage_type": "call"}, {"api_name": "utils.backend.GeneDocMongoDBBackend", "line_number": 177, "usage_type": "call"}, {"api_name": "utils.es.ESIndexer", "line_number": 179, "usage_type": "call"}, {"api_name": "utils.backend.GeneDocESBackend", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 190, "usage_type": "call"}, {"api_name": "biothings.utils.mongo.doc_feeder", "line_number": 191, "usage_type": "call"}, {"api_name": "biothings.utils.mongo.doc_feeder", "line_number": 192, "usage_type": "call"}, {"api_name": "utils.common.get_timestamp", "line_number": 212, "usage_type": "call"}, {"api_name": "utils.common.dump", "line_number": 214, "usage_type": "call"}, {"api_name": "utils.common.get_timestamp", "line_number": 231, "usage_type": "call"}, {"api_name": "utils.common.dump", "line_number": 233, "usage_type": "call"}]}
+{"seq_id": "633369436", "text": "from twisted.trial.unittest import TestCase\n\nfrom vumi.service import Worker\nfrom vumi.tests.utils import get_stubbed_worker, FakeRedis\nfrom vumi.tests.fake_amqp import FakeAMQClient\n\n\nclass ToyWorker(Worker):\n def poke(self):\n return \"poke\"\n\n\nclass UtilsTestCase(TestCase):\n\n def test_get_stubbed_worker(self):\n worker = get_stubbed_worker(ToyWorker)\n self.assertEqual(\"poke\", worker.poke())\n self.assertTrue(isinstance(worker._amqp_client, FakeAMQClient))\n\n def test_get_stubbed_worker_with_config(self):\n options = {'key': 'value'}\n worker = get_stubbed_worker(ToyWorker, options)\n self.assertEqual({}, worker._amqp_client.vumi_options)\n self.assertEqual(options, worker.config)\n\n\nclass FakeRedisIncrTestCase(TestCase):\n\n def test_incr(self):\n self.r_server = FakeRedis()\n self.r_server.set(\"inc\", 1)\n self.assertEqual('1', self.r_server.get(\"inc\"))\n self.assertEqual('2', self.r_server.incr(\"inc\"))\n self.assertEqual('3', self.r_server.incr(\"inc\"))\n\n\n def test_incrby(self):\n self.r_server = FakeRedis()\n self.r_server.set(\"inc\", 1)\n self.assertEqual('1', self.r_server.get(\"inc\"))\n self.assertEqual('2', self.r_server.incrby(\"inc\", 1))\n self.assertEqual('4', self.r_server.incrby(\"inc\", 2))\n self.assertEqual('7', self.r_server.incrby(\"inc\", 3))\n self.assertEqual('11', self.r_server.incrby(\"inc\", 4))\n self.assertEqual('111', self.r_server.incrby(\"inc\", 100))\n\n\n", "sub_path": "vumi/tests/test_testutils.py", "file_name": "test_testutils.py", "file_ext": "py", "file_size_in_byte": 1530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "vumi.service.Worker", "line_number": 8, "usage_type": "name"}, {"api_name": "twisted.trial.unittest.TestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "vumi.tests.utils.get_stubbed_worker", "line_number": 16, "usage_type": "call"}, {"api_name": "vumi.tests.fake_amqp.FakeAMQClient", "line_number": 18, "usage_type": "argument"}, {"api_name": "vumi.tests.utils.get_stubbed_worker", "line_number": 22, "usage_type": "call"}, {"api_name": "twisted.trial.unittest.TestCase", "line_number": 27, "usage_type": "name"}, {"api_name": "vumi.tests.utils.FakeRedis", "line_number": 30, "usage_type": "call"}, {"api_name": "vumi.tests.utils.FakeRedis", "line_number": 38, "usage_type": "call"}]}
+{"seq_id": "459799795", "text": "\"\"\"Markdown to PO files extractor according to mdpo specification.\"\"\"\n\nimport os\n\nimport md4c\nimport polib\n\nfrom mdpo.command import (\n normalize_mdpo_command_aliases,\n parse_mdpo_html_command,\n)\nfrom mdpo.event import debug_events, raise_skip_event\nfrom mdpo.io import filter_paths, to_glob_or_content\nfrom mdpo.md import parse_link_references\nfrom mdpo.md4c import (\n DEFAULT_MD4C_GENERIC_PARSER_EXTENSIONS,\n READABLE_BLOCK_NAMES,\n)\nfrom mdpo.po import (\n find_entry_in_entries,\n mark_not_found_entries_as_obsoletes,\n po_escaped_string,\n remove_not_found_entries,\n save_pofile_checking_file_changed,\n)\nfrom mdpo.text import min_not_max_chars_in_a_row, parse_wrapwidth_argument\n\n\nclass Md2Po:\n __slots__ = {\n 'filepaths',\n 'content',\n 'pofile',\n 'po_filepath',\n 'msgstr',\n 'found_entries',\n 'disabled_entries',\n 'ignore_msgids',\n 'command_aliases',\n 'mark_not_found_as_obsolete',\n 'preserve_not_found',\n 'extensions',\n 'plaintext',\n 'include_codeblocks',\n 'metadata',\n 'events',\n\n 'location',\n '_current_top_level_block_number',\n '_current_top_level_block_type',\n '_current_markdown_filepath',\n\n '_current_msgid',\n '_current_tcomment',\n '_current_msgctxt',\n\n '_disable',\n '_disable_next_line',\n '_enable_next_line',\n '_include_next_codeblock',\n '_disable_next_codeblock',\n '_saved_files_changed',\n\n '_enterspan_replacer',\n '_leavespan_replacer',\n\n 'bold_start_string',\n 'bold_start_string_escaped',\n 'bold_end_string',\n 'bold_end_string_escaped',\n 'italic_start_string',\n 'italic_start_string_escaped',\n 'italic_end_string',\n 'italic_end_string_escaped',\n 'code_start_string',\n 'code_start_string_escaped',\n 'code_end_string',\n 'code_end_string_escaped',\n 'link_start_string',\n 'link_end_string',\n 'strikethrough_start_string',\n 'strikethrough_end_string',\n 'latexmath_start_string',\n 'latexmath_end_string',\n 'latexmathdisplay_start_string',\n 'latexmathdisplay_end_string',\n 'wikilink_start_string',\n 'wikilink_end_string',\n 'underline_start_string',\n 'underline_end_string',\n\n '_inside_uspan',\n '_inside_htmlblock',\n '_inside_codeblock',\n '_inside_pblock',\n '_inside_aspan',\n '_inside_liblock',\n '_inside_codespan',\n '_inside_olblock',\n '_inside_hblock',\n '_quoteblocks_deep',\n '_codespan_start_index',\n '_codespan_backticks',\n '_current_aspan_text',\n '_current_aspan_ref_target',\n '_current_wikilink_target',\n '_current_imgspan',\n '_uls_deep',\n '_link_references',\n }\n\n def __init__(self, glob_or_content, **kwargs):\n is_glob, glob_or_content = to_glob_or_content(glob_or_content)\n if is_glob:\n self.filepaths = filter_paths(\n glob_or_content,\n ignore_paths=kwargs.get('ignore', []),\n )\n else:\n self.content = glob_or_content\n\n self.pofile = None\n self.po_filepath = None\n self.msgstr = kwargs.get('msgstr', '')\n self.found_entries = []\n self.disabled_entries = []\n self._current_msgid = ''\n self._current_tcomment = None\n self._current_msgctxt = None\n\n self.ignore_msgids = kwargs.get('ignore_msgids', [])\n self.command_aliases = normalize_mdpo_command_aliases(\n kwargs.get('command_aliases', {}),\n )\n\n self.mark_not_found_as_obsolete = kwargs.get(\n 'mark_not_found_as_obsolete', True,\n )\n self.preserve_not_found = kwargs.get('preserve_not_found', True)\n\n self.location = kwargs.get('location', True)\n self._current_top_level_block_number = 0\n self._current_top_level_block_type = None\n self._current_markdown_filepath = None\n\n self.extensions = kwargs.get(\n 'extensions',\n DEFAULT_MD4C_GENERIC_PARSER_EXTENSIONS,\n )\n self.events = {}\n if 'events' in kwargs:\n for event_name, functions in kwargs['events'].items():\n self.events[event_name] = (\n [functions] if callable(functions) else functions\n )\n if kwargs.get('debug'):\n for event_name, function in debug_events('md2po').items():\n if event_name not in self.events:\n self.events[event_name] = []\n self.events[event_name].append(function)\n\n self.plaintext = kwargs.get('plaintext', False)\n\n self.include_codeblocks = kwargs.get('include_codeblocks', False)\n\n self._disable = False\n self._disable_next_line = False\n self._enable_next_line = False\n\n self._include_next_codeblock = False\n self._disable_next_codeblock = False\n\n self._saved_files_changed = ( # pragma: no cover\n False if kwargs.get('_check_saved_files_changed') else None\n )\n\n self.metadata = {}\n\n if not self.plaintext:\n self.bold_start_string = kwargs.get('bold_start_string', '**')\n self.bold_start_string_escaped = po_escaped_string(\n self.bold_start_string,\n )\n\n self.bold_end_string = kwargs.get('bold_end_string', '**')\n self.bold_end_string_escaped = po_escaped_string(\n self.bold_end_string,\n )\n\n self.italic_start_string = kwargs.get('italic_start_string', '*')\n self.italic_start_string_escaped = po_escaped_string(\n self.italic_start_string,\n )\n\n self.italic_end_string = kwargs.get('italic_end_string', '*')\n self.italic_end_string_escaped = po_escaped_string(\n self.italic_end_string,\n )\n\n # codespans are built by a indetermined number of 'x' characters\n # so we take only the first\n self.code_start_string = kwargs.get('code_start_string', '`')[0]\n self.code_start_string_escaped = po_escaped_string(\n self.code_start_string,\n )\n\n self.code_end_string = kwargs.get('code_end_string', '`')[0]\n self.code_end_string_escaped = po_escaped_string(\n self.code_end_string,\n )\n\n _include_xheaders = kwargs.get('xheaders', False)\n\n if _include_xheaders:\n self.metadata.update({\n 'x-mdpo-bold-start': self.bold_start_string,\n 'x-mdpo-bold-end': self.bold_end_string,\n 'x-mdpo-italic-start': self.italic_start_string,\n 'x-mdpo-italic-end': self.italic_end_string,\n 'x-mdpo-code-start': self.code_start_string,\n 'x-mdpo-code-end': self.code_end_string,\n })\n\n self._enterspan_replacer = {\n md4c.SpanType.STRONG.value: self.bold_start_string,\n md4c.SpanType.EM.value: self.italic_start_string,\n md4c.SpanType.CODE.value: self.code_start_string,\n }\n\n self._leavespan_replacer = {\n md4c.SpanType.STRONG.value: self.bold_end_string,\n md4c.SpanType.EM.value: self.italic_end_string,\n md4c.SpanType.CODE.value: self.code_end_string,\n }\n\n if 'strikethrough' in self.extensions:\n self.strikethrough_start_string = kwargs.get(\n 'strikethrough_start_string', '~~',\n )\n self._enterspan_replacer[md4c.SpanType.DEL.value] = \\\n self.strikethrough_start_string\n\n self.strikethrough_end_string = kwargs.get(\n 'strikethrough_end_string', '~~',\n )\n self._leavespan_replacer[md4c.SpanType.DEL.value] = \\\n self.strikethrough_end_string\n\n if _include_xheaders:\n self.metadata.update({\n 'x-mdpo-strikethrough-start':\n self.strikethrough_start_string,\n 'x-mdpo-strikethrough-end':\n self.strikethrough_end_string,\n })\n\n if 'latex_math_spans' in self.extensions:\n self.latexmath_start_string = kwargs.get(\n 'latexmath_start_string', '$',\n )\n self._enterspan_replacer[md4c.SpanType.LATEXMATH.value] = \\\n self.latexmath_start_string\n\n self.latexmath_end_string = kwargs.get(\n 'latexmath_end_string', '$',\n )\n self._leavespan_replacer[md4c.SpanType.LATEXMATH.value] = \\\n self.latexmath_end_string\n\n self.latexmathdisplay_start_string = kwargs.get(\n 'latexmathdisplay_start_string', '$$',\n )\n self._enterspan_replacer[\n md4c.SpanType.LATEXMATH_DISPLAY.value\n ] = self.latexmathdisplay_start_string\n\n self.latexmathdisplay_end_string = kwargs.get(\n 'latexmathdisplay_end_string', '$$',\n )\n self._leavespan_replacer[\n md4c.SpanType.LATEXMATH_DISPLAY.value\n ] = self.latexmathdisplay_end_string\n\n if _include_xheaders:\n self.metadata.update({\n 'x-mdpo-latexmath-start': self.latexmath_start_string,\n 'x-mdpo-latexmath-end': self.latexmath_end_string,\n 'x-mdpo-latexmathdisplay-start':\n self.latexmathdisplay_start_string,\n 'x-mdpo-latexmathdisplay-end':\n self.latexmathdisplay_end_string,\n })\n\n if 'wikilinks' in self.extensions:\n self.wikilink_start_string = kwargs.get(\n 'wikilink_start_string', '[[',\n )\n self.wikilink_end_string = kwargs.get(\n 'wikilink_end_string', ']]',\n )\n\n self._enterspan_replacer[md4c.SpanType.WIKILINK.value] = \\\n self.wikilink_start_string\n self._leavespan_replacer[md4c.SpanType.WIKILINK.value] = \\\n self.wikilink_end_string\n\n if _include_xheaders:\n self.metadata.update({\n 'x-mdpo-wikilink-start': self.wikilink_start_string,\n 'x-mdpo-wikilink-end': self.wikilink_end_string,\n })\n\n if 'underline' in self.extensions:\n # underline text is standarized with double '_'\n self.underline_start_string = kwargs.get(\n 'underline_start_string', '__',\n )\n self._enterspan_replacer[md4c.SpanType.U.value] = \\\n self.underline_start_string\n\n self.underline_end_string = kwargs.get(\n 'underline_end_string', '__',\n )\n self._leavespan_replacer[md4c.SpanType.U.value] = \\\n self.underline_end_string\n\n if _include_xheaders:\n self.metadata.update({\n 'x-mdpo-underline-start': self.underline_start_string,\n 'x-mdpo-underline-end': self.underline_end_string,\n })\n\n # optimization to skip checking for\n # ``self.md4c_generic_parser_kwargs.get('underline')``\n # inside spans\n self._inside_uspan = False\n\n self._inside_htmlblock = False\n self._inside_codeblock = False\n self._inside_pblock = False\n self._inside_liblock = False\n self._inside_hblock = False\n self._inside_olblock = False\n self._inside_codespan = False\n\n self._quoteblocks_deep = 0\n self._uls_deep = 0\n\n self._codespan_start_index = None\n self._codespan_backticks = None\n\n self._inside_aspan = False\n self._current_aspan_text = ''\n # indicates the target of the current link, which is referenced and\n # extracted without using MD4C, so we can preserve it as referenced\n self._current_aspan_ref_target = None\n\n self._link_references = None\n self._current_wikilink_target = None\n self._current_imgspan = {}\n\n if 'metadata' in kwargs:\n self.metadata.update(kwargs['metadata'])\n\n def _save_msgid(\n self,\n msgid,\n msgstr='',\n tcomment=None,\n msgctxt=None,\n fuzzy=False,\n ):\n if msgid in self.ignore_msgids:\n return\n entry = polib.POEntry(\n msgid=msgid,\n msgstr=msgstr,\n comment=tcomment,\n msgctxt=msgctxt,\n flags=[] if not fuzzy else ['fuzzy'],\n )\n\n occurrence = None\n if self.location and self._current_markdown_filepath:\n # here could happen a KeyError if someone has aborted an ,\n # enter event, in which case we do not have access to the\n # block type because ``self._current_top_level_block_type is None``\n current_block_name = READABLE_BLOCK_NAMES[\n self._current_top_level_block_type\n ]\n # TODO: when all tests added and the location feature is fully\n # tested, we could ignore the KeyError event keeping an\n # incomplete occurrence place string, and raising a warning\n # if the user has configured an `enter_block` or\n # `leave_block` event remembering that\n # `_current_top_level_block_number` and\n # `_current_top_level_block_type` properties must be handled\n # accordingly\n\n occurrence = (\n self._current_markdown_filepath,\n (\n f'block {self._current_top_level_block_number}'\n f' ({current_block_name})'\n ),\n )\n\n if occurrence not in entry.occurrences:\n entry.occurrences.append(occurrence)\n\n _equal_entry = find_entry_in_entries(\n entry,\n self.pofile,\n compare_obsolete=False,\n compare_msgstr=False,\n compare_occurrences=False,\n )\n\n if _equal_entry and _equal_entry.msgstr:\n entry.msgstr = _equal_entry.msgstr\n if _equal_entry.fuzzy and not entry.fuzzy:\n entry.flags.append('fuzzy')\n if entry not in self.pofile:\n self.pofile.append(entry)\n self.found_entries.append(entry)\n\n def _save_current_msgid(self, msgstr='', fuzzy=False):\n # raise 'msgid' event\n if raise_skip_event(\n self.events,\n 'msgid',\n self,\n self._current_msgid,\n msgstr,\n self._current_msgctxt,\n self._current_tcomment,\n ['fuzzy'] if fuzzy else [],\n ):\n return\n\n if self._current_msgid:\n if (not self._disable_next_line and not self._disable) or \\\n self._enable_next_line:\n self._save_msgid(\n self._current_msgid,\n msgstr=msgstr or self.msgstr,\n msgctxt=self._current_msgctxt,\n tcomment=self._current_tcomment,\n fuzzy=fuzzy,\n )\n else:\n self.disabled_entries.append(\n polib.POEntry(\n msgid=self._current_msgid,\n msgstr=msgstr or self.msgstr,\n msgctxt=self._current_msgctxt,\n tcomment=self._current_tcomment,\n flags=['fuzzy'] if fuzzy else [],\n ),\n )\n self._disable_next_line = False\n self._enable_next_line = False\n self._current_msgid = ''\n self._current_tcomment = None\n self._current_msgctxt = None\n\n def command(self, mdpo_command, comment, original_command):\n # raise 'command' event\n if raise_skip_event(\n self.events,\n 'command',\n self,\n mdpo_command,\n comment,\n original_command,\n ):\n return\n\n if mdpo_command == 'mdpo-disable-next-line':\n self._disable_next_line = True\n elif mdpo_command == 'mdpo-disable':\n self._disable = True\n elif mdpo_command == 'mdpo-enable':\n self._disable = False\n elif mdpo_command == 'mdpo-enable-next-line':\n self._enable_next_line = True\n elif mdpo_command == 'mdpo-include-codeblock':\n self._include_next_codeblock = True\n elif mdpo_command == 'mdpo-disable-codeblock':\n self._disable_next_codeblock = True\n elif mdpo_command == 'mdpo-disable-codeblocks':\n self.include_codeblocks = False\n elif mdpo_command == 'mdpo-include-codeblocks':\n self.include_codeblocks = True\n elif mdpo_command == 'mdpo-translator':\n if not comment:\n raise ValueError(\n 'You need to specify a string for the'\n ' extracted comment with the command'\n f' \\'{original_command}\\'.',\n )\n self._current_tcomment = comment.rstrip()\n elif mdpo_command == 'mdpo-context':\n if not comment:\n raise ValueError(\n 'You need to specify a string for the'\n f' context with the command \\'{original_command}\\'.',\n )\n self._current_msgctxt = comment.rstrip()\n elif mdpo_command == 'mdpo-include':\n if not comment:\n raise ValueError(\n 'You need to specify a message for the'\n ' comment to include with the command'\n f' \\'{original_command}\\'.',\n )\n self._current_msgid = comment.rstrip()\n self._save_current_msgid()\n\n def _process_command(self, text):\n original_command, comment = parse_mdpo_html_command(text)\n if original_command is None:\n return\n\n try:\n command = self.command_aliases[original_command]\n except KeyError: # not custom command\n command = original_command\n\n # process solved command\n self.command(command, comment, original_command)\n\n def enter_block(self, block, details):\n # raise 'enter_block' event\n if raise_skip_event(self.events, 'enter_block', self, block, details):\n return\n\n if block is md4c.BlockType.P:\n self._inside_pblock = True\n if not any([\n self._inside_hblock,\n self._uls_deep,\n self._quoteblocks_deep,\n self._inside_olblock,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.P.value\n elif block is md4c.BlockType.CODE:\n self._inside_codeblock = True\n if not any([\n self._quoteblocks_deep,\n self._uls_deep,\n self._inside_olblock,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.CODE.value\n elif block is md4c.BlockType.LI:\n self._inside_liblock = True\n elif block is md4c.BlockType.UL:\n self._uls_deep += 1\n if self._uls_deep > 1 or self._inside_olblock:\n # changing UL deeep\n self._save_current_msgid()\n elif not any([\n self._quoteblocks_deep,\n self._inside_olblock,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.UL.value\n elif block is md4c.BlockType.H:\n self._inside_hblock = True\n if not any([\n self._quoteblocks_deep,\n self._uls_deep,\n self._inside_olblock,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.H.value\n elif block is md4c.BlockType.QUOTE:\n self._quoteblocks_deep += 1\n if self._inside_liblock:\n self._save_current_msgid()\n if self._quoteblocks_deep == 1:\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.QUOTE.value\n elif block is md4c.BlockType.OL:\n if not any([\n self._quoteblocks_deep,\n self._uls_deep,\n self._inside_olblock,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.OL.value\n\n if self._inside_olblock or self._uls_deep:\n self._save_current_msgid()\n self._inside_olblock = True\n elif block is md4c.BlockType.HTML:\n self._inside_htmlblock = True\n if not any([\n self._quoteblocks_deep,\n self._inside_olblock,\n self._uls_deep,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.HTML.value\n elif block is md4c.BlockType.TABLE:\n if not any([\n self._quoteblocks_deep,\n self._inside_olblock,\n self._uls_deep,\n ]):\n self._current_top_level_block_number += 1\n self._current_top_level_block_type = md4c.BlockType.TABLE.value\n\n def leave_block(self, block, details):\n # raise 'leave_block' event\n if raise_skip_event(self.events, 'leave_block', self, block, details):\n return\n\n if block is md4c.BlockType.CODE:\n self._inside_codeblock = False\n if not self._disable_next_codeblock:\n if self.include_codeblocks or self._include_next_codeblock:\n self._save_current_msgid()\n self._include_next_codeblock = False\n self._disable_next_codeblock = False\n elif block is md4c.BlockType.HTML:\n self._inside_htmlblock = False\n else:\n if block is md4c.BlockType.P:\n self._inside_pblock = False\n elif block is md4c.BlockType.LI:\n self._inside_liblock = True\n elif block is md4c.BlockType.UL:\n self._uls_deep -= 1\n elif block is md4c.BlockType.H:\n self._inside_hblock = False\n elif block is md4c.BlockType.QUOTE:\n self._quoteblocks_deep -= 1\n elif block is md4c.BlockType.OL:\n self._inside_olblock = False\n self._save_current_msgid()\n\n def enter_span(self, span, details):\n # raise 'enter_span' event\n if raise_skip_event(self.events, 'enter_span', self, span, details):\n return\n\n if not self.plaintext:\n # underline spans for double '_' character enters two times\n if not self._inside_uspan:\n if self._inside_aspan: # span inside link text\n try:\n self._current_aspan_text += self._enterspan_replacer[\n span.value\n ]\n except KeyError:\n pass\n else:\n try:\n self._current_msgid += (\n self._enterspan_replacer[span.value]\n )\n except KeyError:\n pass\n\n if span is md4c.SpanType.A:\n # here resides the logic of discover if the current link\n # is referenced\n if self._link_references is None:\n self._link_references = parse_link_references(self.content)\n\n self._inside_aspan = True\n\n current_aspan_href = details['href'][0][1]\n self._current_aspan_ref_target = None\n\n if details['title']:\n current_aspan_title = details['title'][0][1]\n for target, href, title in self._link_references:\n if (\n href == current_aspan_href and\n title == current_aspan_title\n ):\n self._current_aspan_ref_target = target\n break\n else:\n for target, href, title in self._link_references:\n if href == current_aspan_href:\n self._current_aspan_ref_target = target\n break\n\n elif span is md4c.SpanType.CODE:\n self._inside_codespan = True\n\n # entering a code span, literal backticks encountered inside\n # will be escaped\n #\n # save the index char of the opening backtick\n self._codespan_start_index = len(self._current_msgid) - 1\n elif span is md4c.SpanType.IMG:\n self._current_imgspan['src'] = details['src'][0][1]\n self._current_imgspan['title'] = '' if not details['title'] \\\n else details['title'][0][1]\n self._current_imgspan['text'] = ''\n elif span is md4c.SpanType.U:\n self._inside_uspan = True\n elif span is md4c.SpanType.WIKILINK:\n self._current_wikilink_target = details['target'][0][1]\n else:\n if (span is md4c.SpanType.IMG or span is md4c.SpanType.A) and \\\n details['title']:\n self._save_msgid(details['title'][0][1])\n\n def leave_span(self, span, details):\n # raise 'leave_span' event\n if raise_skip_event(self.events, 'leave_span', self, span, details):\n return\n\n if not self.plaintext:\n if not self._inside_uspan:\n if span is md4c.SpanType.WIKILINK:\n self._current_msgid += self._current_wikilink_target\n self._current_wikilink_target = None\n if self._inside_aspan: # span inside link text\n try:\n self._current_aspan_text += self._leavespan_replacer[\n span.value\n ]\n except KeyError:\n pass\n else:\n try:\n self._current_msgid += (\n self._leavespan_replacer[span.value]\n )\n except KeyError:\n pass\n\n if span is md4c.SpanType.A:\n if self._current_aspan_ref_target: # referenced link\n self._current_msgid += (\n f'[{self._current_aspan_text}]'\n f'[{self._current_aspan_ref_target}]'\n )\n self._current_aspan_ref_target = None\n else:\n if self._current_aspan_text == details['href'][0][1]:\n # autolink vs link clash (see implementation notes)\n self._current_msgid += f'<{self._current_aspan_text}'\n if details['title']:\n self._current_msgid += ' \"{}\"'.format(\n details['title'][0][1],\n )\n self._current_msgid += '>'\n else:\n self._current_msgid += '[{}]({}{})'.format(\n self._current_aspan_text,\n details['href'][0][1],\n '' if not details['title'] else ' \"{}\"'.format(\n details['title'][0][1],\n ),\n )\n self._inside_aspan = False\n self._current_aspan_text = ''\n elif span is md4c.SpanType.CODE:\n self._inside_codespan = False\n self._codespan_start_index = None\n\n # add backticks at the end for escape internal backticks\n if self._inside_aspan:\n self._current_aspan_text += (\n self._codespan_backticks * self.code_end_string\n )\n else:\n self._current_msgid += (\n self._codespan_backticks * self.code_end_string\n )\n self._codespan_backticks = None\n elif span is md4c.SpanType.IMG:\n self._current_msgid += '\n if self._current_imgspan['title']:\n self._current_msgid += ' \"%s\"' % (\n self._current_imgspan['title']\n )\n self._current_msgid += ')'\n self._current_imgspan = {}\n elif span is md4c.SpanType.U:\n self._inside_uspan = False\n\n def text(self, block, text):\n # raise 'text' event\n if raise_skip_event(self.events, 'text', self, block, text):\n return\n\n if not self._inside_htmlblock:\n if not self._inside_codeblock:\n if any([ # softbreaks\n self._inside_liblock, self._inside_aspan,\n ]) and text == '\\n':\n text = ' '\n if not self.plaintext:\n if self._current_imgspan:\n self._current_imgspan['text'] = text\n return\n elif self._inside_codespan:\n # fix backticks for codespan start and end to escape\n # internal backticks\n self._codespan_backticks = min_not_max_chars_in_a_row(\n self.code_start_string,\n text,\n ) - 1\n self._current_msgid = '{}{}{}'.format(\n self._current_msgid[:self._codespan_start_index],\n self._codespan_backticks * self.code_start_string,\n self._current_msgid[self._codespan_start_index:],\n )\n if self._inside_aspan:\n self._current_aspan_text += text\n return\n elif self._inside_aspan:\n self._current_aspan_text += text\n return\n elif text == self.italic_start_string:\n text = self.italic_start_string_escaped\n elif text == self.code_start_string:\n text = self.code_start_string_escaped\n elif text == self.italic_end_string: # pragma: no cover\n text = self.italic_end_string_escaped\n elif text == self.code_end_string: # pragma: no cover\n text = self.code_end_string_escaped\n if self._inside_pblock:\n text = text.replace('\\n', ' ')\n if self._current_wikilink_target:\n if text != self._current_wikilink_target:\n # not self-referenced wikilink\n self._current_wikilink_target = '{}|{}'.format(\n self._current_wikilink_target,\n text,\n )\n return\n self._current_msgid += text\n else:\n if not self._disable_next_codeblock:\n if self.include_codeblocks or self._include_next_codeblock:\n self._current_msgid += text\n else:\n self._process_command(text)\n\n def _dump_link_references(self):\n if self._link_references:\n self._disable_next_line = False\n self._disable = False\n\n # 'link_reference' event\n pre_events = self.events.get('link_reference')\n\n for target, href, title in self._link_references:\n if pre_events:\n skip = False\n for event in pre_events:\n if event(self, target, href, title) is False:\n skip = True\n if skip:\n continue\n\n self._current_msgid = '[{}]:{}{}'.format(\n target,\n f' {href}' if href else '',\n f' \"{title}\"' if title else '',\n )\n self._save_current_msgid(\n msgstr=self._current_msgid,\n fuzzy=True,\n )\n\n def extract(\n self,\n po_filepath=None,\n save=False,\n mo_filepath=None,\n po_encoding=None,\n md_encoding='utf-8',\n wrapwidth=78,\n ):\n if not po_filepath:\n self.po_filepath = ''\n\n if save:\n if os.environ.get('_MDPO_RUNNING') == 'true':\n save_arg = '-s/--save'\n po_filepath_arg = '-po/--po-filepath'\n else:\n save_arg, po_filepath_arg = ('save', 'po_filepath')\n raise ValueError(\n f\"The argument '{save_arg}' does not make sense\"\n f\" without passing the argument '{po_filepath_arg}'.\",\n )\n else:\n self.po_filepath = po_filepath\n if not os.path.exists(po_filepath):\n self.po_filepath = ''\n\n pofile_kwargs = (\n dict(autodetect_encoding=False, encoding=po_encoding)\n if po_encoding else {}\n )\n self.pofile = polib.pofile(\n self.po_filepath,\n wrapwidth=parse_wrapwidth_argument(wrapwidth),\n **pofile_kwargs,\n )\n\n parser = md4c.GenericParser(\n 0,\n **{ext: True for ext in self.extensions},\n )\n\n def _parse(content):\n parser.parse(\n content,\n self.enter_block,\n self.leave_block,\n self.enter_span,\n self.leave_span,\n self.text,\n )\n self._dump_link_references()\n\n if hasattr(self, 'content'):\n _parse(self.content)\n else:\n for filepath in self.filepaths:\n with open(filepath, encoding=md_encoding) as f:\n self.content = f.read()\n self._current_markdown_filepath = filepath\n _parse(self.content)\n\n self._disable_next_line = False\n self._disable = False\n self._enable_next_line = False\n self._link_references = None\n\n if self.mark_not_found_as_obsolete:\n mark_not_found_entries_as_obsoletes(\n self.pofile,\n self.found_entries,\n )\n elif not self.preserve_not_found:\n remove_not_found_entries(\n self.pofile,\n self.found_entries,\n )\n\n if self.metadata:\n self.pofile.metadata.update(self.metadata)\n\n if save and po_filepath:\n if self._saved_files_changed is False: # pragma: no cover\n self._saved_files_changed = save_pofile_checking_file_changed(\n self.pofile,\n po_filepath,\n )\n else:\n self.pofile.save(fpath=po_filepath)\n if mo_filepath:\n self.pofile.save_as_mofile(mo_filepath)\n return self.pofile\n\n\ndef markdown_to_pofile(\n glob_or_content,\n ignore=[],\n msgstr='',\n po_filepath=None,\n save=False,\n mo_filepath=None,\n plaintext=False,\n wrapwidth=78,\n mark_not_found_as_obsolete=True,\n preserve_not_found=True,\n location=True,\n extensions=DEFAULT_MD4C_GENERIC_PARSER_EXTENSIONS,\n po_encoding=None,\n md_encoding='utf-8',\n xheaders=False,\n include_codeblocks=False,\n ignore_msgids=[],\n command_aliases={},\n metadata={},\n events={},\n debug=False,\n **kwargs,\n):\n \"\"\"Extracts all the msgids from a string of Markdown content or a group of\n files.\n\n Args:\n glob_or_content (str): Glob path to Markdown files or a string\n with valid Markdown content.\n ignore (list): Paths of files to ignore. Useful when a glob does not\n fit your requirements indicating the files to extract content.\n Also, filename or a dirname can be defined without indicate the\n full path.\n msgstr (str): Default message string for extracted msgids.\n po_filepath (str): File that will be used as :class:`polib.POFile`\n instance where to dump the new msgids and that will be used\n as source checking not found strings that will be marked as\n obsolete if is the case (see ``save`` and\n ``mark_not_found_as_obsolete`` optional parameters).\n save (bool): Save the new content to the pofile indicated in the\n parameter ``po_filepath``. If is enabled and ``po_filepath`` is\n ``None`` a ``ValueError`` will be raised.\n mo_filepath (str): The resulting pofile will be compiled to a mofile\n and saved in the path specified at this parameter.\n plaintext (bool): If you pass ``True`` to this parameter (as default)\n the content will be extracted as is, without markup characters\n included.\n Passing ``plaintext`` as ``False``, extracted msgids\n will contain some markup characters used to appoint the\n location of ```inline code```, ``**bold text**``,\n ``*italic text*`` and ```[links]```, that might be useful\n for you. It depends on the use you are going to give to\n this library activate this mode (``plaintext=False``) or not.\n wrapwidth (int): Wrap width for po file indicated at ``po_filepath``\n parameter. Only useful when the ``-w`` option was passed\n to xgettext.\n mark_not_found_as_obsolete (bool): The strings extracted from markdown\n that will not be found inside the provided pofile will be marked\n as obsolete.\n preserve_not_found (bool): The strings extracted from markdown that\n will not be found inside the provided pofile wouldn't be removed.\n Only has effect if ``mark_not_found_as_obsolete`` is ``False``.\n location (bool): Store references of top-level blocks in which are\n found the messages in PO file `#: reference` comments.\n extensions (list): md4c extensions used to parse markdown content,\n formatted as a list of 'pymd4c' keyword arguments. You can see all\n available at `pymd4c repository <https://github.com/dominickpastore\n /pymd4c#parser-option-flags>`_.\n po_encoding (str): Resulting pofile encoding.\n md_encoding (str): Markdown content encoding.\n xheaders (bool): Indicates if the resulting pofile will have mdpo\n x-headers included. These only can be included if the parameter\n ``plaintext`` is ``False``.\n include_codeblocks (bool): Include all code blocks found inside pofile\n result. This is useful if you want to translate all your blocks\n of code. Equivalent to append ``<!-- mdpo-include-codeblock -->``\n command before each code block.\n ignore_msgids (list): List of msgids ot ignore from being extracted.\n command_aliases (dict): Mapping of aliases to use custom mdpo command\n names in comments. The ``mdpo-`` prefix in command names resolution\n is optional. For example, if you want to use ``<!-- mdpo-on -->``\n instead of ``<!-- mdpo-enable -->``, you can pass the dictionaries\n ``{\"mdpo-on\": \"mdpo-enable\"}`` or ``{\"mdpo-on\": \"enable\"}`` to this\n parameter.\n metadata (dict): Metadata to include in the produced PO file. If the\n file contains previous metadata fields, these will be updated\n preserving the values of the already defined.\n events (dict): Preprocessing events executed during the parsing\n process. You can use these to customize the extraction process.\n Takes functions or list of functions as values. If one of these\n functions returns ``False``, that part of the parsing is skipped\n by md2po (usually a MD4C event). The available events are:\n\n * ``enter_block(self, block, details)``: Executed when the parsing\n a Markdown block starts.\n * ``leave_block(self, block, details)``: Executed when the parsing\n a Markdown block ends.\n * ``enter_span(self, span, details)``: Executed when the parsing of\n a Markdown span starts.\n * ``leave_span(self, span, details)``: Executed when the parsing of\n a Markdown span ends.\n * ``text(self, block, text)``: Executed when the parsing of text\n starts.\n * ``command(self, mdpo_command, comment, original command)``:\n Executed when a mdpo HTML command is found.\n * ``msgid(self, msgid, msgstr, msgctxt, tcomment, flags)``:\n Executed when a msgid is going to be stored.\n * ``link_reference(self, target, href, title)``: Executed when a\n link reference is going to be stored.\n\n All ``self`` arguments are an instance of Md2Po parser. You can\n take advanced control of the parsing process manipulating the\n state of the parser. For example, if you want to skip a certain\n msgid to be included, you can do:\n\n .. code-block:: python\n\n def msgid_event(self, msgid, *args):\n if msgid == 'foo':\n self._disable_next_line = True\n debug (bool): Add events displaying all parsed elements in the\n extraction process.\n\n Examples:\n >>> content = 'Some text with `inline code`'\n >>> entries = markdown_to_pofile(content, plaintext=True)\n >>> {e.msgid: e.msgstr for e in entries}\n {'Some text with inline code': ''}\n >>> entries = markdown_to_pofile(content)\n >>> {e.msgid: e.msgstr for e in entries}\n {'Some text with `inline code`': ''}\n >>> entries = markdown_to_pofile(content, msgstr='Default message')\n >>> {e.msgid: e.msgstr for e in entries}\n {'Some text with `inline code`': 'Default message'}\n\n Returns:\n :class:`polib.POFile` Pofile instance with new msgids included.\n\n Raises\n ValueError: when ``po_filepath`` is ``None`` and ``save`` is ``True``.\n \"\"\"\n return Md2Po(\n glob_or_content,\n ignore=ignore,\n msgstr=msgstr,\n plaintext=plaintext,\n mark_not_found_as_obsolete=mark_not_found_as_obsolete,\n preserve_not_found=preserve_not_found,\n location=location,\n extensions=extensions,\n xheaders=xheaders,\n include_codeblocks=include_codeblocks,\n ignore_msgids=ignore_msgids,\n command_aliases=command_aliases,\n metadata=metadata,\n events=events,\n debug=debug,\n **kwargs,\n ).extract(\n po_filepath=po_filepath,\n save=save,\n mo_filepath=mo_filepath,\n po_encoding=po_encoding,\n md_encoding=md_encoding,\n wrapwidth=wrapwidth,\n )\n", "sub_path": "mdpo/md2po/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 44813, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "mdpo.io.to_glob_or_content", "line_number": 113, "usage_type": "call"}, {"api_name": "mdpo.io.filter_paths", "line_number": 115, "usage_type": "call"}, {"api_name": "mdpo.command.normalize_mdpo_command_aliases", "line_number": 132, "usage_type": "call"}, {"api_name": "mdpo.md4c.DEFAULT_MD4C_GENERIC_PARSER_EXTENSIONS", "line_number": 148, "usage_type": "argument"}, {"api_name": "mdpo.event.debug_events", "line_number": 157, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 181, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 186, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 191, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 196, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 203, "usage_type": "call"}, {"api_name": "mdpo.po.po_escaped_string", "line_number": 208, "usage_type": "call"}, {"api_name": "md4c.SpanType", "line_number": 225, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 226, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 227, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 231, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 232, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 233, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 240, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 246, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 261, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 267, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 274, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 281, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 302, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 304, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 318, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 324, "usage_type": "attribute"}, {"api_name": "polib.POEntry", "line_number": 375, "usage_type": "call"}, {"api_name": "mdpo.md4c.READABLE_BLOCK_NAMES", "line_number": 388, "usage_type": "name"}, {"api_name": "mdpo.po.find_entry_in_entries", "line_number": 411, "usage_type": "call"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 429, "usage_type": "call"}, {"api_name": "polib.POEntry", "line_number": 453, "usage_type": "call"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 469, "usage_type": "call"}, {"api_name": "mdpo.command.parse_mdpo_html_command", "line_number": 521, "usage_type": "call"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 535, "usage_type": "call"}, {"api_name": "md4c.BlockType", "line_number": 538, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 547, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 548, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 556, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 557, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 559, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 569, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 570, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 578, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 579, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 585, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 586, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 593, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 598, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 606, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 607, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 614, "usage_type": "attribute"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 618, "usage_type": "call"}, {"api_name": "md4c.BlockType", "line_number": 621, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 628, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 631, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 633, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 635, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 637, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 639, "usage_type": "attribute"}, {"api_name": "md4c.BlockType", "line_number": 641, "usage_type": "attribute"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 647, "usage_type": "call"}, {"api_name": "md4c.SpanType", "line_number": 668, "usage_type": "attribute"}, {"api_name": "mdpo.md.parse_link_references", "line_number": 672, "usage_type": "call"}, {"api_name": "md4c.SpanType", "line_number": 694, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 702, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 707, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 709, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 712, "usage_type": "attribute"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 718, "usage_type": "call"}, {"api_name": "md4c.SpanType", "line_number": 723, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 741, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 767, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 781, "usage_type": "attribute"}, {"api_name": "md4c.SpanType", "line_number": 792, "usage_type": "attribute"}, {"api_name": "mdpo.event.raise_skip_event", "line_number": 797, "usage_type": "call"}, {"api_name": "mdpo.text.min_not_max_chars_in_a_row", "line_number": 813, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 894, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 894, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 905, "usage_type": "call"}, {"api_name": "os.path", "line_number": 905, "usage_type": "attribute"}, {"api_name": "polib.pofile", "line_number": 912, "usage_type": "call"}, {"api_name": "mdpo.text.parse_wrapwidth_argument", "line_number": 914, "usage_type": "call"}, {"api_name": "md4c.GenericParser", "line_number": 918, "usage_type": "call"}, {"api_name": "mdpo.po.mark_not_found_entries_as_obsoletes", "line_number": 949, "usage_type": "call"}, {"api_name": "mdpo.po.remove_not_found_entries", "line_number": 954, "usage_type": "call"}, {"api_name": "mdpo.po.save_pofile_checking_file_changed", "line_number": 964, "usage_type": "call"}, {"api_name": "mdpo.md4c.DEFAULT_MD4C_GENERIC_PARSER_EXTENSIONS", "line_number": 987, "usage_type": "name"}]}
+{"seq_id": "133833696", "text": "import pandas as pd\nimport numpy as np\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader, Dataset\n\nimport pytorch_lightning as pl\n\nfrom TimeSeriesLearningUtils import CosineWarmupScheduler\n\nMAX_EPOCHS = 80\n\nclass LSTM_based_classification_model(pl.LightningModule):\n def __init__(self,\n train_dataset,\n val_dataset,\n test_dataset,\n calculate_loss_weights,\n currencies,\n num_classes,\n window_size,\n input_size,\n batch_size,\n lstm_hidden_sizes,\n bidirectional,\n last_layer_fsz,\n dropout_ratio = 0.5,\n warmup_epoch = 5,\n learning_rate = 1e-3,\n weight_decay = 1e-2,\n # scheduler_step = 10,\n # scheduler_gamma = 0.1,\n ):\n \n super().__init__()\n self.num_classes = num_classes\n self.currencies = currencies\n self.num_tasks = len(currencies)\n self.window_size = window_size\n self.input_size = input_size\n self.batch_size = batch_size\n \n self.lstm_hidden_sizes = lstm_hidden_sizes\n self.bidirectional = bidirectional \n \n self.dropout_ratio = dropout_ratio\n self.last_layer_fsz = last_layer_fsz\n \n if calculate_loss_weights:\n loss_weights = []\n for i in range(self.num_tasks):\n train_labels = [int(train_dataset[n][self.currencies[i] +\"_label\"] )for n in range(train_dataset.__len__())]\n samples_size = pd.DataFrame({\"label\": train_labels}).groupby(\"label\").size().to_numpy()\n loss_weights.append((1 / samples_size) * sum(samples_size)/2)\n self.weights = loss_weights\n else:\n self.weights = None\n self.lstm_1 = nn.LSTM(input_size = self.input_size, \n num_layers=1, \n batch_first=True, \n hidden_size = self.lstm_hidden_sizes[0], \n bidirectional = bidirectional)\n self.batch_norm1 = nn.BatchNorm2d(num_features=self.lstm_hidden_sizes[0])\n \n if len(self.lstm_hidden_sizes) > 1:\n self.lstm_2 = nn.LSTM(input_size = self.lstm_hidden_sizes[0], \n num_layers=1, \n batch_first=True, \n hidden_size = self.lstm_hidden_sizes[1], \n bidirectional = bidirectional)\n self.batch_norm2 = nn.BatchNorm2d(num_features=self.lstm_hidden_sizes[1])\n\n self.lstm_3 = nn.LSTM(input_size = self.lstm_hidden_sizes[1], \n num_layers=1, \n batch_first=True, \n hidden_size = self.lstm_hidden_sizes[2], \n bidirectional = bidirectional)\n self.batch_norm3 = nn.BatchNorm2d(num_features=self.lstm_hidden_sizes[2])\n \n self.dropout = nn.Dropout(self.dropout_ratio)\n self.lstm_blocks = nn.ModuleList()\n \n for i in range(self.n_lstm_layers):\n\n if i == 0:\n input_size = self.input_size \n else:\n input_size = self.lstm_hidden_sizes[i-1]*2 if self.bidirectional else self.lstm_hidden_sizes[i-1] \n \n lstm_layer = nn.LSTM(input_size = input_size, \n num_layers=1, \n batch_first=True, \n hidden_size = self.lstm_hidden_sizes[i], \n bidirectional = self.bidirectional)\n \n n_feature = self.lstm_hidden_sizes[i]*2 if self.bidirectional else self.lstm_hidden_sizes[i] \n batch_norm = nn.BatchNorm2d(num_features=n_feature)\n lst = [('lstm', lstm_layer), ('batch_norm', batch_norm)]\n \n if self.dropout_after_each_lstm_layer:\n dropout = nn.Dropout(self.dropout_after_each_lstm_layer)\n lst.append(('dropout', dropout))\n \n module_dict = nn.ModuleDict(lst)\n \n self.lstm_blocks.append(module_dict)\n n_feature = self.lstm_hidden_sizes[-1] *2 if bidirectional else self.lstm_hidden_sizes[-1]\n \n self.linear1 =[nn.Linear(n_feature, self.last_layer_fsz)] * self.num_tasks\n self.linear1 = torch.nn.ModuleList(self.linear1)\n self.activation = nn.ReLU()\n \n self.output_layers = [nn.Linear(self.last_layer_fsz, self.num_classes)] * self.num_tasks\n self.output_layers = torch.nn.ModuleList(self.output_layers)\n \n if self.weights != None:\n self.cross_entropy_loss = [nn.CrossEntropyLoss(weight= torch.tensor(weights).float()) for weights in self.weights]\n else:\n self.cross_entropy_loss = [nn.CrossEntropyLoss() for _ in range(self.num_tasks)]\n \n self.cross_entropy_loss = torch.nn.ModuleList(self.cross_entropy_loss)\n \n self.f1_score = pl.metrics.F1(num_classes=self.num_classes, average=\"macro\")\n self.accuracy_score = pl.metrics.Accuracy()\n \n self.train_dl = DataLoader(train_dataset, batch_size=self.batch_size, shuffle = True)\n self.val_dl = DataLoader(val_dataset, batch_size=self.batch_size)\n self.test_dl = DataLoader(test_dataset, batch_size=self.batch_size)\n \n self.learning_rate = learning_rate\n self.warmup_epoch = warmup_epoch\n self.weight_decay = weight_decay\n # self.scheduler_step = scheduler_step\n # self.scheduler_gamma = scheduler_gamma\n \n def forward(self, x, i):\n\n batch_size = x.size()[0]\n \n x = x.view(batch_size, self.window_size, self.input_size) #(batch, window_len, feature_size)\n x, _ = self.lstm_1(x)\n \n x = self.dropout(x)\n\n x = x.reshape(x.size()[-1], batch_size, self.window_size) #(feature_size, batch, window_len)\n x = self.batch_norm1(x.unsqueeze(0))\n \n if len(self.lstm_hidden_sizes) > 1:\n \n x = x.view(batch_size, self.window_size, x.size()[1])\n x, _ = self.lstm_2(x)\n\n x = self.dropout(x)\n\n x = x.reshape(x.size()[-1], batch_size, self.window_size) #(feature_size, batch, window_len)\n x = self.batch_norm2(x.unsqueeze(0))\n\n x = x.view(batch_size, self.window_size, x.size()[1])\n x, _ = self.lstm_3(x)\n\n x = self.dropout(x)\n\n x = x.reshape(x.size()[-1], batch_size, self.window_size) #(feature_size, batch, window_len)\n x = self.batch_norm3(x.unsqueeze(0))\n \n x = x.view(batch_size, self.window_size, x.size()[1])\n x = x[:, -1, :] # equivalent to return sequence = False on keras :)\n \n x = self.dropout(x)\n \n x = self.linear1[i](x)\n x = self.activation(x)\n \n output = self.output_layers[i](x)\n \n return output\n \n def training_step(self, batch, batch_nb):\n \n loss = (torch.tensor(0.0, device=\"cuda:0\", requires_grad=True) + \\\n torch.tensor(0.0, device=\"cuda:0\", requires_grad=True)) \n # araştırılabilir\n for i in range(self.num_tasks):\n x, y = batch[self.currencies[i] + \"_window\"], batch[self.currencies[i] + \"_label\"]\n\n output = self.forward(x, i)\n #loss = F.nll_loss(output, y)\n loss += self.cross_entropy_loss[i](output, y)\n \n acc = self.accuracy_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_train_acc', acc, on_epoch=True, prog_bar=True)\n\n f1 = self.f1_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_train_f1', f1, on_epoch=True, prog_bar=True)\n \n loss = loss / torch.tensor(self.num_tasks)\n self.log('train_loss', loss, on_epoch=True, prog_bar=True)\n \n return loss \n \n def validation_step(self, batch, batch_nb):\n loss = torch.tensor(0.0, device=\"cuda:0\") + torch.tensor(0.0, device=\"cuda:0\")\n \n for i in range(self.num_tasks):\n x, y = batch[self.currencies[i] + \"_window\"], batch[self.currencies[i] + \"_label\"]\n\n output = self(x, i)\n #loss = F.nll_loss(output, y)\n loss += self.cross_entropy_loss[i](output, y)\n \n acc = self.accuracy_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_val_acc', acc, on_epoch=True, prog_bar=True, reduce_fx=torch.mean)\n\n f1 = self.f1_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_val_f1', f1, on_epoch=True, prog_bar=True, reduce_fx=torch.mean)\n \n loss = loss / torch.tensor(self.num_tasks)\n self.log('val_loss', loss, on_epoch=True, prog_bar=True)\n \n def test_step(self, batch, batch_nb):\n loss = torch.tensor(0.0, device=\"cuda:0\") + torch.tensor(0.0, device=\"cuda:0\")\n \n for i in range(self.num_tasks):\n x, y = batch[ self.currencies[i] + \"_window\"], batch[self.currencies[i] + \"_label\"]\n\n output = self(x, i)\n loss += self.cross_entropy_loss[i](output, y)\n \n acc = self.accuracy_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_test_acc', acc, on_epoch=True, reduce_fx=torch.mean)\n\n f1 = self.f1_score(torch.max(output, dim=1)[1], y)\n self.log(self.currencies[i] +'_test_f1', f1, on_epoch=True, reduce_fx=torch.mean)\n \n loss = loss / torch.tensor(self.num_tasks)\n self.log('test_loss', loss, on_epoch=True, reduce_fx=torch.mean)\n \n def configure_optimizers(self):\n \n optimizer = torch.optim.AdamW(self.parameters(), \n lr= self.learning_rate, \n weight_decay=self.weight_decay)\n\n# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, \n# step_size=self.scheduler_step, \n# gamma=self.scheduler_gamma)\n \n self.lr_scheduler = CosineWarmupScheduler(optimizer, \n warmup = self.train_dl.__len__() * self.warmup_epoch, \n max_iters = MAX_EPOCHS * self.train_dl.__len__())\n return [optimizer]#, [{\"scheduler\": scheduler}]\n \n def optimizer_step(self, *args, **kwargs):\n super().optimizer_step(*args, **kwargs)\n self.lr_scheduler.step() # Step per iteration\n \n def train_dataloader(self):\n return self.train_dl\n\n def val_dataloader(self):\n return self.val_dl\n\n def test_dataloader(self):\n return self.test_dl", "sub_path": "notebooks/dev/LSTMModel.py", "file_name": "LSTMModel.py", "file_ext": "py", "file_size_in_byte": 11144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pytorch_lightning.LightningModule", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.LSTM", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pytorch_lightning.metrics.F1", "line_number": 125, "usage_type": "call"}, {"api_name": "pytorch_lightning.metrics", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pytorch_lightning.metrics.Accuracy", "line_number": 126, "usage_type": "call"}, {"api_name": "pytorch_lightning.metrics", "line_number": 126, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 214, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 217, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 232, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 235, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 238, "usage_type": "attribute"}, {"api_name": "torch.optim.AdamW", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 242, "usage_type": "attribute"}, {"api_name": "TimeSeriesLearningUtils.CosineWarmupScheduler", "line_number": 250, "usage_type": "call"}]}
+{"seq_id": "321652249", "text": "import os\nimport io\nimport json\nimport sys\n\n# ignore all SINGLETON samples. these are the ones for which not a single AV\n# engine has a non-generic name. rationale is that if it isn't important\n# enough to get a name, it never had any significant spread.\nrecognized = dict()\nwith io.open('ransomware.labels', 'rb') as infile:\n\tfor line in infile:\n\t\thash = line[0:32]\n\t\tfamily = line[33:].rstrip()\n\t\tif not family.startswith('SINGLETON:'):\n\t\t\trecognized[hash] = family\nsys.stderr.write(str(len(recognized)) + ' non-singleton samples\\n')\n\nstats = dict()\nwith io.open('ransomware.jsons', 'rb') as ransom:\n\ttotal = 0\n\tfor line in ransom:\n\t\tdata = json.loads(line)\n\t\thash = data['md5']\n\t\tfile = data['file']\n\t\tfor family in data['families']:\n\t\t\tif family not in stats:\n\t\t\t\tstats[family] = { 'samples': 0, 'labels': dict(),\n\t\t\t\t\t\t'unicorns': 0, 'detect': 0, 'ransom': 0 }\n\t\t\tstats[family]['samples'] += 1\n\t\t\tif hash in recognized:\n\t\t\t\tlabel = recognized[hash]\n\t\t\t\tif label not in stats[family]['labels']:\n\t\t\t\t\tstats[family]['labels'][label] = 0\n\t\t\t\tstats[family]['labels'][label] += 1\n\t\t\tif len(data['families']) == 1:\n\t\t\t\tstats[family]['unicorns'] += 1\n\t\t\tstats[family]['detect'] += len(data['scans'])\n\t\t\tstats[family]['ransom'] += sum(1 for eng, res in data['scans'].items()\n\t\t\t\t\tif 'ransom' in res['result'].lower())\n\n\t\ttotal += 1\n\t\tif total % 1000 == 0:\n\t\t\tsys.stderr.write('\\r' + str(total / 1000) + 'k ')\n\t\t\tsys.stderr.flush()\nsys.stderr.write('\\r' + str(total) + '\\n')\n\nfor family, stat in stats.items():\n\tsamples = stat['samples']\n\tstat['fraction'] = samples / float(total)\n\tstat['unicorns'] /= float(samples)\n\tstat['labels'] = len(stat['labels'])\n\tstat['ransom'] /= float(stat['detect'])\n\tstat['score'] = stat['samples'] * stat['labels'] / (stat['ransom'] + 1e-5)\nfamilies = sorted(stats.keys(), key=lambda family: stats[family]['score'])\n\nwith io.open('genericide.md', 'wb') as select:\n\tselect.write('| %-14s | %-7s | %-6s | %-8s | %-8s | %-8s |\\n' % ('family', 'samples', 'labels', 'ransom', 'fraction', 'unicorns'))\n\tselect.write('|-%-14s-|-%-7s-|-%-6s-|-%-8s-|-%-8s-|-%-8s-|\\n' % ('-'*14, '-'*7, '-'*6, '-'*8, '-'*8, '-'*8))\n\tfor family in families:\n\t\tstat = stats[family]\n\t\tselect.write('| %-14s | %-7d | %-6d | %8.5f | %8.5f | %8.5f |\\n' %\n\t\t\t\t(family, stat['samples'], stat['labels'], stat['ransom'] * 100, stat['fraction'] * 100, stat['unicorns']))\n", "sub_path": "genericide.py", "file_name": "genericide.py", "file_ext": "py", "file_size_in_byte": 2361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "io.open", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 16, "usage_type": "attribute"}, {"api_name": "io.open", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 45, "usage_type": "attribute"}, {"api_name": "io.open", "line_number": 56, "usage_type": "call"}]}
+{"seq_id": "65263614", "text": "import warnings\nfrom enum import Enum\nfrom textwrap import dedent\nfrom typing import Set\n\nimport structlog\nfrom geoalchemy2 import Geometry\nfrom sqlalchemy import (\n DDL,\n BigInteger,\n CheckConstraint,\n Column,\n Date,\n DateTime,\n Enum as SqlEnum,\n ForeignKey,\n Index,\n Integer,\n MetaData,\n Numeric,\n PrimaryKeyConstraint,\n SmallInteger,\n String,\n Table,\n bindparam,\n func,\n select,\n)\nfrom sqlalchemy.dialects import postgresql as postgres\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.exc import ProgrammingError\n\nfrom cubedash import _utils\nfrom cubedash._utils import ODC_DATASET\n\n_LOG = structlog.get_logger()\n\nCUBEDASH_SCHEMA = \"cubedash\"\nMETADATA = MetaData(schema=CUBEDASH_SCHEMA)\nGRIDCELL_COL_SPEC = f\"{CUBEDASH_SCHEMA}.gridcell\"\n\nDATASET_SPATIAL = Table(\n \"dataset_spatial\",\n METADATA,\n # Note that we deliberately don't foreign-key to datacube tables:\n # - We don't want to add an external dependency on datacube core\n # (breaking, eg, product deletion scripts)\n # - they may be in a separate database.\n Column(\"id\", postgres.UUID(as_uuid=True), primary_key=True, comment=\"Dataset ID\"),\n Column(\n \"dataset_type_ref\",\n SmallInteger,\n comment=\"The ODC dataset_type id\",\n nullable=False,\n ),\n Column(\"center_time\", DateTime(timezone=True), nullable=False),\n # When was the dataset created?\n # Creation_time if it has one, otherwise datacube index time.\n Column(\"creation_time\", DateTime(timezone=True), nullable=False),\n # Nullable: Some products have no region.\n Column(\"region_code\", String, comment=\"\"),\n # Size of this dataset in bytes, if the product includes it.\n Column(\"size_bytes\", BigInteger),\n Column(\"footprint\", Geometry(spatial_index=False)),\n # Default postgres naming conventions.\n Index(\n \"dataset_spatial_dataset_type_ref_center_time_idx\",\n \"dataset_type_ref\",\n \"center_time\",\n ),\n # Faster region pages. Could be removed if faster summary generation is desired...\n Index(\n \"dataset_spatial_dataset_type_ref_region_code_idx\",\n \"dataset_type_ref\",\n \"region_code\",\n postgresql_ops={\"region_code\": \"text_pattern_ops\"},\n ),\n)\n\n\nDATASET_SPATIAL.indexes.add(\n Index(\n \"dataset_spatial_footprint_wrs86_idx\",\n func.ST_Transform(DATASET_SPATIAL.c.footprint, 4326),\n postgresql_using=\"gist\",\n )\n)\n# An index matching the default Stac API Item search and its sort order.\n_COLLECTION_ITEMS_INDEX = Index(\n \"dataset_spatial_collection_items_all_idx\",\n \"dataset_type_ref\",\n \"center_time\",\n \"id\",\n _table=DATASET_SPATIAL,\n)\n# An index matching the default return of '/stac/search' (ie, all collections.)\n_ALL_COLLECTIONS_ORDER_INDEX = Index(\n \"dataset_spatial_all_collections_order_all_idx\",\n \"center_time\",\n \"id\",\n _table=DATASET_SPATIAL,\n)\n\nDATASET_SPATIAL.indexes.add(_COLLECTION_ITEMS_INDEX)\nDATASET_SPATIAL.indexes.add(_ALL_COLLECTIONS_ORDER_INDEX)\n\n# Note that we deliberately don't foreign-key to datacube tables:\n# - We don't want to add an external dependency on datacube core\n# (breaking, eg, product deletion scripts)\n# - they may be in a separate database.\nPRODUCT = Table(\n \"product\",\n METADATA,\n Column(\"id\", SmallInteger, primary_key=True),\n Column(\"name\", String, unique=True, nullable=False),\n Column(\"dataset_count\", Integer, nullable=False),\n Column(\n \"last_refresh\",\n DateTime(timezone=True),\n nullable=False,\n comment=\"Last refresh of this product's extents'\",\n ),\n Column(\n \"last_successful_summary\",\n DateTime(timezone=True),\n nullable=True,\n comment=\"The `last_refresh` time that was current when summaries \"\n \"were last *fully* generated successfully.\",\n ),\n Column(\"source_product_refs\", postgres.ARRAY(SmallInteger)),\n Column(\"derived_product_refs\", postgres.ARRAY(SmallInteger)),\n Column(\"time_earliest\", DateTime(timezone=True)),\n Column(\"time_latest\", DateTime(timezone=True)),\n # A flat key-value set of metadata fields that are the same (\"fixed\") on every dataset.\n # (Almost always includes platform, instrument values)\n Column(\"fixed_metadata\", postgres.JSONB),\n)\nTIME_OVERVIEW = Table(\n \"time_overview\",\n METADATA,\n # Uniquely identified by three values:\n Column(\"product_ref\", None, ForeignKey(PRODUCT.c.id)),\n Column(\n \"period_type\", SqlEnum(\"all\", \"year\", \"month\", \"day\", name=\"overviewperiod\")\n ),\n Column(\"start_day\", Date),\n Column(\"dataset_count\", Integer, nullable=False),\n # Time range (if there's at least one dataset)\n Column(\"time_earliest\", DateTime(timezone=True)),\n Column(\"time_latest\", DateTime(timezone=True)),\n Column(\n \"timeline_period\",\n SqlEnum(\"year\", \"month\", \"week\", \"day\", name=\"timelineperiod\"),\n nullable=False,\n ),\n Column(\n \"timeline_dataset_start_days\",\n postgres.ARRAY(DateTime(timezone=True)),\n nullable=False,\n ),\n Column(\"timeline_dataset_counts\", postgres.ARRAY(Integer), nullable=False),\n Column(\"regions\", postgres.ARRAY(String), nullable=False),\n Column(\"region_dataset_counts\", postgres.ARRAY(Integer), nullable=False),\n # The most newly created dataset\n Column(\"newest_dataset_creation_time\", DateTime(timezone=True)),\n # When this summary was generated\n Column(\n \"generation_time\",\n DateTime(timezone=True),\n server_default=func.now(),\n nullable=False,\n ),\n Column(\n \"product_refresh_time\",\n DateTime(timezone=True),\n # This is nullable in migrated schemas, as the update time is unknown.\n # (Those environments could be made non-null once everything is known to be refreshed)\n nullable=False,\n comment=\"The 'last_refresh' timestamp of the product at the time of generation.\",\n ),\n Column(\"footprint_count\", Integer, nullable=False),\n # SRID is overridden via config.\n Column(\"footprint_geometry\", Geometry(srid=-999, spatial_index=False)),\n Column(\"crses\", postgres.ARRAY(String)),\n # Size of this dataset in bytes, if the product includes it.\n Column(\"size_bytes\", BigInteger),\n PrimaryKeyConstraint(\"product_ref\", \"start_day\", \"period_type\"),\n CheckConstraint(\n r\"array_length(timeline_dataset_start_days, 1) = \"\n r\"array_length(timeline_dataset_counts, 1)\",\n name=\"timeline_lengths_equal\",\n ),\n)\n\n# An SQLAlchemy expression to read the configured SRID.\nFOOTPRINT_SRID_EXPRESSION = func.Find_SRID(\n TIME_OVERVIEW.schema, TIME_OVERVIEW.name, \"footprint_geometry\"\n)\n\n# The geometry of each unique 'region' for a product.\nREGION = Table(\n \"region\",\n METADATA,\n Column(\"dataset_type_ref\", SmallInteger, nullable=False),\n Column(\"region_code\", String, nullable=False),\n Column(\"count\", Integer, nullable=False),\n Column(\n \"generation_time\",\n DateTime(timezone=True),\n server_default=func.now(),\n nullable=False,\n ),\n Column(\"footprint\", Geometry(srid=4326, spatial_index=False)),\n PrimaryKeyConstraint(\"dataset_type_ref\", \"region_code\"),\n)\n\n\n_REF_TABLE_METADATA = MetaData(schema=CUBEDASH_SCHEMA)\n# This is a materialised view of the postgis spatial_ref_sys for lookups.\n# See creation of mv_spatial_ref_sys below.\nSPATIAL_REF_SYS = Table(\n \"mv_spatial_ref_sys\",\n _REF_TABLE_METADATA,\n Column(\"srid\", Integer, primary_key=True),\n Column(\"auth_name\", String(255)),\n Column(\"auth_srid\", Integer),\n Column(\"srtext\", String(2048)),\n Column(\"proj4text\", String(2048)),\n)\n\nSPATIAL_QUALITY_STATS = Table(\n \"mv_dataset_spatial_quality\",\n _REF_TABLE_METADATA,\n Column(\"dataset_type_ref\", SmallInteger, primary_key=True),\n Column(\"count\", Integer),\n Column(\"missing_footprint\", Integer),\n Column(\"footprint_size\", Integer),\n Column(\"footprint_stddev\", Numeric),\n Column(\"missing_srid\", Integer),\n Column(\"has_file_size\", Integer),\n Column(\"has_region\", Integer),\n)\n\n\ndef has_schema(engine: Engine) -> bool:\n \"\"\"\n Does the cubedash schema already exist?\n \"\"\"\n return engine.dialect.has_schema(engine, CUBEDASH_SCHEMA)\n\n\ndef is_compatible_schema(engine: Engine) -> bool:\n \"\"\"Do we have the latest schema changes?\"\"\"\n is_latest = True\n\n if not pg_column_exists(\n engine, f\"{CUBEDASH_SCHEMA}.product\", \"last_successful_summary\"\n ):\n is_latest = False\n\n if pg_exists(engine, f\"{CUBEDASH_SCHEMA}.mv_region\"):\n warnings.warn(\n \"Your database has item `cubedash.mv_region` from an unstable version of Explorer. \"\n \"It will not harm you, but feel free to drop it once all Explorer instances \"\n \"have been upgraded: \"\n \" drop materialised view cubedash.mv_region\"\n )\n\n return is_latest\n\n\ndef is_compatible_generate_schema(engine: Engine) -> bool:\n \"\"\"Is the schema complete enough to run generate/refresh commands?\"\"\"\n is_latest = is_compatible_schema(engine)\n\n # Incremental update scanning requires the optional `update` column on ODC.\n return is_latest and pg_column_exists(engine, ODC_DATASET.fullname, \"updated\")\n\n\nclass SchemaNotRefreshable(Exception):\n \"\"\"The schema is not set-up for running product refreshes\"\"\"\n\n ...\n\n\nclass PleaseRefresh(Enum):\n \"\"\"\n What data should be refreshed/recomputed?\n \"\"\"\n\n # Refresh the product extents.\n PRODUCTS = 2\n # Recreate all dataset extents in the spatial table\n DATASET_EXTENTS = 1\n\n\ndef update_schema(engine: Engine) -> Set[PleaseRefresh]:\n \"\"\"\n Update the schema if needed.\n\n Returns what data should be resummarised.\n \"\"\"\n\n refresh = set()\n\n if not pg_column_exists(engine, f\"{CUBEDASH_SCHEMA}.product\", \"fixed_metadata\"):\n _LOG.warning(\"schema.applying_update.add_fixed_metadata\")\n engine.execute(\n f\"\"\"\n alter table {CUBEDASH_SCHEMA}.product add column fixed_metadata jsonb\n \"\"\"\n )\n refresh.add(PleaseRefresh.DATASET_EXTENTS)\n\n if not pg_exists(\n engine,\n f\"{CUBEDASH_SCHEMA}.{_COLLECTION_ITEMS_INDEX.name}\",\n ):\n _LOG.warning(\"schema.applying_update.add_collection_items_idx\")\n _COLLECTION_ITEMS_INDEX.create(engine)\n\n if not pg_exists(\n engine,\n f\"{CUBEDASH_SCHEMA}.{_ALL_COLLECTIONS_ORDER_INDEX.name}\",\n ):\n _LOG.warning(\"schema.applying_update.add_all_collections_idx\")\n _ALL_COLLECTIONS_ORDER_INDEX.create(engine)\n\n if not pg_column_exists(\n engine, f\"{CUBEDASH_SCHEMA}.time_overview\", \"product_refresh_time\"\n ):\n _LOG.warning(\"schema.applying_update.add_refresh_time\")\n engine.execute(\n f\"\"\"\n alter table {CUBEDASH_SCHEMA}.time_overview\n add column product_refresh_time timestamp with time zone null\n \"\"\"\n )\n\n if not pg_column_exists(\n engine, f\"{CUBEDASH_SCHEMA}.product\", \"last_successful_summary\"\n ):\n _LOG.warning(\"schema.applying_update.add_summary_success_time\")\n engine.execute(\n f\"\"\"\n alter table {CUBEDASH_SCHEMA}.product\n add column last_successful_summary timestamp with time zone null\n \"\"\"\n )\n\n check_or_update_odc_schema(engine)\n\n return refresh\n\n\ndef check_or_update_odc_schema(engine: Engine):\n \"\"\"\n Check that the ODC schema is updated enough to run Explorer,\n\n and either update it safely (if we have permission), or tell the user how.\n \"\"\"\n # We need the `update` column on ODC's dataset table in order to run incremental product refreshes.\n try:\n # We can try to install it ourselves if we have permission, using ODC's code.\n if not pg_column_exists(engine, ODC_DATASET.fullname, \"updated\"):\n _LOG.warning(\"schema.applying_update.add_odc_change_triggers\")\n _utils.install_timestamp_trigger(engine)\n except ProgrammingError as e:\n # We don't have permission.\n raise SchemaNotRefreshable(\n dedent(\n \"\"\"\n Missing update triggers.\n\n No dataset-update triggers are installed on the ODC instance, and Explorer does\n not have enough permissions to add them itself.\n\n It's recommended to run `datacube system init` on your ODC instance to install them.\n\n Then try this again.\n \"\"\"\n )\n ) from e\n\n # Add optional indexes to AGDC if we have permission.\n # (otherwise we warn the user that it may be slow, and how to add it themselves)\n statements = []\n try:\n if not pg_index_exists(\n engine, ODC_DATASET.schema, ODC_DATASET.name, \"ix_dataset_added\"\n ):\n _LOG.warning(\"schema.applying_update.add_odc_added_index\")\n statements.append(\n f\"create index ix_dataset_added on {ODC_DATASET.fullname}(added desc);\"\n )\n if not pg_index_exists(\n engine, ODC_DATASET.schema, ODC_DATASET.name, \"ix_dataset_type_changed\"\n ):\n _LOG.warning(\"schema.applying_update.add_odc_changed_index\")\n statements.append(\n f\"create index ix_dataset_type_changed on \"\n f\"{ODC_DATASET.fullname}(dataset_type_ref, greatest(added, updated, archived) desc);\"\n )\n while statements:\n engine.execute(statements[-1])\n statements.pop()\n except ProgrammingError:\n unexecuted_sql = \"\\n \".join(statements)\n warnings.warn(\n dedent(\n f\"\"\"\n No recently-added index.\n Explorer recommends adding an index for recently-added datasets to your ODC,\n but does not have permission to add it to the current ODC database.\n\n It's recommended to add it manually in Postgres:\n\n {unexecuted_sql}\n \"\"\"\n )\n )\n raise\n\n\ndef pg_exists(conn, name: str) -> bool:\n \"\"\"\n Does a postgres object exist?\n \"\"\"\n return conn.execute(\"select to_regclass(%s)\", name).scalar() is not None\n\n\ndef pg_index_exists(conn, schema_name: str, table_name: str, index_name: str) -> bool:\n \"\"\"\n Does a postgres index exist?\n\n Unlike pg_exists(), we don't need heightened permissions on the table.\n\n So, for example, Explorer's limited-permission user can check agdc/ODC tables\n that it doesn't own.\n \"\"\"\n return (\n conn.execute(\n \"\"\"\n select indexname\n from pg_indexes\n where schemaname=%(schema_name)s and\n tablename=%(table_name)s and\n indexname=%(index_name)s\n \"\"\",\n schema_name=schema_name,\n table_name=table_name,\n index_name=index_name,\n ).scalar()\n is not None\n )\n\n\ndef get_postgis_versions(conn) -> str:\n \"\"\"What versions of Postgis, Postgres and libs do we have?\"\"\"\n return conn.execute(select([func.postgis_full_version()])).scalar()\n\n\ndef pg_column_exists(conn, table_name: str, column_name: str) -> bool:\n \"\"\"\n Does a postgres object exist?\n \"\"\"\n return (\n conn.execute(\n \"\"\"\n select 1\n from pg_attribute\n where attrelid = to_regclass(%s)\n and attname = %s\n and not attisdropped\n \"\"\",\n table_name,\n column_name,\n ).scalar()\n is not None\n )\n\n\ndef _epsg_to_srid(engine: Engine, code: int) -> int:\n \"\"\"\n Convert an epsg code to Postgis' srid number.\n\n They're usually the same in Postgis' default srid table... but they don't\n have to be. We'll do this lookup anyway to be good citizens.\n \"\"\"\n return engine.execute(\n \"select srid from spatial_ref_sys where auth_name = 'EPSG' and auth_srid=%(epsg_code)s\",\n epsg_code=code,\n ).scalar()\n\n\ndef create_schema(engine: Engine, epsg_code: int):\n \"\"\"\n Create any missing parts of the cubedash schema\n \"\"\"\n # Create schema if needed.\n #\n # Note that we don't use the built-in \"if not exists\" because running it *always* requires\n # `create` permission.\n #\n # Doing it separately allows users to run this tool without `create` permission.\n #\n if not engine.dialect.has_schema(engine, CUBEDASH_SCHEMA):\n engine.execute(DDL(f\"create schema {CUBEDASH_SCHEMA}\"))\n\n # Add Postgis if needed\n #\n # Note that, as above, we deliberately don't use the built-in \"if not exists\"\n #\n if (\n engine.execute(\n \"select count(*) from pg_extension where extname='postgis';\"\n ).scalar()\n == 0\n ):\n engine.execute(DDL(\"create extension postgis\"))\n\n srid = _epsg_to_srid(engine, epsg_code)\n if srid is None:\n raise RuntimeError(\n f\"Postgis doesn't seem to know about epsg code {epsg_code!r}.\"\n )\n\n # Our global SRID.\n TIME_OVERVIEW.c.footprint_geometry.type.srid = srid\n\n # We want an index on the spatial_ref_sys table to do authority name/code lookups.\n # But in RDS environments we cannot add indexes to it.\n # So we create our own copy as a materialised view (it's a very small table).\n engine.execute(\n f\"\"\"\n create materialized view if not exists {CUBEDASH_SCHEMA}.mv_spatial_ref_sys\n as select * from spatial_ref_sys;\n \"\"\"\n )\n # The normal primary key.\n engine.execute(\n f\"\"\"\n create unique index if not exists mv_spatial_ref_sys_srid_idx on\n {CUBEDASH_SCHEMA}.mv_spatial_ref_sys(srid);\n \"\"\"\n )\n # For case insensitive auth name/code lookups.\n # (Postgis doesn't add one by default, but we're going to do a lot of lookups)\n engine.execute(\n f\"\"\"\n create unique index if not exists mv_spatial_ref_sys_lower_auth_srid_idx on\n {CUBEDASH_SCHEMA}.mv_spatial_ref_sys(lower(auth_name::text), auth_srid);\n \"\"\"\n )\n\n METADATA.create_all(engine, checkfirst=True)\n\n # Useful reporting.\n engine.execute(\n f\"\"\"\n create materialized view if not exists {CUBEDASH_SCHEMA}.mv_dataset_spatial_quality as (\n select\n dataset_type_ref,\n count(*) as count,\n count(*) filter (where footprint is null) as missing_footprint,\n sum(pg_column_size(footprint)) filter (where footprint is not null) as footprint_size,\n stddev(pg_column_size(footprint)) filter (where footprint is not null) as footprint_stddev,\n count(*) filter (where ST_SRID(footprint) is null) as missing_srid,\n count(*) filter (where size_bytes is not null) as has_file_size,\n count(*) filter (where region_code is not null) as has_region\n from {CUBEDASH_SCHEMA}.dataset_spatial\n group by dataset_type_ref\n ) with no data;\n \"\"\"\n )\n\n engine.execute(\n f\"\"\"\n create unique index if not exists mv_dataset_spatial_quality_dataset_type_ref\n on {CUBEDASH_SCHEMA}.mv_dataset_spatial_quality(dataset_type_ref);\n \"\"\"\n )\n\n\ndef refresh_supporting_views(conn, concurrently=False):\n args = \"concurrently\" if concurrently else \"\"\n conn.execute(\n f\"\"\"\n refresh materialized view {args} {CUBEDASH_SCHEMA}.mv_spatial_ref_sys;\n \"\"\"\n )\n conn.execute(\n f\"\"\"\n refresh materialized view {args} {CUBEDASH_SCHEMA}.mv_dataset_spatial_quality;\n \"\"\"\n )\n\n\ndef get_srid_name(engine: Engine, srid: int):\n \"\"\"\n Convert an internal postgres srid key to a string auth code: eg: 'EPSG:1234'\n \"\"\"\n return engine.execute(\n select(\n [\n func.concat(\n SPATIAL_REF_SYS.c.auth_name,\n \":\",\n SPATIAL_REF_SYS.c.auth_srid.cast(Integer),\n )\n ]\n ).where(SPATIAL_REF_SYS.c.srid == bindparam(\"srid\", srid, type_=Integer))\n ).scalar()\n", "sub_path": "cubedash/summary/_schema.py", "file_name": "_schema.py", "file_ext": "py", "file_size_in_byte": 19985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "structlog.get_logger", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.UUID", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 49, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 52, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 61, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 63, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 64, "usage_type": "call"}, {"api_name": "geoalchemy2.Geometry", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.Index", "line_number": 66, "usage_type": "call"}, {"api_name": "sqlalchemy.Index", "line_number": 72, "usage_type": "call"}, {"api_name": "sqlalchemy.Index", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.func.ST_Transform", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 84, "usage_type": "name"}, {"api_name": "sqlalchemy.Index", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.Index", "line_number": 97, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 111, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 114, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 114, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 115, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 116, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 119, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 123, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 125, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 130, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 130, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 130, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 130, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 131, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 131, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 136, "usage_type": "attribute"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 136, "usage_type": "name"}, {"api_name": "sqlalchemy.Table", "line_number": 138, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 142, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 142, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 143, "usage_type": "call"}, {"api_name": "sqlalchemy.Enum", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 146, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 146, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 147, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 147, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 149, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 149, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 150, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 150, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 151, "usage_type": "call"}, {"api_name": "sqlalchemy.Enum", "line_number": 153, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 156, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 158, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 158, "usage_type": "name"}, {"api_name": "sqlalchemy.DateTime", "line_number": 158, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 161, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 161, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 162, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 162, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 162, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 162, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 163, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 163, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 167, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 169, "usage_type": "call"}, {"api_name": "sqlalchemy.func.now", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 170, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 175, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 181, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 181, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 183, "usage_type": "call"}, {"api_name": "geoalchemy2.Geometry", "line_number": 183, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 184, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.ARRAY", "line_number": 184, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 184, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 184, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 186, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 186, "usage_type": "argument"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 187, "usage_type": "call"}, {"api_name": "sqlalchemy.CheckConstraint", "line_number": 188, "usage_type": "call"}, {"api_name": "sqlalchemy.func.Find_SRID", "line_number": 196, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 196, "usage_type": "name"}, {"api_name": "sqlalchemy.Table", "line_number": 201, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 204, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 204, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 205, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 205, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 206, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 206, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 207, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 209, "usage_type": "call"}, {"api_name": "sqlalchemy.func.now", "line_number": 210, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 210, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 213, "usage_type": "call"}, {"api_name": "geoalchemy2.Geometry", "line_number": 213, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 214, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 218, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 221, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 224, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 224, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 225, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 225, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 226, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 226, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 227, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 227, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 231, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 234, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 234, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 235, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 235, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 236, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 236, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 237, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 237, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 238, "usage_type": "call"}, {"api_name": "sqlalchemy.Numeric", "line_number": 238, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 239, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 239, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 240, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 240, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 241, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 241, "usage_type": "argument"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 245, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 252, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 262, "usage_type": "call"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 272, "usage_type": "name"}, {"api_name": "cubedash._utils.ODC_DATASET.fullname", "line_number": 277, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 277, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 286, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 297, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 297, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 356, "usage_type": "name"}, {"api_name": "cubedash._utils.ODC_DATASET.fullname", "line_number": 365, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 365, "usage_type": "name"}, {"api_name": "cubedash._utils.install_timestamp_trigger", "line_number": 367, "usage_type": "call"}, {"api_name": "cubedash._utils", "line_number": 367, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.ProgrammingError", "line_number": 368, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 371, "usage_type": "call"}, {"api_name": "cubedash._utils.ODC_DATASET.schema", "line_number": 390, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 390, "usage_type": "name"}, {"api_name": "cubedash._utils.ODC_DATASET.name", "line_number": 390, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET.fullname", "line_number": 394, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 394, "usage_type": "name"}, {"api_name": "cubedash._utils.ODC_DATASET.schema", "line_number": 397, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 397, "usage_type": "name"}, {"api_name": "cubedash._utils.ODC_DATASET.name", "line_number": 397, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET.fullname", "line_number": 402, "usage_type": "attribute"}, {"api_name": "cubedash._utils.ODC_DATASET", "line_number": 402, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.ProgrammingError", "line_number": 407, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 409, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 410, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 460, "usage_type": "call"}, {"api_name": "sqlalchemy.func.postgis_full_version", "line_number": 460, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 460, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 483, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 496, "usage_type": "name"}, {"api_name": "sqlalchemy.DDL", "line_number": 508, "usage_type": "call"}, {"api_name": "sqlalchemy.DDL", "line_number": 520, "usage_type": "call"}, {"api_name": "sqlalchemy.engine.Engine", "line_number": 599, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 604, "usage_type": "call"}, {"api_name": "sqlalchemy.func.concat", "line_number": 606, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 606, "usage_type": "name"}, {"api_name": "sqlalchemy.Integer", "line_number": 609, "usage_type": "argument"}, {"api_name": "sqlalchemy.bindparam", "line_number": 612, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 612, "usage_type": "name"}]}
