elements.\r\n for eachTr in listOfTr:\r\n\t#for each tr element, we obtain a list of all the td elements inside it.\r\n listOfTd = eachTr.find_all(\"td\")\r\n artist = 'artist'\r\n song = 'song'\r\n bpm = 'bpm'\r\n # we iterate through the list of | elements\r\n for eachTd in listOfTd:\r\n if eachTd.get('class') == ['artist']:\r\n artist = eachTd.contents[0].text\r\n elif eachTd.get('class') == ['title']:\r\n song = eachTd.text\r\n elif eachTd.get('class') == ['bpm']:\r\n bpm = eachTd.text\r\n print(\"{0} by {1} has a BPM of {2}\".format(song, artist, bpm))\r\n numberOfResults += 1\r\n printNoOfResults()\r\n\r\nelse:\r\n @decor\r\n def noResults():\r\n return \"Sorry. %s results found.\" % numberOfResults\r\n noResults()\r\n","sub_path":"Python/Web Harvesting/BPMParserWifSearch.py","file_name":"BPMParserWifSearch.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"53193630","text":"#!/usr/bin/env python3\n#\n# This file is part of the minifold project.\n# https://github.com/nokia/minifold\n\n__author__ = \"Marc-Olivier Buob\"\n__maintainer__ = \"Marc-Olivier Buob\"\n__email__ = \"marc-olivier.buob@nokia-bell-labs.com\"\n__copyright__ = \"Copyright (C) 2018, Nokia\"\n__license__ = \"BSD-3\"\n\nfrom html.parser import HTMLParser\nfrom html.entities import name2codepoint\n\nfrom minifold.entries_connector import EntriesConnector\n\nclass HtmlTableParser(HTMLParser):\n def __init__(self, columns :list, output_list :list, keep_entry = None):\n \"\"\"\n Constructor.\n Args:\n columns: list of string mapping the attribute name\n corresponding with the index. If data is fetch\n for columns having a greater index than\n len(columns), columns[-1] is used, and this\n key may store a list of string values instead\n of a single string. This allow to store data\n stored among several columns in a single attribute.\n output_list: reference to an output list where the\n data will be outputed (one dict per row, one\n key/value per column).\n keep_entry: callback which determine whether an\n must entry must be kept or discard. Pass None\n to filter nothing. This is the opportunity to\n discard a header or irrelevant row.\n \"\"\"\n HTMLParser.__init__(self)\n self.fetch_data = False\n self.columns = columns\n self.index = 0\n self.entries = output_list\n self.entry = dict()\n self.value = str()\n self.keep_entry = keep_entry\n\n def attributes(self, object :str) -> set:\n return set(self.columns)\n\n def handle_starttag(self, tag, attrs):\n if tag == \"td\":\n # Enable fetch data\n self.fetch_data = True\n\n def handle_endtag(self, tag):\n if tag == \"td\": # Push key/value\n # Disable fetch data\n self.fetch_data = False\n\n # Push new key/value pair\n key = self.columns[self.index] if self.index < len(self.columns) else self.columns[-1]\n if key in self.entry.keys():\n current_value = self.entry[key]\n if not isinstance(current_value, list):\n self.entry[key] = [current_value]\n if self.value:\n self.entry[key].append(self.value)\n else:\n if self.value:\n self.entry[key] = self.value\n\n # Reset key/value pair\n self.value = str()\n self.index += 1\n elif tag == \"tr\":\n # Push entry\n if self.keep_entry == None or self.keep_entry(self.entry):\n self.entries.append(self.entry)\n\n # Reset entry\n self.index = 0\n self.entry = dict()\n\n def handle_data(self, data):\n data = data.strip()\n if self.fetch_data == True and data:\n self.value += data\n\ndef html_table(filename :str, columns :list, keep_entry = None) -> list:\n entries = list()\n parser = HtmlTableParser(columns, self.m_entries, keep_entry)\n with open(filename, \"r\") as f:\n s = f.read()\n parser.feed(s)\n return entries\n\nclass HtmlTableConnector(EntriesConnector):\n def __init__(self, filename :str, columns :list, keep_entry = None):\n \"\"\"\n Constructor.\n Args:\n filename: Input HTML filename.\n columns: list of string mapping the attribute name\n corresponding with the index. If data is fetch\n for columns having a greater index than\n len(columns), columns[-1] is used, and this\n key may store a list of string values instead\n of a single string. This allow to store data\n stored among several columns in a single attribute.\n output_list: reference to an output list where the\n data will be outputed (one dict per row, one\n key/value per column).\n keep_entry: callback which determine whether an\n must entry must be kept or discard. Pass None\n to filter nothing. This is the opportunity to\n discard a header or irrelevant row.\n \"\"\"\n self.m_entries = list()\n self.m_parser = HtmlTableParser(columns, self.m_entries, keep_entry)\n with open(filename, \"r\") as f:\n s = f.read()\n self.m_parser.feed(s)\n super().__init__(self.m_entries)\n\n\n","sub_path":"src/html_table.py","file_name":"html_table.py","file_ext":"py","file_size_in_byte":4685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"69862399","text":"#!/usr/bin/env python3\n\n# use for samtools or bcftoolfs pipelines\n\nimport sys\nimport gzip\n\nIN = sys.argv[1]\n\nif IN.endswith('gz'):\n f = gzip.open(IN, 'rt')\nelse:\n f = open(IN)\n\nwith f:\n print('CHR\\tPOS\\tDP\\tDP4_1\\tDP4_2\\tDP4_3\\tDP4_4\\tMAF')\n for line in f.readlines():\n line = line.strip()\n if line.startswith('#'):\n continue\n line = line.split('\\t')\n info = line[7].split(';')\n # skip INDELs\n if info[0] == 'INDEL':\n continue\n\n chr = line[0]\n pos = line[1]\n features = {}\n for item in info:\n if item.startswith('DP'):\n item = item.replace('=', ',')\n item = item.split(',')\n features[item[0]] = [int(x) for x in item[1:]]\n maf = (features['DP4'][0]+features['DP4'][1])/sum(features['DP4'])\n maf = 1 - maf if maf > 0.5 else maf\n print('{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{:.4f}'.format(chr, pos,\n features['DP'][0],\n features['DP4'][0], features['DP4'][1], features['DP4'][2], features['DP4'][3],\n maf))\n","sub_path":"old/vcf_maf.samtools.py","file_name":"vcf_maf.samtools.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"204672781","text":"#!/usr/bin/python3\n\"\"\" Module that manipulates lists\n\"\"\"\n\n\nclass MyList(list):\n \"\"\" Class that inherits from list\n \"\"\"\n\n def print_sorted(self):\n \"\"\" Prints self sortedly, without modifying the\n original list\n \"\"\"\n\n new_list = self[:]\n new_list.sort()\n print(new_list)\n","sub_path":"0x0A-python-inheritance/1-my_list.py","file_name":"1-my_list.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"541717232","text":"import sys\nsys.path.append('./modules')\nimport httpclient, logging, errors, config_loader, operator as op, json\nimport LocationResponse as loc_res, WeatherResponse as wresp, DistanceResponse as dresp\nfrom flask import Flask, render_template, request\n\nFormat = '%(asctime)-15s:%(name)s:%(levelname)s--> %(message)s'\nlogging.basicConfig (format=Format, filename=\"weather-route-service.log\", level=\"INFO\", filemode=\"a\")\nlog = logging.getLogger(__name__)\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n@app.route('/weatherRoute',methods=['POST','GET'])\ndef main():\n \"\"\"\n This service is used for getting weather along your route.\n\n 1. Gets current location based on wifi.\n 2. Asks for destination e.g SanFrancisco\n 3. Gets weather for current and destination locations.\n 4. Calculates distance between origin and destination.\n 5. Based on how frequency or \n \"\"\"\n try:\n log.info(\"<--------------START----------------->\")\n #destination_city=input(\"Enter destination: \")\n destination_city=request.args.get('destination')\n log.info(\"Destination city is %s \" % destination_city)\n\n # Get current location\n log.info(\"Get current location's latitude and longitude.\")\n configLoader = config_loader.ConfigurationLoader()\n configLoader.set_configsection('Location')\n location = configLoader.getconfig()\n loc_url = location['location.url']\n log.debug(\"Location url: %s\" %loc_url)\n client = httpclient.HttpClient()\n client.set_url(loc_url)\n loc_resp= client.get_request()\n log.info('Location response: %s' % loc_resp)\n location_response = loc_res.LocationResponse()\n location_response.BuildMapping(loc_resp)\n c_loc_city = location_response._city\n c_loc_state = location_response._statecode\n \n #Get distance between current location and destination\n log.info(\"Invoking distance service.\")\n configLoader.set_configsection('Distance')\n distance = configLoader.getconfig()\n dis_url = distance['distance.url']\n log.debug(\"Distance url is %s \" % dis_url)\n places = c_loc_city+\",\"+c_loc_state+\"&\"+destination_city\n log.info(\"Places we need to get distance for is %s \" % places)\n client.set_url(dis_url+\"/\"+places)\n d_resp = client.get_request()\n log.info(\"Distance service response is: %s \" %d_resp)\n dist_resp = dresp.DistanceResponse()\n dist_resp.BuildMapping(d_resp)\n\n # Get Weather for current locaiton\n log.info(\"Get weather for current location.\")\n configLoader.set_configsection('Weather_API')\n weather = configLoader.getconfig()\n weather_url = weather['weather.host']+weather['weather.token']+weather['weather.query']+\"/\"+\\\n c_loc_state+\"/\"+c_loc_city+\".json\"\n log.debug(\"Weather url for current location is: %s \" % weather_url)\n client.set_url(weather_url)\n w_resp=client.get_request()\n log.info(\"Weather response: %s\" %w_resp)\n weather_resp=wresp.WeatherResponse()\n weather_resp.BuildMapping(w_resp)\n log.info(\"Weather for %s is %s \" %(c_loc_city,weather_resp._temperature_string))\n\n \n log.info(\"<--------------END----------------->\")\n return render_template('index.html',location_city=c_loc_city,\n origin=dist_resp._origin,destination=dist_resp._destination,\n distance=dist_resp._distance,duration=dist_resp._duration,\n location=weather_resp._city,temp_string=weather_resp._temperature_string,\n feels_like_string=weather_resp._feels_like_string,wind_speed=weather_resp._wind)\n except errors.Error as err:\n log.error(err.message,err.expression)\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=5000)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"326732417","text":"from django.urls import path\n\nfrom .views import *\n\nurlpatterns = [\n path('', objects_list, name='objects_list_url'),\n path('object/create/', ObjectCreate.as_view(), name='object_create_url'),\n path('object//', ObjectDetail.as_view(), name='object_detail_url'),\n path('object//update/', ObjectUpdate.as_view(), name='object_update_url'),\n path('object//delete/', ObjectDelete.as_view(), name='object_delete_url'),\n path('tags/', tags_list, name='tags_list_url'),\n path('tag/create/', TagCreate.as_view(), name='tag_create_url'),\n path('tag//', TagDetail.as_view(), name='tag_detail_url'),\n path('tag//update/', TagUpdate.as_view(), name='tag_update_url'),\n]\n","sub_path":"objects/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"130675192","text":"from machine import Pin\nimport utime\n\nsensor = Pin(16, Pin.IN, Pin.PULL_UP)\nwhile True:\n print(sensor.value())\n if sensor.value() == 0:\n print(\"The sensor found something\")\n else:\n print(\"The sensor did not find anything\")\n utime.sleep(1)\n","sub_path":"HW-511/HW-511-reader-debug.py","file_name":"HW-511-reader-debug.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"460001258","text":"import logging\nimport re\nimport sys\nimport traceback\nimport urllib.parse\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom threading import RLock\nfrom threading import Timer\n\nimport m3u8\nimport pytz\nimport requests\nimport tzlocal\n\nfrom .configuration import SmoothStreamsProxyConfiguration\nfrom .epg import SmoothStreamsProxyEpg\nfrom .password_manager import SmoothStreamsProxyPasswordManager\nfrom .shelf import SmoothStreamsProxyShelf\nfrom .utilities import SmoothStreamsProxyUtility\n\nlogger = logging.getLogger(__name__)\n\n\nclass SmoothStreamsProxy():\n _nimble_session_id_map = {}\n _nimble_session_id_map_lock = RLock()\n _refresh_session_timer = None\n _serviceable_clients = {}\n _serviceable_clients_lock = RLock()\n _session = {}\n _session_lock = RLock()\n\n @classmethod\n def _add_client_to_serviceable_clients(cls, client_uuid, client_ip_address):\n with cls._serviceable_clients_lock:\n cls._serviceable_clients[client_uuid] = {}\n cls._serviceable_clients[client_uuid]['ip_address'] = client_ip_address\n\n @classmethod\n def _clear_nimble_session_id_map(cls):\n with cls._nimble_session_id_map_lock:\n cls._nimble_session_id_map = {}\n\n @classmethod\n def _do_retrieve_authorization_hash(cls):\n try:\n if datetime.now(pytz.utc) < (cls._get_session_parameter('expires_on') - timedelta(minutes=30)):\n return False\n else:\n logger.info('Authorization hash\\n'\n 'Status => Expired\\n'\n 'Action => Retrieve it')\n\n return True\n except KeyError:\n logger.debug('Authorization hash\\n'\n 'Status => Never retrieved\\n'\n 'Action => Retrieve it')\n\n return True\n\n @classmethod\n def _get_session_parameter(cls, parameter_name):\n with cls._session_lock:\n return cls._session[parameter_name]\n\n @classmethod\n def _get_target_nimble_session_id(cls, hijacked_nimble_session_id):\n with cls._nimble_session_id_map_lock:\n return cls._nimble_session_id_map.get(hijacked_nimble_session_id, None)\n\n @classmethod\n def _hijack_nimble_session_id(cls, hijacked_nimble_session_id, hijacking_nimble_session_id):\n with cls._nimble_session_id_map_lock:\n cls._nimble_session_id_map[hijacked_nimble_session_id] = hijacking_nimble_session_id\n\n @classmethod\n def _process_authorization_hash(cls, hash_response):\n smooth_streams_proxy_session = {}\n\n if 'code' in hash_response:\n if hash_response['code'] == '0':\n logger.error('Failed to retrieved authorization token\\n'\n 'Error => {0}'.format(hash_response['error']))\n elif hash_response['code'] == '1':\n smooth_streams_proxy_session['hash'] = hash_response['hash']\n smooth_streams_proxy_session['expires_on'] = \\\n datetime.now(pytz.utc) + timedelta(seconds=(hash_response['valid'] * 60))\n\n logger.info('Retrieved authorization token\\n'\n 'Hash => {0}\\n'\n 'Expires On => {1}'.format(smooth_streams_proxy_session['hash'],\n smooth_streams_proxy_session['expires_on'].astimezone(\n tzlocal.get_localzone()).strftime('%Y-%m-%d %H:%M:%S')[:-3]))\n else:\n logger.error('Failed to retrieved authorization token\\n'\n 'Error => JSON response contains no [\\'code\\'] field')\n\n return smooth_streams_proxy_session\n\n @classmethod\n def _refresh_serviceable_clients(cls, client_uuid, client_ip_address):\n with cls._serviceable_clients_lock:\n if client_uuid not in cls._serviceable_clients:\n logger.debug('Adding client to serviceable clients\\n'\n 'Client