diff --git "a/1275.jsonl" "b/1275.jsonl" new file mode 100644--- /dev/null +++ "b/1275.jsonl" @@ -0,0 +1,424 @@ +{"seq_id": "367737784", "text": "from behave.runner import Context\n\nfrom pages.main_header import MainHeader\nfrom utils.base_page import BasePage\nfrom utils.browsers.browser_selector import browser\nfrom utils.fake_persons.fake_client import FakeClient\n\n\ndef start_driver():\n driver = browser().driver\n return driver\n\n\ndef before_all(context):\n context.stuff = Stuff(context)\n context.client = context.stuff.client()\n\n\ndef before_scenario(context, scenario):\n context.driver = start_driver()\n context.environment = Environment(context, context.driver)\n pages = context.environment._origin\n behave = (\"feature\", \"text\", \"table\", \"stdout_capture\", \"stderr_capture\", \"log_capture\", \"fail_on_cleanup_errors\")\n for behave_item in behave:\n pages.pop(behave_item, None)\n for page in pages:\n setattr(context, page, getattr(context.environment, page))\n\n\ndef after_scenario(context, scenario):\n context.driver.close()\n\n\ndef after_step(context, step):\n if step.status == 'failed':\n step_str = step.name\n context.driver.save_screenshot(f\"{step_str}.png\")\n\n\nclass Environment(Context):\n\n def __init__(self, runner, driver):\n super().__init__(runner)\n self.base = BasePage(driver)\n self.main_header = MainHeader(driver)\n\n\nclass Stuff(Context):\n\n def __init__(self, runner):\n super().__init__(runner)\n self.client = FakeClient\n", "sub_path": "environment.py", "file_name": "environment.py", "file_ext": "py", "file_size_in_byte": 1383, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "utils.browsers.browser_selector.browser", "line_number": 10, "usage_type": "call"}, {"api_name": "pages.main_header", "line_number": 22, "usage_type": "name"}, {"api_name": "behave.runner", "line_number": 23, "usage_type": "name"}, {"api_name": "behave.runner", "line_number": 24, "usage_type": "name"}, {"api_name": "pages.main_header.pop", "line_number": 25, "usage_type": "call"}, {"api_name": "pages.main_header", "line_number": 25, "usage_type": "name"}, {"api_name": "pages.main_header", "line_number": 26, "usage_type": "name"}, {"api_name": "behave.runner.Context", "line_number": 40, "usage_type": "name"}, {"api_name": "utils.base_page.BasePage", "line_number": 44, "usage_type": "call"}, {"api_name": "pages.main_header.MainHeader", "line_number": 45, "usage_type": "call"}, {"api_name": "behave.runner.Context", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.fake_persons.fake_client.FakeClient", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "47391192", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nimport os\nos.environ.update({\"DJANGO_SETTINGS_MODULE\": \"SqlAudit.settings\"})\nfrom django.conf import settings\nfrom django.core.mail import EmailMultiAlternatives\n\ndef sendMail(subject, html_content,to_email):\n try:\n ToEmail = []\n if type(to_email) != list:\n ToEmail.append(to_email)\n else:\n ToEmail = to_email\n from_email = settings.DEFAULT_FROM_EMAIL\n # subject = '来自SQL审核系统的通知'\n # text_content = '这是一封重要的邮件.'\n # html_content = '

这是一封重要的邮件.

'\n msg = EmailMultiAlternatives(subject, html_content, from_email, ToEmail)\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()\n print(\"Send Email Successful.\")\n return \"Send Email Successful.\"\n except Exception as e:\n print(e)\n return e\n\nif __name__ == '__main__':\n html_content = \"这是一封特别重要的邮件.\"\n print (sendMail(html_content,'baochengcai@lanjingren.com'))", "sub_path": "drf_api/app/celery_tasks/asynchronous/sendmail.py", "file_name": "sendmail.py", "file_ext": "py", "file_size_in_byte": 1104, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "os.environ.update", "line_number": 4, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 15, "usage_type": "name"}, {"api_name": "django.core.mail.EmailMultiAlternatives", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "511271691", "text": "#! /usr/bin/python3\n\nfrom kivy.app import App\nfrom kivy.uix.button import Button\nfrom kivy.uix.scatter import Scatter\nfrom kivy.uix.label import Label\nfrom kivy.uix.floatlayout import FloatLayout\n\nclass TutorialApp(App):\n def build(self):\n f = FloatLayout()\n s = Scatter()\n l = Label(text='Hello!',\n font_size=150)\n f.add_widget(s)\n s.add_widget(l)\n return f\n # This was a button.\n #return Button(text='Hello!',\n # background_color=(0, 0, 1, 1), # List of\n # # rgba components\n # font_size=150)\ndef main():\n print(\"main() function.\")\n TutorialApp().run()\n\nif __name__ == \"main\":\n print(\"main.\")\n main()\nelse:\n print(\"Not main.\")\n #TutorialApp().run()\n\n\n#if __name__ == \"__main__\":\n# TutorialApp().run()", "sub_path": "video1.py", "file_name": "video1.py", "file_ext": "py", "file_size_in_byte": 892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "kivy.app.App", "line_number": 9, "usage_type": "name"}, {"api_name": "kivy.uix.floatlayout.FloatLayout", "line_number": 11, "usage_type": "call"}, {"api_name": "kivy.uix.scatter.Scatter", "line_number": 12, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "453523928", "text": "\"\"\"A configuration file for the school script.\"\"\"\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass CourseType:\n \"\"\"A class for various types of courses (lectures/labs/whatever).\"\"\"\n\n color: int\n has_homework: bool\n\n\n# the relative path to the folder where the courses are stored\ncourses_folder = \"courses/\"\n\n\n# settings regarding course types -- labs/lectures/...\n# the numbers are ANSI colors that the course type will be painted with\n# see https://www.lihaoyi.com/post/BuildyourownCommandLinewithANSIescapecodes.html\ncourse_types = {\n \"cvičení\": CourseType(118, True),\n \"přednáška\": CourseType(39, False),\n}\n\n\n# default handlers for opening course folders/websites/notes...\nfile_browser = [\"ranger\"]\nweb_browser = [\"qutebrowser\", \"--target\", \"window\"]\ntext_editor = [\"vim\"]\nnote_handlers = {\".xopp\": \"xournalpp\", \".md\": \"vim\"}\n\n\n# default handler for Cron class notifications\n# the first argument after this command is the body of the notification\nnotify_command = \"DISPLAY=:0 DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus dunstify 'School Schedule'\"\n\nnotify_started_message = \"právě začal předmět\" # course started message\nnotify_no_more_courses = \"dnes již žádný další předmět není\" # no more courses today\nnotify_next_course_message = (\n \"další předmět je {0} ({1}), \" # {0} is course name, {1} is course type\n \"který začíná {2} minut po tomto \" # {2} is minutes till next course\n \"v učebně {3}\" # {3} is the location\n)\n", "sub_path": "school/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1516, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "dataclasses.dataclass", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "132709075", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/1/16 22:50\n# @Author : Evescn\n# @Site : \n# @File : 