+{"seq_id": "182097996", "text": "from kivy.app import App\nfrom kivy.clock import Clock\nfrom kivy.graphics import Color, Ellipse, Line\nfrom kivy.uix.widget import Widget\n\nfrom control import Control\nfrom simulation import Simulation\n\nfrom control import PIXELS_PER_INCH\n \nclass PlateWidget(Widget):\n def setup_simulation(self):\n self.sim = Simulation(Control(), (0,0))\n\n def render(self, dt):\n with self.canvas:\n self.sim.step()\n self.canvas.clear() \n Color(.5, .5, .5)\n x = (self.sim.center_axis[0] * PIXELS_PER_INCH)+400-4\n y = (self.sim.center_axis[1] * PIXELS_PER_INCH)+300-4\n Ellipse(pos=(x,y), size=(9, 9))\n \n Color(1, 1, 0)\n for p in self.sim.tool_history:\n x = ((p[0]*1) * PIXELS_PER_INCH)+400-1\n y = ((p[1]*1) * PIXELS_PER_INCH)+300-1\n Ellipse(pos=(x,y), size=(3, 3))\n \n Color(1,0,0)\n x = (self.sim.tool_position[0] * PIXELS_PER_INCH)+400-2\n y = (self.sim.tool_position[1] * PIXELS_PER_INCH)+300-2\n Ellipse(pos=(x,y), size=(5, 5))\n \nclass DemoApp(App):\n def build(self):\n w = PlateWidget()\n w.setup_simulation()\n \n # Schedule simulation step\n event = Clock.schedule_interval(w.render, 1 / 60.)\n \n return w\n \nif __name__ == '__main__':\n DemoApp().run()", "sub_path": "demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "kivy.uix.widget.Widget", "line_number": 11, "usage_type": "name"}, {"api_name": "simulation.Simulation", "line_number": 13, "usage_type": "call"}, {"api_name": "control.Control", "line_number": 13, "usage_type": "call"}, {"api_name": "kivy.graphics.Color", "line_number": 19, "usage_type": "call"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 20, "usage_type": "name"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 21, "usage_type": "name"}, {"api_name": "kivy.graphics.Ellipse", "line_number": 22, "usage_type": "call"}, {"api_name": "kivy.graphics.Color", "line_number": 24, "usage_type": "call"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 26, "usage_type": "name"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 27, "usage_type": "name"}, {"api_name": "kivy.graphics.Ellipse", "line_number": 28, "usage_type": "call"}, {"api_name": "kivy.graphics.Color", "line_number": 30, "usage_type": "call"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 31, "usage_type": "name"}, {"api_name": "control.PIXELS_PER_INCH", "line_number": 32, "usage_type": "name"}, {"api_name": "kivy.graphics.Ellipse", "line_number": 33, "usage_type": "call"}, {"api_name": "kivy.app.App", "line_number": 35, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_interval", "line_number": 41, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 41, "usage_type": "name"}]}
+{"seq_id": "103250183", "text": "import requests\nimport json\n\nheaders = {\n 'client-secret': '9923ac9b-8fd3-421f-b0e5-952f807c6885',\n}\n\nx = 1\ny = 1\nplaceholderY = '-0'\nplaceholderX = '-0'\n\n#f = open('entireYear.txt','w')\nwhile x <= 12:\n while y <= 31:\n if y >= 10:\n placeholderY = '-'\n\n if x >= 10:\n placeholderX = '-'\n params = (\n ('startDate', '2018' + placeholderX + str(x) + placeholderY + str(y) + 'T00:00:00Z'),\n ('endDate', '2018' + placeholderX + str(x) + placeholderY + str(y) + 'T23:59:00Z'),\n ('offset', '0'),\n ('artistId', '2884'),\n ('totalRecordsCount', '0'),\n ('limit', 5000)\n )\n\n\n\n response = requests.get('https://conuhacks-playback-api.touchtunes.com/plays', headers=headers, params=params).json()\n\n print(str(x) + '-' + str(y) + \": \" + str(response['totalRecordsCount']))\n with open('data' + str(x) + '-' + str(y) + '.json', 'w') as outfile:\n json.dump(response, outfile)\n\n #f.write(str(x) + '-' + str(y)+ \": \" + str(response['totalRecordsCount']) + ' ' + '\\n')\n\n #with open('data' + str(x) + '-' + str(y) + '.json', 'w') as outfile:\n # json.dump(response, outfile)\n\n y+=1\n\n if (x==2):\n if (y>28):\n y = 33\n\n if (x==4) or (x==6) or (x==9) or (x==11):\n if (y>30):\n y = 33\n\n y = 1\n x += 1\n placeholderY = '-0'\n\n\n#f.close()", "sub_path": "pullYear.py", "file_name": "pullYear.py", "file_ext": "py", "file_size_in_byte": 1463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 36, "usage_type": "call"}]}
+{"seq_id": "187822834", "text": "from multiprocessing import Process\nimport time\n\ndef plus():\n\tfor i in range(10):\n\t\tprint(\"+\")\n\t\ttime.sleep(1)\n\ndef star():\n\tfor i in range(10):\n\t\tprint(\"*\")\n\t\ttime.sleep(1)\n\nif __name__ == '__main__':\n\tp1=Process(target=plus)\n\tp1.start()\n\tp2=Process(target=star)\n\tp2.start()\n\tp1.join()\n\tp2.join()\n\tprint('FINISH')", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 15, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 17, "usage_type": "call"}]}
+{"seq_id": "446739817", "text": "#!/usr/bin/python\n\nimport sys\nfrom redis import Redis\nfrom erigam.lib.request_methods import redis_pool\n\ndb = Redis(connection_pool=redis_pool)\n\nsession_id = sys.argv[2]\n\nif sys.argv[1] == 'add':\n db.sadd('global-mods', session_id)\n print('Added to global mods list.')\n for chat in db.smembers('session.'+session_id+'.chats'):\n print('Setting group in '+chat+' to globalmod.')\n db.hset('session.'+session_id+'.meta.'+chat, 'group', 'globalmod')\n\nelif sys.argv[1] == 'remove':\n print('Removed from global mods list.')\n db.srem('global-mods', session_id)\n for chat in db.smembers('session.'+session_id+'.chats'):\n if db.hget('session.'+session_id+'.meta.'+chat, 'counter') == '1':\n print('Setting group in '+chat+' to mod.')\n db.hset('session.'+session_id+'.meta.'+chat, 'group', 'mod')\n else:\n print('Setting group in '+chat+' to user.')\n db.hset('session.'+session_id+'.meta.'+chat, 'group', 'user')\n", "sub_path": "erigam/extras/globalmod.py", "file_name": "globalmod.py", "file_ext": "py", "file_size_in_byte": 990, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "redis.Redis", "line_number": 7, "usage_type": "call"}, {"api_name": "erigam.lib.request_methods.redis_pool", "line_number": 7, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}]}
+{"seq_id": "439354729", "text": "# _*_ coding:utf-8 _*_\n#\n# Copyright (c) 2018 Baidu.com, Inc. All Rights Reserved\n#\n\"\"\"\nThe use of keras CNN.\n\nAuthors: zhaochaochao(zhaochaochao@baidu.com)\nDate: 2018/9/5 19:03\n\"\"\"\nfrom numpy import *\nimport keras\nfrom keras.datasets import mnist\nfrom keras.layers import Dense, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential\n\n\ndef trainKerasCNN(trainX, trainY, testX, testY):\n \"\"\"使用keras构建CNN训练手写识别系统\n\n Args:\n trainX: 训练输入数据\n trainY: 训练标签数据\n testX: 测试输入数据\n testY: 测试标签数据\n\n Returns:\n errRate: 错误率\n \"\"\"\n batchSize = 10\n epochs = 10\n numClass = 10 # 手写识别有10个数字\n\n # convert class vectors to binary class matrices - this is for use in the\n # categorical_crossentropy loss below\n trainY = keras.utils.to_categorical(trainY, numClass)\n testY = keras.utils.to_categorical(testY, numClass)\n # print(\"trainY:\\n\", str(trainY), \"testY:\\n\", str(testY))\n\n model = Sequential() # 创建序贯模型\n model.add(Conv2D(32, kernel_size=(5, 5), strides=(1, 1),\n activation='relu',\n )) # 添加一个卷积层, 32个卷积核，激活函数用relu\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) # 添加一个max pool层\n model.add(Conv2D(64, (5, 5), activation=\"relu\")) # 添加第二个卷积层\n model.add(MaxPooling2D(pool_size=(2, 2))) # 添加第二个max pool层\n model.add(Flatten()) # 添加flatten层\n model.add(Dense(1000, activation=\"relu\")) # 添加完全连接层，1000个nn，使用relu激活函数\n model.add(Dense(numClass, activation=\"softmax\")) # 添加完全连接层作为输出层，分成10个类\n\n model.compile(loss=keras.losses.categorical_crossentropy, # 标准交叉熵来进行分类\n optimizer=keras.optimizers.Adam(), # 使用Adam优化器\n metrics=['accuracy']) # 在训练和测试时需要评估的度量\n\n model.fit(trainX, # 输入数据列表\n trainY, # 输入标签列表\n batch_size=batchSize, # 梯度更新时样本数\n epochs=epochs, # 训练轮数\n verbose=1, # log等级\n validation_data=(testX, testY) # 测试数据与标签\n )\n\n score = model.evaluate(testX, testY, verbose=0) # 评估模型\n print('Test loss:', score[0])\n print('Test accuracy:', score[1])\n\n\nif __name__ == \"__main__\":\n # input image dimensions\n imgX, imgY = 28, 28\n\n (trainX, trainY), (testX, testY) = mnist.load_data()\n # trainX, trainY = Utils.loadData(\"trainingDigits\")\n # testX, testY = Utils.loadData(\"testDigits\")\n\n # reshape the data into a 4D tensor - (sample_number, x_img_size,\n # y_img_size, num_channels)\n # because the MNIST is greyscale, we only have a single channel - RGB\n # colour images would have 3\n trainX = array(trainX).reshape(trainX.shape[0], imgX, imgY, 1)\n testX = testX.reshape(testX.shape[0], imgX, imgY, 1)\n input_shape = (imgX, imgY, 1)\n\n # convert the data to the right type\n trainX = trainX.astype('float32')\n testX = testX.astype('float32')\n trainX /= 255\n testX /= 255\n print('trainX shape:', trainX.shape)\n print(trainX.shape[0], 'trainX samples')\n print(testX.shape[0], 'testX samples')\n trainKerasCNN(trainX, trainY, testX, testY)\n", "sub_path": "NeuralNetworks/KerasCNN.py", "file_name": "KerasCNN.py", "file_ext": "py", "file_size_in_byte": 3463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "keras.utils.to_categorical", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 37, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 38, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 52, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adam", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 53, "usage_type": "attribute"}, {"api_name": "keras.datasets.mnist.load_data", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.datasets.mnist", "line_number": 73, "usage_type": "name"}]}
+{"seq_id": "283900240", "text": "from random import shuffle\nimport numpy as np\nimport os\nimport multiprocessing as mp\nfrom functools import partial\nfrom core.tapering import taper2D\nfrom preprocess.processing import load_data_3D, select_2D_Data, get_maxmin_info_from_ods_file\n\n\ndef save_2D_LAPNet_data_as_npz(data_setup):\n \"\"\"\n Preprocess data of LAPNet and store them in .npz files\n \"\"\"\n # settings\n workers = data_setup['num_workers']\n saving_dir = data_setup['saving_dir']\n box_num = data_setup['box_num']\n subjectsIDs = data_setup['subjectsIDs']\n num_subject_us = data_setup['num_subject_us']\n ImgPath = data_setup['ImgPath']\n FlowPath = data_setup['FlowPath']\n mask_Flow = data_setup['mask_Flow']\n normalize = data_setup['normalized_img']\n sliceInfo = data_setup['slice_info_coronal']\n # read subjects IDs\n infile = open(subjectsIDs, 'r')\n contents = infile.read().strip().split()\n data_paths = [f for f in contents]\n shuffle(data_paths)\n infile.close()\n\n slice_info = get_maxmin_info_from_ods_file(sliceInfo)\n print('start 2D training data creation ...')\n list_aug = ['real', 'smooth', 'real_x_smooth']\n # create aug file if not existent\n for aug_type in list_aug:\n saving_dir_tapered = f'{saving_dir}/{aug_type}'\n if not os.path.exists(saving_dir_tapered):\n os.makedirs(saving_dir_tapered)\n print(f'start {aug_type} data creation')\n part_pool_func = partial(create_2D_patches,\n saving_dir=saving_dir_tapered,\n aug_type=aug_type,\n ImgPath=ImgPath,\n slice_info=slice_info,\n FlowPath=FlowPath,\n box_num=box_num,\n masking=mask_Flow,\n normalize=normalize,\n num_subject_us=num_subject_us)\n\n pool = mp.Pool(workers)\n pool.map(part_pool_func, data_paths)\n pool.close()\n pool.join()\n print('Creating training Dataset done ... ')\n\n\ndef create_2D_patches(ID, saving_dir, aug_type, ImgPath, FlowPath, box_num, slice_info, num_subject_us, normalize,\n masking,\n direction='coronal'):\n # slicing\n ID_slice_info = slice_info[ID]\n # acceleration list\n list_us = np.arange(0, 31, 2)\n list_us[0] = 1\n np.random.seed()\n us_rate_list = np.random.RandomState().choice(list_us, size=num_subject_us, replace=False)\n for us_rate in us_rate_list:\n print(f'{ID} {aug_type} acc {us_rate} start')\n ref_3D, mov_3D, u_3D, acc = load_data_3D(dataID=ID,\n img_path=ImgPath,\n flow_path=FlowPath,\n aug_type=aug_type,\n us_rate=us_rate,\n mask_type='drUS',\n normalized=normalize,\n masking=masking)\n\n for z_dim in range(int(ID_slice_info[0]), int(ID_slice_info[1])):\n slice_data = select_2D_Data(ref_3D, mov_3D, u_3D, z_dim, direction)\n ref_img = slice_data[:, :, 0]\n mov_img = slice_data[:, :, 1]\n u = slice_data[:, :, :2]\n save_2D_patches_along_depth(ID, ref_img, mov_img, u, us_rate, z_dim, box_num, saving_dir)\n print(f'{ID} {aug_type} done')\n\n\ndef save_2D_patches_along_depth(ID, ref_img, mov_img, u, us_rate, z_dim, box_num, saving_dir, taper_size=33):\n x_dim, y_dim = ref_img.shape\n\n train_kspace = np.zeros((box_num, taper_size, taper_size, 4), dtype=np.float32)\n train_flow = np.zeros((box_num, 2), dtype=np.float32)\n\n x_pos = np.random.randint(0, x_dim - taper_size + 1, box_num)\n y_pos = np.random.randint(0, y_dim - taper_size + 1, box_num)\n\n for num in range(box_num):\n train_kspace[num, :, :, :2], train_kspace[num, :, :, 2:], train_flow[num, :] = taper2D(ref_img,\n mov_img,\n x_pos[num],\n y_pos[num],\n u=u,\n crop_size=taper_size)\n np.savez(f'{saving_dir}/{ID}_acc{us_rate}_slice{z_dim}.npz',\n k_space=train_kspace,\n flow=train_flow)\n", "sub_path": "TF2/preprocess/training_data_2D.py", "file_name": "training_data_2D.py", "file_ext": "py", "file_size_in_byte": 4817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "random.shuffle", "line_number": 29, "usage_type": "call"}, {"api_name": "preprocess.processing.get_maxmin_info_from_ods_file", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 41, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "preprocess.processing.load_data_3D", "line_number": 71, "usage_type": "call"}, {"api_name": "preprocess.processing.select_2D_Data", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "core.tapering.taper2D", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 105, "usage_type": "call"}]}
+{"seq_id": "200816958", "text": "# Code adapté de projets académiques de la professeur Fei Fei Li et\n# de ses étudiants Andrej Karpathy, Justin Johnson et autres.\n# Première version rédigée par Carl Lemaire, Vincent Ducharme et Pierre-Marc Jodoin.\n# Version finale rédigée par Benoit Charbonneau et Pierre-Luc Parent\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom math import ceil, sqrt\n\n\ndef visualize_as_grid(Xs, ubound=255.0, padding=1):\n \"\"\"\n Redimensionne un tenseur en 4D pour faciliter la visualisation.\n\n Inputs:\n - Xs: Numpy array, shape (N, H, W, C)\n - ubound: Les données en sortie vont être entre normalisées entre [0, ubound]\n - padding: Le nombre de pixels entre chaque élément\n \"\"\"\n (N, H, W, C) = Xs.shape\n grid_size = int(ceil(sqrt(N)))\n grid_height = H * grid_size + padding * (grid_size - 1)\n grid_width = W * grid_size + padding * (grid_size - 1)\n grid = np.zeros((grid_height, grid_width, C))\n next_idx = 0\n y0, y1 = 0, H\n for y in range(grid_size):\n x0, x1 = 0, W\n for x in range(grid_size):\n if next_idx < N:\n img = Xs[next_idx]\n low, high = np.min(img), np.max(img)\n grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low)\n next_idx += 1\n x0 += W + padding\n x1 += W + padding\n y0 += H + padding\n y1 += H + padding\n return grid\n\n\ndef visualize_loss(loss_history, y_label='Training loss', x_label='Iterations',\n title='Loss history', infos=\"\", save=\"\"):\n fig = plt.figure(1, figsize=(8, 5))\n ax = fig.add_subplot(111, autoscale_on=True)\n ax.plot(loss_history, lw=3)\n ax.set_title(title)\n ax.set_ylabel(y_label)\n ax.set_xlabel(x_label)\n if infos != \"\":\n ax.text(len(loss_history)/10, np.max(loss_history), infos)\n if save == \"\":\n plt.show()\n else:\n fig.savefig(save + \".png\")\n\n\ndef visualize_accuracy(training_accuracy, validation_accuracy, y_label='Classification accuracy',\n x_label='Epoch', title='Classification accuracy history', infos=\"\", save=\"\"):\n fig = plt.figure(2, figsize=(8, 5))\n ax = fig.add_subplot(111, autoscale_on=True)\n ax.plot(training_accuracy, label='train', lw=3)\n ax.plot(validation_accuracy, label='val', lw=3)\n ax.set_title(title)\n ax.set_ylabel(y_label)\n ax.set_xlabel(x_label)\n ax.legend()\n if infos != \"\":\n ax.text(len(training_accuracy)/10, np.max(training_accuracy), infos)\n if save == \"\":\n plt.show()\n else:\n fig.savefig(save + \".png\")\n", "sub_path": "visualization/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2591, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "math.ceil", "line_number": 21, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]}
+{"seq_id": "263213457", "text": "from flask import render_template, flash, redirect, session, url_for, request, g\nfrom flask_login import login_user, logout_user, current_user, login_required\nfrom app import app, db, lm\nfrom .forms import LoginForm, EditForm, CharacterForm, InviteForm, \\\n SaveCharForm, lvlupForm, RegistrationForm, ResetPasswordRequestForm, ResetPasswordForm\nfrom .models import User, UserTokens\nimport ast\nfrom .hednpc import create_char, load_char, save_char, unique_charname\nimport app.traits as traits\nfrom .emails import invite_user, send_password_reset_email\nfrom config import ADMINS\nfrom werkzeug.urls import url_parse\nimport jwt\n\n\n@app.route('/', methods=['GET','POST'])\n@app.route('/index', methods=['GET','POST'])\ndef index():\n user = g.user\n form = CharacterForm()\n if form.validate_on_submit():\n session['create_values'] = request.form\n return redirect(url_for('character'))\n else:\n return render_template('index.html',\n title='Hem',\n user=user,\n form=form)\n\n@app.route('/register/<token>', methods=['GET', 'POST'])\ndef register(token):\n if current_user.is_authenticated:\n return redirect(url_for('index'))\n try:\n invite_email = jwt.decode(token, app.config['SECRET_KEY'],\n algorithm='HS256')['invite_email']\n except:\n flash('Ogiltig länk. Har det gått mer än en vecka sedan du fick inbjudan?')\n return redirect(url_for('index'))\n form = RegistrationForm()\n form.email.data = invite_email\n if form.validate_on_submit():\n user = User(nickname=form.nickname.data, email=form.email.data)\n user.set_password(form.password.data)\n db.session.add(user)\n db.session.commit()\n flash('Grattis! Du är nu registrerad som användare.')\n return redirect(url_for('login'))\n return render_template('register.html',\n title='Skapa konto',\n form=form)\n\n@app.route('/character', methods=['GET','POST'])\n@app.route('/character/<charname>', methods=['GET','POST'])\ndef character(charname=None):\n form = SaveCharForm()\n if not charname:\n if session['create_values']:\n values = {'Namn':session['create_values']['name'],\n 'Yrke':session['create_values']['job'],\n 'nivå_min':session['create_values']['lvl_min'],\n 'nivå_max':session['create_values']['lvl_max'],\n 'Ras':session['create_values']['race'],\n 'Kön':session['create_values']['gender'],\n 'Ålder':session['create_values']['age'],\n 'ålder_min':session['create_values']['age_min'],\n 'ålder_max':session['create_values']['age_max'],\n 'Längd':session['create_values']['height'],\n 'längd_min':session['create_values']['height_min'],\n 'längd_max':session['create_values']['height_max'],\n 'Huvudhand':session['create_values']['hand']}\n char = create_char(values)\n session['char'] = char.toDict()\n form.notes.data = session['char']['notes']\n if form.validate_on_submit():\n session['char']['start_values']['Namn'] = request.form.get('name',None)\n session['char']['campaign'] = request.form.get('campaign',None)\n session['char']['notes'] = request.form.get('notes',None)\n return redirect(url_for('savecharacter'))\n return render_template('character.html',\n char=char,\n title=char.start_values['Namn'],\n form=form)\n else:\n if g.user.is_authenticated:\n flash('Du måste skapa en karaktär eller ladda en sparad.')\n else:\n flash('Du måste skapa en karaktär först.')\n return redirect(url_for('index'))\n else:\n ### kod för att visa sparad karaktär ###\n if g.user.is_authenticated:\n try:\n char = load_char(charname, g.user)\n session['char'] = char.toDict()\n form.notes.data = session['char']['notes']\n if form.validate_on_submit():\n session['char']['new_name'] = request.form.get('name',None)\n session['char']['campaign'] = request.form.get('campaign',None)\n session['char']['notes'] = request.form.get('notes',None)\n return redirect(url_for('savecharacter'))\n return render_template('character.html',\n char=char,\n title=char.start_values['Namn'],\n form=form,\n charname=charname)\n except:\n flash('Du verkar inte ha någon karaktär med det namnet')\n return redirect(url_for('index'))\n else:\n ### icke inloggad användare ###\n flash('Du måste skapa en karaktär först')\n return redirect(url_for('index'))\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('index'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(nickname=form.nickname.data).first()\n if user is None or not user.check_password(form.password.data):\n flash('Felaktigt användarnamn eller lösenord.')\n return redirect(url_for('login'))\n login_user(user, remember=form.remember_me.data)\n next_page = request.args.get('next')\n if not next_page or url_parse(next_page).netloc != '':\n next_page = url_for('index')\n return redirect(next_page)\n return render_template('login.html',\n title='Logga in',\n form=form)\n\n@app.route('/reset_password_request', methods=['GET', 'POST'])\ndef reset_password_request():\n if current_user.is_authenticated:\n return redirect(url_for('index'))\n form = ResetPasswordRequestForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email=form.email.data).first()\n if user:\n send_password_reset_email(user)\n flash('Kolla din mail för instruktioner om hur du återställer ditt lösenord.')\n return redirect(url_for('login'))\n return render_template('reset_password_request.html',\n title='Återställ lösenord',\n form=form)\n\n@app.route('/reset_password/<token>', methods=['GET', 'POST'])\ndef reset_password(token):\n if current_user.is_authenticated:\n return redirect(url_for('index'))\n user = User.verify_reset_password_token(token)\n if not user:\n flash('Ogiltig länk. Har det gått mer än 10 minuter? ')\n return redirect(url_for('login'))\n form = ResetPasswordForm()\n if form.validate_on_submit():\n user.set_password(form.password.data)\n db.session.commit()\n flash('Ditt lösenord har ändrats.')\n return redirect(url_for('login'))\n return render_template('reset_password.html',\n title='Återställ lösenord',\n form=form)\n\n@lm.user_loader\ndef load_user(id):\n return User.query.get(int(id))\n\n@app.before_request\ndef before_request():\n g.user = current_user\n\n@app.route('/logout')\ndef logout():\n logout_user()\n return redirect(url_for('index'))\n\n@app.route('/user/<nickname>')\n@login_required\ndef user(nickname):\n if not nickname == g.user.nickname:\n flash('Du kan bara se din egen profil.')\n return redirect(url_for('index'))\n user = User.query.filter_by(nickname=nickname).first()\n if user == None:\n flash('Användare %s hittades inte.' % nickname)\n return redirect(url_for('index'))\n chars = user.characters.order_by('timestamp').all()\n if user.email in app.config['ADMINS']:\n is_admin = True\n return render_template('user.html',\n title = 'Profil',\n user=user,\n chars=chars,\n lit_eval=ast.literal_eval,\n is_admin=is_admin)\n\n@app.route('/edituser', methods=['GET', 'POST'])\n@login_required\ndef edituser():\n form = EditForm()\n if form.validate_on_submit():\n g.user.nickname = form.nickname.data\n db.session.add(g.user)\n db.session.commit()\n flash('Ditt användarnamn har ändrats')\n return redirect(url_for('user', nickname=g.user.nickname))\n else:\n form.nickname.data = g.user.nickname\n return render_template('edituser.html',\n title='Ändra namn',\n form=form)\n\n@app.route('/invite', methods=['GET','POST'])\n@login_required\ndef invite():\n if g.user.is_authenticated and g.user.email in ADMINS:\n form = InviteForm()\n if form.validate_on_submit():\n invite_user(form.invite_email.data, g.user)\n return redirect(url_for('user', nickname=g.user.nickname))\n return render_template('invite.html',\n title='Bjud in',\n form=form)\n else:\n flash('Du är inte administratör.')\n return redirect(url_for('index'))\n\n@app.route('/savecharacter', methods=['GET','POST'])\n@login_required\ndef savecharacter():\n ### skillnad om karaktären redan är sparad eller om den är ny ###\n if session['char']['name'] == '': # inte tidigare sparad\n char = save_char(session['char'], g.user)\n flash('Karaktären har sparats som \"%s\".' % char.name)\n return redirect(url_for('character', charname=char.name))\n else: # tidigare sparad\n c = g.user.characters.filter_by(name=session['char']['name']).first()\n if session['char']['new_name'] != session['char']['start_values']['Namn']:\n start_values = ast.literal_eval(c.start_values)\n start_values['Namn'] = session['char']['new_name']\n c.start_values = str(start_values)\n c.name = unique_charname(start_values['Namn'].split()[0], g.user)\n c.campaign = session['char']['campaign']\n c.notes = session['char']['notes']\n db.session.commit()\n flash('Karaktären har sparats som \"%s\".' % c.name)\n return redirect(url_for('character', charname=c.name))\n\n@app.route('/confirm_delete/<charname>')\n@login_required\ndef confirm_delete(charname):\n return render_template('confirm_delete.html',\n title='Bekräfta',\n charname=charname,\n session=session)\n\n@app.route('/deletecharacter/<charname>', methods=['GET','POST'])\n@login_required\ndef deletecharacter(charname):\n c = g.user.characters.filter_by(name=charname).first()\n db.session.delete(c)\n db.session.commit()\n flash('Karaktären har raderats.')\n return redirect(url_for('user', nickname=g.user.nickname))\n\n@app.route('/lvlup/<charname>', methods=['GET','POST'])\n@login_required\ndef lvlup(charname):\n user = g.user\n char = load_char(charname, user)\n form = lvlupForm()\n if form.validate_on_submit():\n levels = request.form.get('levels',None)\n years = request.form.get('years',None)\n token = '%s-%s-%s' % (charname, levels, years)\n return redirect(url_for('ding', dingtoken=token))\n return render_template('lvlup.html',\n title='Dinga %s' % (char.start_values['Namn']),\n user=user,\n char=char,\n form=form)\n\n@app.route('/ding/<dingtoken>', methods=['GET','POST'])\n@login_required\ndef ding(dingtoken):\n charname, levels, years = dingtoken.split('-')\n char = load_char(charname, g.user)\n char.ding(levels, years)\n c = g.user.characters.filter_by(name=charname).first()\n c.start_values = str(char.start_values)\n c.trace_buys = str(char.trace_buys)\n c.points_left = char.points_left\n c.traits = str(char.traits)\n c.skills = str(char.skills)\n c.hitpoints = str(char.hitpoints)\n c.move_carry = str(char.move_carry)\n db.session.commit()\n flash('%s har dingat.' % char.start_values['Namn'])\n return redirect(url_for('character', charname=char.name))\n\n@app.route('/about')\ndef about():\n return render_template('about.html',\n title='Om')\n\n@app.route('/aboutHed')\ndef aboutHed():\n return render_template('aboutHed.html',\n title='Om Hed')\n\n@app.route('/privacy_policy')\ndef privacy_policy():\n return render_template('privacy_policy.html',\n title='Integritetspolicy')\n\n@app.route('/todo')\ndef todo():\n return render_template('todo.html',\n title='Kommande')\n\n@app.errorhandler(404)\ndef not_found_error(error):\n return render_template('404.html',title='Filen hittades inte'), 404\n\n@app.errorhandler(500)\ndef internal_error(error):\n db.session.rollback()\n return render_template('500.html',title='Oväntat fel'), 500\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.g.user", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 19, "usage_type": "name"}, {"api_name": "forms.CharacterForm", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 22, "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.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 16, "usage_type": "call"}, {"api_name": "app.app", "line_number": 16, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 17, "usage_type": "call"}, {"api_name": "app.app", "line_number": 17, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 35, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.RegistrationForm", "line_number": 40, "usage_type": "call"}, {"api_name": "models.User", "line_number": 43, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 45, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 45, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 46, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 46, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 30, "usage_type": "call"}, {"api_name": "app.app", "line_number": 30, "usage_type": "name"}, {"api_name": "forms.SaveCharForm", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 71, "usage_type": "name"}, {"api_name": "hednpc.create_char", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 92, "usage_type": "name"}, {"api_name": "hednpc.load_char", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 113, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 53, "usage_type": "call"}, {"api_name": "app.app", "line_number": 53, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 54, "usage_type": "call"}, {"api_name": "app.app", "line_number": 54, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 118, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 119, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 121, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 124, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "werkzeug.urls.url_parse", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 130, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 115, "usage_type": "call"}, {"api_name": "app.app", "line_number": 115, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 137, "usage_type": "call"}, {"api_name": "forms.ResetPasswordRequestForm", "line_number": 138, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 140, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 140, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 140, "usage_type": "name"}, {"api_name": "emails.send_password_reset_email", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 145, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 134, "usage_type": "call"}, {"api_name": "app.app", "line_number": 134, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 152, "usage_type": "call"}, {"api_name": "models.User.verify_reset_password_token", "line_number": 153, "usage_type": "call"}, {"api_name": "models.User", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 156, "usage_type": "call"}, {"api_name": "forms.ResetPasswordForm", "line_number": 157, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 160, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 160, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 160, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 163, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 149, "usage_type": "call"}, {"api_name": "app.app", "line_number": 149, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 169, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 169, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 169, "usage_type": "name"}, {"api_name": "app.lm.user_loader", "line_number": 167, "usage_type": "attribute"}, {"api_name": "app.lm", "line_number": 167, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 173, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 173, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 173, "usage_type": "name"}, {"api_name": "app.app.before_request", "line_number": 171, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 171, "usage_type": "name"}, {"api_name": "flask_login.logout_user", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 178, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 175, "usage_type": "call"}, {"api_name": "app.app", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 183, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 183, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 185, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 186, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 186, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 189, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 191, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 191, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 193, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 197, "usage_type": "attribute"}, {"api_name": "app.app.route", "line_number": 180, "usage_type": "call"}, {"api_name": "app.app", "line_number": 180, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 181, "usage_type": "name"}, {"api_name": "forms.EditForm", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 205, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 205, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 206, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 206, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 206, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 206, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 206, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 207, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 207, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 208, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 209, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 209, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 211, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 212, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 200, "usage_type": "call"}, {"api_name": "app.app", "line_number": 200, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 219, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 219, "usage_type": "name"}, {"api_name": "config.ADMINS", "line_number": 219, "usage_type": "name"}, {"api_name": "forms.InviteForm", "line_number": 220, "usage_type": "call"}, {"api_name": "emails.invite_user", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 222, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 223, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 223, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 224, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 229, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 229, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 216, "usage_type": "call"}, {"api_name": "app.app", "line_number": 216, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 235, "usage_type": "name"}, {"api_name": "hednpc.save_char", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 236, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 236, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 236, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 237, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.g.user.characters.filter_by", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 240, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 241, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 243, "usage_type": "name"}, {"api_name": "hednpc.unique_charname", "line_number": 245, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 245, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 245, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 246, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 247, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 248, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 248, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 248, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 249, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 250, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 250, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 231, "usage_type": "call"}, {"api_name": "app.app", "line_number": 231, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 255, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 258, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 252, "usage_type": "call"}, {"api_name": "app.app", "line_number": 252, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 253, "usage_type": "name"}, {"api_name": "flask.g.user.characters.filter_by", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 263, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 263, "usage_type": "name"}, {"api_name": "app.db.session.delete", "line_number": 264, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 264, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 264, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 265, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 265, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 265, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 266, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 267, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 267, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 267, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 267, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 260, "usage_type": "call"}, {"api_name": "app.app", "line_number": 260, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 261, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 272, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 272, "usage_type": "name"}, {"api_name": "hednpc.load_char", "line_number": 273, "usage_type": "call"}, {"api_name": "forms.lvlupForm", "line_number": 274, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 276, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 276, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 276, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 277, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 277, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 277, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 279, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 279, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 280, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 269, "usage_type": "call"}, {"api_name": "app.app", "line_number": 269, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 270, "usage_type": "name"}, {"api_name": "hednpc.load_char", "line_number": 290, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 290, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 290, "usage_type": "name"}, {"api_name": "flask.g.user.characters.filter_by", "line_number": 292, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 292, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 292, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 300, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 300, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 300, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 301, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 302, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 302, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 286, "usage_type": "call"}, {"api_name": "app.app", "line_number": 286, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 287, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 306, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 304, "usage_type": "call"}, {"api_name": "app.app", "line_number": 304, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 311, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 309, "usage_type": "call"}, {"api_name": "app.app", "line_number": 309, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 316, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 314, "usage_type": "call"}, {"api_name": "app.app", "line_number": 314, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 321, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 319, "usage_type": "call"}, {"api_name": "app.app", "line_number": 319, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 326, "usage_type": "call"}, {"api_name": "app.app.errorhandler", "line_number": 324, "usage_type": "call"}, {"api_name": "app.app", "line_number": 324, "usage_type": "name"}, {"api_name": "app.db.session.rollback", "line_number": 330, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 330, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 330, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 331, "usage_type": "call"}, {"api_name": "app.app.errorhandler", "line_number": 328, "usage_type": "call"}, {"api_name": "app.app", "line_number": 328, "usage_type": "name"}]}
+{"seq_id": "260213231", "text": "# Copyright 2017 Neural Networks and Deep Learning lab, MIPT\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport sys\nfrom typing import Iterator, List, Union, Optional\n\n\nimport json\nimport numpy as np\nimport tensorflow as tf\nfrom overrides import overrides\n\nfrom deeppavlov.core.commands.utils import expand_path\nfrom deeppavlov.core.common.log import get_logger\nfrom deeppavlov.core.common.registry import register\nfrom deeppavlov.core.data.utils import zero_pad\nfrom deeppavlov.core.models.component import Component\nfrom deeppavlov.core.models.tf_backend import TfModelMeta\n\nfrom deeppavlov.models.bidirectional_lms.elmo.utils import load_model, load_options_latest_checkpoint\nfrom deeppavlov.models.bidirectional_lms.elmo.data import InferBatcher\n\nlog = get_logger(__name__)\n\n@register('elmo_bilm')\nclass ELMoEmbedder(Component, metaclass=TfModelMeta):\n \"\"\"\n\n \"\"\"\n def __init__(self, model_dir: str, forward_direction_sequence: bool = True, backward_direction_sequence: bool = True,\n pad_zero: bool = False, max_token: Optional[int] = None, mini_batch_size: int = 32, **kwargs) -> None:\n\n self.model_dir = model_dir if '://' in model_dir else str(expand_path(model_dir))\n\n self.forward_direction_sequence = forward_direction_sequence\n self.backward_direction_sequence = backward_direction_sequence\n if not (self.forward_direction_sequence or self.backward_direction_sequence):\n log.error(f'At least one direction sequence of forward_direction_sequence or backward_direction_sequence'\\\n ' must be equal to True.')\n sys.exit(1)\n\n self.pad_zero = pad_zero\n self.max_token = max_token\n self.mini_batch_size = mini_batch_size\n self.model, self.sess, self.init_states, self.batcher, self.options = self._load()\n\n def _load(self):\n \"\"\"\n\n Returns:\n \"\"\"\n\n options, ckpt_file, vocab_file = load_options_latest_checkpoint(self.model_dir)\n\n max_token_length = options['char_cnn']['max_characters_per_token']\n batcher = InferBatcher(vocab_file, max_token_length)\n\n model, sess, init_state_tensors, init_state_values, final_state_tensors = load_model(options, ckpt_file, self.mini_batch_size)\n\n init_states = (init_state_tensors, init_state_values, final_state_tensors)\n\n return model, sess, init_states, batcher, options\n\n def _fill_batch(self, batch):\n \"\"\"\n Fill batch correct values.\n\n Args:\n batch: A list of tokenized text samples.\n\n Returns:\n batch: A list of tokenized text samples.\n \"\"\"\n\n if not batch:\n log.Warning('Need implementation')\n\n filled_batch = []\n for batch_line in batch:\n batch_line = batch_line if batch_line else ['']\n filled_batch.append(batch_line)\n\n batch = filled_batch\n\n if self.max_token:\n batch = [batch_line[:self.max_token] for batch_line in batch]\n tokens_length = [len(batch_line) for batch_line in batch]\n tokens_length_max = max(tokens_length)\n batch_notreverse = [batch_line + ['']*(tokens_length_max - len(batch_line)) for batch_line in batch]\n batch_reverse = [list(reversed(batch_line)) + ['']*(tokens_length_max - len(batch_line)) for batch_line in batch]\n\n return batch_notreverse, batch_reverse, tokens_length\n\n def _mini_batch_fit(self, batch: List[List[str]], init_state_tensors, init_state_values, final_state_tensors,\n *args, **kwargs) -> Union[List[np.ndarray], np.ndarray]:\n \"\"\"\n Embed sentences from a batch.\n\n Args:\n batch: A list of tokenized text samples.\n init_state_tensors: ----.\n init_state_values: ----.\n final_state_tensors: ----.\n\n Returns:\n A mini batch of lm predictions.\n \"\"\"\n batch, batch_reverse, tokens_length = self._fill_batch(batch)\n\n\n # time major\n batch = np.expand_dims(self.batcher.batch_sentences(batch).transpose(1,0,2), axis=2)\n batch_reverse = np.expand_dims(self.batcher.batch_sentences(batch_reverse).transpose(1,0,2), axis=2)\n\n pad_size = self.mini_batch_size - batch.shape[1]\n\n #time iterations\n output_batch = np.zeros((batch.shape[0],batch.shape[1],self.options['n_tokens_vocab']))\n output_batch_reverse = np.zeros((batch.shape[0],batch.shape[1],self.options['n_tokens_vocab']))\n for batch_no, (time_sliced_batch, time_sliced_batch_reverse) in enumerate(zip (batch,batch_reverse)):\n\n #batch padding\n complete_batch = np.pad(time_sliced_batch, ((0,pad_size),(0,0),(0,0)), 'constant',constant_values=260)\n complete_batch_reverse = np.pad(time_sliced_batch_reverse, ((0,pad_size),(0,0),(0,0)), 'constant',constant_values=260)\n\n feed_dict = {t: v for t, v in zip(init_state_tensors, init_state_values)}\n feed_dict[self.model.tokens_characters] = complete_batch\n feed_dict[self.model.tokens_characters_reverse] = complete_batch_reverse\n\n ret = self.sess.run([self.model.individual_output_softmaxes, final_state_tensors],\n feed_dict=feed_dict\n )\n individual_output_softmaxes, init_state_values = ret\n \n #remove padded parts of a batch and save in a share matrix\n output_batch[batch_no] = individual_output_softmaxes[0][:batch.shape[1]]\n output_batch_reverse[batch_no] = individual_output_softmaxes[1][:batch.shape[1]]\n\n # remove a prediction of </S> and next token\n output_batch = output_batch[:-2]\n output_batch_reverse = output_batch_reverse[:-2]\n\n # batch major\n output_batch = output_batch.transpose(1,0,2)\n output_batch_reverse = output_batch_reverse.transpose(1,0,2)\n\n # remove pads of time and reverse a reverse\n output_batch = [batch_line[:tok_len] for batch_line, tok_len in zip(output_batch, tokens_length)]\n output_batch_reverse = [np.flip(batch_line[:tok_len],axis=0) for batch_line, tok_len in zip(output_batch_reverse, tokens_length)]\n \n output_full_batch = []\n for batch_line, batch_line_reverse, tok_len in zip(output_batch, output_batch_reverse, tokens_length):\n line = np.concatenate((batch_line,batch_line_reverse), axis=-1)\n # [time x 2*vocab_size] -> [time x 2 x vocab_size]\n line = np.reshape(line, (tok_len, -1, self.options['n_tokens_vocab']))\n output_full_batch.append(line)\n\n\n return output_full_batch, init_state_values\n\n\n @staticmethod\n def chunk_generator(items_list, chunk_size):\n for i in range(0, len(items_list), chunk_size):\n yield items_list[i:i + chunk_size]\n @overrides\n def __call__(self, batch: List[List[str]],\n *args, **kwargs) -> Union[List[np.ndarray], np.ndarray]:\n \"\"\"\n\n Args:\n batch: A list of tokenized text samples.\n\n Returns:\n A batch of lm predictions.\n \"\"\"\n\n init_state_tensors, init_state_values, final_state_tensors = self.init_states\n\n if len(batch) > self.mini_batch_size:\n batch_gen = self.chunk_generator(batch, self.mini_batch_size)\n output_batch = []\n for mini_batch in batch_gen:\n mini_batch_out, init_state_values = self._mini_batch_fit(mini_batch, \n init_state_tensors, init_state_values, \n final_state_tensors, *args, **kwargs)\n output_batch.extend(mini_batch_out)\n else:\n output_batch, init_state_values = self._mini_batch_fit(batch, \n init_state_tensors, init_state_values, \n final_state_tensors, *args, **kwargs)\n \n self.init_states = (init_state_tensors, init_state_values, final_state_tensors)\n return output_batch\n\n def get_vocab_size(self) -> int:\n \"\"\"\n \n Returns:\n vocab size\n \"\"\"\n\n return self.options['n_tokens_vocab']\n \n def get_vocab(self) -> list:\n \"\"\"\n \n Returns:\n vocab size\n \"\"\"\n\n return self.batcher._lm_vocab._id_to_word\n\n\n def destroy(self):\n self.sess.close()\n", "sub_path": "deeppavlov/models/bidirectional_lms/elmo_bilm.py", "file_name": "elmo_bilm.py", "file_ext": "py", "file_size_in_byte": 9116, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "deeppavlov.core.common.log.get_logger", "line_number": 34, "usage_type": "call"}, {"api_name": "deeppavlov.core.models.component.Component", "line_number": 37, "usage_type": "name"}, {"api_name": "deeppavlov.core.models.tf_backend.TfModelMeta", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "deeppavlov.core.commands.utils.expand_path", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}, {"api_name": "deeppavlov.models.bidirectional_lms.elmo.utils.load_options_latest_checkpoint", "line_number": 64, "usage_type": "call"}, {"api_name": "deeppavlov.models.bidirectional_lms.elmo.data.InferBatcher", "line_number": 67, "usage_type": "call"}, {"api_name": "deeppavlov.models.bidirectional_lms.elmo.utils.load_model", "line_number": 69, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 166, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 178, "usage_type": "name"}, {"api_name": "overrides.overrides", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 179, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 179, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 179, "usage_type": "attribute"}, {"api_name": "deeppavlov.core.common.registry.register", "line_number": 36, "usage_type": "call"}]}
+{"seq_id": "403294904", "text": "\nimport numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import make_pipeline\n\nnp.random.seed(0)\npd.set_option('mode.chained_assignment', None)\n\ndef train_batting_model():\n bat_avg_file = \"output/batting_averages.csv\"\n bat_avg = pd.read_csv(bat_avg_file)\n bat_strike_file = \"output/batting_strike_rate.csv\"\n bat_strike = pd.read_csv(bat_strike_file)\n\n batting = pd.concat([bat_avg, bat_strike], axis=1, join='inner')\n batting = batting.iloc[:, [0, 1, 2, 3, 6, 7]]\n\n X = batting.iloc[:, [1, 2, 4, 5]].to_numpy()\n y = batting.iloc[:, 3].to_numpy()\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n reg = LinearRegression().fit(X_train, y_train)\n # print(reg.score(X_train, y_train), reg.score(X_test, y_test))\n # score is: 0.6636614073163727 0.536466419061135\n\n poly_reg = make_pipeline(PolynomialFeatures(3), LinearRegression())\n poly_reg.fit(X_train, y_train)\n # print(poly_reg.score(X_train, y_train), poly_reg.score(X_test, y_test))\n # score is: 0.9567347115912241 0.8210366419205732\n\n return poly_reg\n\ndef train_bowling_model():\n bowl_avg_file = \"output/bowling_averages.csv\"\n bowl_avg = pd.read_csv(bowl_avg_file)\n bowl_strike_file = \"output/bowling_strike_rate.csv\"\n bowl_strike = pd.read_csv(bowl_strike_file)\n\n bowling = pd.concat([bowl_avg, bowl_strike], axis=1, join='inner')\n bowling = bowling.iloc[:, [0, 1, 2, 3, 5, 7]]\n\n X = bowling.iloc[:, [1, 2, 4, 5]].to_numpy()\n y = bowling.iloc[:, 3].to_numpy()\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n reg = LinearRegression().fit(X_train, y_train)\n # print(reg.score(X_train, y_train), reg.score(X_test, y_test))\n # score is: 0.938561403144434 0.9612341999504406\n\n poly_reg = make_pipeline(PolynomialFeatures(2), LinearRegression())\n poly_reg.fit(X_train, y_train)\n # print(poly_reg.score(X_train, y_train), poly_reg.score(X_test, y_test))\n # score is: 0.9679470110434435 0.558182264509151\n\n return reg\n\ndef clean_data_to_predict_batting(df):\n # sum runs for each batter\n runs = df.groupby('batsman')['total_runs'].sum()\n\n # sum outs for each batter\n dismissals = df.iloc[:, [6, 18]] # taking only batters and players dismissed columns\n dismissals['total_dismiss'] = dismissals['batsman'] == dismissals['player_dismissed']\n dismissals['total_dismiss'] = dismissals['total_dismiss'].replace({True: 1, False: 0})\n dismissals = dismissals.groupby('batsman')['total_dismiss'].sum()\n\n # calculate batting average\n averages = pd.concat([runs, dismissals], axis=1, join='inner')\n averages = averages.drop(averages[(averages.total_dismiss <= 5)].index) # remove batters with 5 or less dismissals\n averages['batting_avg'] = averages['total_runs'] / averages['total_dismiss']\n\n # sum balls faced\n balls = df.iloc[:, 4:]\n balls['num_balls_faced'] = 1\n balls = balls.groupby(balls['batsman'])['num_balls_faced'].sum()\n\n # calculate batting strike rate\n bat_strike = pd.concat([runs, balls], axis=1, join='inner')\n bat_strike['strike_rate'] = (bat_strike['total_runs'] / bat_strike['num_balls_faced']) * 100\n\n # concatenate all features into single dataframe\n batting = pd.concat([averages, bat_strike], axis=1, join='inner')\n batting = batting.reset_index()\n batting = batting.iloc[:, [0, 1, 2, 3, 5, 6]]\n # print(batting)\n\n X = batting.iloc[:, [1, 2, 4, 5]].to_numpy()\n y = batting.iloc[:, 3].to_numpy()\n\n return X, y, batting\n\ndef clean_data_to_predict_bowling(df):\n # sums runs that were scored by opposite team for each bowler\n runs_conceded = df.groupby('bowler')['total_runs'].sum()\n\n # sum wickets taken \n wickets_taken = df.iloc[:, [8, 18]] # taking only bowlers and players dismissed columns\n wickets_taken['total_wickets'] = pd.notnull(wickets_taken.player_dismissed)\n wickets_taken['total_wickets'] = wickets_taken['total_wickets'].replace({True: 1, False: 0})\n wickets_taken = wickets_taken.groupby('bowler')['total_wickets'].sum()\n\n # calculate bowling average\n averages = pd.concat([runs_conceded, wickets_taken], axis=1, join='inner')\n averages = averages.drop(averages[(averages.total_wickets <= 0)].index) # remove players with 0 wickets taken\n averages['bowling_avg'] = averages['total_runs'] / averages['total_wickets']\n\n # sum num balls bowled\n balls_bowled = df.iloc[:, 4:]\n balls_bowled['num_bowled'] = 1\n balls_bowled = balls_bowled.groupby(balls_bowled['bowler'])['num_bowled'].sum()\n\n # calculate bowling strike rate\n bowl_strike = pd.concat([balls_bowled, wickets_taken], axis=1, join='inner')\n bowl_strike = bowl_strike.drop(bowl_strike[(bowl_strike.total_wickets <= 0)].index) # remove players with 0 wickets taken\n bowl_strike['strike_rate'] = bowl_strike['num_bowled'] / bowl_strike['total_wickets']\n\n # concatenate all features into single dataframe\n bowling = pd.concat([averages, bowl_strike], axis=1, join='inner')\n bowling = bowling.reset_index()\n bowling = bowling.iloc[:, [0, 1, 2, 3, 4, 6]]\n # print(bowling)\n\n X = bowling.iloc[:, [1, 2, 4, 5]].to_numpy()\n y = bowling.iloc[:, 3].to_numpy()\n\n return X, y, bowling\n\ndef main():\n batting_ranking_2016_file = \"input/2016_batting_ranking.csv\"\n batting_rank_2016 = pd.read_csv(batting_ranking_2016_file)\n bowling_ranking_2016_file = \"input/2016_bowling_ranking.csv\"\n bowling_rank_2016 = pd.read_csv(bowling_ranking_2016_file)\n\n deliveries_file = \"input/deliveries.csv\"\n deliveries = pd.read_csv(deliveries_file)\n\n # keeping only 2016 data to predict batting/bowling averages \n del_2016 = deliveries.drop(deliveries[(deliveries.match_id < 577) | (deliveries.match_id > 636)].index)\n\n # get data for model to predict averages and dataframe with player names to map back to\n X_bat, y_bat, batting_stats = clean_data_to_predict_batting(del_2016)\n X_bowl, y_bowl, bowling_stats = clean_data_to_predict_bowling(del_2016)\n\n # predict averages with trained models\n bat_model = train_batting_model()\n bowl_model = train_bowling_model()\n bat_predict = bat_model.predict(X_bat)\n bowl_predict = bowl_model.predict(X_bowl)\n\n # add predicted averages to table with player names and original stats and sorting by bat/bowl average\n batting_stats['predicted_bat_avg'] = bat_predict\n batting_stats = batting_stats.sort_values(by=['predicted_bat_avg'], ascending=False)\n batting_stats = batting_stats.reset_index()\n batting_stats = batting_stats.drop(columns=['index'])\n bowling_stats['predicted_bowl_avg'] = bowl_predict\n bowling_stats = bowling_stats.sort_values(by=['predicted_bowl_avg'], ascending=False)\n bowling_stats = bowling_stats.reset_index()\n bowling_stats = bowling_stats.drop(columns=['index'])\n\n # add ranks to our dataframe\n batting_stats['rank'] = batting_stats['predicted_bat_avg'].rank(ascending=False)\n bowling_stats['rank'] = bowling_stats['predicted_bowl_avg'].rank(ascending=False)\n\n # re-arrange columns \n batting_stats = batting_stats[['rank', 'batsman', 'total_runs', 'total_dismiss', 'num_balls_faced', 'strike_rate', 'batting_avg', 'predicted_bat_avg']]\n bowling_stats = bowling_stats[['rank', 'bowler', 'total_runs', 'total_wickets', 'num_bowled', 'strike_rate', 'bowling_avg', 'predicted_bowl_avg']]\n\n print(\"_____Batting Stats with Model Prediction_____\")\n print(batting_stats)\n # print(batting_rank_2016)\n print(\"_____Bowling Stats with Model Prediction_____\")\n print(bowling_stats)\n # print(bowling_rank_2016)\n\n batting_stats.to_csv(\"output/batting_player_ranking.csv\")\n bowling_stats.to_csv(\"output/bowling_player_ranking.csv\")\n\n \n\nif __name__=='__main__':\n main()", "sub_path": "predict_2016_player_ranking.py", "file_name": "predict_2016_player_ranking.py", "file_ext": "py", "file_size_in_byte": 7912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.random.seed", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pandas.set_option", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 140, "usage_type": "call"}]}
+{"seq_id": "226232469", "text": "import serial\r\nimport numpy as np\r\nimport argparse\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nfrom datetime import datetime\r\nimport pytz\r\nimport mysql.connector\r\nfrom ClassFrame import Frame\r\nimport runSvm\r\nfrom ClassLand import Land\r\nimport Preprocessing\r\nfrom ClassResults import results\r\nfrom ClassSensor import sensor\r\nfrom ClassLed import Ledsystem\r\n\r\nArduino_Serial = serial.Serial('COM3', 9600)\r\nArduino_Serial2 = serial.Serial('COM4', 9600)\r\n\r\nvalue = 0\r\nLand_ID = 1\r\nstage = 1\r\nLED_ID = 0\r\n\r\nland = Land()\r\nplantid, landownerid = land.getLandowner()\r\nplantname = land.getPlant(plantid)\r\nthreshold_percentage = land.getTrainingThreshold(plantid)\r\nHourtobeOpened, HourtobeClosed = land.getTimer(plantid)\r\ncurrent_time = datetime.now(pytz.timezone('Africa/Cairo'))\r\nprint(plantname)\r\nprint(\"date and time =\", current_time)\r\nprint(\"The time is now: = %s:%s:%s\" % (current_time.hour, current_time.minute, current_time.second))\r\ntime2 = current_time.minute\r\nhour = current_time.hour\r\nmins = current_time.minute\r\nsec = current_time.second\r\ntime = 0\r\n\r\nwhile True:\r\n while (hour>=HourtobeOpened and hour<=HourtobeClosed):\r\n if time == time2 + 5:\r\n frame = Frame()\r\n img = frame.Mainfunc()\r\n # img =CaptureCameraFrames.Main()\r\n # img = cv2.imread('dataset\\\\29.jpg') # dataset\\\\29.jpg dataset\\\\31.jpg dataset\\\\32.jpg 1.jpg With.jpg\r\n classificationresponse = runSvm.runsvm(img)\r\n ratio_green, ratio_red = Preprocessing.convertRGBtoHSV(img)\r\n\r\n if np.round(ratio_green * 100, 2) >= threshold_percentage * 100 and classificationresponse == \"Tomatoes\":\r\n print(\"Change Blue/Green Light to Red light\")\r\n value = 1\r\n stage = 2\r\n # Arduino_Serial.write('1'.encode())\r\n elif np.round(ratio_red * 100, 2) >= threshold_percentage * 100:\r\n print(\"Keep Red light\")\r\n value = 1\r\n stage = 3\r\n # Arduino_Serial.write('1'.encode())\r\n else:\r\n print(\"Keep Blue/Green light\")\r\n value = 2\r\n stage = 1\r\n # Arduino_Serial.write('0'.encode())\r\n res=results()\r\n res.inserttestingthreshold(datetime, stage, ratio_green)\r\n sens=sensor()\r\n sens.insertsensorreadings(Arduino_Serial2, datetime, landownerid)\r\n # cv2.waitKey(0)\r\n # if cv2.waitKey(0):\r\n # Arduino_Serial.write('2'.encode())\r\n current_time = datetime.now(pytz.timezone('Africa/Cairo'))\r\n time2 = current_time.minute\r\n current_time = datetime.now(pytz.timezone('Africa/Cairo'))\r\n hour = current_time.hour\r\n mins = current_time.minute\r\n time = current_time.minute\r\n sec = current_time.second\r\n led = Ledsystem()\r\n led.insertledData(datetime, value, plantid)\r\n\r\n if value == 1:\r\n print(\"R\")\r\n Arduino_Serial.write('1'.encode()) # red led turn on\r\n elif value == 2:\r\n print(\"BG\")\r\n Arduino_Serial.write('0'.encode()) # Blue & Green led turn on\r\n\r\n current_time = datetime.now(pytz.timezone('Africa/Cairo'))\r\n hour = current_time.hour\r\n mins = current_time.minute\r\n time = current_time.minute\r\n sec = current_time.second\r\n", "sub_path": "greenhouseSystem/MainSystem.py", "file_name": "MainSystem.py", "file_ext": "py", "file_size_in_byte": 3342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "serial.Serial", "line_number": 17, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 18, "usage_type": "call"}, {"api_name": "ClassLand.Land", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 30, "usage_type": "call"}, {"api_name": "ClassFrame.Frame", "line_number": 43, "usage_type": "call"}, {"api_name": "runSvm.runsvm", "line_number": 47, "usage_type": "call"}, {"api_name": "Preprocessing.convertRGBtoHSV", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 55, "usage_type": "call"}, {"api_name": "ClassResults.results", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "argument"}, {"api_name": "ClassSensor.sensor", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 74, "usage_type": "call"}, {"api_name": "ClassLed.Ledsystem", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 89, "usage_type": "call"}]}
+{"seq_id": "337755415", "text": "#-*- coding: UTF-8 -*-\nimport logging as log\nfrom OpenGL.GL import *\nimport numpy as np\n\nclass OpenGLVisual(object):\n\n\tdef __init__(self, mode, bodyOrigin, visOrigin, vertices, normals, indices, texCoords, linewidth, color=None):\n\t\tself.__mode = mode\n\t\tself.__bodyOrigin = bodyOrigin\n\t\tself.__visOrigin = visOrigin\n\t\tself.__vertices = vertices\n\t\tself.__normals = normals\n\t\tself.__indices = indices\n\t\tself.__texCoords = texCoords\n\t\tself.__linewidth = linewidth\n\t\tself.__color = color\n\t\tself.__material = None\n\t\tself.__diffuseTexture = None\n\t\tself.__specularTexture = None\n\t\tself.__vao = None\n\t\tself.__vbo = None\n\t\tself.__ebo = None\n\t\tself.__diffuseTextureID = None\n\t\tself.__specularTextureID = None\n\t\tself.__children = []\n\n\tdef partialCopy(self, bodyOrigin, visOrigin):\n\t\tcopy = OpenGLVisual(self.__mode, bodyOrigin, visOrigin, \n\t\t\tself.__vertices, self.__normals, \n\t\t\tself.__indices, self.__texCoords, self.__linewidth, self.__color)\n\t\tcopy.setVAO(self.__vao)\n\t\tcopy.setVBO(self.__vbo)\n\t\tcopy.setEBO(self.__ebo)\n\t\tcopy.setDiffuseTextureID(self.__diffuseTextureID)\n\t\tcopy.setSpecularTextureID(self.__specularTextureID)\n\t\treturn copy\n\n\tdef hash(self):\n\t\treturn hash(self.__vertices.tostring())\n\n\n\tdef getBodyOrigin(self):\n\t\treturn self.__bodyOrigin\n\n\n\tdef getVisOrigin(self):\n\t\treturn self.__visOrigin\n\n\n\tdef setup(self):\n\t\tself.__vao = glGenVertexArrays(1)\n\t\tself.__vbo = glGenBuffers(1)\n\t\tlog.info('New Buffer, vao: %d, vbo: %d, size: %d' %(self.__vao, self.__vbo, self.__vertices.shape[0]))\n\t\tglBindVertexArray(self.__vao)\n\t\tglBindBuffer(GL_ARRAY_BUFFER, self.__vbo)\n\n\t\tif self.__indices is not None:\n\t\t\tself.__ebo = glGenBuffers(1)\n\t\t\tglBindBuffer(GL_ELEMENT_ARRAY_BUFFER, self.__ebo)\n\t\t\tglBufferData(GL_ELEMENT_ARRAY_BUFFER, self.__indices.reshape(-1), GL_STATIC_DRAW)\n\t\t\tlog.info('New Buffer ebo: %d, size: %d' % (self.__ebo, self.__indices.reshape(-1).shape[0]))\n\n\t\tglBufferData(GL_ARRAY_BUFFER, self.__vertices.reshape(-1), GL_STATIC_DRAW)\n\t\tglVertexAttribPointer(0, self.__vertices.shape[1], GL_FLOAT, False, self.__vertices.shape[1]*4, ctypes.c_void_p(0))\n\t\tglEnableVertexAttribArray(0)\n\n\t\tglBindBuffer(GL_ARRAY_BUFFER, 0)\n\t\tglBindVertexArray(0)\n\n\tdef draw(self, shader):\n\t\torigin = np.dot(self.__bodyOrigin, self.__visOrigin)\n\t\tglUniformMatrix4fv(glGetUniformLocation(shader.getProgram(), 'model'), 1, True, origin)\n\t\tif self.__color is not None:\n\t\t\tglUniform4f(glGetUniformLocation(shader.getProgram(), 'color'), self.__color[0], self.__color[1], self.__color[2], self.__color[3])\n\t\t\n\t\tif self.__linewidth is not None:\n\t\t\tglLineWidth(self.__linewidth)\n\n\t\tglBindVertexArray(self.__vao)\n\t\tif self.__indices is None:\n\t\t\tglDrawArrays(self.__mode, 0, self.__vertices.shape[0])\n\t\telse:\n\t\t\tglDrawElements(self.__mode, self.__indices.reshape(-1).shape[0], GL_UNSIGNED_INT, None)\n\t\tglLineWidth(1)\n\t\tglBindVertexArray(0)\n\n\n\tdef delete(self):\n\t\tglDeleteVertexArrays(1, (self.__vao,))\n\t\tglDeleteBuffers(1, (self.__vbo,))\n\t\tlog.info('Deleting Buffer, vao: %d, vbo: %d' %(self.__vao, self.__vbo))\n\t\tif self.__ebo is not None:\n\t\t\tglDeleteBuffers(1, (self.__ebo,))\n\t\t\tlog.info('Deleting Buffer, ebo: %d' % self.__ebo)\n\n\n\tdef addChild(self, child):\n\t\tself.__children.append(child)\n\n\tdef setVAO(self, vao):\n\t\tself.__vao = vao\n\n\tdef setVBO(self, vbo):\n\t\tself.__vbo = vbo\n\n\tdef setEBO(self, ebo):\n\t\tself.__ebo = ebo\n\n\tdef setDiffuseTextureID(self, diffuseTextureID):\n\t\tself.__diffuseTextureID = diffuseTextureID\n\n\tdef setSpecularTextureID(self, specularTextureID):\n\t\tself.__specularTextureID = specularTextureID", "sub_path": "src/view/OpenGLVisual.py", "file_name": "OpenGLVisual.py", "file_ext": "py", "file_size_in_byte": 3501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.info", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 95, "usage_type": "call"}]}
+{"seq_id": "430164610", "text": "from django.test import TestCase\nfrom django.test import Client\nfrom django.core.urlresolvers import reverse\nfrom courses.models import Course, Lesson\n\n\nclass CourseTest(TestCase):\n\n def test_course_list(self):\n client = Client()\n response = client.get('/')\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'No courses here')\n\n course1 = Course.objects.create(name='Eng 101', brief='English 101')\n course2 = Course.objects.create(name='Eng 102', brief='English 102')\n response = client.get('/')\n self.assertEqual(response.status_code, 200)\n self.assertEqual(Course.objects.all().count(), 2)\n\n for contextData in Course.objects.all():\n self.assertContains(response, contextData.name)\n self.assertContains(response, contextData.description)\n\n\nclass CourseDetailTest(TestCase):\n\n def test_course_detail(self):\n courseNumber = 1\n client = Client()\n response = client.get(reverse('courses:course', args=(courseNumber,)))\n self.assertEqual(response.status_code, 404)\n\n nCourse = Course.objects.create(name='Eng 101', brief='English 101',\n description=u'Learn english!')\n\n for i in xrange(10):\n nLesson = Lesson.objects.create(theme=u'Theme # {}'.format(i),\n description='Desc # {}'.format(i),\n course=nCourse,\n number=i,)\n\n response = client.get(reverse('courses:course', args=(courseNumber,)))\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, nCourse.name.upper())\n\n for lesson in nCourse.lesson_set.all():\n self.assertContains(response, lesson.theme)\n\n\n\n", "sub_path": "courses/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 10, "usage_type": "call"}, {"api_name": "courses.models.Course.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 15, "usage_type": "name"}, {"api_name": "courses.models.Course.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 16, "usage_type": "name"}, {"api_name": "courses.models.Course.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 19, "usage_type": "name"}, {"api_name": "courses.models.Course.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 21, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 26, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 30, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 31, "usage_type": "call"}, {"api_name": "courses.models.Course.objects.create", "line_number": 34, "usage_type": "call"}, {"api_name": "courses.models.Course.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "courses.models.Course", "line_number": 34, "usage_type": "name"}, {"api_name": "courses.models.Lesson.objects.create", "line_number": 38, "usage_type": "call"}, {"api_name": "courses.models.Lesson.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "courses.models.Lesson", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 43, "usage_type": "call"}]}
+{"seq_id": "330924215", "text": "import joblib\nimport nerds\nimport os\nimport pandas as pd\nimport random\nimport torch\n\nfrom simpletransformers.ner.ner_model import NERModel as ST_NERModel\n\nfrom nerds.models import NERModel\nfrom nerds.utils import (flatten_list, get_logger, \n write_param_file, get_labels_from_data)\n\nfrom sklearn.model_selection import train_test_split\n\nlog = get_logger()\n\nclass TransformerNER(NERModel):\n\n def __init__(self,\n lang_model_family=\"bert\",\n lang_model_name=\"bert-base-cased\",\n model_dir=\"models\",\n max_sequence_length=128,\n batch_size=32,\n max_iter=4,\n learning_rate=4e-5,\n padding_tag=\"O\",\n random_state=42):\n \"\"\" Construct a Transformer NER model. This is a generic front-end\n NER class that can work with multiple Transformer architectures.\n\n Parameters\n ----------\n model_dir : str, optional, default \"./models\"\n the directory to which model artifacts will be written out to.\n lang_model_family : str, optional, default \"bert\"\n the Transformer Language Model (LM) Family to use. Following LM\n families are supported - BERT, RoBERTa, DistilBERT, CamemBERT,\n and XLM-RoBERTa.\n lang_model_name : str, optional, default \"bert-base-cased\"\n name of the pre-trained LM to use.\n model_dir : string, optional, default \"models\"\n directory path to folder where model artifacts will be written\n max_sequence_length : int, optional, default 128\n maximum number of tokens in each input sentence. Note that\n because of word-piece tokenization, this is not the actual\n number of tokens, but the number of word-pieces.\n batch_size : int, optional, default 32\n the batch size to use during training and prediction.\n max_iter : int, optional, default 4\n the number of epochs to train the model.\n learning_rate: float, optional, default 4e-5\n learning rate for Adam optimizer.\n padding_tag : str, default \"O\"\n padding tag to use when number of predicted tags is smaller\n than the number of label tags because of word-piece tokenization.\n Default value ensures that you won't have to align, at the cost\n of a drop in reported performance. You should choose a non-default\n value and align using nerds.utils.align_labels_and_predictions().\n random_state : int, optional, default 42\n random state to set.\n\n Attributes\n ----------\n model_ : reference to the SimpleTranformers NERModel object.\n model_args_ : flat dictionary composed of values from constructor.\n labels_ : list of labels to use in model.\n \"\"\"\n super().__init__()\n self.model_dir = model_dir\n self.lang_model_family = lang_model_family\n self.lang_model_name = lang_model_name\n self.max_sequence_length = max_sequence_length\n self.batch_size = batch_size\n self.max_iter = max_iter\n self.learning_rate = learning_rate\n self.padding_tag = padding_tag\n self.random_state = random_state\n # attributes\n self.model_ = None\n self.model_args_ = None\n self.labels_ = None\n\n\n def fit(self, X, y):\n \"\"\" Trains the NER model. Input is list of list of tokens and tags.\n\n Parameters\n ----------\n X : list(list(str))\n list of list of tokens\n y : list(list(str))\n list of list of BIO tags.\n\n Returns\n -------\n self\n \"\"\"\n self._build_model_args()\n self.labels_ = get_labels_from_data(y)\n self.model_ = ST_NERModel(\n self.lang_model_family,\n self.lang_model_name,\n labels=self.labels_,\n use_cuda=torch.cuda.is_available(),\n args=self.model_args_)\n \n os.makedirs(self.model_dir, exist_ok=True)\n\n Xtrain, Xval, ytrain, yval = train_test_split(X, y, \n test_size=0.1, random_state=self.random_state)\n train_df = self._build_dataframe_from_data_labels(Xtrain, ytrain)\n eval_df = self._build_dataframe_from_data_labels(Xval, yval)\n self.model_.train_model(train_df, eval_df=eval_df)\n return self\n\n\n def predict(self, X):\n \"\"\" Predicts using the NER model\n\n Parameters\n ----------\n X : list(list(str))\n list of list of tokens\n\n Returns\n -------\n y : list(list(str))\n list of list of predicted BIO tags.\n \"\"\"\n if self.model_ is None:\n raise ValueError(\"No model found, either run fit() to train or load() to load a trained model.\")\n\n predictions, _ = self.model_.predict([\" \".join(toks) for toks in X])\n # predictions are list of {token:tag} dicts\n predictions = [[tag for token_tag_dict in prediction \n for (token, tag) in token_tag_dict.items()] \n for prediction in predictions]\n # handle possible truncation of prediction (and subsequent mismatch\n # with labels) because of too long token list.\n predictions_a = []\n for prediction, tokens in zip(predictions, X):\n if len(prediction) < len(tokens):\n prediction.extend(\n [self.padding_tag] * (len(tokens) - len(prediction)))\n predictions_a.append(prediction)\n return predictions_a\n\n\n def save(self, dirpath=None):\n \"\"\" This is a no-op for this NER, model artifacts are saved automatically\n after every epoch.\n\n Parameters\n ----------\n dirpath : str, optional\n directory to which the param file will be written. If not \n specified, it will use the folder specified by the model's \n output_dir.\n\n Returns\n -------\n None\n \"\"\"\n if self.model_ is None:\n raise ValueError(\"No model artifacts to save, either run fit() to train or load() pretrained model.\")\n if dirpath is None:\n self._build_model_args()\n dirpath = self.model_args_[\"output_dir\"]\n attr_dict = {\n \"model_args\": self.model_args_,\n \"labels\": self.labels_\n }\n joblib.dump(attr_dict, os.path.join(dirpath, \"attr_dict.pkl\"))\n write_param_file(self.get_params(), os.path.join(dirpath, \"params.yaml\"))\n\n\n def load(self, dirpath=None):\n \"\"\" Loads a trained model from specified folder on disk.\n\n Parameters\n ----------\n dirpath : str, optional\n directory from which model artifacts should be loaded. If\n not provided, uses the model_args_[\"output_dir].\n\n Returns\n -------\n self\n \"\"\"\n if dirpath is None:\n self._build_model_args()\n dirpath = self.model_args_[\"output_dir\"]\n if not os.path.exists(dirpath):\n raise ValueError(\"Model directory not found: {:s}\".format(dirpath))\n attr_dict = joblib.load(os.path.join(dirpath, \"attr_dict.pkl\"))\n self.model_args_ = attr_dict[\"model_args\"]\n self.labels_ = attr_dict[\"labels\"]\n self.model_ = ST_NERModel(self.lang_model_family, dirpath,\n args=self.model_args_,\n labels=self.labels_,\n use_cuda=torch.cuda.is_available())\n return self\n\n\n def _build_model_args(self):\n \"\"\" Builds the model_arg dictionary from constructor parameters.\n\n Parameters\n ----------\n none\n\n Returns\n -------\n none\n \"\"\"\n self.model_args_ = {\n \"output_dir\": os.path.join(self.model_dir, \"outputs\"),\n \"cache_dir\": os.path.join(self.model_dir, \"cache\"),\n \"fp16\": False,\n \"fp16_opt_level\": \"01\",\n \"max_seq_length\": self.max_sequence_length,\n \"train_batch_size\": self.batch_size,\n \"gradient_accumulation_steps\": 1,\n \"num_train_epochs\": self.max_iter,\n \"weight_decay\": 0,\n \"learning_rate\": self.learning_rate,\n \"adam_epsilon\": 1e-8,\n \"warmup_ratio\": 0.06,\n \"warmup_steps\": 0,\n \"max_grad_norm\": 1.0,\n \"eval_batch_size\": self.batch_size,\n \"logging_steps\": 50,\n \"save_steps\": 2000,\n \"overwrite_output_dir\": True,\n \"reprocess_input_data\": True,\n \"evaluate_during_training\": True,\n \"process_count\": os.cpu_count() - 2 if os.cpu_count() > 2 else 1,\n \"n_gpu\": torch.cuda.device_count() if torch.cuda.is_available() else 0\n }\n\n\n def _build_dataframe_from_data_labels(self, data, labels):\n \"\"\" Builds Pandas dataframe from data and labels.\n\n Parameters\n ----------\n data : list(list(str))\n list of list of tokens\n labels : list(list(str))\n list of list of tags\n\n Returns\n -------\n Pandas DataFrame with columns (sentence_id, words, labels).\n \"\"\"\n columns = [\"sentence_id\", \"words\", \"labels\"]\n recs = []\n for sid, (tokens, tags) in enumerate(zip(data, labels)):\n for token, tag in zip(tokens, tags):\n recs.append((sid, token, tag))\n data_df = pd.DataFrame.from_records(recs, columns=columns)\n return data_df\n\n", "sub_path": "nerds/models/transformer.py", "file_name": "transformer.py", "file_ext": "py", "file_size_in_byte": 9874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "nerds.utils.get_logger", "line_number": 16, "usage_type": "call"}, {"api_name": "nerds.models.NERModel", "line_number": 18, "usage_type": "name"}, {"api_name": "nerds.utils.get_labels_from_data", "line_number": 101, "usage_type": "call"}, {"api_name": "simpletransformers.ner.ner_model.NERModel", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 111, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "nerds.utils.write_param_file", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "simpletransformers.ner.ner_model.NERModel", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.cpu_count", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 240, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 240, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 263, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 263, "usage_type": "attribute"}]}
+{"seq_id": "319756061", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n # Clay.pp_pygments\n\n Highlight codeblocks with Pygments by passing HTML through it.\n\n\"\"\"\nimport re\n\ntry:\n import pygments\n from pygments import highlight\n from pygments.formatters import HtmlFormatter\n from pygments.lexers import get_lexer_by_name, ClassNotFound\n enabled = True\nexcept ImportError:\n enabled = False\n\n\nRX_CODEBLOCK = re.compile(r'<pre(?: lang=\"([a-z0-9]+#?)\")?><code'\n '(?: class=\"([a-z0-9_\\-]+#?).*?\")?>(.*?)</code></pre>',\n re.IGNORECASE | re.DOTALL)\n\n\ndef _unescape_html(html):\n html = html.replace('<', '<')\n html = html.replace('>', '>')\n html = html.replace('&', '&')\n html = html.replace('"', '\"')\n return html\n\n\ndef _highlight_match(match):\n language, classname, code = match.groups()\n lang_or_class = language or classname\n\n if lang_or_class is None:\n return match.group(0)\n\n linenos = False\n if lang_or_class.endswith('#'):\n lang_or_class = lang_or_class[:-1]\n linenos = True\n\n try:\n lexer = get_lexer_by_name(lang_or_class)\n except ClassNotFound:\n return match.group(0)\n \n formatter = HtmlFormatter(\n cssclass='highlight ' + lang_or_class,\n linenos=linenos\n )\n\n return highlight(_unescape_html(code), lexer, formatter)\n\n\ndef process(html):\n return RX_CODEBLOCK.sub(_highlight_match, html)\n\n", "sub_path": "clay/pp_pygments.py", "file_name": "pp_pygments.py", "file_ext": "py", "file_size_in_byte": 1391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "re.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygments.lexers.get_lexer_by_name", "line_number": 46, "usage_type": "call"}, {"api_name": "pygments.lexers.ClassNotFound", "line_number": 47, "usage_type": "name"}, {"api_name": "pygments.formatters.HtmlFormatter", "line_number": 50, "usage_type": "call"}, {"api_name": "pygments.highlight", "line_number": 55, "usage_type": "call"}]}
+{"seq_id": "594692126", "text": "import bpy\nfrom .utils import *\n\n\nclass LexSmithyEngineComponentProperty(bpy.types.PropertyGroup):\n def get_attribute_value_as_bool(self):\n return bool(self.string_value)\n\n def set_attribute_value_as_bool(self, val):\n self.string_value = str(val) if val else \"\"\n\n def get_attribute_value_as_float(self):\n val = 0\n try: val = float(self.string_value)\n finally: return val\n\n def set_attribute_value_as_float(self, val):\n self.string_value = str(val)\n\n def get_attribute_value_as_int(self):\n val = 0\n try: val = int(round(float(self.string_value)))\n finally: return val\n\n def set_attribute_value_as_int(self, val):\n self.string_value = str(val)\n\n def set_value_generic(self, val):\n if type(val) is bool:\n self.bool_value = val\n elif type(val) is float:\n self.float_value = val\n elif type(val) is int:\n self.int_value = val\n elif type(val) is str:\n self.string_value = val\n\n def value_matches_type(self, value_type):\n if value_type is bool:\n return self.string_value in ['True', \"\"]\n elif value_type is float:\n try:\n val = float(self.string_value)\n return True\n except:\n return False\n elif value_type is int:\n try: \n val = int(round(float(self.string_value)))\n return True\n except:\n return False\n elif value_type is str:\n return True\n\n # properties\n string_value : bpy.props.StringProperty()\n bool_value : bpy.props.BoolProperty(get=get_attribute_value_as_bool, set=set_attribute_value_as_bool)\n float_value : bpy.props.FloatProperty(get=get_attribute_value_as_float, set=set_attribute_value_as_float)\n int_value : bpy.props.IntProperty(get=get_attribute_value_as_int, set=set_attribute_value_as_int)\n\n def draw(self, property_definition, layout):\n name, default = property_definition\n if type(default) is bool:\n layout.prop(self, \"bool_value\", text=name)\n elif type(default) is float:\n layout.prop(self, \"float_value\", text=name)\n elif type(default) is str:\n layout.prop(self, \"string_value\", text=name)\n elif type(default) is int:\n layout.prop(self, \"int_value\", text=name)\n\n\n\nclass LexSmithyEngineComponent(bpy.types.PropertyGroup):\n\n def type_updated(self, context):\n self.init(self.component_type)\n\n def init(self, component_type):\n c_def = fetch_component_definition(component_type)\n if c_def:\n self.component_id = c_def['id']\n\n # properties\n prop_defs = c_def['properties'].items()\n for i, (prop_name, default_value) in enumerate(prop_defs):\n p = self.properties[prop_name] if prop_name in self.properties else None\n if not p:\n p = self.properties.add()\n p.name = prop_name\n p.set_value_generic(default_value)\n\n def valid(self):\n c_def = fetch_component_definition(self.component_type)\n return c_def is not None\n\n def fetch_properties(self):\n properties = []\n c_def = fetch_component_definition(self.component_type)\n prop_defs = c_def['properties'].items()\n for prop_def in prop_defs:\n prop_name, prop_default = prop_def\n p = self.properties[prop_name] if prop_name in self.properties else None\n assert p\n properties.append({'bpy_property': p, 'definition': prop_def})\n return properties\n \n # props\n component_id : bpy.props.IntProperty(default=0)\n component_type : bpy.props.StringProperty(default=\"health\", update=type_updated)\n properties : bpy.props.CollectionProperty(type=LexSmithyEngineComponentProperty)\n", "sub_path": "lex_game/smithy/engine_properties.py", "file_name": "engine_properties.py", "file_ext": "py", "file_size_in_byte": 3934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "bpy.types", "line_number": 5, "usage_type": "attribute"}, {"api_name": "bpy.props.StringProperty", "line_number": 57, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 57, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 58, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bpy.props.FloatProperty", "line_number": 59, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 59, "usage_type": "attribute"}, {"api_name": "bpy.props.IntProperty", "line_number": 60, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 60, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 75, "usage_type": "attribute"}, {"api_name": "bpy.props.IntProperty", "line_number": 110, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 110, "usage_type": "attribute"}, {"api_name": "bpy.props.StringProperty", "line_number": 111, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 111, "usage_type": "attribute"}, {"api_name": "bpy.props.CollectionProperty", "line_number": 112, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 112, "usage_type": "attribute"}]}