IP address => {0}\\n'\n 'Client ID => {1}'.format(client_ip_address, client_uuid))\n\n cls._add_client_to_serviceable_clients(client_uuid, client_ip_address)\n else:\n cls._set_serviceable_client_parameter(client_uuid, 'last_request_date_time', datetime.now(pytz.utc))\n\n @classmethod\n def _retrieve_authorization_hash(cls):\n http_session = requests.Session()\n\n if SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE') == 'viewmmasr':\n url = 'https://www.mma-tv.net/loginForm.php'\n else:\n url = 'https://auth.smoothstreams.tv/hash_api.php'\n\n smooth_streams_username = SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_USERNAME')\n smooth_streams_password = SmoothStreamsProxyPasswordManager.decrypt_password(\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_PASSWORD')).decode()\n smooth_streams_site = SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE')\n\n logger.debug(\n 'Retrieving authorization hash\\n'\n 'URL => {0}\\n'\n ' Parameters\\n'\n ' username => {0}\\n'\n ' password => {1}\\n'\n ' site => {2}'.format(url,\n smooth_streams_username,\n smooth_streams_password,\n smooth_streams_site))\n\n response = SmoothStreamsProxyUtility.make_http_request(\n http_session.get,\n url,\n params={\n 'username': smooth_streams_username,\n 'password': smooth_streams_password,\n 'site': smooth_streams_site\n },\n headers=http_session.headers,\n cookies=http_session.cookies.get_dict())\n\n response_status_code = response.status_code\n if response_status_code != requests.codes.OK and response_status_code != requests.codes.NOT_FOUND:\n logger.error(SmoothStreamsProxyUtility.assemble_response_from_log_message(response))\n\n response.raise_for_status()\n\n # noinspection PyUnresolvedReferences\n logger.trace(SmoothStreamsProxyUtility.assemble_response_from_log_message(response,\n is_content_json=True,\n do_print_content=True))\n\n smooth_streams_proxy_session = cls._process_authorization_hash(response.json())\n\n if response_status_code != requests.codes.OK:\n response.raise_for_status()\n\n if smooth_streams_proxy_session:\n smooth_streams_proxy_session['http_session'] = http_session\n\n return smooth_streams_proxy_session\n\n @classmethod\n def _set_serviceable_client_parameter(cls, client_uuid, parameter_name, parameter_value):\n with cls._serviceable_clients_lock:\n cls._serviceable_clients[client_uuid][parameter_name] = parameter_value\n\n @classmethod\n def _set_session_parameter(cls, parameter_name, parameter_value):\n with cls._session_lock:\n cls._session[parameter_name] = parameter_value\n\n @classmethod\n def _timed_refresh_session(cls):\n logger.debug('Authorization hash refresh timer triggered')\n\n cls.refresh_session(force_refresh=True)\n\n @classmethod\n def cancel_refresh_session_timer(cls):\n if cls._refresh_session_timer:\n cls._refresh_session_timer.cancel()\n\n @classmethod\n def download_chunks_m3u8(cls,\n client_ip_address,\n requested_path,\n channel_number,\n client_uuid,\n nimble_session_id,\n token=None):\n cls._refresh_serviceable_clients(client_uuid, client_ip_address)\n\n smooth_streams_hash = cls._get_session_parameter('hash')\n smooth_streams_session = cls._get_session_parameter('http_session')\n\n target_url = 'https://{0}.smoothstreams.tv/{1}/ch{2}q1.stream{3}'.format(\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVER'),\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE'),\n channel_number,\n re.sub(r'(/.*)?(/.*\\.m3u8)', r'\\2', requested_path))\n\n logger.debug(\n 'Proxying request\\n'\n 'Source IP => {0}\\n'\n 'Requested path => {1}\\n'\n ' Parameters\\n'\n ' channel_number => {2}\\n'\n ' client_uuid => {3}\\n'\n 'Target path => {4}\\n'\n ' Parameters\\n'\n ' nimblesessionid => {5}\\n'\n ' wmsAuthSign => {6}'.format(\n client_ip_address,\n requested_path,\n channel_number,\n client_uuid,\n target_url,\n nimble_session_id,\n smooth_streams_hash))\n\n response = SmoothStreamsProxyUtility.make_http_request(smooth_streams_session.get,\n target_url,\n params={\n 'nimblesessionid': nimble_session_id,\n 'wmsAuthSign': smooth_streams_hash\n },\n headers=smooth_streams_session.headers,\n cookies=smooth_streams_session.cookies.get_dict())\n\n if response.status_code == requests.codes.OK:\n # noinspection PyUnresolvedReferences\n logger.trace(SmoothStreamsProxyUtility.assemble_response_from_log_message(response,\n is_content_text=True,\n do_print_content=True))\n\n return response.text.replace('.ts?', '.ts?channel_number={0}&client_uuid={1}&{2}'.format(\n channel_number,\n client_uuid,\n 'token={0}&'.format(token) if token else ''))\n else:\n logger.error(SmoothStreamsProxyUtility.assemble_response_from_log_message(response))\n\n response.raise_for_status()\n\n @classmethod\n def download_playlist_m3u8(cls,\n client_ip_address,\n requested_path,\n channel_number,\n client_uuid,\n protocol,\n token=None):\n cls._refresh_serviceable_clients(client_uuid, client_ip_address)\n cls.refresh_session()\n\n if protocol == 'hls':\n smooth_streams_hash = cls._get_session_parameter('hash')\n smooth_streams_session = cls._get_session_parameter('http_session')\n\n target_url = 'https://{0}.smoothstreams.tv/{1}/ch{2}q1.stream{3}'.format(\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVER'),\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE'),\n channel_number,\n re.sub(r'(/.*)?(/.*\\.m3u8)', r'\\2', requested_path))\n\n logger.debug(\n 'Proxying request\\n'\n 'Source IP => {0}\\n'\n 'Requested path => {1}\\n'\n ' Parameters\\n'\n ' channel_number => {2}\\n'\n ' client_uuid => {3}\\n'\n ' protocol => {4}\\n'\n 'Target path => {5}\\n'\n ' Parameters\\n'\n ' wmsAuthSign => {6}'.format(\n client_ip_address,\n requested_path,\n channel_number,\n client_uuid,\n protocol,\n target_url,\n smooth_streams_hash))\n\n response = SmoothStreamsProxyUtility.make_http_request(smooth_streams_session.get,\n target_url,\n params={\n 'wmsAuthSign': smooth_streams_hash\n },\n headers=smooth_streams_session.headers,\n cookies=smooth_streams_session.cookies.get_dict())\n\n if response.status_code == requests.codes.OK:\n # noinspection PyUnresolvedReferences\n logger.trace(SmoothStreamsProxyUtility.assemble_response_from_log_message(response,\n is_content_text=True,\n do_print_content=True))\n\n return response.text.replace('chunks.m3u8?',\n 'chunks.m3u8?channel_number={0}&client_uuid={1}&{2}'.format(\n channel_number,\n client_uuid,\n 'token={0}&'.format(token) if token else ''))\n else:\n logger.error(SmoothStreamsProxyUtility.assemble_response_from_log_message(response))\n\n response.raise_for_status()\n elif protocol == 'rtmp':\n smooth_streams_hash = cls._get_session_parameter('hash')\n\n return '#EXTM3U\\n' \\\n '#EXTINF:-1 ,{0}\\n' \\\n 'rtmp://{1}.smoothstreams.tv:3635/{2}/ch{3}q1.stream?' \\\n 'wmsAuthSign={4}'.format(SmoothStreamsProxyEpg.get_channel_name(int(channel_number)),\n SmoothStreamsProxyConfiguration.get_configuration_parameter(\n 'SMOOTH_STREAMS_SERVER'),\n SmoothStreamsProxyConfiguration.get_configuration_parameter(\n 'SMOOTH_STREAMS_SERVICE'),\n channel_number,\n smooth_streams_hash)\n\n @classmethod\n def download_ts_file(cls, client_ip_address, requested_path, channel_number, client_uuid, nimble_session_id):\n cls._refresh_serviceable_clients(client_uuid, client_ip_address)\n\n smooth_streams_hash = cls._get_session_parameter('hash')\n smooth_streams_session = cls._get_session_parameter('http_session')\n\n target_url = 'https://{0}.smoothstreams.tv/{1}/ch{2}q1.stream{3}'.format(\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVER'),\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE'),\n channel_number,\n re.sub(r'(/.*)?(/.*\\.ts)', r'\\2', requested_path))\n\n logger.debug(\n 'Proxying request\\n'\n 'Source IP => {0}\\n'\n 'Requested path => {1}\\n'\n ' Parameters\\n'\n ' channel_number => {2}\\n'\n ' client_uuid => {3}\\n'\n 'Target path => {4}\\n'\n ' Parameters\\n'\n ' nimblesessionid => {5}\\n'\n ' wmsAuthSign => {6}'.format(\n client_ip_address,\n requested_path,\n channel_number,\n client_uuid,\n target_url,\n nimble_session_id,\n smooth_streams_hash))\n\n response = SmoothStreamsProxyUtility.make_http_request(smooth_streams_session.get,\n target_url,\n params={\n 'nimblesessionid': nimble_session_id,\n 'wmsAuthSign': smooth_streams_hash\n },\n headers=smooth_streams_session.headers,\n cookies=smooth_streams_session.cookies.get_dict())\n\n if response.status_code == requests.codes.OK:\n # noinspection PyUnresolvedReferences\n logger.trace(SmoothStreamsProxyUtility.assemble_response_from_log_message(response,\n is_content_binary=True))\n\n return response.content\n else:\n logger.error(SmoothStreamsProxyUtility.assemble_response_from_log_message(response))\n\n response.raise_for_status()\n\n @classmethod\n def generate_channel_playlist_url(cls,\n is_server_secure,\n server_hostname,\n server_port,\n channel_number,\n client_uuid,\n protocol,\n token):\n return '{0}://{1}:{2}/live/playlist.m3u8?channel_number={3:02}&client_uuid={4}&protocol={5}{6}'.format(\n 'https' if is_server_secure else 'http',\n server_hostname,\n server_port,\n channel_number,\n client_uuid,\n protocol,\n '&token={0}'.format(token) if token else '')\n\n @classmethod\n def generate_live_playlist_m3u8(cls, is_server_secure, client_ip_address, client_uuid, protocol, type_, token):\n try:\n playlist_m3u8 = []\n\n client_ip_address_type = SmoothStreamsProxyUtility.determine_ip_address_type(client_ip_address)\n server_hostname = SmoothStreamsProxyConfiguration.get_configuration_parameter(\n 'SERVER_HOSTNAME_{0}'.format(client_ip_address_type.value))\n server_port = SmoothStreamsProxyConfiguration.get_configuration_parameter(\n 'SERVER_HTTP{0}_PORT'.format('S' if is_server_secure else ''))\n\n smooth_streams_hash = cls._get_session_parameter('hash')\n did_retrieve_fresh_smooth_streams_authorization_hash = False\n\n epg_channels = SmoothStreamsProxyEpg.get_channel_number_to_channel()\n for channel_number in sorted(epg_channels):\n epg_channel = epg_channels[channel_number]\n\n epg_channel_icon = epg_channel.icon_url.format(\n 's' if is_server_secure else '',\n server_hostname,\n server_port,\n '?token={0}'.format(token) if token else '').replace(' ', '%20')\n epg_channel_id = epg_channel.id\n epg_channel_name = epg_channel.name\n epg_channel_number = epg_channel.number\n\n playlist_m3u8.append(\n '#EXTINF:-1 group-title=\"{0}\" '\n 'tvg-id=\"{1}\" '\n 'tvg-name=\"{2}\" '\n 'tvg-logo=\"{3}\" '\n 'channel-id=\"{4}\",{2}\\n'.format(\n 'Live TV',\n epg_channel_id,\n epg_channel_name,\n epg_channel_icon,\n epg_channel_number))\n\n if type_ == 'dynamic':\n playlist_m3u8.append('{0}\\n'.format(cls.generate_channel_playlist_url(is_server_secure,\n server_hostname,\n server_port,\n epg_channel_number,\n client_uuid,\n protocol,\n token)))\n elif type_ == 'static':\n if not did_retrieve_fresh_smooth_streams_authorization_hash:\n try:\n smooth_streams_proxy_session = cls._retrieve_authorization_hash()\n smooth_streams_hash = smooth_streams_proxy_session['hash']\n except (KeyError, requests.exceptions.HTTPError):\n logger.error('Failed to retrieve fresh authorization token\\n'\n 'Falling back to main hash')\n\n did_retrieve_fresh_smooth_streams_authorization_hash = True\n\n playlist_m3u8.append(\n '{0}://{1}.smoothstreams.tv:{2}/{3}/ch{4:02}q1.stream?wmsAuthSign={5}\\n'.format(\n 'https' if protocol == 'hls' else 'rtmp',\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVER'),\n '443' if protocol == 'hls' else '3635',\n SmoothStreamsProxyConfiguration.get_configuration_parameter('SMOOTH_STREAMS_SERVICE'),\n epg_channel_number,\n smooth_streams_hash))\n\n playlist_m3u8 = '#EXTM3U x-tvg-url=\"{0}://{1}:{2}/live/epg.xml\"\\n{3}'.format(\n 'https' if is_server_secure else 'http',\n server_hostname,\n server_port,\n ''.join(playlist_m3u8))\n\n logger.debug('Generated live playlist.m3u8')\n\n return playlist_m3u8\n except (KeyError, ValueError):\n (type_, value_, traceback_) = sys.exc_info()\n logger.error('\\n'.join(traceback.format_exception(type_, value_, traceback_)))\n\n @classmethod\n def get_serviceable_client_parameter(cls, client_uuid, parameter_name):\n with cls._serviceable_clients_lock:\n return cls._serviceable_clients[client_uuid][parameter_name]\n\n @classmethod\n def initialize_from_shelf(cls):\n try:\n cls._session = SmoothStreamsProxyShelf.get_shelved_setting('session')\n except KeyError:\n pass\n\n @classmethod\n def map_nimble_session_id(cls,\n client_ip_address,\n channel_number,\n client_uuid,\n nimble_session_id,\n smooth_streams_hash):\n if smooth_streams_hash != cls._get_session_parameter('hash'):\n target_nimble_session_id = cls._get_target_nimble_session_id(nimble_session_id)\n\n if not target_nimble_session_id:\n logger.debug('Authorization hash {0} in request from {1}/{2} expired'.format(smooth_streams_hash,\n client_ip_address,\n client_uuid))\n\n try:\n response_text = cls.download_playlist_m3u8(client_ip_address,\n '/playlist.m3u8',\n channel_number,\n client_uuid,\n 'hls')\n\n m3u8_obj = m3u8.loads(response_text)\n\n requested_path_with_query_string = '/{0}'.format(m3u8_obj.data['playlists'][0]['uri'])\n requested_url_components = urllib.parse.urlparse(requested_path_with_query_string)\n requested_query_string_parameters = dict(urllib.parse.parse_qsl(requested_url_components.query))\n\n target_nimble_session_id = requested_query_string_parameters.get('nimblesessionid',\n nimble_session_id)\n\n logger.debug('Hijacking session\\n'\n 'Expired nimble session ID => {0}\\n'\n 'Target nimble session ID => {1}'.format(nimble_session_id, target_nimble_session_id))\n cls._hijack_nimble_session_id(nimble_session_id, target_nimble_session_id)\n except requests.exceptions.HTTPError:\n target_nimble_session_id = nimble_session_id\n\n (type_, value_, traceback_) = sys.exc_info()\n logger.error('\\n'.join(traceback.format_exception(type_, value_, traceback_)))\n else:\n target_nimble_session_id = nimble_session_id\n\n return target_nimble_session_id\n\n @classmethod\n def refresh_session(cls, force_refresh=False):\n with cls._session_lock:\n do_start_timer = False\n\n if force_refresh or cls._do_retrieve_authorization_hash():\n do_start_timer = True\n\n cls._clear_nimble_session_id_map()\n\n smooth_streams_proxy_session = cls._retrieve_authorization_hash()\n\n if smooth_streams_proxy_session:\n cls._session = smooth_streams_proxy_session\n SmoothStreamsProxyShelf.persist_to_shelf('session', cls._session)\n\n if cls._refresh_session_timer:\n cls._refresh_session_timer.cancel()\n elif not cls._refresh_session_timer:\n do_start_timer = True\n\n if do_start_timer:\n interval = (cls._get_session_parameter('expires_on') - datetime.now(pytz.utc)).total_seconds() - 1800\n cls._refresh_session_timer = Timer(interval, cls._timed_refresh_session)\n cls._refresh_session_timer.start()\n\n logger.debug('Starting authorization hash refresh timer\\n'\n 'Interval => {0} seconds'.format(interval))\n\n @classmethod\n def set_serviceable_client_parameter(cls, client_uuid, parameter_name, parameter_value):\n with cls._serviceable_clients_lock:\n cls._serviceable_clients[client_uuid][parameter_name] = parameter_value\n","sub_path":"smooth_streams_proxy/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":27176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"196327432","text":"# -*- coding:utf-8 -*-\r\n\r\n\"\"\"guba_stock_overview_spider\"\"\"\r\n\r\nimport re\r\nimport json\r\nfrom scrapy import log\r\nfrom scrapy.http import Request\r\nfrom scrapy.conf import settings\r\nfrom scrapy.spider import Spider\r\nfrom BeautifulSoup import BeautifulSoup\r\nfrom guba.items import GubaStocksItem\r\n\r\nHOST_URL = \"http://guba.eastmoney.com/\"\r\nOVERVIEW_URL = \"http://guba.eastmoney.com/remenba.aspx?type=1\"\r\n\r\n\r\nclass GubaStockOverviewSpider(Spider):\r\n \"\"\"usage: scrapy crawl guba_stock_overview_spider --loglevel=INFO\r\n \"\"\"\r\n name = 'guba_stock_overview_spider'\r\n\r\n def start_requests(self):\r\n request = Request(OVERVIEW_URL)\r\n yield request\r\n\r\n def parse(self, response):\r\n results = []\r\n resp = response.body\r\n soup = BeautifulSoup(resp)\r\n \r\n board_list = []\r\n ngbggul_ul = soup.find('ul', {'class': 'ngbggul'})\r\n\r\n if ngbggul_ul:\r\n for li in ngbggul_ul.findAll('li'):\r\n board_list.append(li.string)\r\n \r\n ngbggulbody_div = soup.find('div', {'class':'ngbggulbody'})\r\n if ngbggulbody_div:\r\n for idx, ngbglist_div in enumerate(ngbggulbody_div.findAll('div', {'class': 'ngbglistdiv'})):\r\n if idx >= 4:\r\n continue\r\n stock_type = board_list[idx]\r\n for a in ngbglist_div.findAll('a'):\r\n stock_url = a.get('href')\r\n if 'http://' not in stock_url:\r\n stock_url = HOST_URL + stock_url\r\n stock_id = re.search(r'\\,(.