多进程queue.py\n# @Software: PyCharm\n\nfrom multiprocessing import Process, Queue\n\n\ndef f(q):\n q.put([42, None, 'hello'])\n\ndef f2(q):\n # print('from f2:',q.get()) # prints \"[42, None, 'hello']\"\n q.put([42, None, 'hello'])\n print('from f21:', q.get()) # prints \"[42, None, 'hello']\"\n\nif __name__ == '__main__':\n q = Queue()\n p = Process(target=f, args=(q,))\n p.start()\n print('from parent1:', q.get()) # prints \"[42, None, 'hello']\"\n p.join()\n\n p2 = Process(target=f2, args=(q,))\n p2.start()\n print('from parent2:',q.get()) # prints \"[42, None, 'hello']\"\n p2.join()", "sub_path": "day8/多进程queue.py", "file_name": "多进程queue.py", "file_ext": "py", "file_size_in_byte": 730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "multiprocessing.Queue", "line_number": 21, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 22, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "33840886", "text": "import os\nimport json\nimport logging\nfrom glob import glob\n\nfrom lxml import etree\n\nimport settings as s\nfrom .utils import translate_dir\nfrom .log import set_up_logging\n\nlog = set_up_logging('transform', loglevel=logging.DEBUG)\n\n\ndef transform_sopr(options):\n def _is_array(element):\n return element.getchildren() != []\n\n def _add_element_single(element, json_dict):\n json_dict[element.tag] = dict(element.attrib)\n\n def _add_element_array(root_node, json_dict):\n json_dict[root_node.tag] = []\n for e in root_node.getchildren():\n json_dict[root_node.tag].append(dict(e.attrib))\n\n def _add_element(element, json_dict):\n if _is_array(element):\n _add_element_array(element, json_dict)\n else:\n _add_element_single(element, json_dict)\n\n def _write_to_file(xml_filepath, filing):\n path, destination_dir = translate_dir(xml_filepath,\n from_dir=s.ORIG_DIR,\n to_dir=s.TRANS_DIR)\n filing_id = json_filing['ID']\n output_path = os.path.join(destination_dir,\n '{fid}.json'.format(fid=filing_id))\n if os.path.exists(output_path) and not options['force']:\n raise OSError(os.errno.EEXIST,\n ' '.join([os.strerror(os.errno.EEXIST)+':',\n output_path]))\n\n with open(output_path, 'w') as output_file:\n json.dump(json_filing, output_file)\n\n all_fields = ['AffiliatedOrgs',\n 'Client',\n 'ForeignEntities',\n 'GovernmentEntities',\n 'Issues',\n 'Lobbyists',\n 'Registrant']\n\n xml_files = glob(os.path.join(s.ORIG_DIR, 'sopr/*/*/*.xml'))\n\n for xml_filepath in xml_files:\n for filing in etree.parse(open(xml_filepath)).getroot().iterchildren():\n json_filing = dict.fromkeys(all_fields)\n json_filing.update(dict(filing.attrib))\n\n for element in filing.getchildren():\n _add_element(element, json_filing)\n\n _write_to_file(xml_filepath, json_filing)\n", "sub_path": "tasks/transform.py", "file_name": "transform.py", "file_ext": "py", "file_size_in_byte": 2223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "log.set_up_logging", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "utils.translate_dir", "line_number": 34, "usage_type": "call"}, {"api_name": "settings.ORIG_DIR", "line_number": 35, "usage_type": "attribute"}, {"api_name": "settings.TRANS_DIR", "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.errno", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.strerror", "line_number": 42, "usage_type": "call"}, {"api_name": "os.errno", "line_number": 42, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 46, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 56, "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": "settings.ORIG_DIR", "line_number": 56, "usage_type": "attribute"}, {"api_name": "lxml.etree.parse", "line_number": 59, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 59, "usage_type": "name"}]} +{"seq_id": "228222465", "text": "from art.unittest_lib import testflow\n\nfrom reports.reports_base import ReportsTest\nimport config\n\n\nclass LoggingTest(ReportsTest):\n \"\"\"Base class for logging test\"\"\"\n @staticmethod\n def assert_grep_diff_logs(\n search,\n log_file=config.DWH_LOG,\n backup_log_file=config.DWH_LOG_BACKUP,\n lines=0\n ):\n \"\"\"\n Grep diff of two log files\n\n Args:\n search (str): grepped string\n log_file (str): log file\n backup_log_file (str): backup of the same log file from past\n lines (int): append number of lines to grep\n Returns\n str: return grepped string from diff of two files\n \"\"\"\n testflow.step(\"Grepping dwh log\")\n cmd = [\n 'diff', '-e', backup_log_file, log_file, '|',\n 'grep', '-F', search, '-A' + str(lines), '|',\n 'grep', '-Fv', 'tWarn'\n ]\n result = config.ENGINE_HOST.run_command(command=cmd)\n assert not result[0]\n\n return result[1]\n\n @staticmethod\n def assert_backup_file(file_name, backup_file):\n \"\"\"\n Create a backup for file\n\n Args:\n file (str): file to be backed up\n backup_file (str): service that should run\n \"\"\"\n testflow.step(\"Backing up %s log to %s\", file_name, backup_file)\n cmd = ['cp', file_name, backup_file]\n assert config.ENGINE_HOST.run_command(command=cmd), (\n \"Error: Unable to backup {0}\".format(file_name)\n )\n\n @staticmethod\n def assert_remove_backup(file_name):\n \"\"\"\n Remove backup file\n\n Args:\n file (str): backed up file to be removed\n \"\"\"\n testflow.step(\"Removing backup {0}\".format(file_name))\n assert config.ENGINE_HOST.run_command(['rm', file_name])\n\n @staticmethod\n def assert_setting_variable(settings, var, val):\n \"\"\"\n Check value of application server variable\n\n Args:\n settings (dict): dictionary with settings 'key':value\n var (str): variable to find\n val (str): value to check\n \"\"\"\n assert var in settings.keys(), \"Variable {0} not found.\".format(var)\n assert val in settings[var], (\n \"Option {0} with value {1} not in: {2}\".format(var, val, settings)\n )\n", "sub_path": "art/tests/rhevmtests/integration/reports/logging/logging_base.py", "file_name": "logging_base.py", "file_ext": "py", "file_size_in_byte": 2359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "reports.reports_base.ReportsTest", "line_number": 7, "usage_type": "name"}, {"api_name": "config.DWH_LOG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.DWH_LOG_BACKUP", "line_number": 13, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.step", "line_number": 27, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 27, "usage_type": "name"}, {"api_name": "config.ENGINE_HOST.run_command", "line_number": 33, "usage_type": "call"}, {"api_name": "config.ENGINE_HOST", "line_number": 33, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.step", "line_number": 47, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 47, "usage_type": "name"}, {"api_name": "config.ENGINE_HOST.run_command", "line_number": 49, "usage_type": "call"}, {"api_name": "config.ENGINE_HOST", "line_number": 49, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.step", "line_number": 61, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 61, "usage_type": "name"}, {"api_name": "config.ENGINE_HOST.run_command", "line_number": 62, "usage_type": "call"}, {"api_name": "config.ENGINE_HOST", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "228479760", "text": "from functools import reduce\nfrom pyquery import PyQuery as pq\nfrom random import random\nimport time\nimport json\nfrom pprint import pprint\n\n\ndef fetchPQO(url):\n \"\"\" URLにアクセスし，HTMLを pyQuery Object にして返す\n 1~4秒スリープ後に処理をする．