+{"seq_id": "284363536", "text": "#!/usr/bin/env python\nimport unittest\n\nimport ray\n\nfrom ray.rllib.algorithms.apex_ddpg import ApexDDPGConfig\nfrom ray.rllib.algorithms.sac import SACConfig\nfrom ray.rllib.algorithms.simple_q import SimpleQConfig\nfrom ray.rllib.algorithms.ppo import PPOConfig\nfrom ray.rllib.algorithms.es import ESConfig\nfrom ray.rllib.algorithms.dqn import DQNConfig\nfrom ray.rllib.algorithms.ddpg import DDPGConfig\nfrom ray.rllib.algorithms.ars import ARSConfig\nfrom ray.rllib.algorithms.a3c import A3CConfig\nfrom ray.rllib.utils.test_utils import test_ckpt_restore\nimport os\n\n\n# As we transition things to RLModule API the explore=False will get\n# deprecated. For now, we will just not set it. The reason is that the RLModule\n# API has forward_exploration() method that can be overriden if user needs to\n# really turn of the stochasticity. This test in particular is robust to\n# explore=None if we compare the mean of the distribution of actions for the\n# same observation to be the same.\nalgorithms_and_configs = {\n \"A3C\": (\n A3CConfig()\n .exploration(explore=False)\n .rollouts(num_rollout_workers=1)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"APEX_DDPG\": (\n ApexDDPGConfig()\n .exploration(explore=False)\n .rollouts(observation_filter=\"MeanStdFilter\", num_rollout_workers=2)\n .reporting(min_time_s_per_iteration=1)\n .training(\n optimizer={\"num_replay_buffer_shards\": 1},\n num_steps_sampled_before_learning_starts=0,\n )\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"ARS\": (\n ARSConfig()\n .exploration(explore=False)\n .rollouts(num_rollout_workers=2, observation_filter=\"MeanStdFilter\")\n .training(num_rollouts=10, noise_size=2500000)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"DDPG\": (\n DDPGConfig()\n .exploration(explore=False)\n .reporting(min_sample_timesteps_per_iteration=100)\n .training(num_steps_sampled_before_learning_starts=0)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"DQN\": (\n DQNConfig()\n .exploration(explore=False)\n .training(num_steps_sampled_before_learning_starts=0)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"ES\": (\n ESConfig()\n .exploration(explore=False)\n .training(episodes_per_batch=10, train_batch_size=100, noise_size=2500000)\n .rollouts(observation_filter=\"MeanStdFilter\", num_rollout_workers=2)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"PPO\": (\n # See the comment before the `algorithms_and_configs` dict.\n # explore is set to None for PPO in favor of RLModule API support.\n PPOConfig()\n .training(num_sgd_iter=5, train_batch_size=1000)\n .rollouts(num_rollout_workers=2)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"SimpleQ\": (\n SimpleQConfig()\n .exploration(explore=False)\n .training(num_steps_sampled_before_learning_starts=0)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n \"SAC\": (\n SACConfig()\n .exploration(explore=False)\n .training(num_steps_sampled_before_learning_starts=0)\n .resources(num_gpus=int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")))\n ),\n}\n\n\nclass TestCheckpointRestorePG(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n ray.init()\n\n @classmethod\n def tearDownClass(cls):\n ray.shutdown()\n\n def test_a3c_checkpoint_restore(self):\n # TODO(Kourosh) A3C cannot run a restored algorithm for some reason.\n test_ckpt_restore(\n algorithms_and_configs[\"A3C\"], \"CartPole-v1\", run_restored_algorithm=False\n )\n\n def test_ppo_checkpoint_restore(self):\n test_ckpt_restore(algorithms_and_configs[\"PPO\"], \"CartPole-v1\")\n\n\nclass TestCheckpointRestoreOffPolicy(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n ray.init()\n\n @classmethod\n def tearDownClass(cls):\n ray.shutdown()\n\n def test_apex_ddpg_checkpoint_restore(self):\n test_ckpt_restore(algorithms_and_configs[\"APEX_DDPG\"], \"Pendulum-v1\")\n\n def test_ddpg_checkpoint_restore(self):\n test_ckpt_restore(\n algorithms_and_configs[\"DDPG\"], \"Pendulum-v1\", replay_buffer=True\n )\n\n def test_dqn_checkpoint_restore(self):\n test_ckpt_restore(\n algorithms_and_configs[\"DQN\"],\n \"CartPole-v1\",\n replay_buffer=True,\n )\n\n def test_sac_checkpoint_restore(self):\n test_ckpt_restore(\n algorithms_and_configs[\"SAC\"], \"Pendulum-v1\", replay_buffer=True\n )\n\n def test_simpleq_checkpoint_restore(self):\n test_ckpt_restore(\n algorithms_and_configs[\"SimpleQ\"], \"CartPole-v1\", replay_buffer=True\n )\n\n\nclass TestCheckpointRestoreEvolutionAlgos(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n ray.init()\n\n @classmethod\n def tearDownClass(cls):\n ray.shutdown()\n\n def test_ars_checkpoint_restore(self):\n test_ckpt_restore(algorithms_and_configs[\"ARS\"], \"CartPole-v1\")\n\n def test_es_checkpoint_restore(self):\n test_ckpt_restore(algorithms_and_configs[\"ES\"], \"CartPole-v1\")\n\n\nif __name__ == \"__main__\":\n import pytest\n import sys\n\n # One can specify the specific TestCase class to run.\n # None for all unittest.TestCase classes in this file.\n class_ = sys.argv[1] if len(sys.argv) > 1 else None\n sys.exit(pytest.main([\"-v\", __file__ + (\"\" if class_ is None else \"::\" + class_)]))\n", "sub_path": "rllib/tests/test_algorithm_checkpoint_restore.py", "file_name": "test_algorithm_checkpoint_restore.py", "file_ext": "py", "file_size_in_byte": 5739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "ray.rllib.algorithms.a3c.A3CConfig", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 30, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.apex_ddpg.ApexDDPGConfig", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.ars.ARSConfig", "line_number": 44, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 48, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.ddpg.DDPGConfig", "line_number": 51, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 55, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.dqn.DQNConfig", "line_number": 58, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 61, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 61, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.es.ESConfig", "line_number": 64, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 68, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 68, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.ppo.PPOConfig", "line_number": 73, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.simple_q.SimpleQConfig", "line_number": 79, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 82, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "ray.rllib.algorithms.sac.SACConfig", "line_number": 85, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 88, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 88, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 93, "usage_type": "attribute"}, {"api_name": "ray.init", "line_number": 96, "usage_type": "call"}, {"api_name": "ray.shutdown", "line_number": 100, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 104, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 109, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 112, "usage_type": "attribute"}, {"api_name": "ray.init", "line_number": 115, "usage_type": "call"}, {"api_name": "ray.shutdown", "line_number": 119, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 122, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 125, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 130, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 137, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 142, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 147, "usage_type": "attribute"}, {"api_name": "ray.init", "line_number": 150, "usage_type": "call"}, {"api_name": "ray.shutdown", "line_number": 154, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 157, "usage_type": "call"}, {"api_name": "ray.rllib.utils.test_utils.test_ckpt_restore", "line_number": 160, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 169, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 170, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 170, "usage_type": "call"}]}
+{"seq_id": "282971133", "text": "import similarity\nimport resnet\nimport numpy as np\nimport torchvision as tv\nimport torch\nfrom matplotlib import pyplot as plt\nimport advattack as adv\nimport os\nplt.switch_backend('agg')\n\n\ndef plot_similarity_vs_acc(index_func, std_model, model_list, save_path=None):\n \"\"\"Plot network similarity vs. accuracy. Test data is 500 images in\n CIFAR10 test set\"\"\"\n # Load test data\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n batchsize = 1000\n testset = tv.datasets.CIFAR10(\"data/\", train=False, transform=tv.transforms.ToTensor(), download=True)\n testloader = torch.utils.data.DataLoader(testset, batchsize, shuffle=False)\n testimage, testlabel = iter(testloader).next()\n testimage, testlabel = testimage.to(device), testlabel.to(device)\n # Generate adv. examples\n adv_images = adv.PGD(testimage, testlabel, std_model, iternum=10, eps=1/32, stepsize=1/128)\n # Calculate black box attack accuracy\n acc_list = []\n for model in model_list:\n output = model(adv_images)\n _, predict = torch.max(output.data, 1)\n correct = (predict == testlabel).sum().item()\n acc_list.append(correct/batchsize)\n # Calculate simlarity index after last layer\n\n def preprocess(feature):\n feature = feature.view(batchsize, -1)\n feature -= torch.mean(feature, 0)\n return feature\n\n sim_list = []\n model_feature = resnet.Resnet_20_CIFAR10_feature().to(device)\n model_feature.load_state_dict(std_model.state_dict())\n output = model_feature(testimage)\n std_feature = preprocess(model_feature.get_feature(8)) # Get the feature after last block\n for model in model_list:\n model_feature.load_state_dict(model.state_dict())\n output = model_feature(testimage)\n feature = preprocess(model_feature.get_feature(8))\n sim_list.append(index_func(std_feature, feature).item())\n # Ploting\n accuracy, similarity = np.array(acc_list), np.array(sim_list)\n plt.scatter(accuracy, similarity)\n if save_path:\n plt.savefig(save_path)\n plt.show()\n\n\nif __name__ == \"__main__\":\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n std_model = resnet.Resnet_20_CIFAR10().to(device)\n model_list = []\n os.chdir(\"models\")\n for index, path in enumerate(os.listdir()):\n if index:\n model = resnet.Resnet_20_CIFAR10().to(device)\n model.load_state_dict(torch.load(path, map_location=device))\n model_list.append(model)\n else:\n std_model.load_state_dict(torch.load(path, map_location=device))\n os.chdir(\"..\")\n plot_similarity_vs_acc(similarity.LR, std_model, model_list, save_path=\"fig1.PNG\")\n", "sub_path": "plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 2712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "matplotlib.pyplot.switch_backend", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 19, "usage_type": "attribute"}, {"api_name": "advattack.PGD", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "resnet.Resnet_20_CIFAR10_feature", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 57, "usage_type": "attribute"}, {"api_name": "resnet.Resnet_20_CIFAR10", "line_number": 58, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "resnet.Resnet_20_CIFAR10", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 67, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 68, "usage_type": "call"}, {"api_name": "similarity.LR", "line_number": 69, "usage_type": "attribute"}]}
+{"seq_id": "549492756", "text": "import logging\nimport gossip\n\n\n@gossip.register('session',\n tags=['ssh', 'docker', 'devops_docker', 'core_docker', 'k8s', 'devops_k8s', 'core_k8s'],\n provides=['ssh'])\ndef ssh_direct_connect(host, request):\n init_host_ssh_direct(host)\n mkdir_infra(host)\n\n\ndef init_host_ssh_direct(host):\n if host.pkey:\n host.add_to_ssh_agent()\n logging.info(f\"[{host}] waiting for ssh connection...\")\n host.SshDirect.connect(timeout=60)\n logging.info(f\"[{host}] success!\")\n\n\ndef mkdir_infra(host):\n host.SshDirect.execute(\"mkdir -p -m 777 /tmp/automation_infra\")", "sub_path": "automation/devops_automation_infra/installers/ssh.py", "file_name": "ssh.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "gossip.register", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 18, "usage_type": "call"}]}
+{"seq_id": "54418684", "text": "from shapely.geometry import LineString, Point, Polygon, MultiLineString\nfrom shapely.strtree import STRtree\n#import random, time\n#from rtree import index\nfrom cmath import rect, phase\nfrom math import radians, degrees\nfrom shapely.ops import polygonize, polygonize_full, split\n\n\nline = LineString(((0,0),(5,0),(10,0)))\na = line.project(Point(5,10))\n\next = [(0,0), (0,10),(3,10),(3,14),(7,14),(7,10),(10,10), (10,0),(0,0)]\nint_1 = [[(4,11), (4,12),(6,12),(6,11), (4,11)]]\nint_2 = [[(4,9.5), (4,10.5),(6,10.5),(6,9.5), (4,9.5)]]\n\npol = Polygon(ext, int_1)\nval = pol.is_valid\npol_s = pol.simplify(5, preserve_topology=True)\nval = pol.is_simple\n\n\n\npol = Polygon(ext, int)\nval = pol.is_valid\npol_s = pol.simplify(5, preserve_topology=True)\npol_s1 = pol.simplify(5, preserve_topology=False)\nval2 = pol_s1.is_valid\npol = Polygon([(((0,0), 0,10),(10,10), (10,12), (12,12), (12,10), (20,10)), ((11,0), (11,4),(12,4),(12,5),(11,5), (11,7), (11,11))])\nmline1 = mline.simplify(2, preserve_topology=True)\n\n\nmline = MultiLineString([((0,10),(10,10), (10,12), (12,12), (12,10), (20,10)), ((11,0), (11,4),(12,4),(12,5),(11,5), (11,7), (11,11))])\nmline1 = mline.simplify(2, preserve_topology=True)\n\nline = LineString([(0,0),(10,0), (11,0), (11,1), (10,1), (20,0), (20,1), (22,1), (22,0), (30, 0), (30,-5), (21,-5), (21,.5)])\nline1 = line.simplify(1, preserve_topology=True)\n\n\n\nline = LineString([(0,0),(2,2), (4,-2), (6,2), (7,0), (8,0)])\nsplitter = LineString([(0,0),(8,0)])\n\nline_a = LineString([(0,0),(10,0)])\nline_b = LineString([(10,110),(20,20)])\nline_c = LineString([(7,0),(9,0)])\n\nline_split = split(line, splitter)\nsplitter_split = split(splitter, line)\n\npol = polygonize_full([line_split, splitter_split])\n\nval_a = line_a.intersects(pol[0])\nval_b = line_b.intersects(pol[0])\nval_c = line_c.intersects(pol[0])\n\n\ncoords = [(0, 0), (0, 2), (1, 1), (2, 2), (2, 0), (1, 1), (0, 0)]\ncoords = [(0,0),(5,0), (10,0), (10,10), (5,10), (5,0), (5,-10),(0,-10), (0,0)]\nbowtie = Polygon(coords)\nva11 = bowtie.is_valid\nclean = bowtie.buffer(0)\nval2 = clean.is_valid\n\nl_a = [(0,0),(1,3),(2,-3),(10,10), (0,0)]\npol = Polygon (l_a)\npol1 = pol.buffer(0)\nl_b = [(0,0),(10,10)]\n\n\n\nline_a = LineString(l_a)\nline_b = LineString(l_b)\n\np = polygonize_full([line_a,line_b])\n\np0 = [(0,0),(10,0),(10,10),(0,10),(0,0)]\np1 = [(0,10),(10,10),(10,20),(0,20),(0,10)]\np2 = [(0,0),(20,20),(-5,20),(-5,17),(5,17),(5,12),(-5,12),(0,0)]\n\npol1 = Polygon(p1)\npol2 = Polygon(p2)\n\npols = pol1.symmetric_difference(pol2)\n\np1 = [(0,0),(10,0),(10,10),(0,10),(0,0)]\np2 = [(2,2),(8,2),(8,8),(2,8),(2,2)]\n\npol1 = Polygon(p1)\npol2 = Polygon(p2)\n\npols1 = pol1.symmetric_difference(pol2)\npols2 = pol2.symmetric_difference(pol1)\n\np1 = [(0,0),(7,0),(7,7),(0,7),(0,0)]\np2 = p1 = [(0,10),(10,10),(10,20),(0,20),(0,10)]\n\npol1 = Polygon(p1)\npol2 = Polygon(p2)\n\npols1 = pol1.symmetric_difference(pol2)\npols2 = pol2.symmetric_difference(pol1)\n0/0\n0/0\n\ndef mean_angle(deg):\n a = None\n# a = sum(rect(1, radians(d)) for d,l in deg)\n for d,ll in deg:\n if a is None:\n a = rect(ll, radians(d))\n else:\n a += rect(ll, radians(d))\n\n b = phase(a)\n c = degrees(b)\n\n d = degrees(phase(sum(rect(l, radians(d)) for d,l in deg)/len(deg)))\n\n return (c,d)\n\ndef mean_angle2(degrees):\n\n angle_sum = 0.\n tot_len = 0\n\n a = sum(rect(l, radians(d)) for d,l in degrees)\n a_phase = phase(a)\n a_degree = degrees(a_phase)\n\n\n for deg, len in degrees:\n angle_sum += rect(1, radians(deg))\n tot_len += len\n\n average_sum = degrees(phase(angle_sum))\n average_sum = average_sum / tot_len\n\n d = degrees(angle_sum)\n\n return d\n\n\n\n return degrees(phase(sum(rect(1, radians(d)*l) for d,l in deg)/sum([c[1] for c in deg])))\n\nfor angles in [[(350,1000), (10,1)], [(90,1), (180,1), (270,1), (360,1)], [(10,10), (20,1), (30,1)]]:\n print('The mean angle of', angles, 'is:', round(mean_angle(angles)[0], 12), 'degrees')\n\n#for angles in [[(350,2), (10,4)], [(90,2), (180,2), (270,2), (360,2)], [(10,1), (20,2), (30,3)]]:\n# print('The mean angle of', angles, 'is:', round(mean_angle2(angles), 12), 'degrees')\n\n0/0\n\nfor xy in [(1,.1),(1,1),(0.1,1),(-0.1,1),(-1,1),(-1,-1),(1,-1)]:\n line0 = LineString([(0,0), xy])\n line1 = LineString([xy, (0,0)])\n for line in (line0,line1):\n x0, y0 = line.coords[0][0], line.coords[0][1]\n x1, y1 = line.coords[1][0], line.coords[1][1]\n delta_y = (y1 - y0)\n delta_x = (x1 - x0)\n angle = math.atan(delta_y / delta_x)\n angle = math.degrees(angle)\n print (x0, y0, x1, y1, angle)\n\n0/0\n\n# Create the triangles\nfor i in range(250000):\n x = random.random() * 10000.\n y = random.random() * 10000.\n coords = [(x,y),(x+5, y+5),(x,y+10),(x,y)]\n lst_lines.append(LineString(coords))\n\n# Create the bounding boxes\nfor i in range(10000):\n x = random.random() * 10000.\n y = random.random() * 10000.\n coords = [(x,y),(x+15,y),(x+15,y+15),(x,y+15),(x,y)]\n lst_intersects.append(LineString(coords))\n\n# Create shapely STRtree\ntree = STRtree(lst_lines)\n\n# Create RTree\nidx = index.Index()\nfor i, line in enumerate(lst_lines):\n idx.insert(i, line.bounds)\nprint (time.time())\n\nsec1 = time.time()\n\n# finf the intersection with STRtree\nstr_tree_nbr = 0\nfor intersect in lst_intersects:\n str_tree = tree.query(intersect)\n str_tree_nbr += len(str_tree)\n\nsec2 = time.time()\nprint(\"Seconds for STRtree =\", sec2-sec1)\nprint (\"Str tree number: \", str_tree_nbr)\n\n# Find the intersections with RTree\nrtree_nbr = 0\nfor intersect in lst_intersects:\n rtree = idx.intersection(intersect.bounds)\n rtree_nbr += len(list(rtree))\n\nsec3 = time.time()\nprint(\"Seconds for RTree =\", sec3-sec2)\nprint (\"Rtree number: \", rtree_nbr)\n", "sub_path": ".ipynb_checkpoints/test-checkpoint.py", "file_name": "test-checkpoint.py", "file_ext": "py", "file_size_in_byte": 5715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "shapely.geometry.LineString", "line_number": 10, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 11, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 17, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 24, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 29, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiLineString", "line_number": 33, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 36, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 41, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 42, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 44, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 45, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 46, "usage_type": "call"}, {"api_name": "shapely.ops.split", "line_number": 48, "usage_type": "call"}, {"api_name": "shapely.ops.split", "line_number": 49, "usage_type": "call"}, {"api_name": "shapely.ops.polygonize_full", "line_number": 51, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 60, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 66, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 72, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 73, "usage_type": "call"}, {"api_name": "shapely.ops.polygonize_full", "line_number": 75, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 81, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 82, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 89, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 90, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 98, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 99, "usage_type": "call"}, {"api_name": "cmath.rect", "line_number": 111, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 111, "usage_type": "call"}, {"api_name": "cmath.rect", "line_number": 113, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 113, "usage_type": "call"}, {"api_name": "cmath.phase", "line_number": 115, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 116, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 118, "usage_type": "call"}, {"api_name": "cmath.phase", "line_number": 118, "usage_type": "call"}, {"api_name": "cmath.rect", "line_number": 118, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 118, "usage_type": "call"}, {"api_name": "cmath.rect", "line_number": 127, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 127, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 127, "usage_type": "name"}, {"api_name": "cmath.phase", "line_number": 128, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 129, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 132, "usage_type": "name"}, {"api_name": "cmath.rect", "line_number": 133, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 133, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 136, "usage_type": "call"}, {"api_name": "cmath.phase", "line_number": 136, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 139, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 145, "usage_type": "call"}, {"api_name": "cmath.phase", "line_number": 145, "usage_type": "call"}, {"api_name": "cmath.rect", "line_number": 145, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 145, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 156, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 157, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 163, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 164, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 174, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 181, "usage_type": "call"}, {"api_name": "shapely.strtree.STRtree", "line_number": 184, "usage_type": "call"}]}
+{"seq_id": "365292907", "text": "from aiohttp import web\nimport socketio\nimport time\nimport json\n## creates a new Async Socket IO Server\nsio = socketio.Client()\n#sio.connect(\"https://web-control-game.herokuapp.com\")\nsio.connect(\"http://localhost:3000\")\n#mock data\n\ndata_set = ['scroll up','scroll down','zoom in','zoom out']\n\ntrue = True\nwhile true:\n\n @sio.on('welcome')\n def on_message(data):\n print(\"I received a message, the message is {0}\".format(data))\n for i in range(len(data_set)):\n time.sleep(2.5)\n sio.emit('message',data_set[i])\n\n\n", "sub_path": "test_for_webcontrol.py", "file_name": "test_for_webcontrol.py", "file_ext": "py", "file_size_in_byte": 551, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "socketio.Client", "line_number": 6, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}]}
+{"seq_id": "411174362", "text": "import scrapy\nfrom MuseumNews.items import MuseumnewsItem\nfrom scrapy import Spider, Request\n\nURL = \"http://www.gmc.org.cn/toboalerts/p/{page}.html\"\nprefixURL = \"http://www.gmc.org.cn\"\n\n\nclass ToBoNewsSpyder(scrapy.Spider):\n name = \"ToBoNews\"\n allowed_domains = ['gmc.org.cn']\n page = 1\n start_urls = [URL.format(page=page)]\n\n def parse(self, response):\n news_lists = response.xpath(\"//div[@class='con2']\")[0]\n news_list = news_lists.xpath(\".//div[@class='li']\")\n for news in news_list:\n title = news.xpath(\"./a/div/div[@class='t18']/text()\")\n time = news.xpath(\"./a/div/div[@class='time']/text()\")\n content = news.xpath(\"./a/div/div[@class='p']/text()\")\n href = news.xpath(\"./a/@href\")\n if len(title) == 0 or len(time) == 0 or len(content) == 0 or len(href) == 0:\n continue\n title = title[0].extract()\n time = time[0].extract()\n content = content[0].extract()\n href = prefixURL + href[0].extract()\n author = \"中国地质博物馆\"\n description = \"1\"\n tag = 1\n item = MuseumnewsItem()\n item['title'] = title\n item['author'] = author\n item['time'] = time\n item['description'] = description\n item['content'] = content\n item['url'] = href\n item['tag'] = tag\n yield item\n\n print('page = {}'.format(self.page))\n if self.page < 20:\n self.page += 1\n new_url = URL.format(page=self.page)\n print(new_url)\n yield Request(new_url, callback=self.parse, dont_filter=True)\n", "sub_path": "博物馆新闻采集分析子系统/MuseumNews/MuseumNews/spiders/ToBoNews.py", "file_name": "ToBoNews.py", "file_ext": "py", "file_size_in_byte": 1700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "MuseumNews.items.MuseumnewsItem", "line_number": 32, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 47, "usage_type": "call"}]}
+{"seq_id": "367375582", "text": "import os\nimport numpy as np\nfrom arguments import get_args\nfrom mpi4py import MPI\nimport random\nimport torch\nfrom rl_modules.ddpg_agent import ddpg_agent\n\n\"\"\"\nThe DDPG and HER code are based on a repository \"https://github.com/TianhongDai/hindsight-experience-replay\"\nWe added the sim2real aspects of the code.\n\nTrain the agent, the MPI part code is copy from openai \nbaselines(https://github.com/openai/baselines/blob/master/baselines/her)\n\"\"\"\n\n\ndef get_env_params(env):\n obs = env.reset()\n # close the environment\n params = {'obs': obs['observation'].shape[0], 'goal': obs['desired_goal'].shape[0],\n 'action': env.action_space.shape[0], 'action_max': env.action_space.high[0], 'max_timesteps': 50}\n return params\n\n\ndef launch(args):\n # create the environment\n if args.env_name == 'FetchPush':\n from environments.FetchPush import FetchPushEnv, real_xml_file, sim_xml_file\n env_real = FetchPushEnv(reward_type='sparse', model_xml_path=real_xml_file)\n env_sim = FetchPushEnv(reward_type='sparse', model_xml_path=sim_xml_file)\n envs = (env_real, env_sim)\n else:\n print(\"Invalid env_name. Implement a Gym environment with separate real and sim xml files\")\n\n # set random seeds\n seed = np.random.randint(0, 1000)\n env_real.seed(seed + MPI.COMM_WORLD.Get_rank())\n env_sim.seed(seed + MPI.COMM_WORLD.Get_rank())\n random.seed(seed + MPI.COMM_WORLD.Get_rank())\n np.random.seed(seed + MPI.COMM_WORLD.Get_rank())\n torch.manual_seed(seed + MPI.COMM_WORLD.Get_rank())\n if args.cuda:\n torch.cuda.manual_seed(seed + MPI.COMM_WORLD.Get_rank())\n\n # get the environment parameters\n env_params = get_env_params(env_real)\n env_params['batch_size'] = args.batch_size\n # create the ddpg agent to interact with the environment \n ddpg_trainer = ddpg_agent(args, envs, env_params)\n ddpg_trainer.learn()\n\n\nif __name__ == '__main__':\n # take the configuration for the HER\n os.environ['OMP_NUM_THREADS'] = '1'\n os.environ['MKL_NUM_THREADS'] = '1'\n os.environ['IN_MPI'] = '1'\n # get the params\n args = get_args()\n launch(args)\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2139, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "environments.FetchPush.FetchPushEnv", "line_number": 30, "usage_type": "call"}, {"api_name": "environments.FetchPush.real_xml_file", "line_number": 30, "usage_type": "name"}, {"api_name": "environments.FetchPush.FetchPushEnv", "line_number": 31, "usage_type": "call"}, {"api_name": "environments.FetchPush.sim_xml_file", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 38, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 38, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 38, "usage_type": "name"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 39, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 39, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 40, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 40, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 41, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 42, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 42, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.cuda.manual_seed", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.COMM_WORLD.Get_rank", "line_number": 44, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 44, "usage_type": "name"}, {"api_name": "rl_modules.ddpg_agent.ddpg_agent", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 58, "usage_type": "attribute"}, {"api_name": "arguments.get_args", "line_number": 60, "usage_type": "call"}]}
+{"seq_id": "574020803", "text": "import models\n\nfrom flask import Blueprint, jsonify, request\n\nfrom playhouse.shortcuts import model_to_dict\n\n\n# first argument is blueprints name\n# second argument is it's import_name\n# The third argument is the url_prefix so we don't have\n# to prefix all our apis with /api/v1\ndog = Blueprint('dogs', 'dog')\n\n@dog.route('/', methods=[\"GET\"])\ndef get_all_dogs():\n ## find the dogs and change each one to a dictionary into a new array\n try:\n dogs = [model_to_dict(dog) for dog in models.Dog.select()]\n print(dogs)\n return jsonify(data=dogs, status={\"code\": 200, \"message\": \"Success\"})\n except models.DoesNotExist:\n return jsonify(data={}, status={\"code\": 401, \"message\": \"Error getting the resources\"})\n\n\n\n@dog.route('/', methods=[\"POST\"])\ndef create_dogs():\n ## see request payload anagolous to req.body in express\n payload = request.get_json()\n print(type(payload), 'payload')\n dog = models.Dog.create(**payload)\n ## see the object\n print(dog.__dict__)\n ## Look at all the methods\n print(dir(dog))\n # Change the model to a dict\n print(model_to_dict(dog), 'model to dict')\n dog_dict = model_to_dict(dog)\n return jsonify(data=dog_dict, status={\"code\": 201, \"message\": \"Success\"})", "sub_path": "GA-weeks/w1d5-landscaper/week11/dog_app_flask/dogs.py", "file_name": "dogs.py", "file_ext": "py", "file_size_in_byte": 1251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Dog.select", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Dog", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "models.DoesNotExist", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Dog.create", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Dog", "line_number": 31, "usage_type": "attribute"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 37, "usage_type": "call"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}]}
+{"seq_id": "158069564", "text": "from statistics import mean\r\n\r\ndef odd_even_avg():\r\n\r\n n = int(input())\r\n arr = input().split()\r\n list=[]\r\n odd =[]\r\n even =[]\r\n \r\n for i in range(n):\r\n list.append(int(arr[i]))\r\n \r\n for i in range(len(list)):\r\n if (list[i]%2==0):\r\n even.append(list[i])\r\n else:\r\n odd.append(list[i])\r\n \r\n print(int(mean(odd))+int(mean(even)))\r\n \r\nodd_even_avg()\r\n", "sub_path": "day 24.py", "file_name": "day 24.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "statistics.mean", "line_number": 20, "usage_type": "call"}]}
+{"seq_id": "513006845", "text": "\"\"\"Functionality to extract data from HST filenames.\"\"\"\nimport re\n\nfrom fs.path import basename\n\nfrom pdart.pipeline.suffix_info import ( # type: ignore\n ACCEPTED_LETTER_CODES,\n INSTRUMENT_FROM_LETTER_CODE,\n)\n\n\nclass HstFilename(object):\n \"\"\"\n A wrapper around the name of an HST file with functionality to extract\n data from the filename.\n \"\"\"\n\n def __init__(self, filename: str) -> None:\n self.filename = filename\n if len(basename(filename)) <= 6:\n raise ValueError(\"Filename must be at least six characters long.\")\n basename2 = basename(filename)\n if basename2[0].lower() not in ACCEPTED_LETTER_CODES:\n raise ValueError(\n f\"First char of filename {basename2!r} must be \"\n + f\"in {ACCEPTED_LETTER_CODES!r}.\"\n )\n\n def __str__(self) -> str:\n return self.filename.__str__()\n\n def __repr__(self) -> str:\n return f\"HstFilename({self.filename!r})\"\n\n def _basename(self) -> str:\n return basename(self.filename)\n\n def instrument_name(self) -> str:\n \"\"\"\n Return the instrument name determined by the first character\n of the filename.\n \"\"\"\n filename = self._basename()\n i = filename[0].lower()\n if i not in ACCEPTED_LETTER_CODES.lower():\n raise ValueError(\n f\"First char of filename {filename!r} must be \"\n + f\"in {ACCEPTED_LETTER_CODES!r}.\"\n )\n try:\n return INSTRUMENT_FROM_LETTER_CODE[i]\n except KeyError:\n raise Exception(\n f\"First char of filename {filename!r} must be in {ACCEPTED_LETTER_CODES!r}.\"\n )\n\n def hst_internal_proposal_id(self) -> str:\n \"\"\"\n Return the HST proposal ID determined by the three characters\n after the first of the filename.\n \"\"\"\n return str(self._basename()[1:4].lower())\n\n def rootname(self) -> str:\n \"\"\"\n Return the \"rootname\" of the filename, that is all characters\n before the underscore of the suffix. This is used in\n association tables.\n \"\"\"\n match = re.match(r\"\\A([^_]+)_.*\\Z\", self._basename())\n if not match:\n raise ValueError(\n f\"Filename: {self._basename()} rootname doesn't \"\n + \"match the expected pattern.\"\n )\n return str(match.group(1)).lower()\n\n def suffix(self) -> str:\n \"\"\"\n Return the suffix of the filename, that is all characters\n after the first underscore up to the period before the 'fits'\n extension.\n \"\"\"\n match = re.match(r\"\\A[^_]+_([^.]+)\\..*\\Z\", self._basename())\n if not match:\n raise ValueError(\n f\"Filename: {self._basename()} suffix doesn't \"\n + \"match the expected pattern.\"\n )\n return str(match.group(1)).lower()\n\n def visit(self) -> str:\n \"\"\"\n Return the visit id determined by the two characters after the\n first four of the filename.\n \"\"\"\n return str(self._basename()[4:6].lower())\n", "sub_path": "pdart/pds4/hst_filename.py", "file_name": "hst_filename.py", "file_ext": "py", "file_size_in_byte": 3151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "fs.path.basename", "line_number": 20, "usage_type": "call"}, {"api_name": "fs.path.basename", "line_number": 22, "usage_type": "call"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES", "line_number": 23, "usage_type": "name"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES", "line_number": 26, "usage_type": "name"}, {"api_name": "fs.path.basename", "line_number": 36, "usage_type": "call"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES.lower", "line_number": 45, "usage_type": "call"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES", "line_number": 45, "usage_type": "name"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES", "line_number": 48, "usage_type": "name"}, {"api_name": "pdart.pipeline.suffix_info.INSTRUMENT_FROM_LETTER_CODE", "line_number": 51, "usage_type": "name"}, {"api_name": "pdart.pipeline.suffix_info.ACCEPTED_LETTER_CODES", "line_number": 54, "usage_type": "name"}, {"api_name": "re.match", "line_number": 70, "usage_type": "call"}, {"api_name": "re.match", "line_number": 84, "usage_type": "call"}]}
+{"seq_id": "512431292", "text": "\"\"\"\nCopyright 2020 EraseKesu (class Erase#0027)\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport discord\nfrom discord.ext import commands\nfrom datetime import datetime\nfrom resources import Ban, Mute, Timer\nfrom services import MuteService\nimport argparse\nfrom utils.util import funs, checks, BloodyMenuPages, TextPagesData, converters\n\n\ndef bot_and_author_have_permissions(**perms):\n async def pred(ctx):\n res = await commands.has_permissions(**perms).predicate(ctx)\n if res:\n res = await commands.bot_has_permissions(**perms).predicate(ctx)\n return res\n\n return commands.check(pred)\n\n\n# noinspection PyIncorrectDocstring,PyUnresolvedReferences\nclass Moderation(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.mute_service = MuteService(self.bot.pool)\n\n def cog_check(self, ctx):\n if ctx.guild is None:\n raise commands.NoPrivateMessage(\"Moderation commands can't be used in DMs\")\n return True\n\n @commands.command()\n @bot_and_author_have_permissions(kick_members=True)\n async def kick(self, ctx: commands.Context, victim: discord.Member, *, reason: str = None):\n \"\"\"\n Yeet a user\n Args:\n victim: Member you want to kick\n reason: Reason for kick\n \"\"\"\n\n embed = discord.Embed(title=f\"User was Kicked from {ctx.guild.name}\",\n color=funs.random_discord_color(),\n timestamp=datetime.utcnow())\n embed.add_field(name='Kicked By', value=ctx.author.mention, inline=True)\n embed.add_field(name='Kicked user', value=victim.mention, inline=True)\n if reason:\n embed.add_field(name='Reason', value=reason, inline=False)\n embed.set_thumbnail(url=victim.avatar_url)\n\n await ctx.send(embed=embed)\n await victim.kick(reason=reason)\n\n try:\n embed.title = f\"You have been Kicked from {ctx.guild.name}\"\n await victim.send(embed=embed)\n except discord.Forbidden:\n await ctx.send(\"I can't dm that user. Kicked without notice\")\n\n async def do_ban(self, ctx, victim, reason, time=None):\n if victim.id == ctx.author.id:\n await ctx.send(\"Why do want to ban yourself?\\nI'm not gonna let you do it\")\n return\n\n ban = Ban(\n reason=reason if reason else None,\n banned_by_id=ctx.author.id,\n banned_user_id=victim.id,\n guild_id=ctx.guild.id,\n unban_time=time\n )\n\n saved = await self.ban_service.insert(ban)\n\n embed = discord.Embed(title=f\"User was banned from {ctx.guild.name}\", color=funs.random_discord_color(),\n timestamp=saved.banned_at)\n embed.add_field(name='Banned By', value=ctx.author.mention, inline=True)\n embed.add_field(name='Banned user', value=victim.mention, inline=True)\n if reason:\n embed.add_field(name='Reason', value=reason, inline=False)\n embed.set_thumbnail(url=victim.avatar_url)\n\n await ctx.send(f'ID: {saved.id}', embed=embed)\n\n try:\n embed.title = f\"You have been banned from {ctx.guild.name}\"\n await victim.send(embed=embed)\n except discord.Forbidden:\n await ctx.send(\"I can't DM that user. Banned without notice\")\n\n await victim.ban(reason=f'{reason}\\n(Operation performed by {ctx.author}; ID: {ctx.author.id})')\n\n @commands.command()\n @bot_and_author_have_permissions(ban_members=True)\n async def ban(self, ctx: commands.Context, victim: discord.Member, *, reason: str = None):\n \"\"\"\n Ban a user\n Args:\n victim: Member you want to ban\n reason: Reason for ban - Optional\n \"\"\"\n\n await self.do_ban(ctx, victim, reason)\n\n @commands.command()\n @bot_and_author_have_permissions(ban_members=True)\n async def tempban(self, ctx: commands.Context, victim: discord.Member, *,\n time_and_reason: converters.HumanTime(other=True) = None):\n \"\"\"\n Temporarily ban a user\n Args:\n victim: Member you want to ban\n time: Un-ban time - Optional\n reason: Reason for ban - Optional\n \"\"\"\n if time_and_reason is None:\n time = None\n reason = ''\n else:\n time = time_and_reason.time\n reason = time_and_reason.other if time_and_reason.other is not None else ''\n await self.do_ban(ctx, victim, reason, time)\n\n if time:\n extras = {\n 'ban_id': saved.id,\n 'guild_id': saved.guild_id,\n 'banned_user_id': saved.banned_user_id,\n }\n timer = Timer(\n event='tempban',\n created_at=ctx.message.created_at,\n expires_at=time,\n kwargs=extras\n )\n await self.bot.timers.create_timer(timer)\n\n async def do_unban(self, guild, user_id, reason):\n await self.ban_service.delete(guild.id, user_id)\n await guild.unban(discord.Object(id=user_id), reason=reason)\n\n @commands.Cog.listener()\n async def on_tempban_timer_complete(self, timer):\n kwargs = timer.kwargs\n guild = self.bot.get_guild(kwargs['guild_id'])\n reason = 'Unban from temp-ban timer expiring'\n await self.do_unban(guild, kwargs['banned_user_id'], reason=reason)\n\n async def do_mute(self, ctx, *, victim, reason=None, time=None):\n muted = await self._get_muted_role(ctx.guild, prefix=ctx.prefix)\n\n if muted in victim.roles:\n await ctx.send('User is already muted')\n return\n\n mute = Mute(\n reason=reason,\n muted_by_id=ctx.author.id,\n muted_user_id=victim.id,\n guild_id=ctx.guild.id,\n unmute_time=time\n )\n inserted = await self.mute_service.insert(mute)\n\n await victim.add_roles(muted, reason=f'{reason}\\n(Operation performed by {ctx.author})')\n await ctx.send(f\"**User {victim.mention} has been muted by {ctx.author.mention}**\\nID: {inserted.id}\")\n\n try:\n msg = f\"You have been muted in {ctx.guild.name} {f'for {time}' if time else ''}\" \\\n f\"\\n{'Reason `{reason}`' if reason else ''}\"\n\n await victim.send(msg)\n except discord.Forbidden:\n await ctx.send(\"I can't DM that user. Muted without notice\")\n\n @commands.command()\n @bot_and_author_have_permissions(manage_roles=True)\n async def mute(self, ctx, victim: discord.Member, reason=None):\n \"\"\"\n Permanently Mute a user\n Args:\n victim: Member you want to mute\n reason: Reason for mute - Optional\n \"\"\"\n\n if victim.id == ctx.author.id:\n return await ctx.send(\"Why do want to mute yourself?\\nI'm not gonna let you do it\")\n\n async with ctx.typing():\n await self.do_mute(ctx, victim=victim, reason=reason)\n\n @commands.command(name='temp')\n @bot_and_author_have_permissions(manage_roles=True)\n async def temp_mute(self, ctx: commands.Context, victim: discord.Member, *,\n time_and_reason: converters.HumanTime(other=True)):\n \"\"\"\n Temporarily mute a user\n Args:\n victim: Member you want to mute\n time: Un-mute time - Optional\n reason: Reason for mute - Optional\n \"\"\"\n\n time = time_and_reason.time\n reason = time_and_reason.other\n\n if victim.id == ctx.author.id:\n return await ctx.send(\"Why do want to mute yourself?\\nI'm not gonna let you do it\")\n\n async with ctx.typing():\n await self.do_mute(ctx, victim=victim, time=time, reason=reason)\n\n if time:\n extras = {\n 'mute_id': inserted.id,\n 'guild_id': inserted.guild_id,\n 'muted_user_id': inserted.muted_user_id,\n }\n timer = Timer(\n event='tempmute',\n created_at=ctx.message.created_at,\n expires_at=time,\n kwargs=extras\n )\n await self.bot.timers.create_timer(timer)\n\n async def do_unmute(self, guild, victim):\n muted = self._get_muted_role(guild)\n await victim.remove_roles(muted)\n await self.mute_service.delete(guild.id, victim.id)\n\n @commands.command()\n @bot_and_author_have_permissions(manage_roles=True)\n async def unmute(self, ctx: commands.Context, victim: discord.Member):\n \"\"\"\n Unmute a user\n Args:\n victim: Member you want to unmute\n \"\"\"\n\n await ctx.trigger_typing()\n await self.do_unmute(ctx.guild, victim)\n await ctx.send(f\"**User {victim.mention} has been unmuted by {ctx.author.mention}**\")\n\n @commands.Cog.listener()\n async def on_tempmute_timer_complete(self, timer):\n guild = self.bot.get_guild(timer.kwargs['guild_id'])\n await self.do_unmute(guild, guild.get_member(timer.kwargs['muted_user_id']))\n\n @commands.group(aliases=[\"prune\", \"clean\", \"clear\"])\n @commands.guild_only()\n @bot_and_author_have_permissions(manage_messages=True)\n async def purge(self, ctx):\n\n if ctx.invoked_subcommand is None:\n await ctx.send_help(ctx.command)\n\n async def do_removal(self, ctx, limit, predicate, *, before=None, after=None):\n if limit > 2000:\n return await ctx.send(f'Too many messages to search given ({limit}/2000)')\n\n if before is None:\n before = ctx.message\n else:\n before = discord.Object(id=before)\n\n if after is not None:\n after = discord.Object(id=after)\n\n try:\n deleted = await ctx.channel.purge(limit=limit, before=before, after=after, check=predicate)\n except discord.Forbidden as e:\n return await ctx.send('I do not have permissions to delete messages.')\n except discord.HTTPException as e:\n return await ctx.send(f'Error: {e} (try a smaller search?)')\n\n spammers = Counter(m.author.display_name for m in deleted)\n deleted = len(deleted)\n messages = [f'{deleted} message{\" was\" if deleted == 1 else \"s were\"} removed.']\n if deleted:\n messages.append('')\n spammers = sorted(spammers.items(), key=lambda t: t[1], reverse=True)\n messages.extend(f'**{name}**: {count}' for name, count in spammers)\n\n to_send = '\\n'.join(messages)\n\n if len(to_send) > 2000:\n await ctx.send(f'Successfully removed {deleted} messages.', delete_after=10)\n else:\n await ctx.send(to_send, delete_after=10)\n\n @purge.command()\n async def embeds(self, ctx, search=100):\n \"\"\"Removes messages that have embeds in them.\"\"\"\n await self.do_removal(ctx, search, lambda e: len(e.embeds))\n\n @purge.command()\n async def files(self, ctx, search=100):\n \"\"\"Removes messages that have attachments in them.\"\"\"\n await self.do_removal(ctx, search, lambda e: len(e.attachments))\n\n @purge.command()\n async def images(self, ctx, search=100):\n \"\"\"Removes messages that have embeds or attachments.\"\"\"\n await self.do_removal(ctx, search, lambda e: len(e.embeds) or len(e.attachments))\n\n @purge.command(name='all')\n async def _remove_all(self, ctx, search=100):\n \"\"\"Removes all messages.\"\"\"\n await self.do_removal(ctx, search, lambda e: True)\n\n @purge.command()\n async def user(self, ctx, member: discord.Member, search=100):\n \"\"\"Removes all messages by the member.\"\"\"\n await self.do_removal(ctx, search, lambda e: e.author == member)\n\n @purge.command()\n async def contains(self, ctx, *, substr: str):\n \"\"\"Removes all messages containing a substring.\n The substring must be at least 3 characters long.\n \"\"\"\n if len(substr) < 3:\n await ctx.send('The substring length must be at least 3 characters.')\n else:\n await self.do_removal(ctx, 100, lambda e: substr in e.content)\n\n @purge.command(name='bot')\n async def _bot(self, ctx, prefix=None, search=100):\n \"\"\"Removes a bot user's messages and messages with their optional prefix.\"\"\"\n\n def predicate(m):\n return (m.webhook_id is None and m.author.bot) or (prefix and m.content.startswith(prefix))\n\n await self.do_removal(ctx, search, predicate)\n\n @purge.command(name='emoji', aliases=['emojis'])\n async def _emoji(self, ctx, search=100):\n \"\"\"Removes all messages containing custom emoji.\"\"\"\n custom_emoji = re.compile(r'<a?:[a-zA-Z0-9\\_]+:([0-9]+)>')\n\n def predicate(m):\n return custom_emoji.search(m.content)\n\n await self.do_removal(ctx, search, predicate)\n\n @purge.command(name='reactions')\n async def _reactions(self, ctx, search=100):\n \"\"\"Removes all reactions from messages that have them.\"\"\"\n\n if search > 2000:\n return await ctx.send(f'Too many messages to search for ({search}/2000)')\n\n total_reactions = 0\n async for message in ctx.history(limit=search, before=ctx.message):\n if len(message.reactions):\n total_reactions += sum(r.count for r in message.reactions)\n await message.clear_reactions()\n\n await ctx.send(f'Successfully removed {total_reactions} reactions.')\n\n @purge.command()\n async def custom(self, ctx, *, args: str):\n \"\"\"A more advanced purge command.\n This command uses a powerful \"command line\" syntax.\n Most options support multiple values to indicate 'any' match.\n If the value has spaces it must be quoted.\n The messages are only deleted if all options are met unless\n the `--or` flag is passed, in which case only if any is met.\n The following options are valid.\n `--user`: A mention or name of the user to remove.\n `--contains`: A substring to search for in the message.\n `--starts`: A substring to search if the message starts with.\n `--ends`: A substring to search if the message ends with.\n `--search`: How many messages to search. Default 100. Max 2000.\n `--after`: Messages must come after this message ID.\n `--before`: Messages must come before this message ID.\n Flag options (no arguments):\n `--bot`: Check if it's a bot user.\n `--embeds`: Check if the message has embeds.\n `--files`: Check if the message has attachments.\n `--emoji`: Check if the message has custom emoji.\n `--reactions`: Check if the message has reactions\n `--or`: Use logical OR for all options.\n `--not`: Use logical NOT for all options.\n \"\"\"\n parser = Arguments(add_help=False, allow_abbrev=False)\n parser.add_argument('--user', nargs='+')\n parser.add_argument('--contains', nargs='+')\n parser.add_argument('--starts', nargs='+')\n parser.add_argument('--ends', nargs='+')\n parser.add_argument('--or', action='store_true', dest='_or')\n parser.add_argument('--not', action='store_true', dest='_not')\n parser.add_argument('--emoji', action='store_true')\n parser.add_argument('--bot', action='store_const', const=lambda m: m.author.bot)\n parser.add_argument('--embeds', action='store_const', const=lambda m: len(m.embeds))\n parser.add_argument('--files', action='store_const', const=lambda m: len(m.attachments))\n parser.add_argument('--reactions', action='store_const', const=lambda m: len(m.reactions))\n parser.add_argument('--search', type=int, default=100)\n parser.add_argument('--after', type=int)\n parser.add_argument('--before', type=int)\n\n try:\n args = parser.parse_args(shlex.split(args))\n except Exception as e:\n await ctx.send(str(e))\n return\n\n predicates = []\n if args.bot:\n predicates.append(args.bot)\n\n if args.embeds:\n predicates.append(args.embeds)\n\n if args.files:\n predicates.append(args.files)\n\n if args.reactions:\n predicates.append(args.reactions)\n\n if args.emoji:\n custom_emoji = re.compile(r'<:(\\w+):(\\d+)>')\n predicates.append(lambda m: custom_emoji.search(m.content))\n\n if args.user:\n users = []\n converter = commands.MemberConverter()\n for u in args.user:\n try:\n user = await converter.convert(ctx, u)\n users.append(user)\n except Exception as e:\n await ctx.send(str(e))\n return\n\n predicates.append(lambda m: m.author in users)\n\n if args.contains:\n predicates.append(lambda m: any(sub in m.content for sub in args.contains))\n\n if args.starts:\n predicates.append(lambda m: any(m.content.startswith(s) for s in args.starts))\n\n if args.ends:\n predicates.append(lambda m: any(m.content.endswith(s) for s in args.ends))\n\n op = all if not args._or else any\n\n def predicate(m):\n r = op(p(m) for p in predicates)\n if args._not:\n return not r\n return r\n\n args.search = max(0, min(2000, args.search)) # clamp from 0-2000\n await self.do_removal(ctx, args.search, predicate, before=args.before, after=args.after)\n\n @commands.command()\n @checks.is_mod()\n async def warn(self, ctx, member: discord.Member, *, reason: str = None):\n if reason is None:\n reason = \"No reason provided!\"\n\n if member is None:\n return await ctx.send(\"Please provide a member to warn!\")\n\n res = await self.bot.pool.fetchrow(\"\"\"SELECT warns\n FROM warns\n WHERE guild_id = $1\n AND user_id = $2\"\"\",\n ctx.guild.id,\n member.id\n )\n if res is None:\n await self.bot.pool.execute(\"\"\"INSERT INTO warns(warns, guild_id, user_id)\n VALUES ($1, $2, $3)\"\"\",\n 1,\n ctx.guild.id,\n member.id\n )\n if res is not None:\n await self.bot.pool.execute(\"\"\"UPDATE warns\n SET warns = $1\n WHERE guild_id = $2\n AND user_id = $3\"\"\",\n res['warns'] + 1,\n ctx.guild.id,\n member.id\n )\n\n embed = discord.Embed(\n title=\"Warn\",\n description=f\"{member.mention} has been warned for {reason}\",\n colour=discord.Colour.from_rgb(255, 50, 50)\n )\n embed.add_field(\n name=\"Warns:\",\n value=res['warns'] + 1,\n inline=False\n )\n await ctx.send(embed=embed)\n\n @commands.command(aliases=['warnings'])\n @checks.is_mod()\n async def warns(self, ctx, member: discord.Member = None):\n if member is not None:\n member = member.id\n warnings = await self.bot.pool.fetch(\"\"\"SELECT warns\n FROM warns\n WHERE guild_id = $1\n ORDER BY warns DESC\"\"\",\n ctx.guild.id\n )\n users = await self.bot.pool.fetch(\"\"\"SELECT user_id\n FROM warns\n WHERE guild_id = $1\n ORDER BY warns DESC\"\"\",\n ctx.guild.id\n )\n pages = commands.Paginator(prefix='```md', max_size=1980)\n index = 1\n for warning in warnings:\n member = ctx.guild.get_member(users['user_id'])\n\n line = f\"{users['user_id']}. {funs.format_human_readable_user(member)}\\n\" \\\n f\"Warnings: {warnings['warns']}\"\n pages.add_line(line)\n index += 1\n\n react_paginator = BloodyMenuPages(TextPagesData(pages))\n await react_paginator.start(ctx)\n\n @commands.command()\n @commands.guild_only()\n @checks.is_admin()\n async def massban(self, ctx, *, args):\n \"\"\"Mass bans multiple members from the server.\n This command has a powerful \"command line\" syntax. To use this command\n you and the bot must both have Ban Members permission. **Every option is optional.**\n Users are only banned **if and only if** all conditions are met.\n The following options are valid.\n `--channel` or `-c`: Channel to search for message history.\n `--reason` or `-r`: The reason for the ban.\n `--regex`: Regex that usernames must match.\n `--created`: Matches users whose accounts were created less than specified minutes ago.\n `--joined`: Matches users that joined less than specified minutes ago.\n `--joined-before`: Matches users who joined before the member ID given.\n `--joined-after`: Matches users who joined after the member ID given.\n `--no-avatar`: Matches users who have no avatar. (no arguments)\n `--no-roles`: Matches users that have no role. (no arguments)\n `--show`: Show members instead of banning them (no arguments).\n Message history filters (Requires `--channel`):\n `--contains`: A substring to search for in the message.\n `--starts`: A substring to search if the message starts with.\n `--ends`: A substring to search if the message ends with.\n `--match`: A regex to match the message content to.\n `--search`: How many messages to search. Default 100. Max 2000.\n `--after`: Messages must come after this message ID.\n `--before`: Messages must come before this message ID.\n `--files`: Checks if the message has attachments (no arguments).\n `--embeds`: Checks if the message has embeds (no arguments).\n \"\"\"\n\n # For some reason there are cases due to caching that ctx.author\n # can be a User even in a guild only context\n # Rather than trying to work out the kink with it\n # Just upgrade the member itself.\n if not isinstance(ctx.author, discord.Member):\n try:\n author = await ctx.guild.fetch_member(ctx.author.id)\n except discord.HTTPException:\n return await ctx.send('Somehow, Discord does not seem to think you are in this server.')\n else:\n author = ctx.author\n\n parser = Arguments(add_help=False, allow_abbrev=False)\n parser.add_argument('--channel', '-c')\n parser.add_argument('--reason', '-r')\n parser.add_argument('--search', type=int, default=100)\n parser.add_argument('--regex')\n parser.add_argument('--no-avatar', action='store_true')\n parser.add_argument('--no-roles', action='store_true')\n parser.add_argument('--created', type=int)\n parser.add_argument('--joined', type=int)\n parser.add_argument('--joined-before', type=int)\n parser.add_argument('--joined-after', type=int)\n parser.add_argument('--contains')\n parser.add_argument('--starts')\n parser.add_argument('--ends')\n parser.add_argument('--match')\n parser.add_argument('--show', action='store_true')\n parser.add_argument('--embeds', action='store_const', const=lambda m: len(m.embeds))\n parser.add_argument('--files', action='store_const', const=lambda m: len(m.attachments))\n parser.add_argument('--after', type=int)\n parser.add_argument('--before', type=int)\n\n try:\n args = parser.parse_args(shlex.split(args))\n except Exception as e:\n return await ctx.send(str(e))\n\n members = []\n\n if args.channel:\n channel = await commands.TextChannelConverter().convert(ctx, args.channel)\n before = args.before and discord.Object(id=args.before)\n after = args.after and discord.Object(id=args.after)\n predicates = []\n if args.contains:\n predicates.append(lambda m: args.contains in m.content)\n if args.starts:\n predicates.append(lambda m: m.content.startswith(args.starts))\n if args.ends:\n predicates.append(lambda m: m.content.endswith(args.ends))\n if args.match:\n try:\n _match = re.compile(args.match)\n except re.error as e:\n return await ctx.send(f'Invalid regex passed to `--match`: {e}')\n else:\n predicates.append(lambda m, x=_match: x.match(m.content))\n if args.embeds:\n predicates.append(args.embeds)\n if args.files:\n predicates.append(args.files)\n\n async for message in channel.history(limit=min(max(1, args.search), 2000), before=before, after=after):\n if all(p(message) for p in predicates):\n members.append(message.author)\n else:\n members = ctx.guild.members\n\n # member filters\n predicates = [\n lambda m: isinstance(m, discord.Member) and can_execute_action(ctx, author, m), # Only if applicable\n lambda m: not m.bot, # No bots\n lambda m: m.discriminator != '0000', # No deleted users\n ]\n\n async def _resolve_member(member_id):\n r = ctx.guild.get_member(member_id)\n if r is None:\n try:\n return await ctx.guild.fetch_member(member_id)\n except discord.HTTPException as e:\n raise commands.BadArgument(f'Could not fetch member by ID {member_id}: {e}') from None\n return r\n\n if args.regex:\n try:\n _regex = re.compile(args.regex)\n except re.error as e:\n return await ctx.send(f'Invalid regex passed to `--regex`: {e}')\n else:\n predicates.append(lambda m, x=_regex: x.match(m.name))\n\n if args.no_avatar:\n predicates.append(lambda m: m.avatar is None)\n if args.no_roles:\n predicates.append(lambda m: len(getattr(m, 'roles', [])) <= 1)\n\n now = datetime.datetime.utcnow()\n if args.created:\n def created(member, *, offset=now - datetime.timedelta(minutes=args.created)):\n return member.created_at > offset\n\n predicates.append(created)\n if args.joined:\n def joined(member, *, offset=now - datetime.timedelta(minutes=args.joined)):\n if isinstance(member, discord.User):\n # If the member is a user then they left already\n return True\n return member.joined_at and member.joined_at > offset\n\n predicates.append(joined)\n if args.joined_after:\n _joined_after_member = await _resolve_member(args.joined_after)\n\n def joined_after(member, *, _other=_joined_after_member):\n return member.joined_at and _other.joined_at and member.joined_at > _other.joined_at\n\n predicates.append(joined_after)\n if args.joined_before:\n _joined_before_member = await _resolve_member(args.joined_before)\n\n def joined_before(member, *, _other=_joined_before_member):\n return member.joined_at and _other.joined_at and member.joined_at < _other.joined_at\n\n predicates.append(joined_before)\n\n members = {m for m in members if all(p(m) for p in predicates)}\n if len(members) == 0:\n return await ctx.send('No members found matching criteria.')\n\n if args.show:\n members = sorted(members, key=lambda m: m.joined_at or now)\n fmt = \"\\n\".join(f'{m.id}\\tJoined: {m.joined_at}\\tCreated: {m.created_at}\\t{m}' for m in members)\n content = f'Current Time: {datetime.datetime.utcnow()}\\nTotal members: {len(members)}\\n{fmt}'\n file = discord.File(io.BytesIO(content.encode('utf-8')), filename='members.txt')\n return await ctx.send(file=file)\n\n if args.reason is None:\n return await ctx.send('--reason flag is required.')\n else:\n reason = await ActionReason().convert(ctx, args.reason)\n\n count = 0\n for member in members:\n try:\n await ctx.guild.ban(member, reason=reason)\n except discord.HTTPException:\n pass\n else:\n count += 1\n\n await ctx.send(f'Banned {count}/{len(members)}')\n\n\nclass Arguments(argparse.ArgumentParser):\n def error(self, message):\n raise RuntimeError(message)\n\n\nclass plural:\n def __init__(self, value):\n self.value = value\n\n def __format__(self, format_spec):\n v = self.value\n singular, sep, plural = format_spec.partition('|')\n plural = plural or f'{singular}s'\n if abs(v) != 1:\n return f'{v} {plural}'\n return f'{v} {singular}'\n\n\ndef setup(bot):\n bot.add_cog(Moderation(bot))\n", "sub_path": "cogs/moderation.py", "file_name": "moderation.py", "file_ext": "py", "file_size_in_byte": 30412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "discord.ext.commands.has_permissions", "line_number": 27, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 27, "usage_type": "name"}, {"api_name": "discord.ext.commands.bot_has_permissions", "line_number": 29, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 29, "usage_type": "name"}, {"api_name": "discord.ext.commands.check", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 32, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog", "line_number": 36, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 36, "usage_type": "name"}, {"api_name": "services.MuteService", "line_number": 39, "usage_type": "call"}, {"api_name": "discord.ext.commands.NoPrivateMessage", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 43, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 48, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 48, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 48, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.util.funs.random_discord_color", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.util.funs", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "discord.Forbidden", "line_number": 71, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 46, "usage_type": "name"}, {"api_name": "resources.Ban", "line_number": 79, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.util.funs.random_discord_color", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.util.funs", "line_number": 89, "usage_type": "name"}, {"api_name": "discord.Forbidden", "line_number": 102, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Context", "line_number": 109, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 109, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 109, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 107, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 107, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 121, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 121, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 121, "usage_type": "attribute"}, {"api_name": "utils.util.converters.HumanTime", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.util.converters", "line_number": 122, "usage_type": "name"}, {"api_name": "resources.Timer", "line_number": 144, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 119, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 119, "usage_type": "name"}, {"api_name": "discord.Object", "line_number": 154, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 156, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 156, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 156, "usage_type": "name"}, {"api_name": "resources.Mute", "line_number": 170, "usage_type": "call"}, {"api_name": "discord.Forbidden", "line_number": 187, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 192, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 190, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 190, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 208, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 208, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 208, "usage_type": "attribute"}, {"api_name": "utils.util.converters.HumanTime", "line_number": 209, "usage_type": "call"}, {"api_name": "utils.util.converters", "line_number": 209, "usage_type": "name"}, {"api_name": "resources.Timer", "line_number": 233, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 206, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 206, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 248, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 248, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 248, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 246, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 246, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 259, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 259, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 259, "usage_type": "name"}, {"api_name": "discord.ext.commands.group", "line_number": 264, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 264, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 265, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 265, "usage_type": "name"}, {"api_name": "discord.Object", "line_number": 279, "usage_type": "call"}, {"api_name": "discord.Object", "line_number": 282, "usage_type": "call"}, {"api_name": "discord.Forbidden", "line_number": 286, "usage_type": "attribute"}, {"api_name": "discord.HTTPException", "line_number": 288, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 327, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.MemberConverter", "line_number": 441, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 441, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 474, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 505, "usage_type": "call"}, {"api_name": "discord.Colour.from_rgb", "line_number": 508, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 508, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 472, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 472, "usage_type": "name"}, {"api_name": "utils.util.checks.is_mod", "line_number": 473, "usage_type": "call"}, {"api_name": "utils.util.checks", "line_number": 473, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 519, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Paginator", "line_number": 534, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 534, "usage_type": "name"}, {"api_name": "utils.util.funs.format_human_readable_user", "line_number": 539, "usage_type": "call"}, {"api_name": "utils.util.funs", "line_number": 539, "usage_type": "name"}, {"api_name": "utils.util.BloodyMenuPages", "line_number": 544, "usage_type": "call"}, {"api_name": "utils.util.TextPagesData", "line_number": 544, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 517, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 517, "usage_type": "name"}, {"api_name": "utils.util.checks.is_mod", "line_number": 518, "usage_type": "call"}, {"api_name": "utils.util.checks", "line_number": 518, "usage_type": "name"}, {"api_name": "discord.Member", "line_number": 582, "usage_type": "attribute"}, {"api_name": "discord.HTTPException", "line_number": 585, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.TextChannelConverter", "line_number": 619, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 619, "usage_type": "name"}, {"api_name": "discord.Object", "line_number": 620, "usage_type": "call"}, {"api_name": "discord.Object", "line_number": 621, "usage_type": "call"}, {"api_name": "discord.Member", "line_number": 649, "usage_type": "attribute"}, {"api_name": "discord.HTTPException", "line_number": 659, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.BadArgument", "line_number": 660, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 660, "usage_type": "name"}, {"api_name": "datetime.datetime.datetime.utcnow", "line_number": 676, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 676, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 676, "usage_type": "name"}, {"api_name": "datetime.datetime.timedelta", "line_number": 678, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 678, "usage_type": "name"}, {"api_name": "datetime.datetime.timedelta", "line_number": 683, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 683, "usage_type": "name"}, {"api_name": "discord.User", "line_number": 684, "usage_type": "attribute"}, {"api_name": "datetime.datetime.datetime.utcnow", "line_number": 712, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 712, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 712, "usage_type": "name"}, {"api_name": "discord.File", "line_number": 713, "usage_type": "call"}, {"api_name": "discord.HTTPException", "line_number": 725, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 547, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 547, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 548, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 548, "usage_type": "name"}, {"api_name": "utils.util.checks.is_admin", "line_number": 549, "usage_type": "call"}, {"api_name": "utils.util.checks", "line_number": 549, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 733, "usage_type": "attribute"}]}
+{"seq_id": "481045386", "text": "from collections import deque\n\ndef do1(puzzleInput):\n queue = deque([int(x) for x in puzzleInput])\n\n play(queue, 100)\n\n return cupOrder(queue)\n\ndef do2(puzzleInput):\n queue = [int(x) for x in puzzleInput] + list(range(10, 1000000+1))\n \n linkedList = play2(queue, 10000000)\n\n return findStars(linkedList)\n\ndef cupOrder(queue):\n result = ''\n\n first = queue.index(1)\n queue.rotate(-(first + 1))\n\n for _ in range(len(queue) - 1):\n result += str(queue.popleft())\n\n return result\n\ndef findStars(linkedList):\n first = linkedList[1]\n second = linkedList[first]\n\n return first * second\n\ndef play(queue, turns):\n maxValue = max(queue)\n minValue = min(queue)\n\n for _ in range(1, turns + 1):\n currentElement = queue.popleft()\n removed = [queue.popleft(), queue.popleft(), queue.popleft()]\n\n newElement = currentElement - 1\n\n if newElement < minValue:\n newElement = maxValue\n\n while newElement in removed:\n newElement -= 1\n if newElement < minValue:\n newElement = maxValue\n newIndex = queue.index(newElement)\n\n queue.rotate(- (newIndex + 1))\n queue.extendleft(removed[::-1])\n queue.rotate(newIndex + 1)\n\n queue.append(currentElement)\n\ndef play2(queue, turns):\n maxValue = max(queue)\n minValue = min(queue)\n \n linkedList = {}\n\n for index,item in enumerate(queue[0:-1]):\n linkedList[item] = queue[index + 1]\n linkedList[queue[-1]] = queue[0]\n\n currentIndex = queue[-1]\n\n for _ in range(1, turns + 1):\n currentElement = linkedList[currentIndex]\n removed = [linkedList[currentElement], linkedList[linkedList[currentElement]], linkedList[linkedList[linkedList[currentElement]]]]\n nextafternextafternext = removed[-1]\n newElement = currentElement - 1\n\n if newElement < minValue:\n newElement = maxValue\n while newElement in removed:\n newElement -= 1\n if newElement < minValue:\n newElement = maxValue\n\n tmp1 = linkedList[newElement]\n tmp2 = linkedList[nextafternextafternext]\n linkedList[newElement] = linkedList[currentElement]\n linkedList[nextafternextafternext] = tmp1\n linkedList[currentElement] = tmp2\n\n currentIndex = currentElement\n \n return linkedList\n\ndef do():\n with open ('Input/day23.txt') as f:\n strInput = f.read()\n\n print(do1(strInput))\n print(do2(strInput))\n \ndo()", "sub_path": "2020/day23.py", "file_name": "day23.py", "file_ext": "py", "file_size_in_byte": 2530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]}
+{"seq_id": "231299611", "text": "from flask import render_template, request, redirect, url_for, flash\r\n\r\nfrom login import login\r\nfrom login.forms import LoginForm\r\n\r\n\r\n@login.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n genericData = {\r\n \"title\": \"Sign In\",\r\n \"heading\": \"Login\"\r\n }\r\n form = LoginForm()\r\n if form.validate_on_submit():\r\n flash('Login requested for user {}, remember_me={}'.format(\r\n form.username.data, form.remember_me.data))\r\n return redirect(url_for('home'))\r\n\r\n return render_template(\"login.html\", data=genericData, form=form)\r\n\r\n", "sub_path": "login/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "login.forms.LoginForm", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "login.login.route", "line_number": 7, "usage_type": "call"}, {"api_name": "login.login", "line_number": 7, "usage_type": "name"}]}
+{"seq_id": "432279583", "text": "import toolbox as tb\nimport numpy as np\nimport cv2 \nimport glob\n\n### Single image detection\nsource_img = 'images/process/raw_image.jpg'\n\ntroubleshoot_flag = True\n\n# Pre-processing: \nimg = cv2.imread(source_img, 1)\nfiltered = tb.filterMarkerColour(img)\ncv2.imwrite('images/process/filtered_image.jpg', filtered)\n\n# Scan Images: \nfiltered_img = 'images/process/filtered_image.jpg'\n\nazimuth, elevation = tb.scanPerspectives(filtered_img, troubleshoot_flag) \nprint('Azimuth: ' + str(azimuth) + ' Elevation: ' + str(elevation))\n\nif cv2.waitKey(0) & 0xFF == ord('q'):\n\tcv2.destroyAllWindows() ", "sub_path": "deprecated/main_marker.py", "file_name": "main_marker.py", "file_ext": "py", "file_size_in_byte": 587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "toolbox.filterMarkerColour", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 14, "usage_type": "call"}, {"api_name": "toolbox.scanPerspectives", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 23, "usage_type": "call"}]}
+{"seq_id": "79032960", "text": "import os\nimport json\nfrom unittest.mock import patch\nfrom charmhelpers.core import unitdata\nfrom charms.reactive import is_state\nfrom reactive import containerd\nimport tempfile\n\n\ndef test_series_upgrade():\n \"\"\"Verify series upgrade hook sets the status.\"\"\"\n flags = {\n 'upgrade.series.in-progress': True,\n 'containerd.nvidia.invalid-option': False,\n }\n is_state.side_effect = lambda flag: flags[flag]\n assert containerd.status.blocked.call_count == 0\n with patch('reactive.containerd._check_containerd', return_value=False):\n containerd.charm_status()\n containerd.status.blocked.assert_called_once_with('Series upgrade in progress')\n\n\ndef test_merge_custom_registries():\n \"\"\"Verify merges of registries.\"\"\"\n with tempfile.TemporaryDirectory() as dir:\n config = [{\n \"url\": \"my.registry:port\",\n \"username\": \"user\",\n \"password\": \"pass\"\n }, {\n \"url\": \"my.other.registry\",\n \"ca_file\": \"aGVsbG8gd29ybGQgY2EtZmlsZQ==\",\n \"key_file\": \"aGVsbG8gd29ybGQga2V5LWZpbGU=\",\n }]\n ctxs = containerd.merge_custom_registries(dir, json.dumps(config), None)\n with open(os.path.join(dir, \"my.other.registry.ca\")) as f:\n assert f.read() == \"hello world ca-file\"\n with open(os.path.join(dir, \"my.other.registry.key\")) as f:\n assert f.read() == \"hello world key-file\"\n assert not os.path.exists(os.path.join(dir, \"my.other.registry.cert\"))\n\n for ctx in ctxs:\n assert 'url' in ctx\n\n # Remove 'my.other.registry' from config\n new_config = [{\n \"url\": \"my.registry:port\",\n \"username\": \"user\",\n \"password\": \"pass\"\n }]\n ctxs = containerd.merge_custom_registries(dir, json.dumps(new_config), json.dumps(config))\n assert not os.path.exists(os.path.join(dir, \"my.other.registry.ca\"))\n assert not os.path.exists(os.path.join(dir, \"my.other.registry.key\"))\n assert not os.path.exists(os.path.join(dir, \"my.other.registry.cert\"))\n\n\ndef test_juju_proxy_changed():\n \"\"\"Verify proxy changed bools are set as expected.\"\"\"\n cached = {'http_proxy': 'foo', 'https_proxy': 'foo', 'no_proxy': 'foo'}\n new = {'http_proxy': 'bar', 'https_proxy': 'bar', 'no_proxy': 'bar'}\n\n # Test when nothing is cached\n db = unitdata.kv()\n assert containerd._juju_proxy_changed() is True\n\n # Test when cache hasn't changed\n db.set('config-cache', cached)\n with patch('reactive.containerd.check_for_juju_https_proxy',\n return_value=cached):\n assert containerd._juju_proxy_changed() is False\n\n # Test when cache has changed\n with patch('reactive.containerd.check_for_juju_https_proxy',\n return_value=new):\n assert containerd._juju_proxy_changed() is True\n", "sub_path": "tests/unit/test_containerd_reactive.py", "file_name": "test_containerd_reactive.py", "file_ext": "py", "file_size_in_byte": 2849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "charms.reactive.is_state.side_effect", "line_number": 16, "usage_type": "attribute"}, {"api_name": "charms.reactive.is_state", "line_number": 16, "usage_type": "name"}, {"api_name": "reactive.containerd.status", "line_number": 17, "usage_type": "attribute"}, {"api_name": "reactive.containerd", "line_number": 17, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "reactive.containerd.charm_status", "line_number": 19, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 19, "usage_type": "name"}, {"api_name": "reactive.containerd.status.blocked.assert_called_once_with", "line_number": 20, "usage_type": "call"}, {"api_name": "reactive.containerd.status", "line_number": 20, "usage_type": "attribute"}, {"api_name": "reactive.containerd", "line_number": 20, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 25, "usage_type": "call"}, {"api_name": "reactive.containerd.merge_custom_registries", "line_number": 35, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 35, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "reactive.containerd.merge_custom_registries", "line_number": 51, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 51, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "charmhelpers.core.unitdata.kv", "line_number": 63, "usage_type": "call"}, {"api_name": "charmhelpers.core.unitdata", "line_number": 63, "usage_type": "name"}, {"api_name": "reactive.containerd._juju_proxy_changed", "line_number": 64, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 64, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 68, "usage_type": "call"}, {"api_name": "reactive.containerd._juju_proxy_changed", "line_number": 70, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 70, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 73, "usage_type": "call"}, {"api_name": "reactive.containerd._juju_proxy_changed", "line_number": 75, "usage_type": "call"}, {"api_name": "reactive.containerd", "line_number": 75, "usage_type": "name"}]}
+{"seq_id": "325259715", "text": "import json\nimport re\nfrom datetime import datetime\n\nimport discord\nimport requests\nfrom bs4 import BeautifulSoup\nfrom discord.ext import tasks, commands\n\nfrom web.apps.pricewatchers.models import TikiProduct\nfrom .webspiders import WebSpider\n\n\nclass HonkaiTasks(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.reg = re.compile('\\s*var\\s*defaultProduct\\s*=\\s*({.*})')\n self.fetch_tiki_products_price.start()\n self.site = 'https://tiki.vn'\n self.promo_endpoint = 'https://tiki.vn/api/promon/v1/products/sale-attrs'\n # self.tiki_channel_id = 676382684699820063\n\n @commands.command(name='tikiadd', hidden=True)\n @commands.is_owner()\n async def _add_tiki_page(self, context, page_id):\n tiki_product_obj, created = TikiProduct.objects.get_or_create(page_id=page_id)\n if created:\n message = 'Successfully added Tiki product to check for price.'\n else:\n message = 'This page ID has already been added.'\n await context.say_as_embed(message)\n\n @commands.command(name='tikidel', hidden=True)\n @commands.is_owner()\n async def _delete_tiki_page(self, context, page_id):\n tiki_product_obj = TikiProduct.objects.filter(page_id=page_id).first()\n if tiki_product_obj:\n tiki_product_obj.delete()\n message = 'Successfully deleted Tiki product.'\n else:\n message = 'This page ID does not exist.'\n\n await context.say_as_embed(message)\n\n @tasks.loop(seconds=60)\n async def fetch_tiki_products_price(self):\n\n channel = self.bot.get_channel(676382684699820063)\n if not channel:\n return\n\n tiki_product_objs = TikiProduct.objects.all()\n for product_obj in tiki_product_objs:\n # that extra '-' is for discord quick link to tiki app to work\n url = f'{self.site}/-{product_obj.page_id}.html'\n html = WebSpider.get_content_by_url(url)\n if not html:\n continue\n\n bs = BeautifulSoup(html, 'html.parser')\n\n image_url = bs.select_one('#product-magiczoom')\n image_url = image_url.get('data-zoom-image', None) if image_url else None\n\n product_name = bs.select_one('.item-name')\n product_name = product_name.get_text().strip() if product_name else product_obj.page_id\n\n scripts = bs.find_all('script')\n data = self.parse_product_data(scripts)\n\n embed = discord.Embed(\n title=product_name,\n url=f'{url}',\n color=discord.Color.blue(),\n timestamp=datetime.utcnow()\n )\n embed.set_footer(text='tiki.vn', icon_url='https://tiki.vn/favicon-32x32.png')\n if image_url:\n embed.set_thumbnail(url=image_url)\n\n if not data:\n continue\n elif 'out_of_stock' in data:\n if product_obj.out_of_stock is False:\n embed.description = (\n f'• Trạng thái: **Hết hàng**\\n'\n )\n await channel.send(embed=embed)\n\n product_obj.out_of_stock = True\n product_obj.save()\n continue\n\n all_sellers = [data['current_seller']]\n if 'other_sellers' in data:\n all_sellers += data['other_sellers']\n\n # sorted by price ascending\n for seller in all_sellers:\n product_id = seller.get('product_id', None)\n product_price = seller.get('price', None)\n if not product_id or not product_price:\n continue\n promo_payload = f'[{{\"id\":\"{product_id}\",\"price\":{product_price}}}]'\n promo_response = requests.post(self.promo_endpoint, data=promo_payload)\n if promo_response.status_code != 200:\n continue\n promo_code_list = promo_response.json()['data'][product_id]\n price_list = [code['price'] for code in promo_code_list]\n if not price_list:\n continue\n lowest_price = min(price_list)\n seller['price'] = lowest_price\n\n seller_sorted = sorted(all_sellers, key=lambda abc: abc['price'])\n\n if not seller_sorted:\n continue\n\n best_seller = seller_sorted[0]\n best_price = best_seller['price']\n seller_name = best_seller['name']\n\n product_obj.out_of_stock = False\n\n if product_obj.current_lowest_price == best_price:\n product_obj.save()\n continue\n\n lowest_price = best_price if best_price < product_obj.lowest_price else product_obj.lowest_price\n\n embed.description = (\n f'```bash\\n'\n f'GIÁ\\n'\n f'• Cũ : {product_obj.current_lowest_price:>13,}\\n'\n f'• Mới : {best_price:>13,}\\n'\n f'• Thấp nhất : {lowest_price:>13,}\\n'\n f'---\\n'\n f'NHÀ BÁN HÀNG\\n'\n f'• Tên : {seller_name}\\n'\n f'```'\n )\n await channel.send(embed=embed)\n\n product_obj.current_lowest_price = best_price\n product_obj.product_name = product_name\n\n if product_obj.lowest_price > best_price:\n product_obj.lowest_price = best_price\n product_obj.save()\n\n def parse_product_data(self, script_tags):\n for script in script_tags:\n text = script.get_text()\n reg_result = self.reg.search(text)\n if reg_result:\n try:\n json_data = json.loads(reg_result.group(1))\n return json_data\n except json.JSONDecodeError:\n pass\n elif 'defaultProduct = null' in text:\n return {'out_of_stock': {'status': True}}\n return None\n\n @fetch_tiki_products_price.before_loop\n async def before_parse(self):\n print('[Tiki Product price watcher] Waiting for ready state...')\n\n await self.bot.wait_until_ready()\n\n print('[Tiki Product price watcher] Ready and running!')\n\n def cog_unload(self):\n self.fetch_tiki_products_price.cancel()\n\n\ndef setup(bot):\n bot.add_cog(HonkaiTasks(bot))\n", "sub_path": "francis/tasks/tiki.py", "file_name": "tiki.py", "file_ext": "py", "file_size_in_byte": 6463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 14, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 14, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects.get_or_create", "line_number": 26, "usage_type": "call"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct", "line_number": 26, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 23, "usage_type": "name"}, {"api_name": "discord.ext.commands.is_owner", "line_number": 24, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 24, "usage_type": "name"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct", "line_number": 36, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 33, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 33, "usage_type": "name"}, {"api_name": "discord.ext.commands.is_owner", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 34, "usage_type": "name"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "web.apps.pricewatchers.models.TikiProduct", "line_number": 52, "usage_type": "name"}, {"api_name": "webspiders.WebSpider.get_content_by_url", "line_number": 56, "usage_type": "call"}, {"api_name": "webspiders.WebSpider", "line_number": 56, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 60, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.Color.blue", "line_number": 74, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 74, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 105, "usage_type": "call"}, {"api_name": "discord.ext.tasks.loop", "line_number": 45, "usage_type": "call"}, {"api_name": "discord.ext.tasks", "line_number": 45, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 158, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 160, "usage_type": "attribute"}]}