*?)\\.', stock_url).group(1)\r\n \r\n stock_name_list = a.string.split(')')\r\n\r\n if len(stock_name_list) == 2:\r\n stock_name = stock_name_list[1]\r\n else:\r\n stock_name = stock_name_list[0]\r\n\r\n stock_dict = {'stock_url': stock_url, 'stock_type': stock_type, \\\r\n 'stock_id': stock_id, 'stock_name': stock_name}\r\n \r\n item = GubaStocksItem()\r\n for key in GubaStocksItem.RESP_ITER_KEYS:\r\n item[key] = stock_dict[key]\r\n \r\n results.append(item)\r\n\r\n return results\r\n","sub_path":"guba/spiders/guba_stock_overview_spider.py","file_name":"guba_stock_overview_spider.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"144747781","text":"import movie_trailers\nimport media\n\n# Variable movies with its characteristics\ngoodfelas = media.Movie(\"GoodFelas\", \"1990\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/ca/thumb/1/15/G\\\n\t\t\t\t\t\t\t\t\t\t\t\toodfellas2.jpg/220px-Goodfellas2.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=RWZ78ICQcSk\")\n\nheat = media.Movie(\"Heat\", \"1995\",\n\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/6/6c/Heatposter.jp\\\n\t\t\t\t\t\t\t\t\tg\",\n\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=3UB16UIpgjI\")\n\ninside_job = media.Movie(\"Inside Job\", \"2010\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/a/a1/InsideJ\\\n\t\t\t\t\t\t\t\t\t\t\t\tob2010Poster.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=PH22Z91T2VA\")\n\nmatch_point = media.Movie(\"Match Point\", \"2005\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/0/0a/Match\\\n\t\t\t\t\t\t\t\t\t\t\t\t\tPointPoster.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=Nib7a7w8Yi0\")\n\npulp_fiction = media.Movie(\"Pulp Fiction\", \"1994\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/8/82/Pulp_\\\n\t\t\t\t\t\t\t\t\t\t\t\t\tFiction_cover.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=vzeec59Acnk\")\n\nreservoir_dogs = media.Movie(\"Reservoir Dogs\", \"1992\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/f/f6/Res\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tervoir_dogs_ver1.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=vayksn4Y93A\")\n\nschindlers_list = media.Movie(\"Schindler's List\", \"1993\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/3/38/S\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tchindler%27s_List_movie.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=JdRGC-w9syA\")\n\nse7en = media.Movie(\"Se7en\", \"1995\",\n\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/thumb/6/68/Seven\\\n\t\t\t\t\t\t\t\t\t\t_%28movie%29_poster.jpg/220px-Seven_%28movie%29_poster.jpg\",\n\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=znmZoVkCjpI\")\n\nthe_matrix = media.Movie(\"The Matrix\", \"1999\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/c/c1/The_Mat\\\n\t\t\t\t\t\t\t\t\t\t\t\trix_Poster.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=ItH3RpObRHQ\")\n\nthe_godfather = media.Movie(\"The Godfather\", \"1972\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/1/1c/God\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfather_ver1.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=YYKxj8qiLTg\")\n\nthe_shawshank = media.Movie(\"The Shawshank Redemption\", \"1994\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/8/81/ShawshankRedemptionMoviePoster.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=TaYNFrecwpQ\")\n\nthe_sting = media.Movie(\"The Sting\", \"1973\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://upload.wikimedia.org/wikipedia/en/9/9c/Stingre\\\n\t\t\t\t\t\t\t\t\t\t\t\tdfordnewman.jpg\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"https://www.youtube.com/watch?v=LN2hBOIXhBs\")\n\n# List of all movies\nmovies = [goodfelas, heat, inside_job, match_point, pulp_fiction,\n\t\t\t\t\treservoir_dogs,\tschindlers_list, se7en, the_matrix, the_godfather,\n\t\t\t\t\tthe_shawshank, the_sting]\n\n# Generate the html code with the movies list\nmovie_trailers.open_movies_page(movies)\n","sub_path":"entertainment_center.py","file_name":"entertainment_center.py","file_ext":"py","file_size_in_byte":2925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"579967197","text":"from collections import deque\nfrom typing import List\n\nfrom leetcode.utils.tree_node import TreeNode\n\n\nclass Solution:\n def binaryTreePaths(self, root: TreeNode) -> List[str]:\n if root is None:\n return []\n result = []\n queue = deque()\n queue.append((root, f'{root.val}'))\n while queue:\n node, path = queue.popleft()\n if node.left is None and node.right is None:\n result.append(path)\n continue\n if node.left is not None:\n queue.append((node.left, f'{path}->{node.left.val}'))\n if node.right is not None:\n queue.append((node.right, f'{path}->{node.right.val}'))\n return result\n\n","sub_path":"leetcode/solutions/problem_0257.py","file_name":"problem_0257.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"174884768","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# nezoter.py,\n# Érettségi feladat: 2014. október, Nézőtér\n# Feladatkiírások: http://www.oktatas.hu/kozneveles/erettsegi/feladatsorok\n# Program: Koós Antal, 2016\n\n# --- 1. feladat ---\n# print(\"\\n1. feladat\")\n# Így tároljuk az adatokat:\n# nézőtér=[ sor1,sor2,... ]; sor=[ szék1,szék2,...]; szék=[foglaltság,kategória]\nnézőtér = []\nwith open(\"foglaltsag.txt\") as ffog, open(\"kategoria.txt\") as fkat:\n for fog_sor in ffog:\n fog_sor = fog_sor.strip()\n kat_sor = next(fkat).strip()\n nézőtér.append([[fog_sor[i], kat_sor[i]] for i in range(len(fog_sor))])\n\n# for sor in nézőtér:\n# print(sor)\n\n# --- 2. feladat ---\nprint(\"\\n2. feladat\")\nmegadott_sor = int(input(\"Adja meg egy sor számát! \"))\nmegadott_szék = int(input(\"Adja meg egy szék számát! \"))\n\nszék = nézőtér[megadott_sor - 1][megadott_szék - 1]\nprint(\"{}. sor {}. szék: {}\".format(megadott_sor, megadott_szék, \"foglalt\" if szék[0] == \"x\" else \"szabad\"))\n\n# --- 3. feladat ---\nprint(\"\\n3. feladat\")\nszékek_száma = 0\nfoglaltak = 0\nfor sor in nézőtér:\n székek_száma += len(sor)\n for szék in sor:\n if szék[0] == \"x\":\n foglaltak += 1\n\nprint(\"Az előadásra eddig {} jegyet adtak el, ez a nézőtér {}%-a.\".format(foglaltak,\n round(foglaltak / székek_száma * 100)))\n\n# --- 4. feladat ---\nprint(\"\\n4. feladat\")\nkatstat = dict() # {kategória:darab} statisztika az eladott jegyek kategóriáira\nfor sor in nézőtér:\n for szék in sor:\n if szék[0] == \"x\":\n kategória = szék[1]\n katstat[kategória] = katstat.get(kategória, 0) + 1\n\nlegtöbb = max(katstat.values())\nfor kategória, darab in katstat.items(): # több kategóriában is eladhattak ugyanolyan sok jegyet\n if darab == legtöbb:\n print(\"A legtöbb jegyet a(z) {}. árkategóriában értékesítették.\".format(kategória))\n\n# --- 5. feladat ---\nprint(\"\\n5. feladat\")\nárak = (5000, 4000, 3000, 2000, 1500)\nbevétel = 0\nfor kategória, darab in katstat.items():\n bevétel += árak[int(kategória) - 1] * darab\n\nprint(\"A színház bevétele:\", bevétel, \"Ft\")\n\n# --- 6. feladat ---\nprint(\"\\n6. feladat\")\negyedüliek = 0\n\nfor sor in nézőtér:\n üres_szakasz = 0\n for szék in sor:\n if szék[0] == \"o\":\n üres_szakasz += 1\n else: # \"x\": lezárja az üresek sorozatát\n if üres_szakasz == 1:\n egyedüliek += 1\n üres_szakasz = 0\n if üres_szakasz == 1: # a sor végén is vizsgálni kell\n egyedüliek += 1\n\nprint(\"Az egyedülálló helyek száma:\", egyedüliek)\n\n# --- 7. feladat ---\n# print(\"\\n7. feladat\")\nwith open(\"szabad.txt\", \"w\") as ff:\n for sor in nézőtér:\n for szék in sor:\n ff.write(szék[1] if szék[0] == \"o\" else \"x\")\n ff.write(\"\\n\")\n\n# ---------------------------------------------------------------------------\n# További feladatok: http://sites.google.com/site/eutlantis/erettsegi\n# Ajánlott olvasmány: www.interkonyv.hu/konyvek/koos_antal_python_a_gepben\n","sub_path":"14nezoter/nezoter.py","file_name":"nezoter.py","file_ext":"py","file_size_in_byte":3160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"626198014","text":"#!/usr/bin/env python\nimport signal\nimport subprocess\nimport sys\nimport time\nfrom subprocess import check_output\n\n\ndef find_stream(currentStream):\n \"\"\"Return data to find proper stream to watch.\"\"\"\n liveList = subprocess.Popen(\n ['/bin/sh', 'live.py'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = liveList.communicate()\n output = output.strip().splitlines()\n\n rankedDictionary = {}\n rank = 1\n try:\n with open('rankedstreams.txt') as f:\n for line in f:\n key = line.strip()\n if key[0] == \"#\":\n continue\n rankedDictionary[key] = [rank, 0]\n rank += 1\n except Exception as ex:\n print(ex)\n\n for line in output:\n if line.split()[0] in rankedDictionary:\n rankedDictionary[line.split()[0]][1] = 1\n\n streamChoice = rank\n for key in rankedDictionary:\n if(rankedDictionary[key][1] == 1 and rankedDictionary[key][0] < streamChoice):\n streamChoice = rankedDictionary[key][0]\n for key in rankedDictionary:\n if(rankedDictionary[key][0] == streamChoice):\n streamChoiceName = key\n break\n return rankedDictionary, streamChoice, streamChoiceName, output\n\n\ndef get_pid(name):\n \"\"\"Return pid of window name.\"\"\"\n return map(int, check_output([\"pidof\", name]).split())\n\n\ndef signal_handler(sig, frame):\n \"\"\"Signal handler.\"\"\"\n try:\n videoprocess.kill()\n chatprocess.kill()\n except Exception as ex:\n print(\"Video process and/or chat process could not be killed, already \"\n \"dead?\")\n\n print(\"Stream viewer exited\")\n sys.exit()\n\n\ndef open_videowindow(streamChoiceName, chatMode):\n \"\"\"Open video window.\"\"\"\n if chatMode == \"chat\":\n videoprocess = subprocess.Popen(['streamlink', '--player', 'omxplayer '\n '--win 0,0,1400,1080 --aspect-mode letterbox --timeout 20 '\n '--audio_queue 10 --video_queue 10', '--player-fifo',\n '--hls-segment-threads', '3', '--stream-sorting-excludes', '>=1080p',\n 'twitch.tv/%s' % streamChoiceName, 'best'], stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n else:\n videoprocess = subprocess.Popen(['streamlink', '--player', 'omxplayer '\n '--win 0,0,1920,1080 --aspect-mode letterbox --timeout 20 '\n '--audio_queue 10 --video_queue 10', '--player-fifo',\n '--hls-segment-threads', '3', '--stream-sorting-excludes', '>=1080p',\n 'twitch.tv/%s' % streamChoiceName, 'best'], stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n return videoprocess\n\n\ndef open_chatwindow(streamChoiceName):\n \"\"\"Open chat window.\"\"\"\n windowMade = False\n while not windowMade:\n start_time = time.time()\n chatprocess = subprocess.Popen(['java', '-jar', './Chatty.jar',\n '-channel', streamChoiceName, '-single', '-connect'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n windowFound = False\n while not windowFound:\n chatWindowProcess = subprocess.Popen(['xdotool', 'search',\n '--onlyvisible', '--name', streamChoiceName],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = chatWindowProcess.communicate()\n try:\n chatWID = output.strip().splitlines()[0]\n except IndexError:\n if time.time() - start_time >= 30:\n break\n else:\n continue\n windowFound = True\n windowMade = True\n\n chatWindowProcess = subprocess.Popen(['xdotool', 'windowactivate', chatWID],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = chatWindowProcess.communicate()\n chatWindowProcess = subprocess.Popen(['xdotool', 'key', 'F10'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = chatWindowProcess.communicate()\n chatWindowProcess = subprocess.Popen(['xdotool', 'key', 'shift+F10'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = chatWindowProcess.communicate()\n chatWindowProcess = subprocess.Popen(['xdotool', 'windowsize', chatWID, '472',\n '966'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output, err = chatWindowProcess.communicate()\n chatWindowProcess = subprocess.Popen(['xdotool', 'windowmove', chatWID,\n '1351', '17'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n return chatprocess, chatWindowProcess\n\n\nsignal.signal(signal.SIGINT, signal_handler)\n\n\ndef main():\n \"\"\"Find and display a stream to the user.