\n \"\"\"\n time.sleep(round(random() * 3.0 + 1.0, 3))\n return pq(url)\n\n\ndef fetchPQOs(url, max=100):\n \"\"\" リンクをたどり，page毎に pyQuery Object を取得し，格納したリストを返す\n \"\"\"\n def isLastPage(p):\n return not(p('.pager'))\n\n pqos = []\n for i in range(1, max+1):\n print('page '+str(i)+' fetching...')\n p = fetchPQO(url + '?page=' + str(i))\n pqos.append(p)\n if(isLastPage(p)):\n break\n print('num of page = '+str(len(pqos)))\n return pqos\n\n\ndef getContent(bokeContainer):\n def extractEl(l=['', '']):\n if(l[0] == 'boke-text'):\n return [l[0], pq(l[1])('.boke-text').text()]\n elif(l[0] == 'boke-meta'):\n # 配下に'.boke-stars'があるので取得'\n return ['boke-stars', int(pq(l[1])('.boke-stars').text())]\n else:\n return []\n\n # boke の直接の子要素のうち classを持つもののリスト\n childl = pq(bokeContainer).children('[class]')\n\n # [[class名,html],[]....] となるリストを作成\n datal = [[pq(c).attr('class'), c] for c in childl]\n\n # htmlから必要なデータを抽出し，辞書を作成．(空リストは削除)\n return dict(filter(lambda el: el != [], [extractEl(i) for i in datal]))\n\n\ndef getContents(pqo):\n # pqoから 1つ目の要素(お題), 広告 を除外\n bokediv = pqo('.boke:gt(0)').filter(lambda i, e:\n pq(e).children().length >= 4)\n return [getContent(el) for el in bokediv]\n\n\ndef getAllContents(pqos):\n if(not(len(pqos))):\n return []\n\n ll = [getContents(pqo) for pqo in pqos]\n return list(reduce(lambda tlist, t: tlist+t, ll))\n\n\ndef fetchData(url='', max=100):\n \"\"\" url にアクセスし，1つのお題に対する全てのデータを取得\n url : アクセスするページ(お題のトップページ. ex:http://bokete.jp/odai/2227309)\n max : 取得するbokeのページ数上限\n \"\"\"\n # TODO:中 URLにurl末尾のみ入れても動くようにする\n # TODO:高 fetchしたあと抽出に入る前にお題の画像をとってくる処理、fetchResourceをはさむ\n # TODO:高 返り値に odaiId(ex. odaiId:2227309 ),\n # imgPath(ex. imgPath:'./img/2227309.png')を追加する\n # TODO:高 返り値を辞書に． {odaiId:0, imgPath:'' ,boke:[{},{},{},...]}\n return getAllContents(fetchPQOs(url, max))\n\n\ndef outputJson(obj=[], filename='def.json'):\n \"\"\" obj を jsonファイル(filename)に出力\n \"\"\"\n with open(filename, 'w') as f:\n f.writelines(json.dumps(obj))\n\n\ndef decodeJson(filename='def.json'):\n \"\"\"jsonファイル(filename)をロードし，返す\n \"\"\"\n with open(filename, 'r', encoding='UTF-8') as f:\n return json.loads(f.read(), 'UTF-8')\n\n\ndef printJson(filename='def.json'):\n \"\"\" jsonファイル(filename)をロードし，表示\n \"\"\"\n pprint(decodeJson(filename), indent=4)\n\n\n#\n# main\n#\nif __name__ == '__main__':\n baseUrl = 'http://bokete.jp/odai'\n odaiId = '2227309'\n url = baseUrl+'/'+odaiId\n print('url = \"'+url+'\"')\n\n # 全ての boke を取得\n boke = fetchData(url, max=100)\n\n # 取得データをファイルに保存\n outputJson(boke, filename='boke.json')\n\n # jsonの中身確認\n printJson('boke.json')\n", "sub_path": "bokete_scraping/bokescr.py", "file_name": "bokescr.py", "file_ext": "py", "file_size_in_byte": 3628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "random.random", "line_number": 13, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 14, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 37, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 40, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 45, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 48, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 57, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 93, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "87720264", "text": "import os\n\nimport torch\nfrom torch import optim\nfrom torch.utils.data import sampler\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom common import use_cuda\nfrom widgets import init_pars\nimport os.path as osp\nimport pickle as pkl\nfrom torch.utils.data import Dataset, DataLoader\nimport random\nfrom copy import deepcopy\n\n\nclass LocalDataset(Dataset):\n\n def __init__(self, root_dir):\n \"\"\"\n :param root_dir: the root directory files saved in\n \"\"\"\n super()\n self.root_dir = root_dir\n\n for i, (root, dirs, files) in enumerate(os.walk(self.root_dir)):\n if i == 0:\n self.index_entries = [osp.join(root, filen) for filen in files] \n else:\n print('Directory structure not expected!')\n raise AssertionError()\n random.shuffle(self.index_entries)\n\n def __len__(self):\n return len(self.index_entries)\n\n def __getitem__(self, idx):\n \"\"\"\n\n :param idx:\n :return: (resized img, class)\n \"\"\"\n with open(self.index_entries[idx], 'rb') as f:\n data = pkl.load(f)\n\n return data\n\n\nclass Worker:\n def __init__(self, identifier, dir=osp.join('data', 'debug')):\n self.data_dir = osp.join(dir, str(identifier))\n if not osp.exists(self.data_dir):\n print(f\"Given worker {self.data_dir} doesn't have its data partitioned\")\n raise AssertionError()\n self.dataset = LocalDataset(self.data_dir)\n\n @property\n def num_samples(self):\n return len(self.dataset)\n\n def train(self, model, criterion, current_lr, num_epoches, batch_size=5, verbose=False):\n \"\"\"\n new grads are logged in model itself\n :param num_its: the number of iterations to train locally\n :param current_lr: current learning rate\n :param model: model to be trained in place\n :param criterion:\n :return: model update\n \"\"\"\n running_model = deepcopy(model)\n\n loader = DataLoader(self.dataset, batch_size=batch_size, shuffle=True)\n\n if use_cuda:\n device = torch.device('cuda')\n else:\n device = torch.device('cpu')\n\n running_model = running_model.to(device)\n optimizer = optim.SGD(running_model.parameters(), lr=current_lr)\n\n for epoch in range(num_epoches):\n for it, data in enumerate(loader): # _ start from 0\n inputs, labels = data\n if use_cuda:\n inputs = inputs.to(device=torch.device('cuda'))\n labels = labels.to(device=torch.device('cuda'))\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = running_model(inputs)\n loss = criterion(outputs, labels)\n\n if verbose:\n print(f'Epoch: {epoch}, it: {it}, loss: {loss.item()}')\n\n loss.backward()\n optimizer.step()\n \n return [new_par.data - old_par.data for new_par, old_par in zip(running_model.parameters(), model.parameters())]\n\n\ndef _test():\n worker = Worker(0)\n from model import CNNCifar\n from torch import nn\n\n net = CNNCifar()\n worker.train_(net, nn.CrossEntropyLoss(), 0.01, 10, 5, True)\n\n\nif __name__ == '__main__':\n _test()\n", "sub_path": "worker.py", "file_name": "worker.py", "file_ext": "py", "file_size_in_byte": 3391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 17, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 26, "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": "random.shuffle", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 72, "usage_type": "call"}, {"api_name": "common.use_cuda", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 80, "usage_type": "name"}, {"api_name": "common.use_cuda", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 87, "usage_type": "call"}, {"api_name": "model.CNNCifar", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}]} +{"seq_id": "508242162", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport settings\n\nfrom gensim.models.wrappers import ldamallet\nfrom gensim.corpora import lowcorpus, malletcorpus\n\nif __name__ == '__main__':\n corpus = malletcorpus.MalletCorpus('parsed.txt')\n MALLET_PATH = settings.MALLET_PATH\n # 指定可能なパラメータ及び初期値\n # malletのpath\n # corpus=None コーパス\n # num_topics=100 生成するトピック数\n # alpha=50 初期パラメータ\n # id2word=None 辞書データ（トークンIDとのマッピング）\n # workers=4 スレッド数\n # prefix=None データ用の接頭辞\n # optimize_interval=0 N回のイテレーションごとにハイパーパラメータを最適化する。