+{"seq_id": "215587358", "text": "\n# coding: utf-8\n\n# In[50]:\n\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup, Comment \nimport pandas as pd\nimport html5lib\nimport time \nimport requests\nimport re\n\n\n# In[51]:\n\n# URL for team stats \nurl_template_team = \"http://www.baseball-reference.com/teams/tgl.cgi?team={team}&t=b&year={year}\"\n\n\n# In[52]:\n\n# teams\nteams = [\"LAD\", \"BOS\", \"LAA\", \"CHC\", \"TEX\", \n \"OAK\", \"MIN\", \"CHW\", \"SEA\", \"KCR\", \n \"MIL\", \"TBR\", \"STL\", \"BAL\", \"HOU\", \n \"DET\", \"PIT\", \"NYY\", \"CLE\", \"MIN\", \n \"CIN\", \"COL\", \"NYM\", \"SFG\", \"MIL\", \n \"WSN\", \"ATL\", \"SDP\", \"ARI\", \"MIA\", ]\n\n\n# In[53]:\n\n# function to get column headers (pass in the url and the row at which the column headers begin)\ndef get_columns(url_column, row):\n \n # get the HTML from the url passed in \n html = urlopen(url_column)\n \n # make a BS object\n soup = BeautifulSoup(html, \"lxml\")\n \n # find all the tr tags for the column header of interest \n tr_tag_list = soup.findAll('tr')[row-1] \n \n # extract table header cell elements from the tag object\n cell_el_list = tr_tag_list.findAll('th')\n\n # create an empty list to hold all the elements in the column header \n column_headers = [] \n\n # for each cell element\n for th in cell_el_list: \n col_element = th.getText()\n # append each cell element to the column_header list\n column_headers.append(col_element)\n return column_headers\n\n\n# In[54]:\n\ncolumn_teams = get_columns(\"http://www.baseball-reference.com/teams/tgl.cgi?team=CHC&t=b&year=2016\", 1)\n\n\n# In[55]:\n\nurl_curr = url_template_team\nteam = teams[0]\nyear = 2017\n\n\n# In[56]:\n\nurl = url_curr.format(team=team, year=year)\nurl\n\n\n# In[57]:\n\nhtml = urlopen(url)\nhtml\n\n\n# In[58]:\n\nsoup = BeautifulSoup(html, \"lxml\")\n\n\n# In[59]:\n\nfirst_row = soup.findAll('tr')[1]\n\n\n# In[60]:\n\nfirst_row.find('th').text\n\n\n# In[61]:\n\nfor td in first_row.findAll('td'):\n print (td.text)\n\n\n# In[62]:\n\n# function to run through multiple years and multiple teams and get the regular season game logs \ndef get_game_logs(url_curr, column_headers, teams, start, end):\n \n # create an empty DataFrame to store all the game logs for all the team's seasons \n team_stats_df = pd.DataFrame()\n \n for team in teams:\n for year in range(start, end):\n # get the url\n url = url_curr.format(team=team, year=year)\n \n # check if there are game logs for the current team and the current season\n try:\n html = urlopen(url)\n except:\n print(\"No data for \" + team + \" in the \" + str(year) + \" season\" + \" in the regular season\")\n continue\n \n # get the html\n html = urlopen(url)\n # create the beautiful soup object \n soup = BeautifulSoup(html, \"lxml\")\n \n ### PART 1: REGULAR SEASON DATA \n \n # get regular season data (starts on second row)\n data_rows = soup.findAll('tr')[1:]\n \n # check if data_rows actually contains data\n if len(data_rows)<1:\n print(\"No data for \" + team + \" in the \" + str(year) + \" season\" + \" in the regular season\")\n continue \n\n # create an empty list to hold all the regular season stats for the current season and the current team \n season_data= [] \n\n for i in range(len(data_rows)):\n # create an empty list for each game \n game_row = []\n \n # first value. Must be handled separately since it goes by th tag instead of td tag \n game_row.append(data_rows[i].findAll('th')[0].getText())\n\n # for each table data element from each table row\n for td in data_rows[i].findAll('td'): \n # get the text content and append to the game_row \n game_row.append(td.getText()) \n\n # then append each game to the season_data matrix\n season_data.append(game_row)\n \n # Turn season data into a DatFrame\n season_df = pd.DataFrame(season_data, columns=column_headers)\n # Add game_type column\n season_df.insert(0, 'GameType', 'RegularSeason')\n \n # create and insert the Season and Team column\n season_df.insert(1, 'Season', year)\n season_df.insert(2, 'Team', team)\n \n # Append to the big dataframe\n team_stats_df = team_stats_df.append(season_df, ignore_index=True)\n\n return team_stats_df\n\n\n# In[63]:\n\nurl_box = \"https://www.baseball-reference.com/boxes/{team}/{team}{year}{month}{day}0.shtml\"\n\n\n# In[65]:\n\ndef get_inning_scores(url_box, column_headers, teams, y_start, y_end, m_start, m_end, d_start, d_end):\n\n #initiate data frames\n inning_df = pd.DataFrame()\n date_df = pd.DataFrame()\n start_time_df = pd.DataFrame()\n inning_numbers = list(range(1,50))\n\n for team in teams:\n for year in range(y_start, y_end):\n for month in range(m_start, m_end):\n for day in range (d_start, d_end):\n # get the url\n if month <= 9:\n month = '0' + str(month)\n if day <= 9:\n day = '0' + str(day)\n url = url_box.format(team=team, year=year, month=month, day=day)\n \n # get the html\n html = urlopen(url_box)\n\n # create the beautiful soup object \n soup = BeautifulSoup(html, \"lxml\")\n\n inning_rows = soup.findAll('tr')[2:]\n\n # check if data_rows actually contains data\n if len(data_rows)<1:\n print(\"No data for \" + team + \" in the \" + str(year) + \" season\" + \" in the regular season\")\n continue \n \n \n#####MY ISSUE HERE IS I DON'T KNOW IF THE COLUMN HEADER FOR THE INNING RUNS DF IS COMING IN CORRECTLY-IT SHOULD BE 1-50, FOR EVERY INNING#####\n # create an empty list to hold all the regular season stats for the current season and the current team \n game_data = []\n\n for i in range(len(inning_rows)):\n # create an empty list for each game \n game_row = []\n\n # for each table data element from each table row\n for td in data_rows[i].findAll('td')[:-3]: \n # get the text content and append to the game_row \n game_row.append(td.getText()) \n\n game_data.append(game_row)\n inning_runs_df = pd.DataFrame(game_data, columns = inning_numbers)\n \n\n #get the date data frame\n date_df = pd.DataFrame()\n date = soup.find(\"div\", {\"class\":\"scorebox_meta\"}).text[0]\n date_df = date_df.append(date, column = Date)\n \n #get the start time data frame\n start_time_df = pd.DataFrame()\n start_time = soup.find(\"div\", {\"class\":\"scorebox_meta\"}).text[1]\n start_time_df = start_time_df.append(start_time, column = 'Start Time') \n \n #add a column to the start_time_df with a D for daytime or N for Nighttime\n day_or_night = []\n day_times = [12, 1, 2, 3, 4]\n if start_time[0] in day_times:\n day_or_night.append(D)\n \n else:\n day_or_night.append(N)\n start_time_df.assign(DN = day_or_night)\n \n #get venue and attendance. create a dataframe for each and append to the main dataframe\n \n venue_df = pd.Dataframe()\n attendance_df = pd.Dataframe()\n for strong_tag in soup.find_all('strong'):\n venue = []\n attendance = []\n\n if strong_tag.text == 'Venue':\n venue.append(strong_tag.next_sibling)\n venue_df = venue_df.append(venue, column = 'Venue')\n \n elif strong_tag.text == 'Attendance':\n attendance.append(strong_tag.next_sibling)\n attendance_df = attendance_df.append(attendance, column = Attendance)\n \n \n #get home starting pitcher by creating a data frame for each\n home_sp_df = pd.Dataframe()\n away_sp_df = pd.Dataframe()\n \n #get the div tag where the pitchers are\n sp_tag = soup.find(\"div\", {\"class\":\"section_content\"})\n #create an empty list for both the home and away starting pitchers\n home_sp = []\n away_sp = []\n \n \n \n ###IF YOU GO TO THE LINK AND INSPECT EACH PITCHER, YOU WILL SEE THE AWAY PITCHER LISTED FIRST AND THE HOME PITCHER LISTED NEXT\n ###THERE ARE ONLY 2 A HREF TAGS AND I WANT THE FIRST ONE TO BE THE AWAY, THE SECOND HOME BUT IM NOT SURE HOW TO INDEX IT CORRECTLY\n #append the starting pitcher to each list\n for div in sp_tag:\n for a in div.find_all('a'):\n #the first one should be away but the second should be home, how do i select the index?\n \n h_starting_pitcher = a[1].text\n home_sp.append(h_starting_pitcher)\n \n a_starting_pitcher = a[0].text\n away_sp.append(a_starting_pitcher)\n \n home_sp_df = home_sp_df.append(home_sp, column = 'Home SP')\n away_sp_df = away_sp_df.append(away_sp, column = 'Away SP')\n \n #get away starting pitcher\n \n # then append each game to the season_data matrix\n inning_df = inning_df.append(inning_runs_df, date_df, start_time_df, venue_df, attendance_df, home_sp_df, away_sp_df)\n \n \n \n return inning_df\n\n \n\n\n# In[ ]:\n\nstart_time = time.time()\ninning_numbers = list(range(1,50))\n\n\n# call function for game logs \nteam_game_logs_df = get_game_logs(url_template_team, column_teams, teams, 2017, 2018)\ninning_game_df = get_inning_scores(url_box, inning_numbers, teams, 2016, 2017, 4, 10, 1, 31)\n\nprint(\"%f seconds\" % (time.time() - start_time))\n\n\n# In[ ]:\n\n# look at number of rows\nlen(team_game_logs_df)\n\n\n# In[ ]:\n\n# look at first few rows \nteam_game_logs_df.head()\n\n\n# In[ ]:\n\n# look at last few rows \nteam_game_logs_df.tail()\n\n\n# In[ ]:\n\n# function to clean data\ndef clean_df(df):\n # Convert data to proper data types\n df = df.convert_objects(convert_numeric=True)\n\n # Get rid of the rows full of null values\n df = df[df.Season.notnull()]\n\n # Replace NaNs with 0s\n df = df.fillna(0)\n \n # Change % symbol\n df.columns = df.columns.str.replace('%', '_Perc')\n \n return df\n\n\n# In[ ]:\n\n# clean advanced player stats df\nteam_game_logs_df = clean_df(team_game_logs_df)\ninning_game_df = clean_df(inning_game_df)\n\n\n# In[ ]:\n\nteam_game_logs_df.columns\ninning_game_df.columns\n\n\n# In[ ]:\n\n# Check if missing values in the DataFrame\nteam_game_logs_df.isnull().sum() \ninning_game_df.isnull().sum()\n\n\n# In[ ]:\n\n# write data frame to CSV\nteam_game_logs_df.to_csv(\"team_game_logs_test_new.csv\")\ninning_df.to_csv(\"team_game_logs_test_new.csv\")\n\n\n# In[ ]:\n\n\n\n", "sub_path": "Baseball+Reference+Pull (2).py", "file_name": "Baseball+Reference+Pull (2).py", "file_ext": "py", "file_size_in_byte": 12230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "urllib.request.urlopen", "line_number": 38, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 80, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 120, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 126, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 159, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 183, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 185, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 200, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 203, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.Dataframe", "line_number": 252, "usage_type": "call"}, {"api_name": "pandas.Dataframe", "line_number": 253, "usage_type": "call"}, {"api_name": "pandas.Dataframe", "line_number": 268, "usage_type": "call"}, {"api_name": "pandas.Dataframe", "line_number": 269, "usage_type": "call"}, {"api_name": "time.time", "line_number": 309, "usage_type": "call"}, {"api_name": "time.time", "line_number": 317, "usage_type": "call"}]}
+{"seq_id": "110087749", "text": "\"\"\"\r\nTest script for REST/pilots\r\n\"\"\"\r\n\r\nimport logging\r\nlogger = logging.getLogger('rest_pilots_test')\r\n\r\nimport os\r\nimport sys\r\nimport time\r\nimport random\r\nimport shutil\r\nimport tempfile\r\nimport unittest\r\nimport subprocess\r\nimport json\r\nfrom functools import partial\r\nfrom unittest.mock import patch, MagicMock\r\n\r\nfrom tests.util import unittest_reporter, glob_tests\r\n\r\nimport ldap3\r\nimport tornado.web\r\nimport tornado.ioloop\r\nfrom tornado.httpclient import AsyncHTTPClient, HTTPError\r\nfrom tornado.testing import AsyncTestCase\r\n\r\nfrom rest_tools.server import Auth, RestServer\r\n\r\nfrom iceprod.server.modules.rest_api import setup_rest\r\n\r\nfrom . import RestTestCase\r\n\r\nclass rest_pilots_test(RestTestCase):\r\n def setUp(self):\r\n config = {'rest':{'pilots':{}}}\r\n super(rest_pilots_test,self).setUp(config=config)\r\n\r\n @unittest_reporter(name='REST GET /pilots')\r\n def test_100_pilots(self):\r\n client = AsyncHTTPClient()\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 200)\r\n ret = json.loads(r.body)\r\n self.assertEqual(ret, {})\r\n\r\n @unittest_reporter(name='REST POST /pilots')\r\n def test_105_pilots(self):\r\n client = AsyncHTTPClient()\r\n data = {\r\n 'queue_host': 'foo.bar.baz',\r\n 'queue_version': '1.2.3',\r\n 'resources': {'foo':1}\r\n }\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n method='POST', body=json.dumps(data),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 201)\r\n ret = json.loads(r.body)\r\n pilot_id = ret['result']\r\n\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 200)\r\n ret = json.loads(r.body)\r\n self.assertIn(pilot_id, ret)\r\n for k in data:\r\n self.assertIn(k, ret[pilot_id])\r\n self.assertEqual(data[k], ret[pilot_id][k])\r\n\r\n @unittest_reporter(name='REST GET /pilots/<pilot_id>')\r\n def test_110_pilots(self):\r\n client = AsyncHTTPClient()\r\n data = {\r\n 'queue_host': 'foo.bar.baz',\r\n 'queue_version': '1.2.3',\r\n 'resources': {'foo':1}\r\n }\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n method='POST', body=json.dumps(data),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 201)\r\n ret = json.loads(r.body)\r\n pilot_id = ret['result']\r\n\r\n r = yield client.fetch('http://localhost:%d/pilots/%s'%(self.port,pilot_id),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 200)\r\n ret = json.loads(r.body)\r\n for k in data:\r\n self.assertIn(k, ret)\r\n self.assertEqual(data[k], ret[k])\r\n self.assertIn('tasks', ret)\r\n self.assertEqual(ret['tasks'], [])\r\n\r\n @unittest_reporter(name='REST PATCH /pilots/<pilot_id>')\r\n def test_120_pilots(self):\r\n client = AsyncHTTPClient()\r\n data = {\r\n 'queue_host': 'foo.bar.baz',\r\n 'queue_version': '1.2.3',\r\n 'resources': {'foo':1}\r\n }\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n method='POST', body=json.dumps(data),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 201)\r\n ret = json.loads(r.body)\r\n pilot_id = ret['result']\r\n\r\n new_data = {\r\n 'queues': {'foo': 'HTCondor', 'bar': 'HTCondor'},\r\n 'version': '1.2.8',\r\n 'tasks': ['baz'],\r\n }\r\n r = yield client.fetch('http://localhost:%d/pilots/%s'%(self.port,pilot_id),\r\n method='PATCH', body=json.dumps(new_data),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 200)\r\n ret = json.loads(r.body)\r\n for k in new_data:\r\n self.assertIn(k, ret)\r\n self.assertEqual(new_data[k], ret[k])\r\n\r\n @unittest_reporter(name='REST DELETE /pilots/<pilot_id>')\r\n def test_130_pilots(self):\r\n client = AsyncHTTPClient()\r\n data = {\r\n 'queue_host': 'foo.bar.baz',\r\n 'queue_version': '1.2.3',\r\n 'resources': {'foo':1}\r\n }\r\n r = yield client.fetch('http://localhost:%d/pilots'%self.port,\r\n method='POST', body=json.dumps(data),\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 201)\r\n ret = json.loads(r.body)\r\n pilot_id = ret['result']\r\n\r\n r = yield client.fetch('http://localhost:%d/pilots/%s'%(self.port,pilot_id),\r\n method='DELETE',\r\n headers={'Authorization': 'bearer '+self.token})\r\n self.assertEqual(r.code, 200)\r\n\r\n with self.assertRaises(Exception):\r\n r = yield client.fetch('http://localhost:%d/pilots/%s'%(self.port,pilot_id),\r\n headers={'Authorization': 'bearer '+self.token})\r\n\r\ndef load_tests(loader, tests, pattern):\r\n suite = unittest.TestSuite()\r\n alltests = glob_tests(loader.getTestCaseNames(rest_pilots_test))\r\n suite.addTests(loader.loadTestsFromNames(alltests,rest_pilots_test))\r\n return suite\r\n", "sub_path": "tests/server/rest/pilots_test.py", "file_name": "pilots_test.py", "file_ext": "py", "file_size_in_byte": 5529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 41, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "tests.util.unittest_reporter", "line_number": 39, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 60, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "tests.util.unittest_reporter", "line_number": 48, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 74, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "tests.util.unittest_reporter", "line_number": 72, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 99, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 106, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 109, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 121, "usage_type": "call"}, {"api_name": "tests.util.unittest_reporter", "line_number": 97, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 128, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 135, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 138, "usage_type": "call"}, {"api_name": "tests.util.unittest_reporter", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 151, "usage_type": "call"}, {"api_name": "tests.util.glob_tests", "line_number": 152, "usage_type": "call"}]}
+{"seq_id": "385724161", "text": "import logging\nfrom collections import defaultdict\nfrom xoutil.context import context\nfrom xoutil.objects import get_first_of\nfrom xoutil.decorator.meta import decorator\n\nfrom .errors import MissingCollection, MissingFields\n\nlog = logging.getLogger(__name__)\n\n\ndef merge_dicts(dest, source):\n \"\"\"\n source: {'children': {}, 'parameters': {'ids': [1, 2, 3, 1]}}\n dest: {'children': {'a': {'x': 1}}, 'parameters': {'ids': [1, 1, 4, 1]}}\n\n output: {'children': {'a': {'x': 1}}, 'parameters': {'ids': [1, 2, 3, 4]}}\n \"\"\"\n for k, v in source.items():\n if k not in dest:\n dest[k] = v\n else:\n if isinstance(v, list):\n dest[k] = list(set(dest[k]).union(set(v)))\n elif isinstance(v, dict):\n merge_dicts(dest[k], source[k])\n return dest\n\n\nclass All:\n def __contains__(self, *args, **kwargs):\n return True\n\n\nAll = All()\n\n\nclass Field:\n\n def __init__(self, ref=None, attr=None, call=False, many=False):\n self.attr = attr\n self.call = call\n self.ref = ref\n self.many = many\n\n def to_value(self, instance, children, **kwargs):\n value = self.serialize(instance, children=children, **kwargs)\n\n # ask for new items\n if self.ref:\n query = context['carbon14'].children[self.ref]\n merge_dicts(query['children'], children)\n\n ids = value if self.many else [value]\n ids = set(x for x in ids if x is not None)\n query['parameters']['ids'] = query['parameters']['ids'].union(ids)\n\n return value\n\n def serialize(self, instance, children, **kwargs):\n value = get_first_of(instance, self.attr)\n if value and self.call:\n value = value()\n return value\n\n\nclass MethodField(Field):\n\n def __init__(self, method=None, **kwargs):\n super().__init__(**kwargs)\n self.serialize = method\n\n\n@decorator\ndef field(fn, *args, **kwargs):\n return MethodField(method=fn, *args, **kwargs)\n\n\nclass Node(type):\n\n def __new__(cls, name, bases, attrs):\n fields = {}\n for field_name, field in attrs.items():\n if isinstance(field, Field):\n field.attr = field.attr or field_name\n fields[field_name] = field\n\n attrs['_fields'] = fields\n\n real_class = super().__new__(cls, name, bases, attrs)\n return real_class\n\n\nclass Collection(metaclass=Node):\n\n _source = ()\n id = Field()\n\n def _to_value(\n self, collection_name, level, instances=..., children=None,\n **kwargs):\n instances = self._source if instances is ... else instances\n children = children or {}\n children.setdefault('id', {'parameters': {}, 'children': {}})\n self.permitted_fields = None\n instances = self._resolve(level, instances, **kwargs)\n children = self._filter_children(children, instances, **kwargs)\n\n missing_fields = set(children) - set(self._fields)\n if missing_fields:\n raise MissingFields(collection_name, missing_fields)\n\n return self._serialize(instances, children, ctx=kwargs.get('ctx'))\n\n def _resolve(self, level, instances, **kwargs):\n return instances\n\n def _filter_children(self, children, instances, **kwargs):\n return children\n\n def _serialize(self, instances, children, ctx):\n return [\n {\n child: self._fields[child].to_value(\n instance,\n children=query['children'],\n **dict(query['parameters'], ctx=ctx)\n )\n for child, query in children.items()\n if self.field_is_accessible(child)\n }\n for instance in instances\n ]\n\n def field_is_accessible(self, child):\n if not self._fields:\n self.log_no_fields()\n return False\n\n if child not in self._fields:\n return False\n\n permitted_fields = self.permitted_fields\n\n if not permitted_fields:\n return True\n\n return child in permitted_fields\n\n def log_no_fields(self):\n log.error(\n f\"There are no fields defined for this collection: \"\n f\"self._fields is {self._fields}.\"\n )\n\n\nclass RootNode:\n def __init__(self, **collections):\n self.collections = collections\n\n def serialize(self, children, ctx=None):\n results = defaultdict(lambda: defaultdict(dict))\n more_objects_required = True\n\n level = 0\n\n while more_objects_required:\n future_children = defaultdict(\n lambda: {\n 'children': {},\n 'parameters': {'ids': set(), 'ctx': ctx},\n }\n )\n\n with context('carbon14', children=future_children):\n results = self._serialize(level, results, children, ctx)\n\n more_objects_required = False\n\n for collection, objs in results.items():\n ids = set(objs)\n parameters = future_children[collection]['parameters']\n parameters['ids'] = parameters['ids'].difference(ids)\n\n for query in future_children.values():\n if query['parameters']['ids']:\n more_objects_required = True\n children = future_children\n\n level += 1\n\n return results\n\n def _serialize(self, level, results, children, ctx):\n for child, query in children.items():\n collection = self.collections.get(child)\n if collection:\n collection_results = collection._to_value(\n collection_name=child,\n level=level,\n children=query['children'],\n **dict(query['parameters'], ctx=ctx)\n )\n for r in collection_results:\n results[child][r['id']].update(r)\n else:\n raise MissingCollection(child)\n return results\n", "sub_path": "carbon14/neonode.py", "file_name": "neonode.py", "file_ext": "py", "file_size_in_byte": 6078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "xoutil.context.context", "line_number": 51, "usage_type": "name"}, {"api_name": "xoutil.objects.get_first_of", "line_number": 61, "usage_type": "call"}, {"api_name": "xoutil.decorator.meta.decorator", "line_number": 74, "usage_type": "name"}, {"api_name": "errors.MissingFields", "line_number": 111, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 162, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 168, "usage_type": "call"}, {"api_name": "xoutil.context.context", "line_number": 175, "usage_type": "call"}, {"api_name": "errors.MissingCollection", "line_number": 207, "usage_type": "call"}]}
+{"seq_id": "588265935", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 16 15:10:40 2016\n\n@author: Caiyd\n\"\"\"\n\nimport click\n\n\ndef loadvcf(file, var_dict):\n headline = []\n with open(file) as f:\n for line in f:\n if line[0] != '#':\n line = '\\t'.join(line.strip().split())\n name = '%s-%s' % tuple(line.split('\\t')[:2])\n var_dict[name] = line + '\\n'\n else:\n headline.append(line)\n return headline, var_dict\n\n\ndef output(headline, var_dict, k_list, outfile):\n with open(outfile, 'w') as f:\n for line in headline:\n f.write(line)\n for k in k_list:\n f.write(var_dict[k])\n\n\ndef sort(var_dict):\n k_list = [[x.split('-')[0], int(x.split('-')[1])] for x in var_dict.keys()]\n k_list.sort()\n k_list = ['%s-%s' % tuple(x) for x in k_list]\n return k_list\n\n@click.command()\n@click.option('-O', '--out', default='merge_out.vcf', help='output name, default is merge_out.vcf')\n@click.argument('vcf', nargs=-1)\ndef main(out, vcf):\n var_dict = {}\n for file in vcf:\n headline, var_dict = loadvcf(file, var_dict)\n k_list = sort(var_dict)\n output(headline, var_dict, k_list, out)\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "SNP_analysis/deRedunVcf.py", "file_name": "deRedunVcf.py", "file_ext": "py", "file_size_in_byte": 1236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "click.command", "line_number": 38, "usage_type": "call"}, {"api_name": "click.option", "line_number": 39, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "337599647", "text": "from selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nimport os\nimport time\nimport unittest\n\n\n\n\n# get the path of chromedriver\ndir = os.path.dirname(__file__)\nchrome_driver_path = dir + \"\\chromedriver.exe\" #remove the .exe extension on linux or mac platform\n\n# create a new Chrome session\ndriver = webdriver.Chrome(chrome_driver_path)\ndriver.implicitly_wait(30)\ndriver.maximize_window()\n\n\n# navigate to the Fender American page where the virtual shop is, a workaround is applied here\ndriver.get(\"http://shop.fender.com/en-US/\")\n\nprint(driver.current_url)\nprint(driver.title)\n\nif (driver.current_url != \"http://shop.fender.com/en-US/\"):\n\n change_region_link= driver.find_element_by_link_text(\"Change Your Region\")\n change_region_link.click()\n\n us_shop_link= driver.find_element_by_link_text(\"United States of America (en)\")\n us_shop_link.click()\n\n driver.back()\n us_shop_link= driver.find_element_by_link_text(\"United States of America (en)\")\n us_shop_link.click()\n\n print(driver.title)\n\n#click on the products hyperlink to expand the menu\nproduct= driver.find_element_by_xpath(\"//a[contains(@data-category-id, 'fender-products')]\")\nproduct.click()\n\n#Select Jazzmaster Guitar\njazzmaster= driver.find_element_by_link_text(\"Jazzmaster\")\njazzmaster.click()\n\n#Select Jazzmaster Lacquer Guitar\n\n\njazzmaster_american_lacquer= driver.find_element_by_xpath(\"//a[contains(@title, 'Lacquer')]\")\njazzmaster_american_lacquer.click()\n\n#Add Jazzmaster Lacquer Guitar to cart\nadd_to_cart_button= driver.find_element_by_xpath(\"//button[@title='Add to Cart']\")\nadd_to_cart_button.click()\n\n\n\n# View the shopping cart (see if we can get an independent action to hoover over view cart)\nview_cart_button= driver.find_element_by_xpath(\"//*[@id='mini-cart']/div[2]/div[4]/a\")\ntime.sleep(1)\nview_cart_button.click()\n\n#Click on secure check out\n\n\nsec_checkout_button= driver.find_elements_by_xpath(\"//*[@id='checkout-form']/fieldset/button\")[-1]\nsec_checkout_button.click()\n\ncheckout_guest_button= driver.find_elements_by_xpath(\"//button[@value='Check Out as Guest']\")[-1]\ncheckout_guest_button.click()\n\n# Fill in customer information form\n\ninput_first_name= driver.find_element_by_xpath(\"//input[@id='dwfrm_singleshipping_shippingAddress_addressFields_firstName']\")\ninput_first_name.send_keys('David')\n\ninput_last_name= driver.find_element_by_xpath(\"//input[@id='dwfrm_singleshipping_shippingAddress_addressFields_lastName']\")\ninput_last_name.send_keys('Palomar')\n\ninput_address_1 = driver.find_element_by_xpath(\"//input[@id='dwfrm_singleshipping_shippingAddress_addressFields_address1']\")\ninput_address_1.send_keys('329 North First Street')\n\ninput_city= driver.find_element_by_xpath(\"//input[@id='dwfrm_singleshipping_shippingAddress_addressFields_city']\")\ninput_city.send_keys('San Jose')\n\nselect_state=Select(driver.find_element_by_xpath(\"//*[@id='dwfrm_singleshipping_shippingAddress_addressFields_states_state']\"))\nselect_state.select_by_visible_text(\"California\")\n\nzip_code= driver.find_element_by_xpath(\"//input[@id='dwfrm_singleshipping_shippingAddress_addressFields_zip']\")\nzip_code.send_keys('95110')\n\nphone= driver.find_element_by_xpath(\"//*[@id='dwfrm_singleshipping_shippingAddress_addressFields_phone']\")\nphone.send_keys('4083334446')\n\ncontinue_button= driver.find_elements_by_xpath(\"//label[@value='Continue ']\")[-1]\ncontinue_button.click()\n\nprint('Success! Reached billing page')\nprint(driver.title)\n\nassert \"Billing Checkout | Fender\" == driver.title\n\n# close the browser window\ndriver.quit()\n\n\n\n", "sub_path": "First_Selenium_Sequential/Fender_Challenge.py", "file_name": "Fender_Challenge.py", "file_ext": "py", "file_size_in_byte": 3539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 15, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.Select", "line_number": 88, "usage_type": "call"}]}
+{"seq_id": "185845742", "text": "from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n path('', travel.as_view(), name='travel'),\n path('<int:id>/',travel_id.as_view()),\n path('<int:id>/fork/',travel_fork.as_view()),\n path('<int:id>/travelCommit/', travel_id_travelCommit.as_view()),\n path('travelCommit/<int:id>/photo/', travelCommitPhoto.as_view()),\n path('travelCommit/<int:id>/merge/',travel_commit_merge.as_view()),\n path('popular/',travel_popular.as_view(), name='travel_popular'),\n path('recent/',travel_recent.as_view(), name='travel_recent'),\n path('search/<query>/', TravelSearch.as_view(), name='travel_search'),\n path('user/<int:id>/', user_travel_list.as_view(), name='user_travel_list'),\n path('tag/<tag>/', TagList.as_view(), name='tag_list'),\n path('recommend/<int:id>/', travel_recommend_bytravel.as_view(), name='travel_recommend_bytravel'),\n path('recommend/<int:user_id>/<int:travel_id>/', travel_recommend_byuser.as_view(), name='travel_recommend_byuser'),\n path('settings/<int:id>/', TravelSettings.as_view(), name='travel_settings'),\n path('collaborator/<int:id>/', collaborator_travel_list.as_view(), name='collaborator_travel_list'),\n path('view/<int:id>/',travel_view_update.as_view()),\n path('like/<int:id>/',travel_like_update.as_view()),\n path('<int:tid>/comment/', comments.as_view()),\n path('<int:tid>/comment/<int:cid>/', comments_id.as_view()),\n]\n\n", "sub_path": "backend/triplannet/travel/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}]}
+{"seq_id": "394880464", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.notes, name='notes'),\n path('register', views.register, name='register'),\n path('login', views.login, name='login'),\n path('signout', views.signout, name='signout'),\n path('addFile', views.addFile, name='addFile'),\n path('deleteFile', views.deleteFile, name='deleteFile'),\n path('getFiles', views.getfiles, name='getfiles'),\n]", "sub_path": "notesApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]}
+{"seq_id": "307584005", "text": "#!/usr/bin/env python\n# -*- coding:utf8 -*-\n\nfrom deepdive import *\nimport re\nimport divlaw\nfrom pyvi.pyvi import ViPosTagger, ViTokenizer\nimport handle_string\n\n@tsv_extractor\n@returns(lambda\n law_id =\"text\",\n position = \"text\",\n sentence_index = \"int\",\n sentence_text = \"text\",\n tokens = \"text[]\",\n pos_tags = \"text[]\",\n :[])\n\ndef extract(\n id =\"text\",\n content =\"text\",\n part_index =\"int\",\n chap_index =\"int\",\n sec_index =\"int\",\n law_index =\"int\",\n item_index =\"int\",\n start_index =\"int\",\n end_index =\"int\",\n ):\n sent_index = 0\n for s in content[start_index:end_index].split(\"\\n\"):\n if s != \"\":\n it = re.finditer(r\"(.(?!(\\.\\s)))+.{2}\",s,re.I)\n lent = divlaw.lenIterator(it)\n it = re.finditer(r\"(.(?!(\\.\\s)))+.{2}\",s,re.I)\n listIndex = []\n position = 0\n if item_index is None:\n position = \"{}_{}_{}_{}_{}\".format(part_index+1,chap_index+1,sec_index+1,law_index+1,0) \n else :\n position = \"{}_{}_{}_{}_{}\".format(part_index+1,chap_index+1,sec_index+1,law_index+1,item_index+1) \n if lent > 0:\n for i in it :\n listIndex.append(i.start())\n if (len(s) - i.end()) > 5 :\n listIndex.append(i.end())\n lent += 1\n else :\n listIndex.append(0)\n for j in range(0,lent) :\n if (j != (lent - 1)) :\n string = handle_string.to_unicode(s[listIndex[j]:listIndex[j+1]])\n string = string.replace(\"\\\\\",'')\n tokenize = ViPosTagger.postagging(ViTokenizer.tokenize(string))[0] \n pos_tag = ViPosTagger.postagging(ViTokenizer.tokenize(string))[1]\n tk = []\n sent_index += 1\n for token in tokenize :\n token = token.encode('utf-8')\n tk.append(token)\n if '' in tk :\n continue\n else :\n yield [\n id,\n position,\n sent_index - 1,\n \" \".join(tk),\n tk,\n pos_tag\n ]\n else :\n string = handle_string.to_unicode(s[listIndex[j]:])\n string = string.replace(\"\\\\\",'')\n tokenize = ViPosTagger.postagging(ViTokenizer.tokenize(string))[0]\n pos_tag = ViPosTagger.postagging(ViTokenizer.tokenize(string))[1]\n tk = []\n sent_index+=1\n for token in tokenize :\n token = token.encode('utf-8')\n tk.append(token)\n if '' in tk :\n continue\n else :\n yield [\n id,\n position,\n sent_index -1,\n \" \".join(tk),\n tk,\n pos_tag\n ]\n", "sub_path": "deepdive/udf/extract_sentences.py", "file_name": "extract_sentences.py", "file_ext": "py", "file_size_in_byte": 3298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "re.finditer", "line_number": 34, "usage_type": "call"}, {"api_name": "re.I", "line_number": 34, "usage_type": "attribute"}, {"api_name": "divlaw.lenIterator", "line_number": 35, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 36, "usage_type": "call"}, {"api_name": "re.I", "line_number": 36, "usage_type": "attribute"}, {"api_name": "handle_string.to_unicode", "line_number": 53, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger.postagging", "line_number": 55, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger", "line_number": 55, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViTokenizer.tokenize", "line_number": 55, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViTokenizer", "line_number": 55, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViPosTagger.postagging", "line_number": 56, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger", "line_number": 56, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViTokenizer.tokenize", "line_number": 56, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViTokenizer", "line_number": 56, "usage_type": "name"}, {"api_name": "handle_string.to_unicode", "line_number": 74, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger.postagging", "line_number": 76, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger", "line_number": 76, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViTokenizer.tokenize", "line_number": 76, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViTokenizer", "line_number": 76, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViPosTagger.postagging", "line_number": 77, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViPosTagger", "line_number": 77, "usage_type": "name"}, {"api_name": "pyvi.pyvi.ViTokenizer.tokenize", "line_number": 77, "usage_type": "call"}, {"api_name": "pyvi.pyvi.ViTokenizer", "line_number": 77, "usage_type": "name"}]}
+{"seq_id": "37909233", "text": "\"\"\"added name change in subscribeform Migration\n\nRevision ID: 568794bd8a52\nRevises: 4a2c50cc5743\nCreate Date: 2018-09-16 14:17:13.532087\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '568794bd8a52'\ndown_revision = '4a2c50cc5743'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('subscribers', 'title')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('subscribers', sa.Column('title', sa.VARCHAR(length=255), autoincrement=False, nullable=True))\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/568794bd8a52_added_name_change_in_subscribeform_.py", "file_name": "568794bd8a52_added_name_change_in_subscribeform_.py", "file_ext": "py", "file_size_in_byte": 723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "alembic.op.drop_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "107444491", "text": "\"\"\"isac_simo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.urls import include, path, re_path\nfrom rest_framework import routers\nfrom rest_framework_simplejwt.views import (TokenObtainPairView,\n TokenRefreshView)\n\nfrom api import views as api\nfrom api.views import ImageView, ProfileView, UserView, VideoFrameView\nfrom main import views\n\nrouter = routers.DefaultRouter()\nrouter.register('register', UserView)\nrouter.register('image', ImageView)\nrouter.register('profile', ProfileView)\nrouter.register('video', VideoFrameView)\n\nurlpatterns = [\n # API\n path('api/user/', include('rest_framework.urls')), # REST_FRAMEWORK_URL_FOR_TEST\n path('api/auth/', TokenObtainPairView.as_view(), name='auth'),\n path('api/auth/refresh/', TokenRefreshView.as_view(), name='auth_refresh'),\n path('api/', include(router.urls)),\n # WEB\n path('', views.index, name=\"index\"),\n path('login/', views.login_user, name=\"login\"),\n path('login/<int:id>', views.login_user, name=\"loginpost\"),\n path('register/', views.register, name=\"register\"),\n path('logout/', views.logout_user, name=\"logout\"),\n path('dashboard', views.home, name=\"dashboard\"),\n path('pull', views.pull, name=\"pull\"), # Pull used by circleci trigger to deploy\n path('users/', include('main.urls')),\n path('projects/', include('projects.urls')),\n path('app/', include('api.urls')),\n path('map/', include('map.urls')),\n # Service Worker js\n path('serviceworker.js', views.serviceworker, name=\"serviceworker\"),\n path('offline', views.offline, name=\"offline\")\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n urlpatterns += path('admin/', admin.site.urls),\n", "sub_path": "django-backend/isac_simo/isac_simo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2535, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 28, "usage_type": "name"}, {"api_name": "api.views.UserView", "line_number": 29, "usage_type": "argument"}, {"api_name": "api.views.ImageView", "line_number": 30, "usage_type": "argument"}, {"api_name": "api.views.ProfileView", "line_number": 31, "usage_type": "argument"}, {"api_name": "api.views.VideoFrameView", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "main.views.index", "line_number": 41, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 41, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "main.views.login_user", "line_number": 42, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 42, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "main.views.login_user", "line_number": 43, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "main.views.register", "line_number": 44, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 44, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "main.views.logout_user", "line_number": 45, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 45, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "main.views.home", "line_number": 46, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "main.views.pull", "line_number": 47, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 51, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "main.views.serviceworker", "line_number": 53, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 53, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "main.views.offline", "line_number": 54, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 57, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 58, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.urls.static.static", "line_number": 59, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 59, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 60, "usage_type": "name"}]}
+{"seq_id": "346321316", "text": "\nimport argparse\nfrom configs.default import update_config\nfrom configs import config\nfrom datasets.rssrai import RssraiDataset\nimport torch\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"\")\n parser.add_argument(\n '--cfg',\n metavar='config_json_file',\n default='None',\n help='The Configuration file in json format')\n return parser.parse_args()\n\ndef main():\n global config\n args = parse_args()\n update_config(config, args)\n\n dataset = RssraiDataset('train', config.DATASET, mean=[0,0,0], std=[1,1,1])\n dataloader = torch.utils.data.DataLoader(\n dataset,\n batch_size=10,\n num_workers=4,\n shuffle=False\n )\n\n mean = 0.\n std = 0.\n nb_samples = 0.\n for data, label in dataloader:\n batch_samples = data.size(0)\n data = data.view(batch_samples, data.size(1), -1)\n mean += data.mean(2).sum(0)\n std += data.std(2).sum(0)\n nb_samples += batch_samples\n\n mean /= nb_samples\n std /= nb_samples\n print(\"mean is: %f, std is: %f\", mean, std)\n\nif __name__ == \"__main__\":\n main()", "sub_path": "get_mean_std.py", "file_name": "get_mean_std.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "configs.default.update_config", "line_number": 21, "usage_type": "call"}, {"api_name": "configs.config", "line_number": 21, "usage_type": "argument"}, {"api_name": "datasets.rssrai.RssraiDataset", "line_number": 23, "usage_type": "call"}, {"api_name": "configs.config.DATASET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "configs.config", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 24, "usage_type": "attribute"}]}
+{"seq_id": "80502785", "text": "import numpy\nimport metric_learn\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\n\nclass knn_clf():\n def __init__(self):\n # Hyper param\n self.k = 2\n self.metric = metric_learn.LSML_Supervised()\n self.metric_mtx = None\n\n # kNN classifier\n self.classifier = None\n\n #standarScaler\n self.standar = StandardScaler()\n \n def fit(self, data, label):\n #标准化\n data = self.standar.fit_transform(data)\n #度量学习\n self.metric.fit(data, label)\n self.metric_mtx = self.metric.get_mahalanobis_matrix()\n\n #构造、训练分类器\n self.classifier = KNeighborsClassifier(n_neighbors = self.k, weights='distance', algorithm='brute', metric='mahalanobis', metric_params={'VI' : self.metric_mtx})\n self.classifier.fit(data, label)\n\n def get_neibors(self, data, k):\n data = self.standar.transform(data.reshape(1,-1), copy = True)\n distance, neibors = self.classifier.kneighbors(data, n_neighbors = k)\n return neibors, distance\n\n\n\n", "sub_path": "main/knn_classifier.py", "file_name": "knn_classifier.py", "file_ext": "py", "file_size_in_byte": 1119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "metric_learn.LSML_Supervised", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "643955390", "text": "import copy\nimport httplib\nimport json\nimport os\nimport unittest\nimport uuid\n\nimport mock\nfrom elasticsearch import exceptions\nfrom liblcp import context\n\nimport configuration\nfrom app import services\n\nCORRELATION_ID = str(uuid.uuid4())\nPRINCIPAL = str(uuid.uuid4())\ncontext.set_headers_getter(lambda name: {context.HEADERS_EXTERNAL_BASE_URL: 'http://localhost',\n context.HEADERS_CORRELATION_ID: CORRELATION_ID,\n context.HEADERS_MODE: context.MODE_LIVE,\n context.HEADERS_PRINCIPAL: PRINCIPAL}[name])\n\nNOT_FOUND_EXCEPTION = exceptions.TransportError(httplib.NOT_FOUND,\n 'IndexMissingException[[123] missing]',\n {\n 'status': httplib.BAD_REQUEST,\n 'error': 'IndexMissingException[[123] missing]'\n })\n\nINTERNAL_SERVER_ERROR_EXCEPTION = exceptions.TransportError(httplib.INTERNAL_SERVER_ERROR,\n 'Internal Server error',\n {\n 'status': httplib.INTERNAL_SERVER_ERROR,\n 'error': 'Server error'\n })\n\n\nDELETE_BY_QUERY_RESPONSE_BODY = {\n \"_indices\": {\n \"service\": {\n \"_shards\": {\n \"total\": 5,\n \"successful\": 5,\n \"failed\": 0\n }\n }\n }\n }\n\n\nclass BaseTestElasticSearchService(unittest.TestCase):\n\n def setUp(self):\n self.data = {\n 'url': 'url',\n 'filePath': 'file.csv',\n 'service': 'service',\n 'list_id': 'id',\n 'callbackUrl': 'callback',\n }\n self.member_data = copy.deepcopy(self.data)\n self.member_data['member_id'] = 'member_id'\n self.service = services.ElasticSearch()\n configuration.configure_from(os.path.join(configuration.CONFIGURATION_PATH, 'list_loading_service.cfg'))\n\n @staticmethod\n def _assert_callback(mock_requests_wrapper_post, success, error=None):\n data = {\n 'success': success,\n 'links': {\n 'self': {\n 'href': 'url'\n }\n }\n }\n if success:\n data['links']['member'] = {'href': '/service/id/{member-id}'}\n\n mock_requests_wrapper_post.assert_has_calls([\n mock.call(url='callback',\n headers={\n 'PTS-LCP-Base-URL': 'http://localhost',\n 'PTS-LCP-Mode': context.MODE_LIVE,\n 'PTS-LCP-CID': CORRELATION_ID,\n 'PTS-LCP-Principal': PRINCIPAL,\n 'Content-Type': 'application/json'\n },\n data=json.dumps(data)\n )\n ])\n", "sub_path": "tests/unit/services/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "uuid.uuid4", "line_number": 15, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 16, "usage_type": "call"}, {"api_name": "liblcp.context.set_headers_getter", "line_number": 17, "usage_type": "call"}, {"api_name": "liblcp.context", "line_number": 17, "usage_type": "name"}, {"api_name": "liblcp.context.HEADERS_EXTERNAL_BASE_URL", "line_number": 17, "usage_type": "attribute"}, {"api_name": "liblcp.context.HEADERS_CORRELATION_ID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "liblcp.context", "line_number": 18, "usage_type": "name"}, {"api_name": "liblcp.context.HEADERS_MODE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "liblcp.context", "line_number": 19, "usage_type": "name"}, {"api_name": "liblcp.context.HEADERS_PRINCIPAL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "liblcp.context", "line_number": 20, "usage_type": "name"}, {"api_name": "liblcp.context.MODE_LIVE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "elasticsearch.exceptions.TransportError", "line_number": 22, "usage_type": "call"}, {"api_name": "elasticsearch.exceptions", "line_number": 22, "usage_type": "name"}, {"api_name": "httplib.NOT_FOUND", "line_number": 22, "usage_type": "attribute"}, {"api_name": "httplib.BAD_REQUEST", "line_number": 25, "usage_type": "attribute"}, {"api_name": "elasticsearch.exceptions.TransportError", "line_number": 29, "usage_type": "call"}, {"api_name": "elasticsearch.exceptions", "line_number": 29, "usage_type": "name"}, {"api_name": "httplib.INTERNAL_SERVER_ERROR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "httplib.INTERNAL_SERVER_ERROR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 50, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}, {"api_name": "app.services.ElasticSearch", "line_number": 62, "usage_type": "call"}, {"api_name": "app.services", "line_number": 62, "usage_type": "name"}, {"api_name": "configuration.configure_from", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "configuration.CONFIGURATION_PATH", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mock.call", "line_number": 79, "usage_type": "call"}, {"api_name": "liblcp.context.MODE_LIVE", "line_number": 82, "usage_type": "attribute"}, {"api_name": "liblcp.context", "line_number": 82, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}]}
+{"seq_id": "153665621", "text": "# -*- coding: utf-8 -*-\n\nimport re\nimport json\nfrom scrapy import Spider, Request\nfrom scrapy_redis.spiders import RedisSpider\nfrom scrapy_redis import connection\nfrom crawler.utils import CompleteLinkExtractor\nfrom mongoengine import NotUniqueError\n\nNINE_GAG_WORKER_KEY = 'ninegag_worker:start_urls'\nNINE_GAG_HOME_PAGE_URL = 'http://9gag.com/girl/fresh'\nCRAWLED_NINEGAG_SET = 'crawled_9gag_set'\n\nclass NineGagHomeSpider(Spider):\n\n def _set_crawler(self, crawler):\n super(NineGagHomeSpider, self)._set_crawler(crawler)\n self.server = connection.from_settings(self.crawler.settings)\n\n name = 'nine_gag_homepage'\n start_urls = ['https://www.baidu.com']\n DOWNLOAD_DELAY = 86400\n\n def parse(self, response):\n self.server.rpush(NINE_GAG_WORKER_KEY, NINE_GAG_HOME_PAGE_URL)\n yield Request(response.url, callback=self.parse, dont_filter=True)\n\nfrom bs4 import BeautifulSoup\nfrom crawler.image import download_images, download_video\n\nfrom mongoengine import (Document, StringField, ListField, IntField, DictField, DateTimeField,\n BooleanField, SequenceField)\n\nclass MultimediaArticle(Document):\n title = StringField()\n content = StringField()\n images = ListField(DictField())\n video = StringField()\n source_url = StringField()\n site_url = StringField()\n site_name = StringField()\n published_at = DateTimeField()\n crawled_at = DateTimeField()\n random_score = IntField()\n seq_id = SequenceField(required=True)\n usable = BooleanField()\n\n @classmethod\n def get_random_score(cls):\n import random\n score = random.randint(1, 10000)\n return score\n\nclass NineGagWorkerSpider(RedisSpider):\n\n DOWNLOAD_DELAY = 0\n redis_key = NINE_GAG_WORKER_KEY\n name = 'nine_gag_worker'\n\n headers = {\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\n 'X-Requested-With': 'XMLHttpRequest'\n }\n\n def parse(self, response):\n url = NINE_GAG_HOME_PAGE_URL\n yield Request(url, callback=self.parse_json, headers=self.headers, dont_filter=True)\n\n def get_content(self, article_id, item):\n soup = BeautifulSoup(item, 'lxml')\n tags = soup.select('.badge-item-title')\n if tags:\n content = tags[0].text\n else:\n content = None\n is_nsfw_post = soup.select('.nsfw-post')\n source_video_url = 'http://img-9gag-fun.9cache.com/photo/%s_460sv.mp4' % article_id\n image_url = 'http://img-9gag-fun.9cache.com/photo/%s_460s.jpg' % article_id\n images = download_images([image_url], None)\n video_url = download_video(source_video_url, article_id)\n source_url = 'http://9gag.com/%s' % article_id\n return content, images, video_url, source_url\n\n def parse_json(self, response):\n content = response.body_as_unicode()\n data = json.loads(content)\n items = data.get('items')\n if not items:\n return\n article_tuples = [self.get_content(key, value) for key, value in items.items()]\n for article_tuple in article_tuples:\n content, images, video_url, source_url = article_tuple\n if not source_url:\n continue\n if self.server.sismember(CRAWLED_NINEGAG_SET, source_url):\n continue\n if not images:\n continue\n\n score = MultimediaArticle.get_random_score()\n import datetime\n published_at = datetime.datetime.utcnow()\n crawled_at = published_at\n try:\n MultimediaArticle.objects.create(content=content, images=images, video=video_url,\n source_url=source_url, site_name='9gag', site_url='9gag.com', usable=True,\n random_score=score, published_at=published_at, crawled_at=crawled_at)\n self.server.sadd(CRAWLED_NINEGAG_SET, source_url)\n except NotUniqueError:\n self.server.sadd(CRAWLED_NINEGAG_SET, source_url)\n more_url = data['loadMoreUrl']\n if more_url:\n next_url = 'http://9gag.com%s' % more_url\n return Request(next_url, callback=self.parse_json, headers=self.headers, dont_filter=True)\n", "sub_path": "archive/lonely_chaos/crawler/spiders/jokes.py", "file_name": "jokes.py", "file_ext": "py", "file_size_in_byte": 4231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "scrapy.Spider", "line_number": 15, "usage_type": "name"}, {"api_name": "crawler.utils", "line_number": 18, "usage_type": "argument"}, {"api_name": "scrapy_redis.connection.from_settings", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy_redis.connection", "line_number": 19, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "mongoengine.Document", "line_number": 35, "usage_type": "name"}, {"api_name": "mongoengine.StringField", "line_number": 36, "usage_type": "call"}, {"api_name": "mongoengine.StringField", "line_number": 37, "usage_type": "call"}, {"api_name": "mongoengine.ListField", "line_number": 38, "usage_type": "call"}, {"api_name": "mongoengine.DictField", "line_number": 38, "usage_type": "call"}, {"api_name": "mongoengine.StringField", "line_number": 39, "usage_type": "call"}, {"api_name": "mongoengine.StringField", "line_number": 40, "usage_type": "call"}, {"api_name": "mongoengine.StringField", "line_number": 41, "usage_type": "call"}, {"api_name": "mongoengine.StringField", "line_number": 42, "usage_type": "call"}, {"api_name": "mongoengine.DateTimeField", "line_number": 43, "usage_type": "call"}, {"api_name": "mongoengine.DateTimeField", "line_number": 44, "usage_type": "call"}, {"api_name": "mongoengine.IntField", "line_number": 45, "usage_type": "call"}, {"api_name": "mongoengine.SequenceField", "line_number": 46, "usage_type": "call"}, {"api_name": "mongoengine.BooleanField", "line_number": 47, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "scrapy_redis.spiders.RedisSpider", "line_number": 55, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 68, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 71, "usage_type": "call"}, {"api_name": "crawler.image.download_images", "line_number": 80, "usage_type": "call"}, {"api_name": "crawler.image.download_video", "line_number": 81, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "{'random': 'random'}.get_random_score", "line_number": 101, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "attribute"}, {"api_name": "{'random': 'random'}.objects.create", "line_number": 106, "usage_type": "call"}, {"api_name": "{'random': 'random'}.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mongoengine.NotUniqueError", "line_number": 110, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 115, "usage_type": "call"}]}
+{"seq_id": "501681373", "text": "import argparse, cv2, time\nimport torch.backends.cudnn as cudnn\nfrom easydict import EasyDict as edict\nimport pathlib as Path\n\nfrom visualization.KittiDataset import KittiDataset\nfrom visualization.KittiVisualization import KittiVisualizer\nfrom visualization.KittiUtils import *\n\nfrom utils_classes.SFA3D import SFA3D\nfrom utils_classes.stereo_depth_estimation import Stereo_Depth_Estimation\n\ndef parse_test_configs():\n parser = argparse.ArgumentParser(description='Testing config for the Implementation')\n parser.add_argument('--saved_fn', type=str, default='fpn_resnet_18', metavar='FN', help='The name using for saving logs, models,...')\n parser.add_argument('-a', '--arch', type=str, default='fpn_resnet_18', metavar='ARCH', help='The name of the model architecture')\n parser.add_argument('--pretrained_path', type=str, default='SFA3D/checkpoints/fpn_resnet_18/Model_fpn_resnet_18_epoch_30.pth', metavar='PATH')\n parser.add_argument('--K', type=int, default=50, help='the number of top K')\n parser.add_argument('--no_cuda', action='store_true', help='If true, cuda is not used.')\n parser.add_argument('--gpu_idx', default=0, type=int, help='GPU index to use.')\n parser.add_argument('--num_samples', type=int, default=None, help='Take a subset of the dataset to run and debug')\n parser.add_argument('--num_workers', type=int, default=1, help='Number of threads for loading data')\n parser.add_argument('--batch_size', type=int, default=1, help='mini-batch size (default: 4)')\n parser.add_argument('--peak_thresh', type=float, default=0.2)\n parser.add_argument('--save_test_output', action='store_true', help='If true, the output image of the testing phase will be saved')\n parser.add_argument('--stereo', action='store_true', help=\"Run SFA3D on anynet stereo model pseduo lidar\")\n parser.add_argument('--index', type=int, default=0, help=\"start index in dataset\")\n configs = edict(vars(parser.parse_args()))\n configs.pin_memory = True\n configs.distributed = False # For testing on 1 GPU only\n\n configs.input_size = (608, 608)\n configs.hm_size = (152, 152)\n configs.down_ratio = 4\n configs.max_objects = 50\n\n configs.imagenet_pretrained = False\n configs.head_conv = 64\n configs.num_classes = 3\n configs.num_center_offset = 2\n configs.num_z = 1\n configs.num_dim = 3\n configs.num_direction = 2 # sin, cos\n\n configs.heads = {\n 'hm_cen': configs.num_classes,\n 'cen_offset': configs.num_center_offset,\n 'direction': configs.num_direction,\n 'z_coor': configs.num_z,\n 'dim': configs.num_dim\n }\n configs.num_input_features = 4\n\n # #### set it to empty as this file is inside the root of the project ####\n configs.root_dir = ''\n configs.dataset_dir = os.path.join(configs.root_dir, 'data', 'kitti')\n\n if configs.save_test_output:\n configs.results_dir = os.path.join(configs.root_dir, 'results', configs.saved_fn)\n make_folder(configs.results_dir)\n\n args = parser.parse_args()\n \n return configs, args\n\nfrom full_demo import parse_config\n\ndef main():\n cfg, args = parse_test_configs()\n stereo_args, _ = parse_config()\n cudnn.benchmark = True\n\n dataset_root = os.path.join(cfg.dataset_dir, \"testing\")\n KITTI = KittiDataset(dataset_root, mode='val')\n KITTI_stereo = KittiDataset(dataset_root, stereo_mode=True) \n\n sfa_model = SFA3D(cfg) \n anynet_model = Stereo_Depth_Estimation(stereo_args,None)\n\n visualizer = KittiVisualizer()\n\n\n if args.stereo:\n for i in range(args.index, len(KITTI_stereo)):\n imgL, imgR, _, calib = KITTI_stereo[i]\n\n pointcloud = anynet_model.predict(imgL, imgR, calib.calib_path)\n\n detections = sfa_model.predict(pointcloud)\n objects = SFA3D_output_to_kitti_objects(detections)\n\n visualizer.visualize_scene_2D(pointcloud, imgL, objects, calib=calib)\n if visualizer.user_press == 27:\n cv2.destroyAllWindows()\n break\n\n else:\n for i in range(args.index, len(KITTI)):\n image, pointcloud, labels, calib = KITTI[i]\n\n detections = sfa_model.predict(pointcloud)\n objects = SFA3D_output_to_kitti_objects(detections)\n\n visualizer.visualize_scene_2D(pointcloud, image, objects, calib=calib)\n if visualizer.user_press == 27:\n cv2.destroyAllWindows()\n break\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "sfa_demo.py", "file_name": "sfa_demo.py", "file_ext": "py", "file_size_in_byte": 4491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "easydict.EasyDict", "line_number": 28, "usage_type": "call"}, {"api_name": "full_demo.parse_config", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 71, "usage_type": "name"}, {"api_name": "visualization.KittiDataset.KittiDataset", "line_number": 74, "usage_type": "call"}, {"api_name": "visualization.KittiDataset.KittiDataset", "line_number": 75, "usage_type": "call"}, {"api_name": "utils_classes.SFA3D.SFA3D", "line_number": 77, "usage_type": "call"}, {"api_name": "utils_classes.stereo_depth_estimation.Stereo_Depth_Estimation", "line_number": 78, "usage_type": "call"}, {"api_name": "visualization.KittiVisualization.KittiVisualizer", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 106, "usage_type": "call"}]}
+{"seq_id": "60544031", "text": "# STD python packages\nimport copy\nimport hashlib\nimport io\nimport json\nimport logging\nimport os\nimport platform\nimport time\nimport traceback\nfrom typing import Any, Dict, List, Optional, Tuple\n\n# 3-rd party packages\nimport docker\nimport docker.errors\nimport docker.models.containers\nimport requests.exceptions\nimport urllib3.exceptions\nfrom demisto_sdk.commands.common.constants import (INTEGRATIONS_DIR,\n PACKS_PACK_META_FILE_NAME,\n TYPE_PWSH, TYPE_PYTHON)\n# Local packages\nfrom demisto_sdk.commands.common.tools import (get_all_docker_images,\n run_command_os)\nfrom demisto_sdk.commands.lint.commands_builder import (\n build_bandit_command, build_flake8_command, build_mypy_command,\n build_pwsh_analyze_command, build_pwsh_test_command, build_pylint_command,\n build_pytest_command, build_vulture_command, build_xsoar_linter_command)\nfrom demisto_sdk.commands.lint.helpers import (EXIT_CODES, FAIL, RERUN, RL,\n SUCCESS, WARNING,\n add_tmp_lint_files,\n add_typing_module,\n get_file_from_container,\n get_python_version_from_image,\n pylint_plugin,\n split_warnings_errors,\n stream_docker_container_output)\nfrom jinja2 import Environment, FileSystemLoader, exceptions\nfrom ruamel.yaml import YAML\nfrom wcmatch.pathlib import NEGATE, Path\n\nlogger = logging.getLogger('demisto-sdk')\n\n\nclass Linter:\n \"\"\" Linter used to activate lint command on single package\n\n Attributes:\n pack_dir(Path): Pack to run lint on.\n content_repo(Path): Git repo object of content repo.\n req_2(list): requirements for docker using python2.\n req_3(list): requirements for docker using python3.\n docker_engine(bool): Whether docker engine detected by docker-sdk.\n \"\"\"\n\n def __init__(self, pack_dir: Path, content_repo: Path, req_3: list, req_2: list, docker_engine: bool):\n self._req_3 = req_3\n self._req_2 = req_2\n self._content_repo = content_repo\n self._pack_abs_dir = pack_dir\n self._pack_name = None\n # Docker client init\n if docker_engine:\n self._docker_client: docker.DockerClient = docker.from_env()\n self._docker_hub_login = self._docker_login()\n # Facts gathered regarding pack lint and test\n self._facts: Dict[str, Any] = {\n \"images\": [],\n \"python_version\": 0,\n \"env_vars\": {},\n \"test\": False,\n \"lint_files\": [],\n \"support_level\": None,\n \"is_long_running\": False,\n \"lint_unittest_files\": [],\n \"additional_requirements\": [],\n \"docker_engine\": docker_engine,\n \"is_script\": False,\n \"commands\": None\n }\n # Pack lint status object - visualize it\n self._pkg_lint_status: Dict = {\n \"pkg\": None,\n \"pack_type\": None,\n \"path\": str(self._content_repo),\n \"errors\": [],\n \"images\": [],\n \"flake8_errors\": None,\n \"XSOAR_linter_errors\": None,\n \"bandit_errors\": None,\n \"mypy_errors\": None,\n \"vulture_errors\": None,\n \"flake8_warnings\": None,\n \"XSOAR_linter_warnings\": None,\n \"bandit_warnings\": None,\n \"mypy_warnings\": None,\n \"vulture_warnings\": None,\n \"exit_code\": SUCCESS,\n \"warning_code\": SUCCESS,\n }\n\n def run_dev_packages(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_pylint: bool, no_vulture: bool,\n no_xsoar_linter: bool, no_pwsh_analyze: bool, no_pwsh_test: bool, no_test: bool, modules: dict,\n keep_container: bool, test_xml: str) -> dict:\n \"\"\" Run lint and tests on single package\n Performing the follow:\n 1. Run the lint on OS - flake8, bandit, mypy.\n 2. Run in package docker - pylint, pytest.\n\n Args:\n no_flake8(bool): Whether to skip flake8\n no_bandit(bool): Whether to skip bandit\n no_mypy(bool): Whether to skip mypy\n no_vulture(bool): Whether to skip vulture\n no_pylint(bool): Whether to skip pylint\n no_test(bool): Whether to skip pytest\n no_pwsh_analyze(bool): Whether to skip powershell code analyzing\n no_pwsh_test(bool): whether to skip powershell tests\n modules(dict): Mandatory modules to locate in pack path (CommonServerPython.py etc)\n keep_container(bool): Whether to keep the test container\n test_xml(str): Path for saving pytest xml results\n\n Returns:\n dict: lint and test all status, pkg status)\n \"\"\"\n # Gather information for lint check information\n skip = self._gather_facts(modules)\n # If not python pack - skip pack\n if skip:\n return self._pkg_lint_status\n try:\n # Locate mandatory files in pack path - for more info checkout the context manager LintFiles\n with add_tmp_lint_files(content_repo=self._content_repo, # type: ignore\n pack_path=self._pack_abs_dir,\n lint_files=self._facts[\"lint_files\"],\n modules=modules,\n pack_type=self._pkg_lint_status[\"pack_type\"]):\n # Run lint check on host - flake8, bandit, mypy\n if self._pkg_lint_status[\"pack_type\"] == TYPE_PYTHON:\n self._run_lint_in_host(no_flake8=no_flake8,\n no_bandit=no_bandit,\n no_mypy=no_mypy,\n no_vulture=no_vulture,\n no_xsoar_linter=no_xsoar_linter)\n\n # Run lint and test check on pack docker image\n if self._facts[\"docker_engine\"]:\n self._run_lint_on_docker_image(no_pylint=no_pylint,\n no_test=no_test,\n no_pwsh_analyze=no_pwsh_analyze,\n no_pwsh_test=no_pwsh_test,\n keep_container=keep_container,\n test_xml=test_xml)\n except Exception as ex:\n err = f'{self._pack_abs_dir}: Unexpected fatal exception: {str(ex)}'\n logger.error(f\"{err}. Traceback: {traceback.format_exc()}\")\n self._pkg_lint_status[\"errors\"].append(err)\n self._pkg_lint_status['exit_code'] += FAIL\n return self._pkg_lint_status\n\n def _gather_facts(self, modules: dict) -> bool:\n \"\"\" Gathering facts about the package - python version, docker images, valid docker image, yml parsing\n Args:\n modules(dict): Test mandatory modules to be ignore in lint check\n\n Returns:\n bool: Indicating if to continue further or not, if False exit Thread, Else continue.\n \"\"\"\n # Looking for pkg yaml\n yml_file: Optional[Path] = self._pack_abs_dir.glob([r'*.yaml', r'*.yml', r'!*unified*.yml'], flags=NEGATE)\n\n if not yml_file:\n logger.info(f\"{self._pack_abs_dir} - Skipping no yaml file found {yml_file}\")\n self._pkg_lint_status[\"errors\"].append('Unable to find yml file in package')\n return True\n else:\n try:\n yml_file = next(yml_file)\n except StopIteration:\n return True\n # Get pack name\n self._pack_name = yml_file.stem\n log_prompt = f\"{self._pack_name} - Facts\"\n self._pkg_lint_status[\"pkg\"] = yml_file.stem\n logger.info(f\"{log_prompt} - Using yaml file {yml_file}\")\n # Parsing pack yaml - in order to verify if check needed\n try:\n\n script_obj: Dict = {}\n yml_obj: Dict = YAML().load(yml_file)\n if isinstance(yml_obj, dict):\n script_obj = yml_obj.get('script', {}) if isinstance(yml_obj.get('script'), dict) else yml_obj\n self._facts['is_script'] = True if 'Scripts' in yml_file.parts else False\n self._facts['is_long_running'] = script_obj.get('longRunning')\n self._facts['commands'] = self._get_commands_list(script_obj)\n self._pkg_lint_status[\"pack_type\"] = script_obj.get('type')\n except (FileNotFoundError, IOError, KeyError):\n self._pkg_lint_status[\"errors\"].append('Unable to parse package yml')\n return True\n # return no check needed if not python pack\n if self._pkg_lint_status[\"pack_type\"] not in (TYPE_PYTHON, TYPE_PWSH):\n logger.info(f\"{log_prompt} - Skipping due to not Python, Powershell package - Pack is\"\n f\" {self._pkg_lint_status['pack_type']}\")\n return True\n # Docker images\n if self._facts[\"docker_engine\"]:\n logger.info(f\"{log_prompt} - Pulling docker images, can take up to 1-2 minutes if not exists locally \")\n self._facts[\"images\"] = [[image, -1] for image in get_all_docker_images(script_obj=script_obj)]\n # Gather environment variables for docker execution\n self._facts[\"env_vars\"] = {\n \"CI\": os.getenv(\"CI\", False),\n \"DEMISTO_LINT_UPDATE_CERTS\": os.getenv('DEMISTO_LINT_UPDATE_CERTS', \"yes\")\n }\n lint_files = set()\n # Facts for python pack\n if self._pkg_lint_status[\"pack_type\"] == TYPE_PYTHON:\n self._update_support_level()\n if self._facts[\"docker_engine\"]:\n # Getting python version from docker image - verifying if not valid docker image configured\n for image in self._facts[\"images\"]:\n py_num: float = get_python_version_from_image(image=image[0])\n image[1] = py_num\n logger.info(f\"{self._pack_name} - Facts - {image[0]} - Python {py_num}\")\n if not self._facts[\"python_version\"]:\n self._facts[\"python_version\"] = py_num\n # Checking whatever *test* exists in package\n self._facts[\"test\"] = True if next(self._pack_abs_dir.glob([r'test_*.py', r'*_test.py']),\n None) else False\n if self._facts[\"test\"]:\n logger.info(f\"{log_prompt} - Tests found\")\n else:\n logger.info(f\"{log_prompt} - Tests not found\")\n # Gather package requirements embedded test-requirements.py file\n test_requirements = self._pack_abs_dir / 'test-requirements.txt'\n if test_requirements.exists():\n try:\n additional_req = test_requirements.read_text(encoding='utf-8').strip().split('\\n')\n self._facts[\"additional_requirements\"].extend(additional_req)\n logger.info(f\"{log_prompt} - Additional package Pypi packages found - {additional_req}\")\n except (FileNotFoundError, IOError):\n self._pkg_lint_status[\"errors\"].append('Unable to parse test-requirements.txt in package')\n elif not self._facts[\"python_version\"]:\n # get python version from yml\n pynum = 3.7 if (script_obj.get('subtype', 'python3') == 'python3') else 2.7\n self._facts[\"python_version\"] = pynum\n logger.info(f\"{log_prompt} - Using python version from yml: {pynum}\")\n # Get lint files\n lint_files = set(self._pack_abs_dir.glob([\"*.py\", \"!__init__.py\", \"!*.tmp\"],\n flags=NEGATE))\n # Facts for Powershell pack\n elif self._pkg_lint_status[\"pack_type\"] == TYPE_PWSH:\n # Get lint files\n lint_files = set(\n self._pack_abs_dir.glob([\"*.ps1\", \"!*Tests.ps1\", \"CommonServerPowerShell.ps1\", \"demistomock.ps1'\"],\n flags=NEGATE))\n\n # Add CommonServer to the lint checks\n if 'commonserver' in self._pack_abs_dir.name.lower():\n # Powershell\n if self._pkg_lint_status[\"pack_type\"] == TYPE_PWSH:\n self._facts[\"lint_files\"] = [Path(self._pack_abs_dir / 'CommonServerPowerShell.ps1')]\n # Python\n elif self._pkg_lint_status[\"pack_type\"] == TYPE_PYTHON:\n self._facts[\"lint_files\"] = [Path(self._pack_abs_dir / 'CommonServerPython.py')]\n else:\n test_modules = {self._pack_abs_dir / module.name for module in modules.keys()}\n lint_files = lint_files.difference(test_modules)\n self._facts[\"lint_files\"] = list(lint_files)\n if self._facts[\"lint_files\"]:\n for lint_file in self._facts[\"lint_files\"]:\n logger.info(f\"{log_prompt} - Lint file {lint_file}\")\n else:\n logger.info(f\"{log_prompt} - Lint files not found\")\n\n self._split_lint_files()\n return False\n\n def _split_lint_files(self):\n \"\"\" Remove unit test files from _facts['lint_files'] and put into their own list _facts['lint_unittest_files']\n This is because not all lints should be done on unittest files.\n \"\"\"\n lint_files_list = copy.deepcopy(self._facts[\"lint_files\"])\n for lint_file in lint_files_list:\n if lint_file.name.startswith('test_') or lint_file.name.endswith('_test.py'):\n self._facts['lint_unittest_files'].append(lint_file)\n self._facts[\"lint_files\"].remove(lint_file)\n\n def _run_lint_in_host(self, no_flake8: bool, no_bandit: bool, no_mypy: bool, no_vulture: bool,\n no_xsoar_linter: bool):\n \"\"\" Run lint check on host\n\n Args:\n no_flake8(bool): Whether to skip flake8.\n no_bandit(bool): Whether to skip bandit.\n no_mypy(bool): Whether to skip mypy.\n no_vulture(bool): Whether to skip Vulture.\n \"\"\"\n warning = []\n error = []\n other = []\n exit_code: int = 0\n for lint_check in [\"flake8\", \"XSOAR_linter\", \"bandit\", \"mypy\", \"vulture\"]:\n exit_code = SUCCESS\n output = \"\"\n if self._facts[\"lint_files\"] or self._facts[\"lint_unittest_files\"]:\n if lint_check == \"flake8\" and not no_flake8:\n flake8_lint_files = copy.deepcopy(self._facts[\"lint_files\"])\n # if there are unittest.py then we would run flake8 on them too.\n if self._facts['lint_unittest_files']:\n flake8_lint_files.extend(self._facts['lint_unittest_files'])\n exit_code, output = self._run_flake8(py_num=self._facts[\"python_version\"],\n lint_files=flake8_lint_files)\n\n if self._facts[\"lint_files\"]:\n if lint_check == \"XSOAR_linter\" and not no_xsoar_linter:\n exit_code, output = self._run_xsoar_linter(py_num=self._facts[\"python_version\"],\n lint_files=self._facts[\"lint_files\"])\n elif lint_check == \"bandit\" and not no_bandit:\n exit_code, output = self._run_bandit(lint_files=self._facts[\"lint_files\"])\n\n elif lint_check == \"mypy\" and not no_mypy:\n exit_code, output = self._run_mypy(py_num=self._facts[\"python_version\"],\n lint_files=self._facts[\"lint_files\"])\n elif lint_check == \"vulture\" and not no_vulture:\n exit_code, output = self._run_vulture(py_num=self._facts[\"python_version\"],\n lint_files=self._facts[\"lint_files\"])\n\n # check for any exit code other than 0\n if exit_code:\n error, warning, other = split_warnings_errors(output)\n if exit_code and warning:\n self._pkg_lint_status[\"warning_code\"] |= EXIT_CODES[lint_check]\n self._pkg_lint_status[f\"{lint_check}_warnings\"] = \"\\n\".join(warning)\n if exit_code & FAIL:\n self._pkg_lint_status[\"exit_code\"] |= EXIT_CODES[lint_check]\n # if the error were extracted correctly as they start with E\n if error:\n self._pkg_lint_status[f\"{lint_check}_errors\"] = \"\\n\".join(error)\n # if there were errors but they do not start with E\n else:\n self._pkg_lint_status[f\"{lint_check}_errors\"] = \"\\n\".join(other)\n\n def _run_flake8(self, py_num: float, lint_files: List[Path]) -> Tuple[int, str]:\n \"\"\" Runs flake8 in pack dir\n\n Args:\n py_num(float): The python version in use\n lint_files(List[Path]): file to perform lint\n\n Returns:\n int: 0 on successful else 1, errors\n str: Bandit errors\n \"\"\"\n log_prompt = f\"{self._pack_name} - Flake8\"\n logger.info(f\"{log_prompt} - Start\")\n stdout, stderr, exit_code = run_command_os(command=build_flake8_command(lint_files, py_num),\n cwd=self._content_repo)\n logger.debug(f\"{log_prompt} - Finished exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished stderr: {RL if stderr else ''}{stderr}\")\n if stderr or exit_code:\n logger.info(f\"{log_prompt}- Finished errors found\")\n if stderr:\n return FAIL, stderr\n else:\n return FAIL, stdout\n\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return SUCCESS, \"\"\n\n def _run_xsoar_linter(self, py_num: float, lint_files: List[Path]) -> Tuple[int, str]:\n \"\"\" Runs Xsaor linter in pack dir\n\n Args:\n lint_files(List[Path]): file to perform lint\n\n Returns:\n int: 0 on successful else 1, errors\n str: Xsoar linter errors\n \"\"\"\n status = SUCCESS\n FAIL_PYLINT = 0b10\n with pylint_plugin(self._pack_abs_dir):\n log_prompt = f\"{self._pack_name} - XSOAR Linter\"\n logger.info(f\"{log_prompt} - Start\")\n myenv = os.environ.copy()\n if myenv.get('PYTHONPATH'):\n myenv['PYTHONPATH'] += ':' + str(self._pack_abs_dir)\n else:\n myenv['PYTHONPATH'] = str(self._pack_abs_dir)\n if self._facts['is_long_running']:\n myenv['LONGRUNNING'] = 'True'\n if py_num < 3:\n myenv['PY2'] = 'True'\n myenv['is_script'] = str(self._facts['is_script'])\n # as Xsoar checker is a pylint plugin and runs as part of pylint code, we can not pass args to it.\n # as a result we can use the env vars as a getway.\n myenv['commands'] = ','.join([str(elem) for elem in self._facts['commands']]) \\\n if self._facts['commands'] else ''\n stdout, stderr, exit_code = run_command_os(\n command=build_xsoar_linter_command(lint_files, py_num, self._facts.get('support_level', 'base')),\n cwd=self._pack_abs_dir, env=myenv)\n if exit_code & FAIL_PYLINT:\n logger.info(f\"{log_prompt}- Finished errors found\")\n status = FAIL\n if exit_code & WARNING:\n logger.info(f\"{log_prompt} - Finished warnings found\")\n if not status:\n status = WARNING\n # if pylint did not run and failure exit code has been returned from run commnad\n elif exit_code & FAIL:\n status = FAIL\n # for contrib prs which are not merged from master and do not have pylint in dev-requirements-py2.\n if os.environ.get('CI'):\n stdout = \"Xsoar linter could not run, Please merge from master\"\n else:\n stdout = \"Xsoar linter could not run, please make sure you have\" \\\n \" the necessary Pylint version for both py2 and py3\"\n logger.info(f\"{log_prompt}- Finished errors found\")\n\n logger.debug(f\"{log_prompt} - Finished exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished stderr: {RL if stderr else ''}{stderr}\")\n\n if not exit_code:\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return status, stdout\n\n def _run_bandit(self, lint_files: List[Path]) -> Tuple[int, str]:\n \"\"\" Run bandit in pack dir\n\n Args:\n lint_files(List[Path]): file to perform lint\n\n Returns:\n int: 0 on successful else 1, errors\n str: Bandit errors\n \"\"\"\n log_prompt = f\"{self._pack_name} - Bandit\"\n logger.info(f\"{log_prompt} - Start\")\n stdout, stderr, exit_code = run_command_os(command=build_bandit_command(lint_files),\n cwd=self._pack_abs_dir)\n logger.debug(f\"{log_prompt} - Finished exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished stderr: {RL if stderr else ''}{stderr}\")\n if stderr or exit_code:\n logger.info(f\"{log_prompt}- Finished Finished errors found\")\n if stderr:\n return FAIL, stderr\n else:\n return FAIL, stdout\n\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return SUCCESS, \"\"\n\n def _run_mypy(self, py_num: float, lint_files: List[Path]) -> Tuple[int, str]:\n \"\"\" Run mypy in pack dir\n\n Args:\n py_num(float): The python version in use\n lint_files(List[Path]): file to perform lint\n\n Returns:\n int: 0 on successful else 1, errors\n str: Bandit errors\n \"\"\"\n log_prompt = f\"{self._pack_name} - Mypy\"\n logger.info(f\"{log_prompt} - Start\")\n with add_typing_module(lint_files=lint_files, python_version=py_num):\n stdout, stderr, exit_code = run_command_os(command=build_mypy_command(files=lint_files, version=py_num),\n cwd=self._pack_abs_dir)\n logger.debug(f\"{log_prompt} - Finished exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished stderr: {RL if stderr else ''}{stderr}\")\n if stderr or exit_code:\n logger.info(f\"{log_prompt}- Finished Finished errors found\")\n if stderr:\n return FAIL, stderr\n else:\n return FAIL, stdout\n\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return SUCCESS, \"\"\n\n def _run_vulture(self, py_num: float, lint_files: List[Path]) -> Tuple[int, str]:\n \"\"\" Run mypy in pack dir\n\n Args:\n py_num(float): The python version in use\n lint_files(List[Path]): file to perform lint\n\n Returns:\n int: 0 on successful else 1, errors\n str: Vulture errors\n \"\"\"\n log_prompt = f\"{self._pack_name} - Vulture\"\n logger.info(f\"{log_prompt} - Start\")\n stdout, stderr, exit_code = run_command_os(command=build_vulture_command(files=lint_files,\n pack_path=self._pack_abs_dir,\n py_num=py_num),\n cwd=self._pack_abs_dir)\n logger.debug(f\"{log_prompt} - Finished exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished stderr: {RL if stderr else ''}{stderr}\")\n if stderr or exit_code:\n logger.info(f\"{log_prompt}- Finished Finished errors found\")\n if stderr:\n return FAIL, stderr\n else:\n return FAIL, stdout\n\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return SUCCESS, \"\"\n\n def _run_lint_on_docker_image(self, no_pylint: bool, no_test: bool, no_pwsh_analyze: bool, no_pwsh_test: bool,\n keep_container: bool, test_xml: str):\n \"\"\" Run lint check on docker image\n\n Args:\n no_pylint(bool): Whether to skip pylint\n no_test(bool): Whether to skip pytest\n no_pwsh_analyze(bool): Whether to skip powershell code analyzing\n no_pwsh_test(bool): whether to skip powershell tests\n keep_container(bool): Whether to keep the test container\n test_xml(str): Path for saving pytest xml results\n \"\"\"\n for image in self._facts[\"images\"]:\n # Docker image status - visualize\n status = {\n \"image\": image[0],\n \"image_errors\": \"\",\n \"pylint_errors\": \"\",\n \"pytest_errors\": \"\",\n \"pytest_json\": {},\n \"pwsh_analyze_errors\": \"\",\n \"pwsh_test_errors\": \"\"\n }\n # Creating image if pylint specified or found tests and tests specified\n image_id = \"\"\n errors = \"\"\n for trial in range(2):\n image_id, errors = self._docker_image_create(docker_base_image=image)\n if not errors:\n break\n\n if image_id and not errors:\n # Set image creation status\n for check in [\"pylint\", \"pytest\", \"pwsh_analyze\", \"pwsh_test\"]:\n exit_code = SUCCESS\n output = \"\"\n for trial in range(2):\n if self._pkg_lint_status[\"pack_type\"] == TYPE_PYTHON:\n # Perform pylint\n if not no_pylint and check == \"pylint\" and self._facts[\"lint_files\"]:\n exit_code, output = self._docker_run_pylint(test_image=image_id,\n keep_container=keep_container)\n # Perform pytest\n elif not no_test and self._facts[\"test\"] and check == \"pytest\":\n exit_code, output, test_json = self._docker_run_pytest(test_image=image_id,\n keep_container=keep_container,\n test_xml=test_xml)\n status[\"pytest_json\"] = test_json\n elif self._pkg_lint_status[\"pack_type\"] == TYPE_PWSH:\n # Perform powershell analyze\n if not no_pwsh_analyze and check == \"pwsh_analyze\" and self._facts[\"lint_files\"]:\n exit_code, output = self._docker_run_pwsh_analyze(test_image=image_id,\n keep_container=keep_container)\n # Perform powershell test\n elif not no_pwsh_test and check == \"pwsh_test\":\n exit_code, output = self._docker_run_pwsh_test(test_image=image_id,\n keep_container=keep_container)\n # If lint check perfrom and failed on reason related to enviorment will run twice,\n # But it failing in second time it will count as test failure.\n if (exit_code == RERUN and trial == 1) or exit_code == FAIL or exit_code == SUCCESS:\n if exit_code in [RERUN, FAIL]:\n self._pkg_lint_status[\"exit_code\"] |= EXIT_CODES[check]\n status[f\"{check}_errors\"] = output\n break\n else:\n status[\"image_errors\"] = str(errors)\n self._pkg_lint_status[\"exit_code\"] += EXIT_CODES[\"image\"]\n\n # Add image status to images\n self._pkg_lint_status[\"images\"].append(status)\n try:\n self._docker_client.images.remove(image_id)\n except (docker.errors.ImageNotFound, docker.errors.APIError):\n pass\n\n def _docker_login(self) -> bool:\n \"\"\" Login to docker-hub using environment variables:\n 1. DOCKERHUB_USER - User for docker hub.\n 2. DOCKERHUB_PASSWORD - Password for docker-hub.\n Used in Circle-CI for pushing into repo devtestdemisto\n\n Returns:\n bool: True if logged in successfully.\n \"\"\"\n docker_user = os.getenv('DOCKERHUB_USER')\n docker_pass = os.getenv('DOCKERHUB_PASSWORD')\n try:\n self._docker_client.login(username=docker_user,\n password=docker_pass,\n registry=\"https://index.docker.io/v1\")\n return self._docker_client.ping()\n except docker.errors.APIError:\n return False\n\n def _docker_image_create(self, docker_base_image: List[Any]) -> Tuple[str, str]:\n \"\"\" Create docker image:\n 1. Installing 'build base' if required in alpine images version - https://wiki.alpinelinux.org/wiki/GCC\n 2. Installing pypi packs - if only pylint required - only pylint installed otherwise all pytest and pylint\n installed, packages which being install can be found in path demisto_sdk/commands/lint/dev_envs\n 3. The docker image build done by Dockerfile template located in\n demisto_sdk/commands/lint/templates/dockerfile.jinja2\n\n Args:\n docker_base_image(list): docker image to use as base for installing dev deps and python version.\n\n Returns:\n str, str. image name to use and errors string.\n \"\"\"\n log_prompt = f\"{self._pack_name} - Image create\"\n test_image_id = \"\"\n # Get requirements file for image\n requirements = []\n if 2 < docker_base_image[1] < 3:\n requirements = self._req_2\n elif docker_base_image[1] > 3:\n requirements = self._req_3\n # Using DockerFile template\n file_loader = FileSystemLoader(Path(__file__).parent / 'templates')\n env = Environment(loader=file_loader, lstrip_blocks=True, trim_blocks=True, autoescape=True)\n template = env.get_template('dockerfile.jinja2')\n try:\n dockerfile = template.render(image=docker_base_image[0],\n pypi_packs=requirements + self._facts[\"additional_requirements\"],\n pack_type=self._pkg_lint_status[\"pack_type\"],\n copy_pack=False)\n except exceptions.TemplateError as e:\n logger.debug(f\"{log_prompt} - Error when build image - {e.message()}\")\n return test_image_id, str(e)\n # Trying to pull image based on dockerfile hash, will check if something changed\n errors = \"\"\n test_image_name = f'devtest{docker_base_image[0]}-{hashlib.md5(dockerfile.encode(\"utf-8\")).hexdigest()}'\n test_image = None\n try:\n logger.info(f\"{log_prompt} - Trying to pull existing image {test_image_name}\")\n test_image = self._docker_client.images.pull(test_image_name)\n except (docker.errors.APIError, docker.errors.ImageNotFound):\n logger.info(f\"{log_prompt} - Unable to find image {test_image_name}\")\n # Creatng new image if existing image isn't found\n if not test_image:\n logger.info(\n f\"{log_prompt} - Creating image based on {docker_base_image[0]} - Could take 2-3 minutes at first \"\n f\"time\")\n try:\n with io.BytesIO() as f:\n f.write(dockerfile.encode('utf-8'))\n f.seek(0)\n self._docker_client.images.build(fileobj=f,\n tag=test_image_name,\n forcerm=True)\n\n if self._docker_hub_login:\n for trial in range(2):\n try:\n self._docker_client.images.push(test_image_name)\n logger.info(f\"{log_prompt} - Image {test_image_name} pushed to repository\")\n break\n except (requests.exceptions.ConnectionError, urllib3.exceptions.ReadTimeoutError):\n logger.info(f\"{log_prompt} - Unable to push image {test_image_name} to repository\")\n\n except (docker.errors.BuildError, docker.errors.APIError, Exception) as e:\n logger.critical(f\"{log_prompt} - Build errors occurred {e}\")\n errors = str(e)\n else:\n logger.info(f\"{log_prompt} - Found existing image {test_image_name}\")\n dockerfile_path = Path(self._pack_abs_dir / \".Dockerfile\")\n dockerfile = template.render(image=test_image_name,\n copy_pack=True)\n with open(dockerfile_path, mode=\"w+\") as file:\n file.write(str(dockerfile))\n # we only do retries in CI env where docker build is sometimes flacky\n build_tries = int(os.getenv('DEMISTO_SDK_DOCKER_BUILD_TRIES', 3)) if os.getenv('CI') else 1\n for trial in range(build_tries):\n try:\n logger.info(f\"{log_prompt} - Copy pack dir to image {test_image_name}\")\n docker_image_final = self._docker_client.images.build(path=str(dockerfile_path.parent),\n dockerfile=dockerfile_path.stem,\n forcerm=True)\n test_image_name = docker_image_final[0].short_id\n break\n except Exception as e:\n logger.exception(f\"{log_prompt} - errors occurred when building image in dir {e}\")\n if trial >= build_tries:\n errors = str(e)\n else:\n logger.info(f\"{log_prompt} - sleeping 2 seconds and will retry build after\")\n time.sleep(2)\n if dockerfile_path.exists():\n dockerfile_path.unlink()\n\n if test_image_id:\n logger.info(f\"{log_prompt} - Image {test_image_id} created successfully\")\n\n return test_image_name, errors\n\n def _docker_remove_container(self, container_name: str):\n try:\n container_obj = self._docker_client.containers.get(container_name)\n container_obj.remove(force=True)\n except docker.errors.NotFound:\n pass\n except requests.exceptions.ChunkedEncodingError as err:\n # see: https://github.com/docker/docker-py/issues/2696#issuecomment-721322548\n if platform.system() != 'Darwin' or 'Connection broken' not in str(err):\n raise\n\n def _docker_run_pylint(self, test_image: str, keep_container: bool) -> Tuple[int, str]:\n \"\"\" Run Pylint in created test image\n\n Args:\n test_image(str): test image id/name\n keep_container(bool): True if to keep container after execution finished\n\n Returns:\n int: 0 on successful, errors 1, need to retry 2\n str: Container log\n \"\"\"\n log_prompt = f'{self._pack_name} - Pylint - Image {test_image}'\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pylint\"\n # Check if previous run left container a live if it do, we remove it\n self._docker_remove_container(container_name)\n\n # Run container\n exit_code = SUCCESS\n output = \"\"\n try:\n container_obj: docker.models.containers.Container = self._docker_client.containers.run(\n name=container_name,\n image=test_image,\n command=[\n build_pylint_command(\n self._facts[\"lint_files\"], docker_version=self._facts.get('python_version'))\n ],\n user=f\"{os.getuid()}:4000\",\n detach=True,\n environment=self._facts[\"env_vars\"]\n )\n stream_docker_container_output(container_obj.logs(stream=True))\n # wait for container to finish\n container_status = container_obj.wait(condition=\"exited\")\n # Get container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n container_log = container_obj.logs().decode(\"utf-8\")\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code in [1, 2]:\n # 1-fatal message issued\n # 2-Error message issued\n exit_code = FAIL\n output = container_log\n logger.info(f\"{log_prompt} - Finished errors found\")\n elif container_exit_code in [4, 8, 16]:\n # 4-Warning message issued\n # 8-refactor message issued\n # 16-convention message issued\n logger.info(f\"{log_prompt} - Successfully finished - warnings found\")\n exit_code = SUCCESS\n elif container_exit_code == 32:\n # 32-usage error\n logger.critical(f\"{log_prompt} - Finished - Usage error\")\n exit_code = RERUN\n else:\n logger.info(f\"{log_prompt} - Successfully finished\")\n # Keeping container if needed or remove it\n if keep_container:\n print(f\"{log_prompt} - container name {container_name}\")\n else:\n try:\n container_obj.