\"\"\"\n currentStream = None\n try:\n sys.argv[1]\n except (NameError, IndexError):\n chatMode = \"chat\"\n else:\n if sys.argv[1] == \"--no_chat\":\n chatMode = \"nochat\"\n else:\n print(\"Usage: \" + sys.argv[0] + \" [--no_chat]\")\n exit(1)\n while True:\n rankedDictionary, streamChoice, streamChoiceName, output = find_stream(\n currentStream)\n\n if currentStream is None:\n videoprocess = open_videowindow(streamChoiceName, chatMode)\n if chatMode == \"chat\":\n chatprocess, chatWindowProcess = open_chatwindow(\n streamChoiceName)\n currentStream = streamChoiceName\n\n elif currentStream is not None and not any(currentStream in line for line\n in output):\n videoprocess.kill()\n if chatMode == \"chat\":\n chatprocess.kill()\n currentStream = None\n\n elif rankedDictionary[currentStream][0] > rankedDictionary[streamChoiceName][0]:\n videoprocess.kill()\n if chatMode == \"chat\":\n chatprocess.kill()\n videoprocess = open_videowindow(streamChoiceName, chatMode)\n if chatMode == \"chat\":\n chatprocess, chatWindowProcess = open_chatwindow(\n streamChoiceName)\n\n time.sleep(30)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"raspberry-stream.py","file_name":"raspberry-stream.py","file_ext":"py","file_size_in_byte":6689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"485046053","text":"# 最大的矩形 100分\ndef st201312_3():\n n = int(input())\n nums = list(map(int, input().split()))\n # n=6\n # nums=[3, 1, 6, 5, 2, 3]\n result=[]\n for i in range(n):\n high=nums[i]\n temp = 0\n for a in range(i,-1,-1):\n if nums[a]>=high:\n temp+=1\n else:\n break\n for a in range(i,n):\n if nums[a]>=high:\n temp+=1\n else:\n break\n result.append(temp*high-high)\n # print(result)\n print(max(result))\n\nif __name__ == '__main__':\n st201312_3()","sub_path":"st201312-3.py","file_name":"st201312-3.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"261671181","text":"import random\n\nimport requests\nfrom django.core.cache import cache\n\nfrom common import keys\nfrom swiper import config\nfrom worker import celery_app # 异步任务\n\n# 随机生成验证码\ndef gen_vcode(size=4): # size :多长\n '''1000-9999'''\n start = 10 ** (size - 1)\n stop = 10 ** size - 1\n vcode = random.randint(start, stop)\n return vcode\n\n\n# 发送验证码,去请求官方接口\n@celery_app.task\ndef send_vcode(phone):\n params = config.YZX_PARAMS.copy() # copy 不改变原配置参数\n params['mobile'] = phone\n vcode = gen_vcode()\n params['param'] = vcode\n\n # 生成的验证码存入缓存 过期时间600s (可以保存每个异步任务请求生成的验证码)\n cache.set(keys.VCODE_KEY % phone, str(vcode), timeout=600)\n resp = requests.post(config.YZX_URL, json=params)\n if resp.status_code == 200: # http请求是否成功\n # ok\n result = resp.json() # 接收云之讯返回的json数据转字典\n if result['code'] == '000000':\n return 'OK'\n else:\n return result['msg']\n else:\n return '发送短信有误'","sub_path":"swiper/lib/sms.py","file_name":"sms.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"82398296","text":"import requests\nimport threading\nimport parsel\nimport random\nimport time\n\n# UA池\nuser_agents = [\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60',\n\t\t'Opera/8.0 (Windows NT 5.1; U; en)',\n\t\t'Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50',\n\t\t'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0',\n\t\t'Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2 ',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36',\n\t\t'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',\n\t\t'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.101 Safari/537.36',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11',\n\t\t'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER',\n\t\t'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)',\n\t\t'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 SE 2.X MetaSr 1.0',\n\t\t'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; SE 2.X MetaSr 1.0) ',\n\t]\n\n# 写入所有代理\nproxyList = open('proxy.txt')\n# 写入可用代理ip\nvalidList= open('valid.txt')\n\n# 控制线程\nlock = threading.Lock()\n\ndef getProxy():\n starttime = time.time()\n # 爬取云代理ip\n # http://www.ip3366.net/free\n getCloudProxy()\n\n # 爬取98代理\n # https: // www.89ip.cn / index_10.html\n get89Proxy()\n\n # 爬取快代理\n # https://www.kuaidaili.com/free/inha/1/\n # https://www.kuaidaili.com/free/intr/1/\n getQuickProxy()\n\n endtime = time.time()\n print(f\"爬取代理花费{endtime-starttime}s\")\n\ndef getCloudProxy():\n print(\"爬取云代理IP中--------\")\n num = 0\n import time\n # 打开我们创建的txt文件\n proxyFile = open('proxy.txt', 'a')\n for page in range(1, 8):\n for stype in range(1, 3):\n time.sleep(random.randint(1, 3))\n print(f\"正在抓取{stype}的第{page}页数据\")\n # 数据地址\n url = f'http://www.ip3366.net/free/?stype={stype}&page={page}'\n # 设置随机请求头\n headers = {\n 'User-Agent': random.choice(user_agents)}\n # 发送请求\n response = requests.get(url=url, headers=headers)\n # 自适应编码\n response.encoding = response.apparent_encoding\n html_data = response.text\n # 解析数据\n selector = parsel.Selector(html_data)\n trs = selector.xpath('//table/tbody/tr')\n for tr in trs:\n ip = tr.xpath('./td[1]/text()').get() # ip\n port = tr.xpath('./td[2]/text()').get() # 端口\n protocol = tr.xpath('./td[4]/text()').get() # 协议\n\n # 将获取到的数据按照规定格式写入txt文本中1\n proxyFile.write('%s|%s|%s\\n' % (ip, port, protocol))\n num += 1\n print(f\"爬取云代理IP{num}条\")\n\ndef getQuickProxy():\n print(\"爬取快代理中------\")\n num = 0\n import time\n # 打开我们创建的txt文件\n proxyFile = open('proxy.txt', 'a')\n for page in range(1, 3):\n for type in ['inha', 'intr']:\n\n time.sleep(random.randint(1, 3))\n print(f\"正在抓取类型{type}第{page}页数据\")\n # 数据地址\n url = f'https://www.kuaidaili.com/free/{type}/{page}/'\n # 设置随机请求头\n headers = {\n 'User-Agent': random.choice(user_agents)\n }\n # 发送请求\n response = requests.get(url=url, headers=headers)\n # 自适应编码\n response.encoding = response.apparent_encoding\n html_data = response.text\n # 解析数据\n selector = parsel.Selector(html_data)\n trs = selector.xpath('//table/tbody/tr')\n for tr in trs:\n ip = tr.xpath('./td[1]/text()').get().strip() # ip\n port = tr.xpath('./td[2]/text()').get().strip() # 端口\n protocol = tr.xpath('./td[4]/text()').get().strip() #协议\n # 将获取到的数据按照规定格式写入txt文本中1\n proxyFile.write('%s|%s|%s\\n' % (ip, port, protocol))\n num += 1\n print(f\"爬取快代理IP{num}条\")\n\n\ndef get89Proxy():\n print(\"爬取89代理IP中--------\")\n num = 0\n import time\n # 打开我们创建的txt文件\n proxyFile = open('proxy.txt', 'a')\n for page in range(1, 156):\n time.sleep(random.randint(1, 3))\n print(f\"正在抓取第{page}页数据\")\n # 数据地址\n url = f'https://www.89ip.cn/index_{page}.html'\n # 设置随机请求头\n headers = {\n 'User-Agent': random.choice(user_agents)\n }\n # 发送请求\n response = requests.get(url=url, headers=headers)\n # 自适应编码\n response.encoding = response.apparent_encoding\n html_data = response.text\n # 解析数据\n selector = parsel.Selector(html_data)\n trs = selector.xpath('//table/tbody/tr')\n for tr in trs:\n ip = tr.xpath('./td[1]/text()').get().strip() # ip\n port = tr.xpath('./td[2]/text()').get().strip() # 端口\n protocol = 'HTTP' # 默认协议HTTP\n # 将获取到的数据按照规定格式写入txt文本中1\n proxyFile.write('%s|%s|%s\\n' % (ip, port, protocol))\n num += 1\n print(f\"爬取89代理IP{num}条\")\ndef verifyProxyList():\n '''\n 验证ip有效性并存入valid.txt\n :return:\n '''\n\n valid = open('valid.txt', 'a')\n while True:\n lock.acquire()\n # 读取存放ip的文件\n ipinfo = proxyList.readline().strip()\n lock.release()\n # 读到最后一行\n if len(ipinfo) == 0:\n break\n line = ipinfo.strip().split('|')\n ip = line[0]\n port = line[1]\n realip = ip + ':' + port\n # print(realip)\n # 得到验证码\n code = verifyProxy(realip)\n # 验证通过\n if code == 200:\n lock.acquire()\n print(\"---Success:\" + ip + \":\" + port)\n valid.write(ipinfo + \"\\n\")\n lock.release()\n else:\n pass\n # print(\"---Failure:\" + ip + \":\" + port)\ndef verifyProxy(ip):\n '''\n 验证代理的有效性\n '''\n # 设置随机请求头\n headers = {\n 'User-Agent': random.choice(user_agents)\n }\n url = \"http://www.baidu.com\"\n # 填写代理地址\n proxy = {'http': ip}\n try:\n code = requests.get(url=url, proxies=proxy, timeout=2, headers = headers).status_code\n print(code)\n return code\n except Exception as e:\n return e\n\n\ndef useProxy():\n lock.acquire()\n\n ips = []\n # 获取可用ip池\n # 获取IP列表\n valid = open('/Users/shangyuhu/PycharmProjects/recruitProject/ProxyIP/valid.txt')\n while True:\n # 读取存放ip的文件\n ipinfo = valid.readline().strip()\n # 读到最后一行\n if len(ipinfo) == 0:\n break\n line = ipinfo.strip().split('|')\n ip = line[0]\n port = line[1]\n realip = ip + ':' + port\n ips.append(realip)\n print(ips)\n # 要抓取的目标网站地址\n targetUrl = \"https://news.qq.com/\"\n for i in range(10):\n # 随机使用ip爬虫\n proxyip = random.choice(ips)\n # print(proxyip)\n try:\n response = requests.get(url=targetUrl, proxies={\"http\": proxyip, \"https\": proxyip},\n verify=False, timeout=15)\n except Exception as e:\n continue\n # 自适应编码\n response.encoding = response.apparent_encoding\n html_data = response.text\n print(html_data)\n # 用完了\n lock.release()\n\n\nif __name__ == '__main__':\n # 清空代理列表和有效代理列表\n proxy = open('proxy.txt', 'w')\n proxy.write(\"\")\n proxy.close()\n valid = open('valid.txt', 'w')\n valid.write(\"\")\n valid.close()\n # 获取代理IP\n getProxy()\n\n starttime = time.time()\n # 验证ip有效性\n all_thread = []\n for i in range(30):\n t = threading.Thread(target=verifyProxyList)\n all_thread.append(t)\n t.start()\n\n for t in all_thread:\n t.join()\n\n endtime = time.time()\n print(f\"验证代理IP花费时间{endtime-starttime}s\")\n\n useProxy()\n proxy.close()\n valid.close()\n","sub_path":"getProxyIP/getProxyIP.py","file_name":"getProxyIP.py","file_ext":"py","file_size_in_byte":9345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"636006064","text":"#calander for 1 month\r\nstarting_day=int(input(\"enter starting day(1-7):\"))\r\nnum_of_days=int(input(\"enter no days:\"))\r\nprint(\"sun Mon Tue Wed Thu Fri Sat\")\r\nprint(\"-------------------------------\")\r\nfor i in range(starting_day-1): #for space in begining of month\r\n print(end=\" \")\r\ni=starting_day-1\r\nfor j in range(1,num_of_days+1):\r\n if(i>6): #prints in next line after saturday\r\n print()\r\n i=1\r\n else:\r\n i=i+1\r\n print(j,\" \",end=\" \")","sub_path":"calander.py","file_name":"calander.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"257619076","text":"from __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\nfrom six.moves import xrange\n\nimport tensor_utils_5_channels as utils\n\nFLAGS = tf.flags.FLAGS\ntf.flags.DEFINE_integer(\"batch_size\", \"5\", \"batch size for training\")\ntf.flags.DEFINE_string(\"logs_dir\", \"../logs-vgg19/\", \"path to logs directory\")\ntf.flags.DEFINE_string(\"data_dir\", \"../ISPRS_semantic_labeling_Vaihingen\", \"path to dataset\")\ntf.flags.DEFINE_float(\"learning_rate\", \"1e-4\", \"Learning rate for Adam Optimizer\")\ntf.flags.DEFINE_string(\"model_dir\", \"../pretrained_models/imagenet-vgg-verydeep-19.mat\",\n \"Path to vgg model mat\")\ntf.flags.DEFINE_bool('debug', \"False\", \"Debug mode: True/ False\")\ntf.flags.DEFINE_string('mode', \"train\", \"Mode train/ test/ visualize\")\n\nMODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat'\n\nMAX_ITERATION = int(1e7 + 1)\nNUM_OF_CLASSES = 6\nIMAGE_SIZE = 224\nVALIDATE_IMAGES = [\"top_mosaic_09cm_area7.png\",\"top_mosaic_09cm_area17.png\",\"top_mosaic_09cm_area23.png\",\"top_mosaic_09cm_area37.png\"]\ntf_records_filename = 'Vaihingen.tfrecords'\n\ndef vgg_net(weights, image):\n layers = (\n 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',\n\n 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',\n\n 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',\n 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',\n\n 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',\n 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',\n\n 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',\n 'relu5_3', 'conv5_4', 'relu5_4'\n )\n\n net = {}\n current = image\n for i, name in enumerate(layers):\n kind = name[:4]\n if kind == 'conv':\n kernels, bias = weights[i][0][0][0][0]\n if name == 'conv1_1':\n append_channels= np.random.normal(loc=0,scale=0.02,size=(3,3,12,64))\n kernels = np.concatenate((kernels, append_channels), axis=2)\n kernels = utils.get_variable(np.transpose(kernels, (0, 1, 2, 3)), name=name + \"_w\")\n else:\n kernels = utils.get_variable(np.transpose(kernels, (0, 1, 2, 3)), name=name + \"_w\")\n bias = utils.get_variable(bias.reshape(-1), name=name + \"_b\")\n current = utils.conv2d_basic(current, kernels, bias)\n elif kind == 'relu':\n current = tf.nn.relu(current, name=name)\n\n elif kind == 'pool':\n current = utils.avg_pool_2x2(current)\n net[name] = current\n\n return net\n\n\ndef inference(image, keep_prob):\n print(\"setting up vgg initialized conv layers ...\")\n model_data = utils.get_model_data(FLAGS.model_dir)\n\n mean = model_data['normalization'][0][0][0]\n mean_pixel = np.mean(mean, axis=(0, 1))\n mean_pixel = np.append(mean_pixel, [30.6986130799, 284.97018,106.314329243,124.171918054,109.260369903,182.615729022,\n 75.1762766769,84.3529895303,100.699252985,66.8837693324,98.6030061849,133.955897217])\n weights = np.squeeze(model_data['layers'])\n\n processed_image = utils.process_image(image, mean_pixel)\n\n with tf.variable_scope(\"inference\"):\n image_net = vgg_net(weights, processed_image)\n conv_final_layer = image_net[\"conv5_3\"]\n\n pool5 = utils.max_pool_2x2(conv_final_layer)\n\n W6 = utils.weight_variable([7, 7, 512, 4096], name=\"W6\")\n b6 = utils.bias_variable([4096], name=\"b6\")\n conv6 = utils.conv2d_basic(pool5, W6, b6)\n relu6 = tf.nn.relu(conv6, name=\"relu6\")\n\n relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)\n\n W7 = utils.weight_variable([1, 1, 4096, 4096], name=\"W7\")\n b7 = utils.bias_variable([4096], name=\"b7\")\n conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)\n relu7 = tf.nn.relu(conv7, name=\"relu7\")\n\n relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)\n\n W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSES], name=\"W8\")\n b8 = utils.bias_variable([NUM_OF_CLASSES], name=\"b8\")\n conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)\n\n deconv_shape1 = image_net[\"pool4\"].get_shape()\n W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSES], name=\"W_t1\")\n b_t1 = utils.bias_variable([deconv_shape1[3].value], name=\"b_t1\")\n conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net[\"pool4\"]))\n fuse_1 = tf.add(conv_t1, image_net[\"pool4\"], name=\"fuse_1\")\n\n deconv_shape2 = image_net[\"pool3\"].get_shape()\n W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name=\"W_t2\")\n b_t2 = utils.bias_variable([deconv_shape2[3].value], name=\"b_t2\")\n conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net[\"pool3\"]))\n fuse_2 = tf.add(conv_t2, image_net[\"pool3\"], name=\"fuse_2\")\n\n shape = tf.shape(image)\n deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSES])\n W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSES, deconv_shape2[3].value], name=\"W_t3\")\n b_t3 = utils.bias_variable([NUM_OF_CLASSES], name=\"b_t3\")\n conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)\n\n annotation_pred = tf.argmax(conv_t3, axis=3, name=\"prediction\")\n\n return tf.expand_dims(annotation_pred, dim=3), conv_t3\n\n\ndef train(loss_val, var_list):\n optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)\n grads = optimizer.compute_gradients(loss_val, var_list=var_list)\n return optimizer.apply_gradients(grads)\n\ndef read_and_decode(filename_queue):\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n features = tf.parse_single_example(\n serialized_example,\n features={\n 'image_raw': tf.FixedLenFeature([], tf.string),\n 'annotation_raw': tf.FixedLenFeature([], tf.string)\n })\n image = tf.decode_raw(features['image_raw'], tf.float16)\n annotation = tf.decode_raw(features['annotation_raw'], tf.uint8)\n image = tf.reshape(image, [224, 224, 15])\n annotation = tf.reshape(annotation, [224, 224, 1])\n min_after_deque = 1000\n batch_size = 5\n num_thread = 20\n capacity = min_after_deque + (num_thread + 1) * batch_size\n images, annotations = tf.train.shuffle_batch([image, annotation], batch_size=batch_size, num_threads=num_thread,\n min_after_dequeue=min_after_deque, capacity=capacity)\n return images, annotations\n\ndef main(argv=None):\n filename_queue = tf.train.string_input_producer([tf_records_filename])\n image, annotation = read_and_decode(filename_queue)\n image = tf.cast(image, dtype=tf.float32)\n annotation = tf.cast(annotation, dtype=tf.int32)\n keep_probability = tf.placeholder(tf.float32, name=\"keep_probabilty\")\n\n pred_annotation, logits = inference(image, keep_probability)\n annotation_64 = tf.cast(annotation, dtype=tf.int64)\n\n # calculate accuracy for batch.