：0=無効\n # iterations=1000 イテレーション数\n # topic_threshold=0.0 トピックの分布が疎な場合に使用。トピック選定確率の閾値\n model = ldamallet.LdaMallet(MALLET_PATH,\n corpus=corpus,\n num_topics=20,\n alpha=1,\n id2word=corpus.id2word,\n optimize_interval=100)\n model.save('lda.model')\n", "sub_path": "save_model.py", "file_name": "save_model.py", "file_ext": "py", "file_size_in_byte": 1217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "gensim.corpora.malletcorpus.MalletCorpus", "line_number": 10, "usage_type": "call"}, {"api_name": "gensim.corpora.malletcorpus", "line_number": 10, "usage_type": "name"}, {"api_name": "settings.MALLET_PATH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gensim.models.wrappers.ldamallet.LdaMallet", "line_number": 23, "usage_type": "call"}, {"api_name": "gensim.models.wrappers.ldamallet", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "567869249", "text": "f = open('G:/Textbook-Sem5/BT3051 Data Structures and Algorithms/Class Problems/Avengers_ Endgame - Wikipedia.html')\r\nfrom bs4 import BeautifulSoup\r\nsoup = BeautifulSoup(f,'html.parser')\r\n# print(soup.prettify())\r\n\r\n# for table in soup.find_all('table'):\r\n# \ttry:\r\n# \t\tbb = str(table.a.text)\r\n# \texcept Exception as e:\r\n# \t\tpass\r\n\t\r\n# \tprint(bb)\r\nplot = soup.h2.find('span', id='Plot')\r\ntry:\r\n\tsummary = plot.p.text\r\nexcept Exception as e:\r\n\tsummary=''\r\n\r\nprint(summary)", "sub_path": "Parsing/parsing_Avengers.py", "file_name": "parsing_Avengers.py", "file_ext": "py", "file_size_in_byte": 470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "57203765", "text": "\"\"\"\nUsage of the Solar PV dataset\n\n1. Download the github repository: https://github.com/zae-bayern/elpv-dataset\n2. Extract the zip file elpv-dataset-master.zip\n3. Make sure the utils folder and this script are outside the extracted folder.\n4. Run this file.\n\n\"\"\"\n\n\nfrom utils.elpv_reader import load_dataset\nimport cv2\nimport numpy as np\nimport shutil\nimport os\nfrom sklearn.model_selection import train_test_split\n\n\ndestination_folder = 'solar_panels_products_V2'\n\nif not os.path.exists(destination_folder):\n\tos.mkdir(destination_folder)\n\n\n# datafolder is the relative path for the contents in the elpv dataset\n\nimages, probs, types = load_dataset(datafolder = 'elpv_dataset')\n\ntrain_images, test_images, train_probs, test_probs, train_types, test_types = train_test_split(images, probs, types, test_size= 0.1)\n\ndef create_dataset(images, probs, types, split):\n\n\tthreshold = 0.6\n\n\tdata_folder = os.path.join(destination_folder, split)\n\n\tif not os.path.exists(data_folder):\n\t\tos.mkdir(data_folder)\n\n\tidx = 0\n\n\tfor image , prob, type_ in zip(images, probs, types):\n\n\n\t\tif prob > threshold:\n\n\n\t\t\timage = cv2.resize(image, (300, 300)).reshape(300,300,1)\n\n\t\t\tcategory_folder = os.path.join(data_folder, type_ + '_defective')\n\n\t\t\tif not os.path.exists(category_folder):\n\t\t\t\tos.mkdir(category_folder)\n\n\t\t\timage_name = os.path.join( category_folder, 'elpv_' + str(idx) + '.jpg')\n\n\n\t\t\tif not os.path.exists(image_name):\n\n\t\t\t\tcv2.imwrite(image_name, image )\n\n\n\t\tif prob < threshold:\n\n\n\t\t\timage = cv2.resize(image, (300, 300)).reshape(300,300,1)\n\n\t\t\tcategory_folder = os.path.join(data_folder, type_ + '_non_defective')\n\n\t\t\tif not os.path.exists(category_folder):\n\t\t\t\tos.mkdir(category_folder)\n\n\t\t\timage_name = os.path.join( category_folder, 'elpv_' + str(idx) + '.jpg')\n\n\n\t\t\tif not os.path.exists(image_name):\n\n\t\t\t\tcv2.imwrite(image_name, image )\n\n\t\t\n\t\tidx += 1\n\t\t\t\n\ncreate_dataset(train_images, train_probs, train_types, \"train\")\ncreate_dataset(test_images, test_probs, test_types, \"test\")\n\n", "sub_path": "1.preprocess_dataset/solar_panels_products_v2.py", "file_name": "solar_panels_products_v2.py", "file_ext": "py", "file_size_in_byte": 1984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.elpv_reader.load_dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 30, "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.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 49, "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.mkdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 67, "usage_type": "call"}, {"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.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "472069627", "text": "import bpy\r\n\r\nfrom bpy.types import Panel\r\n\r\nfrom ... utility import active_tool, addon, names\r\n\r\n\r\nclass HARDFLOW_PT_behavior_settings(Panel):\r\n bl_label = 'Behavior'\r\n bl_space_type = 'VIEW_3D'\r\n bl_region_type = 'UI'\r\n bl_category = 'Hardflow'\r\n bl_parent_id = 'HOPS_PT_settings'\r\n bl_options = {'DEFAULT_CLOSED'}\r\n\r\n @classmethod\r\n def poll(cls, context):\r\n return context.region.type == 'UI'\r\n\r\n def draw(self, context):\r\n layout = self.layout\r\n\r\n preference = addon.preference()\r\n\r\n option = None\r\n for tool in context.workspace.tools:\r\n if tool.idname == 'Hardflow':\r\n self.label_row(layout.row(), preference.behavior, 'quick_execute')\r\n\r\n elif tool.idname == 'Hops':\r\n self.label_row(layout.row(), preference.behavior, 'display_gizmo', label='Hide gizmo on Ctrl')\r\n self.label_row(layout.row(), preference.behavior, 'display_dots', label='Display dots on Ctrl')\r\n self.label_row(layout.row(), preference.behavior, 'display_operators', label='Display Operators on Ctrl')\r\n self.label_row(layout.row(), preference.behavior, 'display_boolshapes', label='Display booleans on Ctrl')\r\n self.label_row(layout.row(), preference.behavior, 'display_boolshapes_for_all', label='Display booleans for All Objects')\r\n self.label_row(layout.row(), preference.behavior, 'add_mirror_to_boolshapes', label='Add mirror to boolshapes')\r\n self.label_row(layout.row(), preference.behavior, 'add_WN_to_boolshapes', label='Add WN to boolshapes')\r\n self.label_row(layout.row(), preference.behavior, 'cursor_boolshapes', label='Orient boolshapes to cursor')\r\n\r\n def label_row(self, row, path, prop, label=''):\r\n row.label(text=label if label else names[prop])\r\n row.prop(path, prop, text='')\r\n", "sub_path": "addon/panel/settings/behavior.py", "file_name": "behavior.py", "file_ext": "py", "file_size_in_byte": 1911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "bpy.types.Panel", "line_number": 