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to delete container - {e}\")\n except Exception as e:\n logger.exception(f\"{log_prompt} - Unable to run pylint\")\n exit_code = RERUN\n output = str(e)\n return exit_code, output\n\n def _docker_run_pytest(self, test_image: str, keep_container: bool, test_xml: str) -> Tuple[int, str, dict]:\n \"\"\" Run Pytest in created test image\n\n Args:\n test_image(str): Test image id/name\n keep_container(bool): True if to keep container after execution finished\n test_xml(str): Xml saving path\n\n Returns:\n int: 0 on successful, errors 1, need to retry 2\n str: Unit test json report\n \"\"\"\n log_prompt = f'{self._pack_name} - Pytest - Image {test_image}'\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pytest\"\n # Check if previous run left container a live if it does, Remove it\n self._docker_remove_container(container_name)\n # Collect tests\n exit_code = SUCCESS\n output = ''\n test_json = {}\n try:\n # Running pytest container\n container_obj: docker.models.containers.Container = self._docker_client.containers.run(\n name=container_name, image=test_image, command=[build_pytest_command(test_xml=test_xml, json=True)],\n user=f\"{os.getuid()}:4000\", detach=True, environment=self._facts[\"env_vars\"])\n stream_docker_container_output(container_obj.logs(stream=True))\n # Waiting for container to be finished\n container_status: dict = container_obj.wait(condition=\"exited\")\n # Getting container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code in [0, 1, 2, 5]:\n # 0-All tests passed\n # 1-Tests were collected and run but some of the tests failed\n # 2-Test execution was interrupted by the user\n # 5-No tests were collected\n if test_xml:\n test_data_xml = get_file_from_container(container_obj=container_obj,\n container_path=\"/devwork/report_pytest.xml\")\n xml_apth = Path(test_xml) / f'{self._pack_name}_pytest.xml'\n with open(file=xml_apth, mode='bw') as f:\n f.write(test_data_xml) # type: ignore\n\n test_json = json.loads(get_file_from_container(container_obj=container_obj,\n container_path=\"/devwork/report_pytest.json\",\n encoding=\"utf-8\"))\n for test in test_json.get('report', {}).get(\"tests\"):\n if test.get(\"call\", {}).get(\"longrepr\"):\n test[\"call\"][\"longrepr\"] = test[\"call\"][\"longrepr\"].split('\\n')\n if container_exit_code in [0, 5]:\n logger.info(f\"{log_prompt} - Successfully finished\")\n exit_code = SUCCESS\n elif container_exit_code in [2]:\n output = container_obj.logs().decode('utf-8')\n exit_code = FAIL\n else:\n logger.info(f\"{log_prompt} - Finished errors found\")\n exit_code = FAIL\n elif container_exit_code in [3, 4]:\n # 3-Internal error happened while executing tests\n # 4-pytest command line usage error\n logger.critical(f\"{log_prompt} - Usage error\")\n exit_code = RERUN\n output = container_obj.logs().decode('utf-8')\n # Remove container if not needed\n if keep_container:\n print(f\"{log_prompt} - Container name {container_name}\")\n else:\n try:\n container_obj.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to remove container {e}\")\n except (docker.errors.ImageNotFound, docker.errors.APIError) as e:\n logger.critical(f\"{log_prompt} - Unable to run pytest container {e}\")\n exit_code = RERUN\n\n return exit_code, output, test_json\n\n def _docker_run_pwsh_analyze(self, test_image: str, keep_container: bool) -> Tuple[int, str]:\n \"\"\" Run Powershell code analyze in created test image\n\n Args:\n test_image(str): test image id/name\n keep_container(bool): True if to keep container after excution finished\n\n Returns:\n int: 0 on successful, errors 1, need to retry 2\n str: Container log\n \"\"\"\n log_prompt = f'{self._pack_name} - Powershell analyze - Image {test_image}'\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pwsh-analyze\"\n # Check if previous run left container a live if it do, we remove it\n container_obj: docker.models.containers.Container\n try:\n container_obj = self._docker_client.containers.get(container_name)\n container_obj.remove(force=True)\n except docker.errors.NotFound:\n pass\n\n # Run container\n exit_code = SUCCESS\n output = \"\"\n try:\n container_obj = self._docker_client.containers.run(name=container_name,\n image=test_image,\n command=build_pwsh_analyze_command(\n self._facts[\"lint_files\"][0]),\n user=f\"{os.getuid()}:4000\",\n detach=True,\n environment=self._facts[\"env_vars\"])\n stream_docker_container_output(container_obj.logs(stream=True))\n # wait for container to finish\n container_status = container_obj.wait(condition=\"exited\")\n # Get container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n container_log = container_obj.logs().decode(\"utf-8\")\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code:\n # 1-fatal message issued\n # 2-Error message issued\n logger.info(f\"{log_prompt} - Finished errors found\")\n output = container_log\n exit_code = FAIL\n else:\n logger.info(f\"{log_prompt} - Successfully finished\")\n # Keeping container if needed or remove it\n if keep_container:\n print(f\"{log_prompt} - container name {container_name}\")\n else:\n try:\n container_obj.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to delete container - {e}\")\n except (docker.errors.ImageNotFound, docker.errors.APIError) as e:\n logger.critical(f\"{log_prompt} - Unable to run powershell test - {e}\")\n exit_code = RERUN\n\n return exit_code, output\n\n def _update_support_level(self):\n pack_dir = self._pack_abs_dir.parent if self._pack_abs_dir.parts[-1] == INTEGRATIONS_DIR else \\\n self._pack_abs_dir.parent.parent\n pack_meta_content: Dict = json.load((pack_dir / PACKS_PACK_META_FILE_NAME).open())\n self._facts['support_level'] = pack_meta_content.get('support')\n if self._facts['support_level'] == 'partner' and pack_meta_content.get('Certification'):\n self._facts['support_level'] = 'certified partner'\n\n def _docker_run_pwsh_test(self, test_image: str, keep_container: bool) -> Tuple[int, str]:\n \"\"\" Run Powershell tests in created test image\n\n Args:\n test_image(str): test image id/name\n keep_container(bool): True if to keep container after excution finished\n\n Returns:\n int: 0 on successful, errors 1, neet to retry 2\n str: Container log\n \"\"\"\n log_prompt = f'{self._pack_name} - Powershell test - Image {test_image}'\n logger.info(f\"{log_prompt} - Start\")\n container_name = f\"{self._pack_name}-pwsh-test\"\n # Check if previous run left container a live if it do, we remove it\n self._docker_remove_container(container_name)\n\n # Run container\n exit_code = SUCCESS\n output = \"\"\n try:\n container_obj: docker.models.containers.Container = self._docker_client.containers.run(\n name=container_name, image=test_image, command=build_pwsh_test_command(),\n user=f\"{os.getuid()}:4000\", detach=True, environment=self._facts[\"env_vars\"])\n stream_docker_container_output(container_obj.logs(stream=True))\n # wait for container to finish\n container_status = container_obj.wait(condition=\"exited\")\n # Get container exit code\n container_exit_code = container_status.get(\"StatusCode\")\n # Getting container logs\n container_log = container_obj.logs().decode(\"utf-8\")\n logger.info(f\"{log_prompt} - exit-code: {container_exit_code}\")\n if container_exit_code:\n # 1-fatal message issued\n # 2-Error message issued\n logger.info(f\"{log_prompt} - Finished errors found\")\n output = container_log\n exit_code = FAIL\n else:\n logger.info(f\"{log_prompt} - Successfully finished\")\n # Keeping container if needed or remove it\n if keep_container:\n print(f\"{log_prompt} - container name {container_name}\")\n else:\n try:\n container_obj.remove(force=True)\n except docker.errors.NotFound as e:\n logger.critical(f\"{log_prompt} - Unable to delete container - {e}\")\n except (docker.errors.ImageNotFound, docker.errors.APIError) as e:\n logger.critical(f\"{log_prompt} - Unable to run powershell test - {e}\")\n exit_code = RERUN\n\n return exit_code, output\n\n def _get_commands_list(self, script_obj: dict):\n \"\"\" Get all commands from yml file of the pack\n Args:\n script_obj(dict): the script section of the yml file.\n Returns:\n list: list of all commands\n \"\"\"\n commands_list = []\n try:\n commands_obj = script_obj.get('commands', {})\n for command in commands_obj:\n commands_list.append(command.get('name', ''))\n except Exception:\n logger.debug(\"Failed getting the commands from the yml file\")\n return commands_list\n", "sub_path": "demisto_sdk/commands/lint/linter.py", "file_name": "linter.py", "file_ext": "py", "file_size_in_byte": 50418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 56, "usage_type": "name"}, {"api_name": "docker.DockerClient", "line_number": 64, "usage_type": "attribute"}, {"api_name": "docker.from_env", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 82, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 98, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 99, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.add_tmp_lint_files", "line_number": 133, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PYTHON", "line_number": 139, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 156, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 158, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 170, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 170, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.NEGATE", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 190, "usage_type": "name"}, {"api_name": "ruamel.yaml.YAML", "line_number": 190, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PYTHON", "line_number": 201, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PWSH", "line_number": 201, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.tools.get_all_docker_images", "line_number": 208, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 211, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 212, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PYTHON", "line_number": 216, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.get_python_version_from_image", "line_number": 221, "usage_type": "call"}, {"api_name": "wcmatch.pathlib.NEGATE", "line_number": 249, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PWSH", "line_number": 251, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.NEGATE", "line_number": 255, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PWSH", "line_number": 260, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 261, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PYTHON", "line_number": 263, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 264, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 282, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 303, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 307, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.split_warnings_errors", "line_number": 330, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.EXIT_CODES", "line_number": 332, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 334, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.EXIT_CODES", "line_number": 335, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 343, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 343, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.tools.run_command_os", "line_number": 356, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_flake8_command", "line_number": 356, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 359, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 360, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 364, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 366, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 370, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 343, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 372, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 372, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 382, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.pylint_plugin", "line_number": 384, "usage_type": "call"}, {"api_name": "os.environ.copy", "line_number": 387, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 387, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.common.tools.run_command_os", "line_number": 401, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_xsoar_linter_command", "line_number": 402, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 406, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.WARNING", "line_number": 407, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.WARNING", "line_number": 410, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 412, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 413, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 415, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 415, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 423, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 424, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 372, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 431, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 431, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.tools.run_command_os", "line_number": 443, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_bandit_command", "line_number": 443, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 446, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 447, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 451, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 453, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 457, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 431, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 459, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 459, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.add_typing_module", "line_number": 472, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.tools.run_command_os", "line_number": 473, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_mypy_command", "line_number": 473, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 476, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 477, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 481, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 483, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 487, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 459, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 489, "usage_type": "name"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 489, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.tools.run_command_os", "line_number": 502, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_vulture_command", "line_number": 502, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 507, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RL", "line_number": 508, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 512, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 514, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 518, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 489, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 554, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PYTHON", "line_number": 557, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.TYPE_PWSH", "line_number": 568, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 579, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 579, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 579, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 580, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 580, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.EXIT_CODES", "line_number": 581, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.EXIT_CODES", "line_number": 586, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 592, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 604, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 605, "usage_type": "call"}, {"api_name": "docker.errors", "line_number": 611, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 614, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 614, "usage_type": "name"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 637, "usage_type": "call"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 637, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 638, "usage_type": "call"}, {"api_name": "jinja2.exceptions.TemplateError", "line_number": 645, "usage_type": "attribute"}, {"api_name": "jinja2.exceptions", "line_number": 645, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 650, "usage_type": "call"}, {"api_name": "docker.errors", "line_number": 655, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 663, "usage_type": "call"}, {"api_name": "requests.exceptions.exceptions", "line_number": 676, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 676, "usage_type": "name"}, {"api_name": "urllib3.exceptions.exceptions", "line_number": 676, "usage_type": "attribute"}, {"api_name": "urllib3.exceptions", "line_number": 676, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 679, "usage_type": "attribute"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 684, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 690, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 705, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 614, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 718, "usage_type": "attribute"}, {"api_name": "requests.exceptions.exceptions", "line_number": 720, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 720, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 722, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 743, "usage_type": "name"}, {"api_name": "docker.models", "line_number": 746, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_pylint_command", "line_number": 750, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 753, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.stream_docker_container_output", "line_number": 757, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 768, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 776, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 780, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 789, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 793, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 725, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 815, "usage_type": "name"}, {"api_name": "docker.models", "line_number": 820, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_pytest_command", "line_number": 821, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 822, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.stream_docker_container_output", "line_number": 823, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.get_file_from_container", "line_number": 836, "usage_type": "call"}, {"api_name": "wcmatch.pathlib.Path", "line_number": 838, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 842, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.get_file_from_container", "line_number": 842, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 850, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 853, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 856, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 861, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 869, "usage_type": "attribute"}, {"api_name": "docker.errors", "line_number": 871, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 873, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 797, "usage_type": "name"}, {"api_name": "docker.models", "line_number": 892, "usage_type": "attribute"}, {"api_name": "docker.errors", "line_number": 896, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 900, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_pwsh_analyze_command", "line_number": 905, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 907, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.stream_docker_container_output", "line_number": 910, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 923, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 932, "usage_type": "attribute"}, {"api_name": "docker.errors", "line_number": 934, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 936, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 877, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.common.constants.INTEGRATIONS_DIR", "line_number": 941, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 943, "usage_type": "name"}, {"api_name": "json.load", "line_number": 943, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.common.constants.PACKS_PACK_META_FILE_NAME", "line_number": 943, "usage_type": "name"}, {"api_name": "demisto_sdk.commands.lint.helpers.SUCCESS", "line_number": 966, "usage_type": "name"}, {"api_name": "docker.models", "line_number": 969, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.commands_builder.build_pwsh_test_command", "line_number": 970, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 971, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.stream_docker_container_output", "line_number": 972, "usage_type": "call"}, {"api_name": "demisto_sdk.commands.lint.helpers.FAIL", "line_number": 985, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 994, "usage_type": "attribute"}, {"api_name": "docker.errors", "line_number": 996, "usage_type": "attribute"}, {"api_name": "demisto_sdk.commands.lint.helpers.RERUN", "line_number": 998, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 948, "usage_type": "name"}]}
+{"seq_id": "526904435", "text": "import torch as th\n\nclass Actor(th.nn.Module):\n def __init__(self,\n non_spatial_action_space=549,\n local_feature_shape=(17,84,84),\n global_feature_shape=(7,64,64),\n non_spatial_feature_shape=(1,45)):\n super().__init__()\n self.local_feature_shape=local_feature_shape\n self.global_feature_shape=global_feature_shape\n self.non_spatial_feature_shape=non_spatial_feature_shape\n self.nonlin = th.nn.ReLU(inplace=True)\n\n self.local_feature_extractor = th.nn.Sequential(\n th.nn.Conv2d( local_feature_shape[0], 32, 3, stride=2), self.nonlin,\n th.nn.Conv2d(32, 64, 3, stride=2), self.nonlin,\n th.nn.Conv2d(64, 96, 3, stride=2), self.nonlin,\n th.nn.Conv2d(96,128, 3, stride=2))\n self.global_feature_extractor = th.nn.Sequential(\n th.nn.Conv2d(global_feature_shape[0], 32, 3, stride=2), self.nonlin,\n th.nn.Conv2d(32, 64, 3, stride=2), self.nonlin,\n th.nn.Conv2d(64, 96, 3, stride=2), self.nonlin,\n th.nn.Conv2d(96,128, 3, stride=2))\n self.non_spatial_feature_extractor = th.nn.Sequential(\n th.nn.Linear(non_spatial_feature_shape[-1], 256))\n\n nfeats = self.nfeatures()\n\n self.spatial_policy_x = th.nn.Sequential(\n th.nn.Linear(nfeats, global_feature_shape[-2]),\n th.nn.Softmax(dim=1))\n\n self.spatial_policy_y = th.nn.Sequential(\n th.nn.Linear(nfeats, global_feature_shape[-1]),\n th.nn.Softmax(dim=1))\n\n self.non_spatial_policy= th.nn.Sequential(\n th.nn.Linear(nfeats,non_spatial_action_space),\n th.nn.Softmax(dim=1))\n\n def nfeatures(self):\n x = self.global_feature_extractor(th.zeros((1,*self.global_feature_shape)))\n y = self.local_feature_extractor( th.zeros((1,*self.local_feature_shape)))\n z = self.non_spatial_feature_extractor(th.zeros((1,*self.non_spatial_feature_shape)))\n _ = th.cat((x.view(-1),y.view(-1),z.view(-1)),-1).view(-1)\n return _.size()[-1]\n\n def encode(self, global_features, local_features, non_spatial_features):\n x = self.global_feature_extractor(global_features)\n y = self.local_feature_extractor(local_features)\n z = self.non_spatial_feature_extractor(non_spatial_features)\n bsize = x.size(0)\n _= th.cat((x.view(bsize, -1), y.view(bsize, -1), z.view(bsize, -1)),-1)\n return _\n\n def __call__(self, global_features, local_features, non_spatial_features):\n _= self.encode(global_features, local_features, non_spatial_features)\n\n _= self.nonlin(_)\n\n xlogits = self.spatial_policy_x(_)\n ylogits = self.spatial_policy_y(_)\n\n non_spatial_logits = self.non_spatial_policy(_).squeeze()\n\n return non_spatial_logits, (xlogits, ylogits)\n", "sub_path": "sc2/controllers/actor_critic/actor.py", "file_name": "actor.py", "file_ext": "py", "file_size_in_byte": 2823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "torch.nn", "line_number": 3, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 54, "usage_type": "call"}]}
+{"seq_id": "64520655", "text": "# Cmput 455 sample code\n# 33 Patterns\n# Written by Chenjun Xiao\n# Code is from the michi project on Github:\n# https://github.com/pasky/michi/blob/master/michi.py\n\nfrom functools import reduce\n\npat3src = [ # 3x3 playout patterns; X,O are colors, x,o are their inverses\n [\"XOX\", # hane pattern - enclosing hane\n \"...\",\n \"???\"],\n [\"XO.\", # hane pattern - non-cutting hane\n \"...\",\n \"?.?\"],\n [\"XO?\", # hane pattern - magari\n \"X..\",\n \"x.?\"],\n # [\"XOO\", # hane pattern - thin hane\n # \"...\",\n # \"?.?\", \"X\", - only for the X player\n [\".O.\", # generic pattern - katatsuke or diagonal attachment; similar to magari\n \"X..\",\n \"...\"],\n [\"XO?\", # cut1 pattern (kiri] - unprotected cut\n \"O.o\",\n \"?o?\"],\n [\"XO?\", # cut1 pattern (kiri] - peeped cut\n \"O.X\",\n \"???\"],\n [\"?X?\", # cut2 pattern (de]\n \"O.O\",\n \"ooo\"],\n [\"OX?\", # cut keima\n \"o.O\",\n \"???\"],\n [\"X.?\", # side pattern - chase\n \"O.?\",\n \" \"],\n [\"OX?\", # side pattern - block side cut\n \"X.O\",\n \" \"],\n [\"?X?\", # side pattern - block side connection\n \"x.O\",\n \" \"],\n [\"?XO\", # side pattern - sagari\n \"x.x\",\n \" \"],\n [\"?OX\", # side pattern - cut\n \"X.O\",\n \" \"],\n ]\n\ndef pat3_expand(pat):\n \"\"\" All possible neighborhood configurations matching a given pattern;\n used just for a combinatoric explosion when loading them in an\n in-memory set. \"\"\"\n def pat_rot90(p):\n return [p[2][0] + p[1][0] + p[0][0], p[2][1] + p[1][1] + p[0][1], p[2][2] + p[1][2] + p[0][2]]\n def pat_vertflip(p):\n return [p[2], p[1], p[0]]\n def pat_horizflip(p):\n return [l[::-1] for l in p]\n def pat_swapcolors(p):\n return [l.replace('X', 'Z').replace('x', 'z').replace('O', 'X').replace('o', 'x').replace('Z', 'O').replace('z', 'o') for l in p]\n def pat_wildexp(p, c, to):\n i = p.find(c)\n if i == -1:\n return [p]\n return reduce(lambda a, b: a + b, [pat_wildexp(p[:i] + t + p[i+1:], c, to) for t in to])\n def pat_wildcards(pat):\n return [p for p in pat_wildexp(pat, '?', list('.XO '))\n for p in pat_wildexp(p, 'x', list('.O '))\n for p in pat_wildexp(p, 'o', list('.X '))]\n return [p for p in [pat, pat_rot90(pat)]\n for p in [p, pat_vertflip(p)]\n for p in [p, pat_horizflip(p)]\n for p in [p, pat_swapcolors(p)]\n for p in pat_wildcards(''.join(p))]\n\npat3set = set([p.replace('O', 'x') for p in pat3src for p in pat3_expand(p)])\n\n\n\n", "sub_path": "pattern.py", "file_name": "pattern.py", "file_ext": "py", "file_size_in_byte": 2890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "functools.reduce", "line_number": 70, "usage_type": "call"}]}
+{"seq_id": "576051124", "text": "# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport subprocess\nimport sys\nimport unittest\n\nimport paddle\n\npaddle.enable_static()\n\n\nclass TestNanInf(unittest.TestCase):\n def setUp(self):\n self._python_interp = sys.executable\n if os.getenv('WITH_COVERAGE', 'OFF') == 'ON':\n self._python_interp += \" -m coverage run --branch -p\"\n\n self.env = os.environ.copy()\n\n def check_nan_inf(self):\n cmd = self._python_interp\n\n proc = subprocess.Popen(\n cmd.split(\" \"),\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n env=self.env,\n )\n\n out, err = proc.communicate()\n returncode = proc.returncode\n\n print(out)\n print(err)\n\n # in python3, type(out+err) is 'bytes', need use encode\n if paddle.fluid.core.is_compiled_with_cuda():\n assert (out + err).find('find_nan=1, find_inf=1'.encode()) != -1\n else:\n assert (out + err).find(\n 'There are `nan` or `inf` in tensor'.encode()\n ) != -1\n\n def test_nan_inf_in_static_mode(self):\n self._python_interp += \" check_nan_inf_base.py\"\n self.check_nan_inf()\n\n def test_nan_inf_in_dynamic_mode(self):\n self._python_interp += \" check_nan_inf_base_dygraph.py\"\n self.check_nan_inf()\n\n\nclass TestNanInfEnv(TestNanInf):\n def setUp(self):\n super().setUp()\n # windows python have some bug with env, so need use str to pass ci\n # otherwise, \"TypeError: environment can only contain strings\"\n self.env[str(\"PADDLE_INF_NAN_SKIP_OP\")] = str(\"mul\")\n self.env[str(\"PADDLE_INF_NAN_SKIP_ROLE\")] = str(\"loss\")\n self.env[str(\"PADDLE_INF_NAN_SKIP_VAR\")] = str(\n \"elementwise_add:fc_0.tmp_1\"\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "python/paddle/fluid/tests/unittests/test_nan_inf.py", "file_name": "test_nan_inf.py", "file_ext": "py", "file_size_in_byte": 2421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "paddle.enable_static", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ.copy", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 36, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "paddle.fluid.core.is_compiled_with_cuda", "line_number": 50, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 50, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 79, "usage_type": "call"}]}
+{"seq_id": "323497284", "text": "#import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.optimize import curve_fit\r\nfrom scipy.optimize import minimize\r\n#from scipy.special import erf\r\nfrom sklearn.metrics import r2_score\r\n#from getTxtDataFrame import getTimeInterval\r\n#from astropy.table import Table\r\nimport os\r\n\r\nfrom getTxtDataFrame_v3 import getTimeInterval\r\nfrom getTxtDataFrame_v3 import getTxtDataFrame\r\nfrom getTxtDataFrame_v3 import getTxtPremadeDataFrame\r\nfrom getTxtDataFrameWithTimes_v3 import getTxtDataFrameWithTimes\r\nfrom getTxtDataFrameWithTimes_v3 import getTimes\r\nfrom getTxtDataFrameWithTimes_v3 import get24Hour\r\n\r\n#folderPath = r'C:\\Users\\d.blach-lafleche\\Desktop\\photobleaching- files\\\\'\r\npath = [r'C:\\Users\\d.blach-lafleche\\Desktop\\Photobleaching\\\\',\r\n r'C:\\Users\\d.blach-lafleche\\Documents\\Python Scripts\\Photobleaching_v3\\30kRad\\\\',\r\n r'C:\\Users\\d.blach-lafleche\\Documents\\Python Scripts\\Photobleaching_v3\\60kRad\\\\',\r\n r'C:\\Users\\d.blach-lafleche\\Documents\\Python Scripts\\Photobleaching_v3\\100kRad\\\\',\r\n r'C:\\Users\\d.blach-lafleche\\Documents\\Python Scripts\\Photobleaching_v3\\6.5m\\\\',\r\n ]\r\n\r\n#path, filename, G_0Rad [dBm], G_nRad [dBm],t_0 [hours], name in legend(graph) , getFit , PremadeDf, showFutur, offset\r\nfileNameData = [[1,'6378LF,10m,30kRad_set-1+2+4_offset',19.68,18.38,0,'30 kRad',True,True,False,0], \r\n [2,'6378LF,10m,60kRad_set1+3+5_offset',19.68,17.332,0,'60 kRad',True,True,False,0],#19.54,17.191\r\n [3,'6378Lf,10m,100kRad_set-1+3+5',19.68,16.139,0,'100 kRad set 1+2+3',True,True,False,0],\r\n ]\r\n\r\n#path\r\n#filename\r\n#G_0Rad [dBm]\r\n#G_nRad [dBm]\r\n#t_0 [hours]\r\n#name in legend(graph)\r\n#getFit\r\n#PremadeDf\r\n#showFutur\r\n#offset\r\n\r\n\r\n\r\n#initialGuess = (0.09,0.20)\r\n\r\n#initialGuess\r\nLambdaVec = (0.003,)#(0.001 , 0.0015, 0.002 , 0.0025, 0.003 , 0.0035, 0.004)#np.arange(0.060,0.091,0.005) \r\nAlphaVec = (0.23,)#(0.18,0.19, 0.2 , 0.21, 0.22, 0.23, 0.24, 0.25, 0.26,) #np.arange(-0.200,-0.280,-0.01)\r\n\r\n\r\n\r\n\r\nreadData = True\r\n\r\nsaveGraphPNG = True # Saves plots as .png \r\nshowMaxPower = True # Show the maximum power value, which is power before irradiation (G_n0Rad [dBm]) in graph\r\n#showFutur = False # Show futur values of the fitted function\r\nfuturMuliplier = 9.7 # Factor to multuply orginal dataset length\r\n#setMaxValue = True # Fit curve with max value parameter already set\r\ngetTimeData = True # Define time in dataframe by difference between points, instead of fixed interval\r\nmultiPlot = True\r\nmultiPlotName = 'No set'\r\ncombinePlots = False\r\nunit = 'dBm'\r\n#unit = 'mW'\r\n\r\n# determine limits of graph, to zoom on a part\r\nyLim = np.array([16]) #(17.6,18.6)\r\nsetXLim = False\r\nxLim = (80,120)\r\n\r\n\r\nexportDataFrame = True ## aucune idée ce que ca fait\r\n\r\nfitPar = 'Alpha'\r\n#\r\n#Lambda = [0.67]\r\n#AlphaVec = [-0.213,-0.233,-0.253,-0.273]\r\n####=========================================================================================== \r\n\r\n\r\nWeibull = lambda time,Lambda,Alpha,maxP,minP,: minP + (maxP-minP)*(1 - np.exp(-1*np.power((Lambda*(time-t0)),Alpha)))\r\nFunction = ['AccLoss function',Weibull ,2,[0.09,-0.23],[-300,-16]]\r\n\r\n ###############################################################################\r\ndef function(par,time,data,fit,findPar):\r\n \r\n Lambda= par[0]\r\n Alpha= par[1]\r\n \r\n FVU = np.empty(len(data))\r\n r2vec = np.empty(len(data))\r\n \r\n# print('momo')\r\n for i in range(0,len(data)):\r\n# print(i)\r\n fit[i] = f(time[i],Lambda,Alpha,maxPower[i],minPower[i])\r\n \r\n ssTot = np.sum((data[i]-np.average(data[i]))**2)\r\n ssRes = np.sum((data[i]-fit[i])**2)\r\n \r\n FVU[i] = (ssRes/ssTot) #fraction of variance unexplained\r\n# print(i) \r\n if np.isnan(fit[i]).any() :\r\n r2vec[i] = 0\r\n else:\r\n r2vec[i] = r2_score(data[i],fit[i])\r\n \r\n if findPar:\r\n return 1 - np.average(r2vec)\r\n else:\r\n return r2vec\r\n ###############################################################################\r\n\r\n\r\n# Script start here\r\n\r\n# read text files and load data \r\nif readData:\r\n time = [None]*len(fileNameData)\r\n data = [None]*len(fileNameData)\r\n fit = [None]*len(fileNameData)\r\n maxPower = [None]*len(fileNameData)\r\n minPower = [None]*len(fileNameData)\r\n\r\n radiation = [None]*len(fileNameData)\r\n getFit = [None]*len(fileNameData)\r\n \r\n for j in range(0, len(fileNameData)):\r\n print(fileNameData[j][0])\r\n \r\n # get all elemets from filenameData into independeant varibales with descriptive names\r\n folderPath = path[fileNameData[j][0]]\r\n fileName = fileNameData[j][1]\r\n maxPower[j] = fileNameData[j][2]\r\n minPower[j] = fileNameData[j][3]\r\n t0 = fileNameData[j][4]\r\n radiation[j] = fileNameData[j][5][0:8]\r\n # radiation = fileNameData[j][4]\r\n getFit[j] = fileNameData[j][6]\r\n PremadeDf = fileNameData[j][7]\r\n showFutur = fileNameData[j][8]\r\n offset = fileNameData[j][9]\r\n \r\n # get data depinened on input parameter for configuration of text file\r\n if PremadeDf:\r\n df= getTxtPremadeDataFrame(folderPath,fileName)\r\n else: \r\n if getTimeData:\r\n df= getTxtDataFrameWithTimes(folderPath,fileName,pastTime) \r\n else:\r\n df= getTxtDataFrame(folderPath,fileName)\r\n if offset != 0:\r\n df = offsetData(df,offset) \r\n \r\n time[j] = np.transpose(df)[0]\r\n data[j] = np.transpose(df)[1]\r\n\r\n\r\n f = Weibull\r\n \r\n\r\n \r\n\r\n \r\n\r\n\r\n\r\n\r\n\r\n# loop to fit with different initial guesses\r\nfor i in LambdaVec:\r\n for k in AlphaVec:\r\n \r\n print('Alpha = {0} - Lambda = {1}'.format(i,k))\r\n initialGuess = (i,k)\r\n \r\n res = minimize(function,initialGuess,args=(time,data,fit,True),bounds=[(-2,10),(-2,10)])\r\n \r\n par = res['x']\r\n avgR2 = res['fun']\r\n \r\n fit = [None]*len(fileNameData)\r\n fitTime = [None]*len(fileNameData)\r\n \r\n R2Vec = function(par,time,data,fit,False)\r\n \r\n \r\n for m in range(0,len(data)):\r\n fitTime[m] = np.linspace(time[m][0],time[m][-1])\r\n fit[m] = f(fitTime[m],*par,maxPower[m],minPower[m])\r\n \r\n\r\n\r\n # make figure\r\n plt.figure()\r\n \r\n plt.plot(fitTime[0],maxPower[0]*np.ones(len(fitTime[0])))\r\n \r\n plt.scatter(time[0],data[0],s=0.1,c='red')\r\n plt.plot(fitTime[0],fit[0])\r\n \r\n plt.scatter(time[1],data[1],s=0.1,c='red')\r\n plt.plot(fitTime[1],fit[1])\r\n \r\n plt.scatter(time[2],data[2],s=0.1,c='red')\r\n plt.plot(fitTime[2],fit[2])\r\n \r\n \r\n plt.title('6378LF,10m - Weibull function - Lambda = {0:.3f}, Alpha = {1:.3f}'.format(*par))\r\n plt.legend(['0 kRad','30 kRad - r2 = {:.3f}'.format((R2Vec)[0]),'60 kRad - r2 = {:.3f}'.format((R2Vec)[1]),'100 kRad - r2 = {:.3f}'.format((R2Vec)[2])])\r\n plt.xlabel('Time [Hours]')\r\n plt.ylabel('Power [dBm]')\r\n \r\n title = '6378LF,10m - Weibull function - Initial Guess = {0} - Average R2 = {1:.4f}'.format(initialGuess,avgR2)\r\n \r\n \r\n # Save\r\n if saveGraphPNG:\r\n if os.path.exists(folderPath + 'AverageR2') == False:\r\n os.mkdir(folderPath + 'AverageR2')\r\n plt.savefig(folderPath + 'AverageR2' +'\\\\'+title + '.png', bbox_inches=\"tight\")\r\n \r\n\r\n\r\n", "sub_path": "Old scripct/r2AverageFitting_v2.py", "file_name": "r2AverageFitting_v2.py", "file_ext": "py", "file_size_in_byte": 7602, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 112, "usage_type": "call"}, {"api_name": "getTxtDataFrame_v3.getTxtPremadeDataFrame", "line_number": 149, "usage_type": "call"}, {"api_name": "getTxtDataFrameWithTimes_v3.getTxtDataFrameWithTimes", "line_number": 152, "usage_type": "call"}, {"api_name": "getTxtDataFrame_v3.getTxtDataFrame", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 159, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}]}
+{"seq_id": "559956613", "text": "from tweepy import StreamListener, Stream\nimport tweepy\nfrom textblob import TextBlob\nfrom unidecode import unidecode\nfrom flask import Flask\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\n\n\ncred = credentials.Certificate(\n 'project/apitweet-a60ec-firebase-adminsdk-mgfn0-0628e94c02.json')\nfirebase_admin.initialize_app(cred, {\n 'databaseURL': 'https://apitweet-a60ec.firebaseio.com/'\n})\n\nref = db.reference('/')\n\napp = Flask(__name__)\n\n\nCONSUMER_KEY = \"EwfeKFNaNYS9UrT193GopRXgU\"\nCONSUMER_SECRET = \"EQctHnv9ikhsh4QKaCTkjpwIy58PaL0Qb00GTBQBOoQOnruBcQ\"\nACCESS_TOKEN = \"1131411186006790144-O9qjHQCqDb17pHX3HGl8ZdMwXb5fgp\"\nACCESS_TOKEN_SECRET = \"9aXwtmbieFr29f4VhvwqXXWbSemJ4EkKtEvRyS0mmGeYR\"\n\n\nauth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\nauth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)\napi = tweepy.API(auth)\n\n\ndef make_lowercase(tweet):\n return tweet.lower()\n\n\ndef remove_diacritics(tweet):\n return unidecode(tweet)\n\n\ndef remove_non_alpha_characters(tweet):\n return ''.join(character for character in tweet if character.isalpha() or character == ' ')\n\n\nclass DbStreamListener(StreamListener):\n\n def on_status(self, status):\n cleaned_status_text = self._clean_status_text(status.text)\n analisis = TextBlob(status.text)\n analisis = analisis.sentiment\n opinion = analisis.polarity\n self._insert_status(id_tweet=status.id,\n tweet=cleaned_status_text, sentimiento=opinion)\n\n def _clean_status_text(self, status_text):\n cleaned_status_text = status_text\n for cleaning_function in self._cleaning_functions:\n cleaned_status_text = cleaning_function(cleaned_status_text)\n return cleaned_status_text\n\n def _insert_status(self, id_tweet, tweet, sentimiento):\n tweets_ref = ref.child('tweets')\n tweets_ref.push().set({\n 'id_tweet': id_tweet,\n 'tweet': tweet,\n 'sentimiento': sentimiento\n })\n\n @property\n def _cleaning_functions(self):\n return [make_lowercase, remove_diacritics, remove_non_alpha_characters]\n\n\n@app.route('/tweets/<string:tweets>')\ndef search_tweet(tweets):\n keyword_list = [tweets]\n streaming_api = Stream(auth=auth, listener=DbStreamListener())\n streaming_api.filter(track=keyword_list)\n return \"escuchando...\"\n", "sub_path": "services/tweets/project/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 11, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 11, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 13, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 17, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 19, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 30, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 38, "usage_type": "call"}, {"api_name": "tweepy.StreamListener", "line_number": 45, "usage_type": "name"}, {"api_name": "textblob.TextBlob", "line_number": 49, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 77, "usage_type": "call"}]}
+{"seq_id": "19610553", "text": "# -*- coding:utf-8 -*-\nfrom .thrift2.hbase.ttypes import *\nfrom abc import ABCMeta, abstractmethod\nfrom .thrift2.utils import make_to_dict, make_ordered_to_dict\nfrom collections import Iterable\nimport six\n\n\nclass TableInterface( six.with_metaclass( ABCMeta)):\n '''\n table operating\n All operations are sorted by timestamp\n Timestamp The latest record will take effect, such as deleting the put and so on\n '''\n\n @abstractmethod\n def __del__(self):\n pass\n\n @abstractmethod\n def get(self, rowKey, includeTimestamp=False):\n pass\n\n @abstractmethod\n def gets(self, rowKeys, columns=None, filterString=None):\n pass\n\n @abstractmethod\n def put(self, rowKey, data, timestamp=None, writeToWal=True):\n '''\n :return: The input and query result formats are consistent\n '''\n pass\n\n @abstractmethod\n def delete(self, rowKey, columns=None, timestamp=None, writeToWal=True):\n '''\n :param rowKey:\n :param columns: ['family:qualifier','...'] or [('family:qualifier',timestamp),'...']\n :param timestamp:\n :param writeToWal:\n :return:\n '''\n pass\n\n @abstractmethod\n def scan(self, limit=None, startRow=None, stopRow=None, columns=None, timeRange=None, filterString=None, caching=None, batchSize=1000, includeTimestamp=False, attributes=None, maxVersions=1):\n '''\n :param limit:\n :param startRow:\n :param stopRow:\n :param columns:\n :param timeRange:\n :param filterString:\n :param caching:\n :param batchSize:\n :param includeTimestamp:\n :param attributes:\n :param maxVersions:\n :return:\n '''\n pass\n\n\n\nclass Table(TableInterface):\n\n def __init__(self, tableName, connection):\n self.__tableName = tableName\n self.connection = connection\n self.client = connection.client\n\n def __repr__(self):\n return '<%s.%s tableName=%r>' % (\n __name__,\n self.__class__.__name__,\n self.__tableName,\n )\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n\n def close(self):\n self.connection.close()\n\n def __del__(self):\n self.connection.close()\n\n def get(self, rowKey, columns=None, filterString=None, includeTimestamp=False):\n if columns is not None:\n t_column = []\n t_column.extend(\n TColumn(\n *( family_qualifier.split(':') )\n )\n for family_qualifier in columns\n )\n else: t_column = None\n t_get = TGet(\n row=rowKey,\n columns=t_column,\n filterString=filterString\n )\n item = self.client.get(self.__tableName, t_get)\n row = make_to_dict(item.columnValues, includeTimestamp)\n return item.row,row\n\n\n def gets(self, rowKeys, columns=None, filterString=None, include_timestamp=False):\n if not isinstance(rowKeys, Iterable):\n raise ValueError('rowKeys Can not iterate')\n if columns is not None:\n t_column = []\n t_column.extend(\n TColumn(\n *( family_qualifier.split(':') )\n )\n for family_qualifier in columns\n )\n else: t_column = None\n t_gets = []\n t_gets.extend(\n TGet(\n row=rowKey,\n columns=t_column,\n filterString=filterString\n )\n for rowKey in rowKeys\n )\n items = self.client.getMultiple(self.__tableName,t_gets)\n for item in items:\n row = make_to_dict(item.columnValues, include_timestamp)\n yield item.row, row\n\n\n def getTPut(self, rowKey, data, timestamp, writeToWal):\n if data is None:\n raise ValueError(\"data can not be None,Format reference query results\")\n columns = []\n columns.extend(\n TColumnValue(\n *( family_qualifier.split(':') + list(value_timestamp if isinstance(value_timestamp,tuple) else (value_timestamp,timestamp)) )\n )\n for family_qualifier,value_timestamp in data.items()\n )\n t_put = TPut(\n row=rowKey,\n columnValues=columns,\n timestamp=timestamp, writeToWal=writeToWal\n )\n return t_put\n\n\n def put(self, rowKey, data, timestamp=None, writeToWal=True):\n t_put = self.getTPut(rowKey, data, timestamp, writeToWal)\n # tag storyofus\n # self.client ====> thrift2.hbase.THBaseService.Client\n return self.client.put(self.__tableName, t_put)\n\n\n def puts(self, puts, timestamp=None, writeToWal=True):\n if not isinstance(puts, Iterable):\n raise ValueError(\"puts Can not iterate\")\n t_puts = []\n t_puts.extend(\n self.getTPut(rowKey, data, timestamp=timestamp, writeToWal=writeToWal)\n for rowKey, data in puts\n )\n return self.client.putMultiple(self.__tableName, t_puts)\n\n\n def delete(self, rowKey, columns=None, timestamp=None, writeToWal=True):\n if columns is not None:\n t_columns_list = []\n t_columns_list.extend(\n TColumn(\n *( (family_qualifier.split(':') + [timestamp]) if not isinstance(family_qualifier,tuple) else (family_qualifier[0].split(':') + [family_qualifier[1]]) )\n )\n for family_qualifier in columns\n )\n else: t_columns_list = None\n\n t_delete = TDelete(\n row=rowKey, columns=t_columns_list,\n timestamp=timestamp, deleteType=1,\n writeToWal=writeToWal, attributes=None,\n durability=None\n )\n return self.client.deleteSingle(self.__tableName, t_delete)\n\n\n def deletes(self, rowKeys, columns=None, timestamp=None, writeToWal=True):\n if not isinstance(rowKeys,Iterable):\n raise ValueError('rowKeys Can not iterate')\n if columns is not None:\n t_column = []\n t_column.extend(\n TColumn(\n *( family_qualifier.split(':') )\n )\n for family_qualifier in columns\n )\n else: t_column = None\n t_deletes = []\n t_deletes.extend(\n TDelete(\n row=rowKey,\n columns=t_column,\n timestamp=timestamp,\n writeToWal=writeToWal\n )\n for rowKey in rowKeys\n )\n return self.client.deleteMultiple(self.__tableName, t_deletes)\n\n\n def scan(self, limit=None, startRow=None, stopRow=None, columns=None, timeRange=None, filterString=None, caching=1000, batchSize=None, includeTimestamp=False, attributes=None, maxVersions=1):\n\n if limit is not None and limit < 1:\n raise ValueError(\"'limit' must be >= 1\")\n if caching is not None and caching < 1:\n raise ValueError(\"'caching' must be >= 1\")\n\n if columns is not None:\n t_column = []\n t_column.extend(\n TColumn(\n *( family_qualifier.split(':') )\n )\n for family_qualifier in columns\n )\n else: t_column = None\n timeRange = timeRange and TTimeRange(*timeRange)\n t_scan = TScan(\n startRow=startRow, stopRow=stopRow,\n columns=t_column, timeRange=timeRange,\n filterString=filterString,\n caching=caching, batchSize=batchSize,\n attributes=attributes,maxVersions=maxVersions,\n )\n scannerId = self.client.openScanner(self.__tableName, t_scan)\n n_returned = n_fetched = 0\n try:\n while True:\n if limit is None:\n how_many = caching\n else:\n how_many = min(caching, limit - n_returned)\n\n items = self.client.getScannerRows(scannerId, how_many)\n\n if not items:\n return # scan has finished\n\n n_fetched += len(items)\n\n for n_returned, item in enumerate(items,n_returned + 1):\n row = make_to_dict(item.columnValues, includeTimestamp)\n yield item.row, row\n if limit is not None and n_returned == limit:\n return # scan has finished\n except:\n import traceback\n traceback.print_exc()\n finally:\n self.client.closeScanner(scannerId)", "sub_path": "table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 8676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "six.with_metaclass", "line_number": 9, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 9, "usage_type": "argument"}, {"api_name": "abc.abstractmethod", "line_number": 16, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 20, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 24, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 28, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 35, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 46, "usage_type": "name"}, {"api_name": "thrift2.utils.make_to_dict", "line_number": 108, "usage_type": "call"}, {"api_name": "collections.Iterable", "line_number": 113, "usage_type": "argument"}, {"api_name": "thrift2.utils.make_to_dict", "line_number": 135, "usage_type": "call"}, {"api_name": "collections.Iterable", "line_number": 165, "usage_type": "argument"}, {"api_name": "collections.Iterable", "line_number": 196, "usage_type": "argument"}, {"api_name": "thrift2.utils.make_to_dict", "line_number": 261, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 267, "usage_type": "call"}]}
+{"seq_id": "539622741", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse, HttpRequest\nfrom django.template import RequestContext, loader\nfrom models import *\nfrom django.core.urlresolvers import resolve\n \n\n\ndef index(request):\n latest_question_list = Sermon.objects.order_by('-date')[:5]\n template = loader.get_template('SermonLibrary/index.html')\n context = RequestContext(request, {\n 'latest_sermon_list': latest_question_list,\n })\n return HttpResponse(template.render(context))\n \ndef download(request):\n \n current_url = request.get_full_path()\n fileToDownload = str(current_url).split(\"/\")\n fileToDownload = fileToDownload[-1]\n\n fsock = open('/static/' + str(fileToDownload), 'r')\n response = HttpResponse(fsock, mimetype='audio/mpeg')\n response['Content-Disposition'] = \"attachment; filename=%s - %s.mp3\" % (song.artist, song.title)\n return response\n", "sub_path": "Sermons/SermonLibrary/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.template.loader.get_template", "line_number": 11, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 11, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 12, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 15, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 24, "usage_type": "call"}]}
+{"seq_id": "405318453", "text": "from clientes.models import Cliente, ClienteForm\nfrom historiador.models import Registro\nfrom django.shortcuts import render_to_response\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext\n\ndef update(request, id = None):\n\tinstance = None\n\tif id is not None:\n\t\tinstance = Cliente.objects.get(id = id)\n\t#when POST\n\tif request.method == 'POST':\n\t\tform = ClienteForm(request.POST, instance = instance)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tif id is not None:\n\t\t\t\tregistro = Registro(seccion = 'clientes', accion = 'actualizo', usuario = int(request.session['user_id']))\n\t\t\telse:\n\t\t\t\tregistro = Registro(seccion = 'clientes', accion = 'creo', usuario = int(request.session['user_id']))\n\t\t\tregistro.save()\n\t\treturn HttpResponseRedirect('/clientes/')\n\t#when NOT POST\n\telse:\n\t\tform = ClienteForm(instance = instance)\n\treturn render_to_response('clientes/detail.html',{'form':form}, context_instance = RequestContext(request))", "sub_path": "clientes/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "clientes.models.Cliente.objects.get", "line_number": 10, "usage_type": "call"}, {"api_name": "clientes.models.Cliente.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "clientes.models.Cliente", "line_number": 10, "usage_type": "name"}, {"api_name": "clientes.models.ClienteForm", "line_number": 13, "usage_type": "call"}, {"api_name": "historiador.models.Registro", "line_number": 17, "usage_type": "call"}, {"api_name": "historiador.models.Registro", "line_number": 19, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 21, "usage_type": "call"}, {"api_name": "clientes.models.ClienteForm", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 25, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "200458339", "text": "from django.shortcuts import get_object_or_404\nfrom django.utils import timezone\n\nfrom rest_framework.exceptions import AuthenticationFailed\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\n\nfrom photos_api.permissions import IsAllowedPrivateAPI\nfrom photos_api.serializers import PhotoUploadInitSerializer, PhotoServerRegisterSerializer\nfrom phone_auth.authentication import TokenAuthentication\nfrom phone_auth.models import User\nfrom photos.models import Album, PendingPhoto, Photo, Video\nfrom photos import photo_operations\nfrom photos_api.private_serializers import VideoObjectSerializer, PhotoObjectSerializer\n\n\n@api_view(['POST'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef photo_upload_init(request, photo_id):\n serializer = PhotoUploadInitSerializer(data=request.DATA)\n\n if not serializer.is_valid():\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n try:\n user, token = TokenAuthentication().authenticate_credentials(serializer.object['user_auth_token'])\n except AuthenticationFailed as e:\n return Response({\n 'success': False,\n 'error': 'user_auth_failed',\n 'detail': e.detail\n })\n\n try:\n photo = PendingPhoto.objects.get(pk=photo_id)\n except PendingPhoto.DoesNotExist:\n photo = get_object_or_404(Photo, pk=photo_id)\n\n if photo.author != user:\n return Response({\n 'success': False,\n 'error': 'user_not_permitted'\n })\n\n return Response({\n 'success': True,\n 'storage_id': photo.storage_id,\n 'uploaded': True if isinstance(photo, Photo) else photo.is_file_uploaded()\n })\n\n\n@api_view(['PUT'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef photo_file_uploaded(request, photo_id):\n pending_photo = get_object_or_404(PendingPhoto, pk=photo_id)\n\n now = timezone.now()\n pending_photo.set_uploaded(now)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['PUT'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef photo_processing_done(request, storage_id):\n pending_photo = get_object_or_404(PendingPhoto, storage_id=storage_id)\n\n now = timezone.now()\n pending_photo.set_processing_done(now)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['PUT'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef video_object(request, storage_id):\n serializer = VideoObjectSerializer(data=request.DATA)\n\n if not serializer.is_valid():\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n author_id = serializer.object['author_id']\n client_upload_id = serializer.object['client_upload_id']\n album_id = serializer.object['album_id']\n status_ = serializer.object['status']\n\n try:\n author = User.objects.get(pk=author_id)\n except User.DoesNotExist:\n return Response('Invalid user id: ' + str(author_id), status=status.HTTP_400_BAD_REQUEST)\n\n try:\n album = Album.objects.get(pk=album_id)\n except Album.DoesNotExist:\n return Response('Invalid album id: ' + str(album_id), status=status.HTTP_400_BAD_REQUEST)\n\n # TODO Verify that the author is allowed to add a video into album\n\n now = timezone.now()\n\n if status_ == 'processing':\n Video.objects.set_processing(client_upload_id, storage_id, author, album, now)\n elif status_ == 'ready':\n duration = serializer.object.get('duration')\n if duration is None:\n return Response('Missing required field \"duration\"', status=status.HTTP_400_BAD_REQUEST)\n Video.objects.set_ready(client_upload_id, storage_id, author, album, duration, now)\n elif status_ == 'invalid':\n Video.objects.set_invalid(client_upload_id, storage_id, author, album, now)\n else:\n raise RuntimeError('Unknown status: ' + status_)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['PUT'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef photo_object(request, storage_id):\n serializer = PhotoObjectSerializer(data=request.DATA)\n\n if not serializer.is_valid():\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n author_id = serializer.object['author_id']\n client_upload_id = serializer.object['client_upload_id']\n album_id = serializer.object['album_id']\n status_ = serializer.object['status']\n\n try:\n author = User.objects.get(pk=author_id)\n except User.DoesNotExist:\n return Response('Invalid user id: ' + str(author_id), status=status.HTTP_400_BAD_REQUEST)\n\n try:\n album = Album.objects.get(pk=album_id)\n except Album.DoesNotExist:\n return Response('Invalid album id: ' + str(album_id), status=status.HTTP_400_BAD_REQUEST)\n\n # TODO Verify that the author is allowed to add a video into album\n\n now = timezone.now()\n\n if status_ == 'processing':\n raise RuntimeError('\"processing\" status not yet implemented')\n elif status_ == 'ready':\n photo_operations.add_photo(client_upload_id, storage_id, author, album, now)\n elif status_ == 'invalid':\n raise RuntimeError('\"invalid\" status not yet implemented')\n else:\n raise RuntimeError('Unknown status: ' + status_)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['POST'])\n@permission_classes((IsAllowedPrivateAPI, ))\ndef photo_server_register(request):\n serializer = PhotoServerRegisterSerializer(data=request.DATA)\n\n if not serializer.is_valid():\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n now = timezone.now()\n photo_operations.register_photo_server(\n serializer.object['update_url'],\n serializer.object['subdomain'],\n serializer.object['auth_key'],\n now)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n", "sub_path": "photos_api/private_views.py", "file_name": "private_views.py", "file_ext": "py", "file_size_in_byte": 5927, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "photos_api.serializers.PhotoUploadInitSerializer", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 24, "usage_type": "name"}, {"api_name": "phone_auth.authentication.TokenAuthentication", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "photos.models.PendingPhoto.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "photos.models.PendingPhoto.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "photos.models.PendingPhoto", "line_number": 36, "usage_type": "name"}, {"api_name": "photos.models.PendingPhoto.DoesNotExist", "line_number": 37, "usage_type": "attribute"}, {"api_name": "photos.models.PendingPhoto", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 38, "usage_type": "call"}, {"api_name": "photos.models.Photo", "line_number": 38, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "photos.models.Photo", "line_number": 49, "usage_type": "argument"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 19, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 56, "usage_type": "call"}, {"api_name": "photos.models.PendingPhoto", "line_number": 56, "usage_type": "argument"}, {"api_name": "django.utils.timezone.now", "line_number": 58, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 53, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 54, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 67, "usage_type": "call"}, {"api_name": "photos.models.PendingPhoto", "line_number": 67, "usage_type": "argument"}, {"api_name": "django.utils.timezone.now", "line_number": 69, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 64, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 65, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 65, "usage_type": "name"}, {"api_name": "photos_api.private_serializers.VideoObjectSerializer", "line_number": 78, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 81, "usage_type": "name"}, {"api_name": "phone_auth.models.User.objects.get", "line_number": 89, "usage_type": "call"}, {"api_name": "phone_auth.models.User.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "phone_auth.models.User", "line_number": 89, "usage_type": "name"}, {"api_name": "phone_auth.models.User.DoesNotExist", "line_number": 90, "usage_type": "attribute"}, {"api_name": "phone_auth.models.User", "line_number": 90, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 91, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 91, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 91, "usage_type": "name"}, {"api_name": "photos.models.Album.objects.get", "line_number": 94, "usage_type": "call"}, {"api_name": "photos.models.Album.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "photos.models.Album", "line_number": 94, "usage_type": "name"}, {"api_name": "photos.models.Album.DoesNotExist", "line_number": 95, "usage_type": "attribute"}, {"api_name": "photos.models.Album", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 96, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 96, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 100, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 100, "usage_type": "name"}, {"api_name": "photos.models.Video.objects.set_processing", "line_number": 103, "usage_type": "call"}, {"api_name": "photos.models.Video.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "photos.models.Video", "line_number": 103, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 107, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 107, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 107, "usage_type": "name"}, {"api_name": "photos.models.Video.objects.set_ready", "line_number": 108, "usage_type": "call"}, {"api_name": "photos.models.Video.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "photos.models.Video", "line_number": 108, "usage_type": "name"}, {"api_name": "photos.models.Video.objects.set_invalid", "line_number": 110, "usage_type": "call"}, {"api_name": "photos.models.Video.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "photos.models.Video", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 114, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 76, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 76, "usage_type": "name"}, {"api_name": "photos_api.private_serializers.PhotoObjectSerializer", "line_number": 120, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 123, "usage_type": "name"}, {"api_name": "phone_auth.models.User.objects.get", "line_number": 131, "usage_type": "call"}, {"api_name": "phone_auth.models.User.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "phone_auth.models.User", "line_number": 131, "usage_type": "name"}, {"api_name": "phone_auth.models.User.DoesNotExist", "line_number": 132, "usage_type": "attribute"}, {"api_name": "phone_auth.models.User", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 133, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 133, "usage_type": "name"}, {"api_name": "photos.models.Album.objects.get", "line_number": 136, "usage_type": "call"}, {"api_name": "photos.models.Album.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "photos.models.Album", "line_number": 136, "usage_type": "name"}, {"api_name": "photos.models.Album.DoesNotExist", "line_number": 137, "usage_type": "attribute"}, {"api_name": "photos.models.Album", "line_number": 137, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 138, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 138, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 138, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 142, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 142, "usage_type": "name"}, {"api_name": "photos.photo_operations.add_photo", "line_number": 147, "usage_type": "call"}, {"api_name": "photos.photo_operations", "line_number": 147, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 153, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 153, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 117, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 118, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 118, "usage_type": "name"}, {"api_name": "photos_api.serializers.PhotoServerRegisterSerializer", "line_number": 159, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 162, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 162, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 162, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 164, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 164, "usage_type": "name"}, {"api_name": "photos.photo_operations.register_photo_server", "line_number": 165, "usage_type": "call"}, {"api_name": "photos.photo_operations", "line_number": 165, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 171, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 171, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 171, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 156, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 157, "usage_type": "call"}, {"api_name": "photos_api.permissions.IsAllowedPrivateAPI", "line_number": 157, "usage_type": "name"}]}
+{"seq_id": "294824767", "text": "import requests\nfrom bs4 import BeautifulSoup\nfrom lxml import etree\n\nheader = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36\"\n}\ncer = \"./cer.cer\"\n\nr = requests.get(\n \"https://www.yuncaijing.com/dock/1456/details.html\", headers=header, verify=cer\n)\nr.encoding = \"utf-8\"\ns = BeautifulSoup(r.content, \"html.parser\")\nprint(s)\n", "sub_path": "stock/test/re_yuncaijing.py", "file_name": "re_yuncaijing.py", "file_ext": "py", "file_size_in_byte": 416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]}
+{"seq_id": "617823439", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport datetime\nimport pprint\nimport re\nfrom time import mktime\n\nfrom django.core.management.base import BaseCommand\nimport feedparser\n\nfrom digest.management.commands import _get_http_data_of_url, \\\n apply_parsing_rules, get_tweets_by_url, save_item, apply_video_rules\nfrom digest.models import ITEM_STATUS_CHOICES, \\\n AutoImportResource, Item, ParsingRules, Section, Tag\n\n\ndef get_tweets():\n dsp = []\n for src in AutoImportResource.objects.filter(type_res='twitter',\n in_edit=False):\n\n resource = src.resource\n excl = [s for s in (src.excl or '').split(',') if s]\n\n tweets_data = get_tweets_by_url(src.link)\n\n for text, link, http_code in tweets_data:\n excl_link = bool([i for i in excl if i in link])\n if not excl_link and src.incl in text:\n tw_txt = text.replace(src.incl, '')\n dsp.append([tw_txt, link, resource, http_code])\n return dsp\n\n\ndef import_tweets(**kwargs):\n for i in get_tweets():\n # это помогает не парсить лишний раз ссылку, которая есть\n if Item.objects.filter(link=i[1]).exists():\n continue\n\n # title = u'[!] %s' % i[0] if fresh_google_check(i[1]) else i[0]\n title = i[0]\n item_data = {\n 'title': title,\n 'link': i[1],\n 'http_code': i[3],\n 'resource': i[2]\n }\n data = apply_parsing_rules(item_data, **kwargs) if kwargs.get(\n 'query_rules') else {}\n item_data.update(data)\n save_item(item_data)\n\n\ndef import_rss(**kwargs):\n for src in AutoImportResource.objects.filter(type_res='rss',\n in_edit=False):\n\n rssnews = feedparser.parse(src.link)\n today = datetime.date.today()\n week_before = today - datetime.timedelta(weeks=1)\n for n in rssnews.entries:\n ct = len(Item.objects.filter(link=n.link)[0:1])\n if ct:\n continue\n\n time_struct = getattr(n, 'published_parsed', None)\n if time_struct:\n _timestamp = mktime(time_struct)\n dt = datetime.datetime.fromtimestamp(_timestamp)\n if dt.date() < week_before:\n continue\n\n title = n.title\n # title = u'[!] %s' % n.title if fresh_google_check(\n # n.title) else n.title\n\n http_code, content, raw_content = _get_http_data_of_url(n.link)\n\n item_data = {\n 'title': title,\n 'link': n.link,\n 'raw_content': raw_content,\n 'http_code': http_code,\n 'content': content,\n 'description': re.sub('<.*?>', '', n.summary),\n 'resource': src.resource,\n 'language': src.language,\n }\n item_data.update(\n apply_parsing_rules(item_data, **kwargs)\n if kwargs.get('query_rules') else {})\n item_data = apply_video_rules(item_data.copy())\n save_item(item_data)\n\n\ndef parsing(func):\n \"\"\"\n\n :param func:\n :return:\n \"\"\"\n data = {\n 'query_rules': ParsingRules.objects.filter(is_activated=True).all(),\n 'query_sections': Section.objects.all(),\n 'query_tags': Tag.objects.all(),\n 'query_statuses': [x[0] for x in ITEM_STATUS_CHOICES]\n }\n func(**data)\n\n\nclass Command(BaseCommand):\n\n args = 'no arguments!'\n help = u'News import from external resources'\n\n def handle(self, *args, **options):\n \"\"\"\n Основной метод - точка входа\n \"\"\"\n parsing(import_tweets)\n parsing(import_rss)\n", "sub_path": "digest/management/commands/import_news.py", "file_name": "import_news.py", "file_ext": "py", "file_size_in_byte": 3858, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "digest.models.AutoImportResource.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "digest.models.AutoImportResource.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "digest.models.AutoImportResource", "line_number": 20, "usage_type": "name"}, {"api_name": "digest.management.commands.get_tweets_by_url", "line_number": 26, "usage_type": "call"}, {"api_name": "digest.models.Item.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "digest.models.Item.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "digest.models.Item", "line_number": 39, "usage_type": "name"}, {"api_name": "digest.management.commands.apply_parsing_rules", "line_number": 50, "usage_type": "call"}, {"api_name": "digest.management.commands.save_item", "line_number": 53, "usage_type": "call"}, {"api_name": "digest.models.AutoImportResource.objects.filter", "line_number": 57, "usage_type": "call"}, {"api_name": "digest.models.AutoImportResource.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "digest.models.AutoImportResource", "line_number": 57, "usage_type": "name"}, {"api_name": "feedparser.parse", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 62, "usage_type": "call"}, {"api_name": "digest.models.Item.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "digest.models.Item.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "digest.models.Item", "line_number": 64, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "attribute"}, {"api_name": "digest.management.commands._get_http_data_of_url", "line_number": 79, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 87, "usage_type": "call"}, {"api_name": "digest.management.commands.apply_parsing_rules", "line_number": 92, "usage_type": "call"}, {"api_name": "digest.management.commands.apply_video_rules", "line_number": 94, "usage_type": "call"}, {"api_name": "digest.management.commands.save_item", "line_number": 95, "usage_type": "call"}, {"api_name": "digest.models.ParsingRules.objects.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "digest.models.ParsingRules.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "digest.models.ParsingRules", "line_number": 105, "usage_type": "name"}, {"api_name": "digest.models.Section.objects.all", "line_number": 106, "usage_type": "call"}, {"api_name": "digest.models.Section.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "digest.models.Section", "line_number": 106, "usage_type": "name"}, {"api_name": "digest.models.Tag.objects.all", "line_number": 107, "usage_type": "call"}, {"api_name": "digest.models.Tag.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "digest.models.Tag", "line_number": 107, "usage_type": "name"}, {"api_name": "digest.models.ITEM_STATUS_CHOICES", "line_number": 108, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 113, "usage_type": "name"}]}
+{"seq_id": "18912706", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\n-------------------------------------------------\r\n date： 2018/11/12\r\n File Name： shop_history_data\r\n Description:\r\n-------------------------------------------------\r\n\"\"\"\r\n\r\nimport scrapy\r\nimport json\r\nfrom zz_spiders import env\r\nfrom zz_spiders.service.get_shop_list import get_shops_list\r\nfrom zz_spiders.items import ZTCShopHistoryDataItem\r\nimport datetime\r\nfrom zz_spiders.utils.data import Data\r\nimport requests\r\n\r\n\r\nclass ShopHistoryData(scrapy.Spider):\r\n name = 'zhitongche.shop.shop_history_data'\r\n custom_settings = {\r\n 'ITEM_PIPELINES': {\r\n 'zz_spiders.pipelines.SaveShopDataToMysql': 400,\r\n 'zz_spiders.pipelines.AddDateTimePipeline': 300,\r\n },\r\n }\r\n shop_list = None\r\n\r\n def __init__(self, begin, end, *args, **kwargs):\r\n self.shop_list = get_shops_list(flag='all')\r\n # self.shop_list = shop_list[int(begin):int(end)]\r\n\r\n def start_requests(self):\r\n for i in self.shop_list:\r\n shopname = i['shop_name']\r\n yesterday = datetime.date.today() - datetime.timedelta(days=1)\r\n yesterday = yesterday.strftime('%Y-%m-%d')\r\n url_history_time = ''\r\n url_budget = ''\r\n if i['zhitongche_status'] == 0 and i['zuanzhan_status'] == 0: # 如果是没有授权的店铺，跳过\r\n continue\r\n elif i['zhitongche_status'] == 1:\r\n url_history_time = env.api_zhitongche_url + '/sdk.single.php?shopName={0}&date=' + yesterday\r\n url_budget = env.api_zhitongche_url + '/sdk.budget.php?shopName={}'\r\n elif i['zuanzhan_status'] == 1:\r\n url_history_time = env.api_zuanzhan_url + '/sdk.single.php?shopName={0}&date=' + yesterday\r\n url_budget = env.api_zuanzhan_url + '/sdk.budget.php?shopName={}'\r\n\r\n resp = requests.get(url_budget.format(shopname))\r\n budget = resp.json()['data']\r\n if budget:\r\n budget = budget['budget']\r\n\r\n request_history_time = scrapy.Request(url=url_history_time.format(shopname), callback=self.parse)\r\n request_history_time.meta['shopname'] = shopname\r\n request_history_time.meta['budget'] = budget\r\n yield request_history_time\r\n\r\n def parse(self, response):\r\n resp = json.loads(response.text)\r\n if resp['status'] == 'succ':\r\n shopname = response.meta['shopname']\r\n budget = response.meta['budget']\r\n key = []\r\n value = []\r\n data = resp['data']\r\n if budget:\r\n key.append('f_budget')\r\n value.append(budget)\r\n for i in data:\r\n value.append(data[i])\r\n i = 'f_' + i\r\n key.append(i)\r\n\r\n yield ZTCShopHistoryDataItem(\r\n shopname=shopname,\r\n table='t_shop_data',\r\n key=key,\r\n value=value\r\n )\r\n", "sub_path": "zz_spiders/spiders/zhitongche/shop/shop_history_data.py", "file_name": "shop_history_data.py", "file_ext": "py", "file_size_in_byte": 3028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "scrapy.Spider", "line_number": 20, "usage_type": "attribute"}, {"api_name": "zz_spiders.service.get_shop_list.get_shops_list", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 37, "usage_type": "call"}, {"api_name": "zz_spiders.env.api_zhitongche_url", "line_number": 44, "usage_type": "attribute"}, {"api_name": "zz_spiders.env", "line_number": 44, "usage_type": "name"}, {"api_name": "zz_spiders.env.api_zhitongche_url", "line_number": 45, "usage_type": "attribute"}, {"api_name": "zz_spiders.env", "line_number": 45, "usage_type": "name"}, {"api_name": "zz_spiders.env.api_zuanzhan_url", "line_number": 47, "usage_type": "attribute"}, {"api_name": "zz_spiders.env", "line_number": 47, "usage_type": "name"}, {"api_name": "zz_spiders.env.api_zuanzhan_url", "line_number": 48, "usage_type": "attribute"}, {"api_name": "zz_spiders.env", "line_number": 48, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 61, "usage_type": "call"}, {"api_name": "zz_spiders.items.ZTCShopHistoryDataItem", "line_number": 76, "usage_type": "call"}]}
+{"seq_id": "304977680", "text": "#!/usr/bin/env python\n\nfrom flask import Flask, escape, redirect, render_template, \\\n request, url_for, session\nimport json\nimport md5\nimport os\n\nROOT = os.path.dirname(os.path.abspath(__file__))\nUPLOAD_DIR = os.path.join(ROOT, 'uploads')\nUPLOAD_EXT = 'sof'\n\nACCESS_TOKEN = 'fa12'\nACCESS_TOKEN_KEY = 'access-token'\n\nBAD_FILE_NAME = 'Must be a .sof file.'\nALL_FIELDS_REQUIRED = 'All fields are required.'\n\napp = Flask(__name__)\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1] == UPLOAD_EXT\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n if ACCESS_TOKEN_KEY in session and \\\n session[ACCESS_TOKEN_KEY] == ACCESS_TOKEN:\n return redirect(url_for('upload'))\n\n if request.method == 'POST':\n if request.form['code'] == ACCESS_TOKEN:\n session[ACCESS_TOKEN_KEY] = ACCESS_TOKEN\n return redirect(url_for('upload'))\n else:\n request.error = 'Wrong Access Code!'\n return render_template('access.html')\n\n@app.route('/upload', methods=['GET', 'POST'])\ndef upload():\n if ACCESS_TOKEN_KEY not in session or \\\n session[ACCESS_TOKEN_KEY] != ACCESS_TOKEN:\n return redirect(url_for('index'))\n\n class _RequestError(Exception):\n pass\n\n try:\n if request.method == 'POST':\n try:\n name = request.form['name'].strip()\n email = request.form['email'].lower().strip()\n sof_file = request.files['sof']\n except KeyError:\n request.error = ALL_FIELDS_REQUIRED\n raise _RequestError\n\n if not sof_file:\n request.error = ALL_FIELDS_REQUIRED\n raise _RequestError\n if not allowed_file(sof_file.filename):\n request.error = BAD_FILE_NAME\n raise _RequestError\n\n hashed_name = md5.new(email).hexdigest()\n upload_data = os.path.join(UPLOAD_DIR, hashed_name + '.' + UPLOAD_EXT)\n upload_meta = os.path.join(UPLOAD_DIR, hashed_name + '.json')\n\n sof_file.save(upload_data)\n with open(upload_meta, 'w') as f:\n f.write(json.dumps({'name': name, 'email': email}))\n\n return render_template('thanks.html')\n except _RequestError:\n pass\n\n return render_template('upload.html')\n\napp.secret_key = '/}67@ipYFB=[87aVcFR3D7#;u8C>y]Z4@r$7W3GPZW8:bzE2@Q'\n\nif __name__ == '__main__':\n if not os.path.isdir(UPLOAD_DIR):\n try:\n os.unlink(UPLOAD_DIR)\n except OSError:\n pass\n os.mkdir(UPLOAD_DIR)\n app.run(debug=True)\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.error", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.error", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.error", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.error", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "md5.new", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 83, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 86, "usage_type": "call"}]}
+{"seq_id": "7023428", "text": "# -*- coding: utf-8 -*-\n#encoding=utf-8\nimport sys\n#reload(sys)\n#sys.setdefaultencoding('utf-8')\nimport scrapy\nimport datetime\nfrom scrapy_test.items import ScrapyTestItem\nimport pymysql\nimport hashlib\nimport random\nfrom scrapy_test.db import db_conn\nfrom scrapy_test.db import web_conn\nimport time\n\nclass scrapy_test(scrapy.Spider):\n\n dbObject = db_conn()\n cursor = dbObject.cursor()\n cursor.execute(\"USE bcy\")\n sql = \"SELECT auth_url FROM today_new_come\"\n\n try:\n cursor.execute(sql)\n cursor.connection.commit()\n today_new_come = cursor.fetchall()\n except BaseException as e:\n print(\"MySQL ERROR>>>>>>>>>>>>>\",e,\"<<<<<<<<<<<<<error message\\n\")\n dbObject.rollback()\n\n name = 'bcy_daily'\n allowed_domains = ['bcy.net']\n\n start_urls=list()\n for i in today_new_come:\n start_urls.append(\"\".join(i))\n #\"\".join将tuple单元格转换为str\n\n def parse(self,response):\n\n \n item = ScrapyTestItem()\n item['name']=response.css('h1.js-post-title::text').extract_first()\n urls = response.css('img.detail_std.detail_clickable::attr(src)').extract()\n item['uid'] = response.css('a.fz14.dib.maxw250.cut::attr(href)').extract_first().split('/')[-1]\n #获取uid /u/54497 => 54497\n item['nickname'] = response.css('a.fz14.dib.maxw250.cut::text').extract_first()\n item['album_id'] = response.url.split('/')[-1]\n #相册id https://bcy.net/coser/detail/10000/474890 => 474890\n\n item['file_path']='/' + item['uid'] +'/' + item['album_id'] +'/'\n #保存路径 /54497/474890/\n conn = db_conn()\n cursor = conn.cursor()\n cursor.execute(\"USE bcy_scrapy\")\n\n cp666_conn = web_conn()\n cursor_cp666 = cp666_conn.cursor()\n cursor_cp666.execute(\"USE cosplay\")\n\n #判断BCY用户是否存在,如不存在则在CP666上新建帐号,使用BCY的用户ID作为CP666的用户名\n try:\n sql = \"select cp666_uid from bcy_user where bcy_uid = %s\"\n cursor.execute(sql,(item['uid']))\n result = cursor.fetchone()\n cursor.connection.commit()\n except BaseException as e:\n print(\"mysql daily error>>>>>>>>>>>>>\",e,\"<<<<<<<<<<<<<error message\")\n conn.rollback()\n #如果用户在CP666已经注册过\n if result is not None:\n item['cp666_uid'] = int(result[0])\n #如果用户没有在CP666平台注册过,则建立用户并记录与bcy uid的对应关系\n if result is None:\n sql = \"insert into ct_customer (customer_name,password) VALUES (%s,%s)\"\n cursor_cp666.execute(sql,(item['nickname'],hashlib.md5(''.join(random.sample('zyxwvutsrqponmlkjihgfedcba',6))).hexdigest()))\n item['cp666_uid'] = cp666_conn.insert_id()\n cursor_cp666.connection.commit()\n sql = \"insert into bcy_user (cp666_uid,bcy_uid) VALUES (%s,%s)\"\n cursor.execute(sql,(item['cp666_uid'],item['uid']))\n cursor.connection.commit()\n \n #新建CP666相册,获取相册ID,然后建立与BCY相册的对应关系\n \n try:\n sql = \"INSERT INTO ct_gallery (creator,title,ctime,gallery_type_ids) VALUES (%s,%s,%s,2)\"\n cursor_cp666.execute(sql,(item['cp666_uid'],item['name'],int(time.time())))\n item['cp666_album_id'] = cp666_conn.insert_id()\n cursor_cp666.connection.commit()\n except BaseException as e:\n print(\"mysql daily error>>>>>>>>>>>>>\",e,\"<<<<<<<<<<<<<error message\")\n cp666_conn.rollback()\n \n #try:\n # sql = \"INSERT INTO bcy_album (bcy_album_id,cp666_album_id) VALUES (%s,%s)\"\n # cursor.execute(sql,(item['album_id'],item['cp666_album_id']))\n # cursor.connection.commit()\n #except BaseException as e:\n # print(\"mysql daily error>>>>>>>>>>>>>\",e,\"<<<<<<<<<<<<<error message\")\n # conn.rollback()\n\n \n \n #插入CP666的用户相册表\n \n\n item['image_urls']=list()\n for i in urls:\n item['image_urls'].append(i[:-5])\n #item['image_urls'].append(i)\n yield(item)\n", "sub_path": "scrapy_test/spiders/daily.py", "file_name": "daily.py", "file_ext": "py", "file_size_in_byte": 4218, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "scrapy.Spider", "line_number": 16, "usage_type": "attribute"}, {"api_name": "scrapy_test.db.db_conn", "line_number": 18, "usage_type": "call"}, {"api_name": "scrapy_test.items.ScrapyTestItem", "line_number": 42, "usage_type": "call"}, {"api_name": "scrapy_test.db.db_conn", "line_number": 53, "usage_type": "call"}, {"api_name": "scrapy_test.db.web_conn", "line_number": 57, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 76, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}]}
+{"seq_id": "645252085", "text": "#coding:utf-8\nimport requests\nfrom lxml import etree\nimport json\nfrom PIL import Image\nimport execjs\nimport time\nimport getpass\nimport sys\nfrom keras.models import load_model\nfrom keras.backend import image_data_format\nimport numpy as np\nimport tensorflow as tf\n\nimg_rows, img_cols = 50, 25\n\nif image_data_format() == 'channels_first':\n input_shape = (1, img_rows, img_cols)\nelse:\n input_shape = (img_rows, img_cols, 1)\n \nimport string\nCHRS = string.ascii_lowercase + string.digits\n\nmodel = load_model('ok.h5')\ngraph = tf.get_default_graph()\n\ndef handle_split_image(image):\n '''\n input: image is PIL.Image.open return value\n '''\n \n im = image.convert('1')\n y_min, y_max = 0, 50 # im.height - 1 # 26\n split_lines = [55,80,105,130,155]\n ims = [im.crop([u, y_min, v, y_max]) for u, v in zip(split_lines[:-1], split_lines[1:])]\n \n return ims\n \ndef _predict_image(images): \n global graph\n Y = []\n for i in range(4):\n im = images[i]\n test_input = np.concatenate(np.array(im))\n test_input = test_input.reshape(1, *input_shape)\n y_probs = None\n with graph.as_default():\n y_probs = model.predict(test_input)\n y = CHRS[y_probs[0].argmax(-1)]\n Y.append(y)\n \n return ''.join(Y) \n # plt.show()\n\ndef get_js():\n # f = open(\"D:/WorkSpace/MyWorkSpace/jsdemo/js/des_rsa.js\",'r',encoding='UTF-8')\n f = open('fuckzf.js', 'r')\n line = f.readline()\n htmlstr = ''\n while line:\n htmlstr = htmlstr + line\n line = f.readline()\n return htmlstr\n\ndef processpwd(pwd,modulus,exponent):\n jsstr = get_js()\n ctx = execjs.compile(jsstr)\n\n return ctx.call('passwd', pwd,modulus,exponent)\n\n\nclass fuckzf():\n session = requests.session()\n \n def __init__(self):\n print('------------------------------------------------------\\n')\n print(' Fuck ZhengFang:浙工大正方教务系统\\n')\n print(' by:JCH\\n')\n print('------------------------------------------------------\\n')\n self.url_flag1 = 'www.gdjw.zjut.edu.cn/'\n self.url_flag2 = '172.16.19.160/'\n self.web_state = getpass.getpass('请选择网络，内网1，外网2').strip()\n self.url_flag = self.url_flag1 if (self.web_state == '2') else self.url_flag2\n self.url1 = 'http://%sxtgl/login_slogin.html' % self.url_flag\n self.url2 = 'http://%sxtgl/login_getPublicKey.html' % self.url_flag\n self.url3 = 'http://%skaptcha' % self.url_flag\n self.yhm = input('请输入学号：')\n self.mm = getpass.getpass('请输入密码(密码不回显,输入完回车即可):') .strip()\n\n\n\n def login(self):\n \n response = self.session.get(self.url1)\n html = etree.HTML(response.content)\n csrftoken = html.xpath('//*[@id=\"csrftoken\"]/@value')[0]\n # print(csrftoken)\n res = self.session.get(self.url2)\n res = res.content.decode('utf-8')\n res = json.loads(res)\n exponent = res['exponent']\n modulus = res['modulus']\n with open('./yzm.jpg', 'wb')as f:\n img = self.session.get(self.url3)\n f.write(img.content)\n img = Image.open('yzm.jpg')\n yzm = _predict_image(handle_split_image(img))\n #img.show()\n #yzm = input('请输入验证码：')\n\n\n # print(exponent,modulus,yzm)\n self.mm = processpwd(self.mm, modulus, exponent)\n \n\n data = {\n 'csrftoken': csrftoken,\n 'mm': self.mm, # 对密码进行加密\n 'mm': self.mm, # post的data数据有两个相同mm字段\n 'yhm': self.yhm,\n 'yzm': yzm\n }\n\n self.session.post(self.url1, data=data)\n\n res = self.session.get(self.url1)\n html = etree.HTML(res.content)\n try:\n temp = html.xpath('/html/body/div[4]/div/div[1]/div/h3/span/text()')\n if temp:\n print('登陆成功！')\n return self.session\n except:\n print('登陆失败')\n sys.exit(0)\n\n\nclass get_grades(fuckzf):\n def __init__(self,year,term):\n self.year = year\n self.term = term\n self.url1 = 'http://www.gdjw.zjut.edu.cn/cjcx/cjcx_cxDgXscj.html?gnmkdm=N305005&layout=default&su=%session' % zf.yhm \n self.url2 = 'http://www.gdjw.zjut.edu.cn/cjcx/cjcx_cxDgXscj.html?doType=query&gnmkdm=N305005'\n\n def welcome(self):\n try:\n stu_name = self.req_2['items'][0]['xm']\n sch_stu = self.req_2['items'][0]['xslb']\n institute = self.req_2['items'][0]['jgmc']\n classss = self.req_2['items'][0]['bj']\n print('')\n print('')\n print(stu_name+'同学,欢迎您!!!')\n print('')\n print('姓名:{}\\t学历:{}\\t\\t学院:{}\\t班级:{}'.format(stu_name,sch_stu,institute,classss))\n print('')\n time.sleep(1)\n except:\n print('无当前学期,请重试')\n\n def post_gradedata(self):\n try:\n data = {'_search':'false',\n 'nd':int(time.time()),\n 'queryModel.currentPage':'1',\n 'queryModel.showCount':'15',\n 'queryModel.sortName':'',\t\n 'queryModel.sortOrder':'asc',\n 'time':'0',\n 'xnm':self.year,\n 'xqm':self.term\n }\n req_1 = fuckzf.session.post(self.url1,data = data,headers = fuckzf.session.headers)\n req_2 = fuckzf.session.post(self.url2,data = data , headers = fuckzf.session.headers)\n self.req_2 = req_2.json()\n except:\n print('获取失败,请重试...')\n sys.exit()\n\n def print_geades(self):\n try:\n plt = '{0:{4}<15}\\t{1:{4}<6}\\t{2:{4}<6}\\t{3:{4}<4}' \n gk = 0\n zkm = 0\n print('')\n print('--------------------------------------------------------------------------------')\n print(plt.format('课程','成绩','绩点','教师',chr(12288)))\n print('--------------------------------------------------------------------------------')\n for i in self.req_2['items']:\n print(plt.format(i['kcmc'],i['bfzcj'],i['jd'],i['jsxm'],chr(12288)))\n if i['bfzcj'] < 60:\n gk +=1\n zkm += 1\n print('--------------------------------------------------------------------------------')\n print('')\n print('通过科目数:{}{}'.format(zkm-gk,'门'))\n print('挂科科目数:'+str(gk)+'门')\n print('')\n print('')\n except:\n print('无当前学期,请重试')\n \nzf = fuckzf()\nzf.login()\n\n\nwhile True:\n year = 1\n term = 1 \n while type(year)!=str or type(term)!=str:\n year = input('请输入查询年份(2016-2017即输入2016):').strip()\n term = input('请输入学期(1或2):').strip()\n if term == '1':\n term = '3'\n elif term == '2':\n term = '12'\n else:\n term = ''\n\n cumt_grades = get_grades(str(year),str(term))\n cumt_grades.post_gradedata()\n cumt_grades.welcome()\n cumt_grades.print_geades()\n status = input('输入c继续查询,输入e退出程序:')\n if status == 'c':\n continue\n elif status == 'e':\n sys.exit()\n else:\n print('输入有误,退出...')\n sys.exit()", "sub_path": "正方模拟登陆.py", "file_name": "正方模拟登陆.py", "file_ext": "py", "file_size_in_byte": 7639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "keras.backend.image_data_format", "line_number": 17, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 23, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "execjs.compile", "line_number": 68, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 74, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 83, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 89, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 96, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 96, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 107, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 107, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 128, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 128, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 136, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 179, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 229, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 232, "usage_type": "call"}]}