\n cal_acc = tf.equal(pred_annotation, annotation_64)\n cal_acc = tf.cast(cal_acc, dtype=tf.int8)\n acc = tf.count_nonzero(cal_acc) / (FLAGS.batch_size * IMAGE_SIZE * IMAGE_SIZE)\n loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,\n labels=tf.squeeze(annotation,\n squeeze_dims=[3]),\n name=\"entropy\")))\n loss_summary=tf.summary.scalar(\"entropy\", loss)\n acc_summary=tf.summary.scalar(\"accuracy\", acc)\n\n trainable_var = tf.trainable_variables()\n\n train_op = train(loss, trainable_var)\n\n sess = tf.Session()\n\n print(\"Setting up Saver...\")\n saver = tf.train.Saver()\n\n train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)\n\n sess.run(tf.global_variables_initializer())\n ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path)\n print(\"Model restored...\")\n\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord,sess=sess)\n for itr in xrange(MAX_ITERATION):\n feed_dict = {keep_probability: 0.75}\n sess.run(train_op, feed_dict=feed_dict)\n if itr % 50 == 0:\n train_loss, train_acc, summary_loss, summary_acc = sess.run([loss, acc, loss_summary, acc_summary], feed_dict=feed_dict)\n print(\"Step: %d, Train_loss: %g, Train_acc: %g\" % (itr, train_loss, train_acc))\n train_writer.add_summary(summary_loss, itr)\n train_writer.add_summary(summary_acc, itr)\n if itr % 500 == 0:\n saver.save(sess, FLAGS.logs_dir + \"model.ckpt\", itr)\n coord.request_stop()\n coord.join(threads)\n\nif __name__ == \"__main__\":\n tf.app.run()\n","sub_path":"fully_convnets_15_channels.py","file_name":"fully_convnets_15_channels.py","file_ext":"py","file_size_in_byte":9073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"21133147","text":"import pynput\n\nmouse_drag = pynput.mouse.Controller()\nmouse_button = pynput.mouse.Button\n\ndef mouse_move():\n\tdefault = mouse_drag.position #현재 마우스 커서의 위치를 변수에 대입한다\n\tprint(default) \n\n\tmouse_drag.position=(100,700) #해당 좌표로 마우스커서 이동\n\n\tmouse_drag.press(mouse_button.left) #마우스 왼쪽 버튼을 누른 상태로 유지한다\n\tmouse_drag.release(mouse_button.left) #마우스 왼쪽 버튼을 뗀 상태로 유지한다\n\n\t# mouse_drag.press(mouse_button.right) #마우스 왼쪽 버튼을 누른 상태로 유지한다\n\t# mouse_drag.release(mouse_button.right) #마우스 왼쪽 버튼을 뗀 상태로 유지한다\n\nif __name__ == \"__main__\":\n\tmouse_move()","sub_path":"mouse.py","file_name":"mouse.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"523770013","text":"from django.urls import path\nfrom django.conf.urls import url\nfrom . import views\n\n\n# TEMPLATE TAGGING\napp_name = 'Mylearning'\n\n\nurlpatterns = [\n\n path('', views.Mylearning, name='Mylearning'),\n path('InterviewQuestion/', views.InterviewQuestion, name='InterviewQuestion'),\n path('InterviewQA/', views.InterviewQuestionAnswer.as_view(), name='InterviewQuestionAnswer'),\n path(r'reg/', views.StudentReg, name='StudentReg'),\n\n]\n","sub_path":"Mylearning/url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"499730055","text":"# Imports\n\nimport numpy as np\n\n# --------------------------------------------------------------------------- #\n\nimport matplotlib.pyplot as plt\n\n# --------------------------------------------------------------------------- #\n\nimport pandas as pd\n\nimport las2\n\n# --------------------------------------------------------------------------- #\n\nimport os # importar_pasta\n\n# --------------------------------------------------------------------------- #\n\nclass plotagem:\n \n def __init__(self, n, eixoy=True, comprimento=6, altura=5, dpi=70, titulo = '', titulo_fonte = 16,\n cor_fundo = 'white',transparencia_fundo = 0.5,\n cor_plot_fundo = 'white',transparencia_plot_fundo = 1.0):\n \n self.ax = [0]*n\n self.fig, (self.ax) = plt.subplots(1,n,sharey=eixoy,figsize=(comprimento, altura),\n dpi=dpi)\n self.fig.suptitle(titulo, fontsize=titulo_fonte)\n \n self.fig.patch.set_facecolor(cor_fundo)\n self.fig.patch.set_alpha(transparencia_fundo)\n \n self.cor_plot_fundo = cor_plot_fundo\n self.transparencia_plot_fundo = transparencia_plot_fundo\n \n def plot_s(self,indice,X,Y,\n cor='b',estilo_linha = '-',\n descricao_x = 'x',descricao_y = 'y',fonte_descricao = 16,\n titulo = 'titulo',fonte_titulo = 15\n ):\n \n \"\"\"plot simples\"\"\"\n \n self.ax[indice].plot(X,Y,c = cor,ls = estilo_linha)\n self.ax[indice].grid()\n self.ax[indice].set_ylim(max(Y),min(Y))\n self.ax[indice].set_title(titulo, fontsize=fonte_titulo)\n if indice == 0:\n self.ax[indice].set_ylabel(descricao_y, fontsize=fonte_descricao)\n self.ax[indice].set_xlabel(descricao_x, fontsize=fonte_descricao)\n \n self.ax[indice].patch.set_facecolor(self.cor_plot_fundo)\n self.ax[indice].patch.set_alpha(self.transparencia_plot_fundo)\n \n def plot_m(self,indice,XX,Y,cores = False,estilo_linha = '-',\n descricao_x = 'x',descricao_y = 'y',fonte_descricao = 16,\n titulo = 'titulo',fonte_titulo = 15):\n \n \"\"\"plot multiplo\"\"\"\n \n if cores:\n crs = cores.copy()\n else:\n crs = ['b']*len(XX)\n \n for i in range(len(XX)):\n self.ax[indice].plot(XX[i],Y,c = crs[i],ls = estilo_linha)\n \n if indice == 0:\n self.ax[indice].set_ylabel(descricao_y, fontsize=fonte_descricao)\n self.ax[indice].set_xlabel(descricao_x, fontsize=fonte_descricao)\n self.ax[indice].set_title(titulo, fontsize=fonte_titulo)\n self.ax[indice].set_xticklabels([])\n \n self.ax[indice].patch.set_facecolor(self.cor_plot_fundo)\n self.ax[indice].patch.set_alpha(self.transparencia_plot_fundo)\n \n def plog_s(self,indice,X,Y,\n cor='b',estilo_linha = '-',\n descricao_x = 'x',descricao_y = 'y',fonte_descricao = 16,\n titulo = 'titulo',fonte_titulo = 15\n ):\n \n \"\"\"plot simples\"\"\"\n \n self.ax[indice].semilogx(X,Y,c = cor,ls = estilo_linha)\n self.ax[indice].grid()\n self.ax[indice].set_ylim(max(Y),min(Y))\n self.ax[indice].set_title(titulo, fontsize=fonte_titulo)\n if indice == 0:\n self.ax[indice].set_ylabel(descricao_y, fontsize=fonte_descricao)\n self.ax[indice].set_xlabel(descricao_x, fontsize=fonte_descricao)\n \n self.ax[indice].patch.set_facecolor(self.cor_plot_fundo)\n self.ax[indice].patch.set_alpha(self.transparencia_plot_fundo)\n \n def plot_l(self,indice,litologia,Y,relacao_cor,curva_limite,minimo = False,maximo = False,\n descricao_x = '',descricao_y = 'y',fonte_descricao = 16,\n titulo = 'titulo',fonte_titulo = 15, legend=False):\n \n \"\"\"plot litologia\"\"\"\n\n codigos = []\n for i in relacao_cor:\n codigos.append(i)\n \n if minimo:\n minimo = minimo\n else:\n minimo = min(curva_limite)\n \n if maximo:\n maximo = maximo\n else:\n maximo = max(curva_limite)\n \n num_cores = len(codigos)\n \n matriz_litologias = np.array([[minimo]*len(curva_limite)]*num_cores)\n \n for j in range(num_cores):\n for i in range(len(matriz_litologias[j])):\n if litologia[i] == codigos[j] and ~np.isnan(curva_limite[i]):\n matriz_litologias[j][i] = curva_limite[i]\n \n # =============================== #\n \n for i in range(num_cores):\n self.ax[indice].plot(matriz_litologias[i],Y,c = relacao_cor[codigos[i]][0],lw = 0.1)\n self.ax[indice].fill_betweenx(Y, matriz_litologias[i], facecolor=relacao_cor[codigos[i]][0], label=relacao_cor[codigos[i]][1])\n self.ax[indice].set_ylim(max(Y),min(Y))\n self.ax[indice].set_xlim(minimo,maximo)\n if indice == 0:\n self.ax[indice].set_ylabel(descricao_y, fontsize=fonte_descricao)\n self.ax[indice].set_xlabel(descricao_x, fontsize=fonte_descricao)\n self.ax[indice].set_xticks([])\n self.ax[indice].set_title(titulo, fontsize=fonte_titulo)\n \n self.ax[indice].patch.set_facecolor(self.cor_plot_fundo)\n self.ax[indice].patch.set_alpha(self.transparencia_plot_fundo)\n \n if legend==True:\n self.fig.legend(loc='lower center', fancybox=True, shadow=True, ncol=(3))\n \n def mostrar(self):\n plt.show()\n \n def salvar(self,caminho,transparencia = True):\n self.fig.savefig(caminho, transparent=transparencia)\n \n# --------------------------------------------------------------------------- #\n# ###\n# --------------------------------------------------------------------------- #\n\nclass gerenciamento():\n \n def __init__(self):\n \n self.projetos = {}\n \n # ============================================ #\n \n def importar_pasta(caminho_geral,nomes = False,ext = '.las'):\n\n #------------------------------------------------------------------#\n # vai conter o caminho até os arquivos em geral\n arquivos = []\n # r=root, d=directories, f = files\n for r, d, f in os.walk(caminho_geral):\n for file in f:\n if ext in file:\n arquivos.append(os.path.join(r, file))\n\n #------------------------------------------------------------------#\n # arquivos = caminho geral ate os arquivos\n # names = nomes dos poços\n\n if nomes:\n\n names = []\n for i in arquivos:\n n1 = i.replace(caminho_geral+'/', '')\n names.append(n1.replace(ext,''))\n\n return [arquivos,names]\n\n else:\n\n return arquivos\n \n # ============================================ #\n \n def importar_las(caminho,apelidos=False):\n\n campo = {}\n\n dado_lido = las2.read(caminho)\n\n nomes = [a['mnemonic'] for a in dado_lido['curve']]\n unidades = [a['unit'] for a in dado_lido['curve']]\n dado = {}\n for i in range(len(nomes)):\n dado[nomes[i]] = dado_lido['data'][i]\n\n # ------------------------------------ #\n\n if apelidos:\n dado_final = {}\n for i in dado:\n for j in apelidos:\n for k in apelidos[j]:\n\n if i == k:\n #print(i,'apelidado de',j)\n dado_final[j] = dado[i]\n\n return dado_final\n\n # ------------------------------------ #\n\n else:\n return dado\n \n # ============================================ #\n \n \n def importar_csv(caminho,profundidades,mnemonico):\n\n dado = pd.read_csv(caminho)\n\n print(\"cabecalho =\",dado.columns.values)\n\n dado_final = {}\n\n for i in list(dado.columns.values):\n for j in mnemonico:\n for k in mnemonico[j]:\n\n if i == k:\n print(i,'apelidado de',j)\n dado_final[j] = list(dado[i])\n\n lito = dado_final['codigo']\n ptop = dado_final['topo']\n pbot = dado_final['base']\n\n lito_2 = [0.0]*len(profundidades)\n\n for j in range(len(ptop)):\n for i in range(len(profundidades)):\n if profundidades[i] >= ptop[j] and profundidades[i] < pbot[j]:\n lito_2[i] = lito[j]\n\n return lito_2\n \n # ============================================ #\n \n def importar_dados(caminhos,pocos=False):\n \n # ------------------------------------ #\n \n campo = {}\n for j in range(len(caminhos)):\n\n dado_lido = las2.read(caminhos[j])\n\n nomes = [a['mnemonic'] for a in dado_lido['curve']]\n unidades = [a['unit'] for a in dado_lido['curve']]\n dado = {}\n for i in range(len(nomes)):\n dado[nomes[i]] = dado_lido['data'][i]\n\n campo[caminhos[j]] = [dado,nomes,unidades]\n \n return [nomes,campo]\n \n # ============================================ #\n \n def cropar(profundidade,curvas,topo=0,base=20000,nulos=False):\n\n novas_curvas = []\n for j in range(len(curvas)):\n curva = []\n profundiade_cropada = []\n for i in range(len(profundidade)):\n if profundidade[i] >= topo and profundidade[i] < base:\n curva.append(curvas[j][i])\n profundiade_cropada.append(profundidade[i])\n novas_curvas.append(curva)\n\n novas_curvas_final = []\n novas_curvas_final.append(profundiade_cropada)\n for i in range(len(curvas)):\n novas_curvas_final.append(novas_curvas[i])\n\n return novas_curvas_final\n \n # ============================================ #\n \n def cropar_limpo(profundidade,curvas,topo=0,base=20000,nulos=False):\n\n #nulos_idx = [True]*len(profundidade)\n\n novas_curvas = []\n for j in range(len(curvas)):\n curva = []\n profundiade_cropada = []\n for i in range(len(profundidade)):\n if profundidade[i] >= topo and profundidade[i] < base:\n curva.append(curvas[j][i])\n profundiade_cropada.append(profundidade[i])\n novas_curvas.append(curva)\n\n novas_curvas_final = []\n novas_curvas_final.append(profundiade_cropada)\n for i in range(len(curvas)):\n novas_curvas_final.append(novas_curvas[i])\n\n a = np.array(novas_curvas_final).T\n\n b = a[~np.isnan(a).any(axis=1)]\n if nulos:\n b = b[~np.isin(b,nulos).any(axis=1)]\n\n return list(b.T)\n\n # ============================================ #\n \n def cropar_limpo_2(profundidade,curvas,topo=0,base=20000,nulos=False):\n\n p2 = []\n for j in curvas:\n curva = []\n for i in range(len(curvas[j])):\n if profundidade[i] >= topo and profundidade[i] < base:\n curva.append (curvas[j][i])\n\n p2.append(curva)\n\n a = np.array(p2).T\n b = a[~np.isnan(a).any(axis=1)]\n if nulos:\n b = b[~np.isin(b,nulos).any(axis=1)]\n\n c = b.T\n\n log_limpo = {}\n i = 0\n for key in curvas:\n log_limpo[key] = c[i]\n i += 1\n\n return log_limpo\n \n # ============================================ #\n \n# --------------------------------------------------------------------------- #\n# ###\n# --------------------------------------------------------------------------- #\n\nclass visual:\n \n # ============================================ #\n \n def confusao(lit_1,lit_2,label_1 = False,label_2 = False,log=False,tipo=\"numerico\"):\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # Definição