8, "usage_type": "name"}, {"api_name": "utility.addon.preference", "line_number": 23, "usage_type": "call"}, {"api_name": "utility.addon", "line_number": 23, "usage_type": "name"}, {"api_name": "utility.names", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "591139966", "text": "import bottle, grooveshark, tempfile, urllib2, os, thread, time, Queue\n\nimport pygtk\npygtk.require('2.0')\n\nimport gobject\ngobject.threads_init()\n\nimport pygst\npygst.require('0.10')\n\nimport gst\n\n\nclass Server:\t\n\tdef __init__(self):\n\t\tself.queue = []\n\t\tself.position = -1\n\t\tself.player = gst.parse_launch('appsrc name=source ! decodebin2 ! autoaudiosink')\n\t\tself.buffer = Queue.Queue()\n\t\t\n\t\tsrc = self.player.get_by_name('source')\n\t\tself.player.get_bus().connect('message', self.onmessage)\n\t\tsrc.connect('need-data', self.needdata)\n\t\tself.player.set_state(gst.STATE_PLAYING)\n\t\t\n\tdef needdata(self, src, length):\n\t\tbytes = self.buffer.get()\n\t\tsrc.emit('push-buffer', gst.Buffer(bytes))\n\t\n\tdef _play(self, songId):\t\t\n\t\tip, key = grooveshark.getSong(songId)\n\t\tt = tempfile.mktemp(\".mp3\")\t\t\n\t\t\n\t\tresp = urllib2.urlopen(\"http://\" + ip + \"/stream.php\", \"streamKey=\" + key)\n\t\tf = open(t, \"wb\")\n\t\t\n\t\tchunk = resp.read(4096)\n\t\twhile chunk:\n\t\t\tchunk.write(n)\n\t\t\tself.buffer.put(n)\n\t\t\tchunk = resp.read(4096)\n\t\t\n\t\tf.close()\n\t\t\n\t\t# Tag song\n\t\t# Delete it\n\t\n\t@bottle.route('/play')\n\tdef play(self):\n\t\tself.player.play()\n\n\t@bottle.route('/pause')\n\tdef pause(self):\n\t\tself.player.pause()\n\n\t@bottle.route('/set_volume/:volume')\n\tdef set_volume(self, volume):\n\t\tself.player.set_volume(float(volume))\n\n\t@bottle.route('/stop')\n\tdef stop(self):\n\t\tself.player.stop()\n\n\t@bottle.route('/queue/next')\n\tdef queue_next(self):\n\t\tself.position += 1\n\t\tself._play(self.queue[self.position])\n\n\t@bottle.route('/queue/prev')\n\tdef queue_prev(self):\n\t\tself.position -= 1\n\t\tself._play(self.queue[self.position])\n\t\t\n\t@bottle.route('/queue/add/:song')\n\tdef queue_add(self, song):\n\t\tself.queue.append(song)\n\n\t@bottle.route('/queue/clear')\n\tdef queue_clear(self):\n\t\tself.queue = []\n\t\tself.position = 0\n\t\tself.stop()\n\t\n\t# Listen for http connections\n\tdef listen(self):\n\t\tbottle.run(host='localhost', port=9999)\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "pygtk.require", "line_number": 4, "usage_type": "call"}, {"api_name": "gobject.threads_init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygst.require", "line_number": 10, "usage_type": "call"}, {"api_name": "gst.parse_launch", "line_number": 19, "usage_type": "call"}, {"api_name": "Queue.Queue", "line_number": 20, "usage_type": "call"}, {"api_name": "gst.STATE_PLAYING", "line_number": 25, "usage_type": "attribute"}, {"api_name": "gst.Buffer", "line_number": 29, "usage_type": "call"}, {"api_name": "grooveshark.getSong", "line_number": 32, "usage_type": "call"}, {"api_name": "tempfile.mktemp", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 35, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 49, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 53, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 57, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 61, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 65, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 70, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 75, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 79, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "433316449", "text": "from pylab import *\nimport matplotlib.pyplot as plt\nimport sys\nimport csv\na=input(\"Enter the width of slice \")\nb=input(\"Enter the center of range \")\nfor j in range(1,len(sys.argv)):\n\n\tname=\"wsnapshot_\"+sys.argv[j]+\".csv\"\n\tprint(name);\n\tf=open(name)\n\tr=csv.reader(f)\n\td=list(r)\n\ti=0\n\tprint(\"Plotting \"+sys.argv[j]+\"...\")\n\n\tfor i in range(1,262145):\n\t\tif(float(d[i][2])>b-(a/5) and float(d[i][2]) 5 and epoch % 3 == 0:\n lr = lr_decay * lr\n optimizer = optimizers.Adam(lr)\n print(\"\\n===============================================================\")\n print(f\"Epoch: {epoch + 1}\")\n start_time = time.time()\n training_fn = create_training_step(model, l_losses, training_metrics, optimizer)\n calc_loop(train_ds, training_fn, train_mean_losses, training_metrics)\n # Validation\n validate_fn = create_validate_step(model, l_losses, val_metrics)\n val_loss, val_acc = calc_loop(val_ds, validate_fn, val_mean_losses, val_metrics, mode='val')\n # Update weight\n if val_acc > best_val:\n best_val = val_acc\n model.save_weights(f'{args[\"output_dir\"]}/checkpoint')\n end_time = time.time()\n print(f\"After {end_time - start_time}s\")\n", "sub_path": "classification_training.py", "file_name": "classification_training.py", "file_ext": "py", "file_size_in_byte": 3460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.dataframe.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.dataframe.train_val_split", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.data_generator.ClassifyGenerator", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.data_generator.ClassifyGenerator", "line_number": 53, "usage_type": "call"}, {"api_name": "backbone.model.create_model", "line_number": 55, "usage_type": "call"}, {"api_name": "backbone.losses.get_losses_weights", "line_number": 57, "usage_type": "call"}, {"api_name": "backbone.losses.create_losses", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.BinaryAccuracy", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.BinaryAccuracy", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 66, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 75, "usage_type": "name"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "backbone.model.create_training_step", "line_number": 79, "usage_type": "call"}, {"api_name": "backbone.model.calc_loop", "line_number": 80, "usage_type": "call"}, {"api_name": "backbone.model.create_validate_step", "line_number": 82, "usage_type": "call"}, {"api_name": "backbone.model.calc_loop", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "54939255", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom . import models\n\nclass TripAdmin(admin.ModelAdmin):\n search_fields = [\"code\",]\n list_display = (\"code\",\"file\",\"pilote\")\n list_per_page = 10\nadmin.site.register(models.Trip,TripAdmin)", "sub_path": "Aviation/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 259, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "593709502", "text": "#!