+{"seq_id": "472937848", "text": "from django.shortcuts import render\r\nimport os\r\nfrom os.path import dirname\r\n# Create your views here.\r\nfrom rest_framework import mixins\r\nfrom rest_framework import viewsets\r\nfrom rest_framework import filters\r\nfrom .serializers import DocumentSerializer, CategorySerializer\r\nfrom rest_framework.pagination import PageNumberPagination\r\n\r\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\r\nfrom rest_framework.permissions import IsAuthenticated\r\n\r\nfrom django.contrib.auth.models import User\r\n\r\nfrom django_filters.rest_framework import DjangoFilterBackend\r\nfrom . models import Document, Category\r\nfrom django.template.response import TemplateResponse\r\nfrom django.http.response import HttpResponse\r\n\r\nfrom django.contrib.auth.decorators import login_required\r\n\r\n#email dependency\r\nfrom django.core.mail import send_mail, EmailMessage\r\n\r\nfrom django.db.models.signals import post_save\r\nfrom django.dispatch import receiver\r\nfrom johnstonemart.models import Document\r\n\r\nclass StandardResultsSetPagination(PageNumberPagination):\r\n page_size = 10\r\n page_size_query_param = 'page_size'\r\n page_query_param = 'page'\r\n max_page_size = 100\r\n\r\n\r\nclass DocumentViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):\r\n authentication_classes = [SessionAuthentication, BasicAuthentication]\r\n permission_classes = [IsAuthenticated,]\r\n\r\n serializer_class = DocumentSerializer\r\n pagination_class = StandardResultsSetPagination\r\n filter_backends = (DjangoFilterBackend, filters.SearchFilter)\r\n filter_fields = ('category_id',)\r\n search_fields = ('title', 'user__username')\r\n\r\n def get_queryset(self):\r\n if self.request.user.is_superuser:\r\n return Document.objects.all()\r\n else:\r\n return Document.objects.filter(category_id__level=2)\r\n\r\n\r\n\r\nclass CategoryViewSet(mixins.ListModelMixin, viewsets.GenericViewSet):\r\n serializer_class = CategorySerializer\r\n filter_backends = [filters.OrderingFilter]\r\n filter_backends = (DjangoFilterBackend, filters.SearchFilter)\r\n filter_fields = ('level',)\r\n # ordering_fields = ('level','position','name')\r\n def get_queryset(self):\r\n if self.request.user.is_superuser:\r\n return Category.objects.all().order_by('-position', 'name')\r\n else:\r\n return Category.objects.filter(level=2).order_by('-position', 'name')\r\n\r\n\r\n@login_required(login_url='/login/')\r\ndef index(request):\r\n html = TemplateResponse(request, 'index2.html')\r\n return HttpResponse(html.render())\r\n\r\n\r\n@receiver(post_save, sender=Document)\r\ndef create_document(sender, instance = None,created=False, **kwargs):\r\n if created:\r\n if instance.category_id.level == 1:\r\n emails = superuser_emails()\r\n send_email(instance, \"Admin Information\",emails)\r\n\r\n else:\r\n emails = user_emails()\r\n send_email(instance, \"Notification\", emails)\r\n\r\n\r\ndef user_emails():\r\n user_emails = User.objects.all().values_list('email')\r\n emails = []\r\n for email in user_emails:\r\n if email[0] != \"\":\r\n emails.append(email[0])\r\n return emails\r\n\r\ndef superuser_emails():\r\n user_emails = User.objects.filter(is_superuser=True).values_list('email')\r\n emails = []\r\n for email in user_emails:\r\n if email[0] != \"\":\r\n emails.append(email[0])\r\n return emails\r\n\r\ndef send_email(instance, type, emails = [\"tianyuema0101@gmail.com\"]):\r\n subject = type + \": \" + instance.title\r\n message = \"There is a new notification from John Stonemart Management Team\\n\"\r\n message += \"It is your responsibility, if you do not check your email notification\\n\\n\"\r\n message += (\"Sender: \" + str(instance.user) + \"\\n\")\r\n message += (\"Title: \" + str(instance.title + \"\\n\"))\r\n message += (\"Category: \" + str(instance.category_id) + \"\\n\")\r\n message += (\"Description: \" + str(instance.description) + \"\\n\")\r\n mail = EmailMessage(\r\n subject,\r\n message,\r\n \"info@johnstonemart.com\",\r\n emails,\r\n )\r\n # cur_location = dirname(dirname(os.path.abspath(__file__)))\r\n # document_location = os.path.join(cur_location,\"document\")\r\n # if str(instance.document_file) != \"\":\r\n # mail.attach_file(os.path.join(document_location, str(instance.document_file)))\r\n #\r\n # if str(instance.document_file2) != \"\":\r\n # mail.attach_file(os.path.join(document_location, str(instance.document_file2)))\r\n #\r\n # if str(instance.document_file3) != \"\":\r\n # mail.attach_file(os.path.join(document_location, str(instance.document_file3)))\r\n #\r\n # if str(instance.document_file4) != \"\":\r\n # mail.attach_file(os.path.join(document_location, str(instance.document_file4)))\r\n #\r\n # if str(instance.document_file5) != \"\":\r\n # mail.attach_file(os.path.join(document_location, str(instance.document_file5)))\r\n\r\n\r\n mail.send()\r\n\r\n", "sub_path": "johnstonemart/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "rest_framework.pagination.PageNumberPagination", "line_number": 30, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.authentication.SessionAuthentication", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.authentication.BasicAuthentication", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 39, "usage_type": "name"}, {"api_name": "serializers.DocumentSerializer", "line_number": 41, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.filters.SearchFilter", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.filters", "line_number": 43, "usage_type": "name"}, {"api_name": "johnstonemart.models.Document.objects.all", "line_number": 49, "usage_type": "call"}, {"api_name": "johnstonemart.models.Document.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "johnstonemart.models.Document", "line_number": 49, "usage_type": "name"}, {"api_name": "johnstonemart.models.Document.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "johnstonemart.models.Document.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "johnstonemart.models.Document", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 55, "usage_type": "name"}, {"api_name": "serializers.CategorySerializer", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.filters.OrderingFilter", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.filters", "line_number": 57, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.filters.SearchFilter", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.filters", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Category.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 65, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 68, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 74, "usage_type": "argument"}, {"api_name": "johnstonemart.models.Document", "line_number": 74, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 95, "usage_type": "name"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 110, "usage_type": "call"}]}
+{"seq_id": "320189762", "text": "# -*- coding: utf-8 -*-\nimport numpy as np\nfrom scipy.spatial.distance import cdist\nimport time\n# 使用循环的方式求解两个序列点对的相似度（距离）\n# 即costMatrix右下角的最后一个值为Frechet距离\ndef extractPath(costMatrix,i,j):\n # 初始化路径\n path = []\n # 从右下角循环，寻找对齐点\n while i != 0 and j != 0:\n # 首先加入右下角点\n path.insert(0, (i, j))\n # 循环的元素，三个坐标\n idxArr = [(i-1,j),(i-1,j-1),(i,j-1)]\n # 寻找到最小的那个值\n minArg = np.argmin(np.array([\n costMatrix[i - 1][j],\n costMatrix[i - 1][j - 1],\n costMatrix[i][j - 1]]))\n # 对应的消耗矩阵的元素\n minIndex = idxArr[minArg]\n # 重新迭代\n i = minIndex[0]\n j = minIndex[1]\n # 寻找靠边的点\n while i != 0:\n path.insert(0, (i, 0))\n i = i-1\n # 寻找靠边的点\n while j != 0:\n path.insert(0, (0, j))\n j = j - 1\n # 加入0，0\n path.insert(0, (0, 0))\n return path\n\ndef FrechetDistance(ptSetA, ptSetB):\n # 获得点集ptSetA中点的个数n\n n = ptSetA.shape[0]\n # 获得点集ptSetB中点的个数m\n m = ptSetB.shape[0]\n # 计算任意两个点的距离矩阵\n # disMat[i][j]对应ptSetA的第i个点到ptSetB中第j点的距离\n disMat = cdist(ptSetA, ptSetB, metric='euclidean')\n # 初始化消耗矩阵\n costMatrix = np.full((n, m), -1.0)\n # 逐行给消耗矩阵赋值\n # 首先给第一行赋值\n # 然后依次给2,3,4,...,m行赋值\n for i in range(n):\n for j in range(m):\n if i == 0 and j == 0:\n # 给左上角赋值\n costMatrix[0][0] = disMat[0][0]\n if i == 0 and j > 0:\n # 给第一行赋值\n costMatrix[0][j] = max(costMatrix[0][j-1], disMat[0][j])\n if i > 0 and j == 0:\n # 给第一列赋值\n costMatrix[i][0] = max(costMatrix[i-1][0], disMat[i][0])\n if i > 0 and j > 0:\n # 给其他赋值\n costMatrix[i][j] = max(min(costMatrix[i-1][j],\n costMatrix[i-1][j-1],\n costMatrix[i][j-1]), disMat[i][j])\n path_raw = extractPath(costMatrix, n - 1, m - 1)\n path = [[], []]\n for point in path_raw:\n path[0].append(point[0])\n path[1].append(point[1])\n return costMatrix[n-1][m-1], path\n# data = np.loadtxt(\"./data/traj.csv\",delimiter=\",\")\n# # 加载三条轨迹\n# traj1, traj2, traj3 = data[:8], data[8:15], data[15:]\n# starttime = time.perf_counter()\n# print(\"轨迹1与轨迹2的Frechet距离为：%s\"%(FrechetDistance(traj2,traj1)[0]))\n# print(\"轨迹2与轨迹3的Frechet距离为：%s\"%(FrechetDistance(traj2,traj3)[0]))\n# print(\"轨迹1与轨迹3的Frechet距离为：%s\"%(FrechetDistance(traj1,traj3)[0]))\n# print(\"轨迹1与轨迹3的Frechet轨迹为：%s\"%(FrechetDistance(traj1,traj3)[1]))\n# endtime = time.perf_counter()\n# print(\"运行时间：%s秒\"%(endtime - starttime,))", "sub_path": "trajectory_similarity_matrix_learning_view/SSM/FréchetDistanceLoop.py", "file_name": "FréchetDistanceLoop.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.argmin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 47, "usage_type": "call"}]}
+{"seq_id": "219267659", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport logging\n\nfrom subprocess import Popen, PIPE\n\ndef run_cmd(cmd, shell=False, input=None):\n p = Popen(cmd, shell=shell, stdin=PIPE, stdout=PIPE, stderr=PIPE)\n return p.communicate(input=input)\n\ndef getKsoftirqdList():\n stdout, stderr = run_cmd([\"pgrep\", \"ksoftirqd\"])\n if stderr:\n logging.fatal(\"pgrep ksoftirqd failed: %s\" % stderr)\n exit(1)\n ksoftirqd = stdout.split(\"\\n\")\n return ksoftirqd[0:-1]\n \ndef getKworkerList():\n stdout, stderr = run_cmd([\"pgrep\", \"kworker\"])\n if stderr:\n logging.fatal(\"pgrep kworker failed: %s\" % stderr)\n exit(1)\n kworker = stdout.split(\"\\n\")\n return kworker[0:-1]\n\ndef buildFilter(tnamelist):\n filter = \"prev_comm ~ ksoftirqd* || prev_comm ~ kworker*\"\n\n tnlist = tnamelist.split(\",\")\n for name in tnlist:\n filter += \" || prev_comm ~ %s\" % name\n\n return filter\n\ndef reset(output):\n _, stderr = run_cmd([\"rm\", \"-rf\", \"%s\" % output])\n if stderr:\n logging.fatal(\"reset failed: %s\" % stderr)\n exit(1)\n\n _, stderr = run_cmd([\"mkdir\", \"%s\" % output])\n if stderr:\n logging.fatal(\"reset failed: %s\" % stderr)\n exit(1)\n\n\ndef output(fname, pids):\n with open(fname, \"w\") as f:\n for p in pids[:-1]:\n f.write(p + \",\")\n f.write(pids[-1] + \"\\n\")\n\ndef record_ksoftirqd(fname, ksoftirqd):\n output(fname, ksoftirqd)\n\ndef record_kworker(fname, kworker):\n output(fname, kworker)\n\ndef record_pids(fname, pid, ksoftirqd, kworker):\n l = []\n\n l.extend(ksoftirqd)\n l.extend(kworker)\n\n ps = \"ps -T -p %s\" % pid\n cmd = ps + \" | awk '{printf\\\"%s,\\\",$2}'\"\n stdout, stderr = run_cmd([cmd], shell = True)\n if stderr:\n logging.fatal(\"record_pids failed: %s\" % stderr)\n\n tidlist = stdout.split(\",\")\n l.extend(tidlist[1:-1])\n\n output(fname, l)\n\ndef record_cpufreq(fname):\n stdout, stderr = run_cmd([\"lscpu | grep \\\"GHz\\\" | awk '{print $NF}' | sed 's/GHz//'\"], shell = True)\n if stderr:\n logging.fatal(\"record_cpufreq failed: %s\" % stderr)\n exit(1)\n output(fname, [stdout.strip(\"\\n\")])\n\ndef record_events(filter, disklist, niclist, output, period):\n cmd = [\"./recorder\", \"-f\", \"%s\" % filter ]\n if args.disklist:\n cmd.append(\"-d\")\n cmd.append(args.disklist)\n if args.niclist:\n cmd.append(\"-n\")\n cmd.append(args.niclist)\n cmd.append(\"-o\")\n cmd.append(args.output)\n cmd.append(\"-P\")\n cmd.append(args.period)\n\n _, stderr = run_cmd(cmd)\n if stderr:\n logging.fatal(\"record failed: %s\" % stderr)\n exit(1)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description = \"WPerf events recorder script\")\n parser.add_argument(\"-p\", \"--pid\", action = \"store\", default = None,\n help = \"The target process main thread id\")\n parser.add_argument(\"-T\", \"--tnamelist\", action = \"store\", default = None,\n help = \"The target process's worker thread name list, seprated by ',', support *\")\n parser.add_argument(\"-P\", \"--period\", action = \"store\", default = \"90000\",\n help = \"The recorder run period.\")\n parser.add_argument(\"-d\", \"--disklist\", action = \"store\", default = None,\n help = \"Disk name list seprated by ','.\")\n parser.add_argument(\"-n\", \"--niclist\", action = \"store\", default = None,\n help = \"Nic name list seprated by ','.\")\n parser.add_argument(\"-o\", \"--output\", action = \"store\", default = \"/tmp/wperf/\",\n help = \"Output dir default: /tmp/wperf/.\")\n\n args = parser.parse_args()\n\n if not args.pid or not args.tnamelist:\n logging.fatal(\"no target tidlist or tnamelist\")\n exit(1)\n\n ksoftirqd = getKsoftirqdList()\n kworker = getKworkerList()\n filter = buildFilter(args.tnamelist)\n\n reset(args.output)\n\n record_ksoftirqd(args.output + \"ksoftirqd\", ksoftirqd)\n record_kworker(args.output + \"kworker\", kworker)\n record_pids(args.output + \"pidlist\", args.pid, ksoftirqd, kworker)\n record_cpufreq(args.output + \"cpufreq\")\n record_events(filter, args.disklist, args.niclist, args.output, args.period)\n", "sub_path": "scripts/recorder.py", "file_name": "recorder.py", "file_ext": "py", "file_size_in_byte": 4247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "subprocess.Popen", "line_number": 10, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 10, "usage_type": "name"}, {"api_name": "logging.fatal", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 101, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 122, "usage_type": "call"}]}
+{"seq_id": "653906373", "text": "from scripts.load_dataset import *\nfrom tqdm import tqdm\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport os\n\nclass create:\n def __init__(self, gpu_num=None):\n # Set GPU Number to use. \n # if you don't have any gpu, plz set 'gpu_num' as None.\n if gpu_num != None:\n if type(gpu_num) is not str :\n raise ValueError(\"'gpu_num' is should be String Number like '0' or '0,1'.\")\n try : int(gpu_num)\n except : raise ValueError(\"'gpu_num' is should be String Number like '0' or '0,1'.\")\n \n self.__gpu_exist = True\n os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"]=gpu_num\n \n # check state for skip_gram model built\n self.__builded = False\n \n if self.__gpu_exist : \n config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)\n config.gpu_options.allow_growth = True\n self._sess = tf.Session(config=config)\n else :\n self._sess = tf.Session()\n\n def load_dataset(self, dataset_path):\n self.wordset = load_dataset(dataset_path)\n self.uniq_word_list = sorted(list(set(self.wordset.reshape(-1))))\n \n def train_val_split(self, mode=\"static\", ratio=0.8):\n # Check whether wordset loaded or not\n if not hasattr(self, 'wordset'):\n raise AttributeError(\"Wordset is empty. plz load dataset first.\")\n #if not self.__builded :\n #raise ValueError(\"dataset should be built before split dataset. Built dataset first using 'build_skip_gram_set()'.\")\n \n if mode==\"random\":\n num_valset = int(len(self.wordset)-(len(self.wordset)*ratio))\n val_indices = sorted(np.random.choice(range(len(self.wordset)), num_valset))\n val_indices = list(reversed(val_indices))\n\n dataset = list(self.wordset)\n val_dataset = []\n\n for idx in val_indices:\n val_dataset.append(dataset.pop(idx))\n \n self.train_set = np.array(dataset)\n self.test_set = np.array(val_dataset)\n \n elif mode=='static':\n tr_idx=[0, 1, 3, 4, 6, 7, 8, 9, 12, 13, 15, 20,\n 21, 23, 24, 25, 26, 27, 28, 29, 30, 31,\n 32, 34, 36, 37, 38, 42, 43, 45, 46, 47,\n 49, 50, 51, 53, 54, 56, 59, 60, 61, 62]\n \n te_idx=[2, 5, 10, 11, 14, 16, 17, 18, 19, 22, 33,\n 35, 39, 40, 41, 44, 48, 52, 55, 57, 58]\n\n self.train_set = self.wordset[tr_idx,:,:]\n self.test_set = self.wordset[tr_idx,:,:]\n else :\n raise ValueError(\"mode must be 'static' or 'random'.\")\n \n def build_skip_gram_set(self, dataset , window=1):\n # Check whether wordset loaded or not\n if not hasattr(self, 'wordset'):\n raise AttributeError(\"Wordset is empty. plz load dataset first.\")\n \n skip_grams = []\n\n for b_idx in range(dataset.shape[0]):\n for s_idx in range(dataset.shape[1]):\n for w_idx in range(dataset.shape[2]-2):\n target = self.uniq_word_list.index(dataset[b_idx, s_idx, w_idx+1])\n context = [self.uniq_word_list.index(dataset[b_idx, s_idx, w_idx]),\n self.uniq_word_list.index(dataset[b_idx, s_idx, w_idx+2])]\n\n for word in context:\n skip_grams.append([target, word])\n\n self.__builded = True\n return np.array(skip_grams)\n \n def training(self, training_epoch, batch_size, lr_rate=1e-3, embedding_size = 2, threshold=1.,\n saver_path = \"./w2v_out/\"):\n num_sampled = int(len(self.train_set)*0.01)\n voc_size = len(self.uniq_word_list)\n \n summary_writer = tf.summary.FileWriter(saver_path, self._sess.graph)\n lowest_loss = None\n \n inputs = tf.placeholder(tf.int32, shape=[batch_size])\n labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n\n embeddings = tf.Variable(tf.random_uniform([voc_size, embedding_size], -1.0, 1.0))\n \n selected_embed = tf.nn.embedding_lookup(embeddings, inputs)\n \n nce_weights = tf.Variable(tf.random_uniform([voc_size, embedding_size], -1.0, 1.0))\n nce_biases = tf.Variable(tf.zeros([voc_size]))\n \n loss = tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_biases, \n labels, selected_embed, \n num_sampled, voc_size))\n\n train_op = tf.train.AdamOptimizer(lr_rate).minimize(loss) \n \n with tqdm(total = training_epoch) as pbar:\n with self._sess:\n init = tf.global_variables_initializer()\n self._sess.run(init)\n\n for step in range(1, training_epoch + 1):\n pbar.set_description(\"[ Step : \"+str(step)+\"]\")\n batch_inputs, batch_labels = self._random_batch(self.train_set, batch_size)\n\n _, loss_val = self._sess.run([train_op, loss],\n feed_dict={inputs: batch_inputs, labels: batch_labels})\n\n summ = tf.Summary()\n summ.value.add(tag='Word2Vec - Embedding Dimension ['+str(embedding_size)+'] Loss', simple_value=loss_val)\n summary_writer.add_summary(summ,step)\n \n if step > 0:\n if lowest_loss == None or lowest_loss > loss_val :\n lowest_loss = loss_val\n self.trained_embeddings = embeddings.eval()\n\n pbar.set_postfix_str(\"Loss : \"+'%.3f' % loss_val)\n pbar.update(1)\n if loss_val < threshold: break\n\n def _random_batch(self, data, size):\n random_inputs = []\n random_labels = []\n random_index = np.random.choice(range(len(data)), size, replace=False)\n\n for i in random_index:\n random_inputs.append(data[i][0]) # target\n random_labels.append([data[i][1]]) # context word\n\n return random_inputs, random_labels\n \n def plot(self, Annotate=True):\n if not hasattr(self, 'trained_embeddings'):\n raise AttributeError(\"trained embeddings doesn't exist. plz train dataset first.\")\n \n for i, label in enumerate(self.uniq_word_list):\n x, y = self.trained_embeddings[i]\n plt.scatter(x, y)\n if Annotate :\n plt.annotate(label, xy=(x, y), xytext=(5, 2),\n textcoords='offset points', ha='right', va='bottom')\n plt.show()\n \n def similarity(self,word1,word2):\n if not hasattr(self, 'trained_embeddings'):\n raise AttributeError(\"trained embeddings doesn't exist. plz train dataset first.\")\n \n def dot(A,B): \n return (sum(a*b for a,b in zip(A,B)))\n def cosine_similarity(a,b):\n return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) )\n w1_idx = self.uniq_word_list.index(word1)\n w2_idx = self.uniq_word_list.index(word2)\n w1 = self.trained_embeddings[w1_idx]\n w2 = self.trained_embeddings[w2_idx]\n return cosine_similarity(w1,w2)\n \n def find(self, word):\n return self.trained_embeddings[self.uniq_word_list.index(word)]\n \n def get_distance(self, word1, word2):\n return np.linalg.norm(w2v.find(word1)-w2v.find(word2))\n \n def save(self, path=None):\n if not hasattr(self, 'trained_embeddings'):\n raise AttributeError(\"trained embeddings doesn't exist. plz train dataset first.\")\n \n f = open(path,'w')\n f.write(','.join(map(str,self.trained_embeddings.reshape(-1))))\n f.close()\n \n def read(path, embedding_size, mode='attr'):\n self.trained_embeddings = np.array(list(map(float,open(path,'r').read().split(',')))).reshape(-1,2)", "sub_path": "models/Word2Vec.py", "file_name": "Word2Vec.py", "file_ext": "py", "file_size_in_byte": 8165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.nn.nce_loss", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.Summary", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}]}
+{"seq_id": "586179265", "text": "#!/usr/bin/env python\n\n# A simple HBase wrapper\n# -Andrew Melo\n\nfrom thrift.transport.TSocket import TSocket\nfrom thrift.transport.TTransport import TBufferedTransport\nfrom thrift.protocol import TBinaryProtocol\nfrom ThriftGlue.hbase import Hbase as HBaseThrift\nfrom ThriftGlue.hbase.ttypes import *\n\nimport time\n\nclass HBaseException( Exception ):\n def __init__( self, innerException ):\n self.innerException = innerException\n self.message = innerException.message\n\nclass HBase:\n def __init__( self, host, port ):\n transport = TBufferedTransport(TSocket(host, port))\n transport.open()\n protocol = TBinaryProtocol.TBinaryProtocol(transport)\n\n self.client = HBaseThrift.Client(protocol)\n self.client\n\n def listTables(self):\n return self.client.getTableNames()\n\n def createTable(self, tableName, schema):\n return self.client.createTable( tableName, schema )\n\n def getColumnDescriptors( self, tableName ):\n retval = []\n for oneColumn in self.client.getColumnDescriptors( tableName ):\n retval.append( ColumnDescriptor( oneColumn ) )\n return retval\n\n def deleteTable( self, tableName ):\n self.client.disableTable( tableName )\n return self.client.deleteTable( tableName )\n\n def putSingleCell( self, tableName, rowID, column, data = None ):\n inputCell = Mutation( column = column,\n value = data )\n return self.client.mutateRow( tableName, rowID, [inputCell] )\n\n def getSingleCell( self, tableName, rowID, column ):\n return self.client.getRowWithColumns( tableName, rowID, [column] )[0].columns[column].value\n\n\nimport unittest\nclass TestHBaseBasic( unittest.TestCase ):\n def setUp(self):\n self.hbase = HBase( 'localhost', 9090 )\n\n def tearDown(self):\n if hasattr( self, 'tempTable' ):\n self.hbase.deleteTable( self.tempTable )\n\n def test_listDBs(self):\n self.tempTable = \"listDB_%s\" % int(time.time())\n dummyFamily = ColumnDescriptor( name = 'foo:' )\n self.hbase.createTable( self.tempTable, [dummyFamily] )\n self.assertTrue( self.tempTable in self.hbase.listTables() )\n self.assertEqual( self.hbase.getColumnDescriptors(self.tempTable), [dummyFamily] )\n self.assertRaises( TException, self.hbase.createTable, self.tempTable, [dummyFamily] ) \n \n self.hbase.putSingleCell( self.tempTable, 'dummyRow', 'foo:col1', \"TESTDATA\" )\n self.assertEqual( self.hbase.getSingleCell( self.tempTable,\n 'dummyRow',\n 'foo:col1' ),\n \"TESTDATA\" )\n\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "HBaseR/HBase.py", "file_name": "HBase.py", "file_ext": "py", "file_size_in_byte": 2785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "thrift.transport.TTransport.TBufferedTransport", "line_number": 21, "usage_type": "call"}, {"api_name": "thrift.transport.TSocket.TSocket", "line_number": 21, "usage_type": "call"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocol", "line_number": 23, "usage_type": "call"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 23, "usage_type": "name"}, {"api_name": "ThriftGlue.hbase.Hbase.Client", "line_number": 25, "usage_type": "call"}, {"api_name": "ThriftGlue.hbase.Hbase", "line_number": 25, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 79, "usage_type": "call"}]}
+{"seq_id": "430923927", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Programmer',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('first_name', models.CharField(max_length=256, verbose_name='\\u0418\\u043c\\u044f')),\n ('last_name', models.CharField(max_length=256, verbose_name='\\u0424\\u0430\\u043c\\u0438\\u043b\\u0438\\u044f')),\n ('middle_name', models.CharField(max_length=256, verbose_name='\\u041e\\u0442\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e', blank=True)),\n ('birthday', models.DateField(null=True, verbose_name='\\u0414\\u0435\\u043d\\u044c \\u0412\\u0430\\u0440\\u0435\\u043d\\u044c\\u044f')),\n ('photo', models.ImageField(upload_to=b'', null=True, verbose_name='\\u0424\\u043e\\u0442\\u043e', blank=True)),\n ('notes', models.TextField(verbose_name='\\u0417\\u0430\\u043c\\u0435\\u0442\\u043a\\u0438', blank=True)),\n ],\n options={\n 'verbose_name': '\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0441\\u0442',\n 'verbose_name_plural': '\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0441\\u0442\\u044b',\n },\n ),\n migrations.CreateModel(\n name='Team',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=256, null=True, verbose_name='\\u041d\\u0430\\u0437\\u0432\\u0430\\u043d\\u0438\\u0435')),\n ('notes', models.TextField(verbose_name='\\u0417\\u0430\\u043c\\u0435\\u0442\\u043a\\u0438', blank=True)),\n ('leader', models.OneToOneField(null=True, on_delete=django.db.models.deletion.SET_NULL, blank=True, to='myproject2016app.Programmer', verbose_name='\\u0422\\u0438\\u043c\\u041b\\u0438\\u0434\\u0435\\u0440')),\n ],\n options={\n 'verbose_name': '\\u041a\\u043e\\u043c\\u0430\\u043d\\u0434\\u0430',\n 'verbose_name_plural': '\\u041a\\u043e\\u043c\\u0430\\u043d\\u0434\\u044b',\n },\n ),\n migrations.AddField(\n model_name='programmer',\n name='programmer_team',\n field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, verbose_name='\\u041a\\u043e\\u043c\\u0430\\u043d\\u0434\\u0430', to='myproject2016app.Team', null=True),\n ),\n ]\n", "sub_path": "myproject2016app/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 2648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 46, "usage_type": "name"}]}
+{"seq_id": "478361819", "text": "import rethinkdb as r\nfrom rethinkdb import ReqlDriverError,ReqlOpFailedError\nimport datetime\n\ndef is_rtdb_table_exist(database,table):\n\n r.connect(\"localhost\", 28015).repl()\n table_list = r.db(database).table_list().run()\n return table in table_list\n\ndef gath_latest_node_data(database,node_id):\n\n r.connect(\"localhost\", 28015).repl()\n try :\n return r.db(database).table(str(node_id)).max('date_added').run()\n except ReqlOpFailedError:\n list = {'node_id':\"Not Found\",\"date_added\":\"Not Found\",\"node_status\":\"Not Found\",\"node_value\":\"Not Found\"}\n return list\n\ndef record_counter(database,table):\n\n r.connect(\"localhost\", 28015).repl()\n try:\n return r.db(database).table(str(table)).count().run()\n except ReqlOpFailedError:\n return \"Not Found\"\n\ndef gath_n_latest_node_data(database,node_id,n):\n\n r.connect(\"localhost\",28015).repl()\n raw_data = r.db(str(database)).table(str(node_id)).order_by(index=r.desc(\"date_added\")).limit(n).run()\n data_list = []\n for data in raw_data:\n data_list.append((data))\n return reversed(data_list)\n\n\n# def gath_date_between(d,m,y,date_started,date_ended):\n\ndef gath_date_date_between(database,node_id,starting_date,ending_date):\n\n #connect to the database\n r.connect(\"localhost\", 28015).repl()\n\n data_list = []\n\n dt_start = datetime.datetime.strptime(str(starting_date), '%d/%m/%Y')\n dt_end = datetime.datetime.strptime(str(ending_date), '%d/%m/%Y')\n\n dt_start_timezone_aware = dt_start.replace(tzinfo=r.make_timezone('-00:00'))\n dt_end_timezone_aware = dt_end.replace(tzinfo=r.make_timezone('-00:00'))\n\n datas = r.db(database).table(str(node_id)).between(\n dt_start_timezone_aware,dt_end_timezone_aware,index=\"date_added\").order_by(index=r.asc(\"date_added\")).run()\n\n # append it to list to remove rethinkdb cursor\n data_list = []\n for data in datas:\n data_list.append(data)\n\n return data_list\n\n\n\n\n\n\n\n\n\n", "sub_path": "pantauinv1/rtdb.py", "file_name": "rtdb.py", "file_ext": "py", "file_size_in_byte": 1966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "rethinkdb.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 8, "usage_type": "call"}, {"api_name": "rethinkdb.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 15, "usage_type": "call"}, {"api_name": "rethinkdb.ReqlOpFailedError", "line_number": 16, "usage_type": "name"}, {"api_name": "rethinkdb.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 24, "usage_type": "call"}, {"api_name": "rethinkdb.ReqlOpFailedError", "line_number": 25, "usage_type": "name"}, {"api_name": "rethinkdb.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 31, "usage_type": "call"}, {"api_name": "rethinkdb.desc", "line_number": 31, "usage_type": "call"}, {"api_name": "rethinkdb.connect", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rethinkdb.make_timezone", "line_number": 50, "usage_type": "call"}, {"api_name": "rethinkdb.make_timezone", "line_number": 51, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 53, "usage_type": "call"}, {"api_name": "rethinkdb.asc", "line_number": 54, "usage_type": "call"}]}
+{"seq_id": "75843005", "text": "import logging\nimport urllib.parse\n\nfrom collections import defaultdict\nfrom distutils.version import LooseVersion\n\nfrom detectem.utils import (\n get_most_complete_version,\n get_url,\n)\nfrom detectem.settings import (\n VERSION_TYPE, INDICATOR_TYPE, HINT_TYPE,\n MAIN_ENTRY, RESOURCE_ENTRY, INLINE_SCRIPT_ENTRY,\n GENERIC_TYPE,\n)\nfrom detectem.matchers import (\n UrlMatcher, BodyMatcher, HeaderMatcher, XPathMatcher\n)\n\nlogger = logging.getLogger('detectem')\nMATCHERS = {\n 'url': UrlMatcher(),\n 'body': BodyMatcher(),\n 'header': HeaderMatcher(),\n 'xpath': XPathMatcher(),\n}\n\n\nclass Result():\n def __init__(\n self, name, version=None, homepage=None, from_url=None, type=VERSION_TYPE\n ):\n self.name = name\n self.type = type\n self.version = version\n self.homepage = homepage\n self.from_url = from_url\n\n def __hash__(self):\n return hash((self.name, self.version, self.type))\n\n def __eq__(self, o):\n def to_tuple(rt):\n return (rt.name, rt.version, rt.type)\n return to_tuple(self) == to_tuple(o)\n\n def __lt__(self, o):\n def to_tuple(rt):\n return (rt.name, LooseVersion(rt.version or '0'), rt.type)\n return to_tuple(self) < to_tuple(o)\n\n def __repr__(self):\n return str({'name': self.name, 'version': self.version, 'type': self.type})\n\n\nclass ResultCollection():\n\n def __init__(self):\n self._results = defaultdict(list)\n\n def add_result(self, rt):\n self._results[rt.name].append(rt)\n\n def _normalize_results(self):\n norm_results = defaultdict(list)\n\n for p_name, p_results in self._results.items():\n rdict = defaultdict(set)\n for rt in p_results:\n rdict[rt.type].add(rt)\n\n p_list = []\n if VERSION_TYPE in rdict:\n p_list = list(rdict[VERSION_TYPE])\n assert len(p_list) >= 1\n elif INDICATOR_TYPE in rdict:\n p_list = list(rdict[INDICATOR_TYPE])\n assert len(p_list) == 1\n elif HINT_TYPE in rdict:\n p_list = list(rdict[HINT_TYPE])\n assert len(p_list) == 1\n elif GENERIC_TYPE in rdict:\n p_list = list(rdict[GENERIC_TYPE])\n assert len(p_list) == 1\n\n norm_results[p_name] = p_list\n\n return norm_results\n\n def get_results(self, normalize=True):\n results = self._normalize_results() if normalize else self._results\n return [rt for p_results in results.values() for rt in p_results]\n\n\nclass Detector():\n def __init__(self, response, plugins, requested_url):\n self.requested_url = requested_url\n self.har = self._prepare_har(response)\n\n self._softwares_from_splash = response['softwares']\n self._plugins = plugins\n self._results = ResultCollection()\n\n def _prepare_har(self, response):\n har = response.get('har', [])\n if har:\n self._mark_main_entry(har)\n for script in response.get('scripts', []):\n har.append(self._script_to_har_entry(script))\n return har\n\n def _mark_main_entry(self, entries):\n for entry in entries:\n self._set_entry_type(entry, RESOURCE_ENTRY)\n\n def get_url(entry):\n return entry['request']['url']\n\n def get_location(entry):\n headers = entry['response'].get('headers', [])\n for header in headers:\n if header['name'] == 'Location':\n return header['value']\n return None\n\n main_entry = entries[0]\n main_location = get_location(main_entry)\n if not main_location:\n self._set_entry_type(main_entry, MAIN_ENTRY)\n return\n main_url = urllib.parse.urljoin(get_url(main_entry), main_location)\n\n for entry in entries[1:]:\n url = get_url(entry)\n if url == main_url:\n self._set_entry_type(entry, MAIN_ENTRY)\n break\n else:\n self._set_entry_type(main_entry, MAIN_ENTRY)\n\n def _script_to_har_entry(self, script):\n entry = {\n 'request': {\n 'url': self.requested_url,\n },\n 'response': {\n 'url': self.requested_url,\n 'content': {\n 'text': script\n }\n }\n }\n self._set_entry_type(entry, INLINE_SCRIPT_ENTRY)\n return entry\n\n @staticmethod\n def _set_entry_type(entry, entry_type):\n entry.setdefault('detectem', {})['type'] = entry_type\n\n @staticmethod\n def _get_entry_type(entry):\n return entry['detectem']['type']\n\n\n def get_hints(self, plugin):\n \"\"\" Get plugins hints from `plugin` on `entry`.\n\n Plugins hints return `Result` or `None`.\n\n \"\"\"\n hints = []\n\n for hint_name in getattr(plugin, 'hints', []):\n hint_plugin = self._plugins.get(hint_name)\n if hint_plugin:\n hint_result = Result(\n name=hint_plugin.name,\n homepage=hint_plugin.homepage,\n from_url=self.requested_url,\n type=HINT_TYPE\n )\n hints.append(hint_result)\n logger.debug(\n '%(pname)s & hint %(hname)s detected',\n {'pname': plugin.name, 'hname': hint_result.name}\n )\n else:\n logger.error(\n '%(pname)s hints an invalid plugin: %(hname)s',\n {'pname': plugin.name, 'hname': hint_name}\n )\n\n return hints\n\n def process_from_splash(self):\n for software in self._softwares_from_splash:\n plugin = self._plugins.get(software['name'])\n self._results.add_result(\n Result(\n name=plugin.name,\n version=software['version'],\n homepage=plugin.homepage,\n from_url=self.requested_url,\n )\n )\n for hint in self.get_hints(plugin):\n self._results.add_result(hint)\n\n def process_har(self):\n \"\"\" Detect plugins present in the page.\n\n First, start with version plugins, then software from Splash\n and finish with indicators.\n In each phase try to detect plugin hints in already detected plugins.\n\n \"\"\"\n hints = []\n\n version_plugins = self._plugins.with_version_matchers()\n indicator_plugins = self._plugins.with_indicator_matchers()\n generic_plugins = self._plugins.with_generic_matchers()\n\n for entry in self.har:\n for plugin in version_plugins:\n version = self.get_plugin_version(plugin, entry)\n if version:\n # Name could be different than plugin name in modular plugins\n name = self.get_plugin_name(plugin, entry)\n self._results.add_result(\n Result(\n name=name,\n version=version,\n homepage=plugin.homepage,\n from_url=get_url(entry)\n )\n )\n hints += self.get_hints(plugin)\n\n for plugin in indicator_plugins:\n is_present = self.check_indicator_presence(plugin, entry)\n if is_present:\n name = self.get_plugin_name(plugin, entry)\n self._results.add_result(\n Result(\n name=name,\n homepage=plugin.homepage,\n from_url=get_url(entry),\n type=INDICATOR_TYPE\n )\n )\n hints += self.get_hints(plugin)\n\n for plugin in generic_plugins:\n is_present = self.check_indicator_presence(plugin, entry)\n if is_present:\n plugin_data = plugin.get_information(entry)\n self._results.add_result(\n Result(\n name=plugin_data['name'],\n homepage=plugin_data['homepage'],\n from_url=get_url(entry),\n type=GENERIC_TYPE,\n )\n )\n\n for hint in hints:\n self._results.add_result(hint)\n\n def get_results(self, metadata=False):\n \"\"\" Return results of the analysis. \"\"\"\n results_data = []\n\n self.process_har()\n self.process_from_splash()\n\n for rt in sorted(self._results.get_results()):\n rdict = {'name': rt.name}\n if rt.version:\n rdict['version'] = rt.version\n\n if metadata:\n rdict['homepage'] = rt.homepage\n rdict['type'] = rt.type\n rdict['from_url'] = rt.from_url\n\n results_data.append(rdict)\n\n return results_data\n\n def _get_matchers_for_entry(self, source, plugin, entry):\n grouped_matchers = plugin.get_grouped_matchers(source)\n\n def remove_group(group):\n if group in grouped_matchers:\n del grouped_matchers[group]\n\n if self._get_entry_type(entry) == MAIN_ENTRY:\n remove_group('body')\n remove_group('url')\n else:\n remove_group('header')\n remove_group('xpath')\n\n return grouped_matchers\n\n def get_plugin_version(self, plugin, entry):\n \"\"\" Return version after applying proper ``plugin`` matchers to ``entry``.\n\n The matchers could return many versions, but at the end one is returned.\n\n \"\"\"\n versions = []\n grouped_matchers = self._get_matchers_for_entry(\n 'matchers', plugin, entry\n )\n\n for key, matchers in grouped_matchers.items():\n klass = MATCHERS[key]\n version = klass.get_version(entry, *matchers)\n if version:\n versions.append(version)\n\n return get_most_complete_version(versions)\n\n def get_plugin_name(self, plugin, entry):\n \"\"\" Return plugin name with module name if it's found.\n Otherwise return the normal plugin name.\n\n \"\"\"\n if not plugin.is_modular:\n return plugin.name\n\n grouped_matchers = self._get_matchers_for_entry(\n 'modular_matchers', plugin, entry\n )\n module_name = None\n\n for key, matchers in grouped_matchers.items():\n klass = MATCHERS[key]\n module_name = klass.get_module_name(entry, *matchers)\n if module_name:\n break\n\n if module_name:\n name = '{}-{}'.format(plugin.name, module_name)\n else:\n name = plugin.name\n\n return name\n\n def check_indicator_presence(self, plugin, entry):\n \"\"\" Return presence after applying proper ``plugin`` matchers to ``entry``.\n\n The matchers return boolean values and at least one is enough\n to assert the presence of the plugin.\n\n \"\"\"\n grouped_matchers = self._get_matchers_for_entry(\n 'indicators', plugin, entry\n )\n presences = []\n\n for key, matchers in grouped_matchers.items():\n klass = MATCHERS[key]\n presence = klass.check_presence(entry, *matchers)\n presences.append(presence)\n\n return any(presences)\n", "sub_path": "detectem/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 11656, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "detectem.matchers.UrlMatcher", "line_number": 22, "usage_type": "call"}, {"api_name": "detectem.matchers.BodyMatcher", "line_number": 23, "usage_type": "call"}, {"api_name": "detectem.matchers.HeaderMatcher", "line_number": 24, "usage_type": "call"}, {"api_name": "detectem.matchers.XPathMatcher", "line_number": 25, "usage_type": "call"}, {"api_name": "detectem.settings.VERSION_TYPE", "line_number": 31, "usage_type": "name"}, {"api_name": "distutils.version.LooseVersion", "line_number": 49, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 59, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 65, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 68, "usage_type": "call"}, {"api_name": "detectem.settings.VERSION_TYPE", "line_number": 73, "usage_type": "name"}, {"api_name": "detectem.settings.VERSION_TYPE", "line_number": 74, "usage_type": "name"}, {"api_name": "detectem.settings.INDICATOR_TYPE", "line_number": 76, "usage_type": "name"}, {"api_name": "detectem.settings.INDICATOR_TYPE", "line_number": 77, "usage_type": "name"}, {"api_name": "detectem.settings.HINT_TYPE", "line_number": 79, "usage_type": "name"}, {"api_name": "detectem.settings.HINT_TYPE", "line_number": 80, "usage_type": "name"}, {"api_name": "detectem.settings.GENERIC_TYPE", "line_number": 82, "usage_type": "name"}, {"api_name": "detectem.settings.GENERIC_TYPE", "line_number": 83, "usage_type": "name"}, {"api_name": "detectem.settings.RESOURCE_ENTRY", "line_number": 114, "usage_type": "argument"}, {"api_name": "detectem.settings.MAIN_ENTRY", "line_number": 129, "usage_type": "argument"}, {"api_name": "urllib.parse.parse.urljoin", "line_number": 131, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 131, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 131, "usage_type": "name"}, {"api_name": "detectem.utils.get_url", "line_number": 131, "usage_type": "call"}, {"api_name": "detectem.utils.get_url", "line_number": 134, "usage_type": "call"}, {"api_name": "detectem.settings.MAIN_ENTRY", "line_number": 136, "usage_type": "argument"}, {"api_name": "detectem.settings.MAIN_ENTRY", "line_number": 139, "usage_type": "argument"}, {"api_name": "detectem.settings.INLINE_SCRIPT_ENTRY", "line_number": 153, "usage_type": "argument"}, {"api_name": "detectem.settings.HINT_TYPE", "line_number": 180, "usage_type": "name"}, {"api_name": "detectem.utils.get_url", "line_number": 234, "usage_type": "call"}, {"api_name": "detectem.utils.get_url", "line_number": 247, "usage_type": "call"}, {"api_name": "detectem.settings.INDICATOR_TYPE", "line_number": 248, "usage_type": "name"}, {"api_name": "detectem.utils.get_url", "line_number": 261, "usage_type": "call"}, {"api_name": "detectem.settings.GENERIC_TYPE", "line_number": 262, "usage_type": "name"}, {"api_name": "detectem.settings.MAIN_ENTRY", "line_number": 297, "usage_type": "name"}, {"api_name": "detectem.utils.get_most_complete_version", "line_number": 323, "usage_type": "call"}]}
+{"seq_id": "98726924", "text": "import argparse\nimport os\nimport re\nimport subprocess as sp\nfrom datetime import datetime\n\n\ndef parse_args():\n p = argparse.ArgumentParser()\n p.add_argument('--timeline-log')\n return p.parse_args()\n\n\ndef parse_time_intervals(log_path):\n ret = {\n 'SCALE UP': {'start': None, 'end': None, 'elapsed': None},\n 'COMPUTE PLATEAU': {'start': None, 'end': None, 'elapsed': None},\n 'SCALE DOWN': {'start': None, 'end': None, 'elapsed': None},\n }\n pattern = r'(COMPUTE PLATEAU|SCALE UP|SCALE DOWN) TIME IS (STARTING|FINISHED) AT (.*)'\n with open(log_path) as infile:\n for l in infile:\n match = re.search(pattern, l.strip())\n if match:\n phase = match.group(1)\n start_or_end = 'start' if match.group(2) == 'STARTING' else 'end'\n timestamp = datetime.strptime(match.group(3), '%a %b %d %H:%M:%S UTC %Y')\n ret[phase][start_or_end] = timestamp.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n for phase, phase_dict in ret.items():\n elapsed = {}\n elapsed['start'] = datetime.strptime(phase_dict['start'], '%Y-%m-%dT%H:%M:%SZ')\n elapsed['end'] = datetime.strptime(phase_dict['end'], '%Y-%m-%dT%H:%M:%SZ')\n ret[phase]['elapsed'] = int(round((elapsed['end'] - elapsed['start']).total_seconds() / 60))\n\n # Ensure correct format of returned list\n assert len(ret) == 3\n for phase, phase_dict in ret.items():\n for start_or_end, ts in phase_dict.items():\n assert ts is not None\n\n return ret\n\n\ndef main():\n args = parse_args()\n cluster_name = re.search(r'(.*)-timeline-log.txt', os.path.basename(args.timeline_log)).group(1)\n time_interval_dicts = parse_time_intervals(args.timeline_log)\n ingested_dict = {}\n for phase in ['SCALE UP', 'COMPUTE PLATEAU', 'SCALE DOWN']:\n phase_dict = time_interval_dicts[phase]\n cmd_args = ['scripts/get-ingested-data.sh', cluster_name, phase_dict['start'], phase_dict['end']]\n ingested_bytes = int(sp.check_output(cmd_args).decode().strip())\n ingested_MB = ingested_bytes / 1048576\n ingested_dict[phase] = ingested_MB\n print(f\"{cluster_name}'s log group ingested {ingested_MB} MB during {phase} that elapsed {phase_dict['elapsed']} min\")\n\n print(f\"{cluster_name}'s log group ingested during SCALE UP/SCALE DOWN {round(ingested_dict['SCALE UP'] + ingested_dict['SCALE DOWN'],1 )} MB\")\n print(f\"{cluster_name}'s log group ingested during COMPUTE PLATEAU per hour {round(ingested_dict['COMPUTE PLATEAU'] * 3, 1)} MB\")\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/get-ingested-data-for-phases.py", "file_name": "get-ingested-data-for-phases.py", "file_ext": "py", "file_size_in_byte": 2602, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "re.search", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "re.search", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 53, "usage_type": "call"}]}
+{"seq_id": "455518987", "text": "from django.urls import path\n\n\nfrom . import views\n\napp_name = \"orgApplication\"\n\nurlpatterns = [\n path('', views.home, name='org'),\n path(\"reg/\", views.self_org, name='self_org'),\n path(\"org-profile/\", views.organizationProfile, name='org_profile'),\n path(\"org-project-create/\", views.org_project_create_view, name='org-project-create'),\n path('ajax/load-districts/', views.load_district, name='ajax_load_districts'),\n path('ajax/load-thanas/', views.load_thana, name='ajax_load_thana'),\n path('ajax/load-divisions/', views.load_thana, name='ajax_load_division'),\n]", "sub_path": "orgApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]}
+{"seq_id": "573484876", "text": "import json\nimport tweepy\nfrom tweepy import OAuthHandler\n\nCONSUMER_KEY = '25UmFC2N1YcvXw1CW10dWROQ2'\nCONSUMER_SECRET = 'AMsdjxNMPU7HjCFVzEP30VLivtYgDe2Jb5rMP4apcVaLgN35fO'\nOAUTH_TOKEN = '792959700-JvuvSw66IiCXzyqH7ffmcRu3zEsmgDdv56wW8QmS'\nOAUTH_TOKEN_SECRET = '1rZAkrkM8ecBskwUbIyOegBfbslFcPkATqpM0loupkKfJ'\n\nauth = OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\nauth.set_access_token(OAUTH_TOKEN, OAUTH_TOKEN_SECRET)\napi = tweepy.API(auth)\n\nDUB_WOE_ID = 560743\nUK_WOE_ID = 23424975\n\ndub_trends = api.trends_place(DUB_WOE_ID)\nuk_trends = api.trends_place(UK_WOE_ID)\n\n# trends = dub_trends[0]['trends']\n# trends_uk = uk_trends[0]['trends']\n\ntrend_names = set([t['name'] for t in dub_trends[0]['trends']])\ntrend_names_uk = set([i['name'] for i in uk_trends[0]['trends']])\n\nboth = set.intersection(trend_names, trend_names_uk)\n\n#print(trend_names)\n#print(trend_names_uk)\nprint(both)\n\n\n# trends = api.trends_place(DUB_WOE_ID)\n\n# print (json.dumps(trends, indent=1))", "sub_path": "trending.py", "file_name": "trending.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 12, "usage_type": "call"}]}
+{"seq_id": "491409122", "text": "## Matches patients and controls according age and gender\n# a\n\n#%%\n# import modules\n\nimport subject_match_class\nimport importlib\nimportlib.reload(subject_match_class)\nimport json\nfrom pprint import pprint\nimport numpy as np\nimport pandas as pd\nimport os\n\n\n#%%\n## read in hypnograms again as a dictionary, actually we just require the demographics\noutput_dir ='/media/dagi/DATA/somnonetz/parasomnia/ms/RESULTS'\nwith open(os.path.join(output_dir,'ms-sleepstages-all.json'), 'r') as f:\n patients = json.load(f)\n\n#%%\npatients_df = pd.DataFrame(patients).transpose()\npatients_df = patients_df[['subjectID','age','gender']]\npatients_df.head()\n#%%\n## read in siesta data\nsiesta_dir ='/home/dagi/NC-HTW/PROJECTS/SOMNONETZ/sn-tds/RESULTS_CSV'\ncontrols = pd.read_csv(os.path.join(siesta_dir,'siesta_all_consent.csv'), low_memory=False, skipinitialspace=True)\n# remove trailing blanks\ncontrols.columns = controls.columns.str.lstrip()\n# rename column sex > gender\ncontrols.rename({'sex': 'gender'}, axis=1, inplace=True)\n\n#%%\ncontrols_df = controls.query('status == \"healthy\" & night == 2')\ncontrols_df = controls_df[['subjectID','age','gender']]\ncontrols_df.shape\n\n#%%\n# convert dictionary to dataframe. Is a nested dict, therefore not so easy\n# pprint\ncontrols_df.head()\n\n\n#%%\ncontrols_df.head()\n#print(controls_df['sex'][1])\n\n\n#%%\nresults = subject_match_class.SubjectMatch(patients_df,controls_df)\n\n#%%\nsubject_matching = results.matching\n#%%\nsubject_matching.head()\n#%%\n# write in file\nsubject_matching.to_csv(os.path.join(output_dir,'subject_matches.csv'))\n\n#%%\n# def default(o):\n# if isinstance(o, np.int64): return int(o)\n# raise TypeError\n#\n# with open(os.path.join(output_dir,'ms-sleepparams-all.json'), 'w') as f:\n# json.dump(sleep_parameters, f, default=default)\n\n\n", "sub_path": "run_subject_match_class.py", "file_name": "run_subject_match_class.py", "file_ext": "py", "file_size_in_byte": 1780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "importlib.reload", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "subject_match_class.SubjectMatch", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}]}
+{"seq_id": "240094591", "text": "from starlette import status\nfrom starlette.testclient import TestClient\n\nimport strawberry\nfrom fastapi import FastAPI\nfrom strawberry.fastapi import GraphQLRouter\nfrom strawberry.types import Info\n\n\ndef test_set_custom_http_response_status():\n @strawberry.type\n class Query:\n @strawberry.field\n def abc(self, info: Info) -> str:\n assert info.context.get(\"response\") is not None\n info.context[\"response\"].status_code = status.HTTP_418_IM_A_TEAPOT\n return \"abc\"\n\n app = FastAPI()\n schema = strawberry.Schema(query=Query)\n graphql_app = GraphQLRouter(schema)\n app.include_router(graphql_app, prefix=\"/graphql\")\n\n test_client = TestClient(app)\n response = test_client.post(\"/graphql\", json={\"query\": \"{ abc }\"})\n\n assert response.status_code == 418\n assert response.json() == {\"data\": {\"abc\": \"abc\"}}\n\n\ndef test_set_without_setting_http_response_status():\n @strawberry.type\n class Query:\n @strawberry.field\n def abc(self) -> str:\n return \"abc\"\n\n app = FastAPI()\n schema = strawberry.Schema(query=Query)\n graphql_app = GraphQLRouter(schema)\n app.include_router(graphql_app, prefix=\"/graphql\")\n\n test_client = TestClient(app)\n response = test_client.post(\"/graphql\", json={\"query\": \"{ abc }\"})\n\n assert response.status_code == 200\n assert response.json() == {\"data\": {\"abc\": \"abc\"}}\n", "sub_path": "tests/fastapi/test_response_status.py", "file_name": "test_response_status.py", "file_ext": "py", "file_size_in_byte": 1411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "strawberry.types.Info", "line_number": 14, "usage_type": "name"}, {"api_name": "starlette.status.HTTP_418_IM_A_TEAPOT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "starlette.status", "line_number": 16, "usage_type": "name"}, {"api_name": "strawberry.field", "line_number": 13, "usage_type": "attribute"}, {"api_name": "strawberry.type", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fastapi.FastAPI", "line_number": 19, "usage_type": "call"}, {"api_name": "strawberry.Schema", "line_number": 20, "usage_type": "call"}, {"api_name": "strawberry.fastapi.GraphQLRouter", "line_number": 21, "usage_type": "call"}, {"api_name": "starlette.testclient.TestClient", "line_number": 24, "usage_type": "call"}, {"api_name": "strawberry.field", "line_number": 34, "usage_type": "attribute"}, {"api_name": "strawberry.type", "line_number": 32, "usage_type": "attribute"}, {"api_name": "fastapi.FastAPI", "line_number": 38, "usage_type": "call"}, {"api_name": "strawberry.Schema", "line_number": 39, "usage_type": "call"}, {"api_name": "strawberry.fastapi.GraphQLRouter", "line_number": 40, "usage_type": "call"}, {"api_name": "starlette.testclient.TestClient", "line_number": 43, "usage_type": "call"}]}
+{"seq_id": "24556881", "text": "from django.views.generic import View\nfrom utils.utils import is_none_or_empty, userId2user\nfrom utils.response import EmptyResponse, ErrorResponse, SuccessResponse, UserIdErrorResponse,SuccessWeekResponse\nfrom teaching_system.reptile import scheduleOption\nfrom utils.utils import current_week\nimport json\n\n\n# Create your views here.\n\n# /schedule/exist-schedule-for-semester\nclass ExistScheduleForSemesterView(View):\n \"\"\"当前用户是否导入过课表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n if is_none_or_empty(userId):\n return EmptyResponse()\n user = userId2user(userId)\n\n if user:\n has_schedule = scheduleOption.exist_schedule_for_semester(user)\n if has_schedule:\n return SuccessResponse(\"本学期已存在课表哦\")\n else:\n return ErrorResponse('本学期没有导入课表哦', 2)\n else:\n return UserIdErrorResponse()\n\n\n# /schedule/import-schedule\nclass ImportScheduleView(View):\n \"\"\"导入课程表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n if is_none_or_empty(userId):\n return EmptyResponse()\n user = userId2user(userId)\n\n if user:\n if scheduleOption.exist_schedule_for_semester(user):\n return ErrorResponse('本学期课表已经存在或者正在导入中')\n scheduleOption.import_schedule(user) # 导入课表\n if scheduleOption.exist_schedule_for_semester(user): # 检查课表是否导入成功\n return SuccessResponse('导入课表成功')\n else:\n return ErrorResponse('导入课表失败')\n else:\n return UserIdErrorResponse()\n\n\n# /schedule/schedule-for-week\nclass ScheduleForWeekView(View):\n \"\"\"获取指定周数的课表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n week = request.POST.get('week')\n\n if is_none_or_empty(userId):\n return EmptyResponse()\n if is_none_or_empty(week) or int(week) < 0:\n week = current_week()\n user = userId2user(userId)\n\n if user:\n schedules = scheduleOption.schedule_for_week(user, week)\n if schedules:\n return SuccessWeekResponse('课表获取成功',week,schedules)\n else:\n return ErrorResponse('这周没有课哦', 2)\n else:\n return UserIdErrorResponse()\n\n\n# /schedule/schedule-for-today\nclass ScheduleForTodayView(View):\n \"\"\"获取今天的课表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n\n if is_none_or_empty(userId):\n return EmptyResponse()\n\n user = userId2user(userId)\n if user:\n schedules = scheduleOption.schedule_for_day(user)\n if schedules:\n return SuccessWeekResponse('今日课表获取成功',current_week(),schedules)\n else:\n return ErrorResponse('今天没有课哦', 2)\n else:\n return UserIdErrorResponse()\n\n\n# /schedule/schedule-for-tomorrow\nclass ScheduleForTomorrowView(View):\n \"\"\"获取明天的课表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n\n if is_none_or_empty(userId):\n return EmptyResponse()\n\n user = userId2user(userId)\n if user:\n schedules = scheduleOption.schedule_for_tomorrow(user)\n if schedules:\n return SuccessWeekResponse('明日课表获取成功',current_week(),schedules)\n else:\n return ErrorResponse('明天没有课哦', 2)\n else:\n return UserIdErrorResponse()\n\n\n# /schedule/update-schedule\nclass UpdateScheduleView(View):\n \"\"\"更新课程表\"\"\"\n\n def post(self, request):\n userId = request.POST.get('userId')\n if is_none_or_empty(userId):\n return EmptyResponse()\n\n user = userId2user(userId)\n if user:\n scheduleOption.update_schedule(user)\n return SuccessResponse('课表更新成功')\n else:\n return UserIdErrorResponse()\n", "sub_path": "apps/schedule/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "django.views.generic.View", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 19, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.exist_schedule_for_semester", "line_number": 22, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.response.SuccessResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.response.ErrorResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 28, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 39, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.exist_schedule_for_semester", "line_number": 42, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 42, "usage_type": "name"}, {"api_name": "utils.response.ErrorResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.import_schedule", "line_number": 44, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 44, "usage_type": "name"}, {"api_name": "teaching_system.reptile.scheduleOption.exist_schedule_for_semester", "line_number": 45, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.response.SuccessResponse", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.response.ErrorResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 50, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 63, "usage_type": "call"}, {"api_name": "utils.utils.current_week", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 65, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.schedule_for_week", "line_number": 68, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 68, "usage_type": "name"}, {"api_name": "utils.response.SuccessWeekResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.response.ErrorResponse", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 74, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 78, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 87, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.schedule_for_day", "line_number": 89, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 89, "usage_type": "name"}, {"api_name": "utils.response.SuccessWeekResponse", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.utils.current_week", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.response.ErrorResponse", "line_number": 93, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 95, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 99, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 105, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 106, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 108, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.schedule_for_tomorrow", "line_number": 110, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 110, "usage_type": "name"}, {"api_name": "utils.response.SuccessWeekResponse", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.utils.current_week", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.response.ErrorResponse", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 116, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 120, "usage_type": "name"}, {"api_name": "utils.utils.is_none_or_empty", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.response.EmptyResponse", "line_number": 126, "usage_type": "call"}, {"api_name": "utils.utils.userId2user", "line_number": 128, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption.update_schedule", "line_number": 130, "usage_type": "call"}, {"api_name": "teaching_system.reptile.scheduleOption", "line_number": 130, "usage_type": "name"}, {"api_name": "utils.response.SuccessResponse", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.response.UserIdErrorResponse", "line_number": 133, "usage_type": "call"}]}
+{"seq_id": "637683374", "text": "import ws_flex as gen\nimport random\nfrom tqdm import tqdm\nimport networkx as nx\nimport pandas as pd\n\nnodes = 64\n\nnode_lst = []\nedge_lst = []\nav_short = []\nrows = []\n\n\n\nfor i in tqdm(range(20000)):\n p = random.uniform(0.1, 1)\n k = random.randint(5, 63)\n seed = random.randint(1, 375727574089587287)\n g = gen.connected_ws_graph(nodes,k,p,seed=seed)\n g = g.to_directed()\n\n\n\n cluster, path = gen.compute_stats(g)\n if float(path) > 2.2:\n # for node in g.nodes:\n # for other_node in g.nodes:\n # if node != other_node:\n # print('nx.shortest_path_length(g,source=node,target=other_node): ', nx.shortest_path_length(g,source=node,target=other_node))\n\n # if nx.shortest_path_length(g,source=node,target=other_node) == 2:\n # g.add_edge(node,other_node)\n\n node_lst.append(list(g.nodes))\n edge_lst.append(g.edges)\n\n # av_short.append(nx.average_shortest_path_length(g))\n # path = nx.average_shortest_path_length(g)\n # cluster = nx.average_clustering(g)\n \n\n edg = [] \n edg.append([[edge[0],edge[1]] for edge in g.edges])\n rows.append([list(g.nodes),edg[0],path,cluster,len(g.edges)])\n\n # print(len(g.edges),nx.average_shortest_path_length(g))\n\n\nnew_edge = []\nfor e in edge_lst:\n new_edge.append([[edge[0],edge[1]] for edge in e])\n\n\ndf1 = pd.DataFrame(rows,\n columns=['nodes', 'edge_lst' ,'av_shortest_path','av_cluster','# of edges'])\ndf1.to_excel(r\"C:\\Users\\user\\Desktop\\299B\\code\\Neataptic\\relational\\output.xlsx\") \n", "sub_path": "Neataptic/relational/generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 1607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "tqdm.tqdm", "line_number": 16, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 17, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "ws_flex.connected_ws_graph", "line_number": 20, "usage_type": "call"}, {"api_name": "ws_flex.compute_stats", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}]}
+{"seq_id": "204996661", "text": "import requests\nimport json\nimport re\nfrom bs4 import BeautifulSoup\n\nconfig_file = './data/config/config.ini'\n# one week test data\ndataset_01 = './data/one_week/20170101.json'\nid2url_file = './data/user_infos/id2url.json'\nid2content_file = './data/user_infos/id2content.json'\n\ntest_local_html = \"url.html\"\n\ndef gen_id2url():\n table = []\n data = {}\n f1 = open(dataset_01, 'r', encoding='utf-8')\n for line in f1:\n table.append(json.loads(line))\n #print(json.loads(line))\n f1.close()\n\n f2 = open(id2url_file, 'a+', encoding='utf-8')\n for row in table:\n print(row['eventId'])\n #print(row['url'])\n data['eventId'] = row['eventId']\n data['url'] = row['url']\n json_str = json.dumps(data)\n f2.write(json_str + '\\n')\n print()\n f2.close\n\n\ndef getHTMLText(url):\n try:\n r = requests.get(url, timeout = 20)\n r.raise_for_status()\n #r.encoding = 'utf-8'\n return r.content\n except requests.exceptions.RequestException as e:\n print(e)\n return None\n\ndef removePunctuation(content):\n \"\"\"\n 文本去标点\n \"\"\"\n punctuation = r\"~!@#$%^&*()_+`{}|\\[\\]\\:\\\";\\-\\\\\\='<>?,./，。、《》？；：‘“{【】}|、！@#￥%……&*（）——+=-\"\n content = re.sub(r'[{}]+'.format(punctuation), '', content)\n\n if content.startswith(' ') or content.endswith(' '):\n re.sub(r\"^(\\s+)|(\\s+)$\", \"\", content)\n\n return content.strip().lower()\n\ndef getContent(html, eventId):\n content = ''\n data = {\n 'eventId' : eventId,\n 'title' : '',\n 'content' : ''}\n if not html:\n return data\n soup = BeautifulSoup(html, \"html.parser\")\n #soup = BeautifulSoup(open(test_local_html, encoding='utf-8'), features='html.parser')\n title = soup.select(\"h1.title > span.t100\")\n if not title:\n return data\n\n paras = soup.select(\"div.body > p\")\n if not paras:\n return data\n\n for para in paras:\n if len(para) > 0:\n content += para.get_text()\n #去除标点\n title_str = removePunctuation(title[0].get_text())\n content = removePunctuation(content)\n #将爬取到的文章用字典格式来存\n data['title'] = title_str\n data['content'] = content\n print(data)\n return data\n\ndef gen_id2content():\n table = []\n f1 = open(id2url_file, 'r', encoding='utf-8')\n for line in f1:\n table.append(json.loads(line))\n #print(json.loads(line))\n f1.close()\n\n f2 = open(id2content_file, 'a+', encoding='utf-8')\n for row in table:\n if row['url'] == 'http://adressa.no':\n print('Skip:' + row['url'])\n continue\n html = getHTMLText(row['url'])\n print('Connect:' + row['url'])\n data = getContent(html, row['eventId'])\n json_str = json.dumps(data, ensure_ascii=False)\n print(json_str)\n f2.write(json_str + '\\n')\n f2.close\n\ndef main():\n #url = \"http://news.qq.com/a/20170504/012032.htm\"\n #url = \"https://www.adressa.no/nyheter/2016/12/31/Se-lesernes-nytt%C3%A5rsbilder-14000400.ece\"\n #gen_id2url()\n gen_id2content()\nmain()\n", "sub_path": "src/get_article.py", "file_name": "get_article.py", "file_ext": "py", "file_size_in_byte": 3138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 41, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 50, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 65, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 103, "usage_type": "call"}]}
+{"seq_id": "309635998", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 29 13:35:59 2017\n\n@author: dbanco02\n\"\"\"\n\n## Init\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport RingModel as RM\nimport EllipticModels as EM\n\n#%% Data, Interpolation, Fitting Parameters\ndr = 30\nradius = 370\n \nnum_theta= 2048\nnum_rad = 2*dr\n\nnum_var_t = 15\nnum_var_r = 10\n\ndtheta = 2*np.pi/num_theta\ndrad = 1\n\nvar_theta = np.linspace((dtheta),(np.pi/32),num_var_t)**2\nvar_rad = np.linspace(drad,3,num_var_r)**2\n\nA0_stack = EM.unshifted_basis_matrix_stack(var_theta,\n var_rad,\n dtheta,\n drad,\n num_theta, \n num_rad)\n\nA0_sum = np.sum(np.sum(A0_stack,0),0)\n\nresult_path = os.path.join('E:','CHESS_results','full_2D')\n\n#%% Load results from files\n#var_signal = np.zeros((num_var_t,num_var_r,5,41,5))\n#rel_error = np.zeros((5,41,5))\n\nload_steps = 5\nfor i, step in enumerate(range(3,4)):\n for j, img_num in enumerate(range(156,157)):\n print('Load: ' + str(step) + ' Image: ' + str(img_num))\n file_name = 'ringModel_out_load_' + str(step)+'_img_' + str(img_num) + '.npy'\n file_path = os.path.join(result_path,file_name)\n ringModel = np.load(file_path)\n rel_error[step,img_num//5,img_num%5] = ringModel[()].rel_fit_error\n var_signal[:,:,step,img_num//5,img_num%5] = np.sum(np.sum(ringModel[()].coefs,0),0)*A0_sum\n\n \n#%% Plot results\n\ncutoff_t = 6\ncutoff_r = 4\ntotal_var = np.sum(np.sum(var_signal[:,:,0:load_steps,:,:],0),0)\nfor i in range(load_steps):\n plt.figure(1)\n high_var_theta = np.sum(np.sum(var_signal[cutoff_t::,:,0:load_steps,:,:],0),0)/total_var\n mu = np.mean(high_var_theta[0:load_steps,2:41,:].ravel())\n sig = np.std(high_var_theta[0:load_steps,2:41,:].ravel())\n plt.subplot(1,5, i+1) \n plt.imshow(high_var_theta[i], vmin=0,vmax=np.max(high_var_theta[0:load_steps,2:41,:].ravel())/10, interpolation='nearest')\n if(i ==2 ):\n plt.title('Theta Spread')\n plt.axis('off')\n #if(i==4):\n # plt.colorbar() \n \n plt.figure(2)\n high_var_rad = np.sum(np.sum(var_signal[:,cutoff_r::,0:load_steps,:,:],0),0)/total_var\n mu = np.mean(high_var_rad[0:load_steps,2:41,:].ravel())\n sig = np.std(high_var_rad[0:load_steps,2:41,:].ravel())\n plt.subplot(1,5, i+1) \n plt.imshow(high_var_rad[i], vmin=0,vmax=np.max(high_var_rad[0:load_steps,2:41,:].ravel()), interpolation='nearest')\n if(i ==2 ):\n plt.title('Radial Spread')\n plt.axis('off')\n #if(i==4):\n # plt.colorbar() \n \n plt.figure(3)\n mu = np.mean(rel_error[0:load_steps,2:41,:].ravel())\n sig = np.std(rel_error[0:load_steps,2:41,:].ravel())\n plt.subplot(1,5, i+1) \n plt.imshow(rel_error[i], vmin=0,vmax=1, interpolation='nearest')\n if(i ==2 ):\n plt.title('Fit Error')\n plt.axis('off')\n #if(i==4):\n # plt.colorbar()\n \n#%% Load polar and fit image \n\nimg_num//5,img_num%5\n\nload_step = 5\n#row = 4\n#col = 2\n#img_num = row*\nprint('Load: ' + str(step) + ' Image: ' + str(img_num))\nfile_name = 'ringModel_out_load_' + str(step)+'_img_' + str(img_num) + '.npy'\nfile_path = os.path.join(result_path,file_name)\nringModel = np.load(file_path)\n\nplt.figure(4)\nplt.subplot(3,1,1) \nplt.imshow(ringModel[()].polar_image, vmin=0,vmax=200, interpolation='nearest')\nplt.subplot(3,1,2)\nplt.title(str(ringModel[()].rel_fit_error))\nplt.imshow(ringModel[()].fit_image, vmin=0,vmax=200, interpolation='nearest')\n\nplt.subplot(3,1,3)\nplt.imshow(500*A0_stack[:,:,14,9], vmin=0,vmax=200, interpolation='nearest')\n", "sub_path": "old_python/spread_analysis_2D.py", "file_name": "spread_analysis_2D.py", "file_ext": "py", "file_size_in_byte": 3766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.pi", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 32, "usage_type": "call"}, {"api_name": "EllipticModels.unshifted_basis_matrix_stack", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}]}
+{"seq_id": "223491070", "text": "# pylint: skip-file\n# type: ignore\n# -*- coding: utf-8 -*-\n#\n# tests.models.programdb.test_ramstkmission.py is part of The RAMSTK\n# Project\n#\n# All rights reserved.\n\"\"\"Test class for testing the RAMSTKMission module algorithms and models.\"\"\"\n\n# Third Party Imports\n# noinspection PyPackageRequirements\nimport pytest\n# noinspection PyUnresolvedReferences\nfrom mocks import MockDAO\n\n# RAMSTK Package Imports\nfrom ramstk.models.programdb import RAMSTKMission\n\n\n@pytest.fixture\ndef mock_program_dao(monkeypatch):\n _mission_1 = RAMSTKMission()\n _mission_1.revision_id = 1\n _mission_1.mission_id = 1\n _mission_1.description = 'Test Mission #1'\n _mission_1.mission_time = 0.0\n _mission_1.time_units = 'hours'\n\n _mission_2 = RAMSTKMission()\n _mission_2.revision_id = 1\n _mission_2.mission_id = 1\n _mission_2.description = 'Test Mission #2'\n _mission_2.mission_time = 0.0\n _mission_2.time_units = 'hours'\n\n DAO = MockDAO()\n DAO.table = [\n _mission_1,\n _mission_2,\n ]\n\n yield DAO\n\n\nATTRIBUTES = {\n 'description': 'Test Mission',\n 'mission_time': 0.0,\n 'time_units': 'hours'\n}\n\n\n@pytest.mark.usefixtures('mock_program_dao')\nclass TestRAMSTKMission:\n \"\"\"Class for testing the RAMSTKMission model.\"\"\"\n @pytest.mark.unit\n def test_ramstkmission_create(self, mock_program_dao):\n \"\"\"__init__() should create an RAMSTKMission model.\"\"\"\n DUT = mock_program_dao.do_select_all(RAMSTKMission)[0]\n\n assert isinstance(DUT, RAMSTKMission)\n\n # Verify class attributes are properly initialized.\n assert DUT.__tablename__ == 'ramstk_mission'\n assert DUT.revision_id == 1\n assert DUT.mission_id == 1\n assert DUT.description == 'Test Mission #1'\n assert DUT.mission_time == 0.0\n assert DUT.time_units == 'hours'\n\n @pytest.mark.unit\n def test_get_attributes(self, mock_program_dao):\n \"\"\"get_attributes() should return a tuple of attribute values.\"\"\"\n DUT = mock_program_dao.do_select_all(RAMSTKMission)[0]\n\n _attributes = DUT.get_attributes()\n assert _attributes['description'] == 'Test Mission #1'\n assert _attributes['mission_time'] == 0.0\n assert _attributes['time_units'] == 'hours'\n\n @pytest.mark.unit\n def test_set_attributes(self, mock_program_dao):\n \"\"\"set_attributes() should return a zero error code on success.\"\"\"\n DUT = mock_program_dao.do_select_all(RAMSTKMission)[0]\n\n assert DUT.set_attributes(ATTRIBUTES) is None\n\n @pytest.mark.unit\n def test_set_attributes_none_value(self, mock_program_dao):\n \"\"\"set_attributes() should set an attribute to it's default value when\n the attribute is passed with a None value.\"\"\"\n DUT = mock_program_dao.do_select_all(RAMSTKMission)[0]\n\n ATTRIBUTES['mission_time'] = None\n\n assert DUT.set_attributes(ATTRIBUTES) is None\n assert DUT.get_attributes()['mission_time'] == 0.0\n\n @pytest.mark.unit\n def test_set_attributes_unknown_attributes(self, mock_program_dao):\n \"\"\"set_attributes() should raise an AttributeError when passed an\n unknown attribute.\"\"\"\n DUT = mock_program_dao.do_select_all(RAMSTKMission)[0]\n\n with pytest.raises(AttributeError):\n DUT.set_attributes({'shibboly-bibbly-boo': 0.9998})\n", "sub_path": "tests/models/programdb/ramstkmission_unit_test.py", "file_name": "ramstkmission_unit_test.py", "file_ext": "py", "file_size_in_byte": 3347, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 23, "usage_type": "call"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 30, "usage_type": "call"}, {"api_name": "mocks.MockDAO", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 59, "usage_type": "argument"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 61, "usage_type": "argument"}, {"api_name": "pytest.mark", "line_number": 56, "usage_type": "attribute"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 74, "usage_type": "argument"}, {"api_name": "pytest.mark", "line_number": 71, "usage_type": "attribute"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 84, "usage_type": "argument"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 92, "usage_type": "argument"}, {"api_name": "pytest.mark", "line_number": 88, "usage_type": "attribute"}, {"api_name": "ramstk.models.programdb.RAMSTKMission", "line_number": 103, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 105, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pytest.mark.usefixtures", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 53, "usage_type": "attribute"}]}
+{"seq_id": "21693126", "text": "import csv\nimport json\nfrom pathlib import Path\n\n# step, heart, sleep\nresult = [[0 for x in range(4)] for y in range(98)]\n\nfor i in range(1, 99, 1) :\n userName = 'A0' + str(i)\n result[i - 1][0] = userName\n stepPath = './sokulee/' + userName + '/' + userName + '_' + '20160401' + '_steps.json'\n heartPath = './sokulee/' + userName + '/' + userName + '_' + '20160401' + '_heart.json'\n sleepPath = './sokulee/' + userName + '/' + userName + '_' + '20160401' + '_sleep.json'\n\n if not Path(stepPath).is_file() :\n result[i - 1][1] = 0\n else :\n stepFile = open(stepPath, 'r')\n json_step = json.loads(stepFile.read())\n stepFile.close()\n print(stepPath)\n result[i - 1][1] = json_step['activities-steps'][0]['value'] if ('activities-steps' in json_step) else 0\n\n if not Path(heartPath).is_file() :\n result[i - 1][2] = 0\n else :\n heartFile = open(heartPath, 'r')\n json_heart = json.loads(heartFile.read())\n heartFile.close()\n print(heartPath)\n result[i - 1][2] = len(json_heart['activities-heart-intraday']['dataset']) if ('activities-heart-intraday' in json_heart) else 0\n\n if not Path(sleepPath).is_file() :\n result[i - 1][3] = 0\n else :\n sleepFile = open(sleepPath, 'r')\n json_sleep = json.loads(sleepFile.read())\n sleepFile.close()\n print(sleepPath)\n result[i - 1][3] = json_sleep['summary']['totalTimeInBed'] if ('summary' in json_sleep) else 0\n\n\nwrite_file = open('kmeans_fitbit.txt', 'w')\n\n#for i in range(0, 98, 1) :\n# for j in range(0, 4, 1) :\n# write_file.write(str(result[i][j]) + ',')\n# write_file.write(str(int(result[i][1]) + int(result[i][2]) + int(result[i][3])))\n# write_file.write('\\n')\n#write_file.close()\n\nfor i in range(0, 98, 1) :\n write_file.write(str(i) + ' ')\n write_file.write('1:' + str(result[i][1]) + ' ')\n write_file.write('2:' + str(result[i][2]) + ' ')\n write_file.write('3:' + str(result[i][3]) + ' ')\n write_file.write('\\n')\nwrite_file.close()\n", "sub_path": "Final-Term/2-A/parse_fitbit.py", "file_name": "parse_fitbit.py", "file_ext": "py", "file_size_in_byte": 2057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}]}
+{"seq_id": "470674993", "text": "# coding=utf-8\n\"\"\"Mordoc module\"\"\"\n#!/usr/bin/env python\n\nimport sys\n\nsys.dont_write_bytecode = True\nfrom utils import set_members, json_dumper\nfrom bson.objectid import ObjectId\nfrom bson.errors import InvalidId\n\ndata_exclude = ('_db', 'document_name', 'pk', 'objects', 'count', '_fields')\n\n\nclass ClassProperty(object):\n \"\"\"Classproperty decorator\"\"\"\n\n def __init__(self, getter):\n self.getter = getter\n\n def __get__(self, instance, owner):\n \"\"\"\n Classproperty getter\n @type instance: object\n @param instance:\n @param owner:\n @return:\n \"\"\"\n return self.getter(owner)\n\n\nclass ResultSet(object):\n \"\"\"\n Result set generator\n @param iterable: Iterable to create a generator out of\n \"\"\"\n\n def __init__(self, iterable):\n \"\"\"\n Result set generator init.\n Picks up an iterable and sets up a generator\n \"\"\"\n self.item = None\n self.iterable = iterable if isinstance(\n iterable, (list, tuple)) else []\n\n @property\n def count(self):\n \"\"\"\n Result set count.\n @return: int\n \"\"\"\n return len(self.iterable)\n\n def __iter__(self):\n for item in self.iterable:\n try:\n yield item\n self.item = item\n except StopIteration:\n self.item = None\n\n @classmethod\n def update(cls, *args, **kwargs):\n \"\"\"\n Update wrapper for ResultSet\n @param cls:\n @param args: Arguments for update operation.\n @param kwargs: Keyword arguments for update operation.\n \"\"\"\n set_members(cls)\n kwargs_data = kwargs.get('update_kwargs', None) or kwargs\n update_kwargs = kwargs_data if kwargs_data else args[0] if args and len(args) == 1 else {}\n for item in getattr(cls, 'iterable', []):\n print(item.username)\n item = item.update(update_kwargs=update_kwargs)\n yield item\n\n def delete(self, *args, **kwargs):\n \"\"\"Delete wrapper for ResultSet\n @param args: Arguments for delete.\n @param kwargs: Keyword arguments for delete operation\n \"\"\"\n kwargs_data = kwargs.get('delete_kwargs', {})\n kwargs_data = kwargs_data or kwargs\n delete_kwargs = kwargs_data if kwargs_data else args[0] if args and len(args) == 1 else {}\n for item in self.iterable:\n item = item.update(query_dict=delete_kwargs)\n yield item\n\n\nclass MorDoc(object):\n \"\"\"\n MorDoc document.\n Uses set_members utility method from utils to set members\n and database/collection name.\n Initializes a pk member to track object instance for post save operations.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n MorDoc init.\n Uses set_members utility method from utils to set members\n and database/collection name.\n Initializes a pk member to track object instance for post save\n operations\n @type args: tuple\n @param args:\n @param kwargs:\n \"\"\"\n set_members(self, exclude=data_exclude)\n self.pk = None\n\n # Update object data from constructor kwargs\n for key, val in kwargs.items():\n if hasattr(self, key):\n setattr(self, key, val)\n\n @ClassProperty\n def objects(self):\n \"\"\"\n Document objects generator\n @type self: MorDoc instance\n \"\"\"\n set_members(self)\n objects = getattr(self, '_db')[self.__name__].find({})\n for collection_obj in objects:\n yield collection_obj\n\n @ClassProperty\n def count(self):\n \"\"\"\n Document objects count property\n @type self: MorDoc instance\n \"\"\"\n set_members(self)\n return getattr(self, '_db')[self.__name__].find({}).count()\n\n def clean_data(self):\n \"\"\"Clean data helper pre save\"\"\"\n print({x: y for x, y in self.__dict__.items()\n if x not in data_exclude})\n\n @classmethod\n def find(cls, query=None):\n \"\"\"\n Filter method on MorDoc objects\n @param query: Query dictionary to filter MorDoc objects\n \"\"\"\n set_members(cls)\n query = query if isinstance(query, dict) else {}\n meta_query = {}\n for key, val in query.items():\n key = key.split('__')\n meta_query.update({key[0]: {\"$regex\": val}})\n\n query = meta_query\n objects = getattr(cls, '_db')[cls.__name__].find(query)\n objects = objects if objects.count() > 0 else []\n result_objs = []\n for find_object in objects:\n obj = cls()\n for key, val in find_object.items():\n if hasattr(obj, key):\n setattr(obj, key, val)\n setattr(obj, 'pk', str(find_object['_id']))\n result_objs.append(obj)\n #yield obj\n\n return ResultSet(result_objs)\n\n def __str__(self):\n \"\"\"String representation\"\"\"\n return self.__class__.__name__ + \" object\"\n\n @classmethod\n def update(cls, *args, **kwargs):\n \"\"\"Object instance update method\n @param cls: MorDoc object instance\n @param args:\n @param kwargs:\n \"\"\"\n set_members(cls)\n kwargs_data = kwargs.get('delete_kwargs', {})\n kwargs_data = kwargs_data or kwargs\n update_kwargs = kwargs_data if kwargs_data else args[0] if args and len(args) == 1 else {}\n\n if not update_kwargs:\n raise BaseException(\"No update kwargs provided.\")\n document_name = getattr(cls, 'document_name', None) or cls.__name__\n collection = (getattr(cls, '_db')[document_name])\n\n update_query = {'_id': ObjectId(cls.pk)} if hasattr(cls, 'pk') else {}\n\n collection.update(update_query, {'$set': update_kwargs}, multi=True)\n\n for upd_obj in collection.find(update_query):\n obj = cls.__class__() if hasattr(cls, 'document_name') else cls()\n for key, val in upd_obj.items():\n if hasattr(obj, key):\n setattr(obj, key, val)\n for key, val in update_kwargs.items():\n if hasattr(obj, key) and key not in upd_obj.keys():\n setattr(obj, key, val)\n setattr(obj, 'pk', str(upd_obj['_id']))\n return obj\n\n @classmethod\n def delete(cls, query_dict=None):\n \"\"\"\n Object instance delete method\n @rtype : None\n @param cls: MorDoc object instance\n @param query_dict: Query dictionary to fetch objects for delete\n \"\"\"\n set_members(cls)\n document_name = getattr(cls, 'document_name', None) or cls.__name__\n collection = (getattr(cls, '_db')[document_name])\n query_dict = {'_id': ObjectId(getattr(cls, 'pk'))} if getattr(\n cls, 'pk', None) else query_dict if \\\n isinstance(query_dict, dict) else {}\n collection.remove(**query_dict)\n\n def save(self):\n \"\"\"\n Save method on Mordoc.\n Saves a given data dict to a document if the object instance has no pk.\n Else, figures out the corresponding document based on pk and updates it.\n \"\"\"\n\n def _save(cls):\n \"\"\"\n Helper method for save.\n Sets pk instance member to created document's id\n @type cls: MorDoc object instance\n @param cls: Object instance\n \"\"\"\n collection.save(save_data)\n cls.pk = collection.find_one(**save_data).get('_id')\n\n save_data = {x: y for x, y in self.__dict__.items()\n if x not in data_exclude}\n collection = getattr(self, '_db')[getattr(self, 'document_name')]\n\n # If pk member is None, a new object instance. Create a new document\n if self.pk is None:\n # logging.log(9001,\n # \"No primary key found. Creating a new document\")\n _save(self)\n\n # If pk is found, attempt to update the existing document and\n # fallback to creating a new document. Should raise an Exception?\n else:\n try:\n # logging.log(9001,\n # \"Primary key found. Attempting to update document\")\n collection.update({'_id': ObjectId(self.pk)},\n {'$set': save_data},\n upsert=False)\n for key, val in save_data.items():\n setattr(self, key, val)\n except InvalidId: # Invalid ObjectId has been set to pk.\n # logging.log(9001,\n # \"Invalid document Id. Proceeding to save a new copy\")\n _save(self)\n return self\n\n def json(self, as_string=False, excludes=None):\n \"\"\"Document json dumper\n @param as_string: Boolean to dump document as string or as a dict\n @param excludes: Fields/values to exclude from document in JSON dump\n \"\"\"\n if self.pk is None:\n raise NotImplementedError\n exclude = excludes if isinstance(excludes, (list, tuple)) else ()\n exclude += (data_exclude, '_fields',)\n return json_dumper(self, excludes=exclude, as_string=as_string)\n\n\n__all__ = ('MorDoc',)", "sub_path": "document/document.py", "file_name": "document.py", "file_ext": "py", "file_size_in_byte": 9283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.dont_write_bytecode", "line_number": 7, "usage_type": "attribute"}, {"api_name": "utils.set_members", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 136, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 150, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 183, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 193, "usage_type": "call"}, {"api_name": "utils.set_members", "line_number": 216, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 219, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 257, "usage_type": "call"}, {"api_name": "bson.errors.InvalidId", "line_number": 262, "usage_type": "name"}, {"api_name": "utils.json_dumper", "line_number": 277, "usage_type": "call"}]}