de variáveis\n\n s_1 = sorted(list(set(lit_1))) # lista dos elementos de lit_1\n s_2 = sorted(list(set(lit_2))) # lista dos elementos de lit_2\n\n if log:\n print(s_1)\n print(s_2)\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # salvando as labels (loop dos elementos)\n\n nms_1 = []\n for i in range(len(s_1)):\n if label_1:\n nms_1.append(label_1[int(s_1[i])])\n else:\n nms_1.append(int(s_1[i]))\n\n # ________________________ #\n\n nms_2 = []\n for i in range(len(s_2)):\n if label_1:\n if label_2:\n nms_2.append(label_2[int(s_2[i])])\n else:\n nms_2.append(label_1[int(s_2[i])])\n else:\n nms_2.append(int(s_2[i]))\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # Calculando o erro geral para apresentação\n\n err = []\n for i in range(len(lit_1)):\n if lit_1[i] == lit_2[i]:\n err.append(1)\n else:\n err.append(0)\n\n if log:\n print('acerto = ',sum(err),'de',len(err),'equivalente a',(sum(err)/len(err))*100.0,'%')\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # calculo dos valores (por dicionário)\n\n CM = {}\n M1 = []\n for j in range(len(s_1)):\n CM[int(s_1[j])] = {}\n M0 = []\n for i in range(len(s_2)):\n values = []\n for jj in range(len(lit_1)):\n if lit_1[jj] == int(s_1[j]):\n if lit_2[jj] == int(s_2[i]):\n values.append(1)\n else:\n values.append(0)\n\n sv = sum(values)\n CM[int(s_1[j])][s_2[i]] = sv\n M0.append(sv)\n M1.append(M0)\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # calculando proporções\n\n linhas = np.shape(M1)[0]\n colunas = np.shape(M1)[1]\n tamanho = len(lit_1)\n\n if tipo == \"numerico\": # numeros de elementos contados (padrão)\n M1 = np.array(M1)\n MF = M1.copy()\n\n # ________________________ #\n\n if tipo == \"proporcao\": # proporcao em funcao do total\n M1 = np.array(M1,float)\n MF = M1.copy()\n\n for j in range(linhas):\n for i in range(colunas):\n MF[j,i] = (M1[j,i])/(tamanho)\n\n # ________________________ #\n\n if tipo == \"proporcao_linha\": # proporcao em funcao da linha\n M1 = np.array(M1,float)\n MF = M1.copy()\n\n for j in range(linhas):\n soma = sum(M1[j])\n for i in range(colunas):\n MF[j,i] = (M1[j,i])/(soma)\n\n # ________________________ #\n\n if tipo == \"proporcao_coluna\": # proporcao em funcao da coluna\n M1 = np.array(M1,float)\n MF = M1.copy()\n\n for i in range(colunas):\n soma = sum(MF[:,i])\n for j in range(linhas):\n MF[j,i] = (M1[j,i])/(soma)\n\n # ::::::::::::::::::::::::::::::::::::::::::::::: #\n # Tabela e gráficos\n\n the_table = plt.table(cellText=MF,\n colWidths=[0.1] * len(lit_2),\n rowLabels=nms_1,\n colLabels=nms_2,\n loc='center')\n\n the_table.auto_set_font_size(False)\n the_table.set_fontsize(24)\n the_table.scale(4, 4)\n\n plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False)\n\n for pos in ['right','top','bottom','left']:\n plt.gca().spines[pos].set_visible(False)\n plt.show()\n \n # ============================================ #\n \n \n","sub_path":"ArtigoI/modules/appynho.py","file_name":"appynho.py","file_ext":"py","file_size_in_byte":16163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"319409929","text":"from os import listdir, unlink\nfrom os.path import join\nfrom json import load, dump\nfrom PyQt5.QtWidgets import QDialog\nfrom PyQt5.QtCore import Qt\nfrom placer import __basedir__\nfrom placer.ui.config import Ui_ConfigDialog\nfrom placer.edit import EditConfigDialog\n\n\nclass SettingsDialog(QDialog):\n def __init__(self, config, parent):\n super().__init__(parent)\n self._config = config\n self.Ui = Ui_ConfigDialog()\n self.Ui.setupUi(self)\n self.Ui.configComboBox.currentTextChanged.connect(self.update)\n self.Ui.editPushButton.clicked.connect(lambda: self.editConfig(\n name=self.Ui.configComboBox.currentText()))\n self.Ui.addToolButton.clicked.connect(self.editConfig)\n self.Ui.exitCheckBox.setChecked(\n self._config[\"Placer\"].getboolean(\"saveOnExit\"))\n self.Ui.focusCheckBox.setChecked(\n self._config[\"Placer\"].getboolean(\"refreshOnFocus\"))\n self.Ui.prettyCheckBox.setChecked(\n self._config[\"Placer\"].getboolean(\"prettyPrint\"))\n self.Ui.emptyDataCheckBox.setChecked(\n self._config[\"Placer\"].getboolean(\"emptyData\"))\n self.Ui.apiLineEdit.setText(self._config[\"Updates\"][\"nexusApi\"])\n self.refresh(self._config[\"Placer\"][\"config\"])\n self.show()\n\n def refresh(self, config):\n self.Ui.configComboBox.clear()\n for conf in listdir(__basedir__):\n if conf.endswith(\".json\"):\n self.Ui.configComboBox.addItem(conf)\n if config:\n if self.Ui.configComboBox.findText(config, Qt.MatchExactly) != -1:\n self.Ui.configComboBox.setCurrentIndex(\n self.Ui.configComboBox.findText(config, Qt.MatchExactly))\n else:\n self.Ui.configComboBox.setCurrentIndex(0)\n\n def editConfig(self, *, name=\"\"):\n if name in listdir(__basedir__):\n with open(join(__basedir__, name)) as f:\n config = load(f)\n else:\n config = {}\n if name.endswith(\".json\"):\n name = name[:-5]\n config.setdefault(\"game\", \"\")\n config.setdefault(\"data\", \"\")\n config.setdefault(\"mods\", \"\")\n config.setdefault(\"plugins\", \"\")\n config.setdefault(\"prefix\", \"\")\n config.setdefault(\"ModOrder\", {})\n config.setdefault(\"LoadOrder\", {})\n dialog = EditConfigDialog(name, config, self)\n if dialog.exec_():\n oldName = name + \".json\"\n name, config = dialog.getConfig()\n if not name.endswith(\".json\"):\n name = name + \".json\"\n with open(join(__basedir__, name), \"w\") as f:\n dump(config, f)\n if oldName != name:\n if oldName in listdir(__basedir__):\n unlink(oldName)\n self.refresh(name)\n\n def update(self, text):\n if text:\n self.Ui.editPushButton.setEnabled(True)\n self.Ui.buttonBox.setEnabled(True)\n else:\n self.Ui.editPushButton.setEnabled(False)\n self.Ui.buttonBox.setEnabled(False)\n\n def getConfig(self):\n self._config[\"Placer\"][\"config\"] = self.Ui.configComboBox.currentText()\n self._config[\"Placer\"][\"saveOnExit\"] = str(bool(\n self.Ui.exitCheckBox.isChecked()))\n self._config[\"Placer\"][\"refreshOnFocus\"] = str(bool(\n self.Ui.focusCheckBox.isChecked()))\n self._config[\"Placer\"][\"prettyPrint\"] = str(bool(\n self.Ui.prettyCheckBox.isChecked()))\n self._config[\"Placer\"][\"emptyData\"] = str(bool(\n self.Ui.emptyDataCheckBox.isChecked()))\n self._config[\"Updates\"][\"nexusApi\"] = self.Ui.apiLineEdit.text()\n return self._config\n","sub_path":"placer/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"179332863","text":"# pipenv install twilio in terminal\n# C:\\Users\\Gopi\\.virtualenvs\\PyText-PePtKPDH\nfrom twilio.rest import Client\n#put in config.py\naccount_sid = \"use account sid\"\nauth_token = \"use token\"\nclient = Client(account_sid, auth_token)\n\ncall = client.messages.create(\n to=\"phone nbr\",\n from_=\"phone nbr\",\n body=\"Test Message from gopi using twilio api for python\"\n)\n# call has different attributes\n# call.date_updated\n# call.date_created\n# if not delivered , may be do not disturb is enabled for that number\n","sub_path":"PyText/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"553722162","text":"\"\"\"test_TopLoad.py\n\"\"\"\n\nimport sys\nimport os\nimport pytest\nmyPath = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(myPath, '/../mesh/'))\n\n\ndef test_extract_top_plane_nodes():\n from TopLoad import extract_top_plane_nodes\n\n nodefile = '%s/nodes.dyn' % myPath\n planeNodeIDs = extract_top_plane_nodes(nodefile=nodefile,\n top_face=[0, 0, 0, 0, 0, 1])\n\n assert planeNodeIDs[0][0] == 1211\n assert planeNodeIDs[-1][-1] == 1331\n\n\ndef test_writeNodeLoads_disp(tmpdir):\n from TopLoad import writeNodeLoads\n f = tmpdir.join(\"topload.dyn\")\n writeNodeLoads(loadfile=f.strpath, planeNodeIDs=[[1, 2, 3], [4, 5, 6]],\n loadtype='disp', direction=2, amplitude=-1.0, lcid=1)\n lines = f.readlines()\n assert lines[0] == \"*BOUNDARY_PRESCRIBED_MOTION_NODE\\n\"\n assert lines[1] == \"1,3,2,1,-1.000000\\n\"\n assert lines[-1] == \"*END\\n\"\n\n\ndef test_writeNodeLoads_force(tmpdir):\n from TopLoad import writeNodeLoads\n f = tmpdir.join(\"topload.dyn\")\n writeNodeLoads(loadfile=f.strpath, planeNodeIDs=[[1, 2, 3], [4, 5, 6]],\n loadtype='force', direction=2, amplitude=-1.0, lcid=1)\n lines = f.readlines()\n assert lines[0] == \"*LOAD_NODE_POINT\\n\"\n assert lines[1] == \"1,3,1,-1.000000\\n\"\n assert lines[-1] == \"*END\\n\"\n\n\ndef test_read_cli():\n from TopLoad import read_cli\n import sys\n\n sys.argv = ['TopLoad.py', '--amplitude', '-5.0']\n opts = read_cli()\n assert opts.amplitude == -5.0\n","sub_path":"tests/test_TopLoad.py","file_name":"test_TopLoad.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"422799711","text":"\"\"\"\r\nCSCI 630(Foundation of Intelligent Systems)\r\nLab 1\r\n@author: Shubham Patil (sbp5931)\r\n\"\"\"\r\n\r\nfrom PIL import Image\r\nfrom collections import defaultdict\r\nfrom heapq import heappush, heappop\r\nimport math\r\nimport time\r\nfrom collections import deque\r\n\r\nclass Orienteering:\r\n __slots__ = [\"elevation\", \"terrain\", \"terrain_speed\", \"course\",\r\n \"output_points\", \"season\", \"winter\", \"fall\", \"spring\"]\r\n\r\n def __init__(self, elevation_file, color_file, course_file, season=\"summer\"):\r\n self.terrain_speed = {(248, 148, 18, 255): 9, (255, 192, 0, 255): 5.5, (255, 255, 255, 255): 6.5,\r\n (2, 208, 60, 255): 5, (2, 136, 40, 255): 3, (5, 73, 24, 255): 0.5,\r\n (0, 0, 255, 255): 0.2, (71, 51, 3, 255): 10, (0, 0, 0, 255): 9.5, (205, 0, 101, 255): 0,\r\n (255, 255, 0, 255): 6.5, (121, 76, 19, 255): 2, (0, 191, 255, 255): 4}\r\n self.elevation = {}\r\n self.read_elevation(elevation_file)\r\n self.terrain = []\r\n self.read_terrain(color_file)\r\n self.course = []\r\n self.read_course(course_file)\r\n self.output_points = set()\r\n self.season = season\r\n if season == \"winter\":\r\n self.winter = set()\r\n self.find_boundary((0, 0, 255, 255), (0, 191, 255, 255))\r\n self.make_winter()\r\n if season == \"fall\":\r\n self.fall = set()\r\n self.find_boundary((255, 255, 255, 255), (255, 255, 0, 255))\r\n self.make_fall()\r\n if season == \"spring\":\r\n self.spring = set()\r\n self.find_boundary((0, 0, 255, 255), (121, 76, 19, 255))\r\n self.make_spring()\r\n self.find_path()\r\n self.create_output()\r\n\r\n def find_boundary(self, old_color, new_color):\r\n for i in range(len(self.terrain)):\r\n x = i % 395\r\n y = i // 395\r\n node = (x, y)\r\n current = self.terrain[self.terrain_index(node)]\r\n if current == old_color:\r\n neighbors = [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\r\n for neighbor in neighbors:\r\n i, j = neighbor\r\n cond1 = 0 <= i < 395\r\n cond2 = 0 <= j < 500\r\n if cond1 and cond2:\r\n neighbor_value = self.terrain[self.terrain_index(neighbor)]\r\n if neighbor_value != current:\r\n if self.season == \"winter\":\r\n self.winter.add(neighbor)\r\n self.terrain[self.terrain_index(neighbor)] = new_color\r\n if self.season == \"fall\":\r\n self.fall.add(neighbor)\r\n if self.season == \"spring\":\r\n self.spring.add(neighbor)\r\n\r\n def make_winter(self):\r\n queue = deque(list(self.winter))\r\n visited = defaultdict(lambda: 0)\r\n depth = defaultdict(lambda: math.inf)\r\n for x in queue:\r\n depth[x] = 1\r\n visited[x] = 1\r\n while queue:\r\n current = queue.popleft()\r\n self.winter.add(current)\r\n self.terrain[self.terrain_index(current)] = (0, 191, 255, 255)\r\n if depth[current] > 7:\r\n break\r\n for neighbor in self.get_neighbors(current):\r\n neighbor_value = self.terrain[self.terrain_index(neighbor)]\r\n if neighbor_value == (0, 0, 255, 255):\r\n if visited[neighbor] == 0:\r\n visited[neighbor] = 1\r\n depth[neighbor] = depth[current] + 1\r\n queue.append(neighbor)\r\n\r\n def make_spring(self):\r\n queue = deque(list(self.spring))\r\n visited = defaultdict(lambda: 0)\r\n depth = defaultdict(lambda: math.inf)\r\n delta_height = defaultdict(lambda: math.inf)\r\n for x in queue:\r\n depth[x] = 0\r\n visited[x] = 0\r\n delta_height[x] = 0\r\n while queue:\r\n current = queue.popleft()\r\n self.terrain[self.terrain_index(current)] = (121, 76, 19, 255)\r\n if depth[current] > 15:\r\n break\r\n for neighbor in self.get_neighbors(current):\r\n neighbor_value = self.terrain[self.terrain_index(neighbor)]\r\n current_elevation = self.elevation[self.elevation_ind(current)]\r\n neighbor_elevation = self.elevation[self.elevation_ind(neighbor)]\r\n elevation_difference = (neighbor_elevation - current_elevation)\r\n delta_height[neighbor] = delta_height[current] + elevation_difference\r\n if neighbor_value != (0, 0, 255, 255):\r\n if delta_height[neighbor] > 1:\r\n break\r\n else:\r\n if visited[neighbor] == 0:\r\n visited[neighbor] = 1\r\n depth[neighbor] = depth[current] + 1\r\n queue.append(neighbor)\r\n\r\n def make_fall(self):\r\n for point in self.fall:\r\n for neighbor in self.get_neighbors(point):\r\n neighbor_value = self.terrain[self.terrain_index(neighbor)]\r\n cond1 = (neighbor_value == (71, 51, 3, 255))\r\n cond2 = (neighbor_value == (0, 0, 0, 255))\r\n if cond1 or cond2:\r\n self.terrain[self.terrain_index(neighbor)] = (255, 255, 0, 255)\r\n\r\n def elevation_ind(self, point):\r\n x, y = point\r\n t = (y, x)\r\n return t\r\n\r\n def read_elevation(self, filename):\r\n with open(filename) as elevation:\r\n i = 0\r\n for line in elevation:\r\n line = line.strip()\r\n k = line.split()\r\n for j in range(395):\r\n t = (i, j)\r\n self.elevation[t] = float(k[j])\r\n i = i+1\r\n\r\n def read_terrain(self, filename):\r\n im = Image.open(filename)\r\n self.terrain = list(im.getdata())\r\n\r\n def terrain_index(self, point):\r\n x, y = point\r\n return y * 395 + x\r\n\r\n def speed(self, point):\r\n x, y = point\r\n return self.terrain_speed[self.terrain[self.terrain_index((x, y))]]\r\n\r\n def read_course(self, filename):\r\n with open(filename) as course:\r\n for line in course:\r\n line = line.strip()\r\n x, y = line.split()\r\n x, y = int(x), int(y)\r\n checkpoint = (x, y)\r\n self.course.append(tuple(checkpoint))\r\n\r\n def get_neighbors(self, point):\r\n x, y = point\r\n neighbors = []\r\n for i in (x - 1, x, x + 1):\r\n for j in (y - 1, y, y + 1):\r\n cond1 = not(i == x and j == y)\r\n cond2 = 0 <= i < 395\r\n cond3 = 0 <= j < 500\r\n if cond1 and cond2 and cond3:\r\n neighbors.append((i, j))\r\n return neighbors\r\n\r\n def cost(self, current, neighbor):\r\n x = 10.29\r\n y = 7.55\r\n x1, y1 = current\r\n x2, y2 = neighbor\r\n xy_cost = math.sqrt(((y2-y1)*y)**2 + ((x2-x1)*x)**2)\r\n neighbor_speed = self.speed(neighbor)\r\n current_elevation = self.elevation[(y1, x1)]\r\n neighbor_elevation = self.elevation[(y2, x2)]\r\n elevation_difference = abs(neighbor_elevation - current_elevation)\r\n if not(x1 == x2 and y1 == y2):\r\n if neighbor_speed == 0:\r\n return float(\"inf\")\r\n xyz_distance = math.sqrt(xy_cost**2 + elevation_difference**2)\r\n return xyz_distance/neighbor_speed\r\n else:\r\n return 0\r\n\r\n def heuristic(self, current, target):\r\n x = 10.29\r\n y = 7.55\r\n x1, y1 = current\r\n x2, y2 = target\r\n speed = 10\r\n euclidean_distance = math.sqrt(((y2-y1)*y)**2 + ((x2-x1)*x)**2)\r\n if not(x1 == x2 and y1 == y2):\r\n return euclidean_distance/speed\r\n else:\r\n return 0\r\n\r\n def find_path(self):\r\n cost = 0\r\n for i in range(len(self.course)-1):\r\n start = self.course[i]\r\n goal = self.course[i+1]\r\n cost += self.a_star(start, goal)\r\n print(\"Total cost for \"+self.season+\" is \"+str(cost)+\" seconds\")\r\n\r\n def create_output(self):\r\n image = Image.new(\"RGBA\", (395, 500), \"white\")\r\n im = image.load()\r\n for i in range(len(self.terrain)):\r\n x = i % 395\r\n y = i // 395\r\n node = (x, y)\r\n if node not in self.output_points:\r\n im[node] = self.terrain[i]\r\n for point in self.output_points:\r\n im[point] = (255, 0, 0, 255)\r\n for point in self.course:\r\n im[point] = (138, 43, 226, 255)\r\n image.save(self.season+\".png\")\r\n\r\n def is_in(self, node, list_of_list):\r\n for i in list_of_list:\r\n if i[1] == node:\r\n return True\r\n return False\r\n\r\n def a_star(self, start, goal):\r\n open = []\r\n closed = set()\r\n predecessor = {}\r\n g = defaultdict(lambda: math.inf)\r\n g[start] = 0\r\n f = defaultdict(lambda: math.inf)\r\n f[start] = self.heuristic(start, goal)\r\n heappush(open, [f[start], start])\r\n while open:\r\n current = heappop(open)[1]\r\n if current == goal:\r\n path = [current]\r\n return_value = g[current]\r\n self.output_points.add(current)\r\n while current in predecessor.keys():\r\n current = predecessor[current]\r\n path.append(current)\r\n self.output_points.add(current)\r\n return return_value\r\n closed.add(current)\r\n for neighbor in self.get_neighbors(current):\r\n if self.speed(neighbor) != 0:\r\n if neighbor in closed:\r\n continue\r\n if self.is_in(neighbor, open):\r\n new_g = g[current] + self.cost(current, neighbor)\r\n if new_g < g[neighbor]:\r\n predecessor[neighbor] = current\r\n g[neighbor] = new_g\r\n f[neighbor] = g[neighbor] + self.heuristic(neighbor, goal)\r\n heappush(open, [f[neighbor], neighbor])\r\n else:\r\n predecessor[neighbor] = current\r\n g[neighbor] = g[current] + self.cost(current, neighbor)\r\n f[neighbor] = g[neighbor] + self.heuristic(neighbor, goal)\r\n heappush(open, [f[neighbor], neighbor])\r\n return False\r\n\r\n\r\nif __name__ == '__main__':\r\n p0 = time.time()\r\n for season in [\"summer\", \"fall\", \"winter\", \"spring\"]:\r\n t0 = time.time()\r\n obj = Orienteering(\"mpp.txt\", \"terrain.png\", \"red.txt\", season)\r\n t1 = time.time()\r\n print(\"Execution time for \"+season+\" is \"+str(t1-t0)+\"\\n\")\r\n p1 = time.time()\r\n print(\"Total time:\", (p1-p0))\r\n","sub_path":"orienteering.py","file_name":"orienteering.py","file_ext":"py","file_size_in_byte":11266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"199608204","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr 14 08:01:06 2014\r\n\r\n@author: velimir\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport re\r\n\r\nclass WlReader:\r\n\r\n def __init__(self):\r\n self.kanali = {'SO2':'1-SO2-ppb',\r\n 'CO':'30-CO-ppm',}\r\n \"\"\"\r\n key:regex pattern to match it\r\n \r\n pretpostavka je da je naziv kompaktan i da sadrži:\r\n ime plina i mjernu jedinicu\r\n\r\n\tpattern je \r\n\t[0-9]+-xxx-yyy\r\n\r\n \"\"\"\r\n self.regexKanali={'SO2-ug/m3':r'\\b\\S*so2\\S*ug/m3\\b',\r\n 'NO-ug/m3':r'\\b\\S*no\\S*ug/m3\\b',\r\n 'NOx-ug/m3':r'\\b\\S*nox\\S*ug/m3\\b',\r\n 'NO2-ug/m3':r'\\b\\S*no2\\S*ug/m3\\b',\r\n 'CO-mg/m3':r'\\b\\S*co\\S*mg/m3\\b',\r\n 'O3-ug/m3':r'\\b\\S*o3\\S*ug/m3\\b',\r\n 'PM1-ug/m3':r'\\b\\S*pm1\\S*ug/m3\\b',\r\n 'PM2.5-ug/m3':r'\\b\\S*(pm2\\.5)\\S*ug/m3\\b',\r\n 'PM10-ug/m3':r'\\b\\S*pm10\\S*ug/m3\\b'}\r\n\r\n def set_kanali_za_citanje(self, mapa):\r\n self.kanali = mapa\r\n \r\n def dodaj_kanal(self,kljuc,kanal):\r\n if kljuc not in self.kanali.keys():\r\n self.kanali[kljuc]=kanal\r\n else:\r\n raise KeyError('Unable to create new key, it already exists')\r\n \r\n def brisi_kanal(self,dkey):\r\n del self.kanali[dkey]\r\n\r\n def citaj(self, path):\r\n \"\"\"reads from CSV file into data frame (path is location of file)\"\"\"\r\n df = pd.read_csv(\r\n path,\r\n na_values='-999.00',\r\n index_col=0,\r\n parse_dates=[[0,1]],\r\n dayfirst=True,\r\n header=0,\r\n sep=',',\r\n encoding='latin-1')\r\n\r\n\r\n if self.kanali=={}:\r\n raise KeyError('Neko sranje sa kanalima')\r\n \r\n frejmovi={}\r\n for k in self.kanali:\r\n v=self.kanali[k]\r\n i=df.columns.get_loc(v)\r\n frejmovi[k] = df.iloc[:,i:i+2]\r\n frejmovi[k][u'flag']=pd.Series(0,index=frejmovi[k].index)\r\n tmp=frejmovi[k].columns.values\r\n tmp[0]=u'koncentracija'\r\n tmp[1]=u'status'\r\n frejmovi[k].columns=tmp\r\n \r\n #regex dio\r\n for column in df.columns:\r\n for key in self.regexKanali:\r\n match=re.search(self.regexKanali[key],column,re.IGNORECASE)\r\n if match:\r\n v=column\r\n i=df.columns.get_loc(v)\r\n frejmovi[key] = df.iloc[:,i:i+2]\r\n frejmovi[key][u'flag']=pd.Series(0,index=frejmovi[key].index)\r\n tmp=frejmovi[key].columns.values\r\n tmp[0]=u'koncentracija'\r\n tmp[1]=u'status'\r\n frejmovi[key].columns=tmp\r\n return frejmovi\r\n \r\n \r\nif __name__ == \"__main__\":\r\n data = WlReader().citaj('pj.csv')\r\n","sub_path":"citac.py","file_name":"citac.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"462000016","text":"# Complete the diagonalDifference function below.\ndef diagonalDifference(arr):\n numrows = len(arr) \n numcols = len(arr[0]) \n i = 0\n j = 0\n firstsum = 0\n secondsum = 0\n for i in range(0,numrows):\n print(arr[i][j])\n firstsum += arr[i][j]\n j += 1\n \n i = 0\n j = numcols-1\n #for i in range(numrows-1, 0, -1):\n for i in range(0,numrows):\n print(arr[i][j])\n secondsum += arr[i][j]\n j -= 1\n \n return abs(firstsum-secondsum)\n","sub_path":"Warm Up - Algorithms/Diagonal_Difference.py","file_name":"Diagonal_Difference.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"100223298","text":"#!/usr/bin/python\n\nimport sys, os, argparse, json, urllib\nfrom datetime import datetime\n\n# Define properties\naddress = 'Downtown'\ncity = ''\ncountry = ''\npostalCode = ''\npath = os.path.dirname(os.path.realpath(__file__))\noutput = 'gpxFile'\nlatitude = 0\nlongitude = 0\n\n# Do the process expected\ndef main():\n\tglobal address\n\tglobal city\n\tglobal country\n\tglobal output\n\tglobal path\n\tglobal postalCode\n\t# Get all args\n\targs = options().parse_args()\n\t# Set vars\n\taddress = args.address\n\tcity = args.city\n\tcountry = args.country\n\toutput = args.output\n\tpath = args.path\n\t# Directory exists ?\n\tif os.path.exists(path):\n\t\t# Be sure the path is correct\n\t\tif(path[-1:] != '/'):\n\t\t\tpath = path + '/'\n\t\tpostalCode = args.postalCode\n\t\t# Get coordinate of the location informed\n\t\tprint('Getting location...')\n\t\tcoordinates = getCoordinates()\n\t\tif coordinates == 1:\n\t\t\tprint('Location OK')\n\t\t\tprint('Generating GPX...')\n\t\t\t# Generate the file\n\t\t\tgenerateGPX()\n\t\t\tprint('Congrats GPX generated !')\n\t\telse:\n\t\t\tprint('Location Unknown !')\n\telse:\n\t\tprint('Directory does not exists !')\n\n# Adds all options needed\ndef options():\n\tparser = argparse.ArgumentParser(description='How to use GPX-Generator')\n\tparser.add_argument(\"-a\", \"--address\", type=str, dest=\"address\", help=\"Address\", default=address)\n\tparser.add_argument(\"-ci\", \"--city\", type=str, dest=\"city\", help=\"City\", required=True)\n\tparser.add_argument(\"-co\", \"--country\", type=str, dest=\"country\", help=\"Country\", required=True)\n\tparser.add_argument(\"-o\", \"--output\", type=str, dest=\"output\", help=\"Output file name\", default=output)\n\tparser.add_argument(\"-p\", \"--path\", type=str, dest=\"path\", help=\"Path to save the output file\", default=path)\n\tparser.add_argument(\"-pc\", \"--postalcode\", type=str, dest=\"postalCode\", help=\"Postal Code\", default='0')\n\treturn parser\n\n# Function to get coordinates in terms of address informed\ndef getCoordinates():\n\tglobal latitude\n\tglobal longitude\n\turl = 'http://maps.google.com/maps/api/geocode/json?address='+address+','+postalCode+','+city+','+country+'&sensor=false'\n\trequest = urllib.urlopen(url)\n\tdata = json.loads(request.read())\n\tif(data['status'] == 'OK'):\n\t\tlatitude=data['results'][0]['geometry']['location']['lat']\n\t\tlongitude=data['results'][0]['geometry']['location']['lng']\n\t\treturn 1\n\treturn 0;\n\n# Function that will generate the GPX file\ndef generateGPX():\n\tcontent ='\\n\\\n\\n\\\n\t\\n\\\n\t\t\\n\\\n\t\t'+output+'\\n\\\n\t\t\\n\\\n\t\t\t\\n\\\n\t\t\t\t\\n\\\n\t\t\t\t\t'+address+'\\n\\\n\t\t\t\t\t'+city+'\\n\\\n\t\t\t\t\t'+country[:2].upper()+'\\n\\\n\t\t\t\t\t'+postalCode+'\\n\\\n\t\t\t\t\\n\\\n\t\t\t\\n\\\n\t\t\\n\\\n\t\\n\\\n'\n\t# Create the gpx file\n\tfo = open(path+output+'.gpx', \"wb\")\n\tfo.write(content);\n\tfo.close()\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"External/gpx-generator.py","file_name":"gpx-generator.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"218119886","text":"from __future__ import division\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder,OneHotEncoder\nimport numpy as np\nimport urllib\nimport scipy\nimport os\n\nimport pysmac\nimport pysmac.analyzer\nimport pysmac.utils\n\nimport sklearn.ensemble\nimport sklearn.datasets\nimport sklearn.cross_validation\nfrom sklearn import metrics\nfrom sknn.mlp import Classifier, Layer\nfrom time import time\n\n# Function to calculate the median\ndef median(data):\n data = sorted(data)\n n = len(data)\n if n%2 == 1:\n return data[n//2]\n else:\n i = n//2\n return (data[i - 1] + data[i])/2\n\nstart = time()\nn_iter = 100 ## Number of evaluations (SMAC)\nn_validations = 25 ## Number of Monte-Carlo Cross-Validations for each model's accuracy evaluated\n\n# Dataset 4\n\nurl4 = \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\ndataset4 = np.loadtxt(urllib.urlopen(url4), delimiter=\";\", skiprows = 1)\nX = dataset4[:,0:11]\nY = dataset4[:,11]\nY[Y<6] = 0\nY[Y>0] = 1\n\n# We fit the MLP with the hyperparameters given and return the model's median accuracy from 25 trials\ndef mlp(number_layers, number_neurons_1, number_neurons_2, number_neurons_3, number_neurons_4, dropout_rate):\n\n\tlayers = []\n\tnumber_neurons = []\n\n\tnumber_neurons.append(number_neurons_1)\n\tnumber_neurons.append(number_neurons_2)\n\tnumber_neurons.append(number_neurons_3)\n\tnumber_neurons.append(number_neurons_4)\n\n\tfor i in np.arange(number_layers):\n\t\tlayers.append(Layer(\"Sigmoid\", units=number_neurons[i], dropout = dropout_rate))\n\n\tlayers.append(Layer(\"Softmax\", units=2))\n\n\tscores = []\n\n\tfor i in np.arange(n_validations):\n\n\t\tX_train, X_test, Y_train, Y_test = sklearn.cross_validation.train_test_split(X,Y, test_size=0.3, random_state=1)\n\t\n\t\tpredictor = Classifier(\n\t layers=layers,\n\t learning_rate=0.001,\n\t n_iter=25)\n\n\t\tpredictor.fit(X_train, Y_train)\n\n\t\tscores.append(metrics.accuracy_score(Y_test, predictor.predict(X_test)))\n\t\n\treturn -median(scores)\n\n# We create the optimizer object\nopt = pysmac.SMAC_optimizer( working_directory = './results/dataset4/smac_warm/' % os.environ, persistent_files=True, debug = False)\n\n# Warmstart for Dataset #4 (optimum parameters from Dataset #9)\nparameter_definition=dict(\\\n\t\tnumber_layers = (\"integer\", [1,4], 2),\n\t\tnumber_neurons_1 =(\"integer\", [10,1000], 65, 'log'),\n\t\tnumber_neurons_2 =(\"integer\", [10,1000], 10, 'log'),\n\t\tnumber_neurons_3 =(\"integer\", [10,1000], 94, 'log'),\n\t\tnumber_neurons_4 =(\"integer\", [10,1000], 15, 'log'),\n\t\tdropout_rate =(\"real\", [0,1], 0.002883948210729126),\n\t\t)\n\n# We set some parameters for the optimizer\nvalue, parameters = opt.minimize(mlp,\n\t\t\t\t\tn_iter, parameter_definition,\t# number of evaluations\n\t\t\t\t\tnum_runs = 2,\t\t\t\t\t# number of independent SMAC runs\n\t\t\t\t\tseed = 2,\t\t\t\t\t\t# random seed\n\t\t\t\t\tnum_procs = 2,\t\t\t\t\t# two cores\n\t\t\t\t\tmem_limit_function_mb=1000,\t\t# Memory limit\n\t\t\t\t\tt_limit_function_s = 10000\t # Time limit in seconds\n\t\t\t\t\t)\n\n# We print the best configuration found and its accuracy\nprint(('The highest accuracy found: %f'%(-value)))\nprint(('Parameter setting %s'%parameters))\nprint(\"Bayesian Optimization took %.2f seconds for %d evaluations\" % ((time() - start), n_iter))\n\n\n\n\n\n","sub_path":"smac_warmstart_mlp_4.py","file_name":"smac_warmstart_mlp_4.py","file_ext":"py","file_size_in_byte":3219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"14157407","text":"from sklearn import datasets, metrics, svm\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.linear_model import SGDClassifier\r\n\r\niris = datasets.load_iris()\r\ndigits = datasets.load_digits()\r\n\r\ndigits.data\r\ndigits.target\r\ndigits.images[0]\r\n\r\n#Tutorial\r\nClf = svm.SVC(gamma = 0.001, C=100.)\r\nClf.fit(digits.data[:-1], digits.target[:-1])\r\nexpectedClf = digits.target[-1:]\r\npredictClf = Clf.predict(digits.data[-1:])\r\n\r\nprint(\"Classificação SVC(Tutorial) : \\n %s:\\n%s\\n\" % (Clf, metrics.classification_report(expectedClf, predictClf)))\r\n#Tutorial - fim\r\n\r\n#Primeiro algoritmo\r\nKnc = KNeighborsClassifier(n_neighbors = 1)\r\nKnc.fit(digits.data[:-500], digits.target[:-500])\r\nexpectedKnc = digits.target[-500:]\r\npredictKnc = Knc.predict(digits.data[-500:])\r\n\r\nprint(\"Classificação Kneighbors: \\n %s:\\n%s\\n\" % (Knc, metrics.classification_report(expectedKnc, predictKnc)))\r\n#Primeiro algoritmo - fim\r\n\r\n#Segundo algoritmo\r\nSgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=5) \r\nSgd.fit(digits.data[:-500], digits.target[:-500])\r\nexpectedSgd = digits.target[-500:]\r\npredictSgd = Sgd.predict(digits.data[-500:]) \r\n\r\nprint(\"Classificação SGD: \\n %s:\\n%s\\n\" % (Sgd, metrics.classification_report(expectedSgd, predictSgd)))\r\n \r\n#Segundo algoritmo - fim","sub_path":"Parte1.py","file_name":"Parte1.