/usr/bin/env python\nimport MySQLdb\nimport yaml\nimport re\nimport os\n\n# import json\n# db = MySQLdb.connect(\"bol-db-products-prod-01.ecmwf.int\", \"ecmwf_ro\", \"ecmwf_ro\", \"param\")\ndb = MySQLdb.connect(\"k8s-bol-webapps-test-worker-016.ecmwf.int\", \"products\", \"products\", \"param\", port=30544)\n# db = MySQLdb.connect(\"k8s-bol-webapps-prod-worker-012.ecmwf.int\", \"products\", \"products\", \"param\", port=30545)\n \nPRODGEN = {}\nif os.path.exists(\"prodgen-paramids.yaml\"):\n with open(\"prodgen-paramids.yaml\") as f:\n PRODGEN = yaml.load(f.read(), Loader=yaml.FullLoader)\n\n# print(json.dumps(PRODGEN))\n\nPARAMSIDS = {}\nif os.path.exists(\"paramids.yaml\"):\n with open(\"paramids.yaml\") as f:\n PARAMSIDS = yaml.load(f.read(), Loader=yaml.FullLoader)\n\ncursor = db.cursor()\n\ncursor.execute(\"select * from param\")\n\nfor data in cursor.fetchall():\n paramid, abbr, longname = int(data[0]), data[1].lower(), data[2].lower()\n\n abbr = re.sub(r\"\\W\", \"_\", abbr)\n abbr = re.sub(r\"_+\", \"_\", abbr)\n abbr = re.sub(r\"^_\", \"\", abbr)\n abbr = re.sub(r\"_$\", \"\", abbr)\n\n if not abbr:\n abbr = \"_param_%06d\" % (paramid,)\n\n entry = [abbr.strip(), longname.strip()]\n\n if paramid in PRODGEN:\n pgen = [str(x).lower() for x in PRODGEN[paramid]]\n p = []\n for n in pgen:\n if (\n n not in entry\n ): # and (' ' not in n) and ('.' not in n): # and ('-' not in n):\n entry.append(n)\n p.append(n)\n\n entry = tuple(entry)\n\n if paramid in PARAMSIDS:\n before = tuple(PARAMSIDS[paramid])\n if before != entry:\n print(\n \"WARNING! updated paramid: {}, {} => {}\".format(paramid, before, entry)\n )\n PARAMSIDS[paramid] = list(entry)\n else:\n print(\"new paramid: {} {}\".format(paramid, entry))\n PARAMSIDS[paramid] = list(entry)\n\ncursor.close()\ndb.close()\n\n\nfor paramid, entry in PRODGEN.items():\n if paramid not in PARAMSIDS:\n print(\"WARNING! adding pseudo-paramid: {}, {}\".format(paramid, tuple(entry)))\n PARAMSIDS[paramid] = entry\n\nwith open(\"paramids.yaml\", \"w\") as f:\n f.write(\n \"# File automatically generated by %s\\n# Do not edit\\n\\n\"\n % (os.path.basename(__file__))\n )\n f.write(yaml.safe_dump(PARAMSIDS, default_flow_style=False))\n", "sub_path": "share/metkit/make-paramids-yaml-esuite.py", "file_name": "make-paramids-yaml-esuite.py", "file_ext": "py", "file_size_in_byte": 2357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "MySQLdb.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 15, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 22, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 22, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 32, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 33, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "yaml.safe_dump", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "547975217", "text": "import numpy as np\r\nimport torch\r\nfrom torch import nn\r\nimport time\r\n\r\n\r\nepochs = 2000\r\nSEED = 16\r\nnp.random.seed(SEED)\r\ncuda = torch.cuda.is_available()\r\nif cuda:\r\n torch.cuda.manual_seed(SEED)\r\n\r\n\r\nclass CF(nn.Module):\r\n def __init__(self, input_dim, hidden_dim, layer_num):\r\n super(CF, self).__init__()\r\n\r\n self.input_dim = input_dim\r\n self.hidden_dim = hidden_dim\r\n self.gru = torch.nn.GRU(input_size=self.input_dim, hidden_size=self.hidden_dim,\r\n num_layers=layer_num)\r\n self.out = torch.nn.Linear(self.hidden_dim, 2) # output layer\r\n\r\n def forward(self, miu):\r\n output, hn = self.gru(miu)\r\n output_in_last_timestep = hn[-1, :, :]\r\n x = self.out(output_in_last_timestep)\r\n return x\r\n\r\n\r\ndata_death = []\r\nwith open(\"./data/7/hadm_death_20_7.txt\", \"r\") as file_death:\r\n for line in file_death.readlines():\r\n is_death = float(str(line.strip('\\n')).split('\\t')[1])\r\n data_death.append(is_death)\r\ndata_death_Tensor = torch.LongTensor(data_death)\r\n\r\ngroup = 776\r\n# cf_record = np.load(\"./data/7/CF_node_polynomial_eff_pro.npy\")\r\n# cf_record = np.load(\"./data/7/CF_node_is_fail.npy\")\r\ncf_record = np.load(\"./data/7/CF_node_eff_pro.npy\")\r\n\r\nhidden_dim_all = [32, 64, 128, 256]\r\nweight_CRs = [0.3, 0.5, 0.7, 0.9, 1.0]\r\nMAX_AUC_list = []\r\nfor index in range(5):\r\n # with open(\"./data/7/AUC_result_GRU_polynomial_eff_pro.txt\", \"a\") as f:\r\n with open(\"./data/7/AUC_result_GRU_node_eff_pro.txt\", \"a\") as f:\r\n # with open(\"./data/7/AUC_result_GRU_node_is_fail.txt\", \"a\") as f:\r\n f.write(str(index+1)+'\\n')\r\n max_AUC = 0\r\n for weight_CR in weight_CRs:\r\n for hidden_dim in hidden_dim_all: \r\n length = 60\r\n input_dim = 451\r\n layer_num = 2\r\n learning_rate = 0.0005\r\n\r\n if index == 0:\r\n cal_r_is_fail_train = cf_record[group * 1:]\r\n cal_r_is_fail_test = cf_record[:group * 1]\r\n elif index == 4:\r\n cal_r_is_fail_train = cf_record[:group * 4]\r\n cal_r_is_fail_test = cf_record[group * 4:]\r\n else:\r\n cal_r_is_fail_train = np.concatenate((cf_record[group * 0:group * index], cf_record[group * (index + 1):]))\r\n cal_r_is_fail_test = cf_record[group * index:group * (index + 1)]\r\n\r\n miu_train_new = []\r\n for i in range(cal_r_is_fail_train.shape[1]):\r\n miu_train_new.append(cal_r_is_fail_train[:, i, :])\r\n miu_train_new = torch.Tensor(miu_train_new)\r\n\r\n miu_test_new = []\r\n for i in range(cal_r_is_fail_test.shape[1]):\r\n miu_test_new.append(cal_r_is_fail_test[:, i, :])\r\n miu_test_new = torch.Tensor(miu_test_new)\r\n\r\n net = CF(input_dim=input_dim, hidden_dim=hidden_dim, layer_num=layer_num) # d\r\n optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)\r\n loss_func = torch.nn.CrossEntropyLoss(weight=torch.Tensor([weight_CR, 1.0]).cuda())\r\n\r\n if cuda:\r\n net.cuda()\r\n miu_train_new = miu_train_new.cuda()\r\n miu_test_new = miu_test_new.cuda()\r\n data_death_Tensor = data_death_Tensor.cuda()\r\n\r\n best_epoch = 0\r\n best_loss = 10000\r\n bad_count = 0\r\n patience = 100\r\n start = time.time()\r\n for epoch in range(epochs):\r\n now = time.time()\r\n net.train()\r\n optimizer.zero_grad()\r\n out_train = net(miu_train_new)\r\n\r\n if index == 0:\r\n loss = loss_func(out_train, data_death_Tensor[group*1:])\r\n elif index == 4:\r\n loss = loss_func(out_train, data_death_Tensor[:group*4])\r\n else:\r\n loss = loss_func(out_train, torch.cat((data_death_Tensor[:group*index], data_death_Tensor[group*(index+1):])))\r\n loss.backward()\r\n optimizer.step()\r\n\r\n net.eval()\r\n out_test = net(miu_test_new)\r\n if index == 0:\r\n loss_test = loss_func(out_test, data_death_Tensor[:group*1])\r\n target_y = data_death_Tensor[:group*1].cpu().data.numpy()\r\n elif index == 4:\r\n loss_test = loss_func(out_test, data_death_Tensor[group*4:])\r\n target_y = data_death_Tensor[group*4:].cpu().data.numpy()\r\n else:\r\n loss_test = loss_func(out_test, data_death_Tensor[group*index:group*(index+1)])\r\n target_y = data_death_Tensor[group*index:group*(index+1)].cpu().data.numpy()\r\n\r\n if loss_test < best_loss:\r\n best_loss = loss_test\r\n best_epoch = epoch\r\n bad_count = 0\r\n best_out_test = out_test.cpu()\r\n else:\r\n bad_count += 1\r\n\r\n if bad_count == patience:\r\n break\r\n\r\n True_sample_pro = best_out_test[:, 0].detach().numpy().tolist()\r\n index_dic = {}\r\n for id, pro in enumerate(True_sample_pro):\r\n index_dic[id] = pro\r\n index_dic = sorted(index_dic.items(), key=lambda item:item[1], reverse=True)\r\n\r\n