+{"seq_id": "8847446", "text": "import voluptuous as vol\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.components.sensor import PLATFORM_SCHEMA\nfrom homeassistant.helpers.entity import Entity\nimport urllib.request\nimport re\nimport logging\nfrom homeassistant.const import (CONF_NAME, ATTR_TIME)\n\nDEFAULT_NAME = 'Web Scrapper'\nCONF_WEB_URL = 'web_url'\nCONF_STATE_START_STRING = 'state_start_string'\nCONF_STATE_END_STRING = 'state_end_string'\nCONF_STATUS_START_STRING = 'status_start_string'\nCONF_STATUS_END_STRING = 'status_end_string'\n\n_LOGGER = logging.getLogger(__name__)\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({\n vol.Required(CONF_WEB_URL): cv.string,\n vol.Required(CONF_STATE_START_STRING): cv.string,\n vol.Required(CONF_STATE_END_STRING): cv.string,\n vol.Required(CONF_STATUS_START_STRING): cv.string,\n vol.Required(CONF_STATUS_END_STRING): cv.string,\n vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string\n})\n\n\n\ndef setup_platform(hass, config, add_devices, discovery_info=None):\n \"\"\"Setup the sensor platform.\"\"\"\n web_url = config.get(CONF_WEB_URL)\n state_start_string = config.get(CONF_STATE_START_STRING)\n state_end_string = config.get(CONF_STATE_END_STRING)\n status_start_string = config.get(CONF_STATUS_START_STRING)\n status_end_string = config.get(CONF_STATUS_END_STRING)\n name = config.get(CONF_NAME)\n add_devices([WebScrapperSensor(web_url, state_start_string, state_end_string, status_start_string, status_end_string, name)])\n\n\nclass WebScrapperSensor(Entity):\n \"\"\"Representation of a Sensor.\"\"\"\n\n def __init__(self, web_url, state_start_string, state_end_string, status_start_string, status_end_string, name):\n \"\"\"Initialize the sensor.\"\"\"\n self._state = None\n self._web_url = web_url\n self._state_start_string = state_start_string\n self._state_end_string = state_end_string\n self._status_start_string = status_start_string\n self._status_end_string = status_end_string\n self._name = name\n self._status = None\n\n @property\n def name(self):\n \"\"\"Return the name of the sensor.\"\"\"\n return self._name\n\n @property\n def state(self):\n \"\"\"Return the state of the sensor.\"\"\"\n return self._state\n \n @property\n def state_attributes(self):\n \"\"\"Return the state of the sensor.\"\"\"\n return {\n \"status\": self._state,\n \"details\": self._status\n }\n\n @property\n def unit_of_measurement(self):\n \"\"\"Return the unit of measurement.\"\"\"\n return '' \n\n def update(self):\n \"\"\"Fetch new state data for the sensor.\n\n This is the only method that should fetch new data for Home Assistant.\n \"\"\"\n try:\n url = self._web_url\n _LOGGER.debug(\"URL: %s\", url)\n string_response = str(urllib.request.urlopen(url).read())\n \"\"\"\n calculate state\n \"\"\"\n starting_index = string_response.find(self._state_start_string)\n if starting_index > 0:\n end_index = string_response.find(self._state_end_string, starting_index + len(self._state_end_string))\n starting_index = starting_index + len(self._state_start_string)\n string_result = string_response[starting_index:end_index]\n p = re.compile(r'<.*?>')\n tmp = p.sub('', string_result)\n self._state = tmp\n else:\n self._state = \"no state found\"\n \n \"\"\"\n calculate status\n \"\"\"\n starting_index = string_response.find(self._status_start_string)\n if starting_index > 0:\n end_index = string_response.find(self._status_end_string, starting_index + len(self._status_end_string))\n string_result = string_response[starting_index:end_index]\n p = re.compile(r'<.*?>')\n tmp = p.sub('', string_result)\n self._status = tmp\n else:\n self._status = \"no status found\"\n except:\n _LOGGER.error(\"something happened in web scrapping\")\n self._state = \"something gone bad\"\n\n \n", "sub_path": "custom_components/webscrapper/sensor.py", "file_name": "sensor.py", "file_ext": "py", "file_size_in_byte": 4221, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.PLATFORM_SCHEMA", "line_number": 19, "usage_type": "name"}, {"api_name": "homeassistant.components.sensor.PLATFORM_SCHEMA.extend", "line_number": 19, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 20, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 21, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 22, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 23, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 24, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 25, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_NAME", "line_number": 25, "usage_type": "argument"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 20, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 20, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 21, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 21, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 22, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 22, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 23, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 23, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 24, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 24, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 25, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 25, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_NAME", "line_number": 37, "usage_type": "argument"}, {"api_name": "homeassistant.helpers.entity.Entity", "line_number": 41, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 86, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 86, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 86, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 95, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 108, "usage_type": "call"}]}
+{"seq_id": "91049980", "text": "\nfrom pyqtgraph.Qt import QtGui, QtCore\nimport numpy as np\nimport pyqtgraph as pg\n\nimport SoapySDR\nfrom SoapySDR import * #SOAPY_SDR_ constants\nimport numpy as np\nfrom optparse import OptionParser\nimport time\nimport os\nimport math\nimport sys\nimport matplotlib.pyplot as plt\n\ndef cfloat2uint32(arr, order='IQ'):\n\t\tarr_i = (np.real(arr) * 32767).astype(np.uint16)\n\t\tarr_q = (np.imag(arr) * 32767).astype(np.uint16)\n\t\tif order == 'IQ':\n\t\t\treturn np.bitwise_or(arr_q ,np.left_shift(arr_i.astype(np.uint32), 16))\n\t\telse:\n\t\t\treturn np.bitwise_or(arr_i ,np.left_shift(arr_q.astype(np.uint32), 16))\n\t\ndef uint32tocfloat(arr, order='IQ'):\n\tarr_hi = ((np.right_shift(arr, 16).astype(np.int16))/32768.0)\n\tarr_lo = (np.bitwise_and(arr, 0xFFFF).astype(np.int16))/32768.0\n\tif order == 'IQ':\n\t\treturn (arr_hi + 1j*arr_lo).astype(np.complex64)\n\telse:\n\t\treturn (arr_lo + 1j*arr_hi).astype(np.complex64)\n\nnum_pulses = 16\nserials = [\"RF3E000075\",\"RF3E000069\"]\ntxGain = 30 \nrxGain = 30\nrate = 30e6\nfreq = 2575e6\ntxAnt = \"TRX\"\nrxAnt = \"TRX\"\n\n\n\nnum_sdrs=len(serials)\nnum_samps=1024*num_sdrs*4\nnum_plots = (num_sdrs-1)*2\nprint(num_sdrs)\n\n# Read transmit buffer from stdin\npulseFile = open(\"skylark_transmit.txt\", 'r')\nfor line in pulseFile:\n\tinput_interleaved = np.fromstring(line, dtype=np.float32, count=-1, sep=' ')\n\tsigLength = int(len(input_interleaved) / 2)\n\tinterleavedValues = input_interleaved.reshape((sigLength, 2))\n\trealSamples = interleavedValues[:, 0]\n\timagSamples = interleavedValues[:, 1]\n\tinputbuffer = realSamples + 1j * imagSamples\n\nnum_samps = num_pulses*len(inputbuffer)\nprint(num_samps)\n\nfrom pulseDopplerTxRx import MIMO_SDR\n\nsdrs = [SoapySDR.Device(dict(driver=\"iris\", serial = serial)) for serial in serials]\nfor serial in serials:\n\tprint(serial)\n#\tSoapySDR.Device(dict(driver=\"iris\", serial = serial))\n\ntx_sdrs = sdrs[0:len(sdrs)//2]\nrx_sdrs = sdrs[len(sdrs)//2:]\ntrig_sdr = sdrs[0]\n\n\nprint(\"Using %i tx Irises and %i rx Irises.\" % (len(tx_sdrs), len(rx_sdrs)) )\n\n#override default settings\nfor sdr in sdrs:\n\tfor chan in [0, 1]:\n\t\tsdr.setSampleRate(SOAPY_SDR_RX, chan, rate)\n\t\t# sdr.setBandwidth(SOAPY_SDR_RX, chan, bw)\n\t\tsdr.setGain(SOAPY_SDR_RX, chan, rxGain)\n\t\tsdr.setFrequency(SOAPY_SDR_RX, chan, \"RF\", freq)\n\t\tsdr.setAntenna(SOAPY_SDR_RX, chan, rxAnt)\n\t\tsdr.setFrequency(SOAPY_SDR_RX, chan, \"BB\", 0) #don't use cordic\n\t\tsdr.setDCOffsetMode(SOAPY_SDR_RX, chan, True) #dc removal on rx\n\n\t\tsdr.setSampleRate(SOAPY_SDR_TX, chan, rate)\n\t\t# sdr.setBandwidth(SOAPY_SDR_TX, chan, bw)\n\t\tsdr.setGain(SOAPY_SDR_TX, chan, txGain)\n\t\tsdr.setFrequency(SOAPY_SDR_TX, chan, \"RF\", freq)\n\t\tsdr.setAntenna(SOAPY_SDR_TX, chan, txAnt)\n\t\tsdr.setFrequency(SOAPY_SDR_TX, chan, \"BB\", 0) #don't use cordic\n\n\t\t# NO DOCUMENTATION ON THESE SETTINGS\n\t\tsdr.writeSetting(SOAPY_SDR_RX, chan, 'CALIBRATE', 'SKLK')\n\t\tsdr.writeSetting(SOAPY_SDR_TX, chan, 'CALIBRATE', 'SKLK')\n\t\tsdr.writeSetting('SPI_TDD_MODE', 'MIMO')\n\n\n#create rx streams\nrxStreams = [sdr.setupStream(SOAPY_SDR_RX, SOAPY_SDR_CF32, [0, 1], {\"remote:prot\":\"tcp\", \"remote:mtu\":\"1024\"}) for sdr in rx_sdrs]\nnum_rx_r = len(rx_sdrs)*2\nsampsRecv = [np.empty(num_samps).astype(np.complex64) for r in range(num_rx_r)]\nprint(\"Receiving chunks of %i\" % len(sampsRecv[0]))\n\n#create tx stream\ntxStreams = []\nfor sdr in tx_sdrs:\n\ttxStream = sdr.setupStream(SOAPY_SDR_TX, SOAPY_SDR_CF32, [0], {\"REPLAY\": 'true'})\n\tsdr.activateStream(txStream)\n\ttxStreams.append(txStream)\n\n\n#create our own sinusoid\nif inputbuffer is None:\n\tTs = 1/rate\n\ts_length = 768*4\n\ts_freq = 1e6\n\ts_time_vals = np.array(np.arange(0,s_length)).transpose()*Ts\n\ts = np.exp(s_time_vals*1j*2*np.pi*s_freq).astype(np.complex64)*.25\n\tnum_tx_r = len(tx_sdrs)*2\n\tsampsToSend = [np.zeros(int(num_samps/4)).astype(np.complex64) for r in range(num_tx_r)]\nelse:\n\ts = inputbuffer\n\ts_length = len(s)\n\tnum_tx_r = len(tx_sdrs)*2\n\tprint(int(num_samps/4))\n\tsampsToSend = [np.zeros(int(num_samps/num_pulses)).astype(np.complex64) for r in range(num_tx_r)]\n\n#samples to send is a two channel array of complex floats\nfor r in range(num_tx_r):\n\tprint('R-',r)\n\tsampsToSend[r] = s\n\tprint(sampsToSend)\n\nprint(\"Done initializing\")\n\n\n#clear out socket buffer from old requests\nfor r,sdr in enumerate(rx_sdrs):\n\trxStream = rxStreams[r]\n\tsr = sdr.readStream(rxStream, [sampsRecv[r*2][:], sampsRecv[r*2+1][:]], len(sampsRecv[0]), timeoutUs = 0)\n\twhile sr.ret != SOAPY_SDR_TIMEOUT:\n\t\tsr = sdr.readStream(rxStream, [sampsRecv[r*2][:], sampsRecv[r*2+1][:]], len(sampsRecv[0]), timeoutUs = 0)\n\n\n\ntx_sdrs[0].writeRegisters('TX_RAM_A', 0, cfloat2uint32(inputbuffer).tolist())\n\ntime.sleep(0.1)\n\n#receive a waveform at the same time\nfor r,sdr in enumerate(rx_sdrs):\n\trxStream = rxStreams[r]\n\tflags = SOAPY_SDR_WAIT_TRIGGER | SOAPY_SDR_END_BURST\n\t#flags = SOAPY_SDR_HAS_TIME | SOAPY_SDR_END_BURST\n\tsdr.activateStream(rxStream, flags, 0, len(sampsRecv[0]))\n\ntrig_sdr.writeSetting(\"TRIGGER_GEN\", \"\")\ntx_sdrs[0].writeSetting(\"TX_REPLAY\", str(len(inputbuffer)))\n\n#time.sleep(.5)\n\nfor r,sdr in enumerate(rx_sdrs):\n\t\n\trxStream = rxStreams[r]\n\tsr = sdr.readStream(rxStream, [sampsRecv[r*2], sampsRecv[r*2+1]], len(sampsRecv[0]), timeoutUs=int(1e6))\n\tif sr.ret != len(sampsRecv[0]):\n\t\tprint(\"Bad read!!!\")\n\t\t\n\t#remove residual DC offset\n\tsampsRecv[r*2][:] -= np.mean(sampsRecv[r*2][:])\n\tsampsRecv[r*2+1][:] -= np.mean(sampsRecv[r*2+1][:])\n\n#look at any async messages\nprint('Issues:')\nfor r,sdr in enumerate(tx_sdrs):\n\ttxStream = txStreams[r]\n\tsr = sdr.readStreamStatus(txStream, timeoutUs=int(1e6))\n\tprint(sr)\n\n#cleanup streams\nprint(\"Cleanup streams\")\nfor r,sdr in enumerate(tx_sdrs):\n\tsdr.deactivateStream(txStreams[r])\n\tsdr.closeStream(txStreams[r])\n#for sdr,rxStream in (rx_sdrs,rxStreams):\nfor r,sdr in enumerate(rx_sdrs):\n\tsdr.deactivateStream(rxStreams[r])\n\tsdr.closeStream(rxStreams[r])\nprint(\"Done!\")\n\n\n\nt = np.arange(inputbuffer.size)\nwaveFormFig, waveformAxList = plt.subplots(3,1)\nwaveformAxList[0].plot(t,np.real(sampsToSend[0]))\nwaveformAxList[0].plot(t,np.imag(sampsToSend[0]))\nt = np.arange(sampsRecv[0].size)\nwaveformAxList[1].plot(t,np.real(sampsRecv[0]))\nwaveformAxList[1].plot(t,np.imag(sampsRecv[0]))\nwaveformAxList[2].plot(t,np.real(sampsRecv[1]))\nwaveformAxList[2].plot(t,np.imag(sampsRecv[1]))\n\nplt.show()", "sub_path": "sdr_tests/singlePktTxRx_2.py", "file_name": "singlePktTxRx_2.py", "file_ext": "py", "file_size_in_byte": 6177, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "numpy.real", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.imag", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.bitwise_or", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.left_shift", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.bitwise_or", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.left_shift", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.right_shift", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.bitwise_and", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.complex64", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.complex64", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.fromstring", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "SoapySDR.Device", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.complex64", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 127, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.real", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}]}
+{"seq_id": "357619727", "text": "import pickle\n\nimport numpy as np\nimport pandas as pd\nfrom rdt.transformers import OneHotEncodingTransformer\nfrom sklearn.exceptions import ConvergenceWarning\nfrom sklearn.mixture import BayesianGaussianMixture\n\n# NOTE: 2020-11-09. To fix FutureWarning. Use filterwarnings instead of ignore_warnings.\n# from sklearn.utils.testing import ignore_warnings\nimport warnings\nwarnings.filterwarnings('ignore', category=ConvergenceWarning)\nfrom warnings import simplefilter\n\nsimplefilter(action='ignore', category=FutureWarning)\n\n\nclass DataTransformer(object):\n \"\"\"Data Transformer.\n\n Model continuous columns with a BayesianGMM and normalized to a scalar\n [0, 1] and a vector.\n Discrete columns are encoded using a scikit-learn OneHotEncoder.\n\n Args:\n n_cluster (int):\n Number of modes.\n epsilon (float):\n Epsilon value.\n \"\"\"\n\n def __init__(self, n_clusters=15, epsilon=0.005): ## n_clusters increased to 15\n self.n_clusters = n_clusters\n self.epsilon = epsilon\n self.side = 0 # for tablegan\n self.datalen = 0 # for tablegan\n\n # @ignore_warnings(category=ConvergenceWarning)\n def _fit_continuous(self, column, data):\n # (JY)\n # 'gm' gives approximate parameters of a Gaussian mixture distribution\n # 'epsilon' is threshold to prevent mixtures with many negligible components\n # 'weight_concentration_prior': the higher concentration puts more mass in\n # the centre and will lead to more components being active,\n # a lower concentration parameter will lead to more mass at the edge\n # of the mixture weights simplex\n gm = BayesianGaussianMixture(\n self.n_clusters,\n weight_concentration_prior_type='dirichlet_process',\n weight_concentration_prior=0.001,\n n_init=1,\n random_state=2\n )\n # (JY)\n # 'gm.fit(data)' estimates model parameters using 'data' and predict labels\n # for 'data'; fits the model 'n_init' times and sets parameters with which\n # the model has largest likelihood or lower bound; after fitting, predicts\n # the most probable label for the input data points\n gm.fit(data)\n components = gm.weights_ > self.epsilon\n # (JY) 'num_components' is the optimal number of modes identified\n num_components = components.sum()\n print(\"num_components\", num_components)\n\n return {\n 'name': column,\n 'model': gm,\n 'components': components,\n 'output_info': [(1, 'tanh'), (num_components, 'softmax')],\n 'output_dimensions': 1 + num_components,\n }\n\n def _fit_discrete(self, column, data):\n ohe = OneHotEncodingTransformer()\n data = data[:, 0]\n ohe.fit(data)\n num_categories = len(ohe.dummies)\n\n return {\n 'name': column,\n 'encoder': ohe,\n 'output_info': [(num_categories, 'softmax')],\n 'output_dimensions': num_categories\n }\n\n def fit(self, data, discrete_columns=tuple(), trans=\"VGM\"):\n self.output_info = []\n self.output_dimensions = 0\n self.trans = trans\n\n if not isinstance(data, pd.DataFrame):\n self.dataframe = False\n data = pd.DataFrame(data)\n else:\n self.dataframe = True\n\n self.dtypes = data.infer_objects().dtypes\n self.meta = []\n if self.trans == \"VGM\": ##use VGM transformation for continuous variables\n for column in data.columns:\n column_data = data[[column]].values\n if column in discrete_columns:\n meta = self._fit_discrete(column, column_data)\n else:\n meta = self._fit_continuous(column, column_data)\n self.output_info += meta['output_info']\n self.output_dimensions += meta['output_dimensions']\n self.meta.append(meta)\n else: ##use Min-Max transformation for continuous variables\n for column in data.columns:\n column_data = data[[column]].values\n if column in discrete_columns:\n meta = self._fit_discrete(column, column_data)\n else:\n meta = {\n \"name\": column,\n \"type\": \"continuous\",\n \"min\": column_data.min(),\n \"max\": column_data.max(),\n # 'output_info': [(1, 'tanh'), (0, 'softmax')],\n 'output_info': [(1, 'tanh')],\n 'output_dimensions': 1\n }\n self.output_info += meta['output_info']\n self.output_dimensions += meta['output_dimensions']\n self.meta.append(meta)\n\n def _transform_continuous(self, column_meta, data):\n components = column_meta['components']\n model = column_meta['model'] # corresponds to gm.\n\n means = model.means_.reshape((1, self.n_clusters))\n stds = np.sqrt(model.covariances_).reshape((1, self.n_clusters))\n features = (data - means) / (4 * stds)\n\n probs = model.predict_proba(data)\n\n n_opts = components.sum()\n features = features[:, components]\n probs = probs[:, components]\n\n opt_sel = np.zeros(len(data), dtype='int')\n for i in range(len(data)):\n pp = probs[i] + 1e-6\n pp = pp / pp.sum()\n opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)\n\n idx = np.arange((len(features)))\n features = features[idx, opt_sel].reshape([-1, 1])\n features = np.clip(features, -.99, .99)\n\n probs_onehot = np.zeros_like(probs)\n probs_onehot[np.arange(len(probs)), opt_sel] = 1\n return [features, probs_onehot]\n\n def _transform_discrete(self, column_meta, data):\n encoder = column_meta['encoder']\n return encoder.transform(data[:, 0])\n\n def transform(self, data):\n if not isinstance(data, pd.DataFrame):\n data = pd.DataFrame(data)\n values = []\n if self.trans == \"VGM\":\n for meta in self.meta:\n column_data = data[[meta['name']]].values\n if 'model' in meta:\n values += self._transform_continuous(meta, column_data)\n else:\n values.append(self._transform_discrete(meta, column_data))\n else:\n for meta in self.meta:\n column_data = data[[meta['name']]].values\n if 'type' in meta:\n minn = meta['min'] - 1e-3\n maxx = meta['max'] + 1e-3\n values.append((column_data - minn) / (maxx - minn) * 2 - 1) ##range (-1,1)\n else:\n ## use one-hot encoder\n values.append(self._transform_discrete(meta, column_data))\n # minn = -1e-3\n # maxx = meta['output_dimensions'] - 1 + 1e-3\n # values.append(np.expand_dims((column_data - minn) / (maxx - minn) * 2 - 1,axis=1))\n return np.concatenate(values, axis=1).astype(float)\n\n # For tablegan, there is an additional transformation of training data to square matrices.\n def transform_tablegan(self, data):\n print('shape of input data:', data.shape)\n data = self.transform(data)\n print('shape of data after VGM transformation:', data.shape)\n self.datalen = data.shape[1]\n\n sides = [4, 8, 16, 24, 32, 48, 64] # added 48 and 64 to accommodate OVS dataset\n for i in sides:\n if i * i >= self.datalen:\n self.side = i\n break\n\n data = data.copy().astype('float32')\n if self.side * self.side > len(data[1]):\n padding = np.zeros((len(data), self.side * self.side - len(data[1])))\n data = np.concatenate([data, padding], axis=1)\n return data.reshape(-1, 1, self.side, self.side)\n\n def _inverse_transform_continuous(self, meta, data, sigma):\n model = meta['model']\n components = meta['components']\n\n u = data[:, 0]\n v = data[:, 1:]\n\n if sigma is not None:\n u = np.random.normal(u, sigma)\n\n u = np.clip(u, -1, 1)\n v_t = np.ones((len(data), self.n_clusters)) * -100\n v_t[:, components] = v\n v = v_t\n means = model.means_.reshape([-1])\n stds = np.sqrt(model.covariances_).reshape([-1])\n p_argmax = np.argmax(v, axis=1)\n std_t = stds[p_argmax]\n mean_t = means[p_argmax]\n column = u * 4 * std_t + mean_t\n\n return column\n\n def _inverse_transform_discrete(self, meta, data):\n encoder = meta['encoder']\n return encoder.reverse_transform(data)\n\n def inverse_transform(self, data, sigmas):\n start = 0\n output = []\n column_names = []\n\n if self.trans == \"VGM\": ##use VGM transformation\n for meta in self.meta:\n dimensions = meta['output_dimensions']\n columns_data = data[:, start:start + dimensions]\n if 'model' in meta: ##choose continuous variables\n # NOTE: 2020-11-09.\n # ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n # sigma = sigmas[start] if sigmas else None\n if sigmas is not None:\n sigma = sigmas[start]\n else:\n sigma = None\n inverted = self._inverse_transform_continuous(meta, columns_data, sigma)\n else:\n inverted = self._inverse_transform_discrete(meta, columns_data)\n output.append(inverted)\n column_names.append(meta['name'])\n start += dimensions\n else: ## use Min-Max transformation\n for meta in self.meta:\n dimensions = meta['output_dimensions']\n columns_data = data[:, start:start + dimensions]\n if 'type' in meta:\n minn = meta['min'] - 1e-3\n maxx = meta['max'] + 1e-3\n inverted = ((columns_data + 1)/2)*(maxx-minn) + minn\n else:\n inverted = self._inverse_transform_discrete(meta, columns_data)\n output.append(inverted)\n column_names.append(meta['name'])\n start += dimensions\n output = np.column_stack(output)\n output = pd.DataFrame(output, columns=column_names).astype(self.dtypes)\n if not self.dataframe:\n output = output.values\n\n return output\n\n def save(self, filepath):\n with open(filepath, \"wb\") as f:\n pickle.dump(self, f)\n\n def covert_column_name_value_to_id(self, column_name, value):\n discrete_counter = 0\n column_id = 0\n for info in self.meta:\n if info[\"name\"] == column_name:\n break\n if len(info[\"output_info\"]) == 1: # is discrete column\n discrete_counter += 1\n column_id += 1\n\n return {\n \"discrete_column_id\": discrete_counter,\n \"column_id\": column_id,\n \"value_id\": np.argmax(info[\"encoder\"].transform(np.array([value]))[0])\n }\n\n @classmethod\n def load(cls, filepath):\n with open(filepath, \"rb\") as f:\n return pickle.load(f)\n", "sub_path": "ctgan/transformer.py", "file_name": "transformer.py", "file_ext": "py", "file_size_in_byte": 11571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "warnings.filterwarnings", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.exceptions.ConvergenceWarning", "line_number": 12, "usage_type": "name"}, {"api_name": "warnings.simplefilter", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.mixture.BayesianGaussianMixture", "line_number": 47, "usage_type": "call"}, {"api_name": "rdt.transformers.OneHotEncodingTransformer", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 213, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 268, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 269, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 298, "usage_type": "call"}]}
+{"seq_id": "143085446", "text": "import logging\n\nimport dill\n\nimport blocks\nfrom blocks.extensions.saveload import SAVED_TO\nfrom examples.sqrt import main as sqrt_test\nfrom examples.mnist import main as mnist_test\nfrom examples.markov_chain.main import main as markov_chain_test\nfrom tests import temporary_files, silence_printing\n\n\ndef setup():\n # Silence Block's logger\n logger = logging.getLogger(blocks.__name__)\n logger.setLevel(logging.ERROR)\n\n\n@temporary_files('__sqrt')\n@silence_printing\ndef test_sqrt():\n filename = '__sqrt'\n sqrt_test(filename, 7)\n main_loop = sqrt_test(filename, 14, continue_=True)\n assert main_loop.log[7][SAVED_TO] == filename\n\n\n@temporary_files('mnist.pkl')\n@silence_printing\ndef test_mnist():\n filename = 'mnist.pkl'\n mnist_test(filename, 1)\n with open(filename, \"rb\") as source:\n main_loop = dill.load(source)\n main_loop.find_extension(\"FinishAfter\").invoke_after_n_epochs(2)\n main_loop.run()\n assert main_loop.log.status.epochs_done == 2\n\ntest_mnist.setup = setup\n\n\n@temporary_files('chain.pkl')\n@silence_printing\ndef test_markov_chain():\n filename = 'chain.pkl'\n markov_chain_test(\"train\", filename, None, 10)\n\ntest_mnist.setup = setup\n", "sub_path": "tests/test_examples.py", "file_name": "test_examples.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "blocks.__name__", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "examples.sqrt.main", "line_number": 23, "usage_type": "call"}, {"api_name": "examples.sqrt.main", "line_number": 24, "usage_type": "call"}, {"api_name": "blocks.extensions.saveload.SAVED_TO", "line_number": 25, "usage_type": "name"}, {"api_name": "tests.temporary_files", "line_number": 19, "usage_type": "call"}, {"api_name": "tests.silence_printing", "line_number": 20, "usage_type": "name"}, {"api_name": "examples.mnist.main", "line_number": 32, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 34, "usage_type": "call"}, {"api_name": "tests.temporary_files", "line_number": 28, "usage_type": "call"}, {"api_name": "tests.silence_printing", "line_number": 29, "usage_type": "name"}, {"api_name": "examples.markov_chain.main.main", "line_number": 46, "usage_type": "call"}, {"api_name": "tests.temporary_files", "line_number": 42, "usage_type": "call"}, {"api_name": "tests.silence_printing", "line_number": 43, "usage_type": "name"}]}
+{"seq_id": "603784608", "text": "from selenium.webdriver.remote.webdriver import WebDriver\nfrom page_objects.login_page import LoginPage\nfrom page_objects.user_workspace_page import UserWorkSpacePage\n\n\nclass MainPage(object):\n \"\"\"\n Test Adaptation Layer\n \"\"\"\n def __init__(self, web_driver: WebDriver):\n # Initialize web driver\n self.web_driver = web_driver\n self.web_driver.maximize_window()\n self.web_driver.get(\"https://github.com/\")\n\n # Instantiating web elements\n self.github_logo = self.web_driver.find_element_by_css_selector(\"svg\")\n self.sing_in_button = self.web_driver.find_element_by_css_selector(\"a[href='/login']\")\n self.sing_up_form = self.web_driver.find_element_by_css_selector(\"form[action='/join']\")\n self.sign_up_button = self.web_driver.find_elements_by_xpath(\"a[href='/join?source=header-home']\")\n\n def click_on_sing_in_button(self):\n self.sing_in_button.click()\n return LoginPage(self.web_driver)\n", "sub_path": "page_objects/main_page.py", "file_name": "main_page.py", "file_ext": "py", "file_size_in_byte": 978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 10, "usage_type": "name"}, {"api_name": "page_objects.login_page.LoginPage", "line_number": 24, "usage_type": "call"}]}
+{"seq_id": "476674876", "text": "import IPython\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef chirp(a, b, sf, len=1):\n t = np.arange(0, len, 1 / sf)\n return np.cos(np.pi * a * t**2 + 2 * np.pi * b * t), t\n\n\nif __name__ == '__main__':\n # Generate samples\n samples_40, time_40 = chirp(40, 4, 400)\n samples_400, time_400 = chirp(400, 4, 400)\n # Plot samples\n fig, ax = plt.subplots(1, 2, figsize=(20, 4))\n ax[0].plot(time_40, samples_40)\n ax[1].plot(time_400, samples_400)\n plt.show()\n", "sub_path": "Semester 2/Signal Processing/Homework/Hw9/ex4.py", "file_name": "ex4.py", "file_ext": "py", "file_size_in_byte": 490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.arange", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "428349421", "text": "# coding=utf-8\n# Signal_FT2.py\n#\n\nimport numpy as np\nimport scipy.stats as st\nfrom simlib.signal.base import Signal\nfrom simlib.signal.lib import *\n\n\nclass Signal_FT2(Signal):\n\n def prebatch(self):\n F1 = self.factors.BP\n F1_msk = np.ma.array(F1,mask=np.isnan(F1))\n F1_z = st.zscore(F1_msk)\n \n self.names = self.eod.ticker_names\n self.F1 = F1\n self.s = F1_z\n\n def generate(self, di):\n r = []\n last_wgt = self.weights[-1]\n\n F1 = self.F1[di-1,:].T\n s = self.s[di-1]\n\n score = st.scoreatpercentile(F1[~np.isnan(F1)], 90)\n\n for ix, ticker in enumerate(self.names):\n w = last_wgt[ix]\n if ticker >= score:\n w = s[ix]\n # w = np.exp(s[ix])\n else:\n w = 0.\n\n r.append(w)\n res = np.array(r)\n return res \n\nsignal = Signal_FT2", "sub_path": "*TDK/TDK_Library/FT2.py", "file_name": "FT2.py", "file_ext": "py", "file_size_in_byte": 912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "simlib.signal.base.Signal", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.ma.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 16, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "319093348", "text": "from collections import namedtuple as nt\n\nField = nt('Field', 'name spec default')\n\ndef line(**field_defs):\n '''Return a cid file line definition composed of a tuple of fields'''\n return tuple(Field(n, s, d) for n, (s, d) in field_defs.items())\n\ndef defaults(linedef):\n '''Generate the defaults from a line definition sequence'''\n try:\n yield from (field.default for field in linedef)\n except AttributeError as e:\n raise TypeError('The lindef argument did not contain '\n 'valid fields') from e\n\ndef names(linedef):\n '''Generate the names from a line definition sequence'''\n try:\n yield from (field.name for field in linedef)\n except AttributeError as e:\n raise TypeError('The lindef argument did not contain '\n 'valid fields') from e\n", "sub_path": "src/candemaker/cid/linedef/general.py", "file_name": "general.py", "file_ext": "py", "file_size_in_byte": 832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "collections.namedtuple", "line_number": 3, "usage_type": "call"}]}
+{"seq_id": "96729352", "text": "from quart import Quart, render_template, request, session, redirect, url_for,jsonify\nfrom quart_discord import DiscordOAuth2Session\nfrom discord.ext import ipc\nimport html\nimport json\n\n\napp = Quart(__name__)\nipc_client = ipc.Client(secret_key = \"JamEater\")\n\napp.config[\"SECRET_KEY\"] = \"test123\"\napp.config[\"DISCORD_CLIENT_ID\"] = 813008914207080449\napp.config[\"DISCORD_CLIENT_SECRET\"] = \"zn-ifplp8Z8XVatyFN5DGmRsiOMmdnsJ\"\napp.config[\"DISCORD_REDIRECT_URI\"] = \"https://127.0.0.1:5000/callback\"\n\ndiscord = DiscordOAuth2Session(app)\n\n@app.route(\"/\")\nasync def home():\n\treturn await render_template(\"index.html\")\n\n@app.route(\"/login\")\nasync def login():\n\treturn await discord.create_session()\n\n@app.route(\"/invite/<guildid>\")\nasync def invite(guildid):\n\treturn await discord.create_session(scope=[\"bot\"], permissions=8, guild_id=guildid, disable_guild_select=True)\n\n@app.route(\"/callback\")\nasync def callback():\n\ttry:\n\t\tawait discord.callback()\n\texcept:\n\t\treturn redirect(url_for(\"login\"))\n\n\tuser = await discord.fetch_user()\n\treturn redirect(url_for(\"servers\"))\n\n@app.route(\"/dashboard/<serverid>\")\nasync def dashboard(serverid):\n\tnickname = await ipc_client.request(\"getnickname\", guildid=int(serverid))\n\tprefix = await ipc_client.request(\"getprefix\", guildid=int(serverid))\n\treturn await render_template(\"dashboard.html\", something=nickname, prefix=prefix,limit=limit,channels=json.dumps(channels), welchannels=json.dumps(welchannels), leachannels=json.dumps(leachannels))\n\n@app.route(\"/servers\")\nasync def servers():\n\tguild_count = await ipc_client.request(\"get_guild_count\")\n\tguild_ids = await ipc_client.request(\"get_guild_ids\")\n\n\ttry:\n\t\tuser_guilds = await discord.fetch_guilds()\n\t\tuser = await discord.fetch_user()\n\texcept:\n\t\treturn redirect(url_for(\"login\"))\n\n\n\treturn await render_template(\"serverselect.html\", user = f\"{user.name}#{user.discriminator}\", guilds =[guild for guild in user_guilds if ((guild.permissions.value & 0x00000020) == 0x00000020) or ((guild.permissions.value & 0x00000008) == 0x00000008)])\n\n@app.route(\"/switchto/<serverid>\")\nasync def switchto(serverid):\n\tres = await ipc_client.request(\"checkforguild\",guildid=int(serverid))\n\tif res == None:\n\t\treturn redirect(f\"/invite/{serverid}\")\n\treturn redirect(f\"/dashboard/{serverid}\")\n\n@app.route(\"/change/<method>/<serverid>\", methods=[\"POST\"])\nasync def nick(method,serverid):\n\tif method == \"nicknameSave\":\n\t\tdata = await request.form\n\t\tprint(data[\"data\"])\n\t\tawait ipc_client.request(\"changenick\", guildid=int(serverid), name=data[\"data\"])\n\t\treturn redirect(f\"/dashboard/{serverid}\")\n\tif method == \"prefixSave\":\n\t\tdata = await request.form\n\t\tawait ipc_client.request(\"changeprefix\", guildid=int(serverid), newprefix=data[\"data\"])\n\t\treturn redirect(f\"/dashboard/{serverid}\")\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\")\n\n", "sub_path": "Dashboard/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "quart.Quart", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.ext.ipc.Client", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.ipc", "line_number": 9, "usage_type": "name"}, {"api_name": "discord.ext", "line_number": 16, "usage_type": "name"}, {"api_name": "quart_discord.DiscordOAuth2Session", "line_number": 16, "usage_type": "call"}, {"api_name": "quart.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.ext.create_session", "line_number": 24, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 24, "usage_type": "name"}, {"api_name": "discord.ext.create_session", "line_number": 28, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 28, "usage_type": "name"}, {"api_name": "discord.ext.callback", "line_number": 33, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 33, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 35, "usage_type": "call"}, {"api_name": "discord.ext.fetch_user", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 37, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 38, "usage_type": "call"}, {"api_name": "quart.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "discord.ext.fetch_guilds", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 52, "usage_type": "name"}, {"api_name": "discord.ext.fetch_user", "line_number": 53, "usage_type": "call"}, {"api_name": "discord.ext", "line_number": 53, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 55, "usage_type": "call"}, {"api_name": "quart.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "quart.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "quart.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "quart.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 70, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "quart.request.form", "line_number": 75, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 75, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 77, "usage_type": "call"}]}
+{"seq_id": "383983982", "text": "import os\n\nimport pandas as pd\nfrom hypothesis import given, settings\nfrom hypothesis.strategies import composite, integers, sampled_from\nfrom pytest import fixture\nfrom sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.utils.validation import check_is_fitted\n\nfrom tigerml.core.preprocessing import prep_data\nfrom tigerml.model_eval.base import *\n\n\n# ---------------------------------------------------------------------------------\n# ---------- composite strategy to generate Classification dataset ----------------\n@composite\ndef classification_data(draw):\n \"\"\"Creates dataset of sizes upto 100k using hypothesis library and makes it into classfication data using sklearn.make_classfication.\"\"\"\n n_samples_val = draw(integers(min_value=1000, max_value=100000))\n # n_samples_val = draw(integers(min_value=100, max_value=1000))\n n_features_val = draw(integers(min_value=7, max_value=50))\n n_informative_val = draw(integers(min_value=3, max_value=n_features_val - 2))\n hypercube_val = draw(sampled_from([True, False]))\n random_state_val = draw(integers(min_value=10, max_value=1000))\n array_data = make_classification(\n n_samples=n_samples_val,\n n_features=n_features_val,\n n_informative=n_informative_val,\n hypercube=hypercube_val,\n random_state=random_state_val,\n )\n x_data = array_data[0]\n y_data = array_data[1]\n df = pd.DataFrame(\n data=x_data[0:, 0:],\n index=[i for i in range(x_data.shape[0])],\n columns=[\"Col_\" + str(i + 1) for i in range(x_data.shape[1])],\n )\n df[\"DV\"] = y_data\n return df\n\n\n# ---------------------------------------------------------------------------------\n# ----------- setting up ClassificationReport object for the tests ------------\n@fixture\ndef sample_report():\n \"\"\"Simplifies the task of fitting and scoring the models created using ClassificationReport and uses them in all the test cases later.\"\"\"\n\n def _get_data(df, scoring=True, return_test_df=False):\n x_train, x_test, y_train, y_test = prep_data(df, dv_name=\"DV\")\n model = LogisticRegression(solver=\"lbfgs\", max_iter=1000)\n lr = model.fit(x_train, y_train)\n yhat_test = lr.predict_proba(x_test)[:, 1]\n yhat_train = lr.predict_proba(x_train)[:, 1]\n report = ClassificationReport(\n y_train, model, x_train, yhat_train, x_test, y_test, yhat_test, refit=True\n )\n return_val = [report, model, x_train, y_train]\n if return_test_df:\n return_val += [x_test, y_test]\n return return_val\n\n return _get_data\n\n\n# ---------------------------------------------------------------------------------\n# ------------------------------ fit testing -------------------------------------\n@settings(max_examples=1, deadline=None)\n@given(test_df=classification_data())\ndef test_fit(test_df, sample_report):\n \"\"\"Checks if the models are fitting on the data or not.\"\"\"\n report, model, x_train, y_train = sample_report(test_df, scoring=False)\n # check_is_fitted() raises a 'NotFittedError' error if the model is no fitted\n check_is_fitted(model)\n\n\n# ---------------------------------------------------------------------------------\n# ------------------------------ score testing -------------------------------------\n@settings(max_examples=1, deadline=None)\n@given(test_df=classification_data())\ndef test_score(test_df, sample_report):\n report, model, x_train, y_train = sample_report(test_df, scoring=True)\n assert report.x_test is not None\n assert report.y_test is not None\n\n\n# ---------------------------------------------------------------------------------\n# ------------------------------ get_report testing -------------------------------------\n@settings(max_examples=1, deadline=None)\n@given(test_df=classification_data())\ndef test_get_report(test_df, sample_report):\n \"\"\"Checks if the report is generated.\"\"\"\n report, model, x_train, y_train = sample_report(test_df)\n report.get_report(file_path=\"classification_report\")\n assert os.path.exists(\"classification_report.html\")\n", "sub_path": "src/ta_lib/_vendor/tigerml/model_eval/tests/test_ClassificationReport.py", "file_name": "test_ClassificationReport.py", "file_ext": "py", "file_size_in_byte": 4140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "hypothesis.strategies.integers", "line_number": 20, "usage_type": "call"}, {"api_name": "hypothesis.strategies.integers", "line_number": 22, "usage_type": "call"}, {"api_name": "hypothesis.strategies.integers", "line_number": 23, "usage_type": "call"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 24, "usage_type": "call"}, {"api_name": "hypothesis.strategies.integers", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.datasets.make_classification", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "hypothesis.strategies.composite", "line_number": 17, "usage_type": "name"}, {"api_name": "tigerml.core.preprocessing.prep_data", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 46, "usage_type": "name"}, {"api_name": "sklearn.utils.validation.check_is_fitted", "line_number": 75, "usage_type": "call"}, {"api_name": "hypothesis.settings", "line_number": 69, "usage_type": "call"}, {"api_name": "hypothesis.given", "line_number": 70, "usage_type": "call"}, {"api_name": "hypothesis.settings", "line_number": 80, "usage_type": "call"}, {"api_name": "hypothesis.given", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "hypothesis.settings", "line_number": 90, "usage_type": "call"}, {"api_name": "hypothesis.given", "line_number": 91, "usage_type": "call"}]}
+{"seq_id": "410997360", "text": "#!/usr/bin/python\n'''\nCopyright 2017, United States Government, as represented by the Administrator of the National Aeronautics and Space Administration. All rights reserved.\n\nThe pyCMR platform is licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n\n'''\nfrom __future__ import print_function\nimport os, sys, datetime, subprocess\n\ndateTimeFormatISO = \"%Y-%m-%dT%H:%M:%SZ\"\n\nVARIABLE_NCDUMP_AWK='''\\\n$1 == \"variables:\" {inVariables=1; inData=0; next; }\n$1 == \"data:\" {inVariables=0; inData=1; next; }\ninData && $1 == timeFieldName { inTime = 1; maxTime = minTime = $3 }\ninData && $1 == latFieldName { inLatitude = 1; NLat = SLat = 0 }\ninData && $1 == lonFieldName { inLongitude = 1; ELon = WLon = 0 }\n$1 == timeFieldName \":units\" && $4 == \"since\" {\n sub(/\"/, \"\", $5);\n baseTime=$5;\n sub(/\"/, \"\", $3);\n timeUnits=$3;\n}\ninTime{\n for (i=1; i<=NF; i++)\n if ($i != \"=\" && $i != \";\" && $i != timeFieldName)\n if ($i > maxTime) maxTime = $i;\n}\ninLatitude{\n for (i=1; i<=NF; i++)\n if ($i != \"=\" && $i != \";\" && $i != latFieldName)\n {\n if ($i <=90 && $i >= -90 && $i != 0)\n {\n if (NLat==0 || $i > NLat) NLat = $i;\n if (SLat==0 || $i < SLat) SLat = $i;\n }\n }\n}\ninLongitude{\n for (i=1; i<=NF; i++)\n if ($i != \"=\" && $i != \";\" && $i != lonFieldName)\n {\n if ($i <=180 && $i >= -180 && $i != 0)\n {\n if (ELon==0 || $i > ELon) ELon = $i;\n if (WLon==0 || $i < WLon) WLon = $i;\n }\n }\n}\ninTime && $NF == \";\"{ inTime = 0; }\ninLatitude && $NF == \";\"{ inLatitude = 0; }\ninLongitude && $NF == \";\"{ inLongitude = 0; }\nEND{\n print \"minTime=\" minTime;\n print \"maxTime=\" maxTime;\n if (baseTime) print \"baseTime=\" baseTime;\n if (timeUnits) print \"timeUnits=\" timeUnits;\n if (NLat) print \"NLat=\" NLat;\n if (SLat) print \"SLat=\" SLat;\n if (ELon) print \"ELon=\" ELon;\n if (WLon) print \"WLon=\" WLon;\n}\n'''\n\ndef read_variable_nc(filename, fp, timeFieldName, latFieldName, lonFieldName):\n\n fp.close()\n results = {} # Where the keys,values go to be put on the 'q' to go back to the main thread\n # Use ncdump to get the values of the variables dumped out of the file\n ncdump_sp = \\\n subprocess.Popen((\"ncdump\", \"-v\",\n \"{0},{1},{2}\".format(timeFieldName,\n latFieldName,\n lonFieldName), filename), stdout=subprocess.PIPE)\n # Use tr to get rid of all the commas so our awk script doesn't have to deal with them\n tr_sp = \\\n subprocess.Popen((\"tr\", \"-d\", \",\"), stdin=ncdump_sp.stdout, stdout=subprocess.PIPE)\n # Run the awk script to read the ncdump and write out information on\n # min and max latitudes, longitudes, times, time units, and base time\n awk_result = \\\n subprocess.Popen((\"awk\",\n \"-v\", \"timeFieldName=\" + timeFieldName,\n \"-v\", \"latFieldName=\" + latFieldName,\n \"-v\", \"lonFieldName=\" + lonFieldName,\n VARIABLE_NCDUMP_AWK),\n stdin=tr_sp.stdout, stdout=subprocess.PIPE)\n # Create a hashtable for the results from awk\n a = {}\n for line in awk_result.communicate()[0].split(\"\\n\"):\n terms = line.split(\"=\")\n if len(terms) == 2:\n a[terms[0].strip()] = terms[1].strip()\n # If Awk found latitudes, put them in the results hashtable\n for k in [\"NLat\", \"SLat\", \"ELon\", \"WLon\"]:\n if k in a:\n results[k] = a[k]\n # If Awk found baseTime, timeUnits, and a min and max, we can set start and end\n if \"minTime\" in a and \"maxTime\" in a and \"baseTime\" in a and \"timeUnits\" in a:\n bt = datetime.datetime.strptime(a[\"baseTime\"], dateTimeFormatISO)\n # Flag to tell if we understood the timeUnits\n timeUnitsValid = True\n if a[\"timeUnits\"] == \"hours\":\n std = datetime.timedelta(hours=float(a[\"minTime\"]))\n etd = datetime.timedelta(hours=float(a[\"maxTime\"]))\n elif a[\"timeUnits\"] == \"minutes\":\n std = datetime.timedelta(minutes=float(a[\"minTime\"]))\n etd = datetime.timedelta(minutes=float(a[\"maxTime\"]))\n elif a[\"timeUnits\"] == \"seconds\":\n std = datetime.timedelta(seconds=float(a[\"minTime\"]))\n etd = datetime.timedelta(seconds=float(a[\"maxTime\"]))\n else:\n timeUnitsValid = False\n if timeUnitsValid:\n results[\"start\"] = (bt + std)\n results[\"end\"] = (bt + etd)\n return results\n\nif __name__ == \"__main__\":\n filename = sys.argv[1]\n args = sys.argv[2:]\n r = read_variable_nc(filename, open(filename), *args)\n for k, v in r.iteritems():\n print(\"{0}={1}\".format(k, v))\n", "sub_path": "pyCMR/read_variable_nc.py", "file_name": "read_variable_nc.py", "file_ext": "py", "file_size_in_byte": 5244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "subprocess.Popen", "line_number": 75, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 81, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 85, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 114, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 124, "usage_type": "attribute"}]}
+{"seq_id": "364656276", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright 2015 OpenMarket Ltd\n# Copyright 2018 New Vector Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom math import inf, nan\n\nfrom canonicaljson import (\n encode_canonical_json,\n encode_pretty_printed_json,\n iterencode_canonical_json,\n iterencode_pretty_printed_json,\n set_json_library,\n)\n\nfrom frozendict import frozendict\n\nimport unittest\nfrom unittest import mock\n\n\nclass TestCanonicalJson(unittest.TestCase):\n def test_encode_canonical(self):\n self.assertEqual(encode_canonical_json({}), b\"{}\")\n\n # ctrl-chars should be encoded.\n self.assertEqual(\n encode_canonical_json(u\"text\\u0003\\r\\n\"),\n b'\"text\\\\u0003\\\\r\\\\n\"',\n )\n\n # quotes and backslashes should be escaped.\n self.assertEqual(\n encode_canonical_json(r'\"\\ test'),\n b'\"\\\\\"\\\\\\\\ test\"',\n )\n\n # non-ascii should come out utf8-encoded.\n self.assertEqual(\n encode_canonical_json({u\"la merde amusée\": u\"💩\"}),\n b'{\"la merde amus\\xc3\\xa9e\":\"\\xF0\\x9F\\x92\\xA9\"}',\n )\n\n # so should U+2028 and U+2029\n self.assertEqual(\n encode_canonical_json({u\"spaces\": u\"\\u2028 \\u2029\"}),\n b'{\"spaces\":\"\\xe2\\x80\\xa8 \\xe2\\x80\\xa9\"}',\n )\n\n # but we need to watch out for 'u1234' after backslash, which should\n # get encoded to an escaped backslash, followed by u1234\n self.assertEqual(\n encode_canonical_json(u\"\\\\u1234\"),\n b'\"\\\\\\\\u1234\"',\n )\n\n # Iteratively encoding should work.\n self.assertEqual(list(iterencode_canonical_json({})), [b\"{}\"])\n\n def test_ascii(self):\n \"\"\"\n Ensure the proper ASCII characters are escaped.\n\n See https://matrix.org/docs/spec/appendices#grammar.\n \"\"\"\n # Some characters go to their common shorthands.\n escaped = {\n 0x08: b'\"\\\\b\"',\n 0x09: b'\"\\\\t\"',\n 0x0A: b'\"\\\\n\"',\n 0x0C: b'\"\\\\f\"',\n 0x0D: b'\"\\\\r\"',\n 0x22: b'\"\\\\\"\"',\n 0x5C: b'\"\\\\\\\\\"',\n }\n for c, expected in escaped.items():\n self.assertEqual(encode_canonical_json(chr(c)), expected)\n\n # Others go to the \\uXXXX.\n hex_escaped = list(range(0x08)) + [0x0B] + list(range(0x0E, 0x20))\n for c in hex_escaped:\n self.assertEqual(encode_canonical_json(chr(c)), b'\"\\\\u00%02x\"' % (c,))\n\n # And other characters are passed unescaped.\n unescaped = [0x20, 0x21] + list(range(0x23, 0x5C)) + list(range(0x5D, 0x7E))\n for c in unescaped:\n c = chr(c)\n self.assertEqual(encode_canonical_json(c), b'\"' + c.encode(\"ascii\") + b'\"')\n\n def test_encode_pretty_printed(self):\n self.assertEqual(encode_pretty_printed_json({}), b\"{}\")\n self.assertEqual(list(iterencode_pretty_printed_json({})), [b\"{}\"])\n\n # non-ascii should come out utf8-encoded.\n self.assertEqual(\n encode_pretty_printed_json({u\"la merde amusée\": u\"💩\"}),\n b'{\\n \"la merde amus\\xc3\\xa9e\": \"\\xF0\\x9F\\x92\\xA9\"\\n}',\n )\n\n def test_frozen_dict(self):\n self.assertEqual(\n encode_canonical_json(frozendict({\"a\": 1})),\n b'{\"a\":1}',\n )\n self.assertEqual(\n encode_pretty_printed_json(frozendict({\"a\": 1})), b'{\\n \"a\": 1\\n}'\n )\n\n def test_unknown_type(self):\n class Unknown(object):\n pass\n\n unknown_object = Unknown()\n with self.assertRaises(Exception):\n encode_canonical_json(unknown_object)\n\n with self.assertRaises(Exception):\n encode_pretty_printed_json(unknown_object)\n\n def test_invalid_float_values(self):\n \"\"\"Infinity/-Infinity/NaN are not allowed in canonicaljson.\"\"\"\n\n with self.assertRaises(ValueError):\n encode_canonical_json(inf)\n\n with self.assertRaises(ValueError):\n encode_pretty_printed_json(inf)\n\n with self.assertRaises(ValueError):\n encode_canonical_json(-inf)\n\n with self.assertRaises(ValueError):\n encode_pretty_printed_json(-inf)\n\n with self.assertRaises(ValueError):\n encode_canonical_json(nan)\n\n with self.assertRaises(ValueError):\n encode_pretty_printed_json(nan)\n\n def test_set_json(self):\n \"\"\"Ensure that changing the underlying JSON implementation works.\"\"\"\n mock_json = mock.Mock(spec=[\"JSONEncoder\"])\n mock_json.JSONEncoder.return_value.encode.return_value = \"sentinel\"\n try:\n set_json_library(mock_json)\n self.assertEqual(encode_canonical_json({}), b\"sentinel\")\n finally:\n # Reset the JSON library to whatever was originally set.\n from canonicaljson import json\n\n set_json_library(json)\n", "sub_path": "test_canonicaljson.py", "file_name": "test_canonicaljson.py", "file_ext": "py", "file_size_in_byte": 5393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "unittest.TestCase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 35, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 39, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 45, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 51, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 57, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 64, "usage_type": "call"}, {"api_name": "canonicaljson.iterencode_canonical_json", "line_number": 69, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 88, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 93, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 99, "usage_type": "call"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 102, "usage_type": "call"}, {"api_name": "canonicaljson.iterencode_pretty_printed_json", "line_number": 103, "usage_type": "call"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 107, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 113, "usage_type": "call"}, {"api_name": "frozendict.frozendict", "line_number": 113, "usage_type": "call"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 117, "usage_type": "call"}, {"api_name": "frozendict.frozendict", "line_number": 117, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 126, "usage_type": "call"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 129, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 135, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 135, "usage_type": "argument"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 138, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 138, "usage_type": "argument"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 141, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 141, "usage_type": "name"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 144, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 144, "usage_type": "name"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 147, "usage_type": "call"}, {"api_name": "math.nan", "line_number": 147, "usage_type": "argument"}, {"api_name": "canonicaljson.encode_pretty_printed_json", "line_number": 150, "usage_type": "call"}, {"api_name": "math.nan", "line_number": 150, "usage_type": "argument"}, {"api_name": "unittest.mock.Mock", "line_number": 154, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 154, "usage_type": "name"}, {"api_name": "canonicaljson.set_json_library", "line_number": 157, "usage_type": "call"}, {"api_name": "canonicaljson.encode_canonical_json", "line_number": 158, "usage_type": "call"}, {"api_name": "canonicaljson.set_json_library", "line_number": 163, "usage_type": "call"}, {"api_name": "canonicaljson.json", "line_number": 163, "usage_type": "name"}]}
+{"seq_id": "348903092", "text": "from astropy.table import Table, Column\nfrom astropy import units as u\nfrom latex_info import latexdict\n\ntbl = Table.read('spw_table.txt', format='ascii')\n\nfrq_range = list(zip(tbl['Ch0(MHz)']*u.MHz, (tbl['Ch0(MHz)'] + tbl['ChanWid(kHz)']/1e3*tbl['#Chans'])*u.MHz))\n\ntbl.add_column(Column(data=u.Quantity([min(x) for x in frq_range], u.GHz), name='Minimum Frequency'))\ntbl.add_column(Column(data=u.Quantity([max(x) for x in frq_range], u.GHz), name='Maximum Frequency'))\ntbl.rename_column('ChanWid(kHz)', 'Channel Width')\ntbl['Channel Width'].unit = u.kHz\n\ntbl = tbl['SpwID', 'Minimum Frequency', 'Maximum Frequency', 'Channel Width']\n\nlatexdict['header_start'] = '\\label{tab:spw}'\nlatexdict['caption'] = 'Spectral Setup'\n#latexdict['tablefoot'] = ('\\par\\nJy-Kelvin gives the conversion factor from Jy '\n# 'to Kelvin given the synthesized beam size and '\n# 'observation frequency.')\nlatexdict['col_align'] = 'l'*len(tbl.columns)\n#latexdict['tabletype'] = 'longtable'\n#latexdict['tabulartype'] = 'longtable'\nlatexdict['units'] = {}\n\ntbl.write('../cores_and_outflows/spwtable.tex', latexdict=latexdict,\n format='ascii.latex',\n overwrite=True)\n", "sub_path": "tables/make_spw_tex_tbl.py", "file_name": "make_spw_tex_tbl.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "astropy.table.Table.read", "line_number": 5, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 5, "usage_type": "name"}, {"api_name": "astropy.units.MHz", "line_number": 7, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 7, "usage_type": "name"}, {"api_name": "astropy.table.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "astropy.units.Quantity", "line_number": 9, "usage_type": "call"}, {"api_name": "astropy.units", "line_number": 9, "usage_type": "name"}, {"api_name": "astropy.units.GHz", "line_number": 9, "usage_type": "attribute"}, {"api_name": "astropy.table.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "astropy.units.Quantity", "line_number": 10, "usage_type": "call"}, {"api_name": "astropy.units", "line_number": 10, "usage_type": "name"}, {"api_name": "astropy.units.GHz", "line_number": 10, "usage_type": "attribute"}, {"api_name": "astropy.units.kHz", "line_number": 12, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 12, "usage_type": "name"}, {"api_name": "latex_info.latexdict", "line_number": 16, "usage_type": "name"}, {"api_name": "latex_info.latexdict", "line_number": 17, "usage_type": "name"}, {"api_name": "latex_info.latexdict", "line_number": 21, "usage_type": "name"}, {"api_name": "latex_info.latexdict", "line_number": 24, "usage_type": "name"}, {"api_name": "latex_info.latexdict", "line_number": 26, "usage_type": "name"}]}
+{"seq_id": "643764245", "text": "# -*- coding: utf-8 -*-\n\nimport sys, socket, logging, ctypes, array\nlog=logging.getLogger(__name__)\nfrom socket import htonl\nfrom fcntl import ioctl\n\nfrom TwCAS.util.ifinspect import interface\nfrom TwCAS.protocol.caproto import int2addr\n\n__all__ = ['unix']\n\nif sys.version_info < (2, 6, 0):\n def str2struct(string, cstruct):\n \"\"\"Cast a python string containing a byte\n array into a C structure\n \"\"\"\n from ctypes import sizeof, pointer, POINTER, memmove\n\n assert not hasattr(cstruct, 'contents'), \"must _not_ be a POINTER type\"\n # copy to a writable buffer first\n a=array.array('c')\n a.fromstring(string)\n\n # find address\n base, l = a.buffer_info()\n assert l==sizeof(cstruct), 'string size must match struct size'\n\n # cast buffer pointer to struct pointer\n b=ctypes.cast(base, POINTER(cstruct))\n\n # make a copy\n c=cstruct()\n memmove(pointer(c), b, sizeof(c))\n\n return c\nelse:\n def str2struct(string,cstruct):\n return cstruct.from_buffer_copy(buffer(string))\n\ndef unix():\n \"\"\"Query interfaces\n \"\"\"\n \n SIOCGIFCONF =0x8912\n SIOCGIFFLAGS =0x8913\n SIOCGIFBRDADDR=0x8919\n\n # Select interface flags\n IFF_UP=0x1\n IFF_BROADCAST=0x2\n IFF_LOOPBACK=0x8\n \n class sockaddr(ctypes.Structure):\n _fields_ = [('family', ctypes.c_uint16),\n ('data', ctypes.c_uint8*14)]\n\n class sockaddr_in(ctypes.Structure):\n _fields_ = [('family', ctypes.c_uint16),\n ('port', ctypes.c_uint16),\n ('addr', ctypes.c_uint32),\n ('zero', ctypes.c_uint8*8)]\n\n assert ctypes.sizeof(sockaddr)==ctypes.sizeof(sockaddr_in)\n \n class ifmap(ctypes.Structure):\n _fields = [('start', ctypes.c_ulong),\n ('end', ctypes.c_ulong),\n ('addr', ctypes.c_ushort),\n ('irq', ctypes.c_char),\n ('dma', ctypes.c_char),\n ('port', ctypes.c_char)]\n \n class ifreq_ifru(ctypes.Union):\n _fields_ = [('addr', sockaddr),\n ('sval', ctypes.c_short),\n ('ival', ctypes.c_int),\n ('map', ifmap),\n ('strval', ctypes.c_char*16)]\n \n class ifreq(ctypes.Structure):\n _anonymous_ = (\"ifru\",)\n _fields_ = [('name', ctypes.c_char*16),\n ('ifru', ifreq_ifru)]\n\n class ifconf(ctypes.Structure):\n _fields_ = [(\"len\", ctypes.c_int),\n (\"req\", ctypes.POINTER(ifreq))]\n\n if ctypes.sizeof(ctypes.c_int)==4:\n # lengths found on 32-bit Linux x86\n assert ctypes.sizeof(ifreq_ifru)==16, 'expect 16 not %u'%ctypes.sizeof(ifreq_ifru)\n assert ctypes.sizeof(ifreq)==32\n \n\n ifarr=ifreq*100\n arr=ifarr()\n conf=ifconf(len=ctypes.sizeof(ifarr),\n req=arr)\n\n a = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n ioctl(a.fileno(), SIOCGIFCONF, buffer(conf))\n\n iflist=set()\n\n for intr in arr:\n if len(intr.name)==0:\n break\n\n if intr.addr.family==socket.AF_INET:\n iface=interface()\n iface.name=intr.name\n\n # cast from sockaddr to sockaddr_in\n addr=ctypes.cast(ctypes.byref(intr.addr),\n ctypes.POINTER(sockaddr_in))[0]\n # go from integer in host order to string\n ip=int2addr(socket.htonl(addr.addr))\n iface.addr=ip\n \n x=ioctl(a.fileno(), SIOCGIFFLAGS, buffer(intr))\n\n intrflags=str2struct(x,ifreq)\n assert intrflags.name==intr.name\n\n flags=intrflags.sval\n \n if not flags&IFF_UP:\n # only include active interfaces\n log.debug('%s is down, skipping...',iface.name)\n continue\n\n iface.loopback=bool(flags&IFF_LOOPBACK)\n\n if flags&IFF_BROADCAST:\n x=ioctl(a.fileno(), SIOCGIFBRDADDR, buffer(intr))\n #intr=ifreq.from_buffer_copy(x)\n intr=str2struct(x,ifreq)\n addr=ctypes.cast(ctypes.byref(intr.addr),\n ctypes.POINTER(sockaddr_in))[0]\n ip=int2addr(htonl(addr.addr))\n iface.broadcast=ip\n\n iflist.add(iface)\n else:\n log.debug('Ignoring non IPv4 interface %s',intr.name)\n\n return iflist\n", "sub_path": "TwCAS/util/ifinspect/unix.py", "file_name": "unix.py", "file_ext": "py", "file_size_in_byte": 4475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 13, "usage_type": "attribute"}, {"api_name": "array.array", "line_number": 22, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 27, "usage_type": "call"}, {"api_name": "ctypes.cast", "line_number": 30, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 30, "usage_type": "call"}, {"api_name": "ctypes.memmove", "line_number": 34, "usage_type": "call"}, {"api_name": "ctypes.pointer", "line_number": 34, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 34, "usage_type": "call"}, {"api_name": "ctypes.Structure", "line_number": 54, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint16", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint8", "line_number": 56, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 58, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint16", "line_number": 59, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint16", "line_number": 60, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint8", "line_number": 62, "usage_type": "attribute"}, {"api_name": "ctypes.sizeof", "line_number": 64, "usage_type": "call"}, {"api_name": "ctypes.Structure", "line_number": 66, "usage_type": "attribute"}, {"api_name": "ctypes.c_ulong", "line_number": 67, "usage_type": "attribute"}, {"api_name": "ctypes.c_ulong", "line_number": 68, "usage_type": "attribute"}, {"api_name": "ctypes.c_ushort", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ctypes.c_char", "line_number": 70, "usage_type": "attribute"}, {"api_name": "ctypes.c_char", "line_number": 71, "usage_type": "attribute"}, {"api_name": "ctypes.c_char", "line_number": 72, "usage_type": "attribute"}, {"api_name": "ctypes.Union", "line_number": 74, "usage_type": "attribute"}, {"api_name": "ctypes.c_short", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 77, "usage_type": "attribute"}, {"api_name": "ctypes.c_char", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 81, "usage_type": "attribute"}, {"api_name": "ctypes.c_char", "line_number": 83, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 87, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 88, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 90, "usage_type": "call"}, {"api_name": "ctypes.c_int", "line_number": 90, "usage_type": "attribute"}, {"api_name": "ctypes.sizeof", "line_number": 92, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 93, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 98, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 101, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 101, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 101, "usage_type": "attribute"}, {"api_name": "fcntl.ioctl", "line_number": 103, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 111, "usage_type": "attribute"}, {"api_name": "TwCAS.util.ifinspect.interface", "line_number": 112, "usage_type": "call"}, {"api_name": "ctypes.cast", "line_number": 116, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 116, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 117, "usage_type": "call"}, {"api_name": "TwCAS.protocol.caproto.int2addr", "line_number": 119, "usage_type": "call"}, {"api_name": "socket.htonl", "line_number": 119, "usage_type": "call"}, {"api_name": "fcntl.ioctl", "line_number": 122, "usage_type": "call"}, {"api_name": "fcntl.ioctl", "line_number": 137, "usage_type": "call"}, {"api_name": "ctypes.cast", "line_number": 140, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 140, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 141, "usage_type": "call"}, {"api_name": "TwCAS.protocol.caproto.int2addr", "line_number": 142, "usage_type": "call"}, {"api_name": "socket.htonl", "line_number": 142, "usage_type": "call"}]}
+{"seq_id": "288411166", "text": "from setuptools import setup\nimport os\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetup(\n name='cleverbot_io',\n version='1.0.1',\n packages=['cleverbot_io'],\n\tinstall_requires=['requests'],\n\tinclude_package_data=True,\n url='http://www.cleverbot.io',\n license='MIT',\n author='Underforest',\n author_email='neovisatoons@gmail.com',\n description='An unofficial Python wrapper for Cleverbot.io',\n\tlong_description=read('README')\n\t)\n", "sub_path": "pypi_install_script/cleverbot_io-1.0.1.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 7, "usage_type": "call"}]}
+{"seq_id": "308428428", "text": "# Last Updated: 2019-Jul-08\n# Udaya Maurya (udaya_cbscients@yahoo.com, telegram: https://t.me/udy11)\n# Source: https://github.com/udy11, https://gitlab.com/udy11\n\n# This program runs periodic convection problems using lcpfct and utility routines. The profile is a semicircular hump. The velocity is constant in space and time and the grid is kept stationary.\n\n# Tested with Python 3.6.4, Numpy 1.13.3, Matplotlib 2.1.1\n\nimport numpy as np\nfrom lcpfct import lcpfct\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\ndef prof(t, x, n, v):\n width = 10.0\n height = 1.0\n x0 = 20.0\n n1 = n + 1\n\n # Compute the profile of the semicircular hump...\n xleft = (x0 - width) + v * t\n while xleft > x[n]:\n xleft -= x[n]\n while xleft < 0.0:\n xleft += x[n]\n xright = xleft + 2.0 * width\n\n # Loop over the cells in the numerical profile to be determined...\n a = np.zeros(n)\n for i in range(n):\n for k in range(10):\n xk = x[i] + 0.1 * (k + 0.5) * (x[i+1] - x[i])\n if xk > xleft and xk < xright:\n xcent = xleft + width\n a[i] += 0.1 * height * np.sqrt(1 - ((xk - xcent) / width)**2)\n else:\n xk += x[n]\n if xk > xleft and xk < xright:\n xcent = xleft + width\n a[i] += 0.1 * height * np.sqrt(1 - ((xk - xcent) / width)**2)\n return a\n\n# The Constant Velocity Convection control parameters are initialized...\nnx = 50\ndx = 1.0\ndt = 0.2\nvx = 1.0\nmxstp = 501\ntym = 0.0\n\n# The grid, velocity and the density profile are initialized...\nnxp = nx + 1\nxint = np.linspace(0, dx * nx, nxp)\nvint = vx * np.ones(nxp)\nscr = prof(tym, xint, nx, vx)\n\n# Set up plotting...\nfig = plt.figure()\nplt.title('Semicircular Wave Convection')\nplt.ylabel('Density')\nplt.xlabel('X')\nplots = []\nplots.append(plt.plot(scr, 'k'))\n\n# Set residual diffusion, grid, and velocity factors in lcpfct...\nlh = lcpfct(nxp)\nlh.residiff(1.0)\nlh.makegrid(xint, xint, 0, nx, 0)\nlh.velocity(vint, 0, nx, dt)\n\n# Begin loop over timesteps...\nfor it in range(1, mxstp):\n tym = it * dt\n scr = lh.lcpfct(scr, 0, nx-1, 0.0, 0.0, 0.0, 0.0, True)\n if it % 1 == 0:\n plots.append(plt.plot(scr, 'k'))\nvid = animation.ArtistAnimation(fig, plots, interval = 20, repeat = True, repeat_delay = 0, blit = True)\n#vidwriter = animation.FFMpegWriter(fps = 30, codec ='libx264', extra_args=['-tune', 'animation'])\n#vid.save('vid_scr.mp4', writer = vidwriter)\nplt.show()\n\n", "sub_path": "mathematics/differential_equations/flux_corrected_transport/examples/convect_semicircle.py", "file_name": "convect_semicircle.py", "file_ext": "py", "file_size_in_byte": 2513, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "lcpfct.lcpfct", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.animation.ArtistAnimation", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}]}
+{"seq_id": "392509529", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport uuid\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('frontend', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='snippet',\n name='snippets_uuid',\n field=models.UUIDField(default=uuid.uuid4, unique=True, editable=False),\n ),\n ]\n", "sub_path": "frontend/migrations/0002_snippet_snippets_uuid.py", "file_name": "0002_snippet_snippets_uuid.py", "file_ext": "py", "file_size_in_byte": 445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.UUIDField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 19, "usage_type": "attribute"}]}
+{"seq_id": "319859979", "text": "\"\"\"\nKochka core related lib\n\nCopyright 2017 Pavel Folov\n\"\"\"\n\nimport re\nfrom enum import Enum\nfrom textwrap import shorten\n\nfrom patterns import Event\nfrom oslib import filebackup\n\n\nclass Set:\n \"\"\"Подходы: вес, повторения, подходы\"\"\"\n\n __slots__ = ['weight', 'count', 'set_count']\n\n # to access by index\n # getattr(set_, Set.ATTRS[col])\n ATTRS = __slots__\n\n def __init__(self, weight, count, set_count=1):\n self.weight = int(weight)\n self.count = int(count)\n self.set_count = int(set_count)\n\n @property\n def total_weight(self) -> int:\n return self.weight * self.count * self.set_count\n\n def __str__(self):\n if self.set_count > 1:\n return '{} {} x{}'.format(self. weight, self.count, self.set_count)\n else:\n return '{} {}'.format(self.weight, self.count)\n\n\nclass Exercise:\n \"\"\"Упражнение из подходов\"\"\"\n\n __slots__ = ['date', 'name', 'sets', 'note']\n\n # to access by index\n # getattr(exercise, Exercise.ATTRS[col])\n ATTRS = __slots__\n\n def __init__(self, date=None, name=None, sets=None, note=None):\n self.date = date\n self.name = name\n self.sets = sets or []\n self.note = note\n\n def add_set(self, set_: Set):\n self.sets.append(set_)\n\n @property\n def total_weight(self) -> int:\n return sum(s.total_weight for s in self.sets)\n\n @property\n def sets_str(self) -> str:\n return ' '.join('[{}]'.format(str(s)) for s in self.sets)\n\n def str_to_save(self) -> str:\n record_list = [self.date, self.name]\n if self.note:\n record_list.append(self.note_to_save)\n record_list.append('\\n'.join('{}'.format(str(s)) for s in self.sets))\n return '\\n'.join(record_list)\n\n @property\n def note_to_save(self):\n return '# {}'.format(self.note)\n\n @property\n def name_with_note(self):\n if self.note is None:\n return self.name\n else:\n return '{} ({})'.format(\n self.name,\n shorten(self.note, width=20, placeholder='...')\n )\n\n def __str__(self):\n return 'date({0.date}), name({0.name}), sets({0.sets_str})'.format(\n self)\n\n\nclass ParserState(Enum):\n START = 0\n DATE_GOT = 1\n NAME_GOT = 2\n NOTE_GOT = 3\n SETS_GETTING = 4\n DONE = 5\n\n\nclass ExerciseTxtParser:\n \"\"\"\n Training text file parser\n\n 2017.01.13\n жим\n #травма, поясница\n 35 5\n 45 10 х5\n 50 6 х2\n\n >>> parser = ExerciseTxtParser('data.txt')\n >>> for e in parser: print(e)\n >>> # date(2017.01.13), name(жим), sets([35 5] [45 10 x5] [50 6 x2])\n \"\"\"\n\n date_pattern = re.compile(r'(?P<Y>\\d{4})\\.(?P<M>\\d{2})\\.(?P<D>\\d{2})$')\n name_pattern = re.compile(r'(?P<NAME>\\w+)$')\n set_pattern = re.compile(r'(?P<WEIGHT>\\d{2,3}) (?P<COUNT>\\d{1,2})'\n r'(?:\\s?[\\*xXхХ]\\s?(?P<SET_COUNT>\\d{1,2}))?$')\n\n def __init__(self, filename: str):\n self.filename = filename\n self.state = ParserState.START\n self.currentExercise = None\n self.on_error = Event()\n\n def __iter__(self):\n # из cookbook, может лучше просто итератор реализовать\n with open(self.filename) as f:\n for lineno, line in enumerate(f, start=1):\n self.dispatch_line(line.rstrip(), lineno)\n if self.is_done:\n self.state = ParserState.START\n yield self.currentExercise\n self.currentExercise = None\n\n @property\n def is_start(self):\n return self.state is ParserState.START\n\n @property\n def is_date_got(self):\n return self.state is ParserState.DATE_GOT\n\n @property\n def is_name_got(self):\n return self.state is ParserState.NAME_GOT\n\n @property\n def is_note_got(self):\n return self.state is ParserState.NOTE_GOT\n\n @property\n def is_sets_getting(self):\n return self.state is ParserState.SETS_GETTING\n\n @property\n def is_done(self):\n return self.state is ParserState.DONE\n\n # todo: избавиться от условий (конечный автомат, или еще чего)\n def dispatch_line(self, line: str, lineno: int):\n done_cond = (not line) and self.is_sets_getting\n if done_cond:\n self.state = ParserState.DONE\n return\n elif not line:\n self.on_error('Unexpected new line: lineno({}), detail{}'.format(\n lineno, str(self)))\n return\n elif self.is_start and self.date_pattern.match(line):\n self.currentExercise = Exercise(date=line)\n self.state = ParserState.DATE_GOT\n return\n elif self.is_date_got and self.name_pattern.match(line):\n self.currentExercise.name = line\n self.state = ParserState.NAME_GOT\n return\n elif self.is_name_got and line.startswith('#'):\n self.currentExercise.note = line.lstrip('# ')\n self.state = ParserState.NOTE_GOT\n return\n elif self.is_name_got or self.is_note_got or self.is_sets_getting:\n match = self.set_pattern.match(line)\n if match:\n self.currentExercise.sets.append(\n self._create_set_by_match(match))\n self.state = ParserState.SETS_GETTING\n return\n err_msg = 'Parser sequence error: lineno({}), line({}), detail({})'\n self.on_error(err_msg.format(lineno, line, str(self)))\n\n def _create_set_by_match(self, match):\n set_dict = match.groupdict()\n if set_dict['SET_COUNT']:\n return Set(set_dict['WEIGHT'], set_dict['COUNT'],\n set_dict['SET_COUNT'])\n else:\n return Set(set_dict['WEIGHT'], set_dict['COUNT'])\n\n def __str__(self):\n return 'Parser: {0.state}, exercise({0.currentExercise})'.format(self)\n\n\ndef save_exercises_to_file(filename, exercises):\n \"\"\"Writes exercises to file\"\"\"\n if not exercises:\n raise ValueError('Exercises cannot be empty')\n\n with filebackup(filename), open(filename, 'w') as f:\n for exercise in exercises:\n f.write(''.join([\n exercise.str_to_save(),\n '\\n\\n'\n ]))\n", "sub_path": "kochkalib.py", "file_name": "kochkalib.py", "file_ext": "py", "file_size_in_byte": 6412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "textwrap.shorten", "line_number": 84, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 92, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 117, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 118, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 119, "usage_type": "call"}, {"api_name": "patterns.Event", "line_number": 126, "usage_type": "call"}, {"api_name": "oslib.filebackup", "line_number": 211, "usage_type": "call"}]}
+{"seq_id": "4776476", "text": "import os\n\nfrom flask import Flask\nfrom flask import redirect, url_for, request\n\nfrom pid_getter import get_pids_and_names\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef pid_viewer():\n res = \"\"\n\n s = \"\"\" <form role=\"form\" action=\"/pid_kill/\" method=\"post\">\n\n <button type=\"submit\" class=\"btn btn-primary\"> %s </button>\n %s\n <input id=\"email\" type=\"hidden\" class=\"form-control\" name=\"pid\" value=\"%s\"\n placeholder=\"Email Address\">\n\n </form>\"\"\"\n\n for i in get_pids_and_names():\n res = res + \"<p>\" + s % (\"[pid: \" + i[0] + \"] \", i[1], i[0]) + \"</p>\" + \"\\n\"\n\n return res\n\n\n@app.route('/pid_kill/', methods=['GET', 'POST'])\ndef pid_kill():\n res = \"\"\n if request.method == 'POST':\n pid = request.form[\"pid\"]\n os.system(\"kill %s\" % pid)\n\n for i in get_pids_and_names():\n res = res + \"<p>\" + \"[pid: \" + i[0] + \"] \" + i[1] + \"</p>\" + \"\\n\"\n\n return redirect(url_for('pid_viewer'))\n\n\nif __name__ == '__main__':\n app.debug = True\n app.secret_key = 'AAJFjp3141`15123`;ewr[][/fw;jq'\n app.run()\n", "sub_path": "SHU/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "pid_getter.get_pids_and_names", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "os.system", "line_number": 35, "usage_type": "call"}, {"api_name": "pid_getter.get_pids_and_names", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "356252234", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/dmulholl/dev/src/ivy/ivy/extensions/ivy_include.py\n# Compiled at: 2020-04-04 05:06:35\n# Size of source mod 2**32: 1044 bytes\nimport shortcodes, ivy, os\n\n@shortcodes.register('include')\ndef handler(node, content, pargs, kwargs):\n if pargs:\n path = ivy.site.inc(pargs[0])\n if os.path.exists(path):\n with open(path) as (file):\n return file.read()\n return ''", "sub_path": "pycfiles/ivy-2.8.0.tar/ivy_include.cpython-37.py", "file_name": "ivy_include.cpython-37.py", "file_ext": "py", "file_size_in_byte": 565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "ivy.site.inc", "line_number": 13, "usage_type": "call"}, {"api_name": "ivy.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "shortcodes.register", "line_number": 10, "usage_type": "call"}]}
+{"seq_id": "75073235", "text": "import bpy\nfrom cgl.plugins.blender import lumbermill as lm\n\nclass Defaultshader(bpy.types.Operator):\n \"\"\"\n This class is required to register a button in blender.\n \"\"\"\n bl_idname = 'object.defaultshader'\n bl_label = 'Defaultshader'\n\n @classmethod\n def poll(cls, context):\n return context.active_object is not None\n\n def execute(self, context):\n run()\n return {'FINISHED'}\n\n\ndef run():\n \"\"\"\n This run statement is what's executed when your button is pressed in blender.\n :return:\n \"\"\"\n\n def assignToon(context):\n def instanciate_group(nodes, group_name):\n group = nodes.new(type='ShaderNodeGroup')\n group.node_tree = bpy.data.node_groups[group_name]\n return group\n\n def assignToonShader(material):\n '''To do Handle if the material output doesnt exist'''\n toonShader = instanciate_group(material.node_tree.nodes, \"ToonShader_2\")\n node2 = material.node_tree.nodes['Material Output']\n material.node_tree.links.new(toonShader.outputs[0], node2.inputs[0])\n\n objects = bpy.context.selected_objects\n for obj in objects:\n if len(obj.material_slots) < 1:\n\n bpy.ops.object.material_slot_add()\n\n if obj.name not in bpy.data.materials:\n\n mat = bpy.data.materials.new(obj.name)\n else:\n mat = bpy.data.materials[obj.name]\n\n obj.data.materials[0] = mat\n mat.use_nodes = True\n\n for mat in obj.data.materials:\n if mat.name == '':\n mat.name = obj.name\n\n matNodes = mat.node_tree.nodes\n\n assignToonShader(mat)\n if 'Principled BSDF' in matNodes:\n matNodes.remove(matNodes['Principled BSDF'])\n # else:\n # for n in matNodes:\n # if n != material.node_tree.nodes['Material Output']:\n # matNodes.remove(n)\n\n\n shaderPath = r'D:/COMPANIES/loneCoconut/render/MILVIO_CGL/assets/lib/TOONSCEENSETUP/shd/publish/001.000/high/lib_TOONSCEENSETUP_shd.blend'\n collection_name = 'ToonSceneSetup'\n # dict_ = {'company': 'loneCoconut',\n # 'context': 'render',\n # 'project': 'MILVIO',\n # 'scope': 'assets',\n # 'seq': 'lib',\n # 'shot': 'TOONSCEENSETUP',\n # 'task': 'shd',\n # 'user': 'publish',\n # 'resolution': 'high'}\n # shaderPath = lm.LumberObject(dict_)\n # print(shaderPath.latest_version().path_root)\n #\n # collection_name = shaderPath.shot\n\n if collection_name not in bpy.data.collections:\n\n # link all collections starting with 'MyCollection'\n with bpy.data.libraries.load(shaderPath, link=False) as (data_from, data_to):\n data_to.collections = [c for c in data_from.collections if c.startswith(collection_name)]\n\n # link collection to scene collection\n for coll in data_to.collections:\n if coll is not None:\n bpy.data.scenes['Scene'].collection.children.link(coll)\n\n else:\n print(\"Toon Shader Exist\")\n\n\n assignToon(bpy.context)\n\n", "sub_path": "cookbook/.stversions/blender/menus/lumbermill/Defaultshader~20201019-122407.py", "file_name": "Defaultshader~20201019-122407.py", "file_ext": "py", "file_size_in_byte": 3276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "bpy.types", "line_number": 4, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 29, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 38, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.material_slot_add", "line_number": 42, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 44, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 46, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 46, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 48, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 84, "usage_type": "attribute"}, {"api_name": "bpy.data.libraries.load", "line_number": 87, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 87, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 93, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 99, "usage_type": "attribute"}]}
+{"seq_id": "335545587", "text": "from datetime import datetime\nfrom typing import Any, Dict, Optional, Tuple\n\nfrom django.conf import settings\nfrom django.db.models.expressions import F\nfrom django.utils import timezone\nfrom rest_framework.utils.serializer_helpers import ReturnDict\nfrom sentry_sdk.api import capture_exception\n\nfrom ee.clickhouse.client import sync_execute\nfrom ee.clickhouse.models.action import format_action_filter\nfrom ee.clickhouse.models.person import ClickhousePersonSerializer\nfrom ee.clickhouse.models.property import parse_prop_clauses\nfrom ee.clickhouse.queries.util import get_trunc_func_ch, parse_timestamps\nfrom ee.clickhouse.sql.person import (\n GET_LATEST_PERSON_DISTINCT_ID_SQL,\n GET_LATEST_PERSON_SQL,\n INSERT_COHORT_ALL_PEOPLE_SQL,\n PEOPLE_SQL,\n PERSON_STATIC_COHORT_TABLE,\n)\nfrom ee.clickhouse.sql.stickiness.stickiness import STICKINESS_SQL\nfrom ee.clickhouse.sql.stickiness.stickiness_actions import STICKINESS_ACTIONS_SQL\nfrom ee.clickhouse.sql.stickiness.stickiness_people import STICKINESS_PEOPLE_SQL\nfrom posthog.constants import TREND_FILTER_TYPE_ACTIONS\nfrom posthog.models.action import Action\nfrom posthog.models.cohort import Cohort\nfrom posthog.models.entity import Entity\nfrom posthog.models.filters.stickiness_filter import StickinessFilter\nfrom posthog.models.team import Team\nfrom posthog.queries.stickiness import Stickiness\n\n\nclass ClickhouseStickiness(Stickiness):\n def stickiness(self, entity: Entity, filter: StickinessFilter, team_id: int) -> Dict[str, Any]:\n\n parsed_date_from, parsed_date_to, _ = parse_timestamps(filter=filter, team_id=team_id)\n prop_filters, prop_filter_params = parse_prop_clauses(filter.properties, team_id)\n trunc_func = get_trunc_func_ch(filter.interval)\n\n params: Dict = {\"team_id\": team_id}\n params = {**params, **prop_filter_params, \"num_intervals\": filter.total_intervals}\n if entity.type == TREND_FILTER_TYPE_ACTIONS:\n action = Action.objects.get(pk=entity.id)\n action_query, action_params = format_action_filter(action)\n if action_query == \"\":\n return {}\n\n params = {**params, **action_params}\n content_sql = STICKINESS_ACTIONS_SQL.format(\n team_id=team_id,\n actions_query=action_query,\n parsed_date_from=parsed_date_from,\n parsed_date_to=parsed_date_to,\n filters=prop_filters,\n trunc_func=trunc_func,\n latest_distinct_id_sql=GET_LATEST_PERSON_DISTINCT_ID_SQL,\n )\n else:\n content_sql = STICKINESS_SQL.format(\n team_id=team_id,\n event=entity.id,\n parsed_date_from=parsed_date_from,\n parsed_date_to=parsed_date_to,\n filters=prop_filters,\n trunc_func=trunc_func,\n latest_distinct_id_sql=GET_LATEST_PERSON_DISTINCT_ID_SQL,\n )\n\n counts = sync_execute(content_sql, params)\n return self.process_result(counts, filter)\n\n def _retrieve_people(self, target_entity: Entity, filter: StickinessFilter, team: Team) -> ReturnDict:\n return retrieve_stickiness_people(target_entity, filter, team)\n\n\ndef _format_entity_filter(entity: Entity) -> Tuple[str, Dict]:\n if entity.type == TREND_FILTER_TYPE_ACTIONS:\n try:\n action = Action.objects.get(pk=entity.id)\n action_query, params = format_action_filter(action)\n entity_filter = \"AND {}\".format(action_query)\n\n except Action.DoesNotExist:\n raise ValueError(\"This action does not exist\")\n else:\n entity_filter = \"AND event = %(event)s\"\n params = {\"event\": entity.id}\n\n return entity_filter, params\n\n\ndef _process_content_sql(target_entity: Entity, filter: StickinessFilter, team: Team) -> Tuple[str, Dict[str, Any]]:\n parsed_date_from, parsed_date_to, _ = parse_timestamps(filter=filter, team_id=team.pk)\n prop_filters, prop_filter_params = parse_prop_clauses(\n filter.properties, team.pk, filter_test_accounts=filter.filter_test_accounts\n )\n entity_sql, entity_params = _format_entity_filter(entity=target_entity)\n trunc_func = get_trunc_func_ch(filter.interval)\n\n params: Dict = {\n \"team_id\": team.pk,\n **prop_filter_params,\n \"stickiness_day\": filter.selected_interval,\n **entity_params,\n \"offset\": filter.offset,\n }\n\n content_sql = STICKINESS_PEOPLE_SQL.format(\n entity_filter=entity_sql,\n parsed_date_from=parsed_date_from,\n parsed_date_to=parsed_date_to,\n filters=prop_filters,\n trunc_func=trunc_func,\n latest_distinct_id_sql=GET_LATEST_PERSON_DISTINCT_ID_SQL,\n )\n return content_sql, params\n\n\ndef retrieve_stickiness_people(target_entity: Entity, filter: StickinessFilter, team: Team) -> ReturnDict:\n\n content_sql, params = _process_content_sql(target_entity, filter, team)\n\n people = sync_execute(\n PEOPLE_SQL.format(\n content_sql=content_sql,\n query=\"\",\n latest_person_sql=GET_LATEST_PERSON_SQL.format(query=\"\"),\n latest_distinct_id_sql=GET_LATEST_PERSON_DISTINCT_ID_SQL,\n ),\n params,\n )\n return ClickhousePersonSerializer(people, many=True).data\n\n\ndef insert_stickiness_people_into_cohort(cohort: Cohort, target_entity: Entity, filter: StickinessFilter) -> None:\n content_sql, params = _process_content_sql(target_entity, filter, cohort.team)\n try:\n sync_execute(\n INSERT_COHORT_ALL_PEOPLE_SQL.format(\n content_sql=content_sql,\n query=\"\",\n latest_person_sql=GET_LATEST_PERSON_SQL.format(query=\"\"),\n cohort_table=PERSON_STATIC_COHORT_TABLE,\n latest_distinct_id_sql=GET_LATEST_PERSON_DISTINCT_ID_SQL,\n ),\n {\"cohort_id\": cohort.pk, \"_timestamp\": datetime.now(), **params},\n )\n cohort.is_calculating = False\n cohort.last_calculation = timezone.now()\n cohort.errors_calculating = 0\n cohort.save()\n except Exception as err:\n if settings.DEBUG:\n raise err\n cohort.is_calculating = False\n cohort.errors_calculating = F(\"errors_calculating\") + 1\n cohort.save()\n capture_exception(err)\n", "sub_path": "ee/clickhouse/queries/clickhouse_stickiness.py", "file_name": "clickhouse_stickiness.py", "file_ext": "py", "file_size_in_byte": 6354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "posthog.queries.stickiness.Stickiness", "line_number": 34, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 35, "usage_type": "name"}, {"api_name": "posthog.models.filters.stickiness_filter.StickinessFilter", "line_number": 35, "usage_type": "name"}, {"api_name": "ee.clickhouse.queries.util.parse_timestamps", "line_number": 37, "usage_type": "call"}, {"api_name": "ee.clickhouse.models.property.parse_prop_clauses", "line_number": 38, "usage_type": "call"}, {"api_name": "ee.clickhouse.queries.util.get_trunc_func_ch", "line_number": 39, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "name"}, {"api_name": "posthog.constants.TREND_FILTER_TYPE_ACTIONS", "line_number": 43, "usage_type": "name"}, {"api_name": "posthog.models.action.Action.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "posthog.models.action.Action.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "posthog.models.action.Action", "line_number": 44, "usage_type": "name"}, {"api_name": "ee.clickhouse.models.action.format_action_filter", "line_number": 45, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness_actions.STICKINESS_ACTIONS_SQL.format", "line_number": 50, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness_actions.STICKINESS_ACTIONS_SQL", "line_number": 50, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_DISTINCT_ID_SQL", "line_number": 57, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness.STICKINESS_SQL.format", "line_number": 60, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness.STICKINESS_SQL", "line_number": 60, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_DISTINCT_ID_SQL", "line_number": 67, "usage_type": "name"}, {"api_name": "ee.clickhouse.client.sync_execute", "line_number": 70, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 35, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 73, "usage_type": "name"}, {"api_name": "posthog.models.filters.stickiness_filter.StickinessFilter", "line_number": 73, "usage_type": "name"}, {"api_name": "posthog.models.team.Team", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.utils.serializer_helpers.ReturnDict", "line_number": 73, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 77, "usage_type": "name"}, {"api_name": "posthog.constants.TREND_FILTER_TYPE_ACTIONS", "line_number": 78, "usage_type": "name"}, {"api_name": "posthog.models.action.Action.objects.get", "line_number": 80, "usage_type": "call"}, {"api_name": "posthog.models.action.Action.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "posthog.models.action.Action", "line_number": 80, "usage_type": "name"}, {"api_name": "ee.clickhouse.models.action.format_action_filter", "line_number": 81, "usage_type": "call"}, {"api_name": "posthog.models.action.Action.DoesNotExist", "line_number": 84, "usage_type": "attribute"}, {"api_name": "posthog.models.action.Action", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 77, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 93, "usage_type": "name"}, {"api_name": "posthog.models.filters.stickiness_filter.StickinessFilter", "line_number": 93, "usage_type": "name"}, {"api_name": "posthog.models.team.Team", "line_number": 93, "usage_type": "name"}, {"api_name": "ee.clickhouse.queries.util.parse_timestamps", "line_number": 94, "usage_type": "call"}, {"api_name": "ee.clickhouse.models.property.parse_prop_clauses", "line_number": 95, "usage_type": "call"}, {"api_name": "ee.clickhouse.queries.util.get_trunc_func_ch", "line_number": 99, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 101, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness_people.STICKINESS_PEOPLE_SQL.format", "line_number": 109, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.stickiness.stickiness_people.STICKINESS_PEOPLE_SQL", "line_number": 109, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_DISTINCT_ID_SQL", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 93, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 120, "usage_type": "name"}, {"api_name": "posthog.models.filters.stickiness_filter.StickinessFilter", "line_number": 120, "usage_type": "name"}, {"api_name": "posthog.models.team.Team", "line_number": 120, "usage_type": "name"}, {"api_name": "ee.clickhouse.client.sync_execute", "line_number": 124, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.PEOPLE_SQL.format", "line_number": 125, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.PEOPLE_SQL", "line_number": 125, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_SQL.format", "line_number": 128, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_SQL", "line_number": 128, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_DISTINCT_ID_SQL", "line_number": 129, "usage_type": "name"}, {"api_name": "ee.clickhouse.models.person.ClickhousePersonSerializer", "line_number": 133, "usage_type": "call"}, {"api_name": "rest_framework.utils.serializer_helpers.ReturnDict", "line_number": 120, "usage_type": "name"}, {"api_name": "posthog.models.cohort.Cohort", "line_number": 136, "usage_type": "name"}, {"api_name": "posthog.models.entity.Entity", "line_number": 136, "usage_type": "name"}, {"api_name": "posthog.models.filters.stickiness_filter.StickinessFilter", "line_number": 136, "usage_type": "name"}, {"api_name": "ee.clickhouse.client.sync_execute", "line_number": 139, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.INSERT_COHORT_ALL_PEOPLE_SQL.format", "line_number": 140, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.INSERT_COHORT_ALL_PEOPLE_SQL", "line_number": 140, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_SQL.format", "line_number": 143, "usage_type": "call"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_SQL", "line_number": 143, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.PERSON_STATIC_COHORT_TABLE", "line_number": 144, "usage_type": "name"}, {"api_name": "ee.clickhouse.sql.person.GET_LATEST_PERSON_DISTINCT_ID_SQL", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 150, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 150, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 154, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 154, "usage_type": "name"}, {"api_name": "django.db.models.expressions.F", "line_number": 157, "usage_type": "call"}, {"api_name": "sentry_sdk.api.capture_exception", "line_number": 159, "usage_type": "call"}]}
+{"seq_id": "472914162", "text": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\nimport gensim\r\nimport numpy as np\r\n\r\nDELIMITER = '\\t'\r\n\r\n\r\ndef load_voc(fn, delimiter):\r\n voc2id = {}\r\n id2voc = {}\r\n with open(fn, encoding='utf-8') as f:\r\n for l in f:\r\n if l.strip():\r\n items = l.split(delimiter)\r\n if not len(items) == 2:\r\n print(l)\r\n continue\r\n v, vid = items[0].strip(), items[1].strip()\r\n if v and vid:\r\n voc2id[v] = vid\r\n id2voc[vid] = v\r\n return voc2id, id2voc\r\n\r\n\r\nVOC2ID, ID2VOC = load_voc('./models/voc.txt', DELIMITER)\r\nMAX_VOCAB = len(VOC2ID.items()) + 2\r\nprint(MAX_VOCAB)\r\nEMBEDDING_DIM = 150\r\n\r\n\r\nclass LoadVec:\r\n def __init__(self):\r\n self.model = gensim.models.Word2Vec.load('./models/vec.model')\r\n\r\n def getVec(self, word):\r\n return self.model[word]\r\n\r\n\r\nlv = LoadVec()\r\n\r\n\r\ndef get_embedding_metrix():\r\n embedding_matrix = np.zeros((MAX_VOCAB, EMBEDDING_DIM))\r\n count = 0\r\n for w, id in VOC2ID.items():\r\n try:\r\n assert int(id) < MAX_VOCAB\r\n count += 1\r\n embedding_matrix[int(id)] = lv.getVec(w)\r\n except:\r\n embedding_matrix[int(id)] = np.random.rand(150)\r\n\r\n print('#words in embedding matrix: ' + str(count))\r\n return embedding_matrix\r\n\r\n\r\nEMBEDDING_MATRIX = get_embedding_metrix()\r\n\r\n\r\ndef main():\r\n voc2id, id2voc = load_voc('./models/voc.txt', DELIMITER)\r\n print(len(voc2id.items()))\r\n print(len(id2voc.items()))\r\n EMBEDDING_MATRIX = get_embedding_metrix()\r\n print(EMBEDDING_MATRIX)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "NLP (bert, lda kmeans cnn)/lstm/load_voc.py", "file_name": "load_voc.py", "file_ext": "py", "file_size_in_byte": 1687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "gensim.models.Word2Vec.load", "line_number": 34, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}]}
+{"seq_id": "432183762", "text": "import re\nfrom . import *\nfrom db.queries import *\nfrom fs.get_fileinfo import get_fileinfo\nimport settings\nimport shutil\nimport glob\n\n\ndef import_files(directories):\n \"\"\"\n Attempts to recursively import files from values in directories and writes log files with actions taken\n @param directories: a list of directories to import from\n \"\"\"\n print(\"Importing from '{}'\".format(\",\".join(directories)))\n for directory in directories:\n directory = directory.strip()\n if os.path.isdir(directory):\n\n import_history = check_import_path_in_db(directory)\n\n if len(import_history) > 0:\n answer = input(\n \"\\n\\n**** '{}' has already been imported on:\\n\\n{}\\n\\nContinue: [y|N]: \".format(directory,\n '\\n'.join(\n import_history)))\n if not answer.lower() == 'y':\n print(\"**** Skipping '{}'\\n\".format(directory))\n continue\n\n (files_added_to_database, total_files, files_deleted, files_copied, files_with_duplicate_hashes,\n files_with_invalid_extensions) = import_files_work(directory)\n\n add_import_path_to_db(directory, files_added_to_database, total_files, files_deleted,\n files_copied, files_with_duplicate_hashes, files_with_invalid_extensions)\n\n print(\n '\\n' + '*' * 4 + \"\"\" {:,d} total files found. {:,d} copied to file store and {:,d} files were added to the database. {:,d} files had duplicate hashes. {:,d} files had invalid extensions (see log file for details)\"\"\".format(\n total_files, files_copied, files_added_to_database, len(files_with_duplicate_hashes),\n len(files_with_invalid_extensions)))\n\n directory_clean = re.sub('[^\\w\\-_\\. ]', '_', directory)\n\n logfile_name = os.path.join(settings.base_directory,\n \"Import log for \" + directory_clean + \" \" + datetime.datetime.now().strftime(\n \"%H%M%S%f\") + '.txt')\n\n with open(logfile_name, 'w+', encoding=\"utf-16\") as logfile:\n logfile.write('Directory processed: {}\\n\\n'.format(directory))\n logfile.write('Files found: {:,d}\\n'.format(total_files))\n logfile.write('Files copied to file store: {:,d}\\n'.format(files_copied))\n logfile.write('Files added to database: {:,d}\\n'.format(files_added_to_database))\n\n logfile.write('Files with duplicate hashes: {:,d}\\n\\n'.format(len(files_with_duplicate_hashes)))\n\n if files_deleted > 0:\n logfile.write('Number of deleted files: {:,d}\\n\\n'.format(files_deleted))\n\n logfile.write('*' * 78 + '\\n\\n')\n\n logfile.write('The following files had duplicate hashes and were not imported:\\n\\n')\n for item in files_with_duplicate_hashes:\n logfile.write(\"{}\\n\".format(item))\n\n logfile.write('\\n\\nThe following files had invalid extensions and were not imported:\\n\\n')\n for item in files_with_invalid_extensions:\n logfile.write(\"{}\\n\".format(item))\n\n if settings.delete_existing and files_deleted > 0:\n print(' ' * 5 + '{:,d} files were deleted'.format(files_deleted))\n else:\n print(\"\\t'{}' does not exist!\".format(directory))\n\n # after import, tell the user to see generated logs (one per directory) in the main directory\n # but only if we actually attempted to import something\n if len(directories) > 0 and 'logfile_name' in locals():\n print(\"\\n\\nSee log files in {} for details.\".format(settings.base_directory))\n\n\ndef import_files_work(dirname):\n files_with_invalid_extensions = [] # list of files we didn't import.\n\n total_files = 0\n files_added_to_database = 0\n files_deleted = 0\n files_with_duplicate_hashes = []\n files_copied = 0\n\n # Looking up each hash is sllllllow, so pull em all in as a set and just look there!\n print(\"Getting existing hashes from database...\", end='')\n existing_hashes = get_sha1b32_from_database()\n\n print(\"Got {:,d} hashes from database. Looking for files.\\n\".format(len(existing_hashes)))\n\n for dirpath, dirnames, files in os.walk(dirname, topdown=False):\n\n total_files += len(files)\n\n file_counter = 0\n\n if len(files) > 0:\n safeprint(\"\\n\\tFound {:,d} files in {}. Processing...\".format(len(files), dirpath))\n\n # logger.info(\"Found {:,d} files in {}\".format(len(files), dirpath))\n\n for name in files:\n full_path_name = os.path.join(dirpath, name)\n\n file_counter += 1\n\n if os.path.isfile(full_path_name):\n\n if os.path.getsize(full_path_name) == 0:\n safeprint(\"\\t\\tDeleting 0 byte file '{}'.\".format(full_path_name))\n os.remove(full_path_name)\n continue\n\n parts = os.path.splitext(name.lower())\n if len(parts) == 2:\n ext = parts[1]\n\n # some files are always bad, so just make em go away.\n if ext in auto_delete_extensions:\n safeprint(\n '\\t\\t({} [{:,d}/{:,d}]): File {} has an autonuke extension. Deleting...'.format(\n datetime.datetime.now().strftime('%x %X'),\n file_counter,\n len(files), full_path_name))\n os.remove(full_path_name)\n continue\n\n if ext in extensions:\n # logger.info(\n # \"{} before fileinfo = get_file_data(full_path_name)\".format(\n # datetime.datetime.now().strftime('%x %X')))\n\n fileinfo = get_fileinfo(full_path_name)\n\n # logger.info(\"{} after fileinfo = get_file_data(full_path_name)\".format(\n # datetime.datetime.now().strftime('%x %X')))\n\n if not fileinfo['hashes']['sha1b32'] in existing_hashes:\n files_added_to_database += 1\n\n safeprint(\"\\t\\t({} [{:,d}/{:,d}]): '{}' does not exist in database! Adding...\".format\n (datetime.datetime.now().strftime('%x %X'),\n file_counter,\n len(files),\n full_path_name))\n\n # since this is a new file, we add it to our set for future import operations\n existing_hashes.add(fileinfo['hashes']['sha1b32'])\n\n add_file_to_db(fileinfo)\n else:\n pass # do anything else here? should i check if file exists in file system? who cares tho\n # as this syncs it up maybe here is where you do extra hashing of what is on file\n # system to make sure the 2 match, properly named, etc\n\n # logger.info(\"{} before copied = copy_file_to_store(fileinfo)):\".format(\n # datetime.datetime.now().strftime('%x %X')))\n\n copied = copy_file_to_store(fileinfo)\n\n if copied:\n safeprint(\n '\\t\\t({} [{:,d}/{:,d}]): File with SHA-1 Base32 hash {} does not exist in file store! Copying {:,d} bytes...'.format(\n datetime.datetime.now().strftime('%x %X'),\n file_counter,\n len(files), fileinfo['hashes']['sha1b32'], fileinfo['filesize']))\n\n # logger.info(\"{} after copied = copy_file_to_store(fileinfo)):\".format(\n # datetime.datetime.now().strftime('%x %X')))\n\n if not copied:\n files_with_duplicate_hashes.append(full_path_name)\n else:\n files_copied += 1\n\n if len(settings.copy_new_destination) > 0 and copied:\n if not os.path.exists(settings.copy_new_destination):\n os.mkdir(settings.copy_new_destination)\n\n # TODO should this create the 2 char structure too? for now, just copy it\n\n copy_name = os.path.join(settings.copy_new_destination, name)\n\n unique_prefix = 0\n\n while os.path.isfile(copy_name):\n # file exists, so get a unique name\n copy_name = os.path.join(settings.copy_new_destination,\n str(unique_prefix) + \"_\" + name)\n unique_prefix += 1\n\n shutil.copyfile(full_path_name, copy_name)\n\n outfile = os.path.join(settings.copy_new_destination,\n \"!!\" + datetime.datetime.now().strftime(\n \"%Y-%m-%d\") + \" File copy log \" + '.txt')\n with open(outfile, 'a', encoding=\"utf-16\") as logfile:\n logfile.write(\n \"{}: Copied {} to {}.\\n\".format(datetime.datetime.now(), full_path_name, copy_name))\n\n if settings.delete_existing:\n safeprint(\"\\t\\t({} [{:,d}/{:,d}]): Deleting '{}'...\".format(\n datetime.datetime.now().strftime('%x %X'),\n file_counter,\n len(files),\n full_path_name))\n\n if settings.delete_existing == 'yes':\n os.remove(full_path_name)\n\n files_deleted += 1\n else:\n files_with_invalid_extensions.append(os.path.join(dirpath, name))\n\n if settings.delete_empty_directories:\n if not os.listdir(dirpath):\n safeprint(\"\\t\\t({} [{:,d}/{:,d}]): Deleting empty directory '{}'...\".format(\n datetime.datetime.now().strftime('%x %X'), file_counter, len(files), dirpath))\n if settings.delete_empty_directories == 'yes':\n os.rmdir(dirpath)\n\n return (files_added_to_database, total_files, files_deleted, files_copied, files_with_duplicate_hashes,\n files_with_invalid_extensions)\n\n\ndef copy_file_to_store(fileinfo):\n \"\"\"Checks datastore for a file with identical sha1b32 hash.\n if one exists, optionally delete the source file\n optionally copy new file to separate directory for sharing purposes\n \"\"\"\n\n filename = fileinfo['inputfile']\n base_filename = os.path.split(filename)[-1]\n base_filename_parts = os.path.splitext(base_filename)\n file_ext = base_filename_parts[1].lower()\n\n files_directory = os.path.join(settings.base_directory, 'files')\n\n file_directory = os.path.join(files_directory, fileinfo['hashes']['sha1b32'][0:2])\n\n if not os.path.exists(file_directory):\n os.mkdir(file_directory)\n\n target_filemask = os.path.join(file_directory, fileinfo['hashes']['sha1b32'] + '*')\n\n dest_filename = os.path.join(file_directory, fileinfo['hashes']['sha1b32'] + file_ext)\n\n listing = glob.glob(target_filemask)\n\n file_copied = False\n\n if len(listing) == 0:\n shutil.copyfile(filename, dest_filename)\n file_copied = True\n\n return file_copied", "sub_path": "ops/import_files.py", "file_name": "import_files.py", "file_ext": "py", "file_size_in_byte": 12150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "settings.base_directory", "line_number": 44, "usage_type": "attribute"}, {"api_name": "settings.delete_existing", "line_number": 69, "usage_type": "attribute"}, {"api_name": "settings.base_directory", "line_number": 77, "usage_type": "attribute"}, {"api_name": "fs.get_fileinfo.get_fileinfo", "line_number": 137, "usage_type": "call"}, {"api_name": "settings.copy_new_destination", "line_number": 180, "usage_type": "attribute"}, {"api_name": "settings.copy_new_destination", "line_number": 181, "usage_type": "attribute"}, {"api_name": "settings.copy_new_destination", "line_number": 182, "usage_type": "attribute"}, {"api_name": "settings.copy_new_destination", "line_number": 186, "usage_type": "attribute"}, {"api_name": "settings.copy_new_destination", "line_number": 192, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 196, "usage_type": "call"}, {"api_name": "settings.copy_new_destination", "line_number": 198, "usage_type": "attribute"}, {"api_name": "settings.delete_existing", "line_number": 205, "usage_type": "attribute"}, {"api_name": "settings.delete_existing", "line_number": 212, "usage_type": "attribute"}, {"api_name": "settings.delete_empty_directories", "line_number": 219, "usage_type": "attribute"}, {"api_name": "settings.delete_empty_directories", "line_number": 223, "usage_type": "attribute"}, {"api_name": "settings.base_directory", "line_number": 241, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 252, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 257, "usage_type": "call"}]}
+{"seq_id": "484125408", "text": "from rest_framework import serializers\n\nfrom level.models import UserLevelProgress, Level\n\n\nclass AddLevelProgressSerializer(serializers.ModelSerializer):\n level = serializers.SlugRelatedField(slug_field='num', queryset=Level.objects.all())\n\n class Meta:\n model = UserLevelProgress\n fields = ('level', 'points')\n\n def create(self, validated_data):\n level = validated_data.get('level')\n points = validated_data.get('points')\n\n progress = UserLevelProgress.objects.update_or_create(user=self.context['request'].user, level=level,\n defaults={'points': points})\n self.context['request'].user.rating += points\n self.context['request'].user.save()\n return progress\n", "sub_path": "level/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "level.models", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "level.models.Level.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "level.models.Level.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "level.models.Level", "line_number": 7, "usage_type": "name"}, {"api_name": "level.models.UserLevelProgress", "line_number": 10, "usage_type": "name"}, {"api_name": "level.models", "line_number": 14, "usage_type": "name"}, {"api_name": "level.models.UserLevelProgress.objects.update_or_create", "line_number": 17, "usage_type": "call"}, {"api_name": "level.models.UserLevelProgress.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "level.models.UserLevelProgress", "line_number": 17, "usage_type": "name"}, {"api_name": "level.models", "line_number": 17, "usage_type": "name"}]}
+{"seq_id": "84837894", "text": "from ase.io import read\nfrom espresso import espresso\nfrom ase.optimize import QuasiNewton\n\nslab_ads=read('Pt+CO.traj')\nslab_ads.calc=espresso(pw=450,\n dw=4500,\n kpts=(5,7,1),\n xc='PBE',\n outdir='E_slab_ads',#espresso outdirectory saved\n #here\n convergence={'energy':1e-6,\n 'mixing':0.05,\n 'mixing_mode':'local-TF',\n 'maxsteps':1000,\n 'diag':'cg'})\n\nrelax_slab_ads=QuasiNewton(slab_ads,\n logfile='opt.log',\n trajectory='opt.traj',\n restart='opt.pckl') #ase output\nrelax_slab_ads.run(fmax=0.05)\n\nE_slab_ads=slab_ads.get_potential_energy()\n\nprint(E_slab_ads)\n", "sub_path": "tutorial/single_point_geometry_optimization.py", "file_name": "single_point_geometry_optimization.py", "file_ext": "py", "file_size_in_byte": 928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "ase.io.read", "line_number": 5, "usage_type": "call"}, {"api_name": "espresso.espresso", "line_number": 6, "usage_type": "call"}, {"api_name": "ase.optimize.QuasiNewton", "line_number": 18, "usage_type": "call"}]}
+{"seq_id": "503512255", "text": "from bs4 import BeautifulSoup\n\ndef weatherToday(pageString):\n bsObj = BeautifulSoup(pageString, \"html.parser\")\n div = bsObj.find(\"div\", {\"class\": \"main_info\"})\n if div is None:\n return None\n ondo = div.find(\"p\", {\"class\": \"info_temperature\"})\n weather = div.find(\"ul\", {\"class\": \"info_list\"})\n week = weather.findAll(\"li\", {\"class\": \"\"})\n todayAll = {}\n # 오늘\n # 두번째칸-1\n todayTemp = bsObj.findAll(\"ul\", {\"class\": \"list_area\"})\n todayTemp1 = todayTemp[0].findAll(\"li\", {\"class\": \"on\"})\n todayTemp2 = todayTemp[0].findAll(\"li\", {\"class\": \"\"})\n todayTemp3 = todayTemp[0].find(\"li\", {\"class\": \"last\"})\n\n # 세번째칸\n munji = bsObj.find(\"dl\", {\"class\": \"indicator\"})\n mise = munji.text.split()\n\n weekWeather = bsObj.findAll(\"ul\", {\"class\": \"list_area _pageList\"})\n weekWeather1 = weekWeather[0].findAll(\"li\", {\"class\": \"date_info today\"})\n weekWeather2 = weekWeather[1].findAll(\"li\", {\"class\": \"date_info today\"})\n\n area = bsObj.find(\"div\", {\"class\": \"select_box\"})\n areaA = area.find(\"em\")\n # 오늘 끝\n\n\n # 오늘 시작\n weekWeather1_1 = []\n weekWeather2_1 = []\n weekWeatherSum = {}\n weekWeatherOndo = []\n for i in range(0, 5):\n weekWeather1_1.append(weekWeather1[i].text.split())\n\n weekWeatherOndo.append(weekWeather1_1[i][8].split('/'))\n for i in range(0, 5):\n weekWeather2_1.append(weekWeather2[i].text.split())\n weekWeatherOndo.append(weekWeather2_1[i][8].split('/'))\n\n weekWeatherSum = {0: {\"요일\": weekWeather1_1[0][0], \"날짜\": weekWeather1_1[0][1], \"오전강수\": weekWeather1_1[0][3],\n \"오후강수\": weekWeather1_1[0][5], \"최고기온\": weekWeatherOndo[0][0], \"최저기온\": weekWeatherOndo[0][1]},\n 1: {\"요일\": weekWeather1_1[1][0], \"날짜\": weekWeather1_1[1][1], \"오전강수\": weekWeather1_1[1][3],\n \"오후강수\": weekWeather1_1[1][5], \"최고기온\": weekWeatherOndo[1][0], \"최저기온\": weekWeatherOndo[1][1]},\n 2: {\"요일\": weekWeather1_1[2][0], \"날짜\": weekWeather1_1[2][1], \"오전강수\": weekWeather1_1[2][3],\n \"오후강수\": weekWeather1_1[2][5], \"최고기온\": weekWeatherOndo[2][0], \"최저기온\": weekWeatherOndo[2][1]},\n 3: {\"요일\": weekWeather1_1[3][0], \"날짜\": weekWeather1_1[3][1], \"오전강수\": weekWeather1_1[3][3],\n \"오후강수\": weekWeather1_1[3][5], \"최고기온\": weekWeatherOndo[3][0], \"최저기온\": weekWeatherOndo[3][1]},\n 4: {\"요일\": weekWeather1_1[4][0], \"날짜\": weekWeather1_1[4][1], \"오전강수\": weekWeather1_1[4][3],\n \"오후강수\": weekWeather1_1[4][5], \"최고기온\": weekWeatherOndo[4][0], \"최저기온\": weekWeatherOndo[4][1]},\n 5: {\"요일\": weekWeather2_1[0][0], \"날짜\": weekWeather2_1[0][1], \"오전강수\": weekWeather2_1[0][3],\n \"오후강수\": weekWeather2_1[0][5], \"최고기온\": weekWeatherOndo[5][0], \"최저기온\": weekWeatherOndo[5][1]},\n 6: {\"요일\": weekWeather2_1[1][0], \"날짜\": weekWeather2_1[1][1], \"오전강수\": weekWeather2_1[1][3],\n \"오후강수\": weekWeather2_1[1][5], \"최고기온\": weekWeatherOndo[6][0], \"최저기온\": weekWeatherOndo[6][1]},\n 7: {\"요일\": weekWeather2_1[2][0], \"날짜\": weekWeather2_1[2][1], \"오전강수\": weekWeather2_1[2][3],\n \"오후강수\": weekWeather2_1[2][5], \"최고기온\": weekWeatherOndo[7][0], \"최저기온\": weekWeatherOndo[7][1]},\n 8: {\"요일\": weekWeather2_1[3][0], \"날짜\": weekWeather2_1[3][1], \"오전강수\": weekWeather2_1[3][3],\n \"오후강수\": weekWeather2_1[3][5], \"최고기온\": weekWeatherOndo[8][0], \"최저기온\": weekWeatherOndo[8][1]},\n 9: {\"요일\": weekWeather2_1[4][0], \"날짜\": weekWeather2_1[4][1], \"오전강수\": weekWeather2_1[4][3],\n \"오후강수\": weekWeather2_1[4][5], \"최고기온\": weekWeatherOndo[9][0], \"최저기온\": weekWeatherOndo[9][1]}\n }\n\n todayFirstWeather = weather.text.split()\n todayLowOndo = todayFirstWeather[4].split('/')\n todayTempEnd = todayTemp[0].text.split()\n todayTempSum = {}\n\n num=0\n for i in range(0, 8):\n # 2,9,16,\n todayTempSum[i] = {}\n if todayTempEnd[6 + i * 7]==\"내일\":\n num=1\n todayTempSum[i][\"시간\"] = todayTempEnd[7 + i * 7]\n todayTempSum[i][\"온도\"] = todayTempEnd[2 + i * 7]\n todayTempSum[i][\"날씨\"] = todayTempEnd[4 + i * 7]\n elif num==1:\n todayTempSum[i][\"시간\"] = todayTempEnd[7 + i * 7]\n todayTempSum[i][\"온도\"] = todayTempEnd[3 + i * 7]\n todayTempSum[i][\"날씨\"] = todayTempEnd[5 + i * 7]\n else:\n todayTempSum[i][\"시간\"] = todayTempEnd[6 + i * 7]\n todayTempSum[i][\"온도\"] = todayTempEnd[2 + i * 7]\n todayTempSum[i][\"날씨\"] = todayTempEnd[4 + i * 7]\n\n\n todayWeather = {\"지역\": areaA.text, \"날씨\": week[0].text, \"온도\": ondo.text, \"최저기온\": todayLowOndo[0],\n \"최고기온\": todayLowOndo[1], \"체감온도\": todayFirstWeather[6], \"미세먼지\": mise[1],\n \"초미세먼지\": mise[3], \"오존지수\": mise[5]}\n\n # 오늘 끝\n\n todayAll={\"날씨\":todayWeather,\"시간별날씨\":todayTempSum,\"주간날씨\":weekWeatherSum}\n return todayAll", "sub_path": "crawler/weatherToday.py", "file_name": "weatherToday.py", "file_ext": "py", "file_size_in_byte": 5611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 4, "usage_type": "call"}]}
+{"seq_id": "431642582", "text": "# -*- coding: utf-8 -*-\n\nimport sys\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\nfrom common import public\n\n\n\n\nclass dishonesty():\n \"\"\"失信被执行人\"\"\"\n need_check_ziduan = [u'bbd_dotime',\n u'pname',\n u'pname_id',\n u'frname',\n u'province',\n u'exe_code',\n u'case_create_time',\n u'case_code',\n u'pubdate'\n ]\n ########################规则######################################\n\n def check_bbd_dotime(self, indexstr, ustr):\n \"\"\"bbd_dotime 校验\n 可以为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if not public.bbd_dotime_date_format(ustr):\n ret = u\"不合法日期\"\n return ret\n\n def check_pname(self, indexstr, ustr):\n \"\"\" 被执行人姓名/名称 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if any(c in u\"()、,，；/\\\\\" for c in unicode(ustr)):\n ret = u\"存在特殊字符\"\n elif unicode(ustr).endswith(u'。'):\n ret = u\"以。结尾\"\n return ret\n\n def check_pname_id(self, indexstr, ustr):\n \"\"\"身份证号码/组织机构代码 校验\n 可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n for c in ustr:\n if public.is_number(c) \\\n or public.is_alphabet(c) \\\n or public.is_chinese(c) \\\n or c in '-*':\n pass\n else:\n ret = u\"存在特殊字符\"\n if u'--' in ustr:\n ret = u'有--'\n return ret\n\n def check_frname(self, indexstr, ustr):\n \"\"\"法定代表人或者负责人姓名 校验\n 可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if any(c in u\" ()、,，/／。\\\\＼\" for c in ustr):\n ret = u\"存在特殊字符\"\n return ret\n\n def check_province(self, indexstr, ustr):\n \"\"\"省份 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if ustr not in public.PROVINCE:\n ret = u\"不为省名\"\n return ret\n\n def check_exe_code(self, indexstr, ustr):\n \"\"\"执行依据文号 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if any(c in u\" ()【】[]］［、,，；。.．：:\" for c in unicode(ustr)):\n ret = u\"存在特殊字符\"\n return ret\n\n def check_case_create_time(self, indexstr, ustr):\n \"\"\"立案时间 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if not public.date_format(ustr):\n ret = u\"不合法日期\"\n return ret\n\n def check_case_code(self, indexstr, ustr):\n \"\"\"案号 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if public.is_include_specialchar(u'()', ustr):\n ret = u\"包含半角括号\"\n return ret\n\n def check_exec_basunit(self, indexstr, ustr):\n \"\"\"做出执行依据单位 校验\n 可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if any(c in u\" ()、,，；\" for c in ustr):\n ret = u\"存在特殊字符\"\n return ret\n\n def check_pubdate(self, indexstr, ustr):\n \"\"\"发布时间 校验\n 不可为空\n \"\"\"\n ret = None\n if ustr and len(ustr):\n if not public.date_format(ustr):\n ret = u\"pubdate不合法日期\"\n return ret\n ##############################################################\n\n\n", "sub_path": "src/clean/dishonesty.py", "file_name": "dishonesty.py", "file_ext": "py", "file_size_in_byte": 3969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 6, "usage_type": "call"}, {"api_name": "common.public.bbd_dotime_date_format", "line_number": 32, "usage_type": "call"}, {"api_name": "common.public", "line_number": 32, "usage_type": "name"}, {"api_name": "common.public.is_number", "line_number": 55, "usage_type": "call"}, {"api_name": "common.public", "line_number": 55, "usage_type": "name"}, {"api_name": "common.public.is_alphabet", "line_number": 56, "usage_type": "call"}, {"api_name": "common.public", "line_number": 56, "usage_type": "name"}, {"api_name": "common.public.is_chinese", "line_number": 57, "usage_type": "call"}, {"api_name": "common.public", "line_number": 57, "usage_type": "name"}, {"api_name": "common.public.PROVINCE", "line_number": 82, "usage_type": "attribute"}, {"api_name": "common.public", "line_number": 82, "usage_type": "name"}, {"api_name": "common.public.date_format", "line_number": 102, "usage_type": "call"}, {"api_name": "common.public", "line_number": 102, "usage_type": "name"}, {"api_name": "common.public.is_include_specialchar", "line_number": 112, "usage_type": "call"}, {"api_name": "common.public", "line_number": 112, "usage_type": "name"}, {"api_name": "common.public.date_format", "line_number": 132, "usage_type": "call"}, {"api_name": "common.public", "line_number": 132, "usage_type": "name"}]}
+{"seq_id": "598724996", "text": "import MySQLdb\nimport csv\nclass ConnectionClass:\n #intialize the mysql\n tableName='agentsale'\n forecastTableName='agentsaleforecast'\n createTableQ='create table '+tableName +' (agentid int, sales double, month int,year int )'\n getMarchQuery=\"select SalesValue from Agents where Month='Mar' and Year='17' and AgentName=%s\"\n conInfo={'host':'localhost','user':'root','password':'root','db':'manchester','saleTable':tableName }\n agentCount=13\n insertQuery='insert into '+tableName+' values (%s,%s,%s,%s)'\n dataFolder='timeSeriesData'\n createResultTableQuery='create table if not exists '+forecastTableName +' (agentid int, sales double, month int,year int)'\n insertForecastQuery='insert into '+forecastTableName+' values (%s,%s,%s,%s)'\n fetchForecastResultQuery='select * from '+forecastTableName+' where agentid=%s'\n month=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n #insert into table\n def getMonth(self,id):\n return self.month[id]\n def insertToTable(self,name,value,year,month):\n cursor=self.db.cursor()\n #print(\"## insert start\")\n #print(str(type(name))+':'+str(type(value))+':'+str(type(year))+str(type(month)))\n cursor.execute(self.insertQuery,[str(name),str(value),str(month),str(year)])\n self.db.commit()\n #print(\"## insert end\")\n #initialize after creating table\n def __init__(self,inAgentCount=-1):\n print(\"## starting init\")\n #self.db=MySQLdb.connect(ConnectionClass.conInfo['shailesh.mysql.pythonanywhere-services.com'],ConnectionClass.conInfo['shailesh'],ConnectionClass.conInfo['tayde123'],ConnectionClass.conInfo['shailesh$manchester'])\n _dbname = \"shailesh$manchester\"\n user = \"shailesh\"\n password = \"tayde123\"\n self.db = MySQLdb.connect(\"shailesh.mysql.pythonanywhere-services.com\", user, password, _dbname)\n if(inAgentCount!=-1):\n self.agentCount=inAgentCount\n cursor=self.db.cursor()\n try:\n print(\"## Table creation started\")\n #self.insertToTable(1,20,100,90)\n cursor.execute(ConnectionClass.createTableQ)\n\n print(\"## table created\")\n baseName='Agent '\n for ele in range(1,self.agentCount+1):\n result=cursor.execute(self.getMarchQuery,[baseName+str(ele)])\n #result=cursor.execute(\"select SalesValue from Agents where Month='Mar' and Year='17' and AgentName='Agent 12'\")\n result=cursor.fetchone()[0]\n #result=result.fetchall()\n #if(len(result)==0):\n # raise Exception(\"result is null\")\n #result=long(result)\n print(\"Fetch result\"+str(result))\n month=3\n year=17\n if(result!=None):\n marValue=float(result)\n if(int(marValue)==0):\n marValue=1\n dictF=self.readAndGetFromFile(ele)\n ratio=-1\n j=0\n for row in dictF:\n #for row in dictF:\n ##row=dictF.\n j=j+1\n if(j==36):\n break\n\n value=float(row['value'])\n ##calculate the ration first\n if(ratio==-1):\n ratio=marValue/value\n self.insertToTable(ele,marValue,year,month)\n else:\n value=float(value)*ratio\n self.insertToTable(ele,value,year,month)\n month=month-1\n if(month==0):\n month=12\n year-=1\n\n\n\n\n except Exception as e:\n print(\"Error in executing \"+str(e))\n #raise e\n pass\n def fetchForecastResult(self,id):\n cursor=self.db.cursor()\n try:\n count=cursor.execute(self.fetchForecastResultQuery,[id])\n if(count>0):\n result=cursor.fetchall()\n value=[]\n for ele in result:\n temp={'value':ele[1],'month':self.getMonth(ele[2]),'year':ele[3]}\n #print(temp)\n value.append(temp)\n return value\n except Exception as e:\n print(e)\n return None\n\n\n def readAndGetFromFile(self,id):\n id=id%4+1\n file=open(self.dataFolder+'/'+'Agent-'+str(id)+'.csv')\n return csv.DictReader(file)\n def getConnection(self):\n return self.db\n\n def close(self):\n self.db.close()\n def getReadValueQuery(self,id):\n return 'select sales from '+self.tableName+' where agentid='+str(id)\n def createResultTable(self):\n cursor=self.db.cursor()\n cursor.execute(self.createResultTableQuery)\n self.db.commit()\n\n def insertForecast(self,id,prediction,month,year,create=False):\n try:\n\n self.createResultTable()\n cursor=self.db.cursor()\n for i in range(0,len(prediction)):\n cursor.execute(self.insertForecastQuery,(str(id),str(prediction[i]),str(month[i]),str(year[i])))\n self.db.commit()\n except Exception as e :\n print(e)\n\n\n\n\nprint(\"## Executing class\")\ncl= ConnectionClass()\n", "sub_path": "manchester/manchesterV1/ConnectionClass.py", "file_name": "ConnectionClass.py", "file_ext": "py", "file_size_in_byte": 5473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "MySQLdb.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 110, "usage_type": "call"}]}
+{"seq_id": "9620543", "text": "\"\"\"\nLogin and authentication views and related authentication setup logic\n\"\"\"\n\n__author__ = \"Graham Klyne (GK@ACM.ORG)\"\n__copyright__ = \"Copyright 2016, G. Klyne\"\n__license__ = \"MIT (http://opensource.org/licenses/MIT)\"\n\n# @@TODO: define a view decorator to apply authentication requirement\n\nimport os\nimport re\nimport json\nimport markdown\nimport copy\nimport uuid\nimport urllib\nfrom urlparse import urlparse, urljoin\nfrom importlib import import_module\n\nimport logging\nlog = logging.getLogger(__name__)\n\nfrom django.core.urlresolvers import resolve, reverse\nfrom django.http import HttpResponse\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext, loader\nfrom django.views import generic\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.models import User\n\nfrom utils.http_errors import error400values\n\nfrom login.auth_django_client import django_flow_from_user_id\nfrom auth_oidc_client import (\n oauth2_flow_from_provider_details, oauth2_flow_to_dict, oauth2_get_state_token, \n SCOPE_DEFAULT\n )\nfrom login_utils import HttpResponseRedirectWithQuery, HttpResponseRedirectLogin\nfrom models import CredentialsModel, get_user_credential\nimport login_message\n\n# Per-instance generated secret key for CSRF protection via OAuth2 state value.\n# Regenerated each time this service is started.\n\nPROVIDER_FILES = None\n\nPROVIDER_DETAILS = None\n\nsettings = import_module(os.environ[\"DJANGO_SETTINGS_MODULE\"])\n\ndef collect_provider_details():\n global PROVIDER_FILES, PROVIDER_DETAILS\n if PROVIDER_DETAILS is None:\n PROVIDER_DETAILS = {}\n PROVIDER_FILES = {}\n clientsecrets_dirname = os.path.join(settings.CONFIG_BASE, \"providers/\")\n if os.path.isdir(clientsecrets_dirname):\n clientsecrets_files = os.listdir(clientsecrets_dirname)\n for f in clientsecrets_files:\n if f.endswith(\".json\"):\n p = os.path.join(clientsecrets_dirname,f)\n j = json.load(open(p, \"r\"))\n n = j['web']['provider']\n PROVIDER_FILES[n] = p\n PROVIDER_DETAILS[n] = j['web']\n if 'provider_label' not in PROVIDER_DETAILS[n]:\n PROVIDER_DETAILS[n]['provider_label'] = n\n return\n\ndef _untested_authentication_required(\n login_form_url=None, login_post_url=None, login_done_url=None, \n continuation_url=None):\n \"\"\"\n Decorator for view handler function that activates authentication flow\n if the current request is not already associated with an authenticated user.\n \"\"\"\n # @@NOTE: not tested; the mix of static and dynamic parameters required makes\n # the in-line form easier to use than a decorator.\n def decorator(func):\n def guard(view, values):\n return (\n confirm_authentication(view, \n login_form_url, login_post_url, login_done_url, \n continuation_url)\n or\n func(view, values)\n )\n return guard\n return decorator\n\ndef confirm_authentication(view, \n login_form_url=None, login_post_url=None, login_done_url=None, \n user_profile_url=None, continuation_url=None, \n help_path=\"annalist/views/help/\"):\n \"\"\"\n Return None if required authentication is present, otherwise\n a login redirection response to the supplied URI\n\n view.credential is set to credential that can be used to access resource\n\n Five URL parameters are passed in from the calling application:\n\n login_form_url Page to gather information to initiate login process\n login_post_url URL to which login information is posted\n login_done_url URL retrieved with additional parameters when authentication\n is complete (maybe failed). In the OAuth2 flow, this triggers\n retrieval of user profile information. Not used for local\n authentication.\n user_profile_url URL retrieved when user profile details have been set up.\n continuation_url URL from which the login process was initiated.\n \"\"\"\n if view.request.user.is_authenticated():\n view.credential = get_user_credential(view.request.user)\n # log.info(\"view.credential %r\"%(view.credential,))\n if view.credential is not None:\n if not view.credential.invalid:\n return None # Valid credential present: proceed...\n else:\n # Django login with local credential: check for user email address\n #\n # @@TODO: is this safe?\n # \n # NOTE: currently, view.credential is provided by the oauth2 and used\n # only for the .invalid test above. If it is ever used by other \n # application components, it may be necessary to construct a\n # credential for local logins. In the long run, if credentials will\n # be used to access third party services or resources, it may not be \n # possible to use non-Oauth2 credentials here. In the meanwhile,\n # allowing local Django user credentials provides an easier route for\n # getting a software instance installed for evaluation purposes.\n #\n if view.request.user.email:\n return None # Assume valid login: proceed...\n else:\n return error400values(view, \"Local user has no email address\")\n if not login_form_url:\n return error400values(view, \"No login form URI specified\")\n if not login_done_url:\n return error400values(view, \"No login completion URI specified\")\n if not login_post_url:\n login_post_url = login_form_url\n if not continuation_url:\n continuation_url = view.request.path\n # Redirect to initiate login sequence \n view.request.session['login_form_url'] = login_form_url\n view.request.session['login_post_url'] = login_post_url\n view.request.session['login_done_url'] = login_done_url\n view.request.session['user_profile_url'] = user_profile_url\n view.request.session['continuation_url'] = continuation_url\n view.request.session['help_dir'] = os.path.join(settings.SITE_SRC_ROOT, help_path)\n userid = view.request.POST.get(\"userid\", \n view.request.GET.get(\"userid\",\n view.request.session.get('recent_userid', \"\")\n )\n ) \n query_params = (\n { \"userid\": userid\n , \"continuation_url\": continuation_url\n })\n query_params.update(view.get_message_data())\n return HttpResponseRedirectWithQuery(login_form_url, query_params)\n\nclass LoginUserView(generic.View):\n \"\"\"\n View class to present login form to gather user id and other login information.\n\n The login page solicits a user id and an identity provider\n\n The login page supports the following request parameters:\n\n continuation_url={uri}\n - a URL for a page that is displayed when the login process is complete.\n \"\"\"\n\n def get(self, request):\n collect_provider_details()\n # @@TODO: check PROVIDER_FILES, report error if none here\n # Retrieve request parameters\n continuation_url = request.GET.get(\"continuation_url\", \"/no-login-continuation/\")\n # Check required values in session - if missing, restart sequence from original URI\n # This is intended to avoid problems if this view is invoked out of sequence\n login_post_url = request.session.get(\"login_post_url\", None)\n login_done_url = request.session.get(\"login_done_url\", None)\n user_profile_url = request.session.get(\"user_profile_url\", None)\n help_dir = request.session.get(\"help_dir\", None)\n recent_userid = request.session.get(\"recent_userid\", \"\")\n if ( (login_post_url is None) or \n (login_done_url is None) or \n (user_profile_url is None) or \n (help_dir is None) ):\n log.warning(\n \"LoginUserView: missing details \"+\n \"login_post_url %s, login_done_url %s, user_profile_url %s, help_dir %s\"%\n (login_post_url, login_done_url, user_profile_url, help_dir)\n )\n return HttpResponseRedirect(continuation_url)\n # Display login form\n default_provider = \"\"\n provider_labels = map( \n lambda pair: pair[1], \n sorted(\n [ ( p.get('provider_order', 5),\n (k, p.get('provider_label', k), p.get('provider_image', None))\n )\n for k, p in PROVIDER_DETAILS.items()\n ])\n )\n for p in PROVIDER_DETAILS:\n if \"default\" in PROVIDER_DETAILS[p]: \n default_provider = PROVIDER_DETAILS[p][\"default\"]\n logindata = (\n { \"login_post_url\": login_post_url\n , \"login_done_url\": login_done_url\n , \"user_profile_url\": user_profile_url\n , \"continuation_url\": continuation_url\n , \"provider_keys\": PROVIDER_DETAILS.keys()\n , \"provider_labels\": provider_labels\n , \"provider\": default_provider\n , \"suppress_user\": True\n , \"help_filename\": \"login-help\"\n , \"userid\": request.GET.get(\"userid\", recent_userid)\n , \"info_head\": request.GET.get(\"info_head\", None)\n , \"info_message\": request.GET.get(\"info_message\", None)\n , \"error_head\": request.GET.get(\"error_head\", None)\n , \"error_message\": request.GET.get(\"error_message\", None)\n })\n # Load help text if available\n if \"help_filename\" in logindata:\n help_filepath = help_dir + \"%(help_filename)s.md\"%(logindata)\n if os.path.isfile(help_filepath):\n with open(help_filepath, \"r\") as helpfile:\n logindata[\"help_markdown\"] = helpfile.read()\n if \"help_markdown\" in logindata:\n logindata[\"help_text\"] = markdown.markdown(logindata[\"help_markdown\"])\n # Render form & return control to browser\n template = loader.get_template(\"login.html\")\n context = RequestContext(self.request, logindata)\n return HttpResponse(template.render(context))\n\nclass LoginPostView(generic.View):\n \"\"\"\n View class to initiate an authentication flow, typically on POST \n of the login form.\n\n It saves the supplied user id in a session value, and redirects the user to the \n identity provider, which in due course returns control to the application along \n with a suitable authorization grant.\n\n The login form provides the following values:\n\n userid={string}\n - a user identifying string that will be associated with the external service\n login credentials.\n provider={string}\n - a string that identifies a provider selectred to perform authentication\n of the indicated user. This string is an index to PROVIDER_FILES,\n which in turn contains filenames for client secrets to user when accessing\n the indicated identity provider.\n login_done={uri}\n - a URI that is retrieved, with a suitable authorization grant as a parameter, \n when appropriate permission has been confirmed by an authenticated user.\n Used to obtain user information following completion of authentication.\n Communicated via a hidden form value.\n user_profile_url={uri}\n - a URI that is retrieved, when user information has been obtained. Expected use\n is to display user information, thenm continue tyo the page from which the\n login sequence was invoked. Communicated via a hidden form value.\n continuation_url={uri}\n - URL of page from which logon sequence was invoked, and to which control is\n eventually returned. Communicated via a hidden form value.\n \"\"\"\n\n def post(self, request):\n # Retrieve request parameters\n userid = request.POST.get(\"userid\", \"\")\n provider = request.POST.get(\"provider\", \"No_provider\")\n provider = request.POST.get(\"login\", provider)\n login_done_url = request.POST.get(\"login_done_url\", \"/no_login_done_url_in_form/\")\n user_profile_url = request.POST.get(\"user_profile_url\", \"/no_user_profile_url_in_form/\")\n continuation_url = request.POST.get(\"continuation_url\", \"/no_continuation_url_in_form/\")\n if request.POST.get(\"login\", None):\n collect_provider_details()\n provider_details = PROVIDER_DETAILS[provider]\n provider_details_file = PROVIDER_FILES[provider]\n provider_mechanism = provider_details.get(\"mechanism\", \"OIDC\")\n provider_scope = provider_details.get(\"scope\", SCOPE_DEFAULT)\n if userid and not re.match(r\"\\w+$\", userid):\n return HttpResponseRedirectLogin(\n request, \n login_message.USER_ID_SYNTAX%(userid)\n )\n request.session['recent_userid'] = userid\n request.session['provider_details'] = provider_details\n request.session['continuation_url'] = continuation_url\n if provider_mechanism == \"OIDC\":\n # Create and initialize flow object\n flow = oauth2_flow_from_provider_details(\n provider_details_file,\n scope=provider_scope,\n redirect_uri=request.build_absolute_uri(login_done_url)\n )\n flow.params['state'] = oauth2_get_state_token(request.user)\n flow.params['userid'] = userid\n # Save flow object in Django session\n request.session['oauth2flow'] = oauth2_flow_to_dict(flow)\n # Initiate OAuth2 dance\n auth_uri = flow.step1_get_authorize_url()\n return HttpResponseRedirect(auth_uri)\n if provider_mechanism == \"django\":\n flow = django_flow_from_user_id(\n provider_details,\n userid=userid,\n auth_uri=reverse(\"LocalUserPasswordView\"),\n redirect_uri=request.build_absolute_uri(user_profile_url)\n )\n # Initiate django authentication\n auth_uri = flow.step1_get_authorize_url()\n return HttpResponseRedirect(auth_uri)\n return HttpResponseRedirectLogin(\n request,\n login_message.UNRECOGNIZED_PROVIDER%(provider_mechanism, provider_details_file)\n )\n # Login cancelled: redirect to continuation\n # (which may just redisplay the login page)\n return HttpResponseRedirect(continuation_url)\n\nclass LogoutUserView(generic.View):\n \"\"\"\n View class to handle logout\n \"\"\"\n\n def get(self, request):\n recent_userid = request.session.get('recent_userid', \"\")\n logout(request)\n request.session['recent_userid'] = recent_userid\n continuation_url = request.GET.get(\"continuation_url\", \n urljoin(urlparse(request.path).path, \"../\")\n )\n return HttpResponseRedirect(continuation_url)\n\n# End.\n", "sub_path": "src/annalist_root/login/login_views.py", "file_name": "login_views.py", "file_ext": "py", "file_size_in_byte": 15579, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 52, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "models.get_user_credential", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.http_errors.error400values", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.http_errors.error400values", "line_number": 140, "usage_type": "call"}, {"api_name": "utils.http_errors.error400values", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "login_utils.HttpResponseRedirectWithQuery", "line_number": 164, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 166, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 166, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "markdown.markdown", "line_number": 237, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 239, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 239, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 240, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 241, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 243, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 243, "usage_type": "name"}, {"api_name": "auth_oidc_client.SCOPE_DEFAULT", "line_number": 289, "usage_type": "argument"}, {"api_name": "re.match", "line_number": 290, "usage_type": "call"}, {"api_name": "login_utils.HttpResponseRedirectLogin", "line_number": 291, "usage_type": "call"}, {"api_name": "login_message.USER_ID_SYNTAX", "line_number": 293, "usage_type": "attribute"}, {"api_name": "auth_oidc_client.oauth2_flow_from_provider_details", "line_number": 300, "usage_type": "call"}, {"api_name": "auth_oidc_client.oauth2_get_state_token", "line_number": 305, "usage_type": "call"}, {"api_name": "auth_oidc_client.oauth2_flow_to_dict", "line_number": 308, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 311, "usage_type": "call"}, {"api_name": "login.auth_django_client.django_flow_from_user_id", "line_number": 313, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 316, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 321, "usage_type": "call"}, {"api_name": "login_utils.HttpResponseRedirectLogin", "line_number": 322, "usage_type": "call"}, {"api_name": "login_message.UNRECOGNIZED_PROVIDER", "line_number": 324, "usage_type": "attribute"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 328, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 330, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 330, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 337, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 340, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 340, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 342, "usage_type": "call"}]}
+{"seq_id": "405641893", "text": "import logging\nimport math\nimport numpy as np\nimport random\nimport time\nimport cv2\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\nfrom gym.spaces import Tuple, Box, Discrete, MultiDiscrete, Dict\nfrom gym.spaces.box import Box\nimport airsim\n\nfrom envs.airsim.myAirSimCarClient import *\n\nlogger = logging.getLogger(__name__)\n\nclass AirSimCarEnv(gym.Env):\n\n airsimClient = None\n def __init__(self):\n # left depth, center depth, right depth, steering\n self.low = np.array([0.0, 0.0, 0.0, 0, 0, 0])\n self.high = np.array([100.0, 100.0, 100.0, 5, 5000.0, 5000.0])\n \n self.observation_space = spaces.Box(self.low, self.high)\n self.action_space = spaces.Discrete(5)\n \n self.state = (100, 100, 100, random.uniform(-1.0, 1.0))\n \n self.episodeN = 0\n self.stepN = 0 \n self.allLogs = {'speed':[0]}\n self.dist = 0\n self.last_pos = [0,0]\n self.collision = False\n self.close_l, self.close_r = 5000, 5000\n \n self._seed()\n self.stallCount = 0\n self.last_collision = None\n global airsimClient\n airsimClient = myAirSimCarClient()\n \n self.LABELS = open(\"../Yolo-Fastest/data/coco.names\").read().strip().split(\"\\n\")\n self.path_weights = \"../Yolo-Fastest/Yolo-Fastest/COCO/yolo-fastest.weights\"\n self.path_config = \"../Yolo-Fastest/Yolo-Fastest/COCO/yolo-fastest.cfg\"\n \n np.random.seed(42)\n self.net = cv2.dnn.readNetFromDarknet(self.path_config, self.path_weights)\n self.dirname = time.strftime(\"%Y_%m_%d_%H_%M\") + '_yolo' \n os.mkdir(self.dirname)\n self.f = open(self.dirname+ '/log.txt','w')\n firstline = 'x,y,reward,collision\\n'\n self.f.write(firstline)\n \n \n def _seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n \n def computeReward(self, mode='roam'):\n speed = self.car_state.speed \n steer = self.steer\n reward = 0\n \n this_pos = [self.car_state.kinematics_estimated.position.x_val, self.car_state.kinematics_estimated.position.y_val]\n this_dist = ((self.last_pos[0]-this_pos[0])**2 + (self.last_pos[1]-this_pos[1])**2)** 0.5\n self.dist += this_dist\n self.last_pos = this_pos\n \n if mode == 'roam' or mode == 'smooth':\n reward += this_dist\n if self.collision:\n reward -= 10\n \n if mode == 'smooth':\n # also penalize on jerky motion, based on a fake G-sensor\n steerLog = self.allLogs['steer']\n g = abs(steerLog[-1] - steerLog[-2]) * 5\n reward -= g\n \n return [reward, 0]\n \n\n def get_closeness(self,image,x1,y1,w1,h1):\n #color2 = [110,220,69]\n \n k = 3\n start_pt = (int(x1+w1/2),144)\n end_pt = (int(x1+w1/2),y1+h1)\n #cv2.line(image, start_pt, end_pt, color2,2)\n distance = (start_pt[0]-end_pt[0])**2 + (start_pt[1]-end_pt[1])**2\n distance = math.sqrt(distance)\n area = w1*h1\n return round(k*area/(distance+0.0000000001),3)\n \n '''\n def get_closeness_(self,image,x1,y1,w1,h1):\n distance = 144 - y1\n return distance\n '''\n \n def yolo(self, img, net, confidence_threshold, threshold):\n #print(\"In YOLO method\")\n #image = cv2.imread(img)\n image = img\n \n #print(\"shape of image is:\",image.shape)\n #since image is 4 channel, we consider height and width\n (H, W) = image.shape[:2]\n layerNames = net.getLayerNames()\n layerNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n \n ##construct a blob from the input image\n blob = cv2.dnn.blobFromImage(image, 1 / 255.0,(416,416),swapRB=True,crop=False)\n net.setInput(blob)\n netOutputs = net.forward(layerNames)\n \n # Constructing bounding box\n boxes = []\n confidences = []\n classIDs = []\n\n for output in netOutputs:\n for detection in output:\n scores = detection[5:]\n classID = np.argmax(scores)\n confidence = scores[classID]\n if(confidence>confidence_threshold):\n box = detection[0:4] * np.array([W, H, W, H])\n (Xcenter, Ycenter, width, height) = box.astype(\"int\")\n #obtain coordinates for top left corner\n tl_x = int(Xcenter - (width/2))\n tl_y = int(Ycenter - (height/2))\n boxes.append([tl_x,tl_y,int(width),int(height)])\n confidences.append(float(confidence))\n classIDs.append(classID)\n \n #applying non maxima suppression\n idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence_threshold,threshold)\n close = dict()\n if(len(idxs)>0):\n for i in idxs.flatten():\n (x,y) = (boxes[i][0], boxes[i][1])\n (w,h) = (boxes[i][2], boxes[i][3])\n cv2.rectangle(image, (x,y), (x+w,y+h), (100,220,210), 2)\n text = self.LABELS[classIDs[i]]\n cv2.putText(image, text, (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,\n 0.25, (180,100,70), 1)\n cl = self.get_closeness(image,x,y,w,h)\n close.update({(self.LABELS[classIDs[i]],confidences[i]):[cl,x+w/2,int((x+w/2)>128)]})\n #df = pd.DataFrame(columns=['Object','Confidence','Closeness'])\n \n #close_metric = sorted(close.items(), key=lambda x: x[1], reverse=True)\n \n close_l = 5000\n close_r = 5000\n \n for i in close_metric:\n if i[1][-1]:\n close_r = min(i[1][0],close_r) \n else:\n close_l = min(i[1][0],close_l)\n \n return image, close_l, close_r\n\n \n def _step(self, action):\n assert self.action_space.contains(action), \"%r (%s) invalid\"%(action, type(action))\n time.sleep(0.05)\n car_state = airsimClient.getCarState()\n self.car_state = car_state\n speed = car_state.speed \n \n self.stepN += 1\n steer = (action-2)/2\n gas = max(min(20,(speed-20)/-15),0)\n \n airsimClient.setCarControls(gas, steer) \n self.steer = steer\n\n collision_info = airsimClient.simGetCollisionInfo()\n if collision_info.time_stamp != self.last_collision and collision_info.time_stamp != 0:\n done = True\n self.collision = True\n else:\n done = False\n self.collision = False\n\n '''\n elif speed < 0.5:\n self.stallCount += 1\n else:\n self.stallCount = 0\n if self.stallCount > 6:\n done = False\n else:\n done = False\n '''\n \n self.last_collision = collision_info.time_stamp\n \n self.sensors = airsimClient.getSensorStates()\n cdepth = self.sensors[1]\n self.state = self.sensors\n self.state.append(action)\n self.state += [self.close_l, self.close_r]\n\n self.addToLog('speed', speed)\n self.addToLog('steer', steer)\n steerLookback = 17\n steerAverage = np.average(self.allLogs['steer'][-steerLookback:])\n self.steerAverage = steerAverage\n \n responses2 = airsimClient.simGetImages([airsim.ImageRequest(\"0\", airsim.ImageType.Scene, False, False)])\n cam_image = responses2[0]\n img1d = np.fromstring(cam_image.image_data_uint8, dtype=np.uint8)\n try:\n img_rgb = img1d.reshape(cam_image.height, cam_image.width, 3)\n self.yolores, self.close_r, self.close_l = self.yolo(img_rgb,self.net,0.5,0.5)\n except:\n pass\n \n # Training using the Roaming mode \n reward, dSpeed = self.computeReward('roam')\n self.addToLog('reward', reward)\n rewardSum = np.sum(self.allLogs['reward'])\n\n # Terminate the episode on large cumulative amount penalties, \n # since car probably got into an unexpected loop of some sort\n if rewardSum < -1000:\n done = True\n \n sys.stdout.write(\"\\r\\x1b[K{}/{}==>reward/depth/steer/speed: {:.0f}/{:.0f} \\t({:.1f}/{:.1f}/{:.1f}) \\t{:.1f}/{:.1f} \\t{:.2f}/{:.2f} \".format(self.episodeN, self.stepN, reward, rewardSum, self.state[0], self.state[1], self.state[2], steer, steerAverage, speed, dSpeed))\n sys.stdout.flush()\n \n return np.array(self.state), reward, done, {}\n\n def addToLog (self, key, value):\n if key not in self.allLogs:\n self.allLogs[key] = []\n self.allLogs[key].append(value)\n \n def _reset(self):\n airsimClient.reset()\n airsimClient.setCarControls(1, 0)\n time.sleep(0.8)\n \n self.stepN = 0\n self.stallCount = 0\n self.episodeN += 1\n self.dist = 0\n \n print(\"\")\n self.allLogs = {'speed':[0]}\n \n # Randomize the initial steering to broaden learning\n self.state = (100, 100, 100, random.uniform(-1.0, 1.0), 5000, 5000)\n \n self.f.close()\n self.f = open(self.dirname+ '/log.txt','a')\n \n return np.array(self.state)", "sub_path": "envs/airsim/airsimcarenv.py", "file_name": "airsimcarenv.py", "file_ext": "py", "file_size_in_byte": 9438, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "gym.Env", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 27, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 27, "usage_type": "name"}, {"api_name": "gym.spaces.Discrete", "line_number": 28, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 28, "usage_type": "name"}, {"api_name": "random.uniform", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.dnn.readNetFromDarknet", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 51, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 52, "usage_type": "call"}, {"api_name": "gym.utils.seeding.np_random", "line_number": 60, "usage_type": "call"}, {"api_name": "gym.utils.seeding", "line_number": 60, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.dnn.NMSBoxes", "line_number": 142, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 142, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 150, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 214, "usage_type": "call"}, {"api_name": "airsim.ImageRequest", "line_number": 217, "usage_type": "call"}, {"api_name": "airsim.ImageType", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.fromstring", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 239, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 249, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 265, "usage_type": "call"}]}
+{"seq_id": "79226244", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 4 19:34:32 2021\n\n@author: User\n\"\"\"\nimport random\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom plot import save,pie\n\n\n\ndata = pd.read_excel('C:/Work/Data/pdx.xlsx',sheet_name='Sheet1',index_col=0)\neggs_connection = pd.read_excel('C:/Work/Data/eggs_connection.xlsx',sheet_name='Лист1',index_col=0)\nmax_id = max(data.index)\n\n\n\n# one,two=int(input(\"Введите 1-ое id: \")),int(input(\"Введите 2-ое id: \"))\n# kolvo=int(input(\"Введите количество размножений для анализа вероятностей: \"))\n\n\ndef graphs(fig,win):\n '''\n Parameters\n ----------\n fig : TYPE list\n item of list of figures\n win : TYPE Tk\n window\n -------\n '''\n canvas = FigureCanvasTkAgg(fig, win)\n canvas.draw()\n canvas.get_tk_widget().place(x = 10, y = 10, width = 340, height = 250)\n\ndef reproduction(raz,dva,baza,kol):\n '''\n Parameters\n ----------\n raz : TYPE Integer\n id of pokemon\n dva : TYPE Integer\n id of pokemon\n baza : TYPE DataFrame\n database of pok_typelist\n kol : TYPE Integer\n ammount of cicle\n Returns array\n -------\n\n '''\n hidden = []\n pok_hidden = []\n abilities_pok = []\n pok_abilities = []\n typelist = []\n pok_typelist = []\n flag = True\n hidden.append(baza[\"Hidden Ability\"][raz])\n if baza[\"Hidden Ability\"][raz]!=baza[\"Hidden Ability\"][dva]:\n hidden.append(baza[\"Hidden Ability\"][dva])\n abilities_pok.append(baza[\"Ability I\"][raz])\n if baza[\"Ability II\"][raz]!=0:\n abilities_pok.append(baza[\"Ability II\"][raz])\n if baza[\"Ability I\"][dva]!=0:\n for i in abilities_pok:\n if i == baza[\"Ability I\"][dva]:\n flag = False\n if flag:\n abilities_pok.append(baza[\"Ability I\"][dva])\n flag = True\n if baza[\"Ability II\"][dva]!=0:\n for i in abilities_pok:\n if i == baza[\"Ability II\"][dva]:\n flag = False\n if flag:\n abilities_pok.append(baza[\"Ability II\"][dva])\n for i in range(kol):\n randnum = random.randint(0,len(abilities_pok)-1)\n pok_abilities.append(abilities_pok[randnum])\n promejutok = randnum\n while randnum == promejutok:\n randnum = random.randint(0,len(abilities_pok))\n if randnum == 0:\n pok_abilities.append(abilities_pok[0])\n elif randnum == len(abilities_pok):\n pok_abilities.append(0)\n else:\n pok_abilities.append(abilities_pok[randnum])\n randnum = random.randint(0,len(hidden)-1)\n pok_hidden.append(hidden[randnum])\n typelist.append(baza[\"Type I\"][raz])\n if baza[\"Type II\"][raz]!=0:\n typelist.append(baza[\"Type II\"][raz])\n if baza[\"Type I\"][dva]!=0:\n for i in typelist:\n if i == baza[\"Type I\"][dva]:\n flag = False\n if flag:\n typelist.append(baza[\"Type I\"][dva])\n flag = True\n if baza[\"Type II\"][dva]!=0:\n for i in typelist:\n if i == baza[\"Type II\"][dva]:\n flag = False\n if flag:\n typelist.append(baza[\"Type II\"][dva])\n for i in range(kol):\n randnum = random.randint(0,len(typelist)-1)\n pok_typelist.append(typelist[randnum])\n promejutok = randnum\n while randnum == promejutok:\n randnum = random.randint(0,len(typelist))\n if randnum == 0:\n pok_typelist.append(typelist[0])\n elif randnum == len(typelist):\n pok_typelist.append(0)\n else:\n pok_typelist.append(typelist[randnum])\n boxplot()\n return (analyze(pok_typelist,pok_abilities,pok_hidden)+\n avrstats(pok_typelist,baza)+raspredelenie(typelist))\n\ndef analyze(tipes,abilki,pryatki):\n '''\n Parameters\n ----------\n tipes : TYPE Array\n list of pok_typelist type\n Returns array\n -------\n\n '''\n array = []\n names = [\"Ability I\",\"Ability II\",\"Hidden Ability\",\"Type I\",\"Type II\"]\n colors = list('rbygkm')\n eggs_data = pd.read_excel('C:/Work/Data/Types.xlsx',sheet_name='Лист1',index_col=0)\n ability_data = pd.read_excel('C:/Work/Data/Ability.xlsx',sheet_name='Лист1',index_col=0)\n t_1 = {}\n t_2 = {}\n for i,abilki in enumerate(abilki):\n if i%2>0:\n if t_2.get(abilki) is not None:\n t_2[abilki]+=1\n else:\n t_2[abilki]=1\n else:\n if t_1.get(abilki) is not None:\n t_1[abilki]+=1\n else:\n t_1[abilki]=1\n klv = list(t_1.keys())\n for i,klvshka in enumerate(klv):\n klv[i]=ability_data[\"Ability\"][klvshka]\n znch = list(t_1.values())\n klv2 = list(t_2.keys())\n for i,klvshka2 in enumerate(klv2):\n if klvshka2!=0:\n klv2[i]=ability_data[\"Ability\"][klvshka2]\n else:\n klv2[i]=\"No Ability\"\n znch2 = list(t_2.values())\n\n array.append(pie(znch,klv,colors,names[0]))\n array.append(pie(znch2,klv2,colors,names[1]))\n\n t_1.clear()\n t_2.clear()\n for i,pryatki in enumerate(pryatki):\n if t_1.get(pryatki) is not None:\n t_1[pryatki]+=1\n else:\n t_1[pryatki]=1\n klv = list(t_1.keys())\n for i,klvshka in enumerate(klv):\n if klvshka!=0:\n klv[i]=ability_data[\"Ability\"][klvshka]\n else:\n klv[i]=\"No Hidden Ability\"\n znch = list(t_1.values())\n\n array.append(pie(znch,klv,colors,names[2]))\n\n t_1.clear()\n t_2.clear()\n for i,spis in enumerate(tipes):\n if i%2>0:\n if t_2.get(spis) is not None:\n t_2[spis]+=1\n else:\n t_2[spis]=1\n else:\n if t_1.get(spis) is not None:\n t_1[spis]+=1\n else:\n t_1[spis]=1\n klv = list(t_1.keys())\n for i,klvshka in enumerate(klv):\n klv[i]=eggs_data[\"Type_name\"][klvshka]\n znch = list(t_1.values())\n klv2 = list(t_2.keys())\n for i,klvshka2 in enumerate(klv2):\n if klvshka2!=0:\n klv2[i]=eggs_data[\"Type_name\"][klvshka2]\n else:\n klv2[i]=\"No Type\"\n znch2 = list(t_2.values())\n array.append(pie(znch,klv,colors,names[3]))\n array.append(pie(znch2,klv2,colors,names[4]))\n\n return array\ndef raspredelenie(typelist):\n '''\n Parameters\n ----------\n typelist : TYPE Array\n list of type\n Returns array\n -------\n False.\n\n '''\n array = []\n\n plt.close()\n listochek = pd.read_excel('C:/Work/Data/pdx.test.xlsx',sheet_name='Sheet1',index_col=0)\n for i in range(0,max_id+1):\n for j in typelist:\n if j == data[\"Type I\"][i] or j==data[\"Type II\"][i]:\n stats = pd.Series([data[\"HP\"][i],data[\"Atk\"][i],data[\"Def\"][i],\n data[\"SpA\"][i],data[\"SpD\"][i],data[\"Spe\"][i]],\n index=[\"HP\",\"Atk\",\"Def\",\"SpA\",\"SpD\",\"Spe\"])\n listochek = listochek.append(stats,ignore_index=True)\n fig, axi = plt.subplots(figsize=(10, 10))\n axi.scatter(x = listochek['Atk'], y = listochek['Def'])\n plt.xlabel(\"Attack\")\n plt.ylabel(\"Defense\")\n plt.rc('xtick', labelsize=10)\n array.append(fig)\n save(\"C:/Work/Graphics/AtkDef\")\n # plt.show()\n plt.close()\n fig, axi = plt.subplots(figsize=(10, 10))\n axi.scatter(x = listochek['SpA'], y = listochek['SpD'])\n plt.xlabel(\"SpA\")\n plt.ylabel(\"SpD\")\n plt.rc('xtick', labelsize=10)\n array.append(fig)\n save(\"C:/Work/Graphics/SpASpD\")\n plt.close()\n fig, axi = plt.subplots(figsize=(10, 10))\n axi.scatter(x = listochek['HP'], y = listochek['Spe'])\n plt.xlabel(\"HP\")\n plt.ylabel(\"Spe\")\n plt.rc('xtick', labelsize=10)\n array.append(fig)\n save(\"C:/Work/Graphics/HPSpe\")\n plt.close()\n # plt.show()\n return array\ndef avrstats(listok,b_z):\n '''\n Parameters\n ----------\n listok : TYPE Array\n list of pok_typelist type\n d_z : TYPE DataFrame\n database of pok_typelist\n Returns array\n -------\n\n '''\n plt.close()\n array = []\n stats = [0,0,0,0,0,0]\n avr = 0\n podhodyat = {}\n for i in range (0,max_id+1):\n j=0\n while j<len(listok):\n if b_z[\"Type I\"][i]==listok[j] and b_z[\"Type II\"][i]==listok[j+1]:\n if podhodyat.get(i) is None:\n podhodyat[i]=0\n j+=2\n for i in list(podhodyat):\n stats[0] += b_z[\"HP\"][i]\n stats[1] += b_z[\"Atk\"][i]\n stats[2] += b_z[\"Def\"][i]\n stats[3] += b_z[\"SpA\"][i]\n stats[4] += b_z[\"SpD\"][i]\n stats[5] += b_z[\"Spe\"][i]\n avr +=1\n arr = [stats[0]/avr,stats[1]/avr,stats[2]/avr,stats[3]/avr,stats[4]/avr,stats[0]/avr]\n arr2 = [\"HP\",\"Atk\",\"Def\",\"SpA\",\"SpD\",\"Spe\"]\n colors = list('rgbkym')\n fig = plt.figure()\n a_x = fig.add_axes([0,0,1,1])\n a_x.bar(arr2,arr,color = colors)\n fig.set_figwidth(16)\n plt.title(\"Average Stats\")\n plt.rc('xtick', labelsize=10)\n save(\"C:/Work/Graphics/Avr_stats\")\n array.append(fig)\n # plt.show()\n plt.close()\n return array\n\ndef boxplot():\n '''\n Parameters\n ----------\n -------\n None.\n '''\n array=[]\n plt.close()\n fig = data.boxplot(column=[\"HP\",\"Atk\",\"Def\",\"SpA\",\"SpD\",\"Spe\"])#, by='Type I')\n array.append(fig)\n plt.show()\n\ndef proverka(t_1,b_1):\n '''\n Parameters\n ----------\n a : TYPE Integer\n id of egg group\n b : TYPE Integer\n id of egg group\n\n Returns 1\n -------\n Returns 0\n\n '''\n if eggs_connection[t_1][b_1]==1:\n return 1\n return 0\ndef cheker(id1,id2,d_b):\n '''\n Parameters\n ----------\n id1 : TYPE Integer\n id of pokemon\n id2 : TYPE Integer\n id of pokemon\n d_b : TYPE DataFrame\n database of pok_typelist\n Returns True\n -------\n False.\n\n '''\n if(d_b[\"Egg Group I\"][id1]!=0 and d_b[\"Egg Group II\"][id1]!=0):\n\n if(d_b[\"Egg Group I\"][id2]!=0 and d_b[\"Egg Group II\"][id2]!=0):\n if(( proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group I\"][id2])\n +proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group II\"][id2])\n +proverka(d_b[\"Egg Group II\"][id1],d_b[\"Egg Group I\"][id2])\n +proverka(d_b[\"Egg Group II\"][id1],d_b[\"Egg Group II\"][id2]))>0):\n\n return True\n\n elif(d_b[\"Egg Group I\"][id2]!=0 and d_b[\"Egg Group II\"][id2]==0):\n if(( proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group I\"][id2])\n +proverka(d_b[\"Egg Group II\"][id1],d_b[\"Egg Group I\"][id2]))>0):\n\n return True\n\n elif(d_b[\"Egg Group I\"][id1]!=0 and d_b[\"Egg Group II\"][id1]==0):\n if(d_b[\"Egg Group I\"][id2]!=0 and d_b[\"Egg Group II\"][id2]!=0):\n if(( proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group I\"][id2])\n +proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group II\"][id2]))>0):\n\n return True\n\n elif(d_b[\"Egg Group I\"][id2]!=0 and d_b[\"Egg Group II\"][id2]==0):\n if (proverka(d_b[\"Egg Group I\"][id1],d_b[\"Egg Group I\"][id2]))>0:\n\n return True\n\n return False\n", "sub_path": "Library/pokemons_reproduction.py", "file_name": "pokemons_reproduction.py", "file_ext": "py", "file_size_in_byte": 11253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "56", "api": [{"api_name": "pandas.read_excel", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 93, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 112, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 141, "usage_type": "call"}, {"api_name": "plot.pie", "line_number": 167, "usage_type": "call"}, {"api_name": "plot.pie", "line_number": 168, "usage_type": "call"}, {"api_name": "plot.pie", "line_number": 185, "usage_type": "call"}, {"api_name": "plot.pie", "line_number": 211, "usage_type": "call"}, {"api_name": "plot.pie", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 229, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "plot.save", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "plot.save", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "plot.save", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "plot.save", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}]}
+{"seq_id": "249505983", "text": "# -*- coding: utf-8 -*-\n\nimport regex\nfrom tldextract import extract\nimport ssl\nimport socket\nfrom bs4 import BeautifulSoup\nimport urllib.request\nimport re\nfrom datetime import datetime\nimport ipaddress\nfrom urllib.request import urlopen\nimport requests\n\n\ndef url_having_ip(url):\n try:\n ipaddress.ip_address(url)\n ip = 1\n except:\n ip = 0\n return ip\n\n\ndef url_length(url):\n length = len(url)\n if length < 54:\n return -1\n elif 54 <= length <= 75:\n return 0\n else:\n return 1\n\nshortening_services = r\"bit\\.ly|goo\\.gl|shorte\\.st|go2l\\.ink|x\\.co|ow\\.ly|t\\.co|tinyurl|tr\\.im|is\\.gd|cli\\.gs|\" \\\n r\"yfrog\\.com|migre\\.me|ff\\.im|tiny\\.cc|url4\\.eu|twit\\.ac|su\\.pr|twurl\\.nl|snipurl\\.com|\" \\\n r\"short\\.to|BudURL\\.com|ping\\.fm|post\\.ly|Just\\.as|bkite\\.com|snipr\\.com|fic\\.kr|loopt\\.us|\" \\\n r\"doiop\\.com|short\\.ie|kl\\.am|wp\\.me|rubyurl\\.com|om\\.ly|to\\.ly|bit\\.do|t\\.co|lnkd\\.in|db\\.tt|\" \\\n r\"qr\\.ae|adf\\.ly|goo\\.gl|bitly\\.com|cur\\.lv|tinyurl\\.com|ow\\.ly|bit\\.ly|ity\\.im|q\\.gs|is\\.gd|\" \\\n r\"po\\.st|bc\\.vc|twitthis\\.com|u\\.to|j\\.mp|buzurl\\.com|cutt\\.us|u\\.bb|yourls\\.org|x\\.co|\" \\\n r\"prettylinkpro\\.com|scrnch\\.me|filoops\\.info|vzturl\\.com|qr\\.net|1url\\.com|tweez\\.me|v\\.gd|\" \\\n r\"tr\\.im|link\\.zip\\.net\"\n\ndef url_short(url):\n match = re.search(shortening_services, url)\n if match:\n return 1\n else:\n return 0\n\n\ndef having_at_symbol(url):\n symbol = regex.findall(r'@', url)\n if (len(symbol) == 0):\n return -1\n else:\n return 1\n\n\ndef doubleSlash(url):\n pos = url.rfind(\"//\") # Finds last occurence of // in url\n\n if pos < 7:\n return 0\n\n if url[:5].upper() == \"HTTPS\" and pos == 7:\n return 0\n\n return 1 # only 1 // must be present and that only at index less than 7\n\n\ndef prefix_suffix(url):\n subDomain, domain, suffix = extract(url)\n if (domain.count('-')):\n return 1\n else:\n return -1\n\n\ndef sub_domain(url):\n subDomain, domain, suffix = extract(url)\n if (subDomain.count('.') == 0):\n return -1\n elif (subDomain.count('.') == 1):\n return 0\n else:\n return 1\n\n\ndef SSLfinal_State(url):\n try:\n # check wheather contains https\n if (regex.search('^https', url)):\n usehttps = 1\n else:\n usehttps = 0\n # getting the certificate issuer to later compare with trusted issuer\n # getting host name\n subDomain, domain, suffix = extract(url)\n host_name = domain + \".\" + suffix\n context = ssl.create_default_context()\n sct = context.wrap_socket(socket.socket(), server_hostname=host_name)\n sct.connect((host_name, 443))\n certificate = sct.getpeercert()\n issuer = dict(x[0] for x in certificate['issuer'])\n certificate_Auth = str(issuer['commonName'])\n certificate_Auth = certificate_Auth.split()\n if (certificate_Auth[0] == \"Network\" or certificate_Auth == \"Deutsche\"):\n certificate_Auth = certificate_Auth[0] + \" \" + certificate_Auth[1]\n else:\n certificate_Auth = certificate_Auth[0]\n trusted_Auth = ['Comodo', 'Symantec', 'GoDaddy', 'GlobalSign', 'DigiCert', 'StartCom', 'Entrust', 'Verizon',\n 'Trustwave', 'Unizeto', 'Buypass', 'QuoVadis', 'Deutsche Telekom', 'Network Solutions',\n 'SwissSign', 'IdenTrust', 'Secom', 'TWCA', 'GeoTrust', 'Thawte', 'Doster', 'VeriSign']\n # getting age of certificate\n startingDate = str(certificate['notBefore'])\n endingDate = str(certificate['notAfter'])\n startingYear = int(startingDate.split()[3])\n endingYear = int(endingDate.split()[3])\n Age_of_certificate = endingYear - startingYear\n\n # checking final conditions\n if ((usehttps == 1) and (certificate_Auth in trusted_Auth) and (Age_of_certificate >= 1)):\n return -1 # legitimate\n elif ((usehttps == 1) and (certificate_Auth not in trusted_Auth)):\n return 0 # suspicious\n else:\n return 1 # phishing\n\n except Exception as e:\n\n return 1\n\n\ndef domain_registration(URL):\n if not URL :\n return 1\n try:\n with urlopen(URL) as f:\n s = dict(f.getheaders())['Set-Cookie'].split(\";\")\n expire = \"\"\n for i in s:\n if \"Expires\" in i:\n expire = i.split(\"=\")[-1][5:16]\n\n expiration_date = datetime.strptime(expire, '%d-%b-%Y').date()\n\n today = datetime.today().date()\n end = abs((expiration_date - today).days)\n if ((end / 30) < 36):\n end = 0\n else:\n end = 1\n return end\n\n except:\n print(\"Domain Registration Error\")\n return 1\n\n\"\"\"\ndef domain_registration(url):\n try:\n w = whois.whois(url)\n updated = w.updated_date\n exp = w.expiration_date\n length = (exp[0] - updated[0]).days\n if (length <= 365):\n return 1\n else:\n return -1\n except:\n return 0\"\"\"\n\n\ndef favicon(url):\n # ongoing\n return 0\n\n\ndef port(url):\n # ongoing\n return 0\n\n\ndef https_token(url):\n subDomain, domain, suffix = extract(url)\n host = subDomain + '.' + domain + '.' + suffix\n if (host.count('https')): # attacker can trick by putting https in domain part\n return 1\n else:\n return -1\n\n\ndef request_url(url):\n try:\n subDomain, domain, suffix = extract(url)\n websiteDomain = domain\n\n opener = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(opener, 'lxml')\n imgs = soup.findAll('img', src=True)\n total = len(imgs)\n\n linked_to_same = 0\n avg = 0\n for image in imgs:\n subDomain, domain, suffix = extract(image['src'])\n imageDomain = domain\n if (websiteDomain == imageDomain or imageDomain == ''):\n linked_to_same = linked_to_same + 1\n vids = soup.findAll('video', src=True)\n total = total + len(vids)\n\n for video in vids:\n subDomain, domain, suffix = extract(video['src'])\n vidDomain = domain\n if (websiteDomain == vidDomain or vidDomain == ''):\n linked_to_same = linked_to_same + 1\n linked_outside = total - linked_to_same\n if (total != 0):\n avg = linked_outside / total\n\n if (avg < 0.22):\n return -1\n elif (0.22 <= avg <= 0.61):\n return 0\n else:\n return 1\n except:\n return 0\n\n\ndef url_of_anchor(url):\n try:\n subDomain, domain, suffix = extract(url)\n websiteDomain = domain\n\n opener = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(opener, 'lxml')\n anchors = soup.findAll('a', href=True)\n total = len(anchors)\n linked_to_same = 0\n avg = 0\n for anchor in anchors:\n subDomain, domain, suffix = extract(anchor['href'])\n anchorDomain = domain\n if (websiteDomain == anchorDomain or anchorDomain == ''):\n linked_to_same = linked_to_same + 1\n linked_outside = total - linked_to_same\n if (total != 0):\n avg = linked_outside / total\n\n if (avg < 0.31):\n return -1\n elif (0.31 <= avg <= 0.67):\n return 0\n else:\n return 1\n except:\n return 0\n\n\ndef Links_in_tags(url):\n try:\n opener = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(opener, 'lxml')\n\n no_of_meta = 0\n no_of_link = 0\n no_of_script = 0\n anchors = 0\n avg = 0\n for meta in soup.find_all('meta'):\n no_of_meta = no_of_meta + 1\n for link in soup.find_all('link'):\n no_of_link = no_of_link + 1\n for script in soup.find_all('script'):\n no_of_script = no_of_script + 1\n for anchor in soup.find_all('a'):\n anchors = anchors + 1\n total = no_of_meta + no_of_link + no_of_script + anchors\n tags = no_of_meta + no_of_link + no_of_script\n if (total != 0):\n avg = tags / total\n\n if (avg < 0.25):\n return -1\n elif (0.25 <= avg <= 0.81):\n return 0\n else:\n return 1\n except:\n return 0\n\n\ndef sfh(url):\n # ongoing\n return 0\n\n\ndef email_submit(url):\n try:\n opener = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(opener, 'lxml')\n if (soup.find('mailto:')):\n return 1\n else:\n return -1\n except:\n return 0\n\n\ndef abnormal_url(url):\n # ongoing\n return 0\n\n\ndef redirect(url):\n # ongoing\n return 0\n\n\ndef on_mouseover(url):\n try:\n response = requests.get(url)\n except:\n response = \"\"\n if response == \"\":\n return 1\n else:\n if re.findall(\"<script>.+onmouseover.+</script>\", response.text):\n return 1\n else:\n return 0\n\ndef rightClick(url):\n try:\n response = requests.get(url)\n except:\n response = \"\"\n\n if response == \"\":\n return 1\n else:\n if re.findall(r\"event.button ?== ?2\", response.text):\n return 0\n else:\n return 1\n\ndef popup(url):\n try:\n response = requests.get(url)\n except:\n response = \"\"\n\n if response == \"\":\n return 1\n else:\n if len(response.history) <= 2:\n return 0\n else:\n return 1\n\n\ndef iframe(url):\n # ongoing\n return 0\n\n\n\ndef age_of_domain(URL):\n if URL == None:\n return 1\n try:\n from htmldate import find_date\n creation_date = find_date(URL)\n\n with urlopen(URL) as f:\n s=dict(f.getheaders())['Set-Cookie'].split(\";\")\n expire=\"\"\n for i in s:\n if \"Expires\" in i:\n expire = i.split(\"=\")[-1][5:16]\n\n from datetime import datetime\n\n expire_date = datetime.strptime(expire, '%d-%b-%Y').date()\n creation_date = datetime.strptime(creation_date, '%Y-%m-%d').date()\n\n delta = expire_date - creation_date\n\n if delta.days<180:\n return 1\n\n return 0\n\n except:\n return 1\n\n\n\"\"\"def age_of_domain(url):\n try:\n w = whois.whois(url)\n start_date = w.creation_date\n current_date = datetime.datetime.now()\n age = (current_date - start_date[0]).days\n if (age >= 180):\n return -1\n else:\n return 1\n except Exception as e:\n print(e)\n return 0\"\"\"\n\n\ndef dns(url):\n # ongoing\n return 0\n\n\ndef web_traffic(url):\n # ongoing\n return 0\n\n\ndef page_rank(url):\n # ongoing\n return 0\n\n\ndef google_index(url):\n # ongoing\n return 0\n\n\ndef links_pointing(url):\n # ongoing\n return 0\n\n\ndef statistical(url):\n # ongoing\n return 0\n\n\ndef main(url):\n check = [[url_having_ip(url), url_length(url), url_short(url), having_at_symbol(url),\n doubleSlash(url), prefix_suffix(url), sub_domain(url), SSLfinal_State(url),\n domain_registration(url), favicon(url), port(url), https_token(url), request_url(url),\n url_of_anchor(url), Links_in_tags(url), sfh(url), email_submit(url), abnormal_url(url),\n redirect(url), on_mouseover(url), rightClick(url), popup(url), iframe(url),\n age_of_domain(url), dns(url), web_traffic(url), page_rank(url), google_index(url),\n links_pointing(url), statistical(url)]]\n\n print(check)\n return check\n\n", "sub_path": "FeatureExtraction.py", "file_name": "FeatureExtraction.py", "file_ext": "py", "file_size_in_byte": 11817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "53", "api": [{"api_name": "ipaddress.ip_address", "line_number": 18, "usage_type": "call"}, {"api_name": "re.search", "line_number": 44, "usage_type": "call"}, {"api_name": "regex.findall", "line_number": 52, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 72, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 80, "usage_type": "call"}, {"api_name": "regex.search", "line_number": 92, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 98, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 100, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 101, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "tldextract.extract", "line_number": 185, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 195, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 198, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 198, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 198, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 199, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 206, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 214, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 234, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 237, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 237, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 237, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 238, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 244, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 264, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 264, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 264, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 265, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 302, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 302, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 302, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 303, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 324, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 330, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 337, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 344, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 351, "usage_type": "call"}, {"api_name": "htmldate.find_date", "line_number": 375, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 377, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 386, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 386, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 387, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 387, "usage_type": "name"}]}