FPR_list = [0.0]\r\n TPR_list = [0.0]\r\n TP = 0\r\n FN = 0\r\n FP = 0\r\n TN = 0\r\n\r\n for id in index_dic:\r\n if target_y[id[0]] == 0:\r\n TP += 1\r\n else:\r\n FP += 1\r\n if index == 4:\r\n FN = group + 4 - target_y.sum() - TP\r\n else:\r\n FN = group - target_y.sum() - TP\r\n\r\n TN = target_y.sum() - FP\r\n FPR = FP / (TN + FP)\r\n TPR = TP / (TP + FN)\r\n FPR_list.append(FPR)\r\n TPR_list.append(TPR)\r\n\r\n AUC = 0\r\n for id, x in enumerate(FPR_list[:-1]):\r\n AUC += (FPR_list[id+1] - x) * (TPR_list[id] + TPR_list[id+1])\r\n AUC = AUC/2\r\n if AUC > max_AUC:\r\n max_AUC = AUC\r\n\r\n f.write(\"seq_len=\" + str(length) + ' ')\r\n f.write(\"input=\" + str(input_dim) + ' ')\r\n f.write(\"hidden=\" + str(hidden_dim) + ' ')\r\n f.write(\"layer_num=\" + str(layer_num) + ' ')\r\n f.write(\"w_CR=\" + str(weight_CR) + ' ')\r\n f.write(\"lr=\" + str(learning_rate) + ' ')\r\n f.write(\"AUC=\"+str(AUC)+'\\n')\r\n f.write(\"MAX_AUC=\"+str(max_AUC)+'\\n')\r\n MAX_AUC_list.append(max_AUC)\r\n if index == 4:\r\n sum = 0\r\n f.write('(')\r\n for i, auc in enumerate(MAX_AUC_list):\r\n sum += auc\r\n if i < 4: \r\n f.write(str(auc) +'+')\r\n else:\r\n f.write(str(auc))\r\n f.write(')/5=')\r\n f.write(str(sum/5) + '\\n')\r\n", "sub_path": "predict_GRU_CUDA.py", "file_name": "predict_GRU_CUDA.py", "file_ext": "py", "file_size_in_byte": 7589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "numpy.random.seed", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "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.GRU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 82, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "578414217", "text": "# COLLECT SUBLET INFORMATION FROM WOKO.CH\n\n# IMPORT LIBRARIES\nfrom bs4 import BeautifulSoup as soup\nfrom urllib import request\nimport csv\nimport time\n\n# get the page and\nhome = \"http://www.woko.ch\"\nurl = \"http://www.woko.ch/en/untermieter-gesucht\"\npage = request.urlopen(url)\nscrap = soup(page,'html.parser')\n\n# find result rows for region Zurich\nzurich = scrap.find('div',{'id':'GruppeID_98'})\nitems = zurich.find_all('div',{'class':'inserat'})\n\n# set titles\nrows = []\nrow = ['Title','PostedTime','Duration','Address','Rent (CHF/Month)','Link','Room Info']\nrows.append(row)\n\n# loop through items and get info for each one\nfor item in items:\n\theader = item.find('div',{'class':'titel'})\n\ttitle = header.find('h3').getText().replace('ä','ae').replace('ö','oe').replace('ü','ue')\n\tpostedtime = header.find('span').getText()\n\tlink = home+item.find('a').get('href')\n\tdata = item.find_all('td')\n\tduration = data[1].getText().strip('as from ').strip().replace('until','-')\n\taddress = data[3].getText().replace('ä','ae').replace('ö','oe').replace('ü','ue')\n\trent = item.find('div',{'class':'preis'}).getText().replace('.--','')\n\tdetailpage = soup(request.urlopen(link),'html.parser')\n\troominfo = home+detailpage.find('a',{'class':'btn btn-primary'}).get('href')\n\trows.append([title,postedtime,duration,address,rent,link,roominfo])\n\n# output to csv file\ndate = time.strftime(\"%m%d\",time.localtime())\nfilename = \"sublet_info_\"+date+\".csv\"\nwith open(filename,\"w\",newline = \"\",encoding=\"utf-8\") as output:\n\twriter = csv.writer(output)\n\twriter.writerows(rows)\n\nprint(\"finished scrapying \"+filename)\n\n\n", "sub_path": "woko-info/sublet.py", "file_name": "sublet.py", "file_ext": "py", "file_size_in_byte": 1595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "urllib.request.urlopen", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 12, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 34, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 39, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 39, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "314241276", "text": "import argparse\n\nparser = argparse.ArgumentParser(\"Add scores from transformer and Bi-LM\")\nparser.add_argument(\"-score_path_1\", type=str, required=True)\nparser.add_argument(\"-score_path_2\", type=str, required=True)\nparser.add_argument(\"-saveto\", type=str, required=True)\n\nargs = parser.parse_args()\n\nwith open(args.score_path_1, 'r') as f_1, open(args.score_path_2, 'r') as f_2:\n scores_1 = f_1.readlines()\n scores_2 = f_2.readlines()\n if len(scores_1) != len(scores_2):\n raise ValueError(\"Two file should have same line number.\")\n\nwith open(args.saveto, 'w') as f:\n for s1, s2 in zip(scores_1, scores_2):\n s = float(s1.strip()) + (-float(s2.strip()))\n f.write(str(s) + '\\n')\n \n", "sub_path": "thumt/thumt/scripts/ensemble_score.py", "file_name": "ensemble_score.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "153004146", "text": "from requests import get\nimport requests.exceptions\nimport logging\nfrom time import sleep\nimport re\n\n\nAPI_URL = \"https://api.freelancehunt.com/v2\"\nEVENT_TEMPLATE = '{type} {login} {message}'\nOK, NETWORK_ERROR, TOKEN_ERROR, TOO_MANY_REQUESTS = range(4)\nlogger = logging.getLogger(__name__)\n\n\ndef _api_get(token, rel_url=\"/\"):\n return get(API_URL + rel_url, headers={'Authorization': f'Bearer {token}'})\n\n\ndef validate(token):\n try:\n response = _api_get(token)\n except requests.exceptions.RequestException as error:\n return NETWORK_ERROR\n res = response.json()\n if 'error' in res and response.status_code != 404:\n if response.status_code == 429:\n logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n token[:len(token) // 2])\n return TOO_MANY_REQUESTS\n logger.warning(\"Received error '%s' for token %s...\", res['error'], token[:len(token) // 2])\n return TOKEN_ERROR\n else:\n return OK\n\n\ndef _get_feed(token):\n try:\n response = _api_get(token, \"/my/feed\")\n except requests.exceptions.RequestException as error:\n logger.critical(\"Request raised error '%s'. Returned 'NETWORK_ERROR'\", error)\n return NETWORK_ERROR\n feed = response.json()\n if 'error' in feed and response.status_code != 404:\n if response.status_code == 429:\n logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n token[:len(token) // 2])\n return TOO_MANY_REQUESTS\n logger.warning(\"Received error '%s' for token %s...\", feed['error'], token[:len(token) // 2])\n return TOKEN_ERROR\n return _prepare_feed(feed)\n\n\ndef _prepare_event_msg(text):\n msg = re.sub(r\"\", \"\", text)\n msg = re.sub(r'', lambda m: f'', msg)\n return msg\n\n\ndef _prepare_feed(feed):\n events = []\n for i in feed['data']:\n attrs = i['attributes']\n if not attrs['is_new']:\n break\n from_type = \"закачкик\" if attrs['from']['type'] == \"employer\" else \"исполнитель\"\n login = attrs['from']['login']\n msg = _prepare_event_msg(attrs['message'])\n event = EVENT_TEMPLATE.format(type=from_type, login=login, message=msg)\n events.append(event)\n return events[::-1]\n\n\ndef _get_threads(token):\n try:\n response = _api_get(token, \"/threads\")\n except requests.exceptions.RequestException as error:\n logger.critical(\"Request raised error '%s'. Returned 'NETWORK_ERROR'\", error)\n return NETWORK_ERROR\n threads = response.json()\n if 'error' in threads and response.status_code != 404:\n if response.status_code == 429:\n logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n token[:len(token) // 2])\n return TOO_MANY_REQUESTS\n logger.warning(\"Received error '%s' for token %s...\", msgs['error'], token[:len(token) // 2])\n return TOKEN_ERROR\n return threads['data']\n\n\ndef _get_message(token):\n return \"some text\"\n\n\ndef _get_messages(token):\n threads = _get_threads(token)\n msgs = []\n for thread in threads:\n attrs = thread['attributes']\n if not attrs['is_unread']:\n continue\n\n\n\ndef get_updates(settings):\n token = settings['token']\n logger.debug(\"Starting 'get_updates' for token %s...\", token[:len(token) // 2])\n validation = validate(token)\n if validation == TOO_MANY_REQUESTS:\n sleep(20)\n return get_updates(settings)\n elif validation != OK:\n return validation\n\n feed = _get_feed(token)\n if not isinstance(feed, list):\n return feed\n return feed\n\n\nif __name__ == \"__main__\":\n TEST_TOKEN = \"a10207aff6d23d9d735bcd6e36eefa9f2ba2a0d0\"\n TEST_SETTINGS = {\n 'token': TEST_TOKEN\n }\n TEST_MSG = 'От закачкика freelancehunt:\\nИтоги 2019 года Freelancehunt: рекорды в цифрах! '\n #print(get_updates(TEST_SETTINGS))\n #print(_get_feed(TEST_TOKEN))\n print(_get_threads(TEST_TOKEN))\n #print(_prepare_msg(TEST_MSG))\n", "sub_path": "app/fhapi.py", "file_name": "fhapi.py", "file_ext": "py", "file_size_in_byte": 4507, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 21, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 38, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 75, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "211177572", "text": "from django.urls import path\nfrom django.conf.urls import url\nfrom webapp import views\n\nurlpatterns = [\n path('', views.inicio , name='inicio'),\n path('buscar', views.buscar , name='buscar'),\n path('herramientas/', views.info_herramienta , name='herramientas'),\n path('ejemplos/', views.info_ejemplo_de_uso , name='ejemplos'),\n path('personal/', views.info_persona_de_conectate , name='personal'),\n path('personal', views.personal , name='personal_dashboard'),\n path('herramientas//tutoriales/', views.tutoriales , name='tutoriales'),\n path('auth/login', views.rest_login, name='login'),\n path('auth/logout', views.logout_view, name='logout'),\n path('historial', views.historial, name=\"historial\"),\n]\n", "sub_path": "webapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "webapp.views.inicio", "line_number": 6, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "webapp.views.buscar", "line_number": 7, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "webapp.views.info_herramienta", "line_number": 8, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "webapp.views.info_ejemplo_de_uso", "line_number": 9, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "webapp.views.info_persona_de_conectate", "line_number": 10, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "webapp.views.personal", "line_number": 11, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "webapp.views.tutoriales", "line_number": 12, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "webapp.views.rest_login", "line_number": 13, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "webapp.views.logout_view", "line_number": 14, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "webapp.views.historial", "line_number": 15, "usage_type": "attribute"}, {"api_name": "webapp.views", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "596648555", "text": "#!/usr/bin/env python3\n\nimport re\nimport subprocess\n\nimport utils\n\n\ndef volume():\n conf = utils.read_conf(\"volume\")\n color = conf.get(\"color\", \"FFFFFF\")\n color_inactive = conf.get(\"color_inactive\", \"000000\")\n emblem = conf.get(\"emblem_default\")\n label = \"N.A\"\n\n try:\n output_raw = subprocess.check_output(\n [\n conf.get(\"amixer_path\", \"amixer\"),\n \"get\", \"Master\"\n ],\n timeout=conf.getint(\"timeout\", 1)\n )\n\n if re.search('\\[on\\]', output_raw.decode()): # volume is on\n re_output = re.search('(?<=\\[)\\d+(?=%\\])', output_raw.decode())\n vol_percent = int(re_output.group(0))\n\n if conf.getboolean(\"dynamic_emblem\", False):\n if vol_percent >= 65:\n emblem = conf.get(\"emblem_high\", \"\")\n elif vol_percent >= 35:\n emblem = conf.get(\"emblem_medium\", \"\")\n else:\n emblem = conf.get(\"emblem_low\", \"\")\n label = \"{0}%\".format(vol_percent) if vol_percent else \"N.A\"\n else: # volume if off\n color = color_inactive\n label = \"Off\"\n emblem = conf.get(\"emblem_muted\", \"\")\n\n except (subprocess.CalledProcessError, subprocess.CalledProcessError,\n re.error, ValueError, Exception):\n color = color_inactive\n\n if conf.getboolean(\"uppercase\", False):\n label = label.upper()\n\n return utils.print_u(emblem, label, color)\n\n\nif __name__ == '__main__':\n print(volume())\n", "sub_path": "src/volume.py", "file_name": "volume.py", "file_ext": "py", "file_size_in_byte": 1565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "utils.read_conf", "line_number": 10, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 17, "usage_type": "call"}, {"api_name": "re.search", "line_number": 25, "usage_type": "call"}, {"api_name": "re.search", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 42, "usage_type": "attribute"}, {"api_name": "re.error", "line_number": 43, "usage_type": "attribute"}, {"api_name": "utils.print_u", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "154318582", "text": "from django.shortcuts import render_to_response, redirect\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import login\nfrom django.template import RequestContext\n\n\n\ndef index(request):\n if request.POST:\n form = AuthenticationForm(request,data=request.POST)\n if form.is_valid():\n login(request, form.get_user())\n if request.session.test_cookie_worked():\n request.session.delete_test_cookie() \n return redirect('device-index')\n else:\n form = AuthenticationForm(request)\n request.session.set_test_cookie()\n return render_to_response('index.html', {'form': form },\n context_instance = RequestContext(request) )\n\n", "sub_path": "whitelist/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "61", "api": [{"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 19, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "487275919", "text": "# -*- encoding: utf-8 -*-\n##############################################################################\n#\n# OpenERP, Open Source Management Solution\t\n# Copyright (C) 2004-2009 Tiny SPRL (). All Rights Reserved\n# $Id$\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n#\n##############################################################################\n\n\"\"\"wizard that replace completely the wizard on stock with same name,\nbeacause adds the functionally that set zero prodlotas not products\"\"\"\n\nimport wizard\nimport pooler\nfrom tools.translate import _\n\n\nFORM = \"\"\"\n
\n \n \n \n