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"262172188","text":"\n\n\nfrom random import randint\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\n\n\n__author__ = 'hamid'\n\n\n\n\n\ndef search_base(request):\n movie_names=[]\n movie_descriptions=[]\n movie_images=[]\n movie_yearProduction=[]\n celeb_names=[]\n celeb_descriptions=[]\n celeb_images=[]\n\n random=randint(0,2)\n print('random: '+str(random))\n if random==0:\n movie_names.append('پرویز پرستویی')\n movie_names.append('ثریا قاسمی')\n celeb_names.append('بادیگارد')\n celeb_names.append('جدایی نادر از سیمین')\n movie_yearProduction.append('۱۳۹۴')\n movie_yearProduction.append('۱۳۸۹')\n celeb_descriptions.append('بازیگر')\n celeb_descriptions.append('بازیگر')\n movie_descriptions.append('کارگردان: ابراهیم حاتمی کیا')\n movie_descriptions.append('کارگردان: اصغر فرهادی')\n celeb_images.append('/media/images/base/0.jpg')\n celeb_images.append('/media/images/base/1.jpg')\n movie_images.append('/media/images/base/2.jpg')\n movie_images.append('/media/images/base/3.jpg')\n else:\n movie_names.append('بادیگارد')\n celeb_names.append('باران کوثری')\n movie_names.append('بارکد')\n celeb_names.append('بهرام رادان')\n celeb_names.append('بابک حمیدیان')\n movie_yearProduction.append('۱۳۹۴')\n movie_yearProduction.append('۱۳۹۴')\n movie_descriptions.append('کارگردان: ابراهیم حاتمی کیا')\n celeb_descriptions.append('بازیگر')\n movie_descriptions.append('کارگردان: مصطفی کیایی')\n celeb_descriptions.append('بازیگر')\n celeb_descriptions.append('بازیگر')\n movie_images.append('/media/images/base/4.jpg')\n celeb_images.append('/media/images/base/0.jpg')\n movie_images.append('/media/images/base/1.jpg')\n celeb_images.append('/media/images/base/2.jpg')\n celeb_images.append('/media/images/base/3.jpg')\n\n return JsonResponse({'celeb_names':celeb_names,'celeb_descriptions':celeb_descriptions,'celeb_images':celeb_images,\n 'movie_names':movie_names,'movie_descriptions':movie_descriptions,'movie_images':movie_images,\n 'movie_yearProduction':movie_yearProduction})\n\n\n\nclass SearchSuggestion:\n name=''\n description=''\n image=''\n\n def __init__(self,name,job,image):\n self.name=name\n self.job=job\n self.image=image\n\n\ndef mostVisited(request):\n mostVisited_names=[]\n mostVisited_names.append('جدایی نادر از سیمین')\n mostVisited_names.append('آژانس شیشه ای')\n mostVisited_names.append('نمی دونم')\n mostVisited_names.append('قیصر')\n mostVisited_names.append('مارمولک')\n return JsonResponse({'mostVisited_names':mostVisited_names})\n\n\n\n\ndef index(request):\n\n news=[]\n last_images=[]\n poll_images=[]\n\n i=0\n while i<4:\n i+=1\n j=0\n item=[]\n while j<4:\n j+=1\n item.append(Item(news_title='news_title_'+str(i)+'_'+str(j)\\\n ,news_link='https://www.google.com/?gws_rd=ssl'))\n\n news.append(News(str(i-1)+'.jpg','main_news_title_'+str(i),'main_news_article_'+str(i),item))\n\n news[0].button_label=\"سینمای ایران\"\n news[1].button_label=\"سینمای جهان\"\n news[2].button_label=\"تلویزیون\"\n news[3].button_label=\"هنرمندان\"\n\n news[3].main_news_title=\"hay\"\n\n\n i=0\n while i<4:\n i+=1\n last_images.append(LastImage(str(i-1)+\".jpg\",\"group title \"+str(i),\"number of images \"+str(randint(1,5))))\n\n\n i=-1\n while i<4:\n i+=1\n poll_images.append('/media/images/index/Poll/'+str(i)+'.jpg')\n\n\n bestReviewImage='/media/images/index/sideBar/bestReview.jpg'\n bestReviewDescription='عباس کیارستمی حرفه ای ترین کارگردان ایرانی است و بیشترین همکاری ها را با کمپانی های خارجی داشته است' \\\n 'طبق افشاگری اخیر خانم ژولیت بینوش او در تازه ترین تجربه اش به چین رفته و فیلمی در این کشور می سازد.'\n\n mostVisited_images=[]\n mostVisited_images.append('/media/images/base/0.jpg')\n mostVisited_images.append('/media/images/base/1.jpg')\n mostVisited_images.append('/media/images/base/2.jpg')\n mostVisited_images.append('/media/images/base/3.jpg')\n mostVisited_images.append('/media/images/base/4.jpg')\n\n mostVisited_names=[]\n mostVisited_names.append('جدایی نادر از سیمین')\n mostVisited_names.append('آژانس شیشه ای')\n mostVisited_names.append('نمی دونم')\n mostVisited_names.append('قیصر')\n mostVisited_names.append('مارمولک')\n\n\n return render(request,'index.html',{'news':news,'last_images':last_images,'poll_images':poll_images,\n 'best_review':bestReviewImage,'best_review_description':bestReviewDescription,\n 'mostVisited_names':mostVisited_names,'mostVisited_images':mostVisited_images})\n\n\n\n\nclass Item:\n news_title=\"\"\n news_link=\"\"\n\n def __init__(self,news_title,news_link):\n self.news_title=news_title\n self.news_link=news_link\n\n\nclass News:\n image_name=\"\"\n button_label=\"\"\n main_news_title=\"\"\n main_news_article=\"\"\n item={}\n\n def __init__(self,image_name,main_news_title,main_news_article,item):\n self.image_name=image_name\n self.main_news_title=main_news_title\n self.main_news_article=main_news_article\n self.item=item\n\n\nclass LastImage:\n\n # title of this group of images.\n title=\"\"\n #number of images in this group.\n images_number=\"\"\n #this image name.\n name=\"\"\n\n def __init__(self,name,title,images_number):\n self.name=name\n self.title=title\n self.images_number=images_number\n\n\n\n\n\n\n\n\n\n\n\n\ndef top100(request):\n movies=[]\n sideBar_mostPoints=[]\n\n\n i=-1\n while(i<9):\n i+=1\n rand=randint(1000,2000)\n point=randint(0,10)\n temp=Movie1(\"اسم\"+str(i),str(i)+\".jpg\",1394-i,rand,point)\n movies.append(temp)\n\n sideBar_mostPoints.append(SideBar_mostGenrePoints('اکشن',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('درام',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('عاشقانه',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('انیمیشن',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('فانتزی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('علمی تخیلی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('بیوگرافی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('تاریخی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('کوتاه',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('کمدی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('ترسناک',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('تریلر',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('جنایی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('موزیکال',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('جنگی',''))\n sideBar_mostPoints.append(SideBar_mostGenrePoints('مستند',''))\n\n\n return render(request,'top100.html',{'movies':movies,'sideBar_mostPoints':sideBar_mostPoints})\n\n\n\nclass Movie1:\n url=\"\"\n name=\"\"\n year=0\n commentNumber=0\n point=0\n\n def __init__(self,name,url,year,commentNumber,point):\n self.name=name\n self.url=url\n self.year=year\n self.commentNumber=commentNumber\n self.point=point\n\n\n\nclass SideBar_mostGenrePoints:\n name=''\n url=''\n\n def __init__(self,name,url):\n self.name=name\n self.url=url\n\n\n\n\n\ndef movie(request):\n\n slider_images=[]\n i=-1\n while(i<4):\n i+=1\n slider_images.append('/media/images/test/slider'+str(i)+'.jpg')\n\n\n\n mainActors=[]\n i=-1\n while(i<3):\n i+=1\n mainActors.append(\"/media/images/movie/actor\"+str(i)+\".jpg\")\n\n\n\n fActors=[]\n i=-1\n while(i<4):\n i+=1\n temp1=FActor(\"/media/images/movie/fActor\"+str(i)+\".jpg\",\"اسم\"+str(i),\"اسم تو فیلم\"+str(i))\n fActors.append(temp1)\n fActors.append(FActor(\"/static/icons/movie/defaultUserImg.jpg\",\"اسم بازیگر\",\"اسم تو فیلم بازیگر\"))\n fActors[0].name=\"چرا من اینو\"\n fActors.append(FActor('/media/images/actor/image0.jpg','مریم','سرباز اول'))\n\n\n\n keywords=[]\n keywords.append(\"خانواده\")\n keywords.append(\"طلاق\")\n keywords.append(\"مهاجرت\")\n keywords.append(\"دروغ\")\n keywords.append(\"پدر\")\n\n\n\n bestDialogue=[]\n bestDialogue.append(\"اون نمی دونه من پسرشم. من که می دونم اون پدرمه\")\n bestDialogue.append(\"چرا من آخه\")\n bestDialogue.append(\"بودن یا نبودن مساله این است.\")\n bestDialogue.append(\"دیالوگ ماندگار\")\n bestDialogue.append(\"دیالوگ ماندگار بعدی\")\n\n\n mov=Movie2(\"جدایی نادر از سیمین\",1389,119,\"درام\",8.9,400,\"اصغر فرهادی\",\"اصغر فرهادی\",2,34\n ,\"این فیلم جدایی نادر از سیمینه دیگه\",mainActors,fActors,keywords,3500000000,bestDialogue)\n\n\n\n circles=[]\n point=mov.point\n i=0\n while(i<10):\n if(i 3:\n return False, []\n if dist(node, cars[car][0]) > 10000:\n return False, []\n if len(cars[car][1]) > 0:\n detours, order = detour(cars[car][0], node, destnode, cars[car][1])\n if (max(detours) > 10000):\n return False, []\n else:\n return True, order\n return True, [0]\n\ndef deepValidate(node, car, destnode, order):\n global nodes, cars\n dist, path = shortpath.getRoad(node, cars[car][0])\n if (dist > 10000):\n return False, [0]\n\n distpick, path = shortpath.getRoad(cars[car][0], node)\n disttmp = distpick\n nodetmp = node\n info = [[] for i in order]\n for sp in order:\n if (sp == len(cars[car][1])):\n dist, path = shortpath.getRoad(nodetmp, destnode)\n disttmp += dist\n dist, path = shortpath.getRoad(node, destnode)\n if (disttmp - distpick - dist > 10000):\n return False, [0]\n info[sp] = [dist, disttmp - distpick]\n else:\n dist, path = shortpath.getRoad(nodetmp, cars[car][1][sp])\n disttmp += dist\n dist, path = shortpath.getRoad(cars[car][0], cars[car][1][sp])\n if (disttmp - dist > 10000):\n return False, [0]\n info[sp] = [dist, disttmp]\n dist, path = shortpath.getRoad(cars[car][0], node)\n return True, [dist, info]\n\ndef search(node, destnode):\n global nodes, cars, gridcars\n grid = 5000\n stepList = [[0, 0], [0, 1], [1, 0], [0, -1], [-1, 0], [1, 1], [1, -1], [-1, -1],\n [-1, 1], [0, 2], [2, 0], [0, -2], [-2, 0], [1, 2], [2, 1], [-1, 2],\n [-2, 1], [1, -2], [2, -1], [-1, -2], [-2, -1], [2, 2], [-2, 2],\n [2, -2], [-2, -2]]\n\n gx = int(nodes[node][0] / 5000)\n gy = int(nodes[node][1] / 5000)\n\n res = []\n for step in stepList:\n px = gx + step[0]\n py = gy + step[1]\n if px < 0 or px >= 38:\n continue\n if py < 0 or py >= 32:\n continue\n for car in gridcars[px * 32 + py]:\n flag, order = validate(node, car, destnode)\n if (flag):\n detourInfo = [0]\n if deepValidateFlag:\n flag, detourInfo = deepValidate(node, car, destnode, order)\n if not flag:\n continue\n res.append([car, order, detourInfo])\n if len(res) == 5:\n break\n if len(res) == 5:\n break\n return res\n\ndef init(ns, cs, gs):\n global nodes, cars, gridcars\n nodes = ns\n cars = cs\n gridcars = gs\n\nif __name__ == \"__main__\":\n res = search(5000, 5001)\n print(res)\n","sub_path":"TAXISearcher/src/getcar.py","file_name":"getcar.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"560570981","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\n\nfrom symposion.proposals.models import ProposalBase\n\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass Proposal(ProposalBase):\n \n #AUDIENCE_LEVEL_NOVICE = 1\n #AUDIENCE_LEVEL_EXPERIENCED = 2\n #AUDIENCE_LEVEL_INTERMEDIATE = 3\n \n TOPIC_AREAS = [\n (1, u\"Redes de Atenção à Saúde\"),\n (2, u\"Saúde Mental, Álcool, Crack e outras Drogas\"),\n (3, u\"Vigilância em Saúde\"),\n (4, u\"Experiências Pedagógicas Inovadoras\"),\n (5, u\"Etnia, Raça e Saúde\"),\n (6, u\"Educação Permanente em Saúde\"),\n (7, u\"Educação Popular em Saúde, mobilização, controle e participação social em experiências de formação\"),\n (8, u\"Formação em Residências para o SUS\"),\n ]\n \n audience_level = models.IntegerField(_(u\"Eixo Temático\"),choices=TOPIC_AREAS)\n #topic_area = models.IntegerField(_(u\"Eixo Temático\"),choices=TOPIC_AREAS)\n \n recording_release = models.BooleanField(\n _(u\"Liberação para gravação\"), \n default=True,\n help_text=_(u\"Ao enviar sua proposta, você concorda em dar permissão para os organizadores do congresso para gravar, editar e lançar áudio e/ou vídeo de sua apresentação. Se você não concorda com isso, por favor, desmarque esta caixa.\")\n )\n \n \n class Meta:\n abstract = True\n ProposalBase._meta.get_field(\"title\").verbose_name = u\"Título\"\n \n ProposalBase._meta.get_field('description').verbose_name = u\"Breve Resumo\"\n ProposalBase._meta.get_field('description').help_text = u\"Se a sua apresentação for aceita isso será tornado público e impresso no programa. Deve ser um parágrafo, no máximo 400 caracteres.\"\n \n ProposalBase._meta.get_field('abstract').verbose_name = u\"Resumo Detalhado\"\n ProposalBase._meta.get_field('abstract').help_text = u\"Descrição detalhada e esboço. Isso será tornado público, se sua apresentação for aceita. Edite usando Markdown.\"\n \n ProposalBase._meta.get_field('additional_notes').verbose_name = u\"Observações\"\n ProposalBase._meta.get_field('additional_notes').help_text = u\"Qualquer outra informação que você gostaria que a comissão científica soubesse ao fazer a sua seleção: sua experiência passada como palestrante, as experiências relevantes deste trabalho, etc. Edite usando Markdown.\"\n\n\nclass TalkProposal(Proposal):\n class Meta:\n verbose_name = \"talk proposal\"\n\n\nclass TutorialProposal(Proposal):\n class Meta:\n verbose_name = \"tutorial proposal\"\n","sub_path":"congrefor/proposals/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"490498501","text":"from cooperative_transport.utils import saturation\nimport numpy as np\n\ndef proportional_control(k_p, r, y, u_max, avoid_overturning):\n \"\"\"Implement proportional control law.\n\n Arguments:\n k_p (float): Proportional gain\n r (float): reference signal\n y (float): system output signal\n u_max (float): maximum control effort\n avoid_overturning (bool): if True avoids rotation greater than pi\n \"\"\"\n error = r - y\n if avoid_overturning:\n if abs(error) > np.pi:\n error += -2 * np.pi * np.sign(error)\n\n u = k_p * error\n saturated_u = saturation(u ,u_max)\n return saturated_u\n","sub_path":"src/cooperative_transport/control/proportional_control.py","file_name":"proportional_control.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"642347323","text":"import socket, os, sys, subprocess, time, signal, uuid\nimport urllib.request\nglobal ip_str, discovered_ip, max_range, no_reach\nno_reach = False\nmax_range = 255\ndiscovered_ip = []\nver = str(2.0)\ndef lip():\n ip_string=str(os.popen('ifconfig | grep -E \"([0-9]{1,3}[\\.]){3}[0-9]{1,3}\"').readlines())[15:];result=\"\";i=0\n while ip_string[i]!=\" \":result=result+ip_string[i];i=i+1\n return result\n\nip_str = lip()\nBLUE, RED, WHITE, YELLOW, MAGENTA, GREEN, END = '\\33[94m', '\\033[91m', '\\33[97m', '\\33[93m', '\\033[1;35m', '\\033[1;32m', '\\033[0m'\n\ndef getip():\n\treturn socket.gethostbyname(socket.gethostname())\n\ndef quit():\n os.system('clear')\n exit()\n\ndef line():\n sys.stdout.write(MAGENTA + \"\"\"--------------------------------------------------------------------\"\"\" + '\\n')\n\ndef banner():\n spaces = \" \" * 76\n sys.stdout.write(GREEN + spaces + \"\"\"\n ███▄ ▄███▓ ██▀███ ██████ ▄████▄ ▄▄▄ ███▄ █\n ▓██▒▀█▀ ██▒▓██ ▒ ██▒▒██ ▒ ▒██▀ ▀█ ▒████▄ ██ ▀█ █\n ▓██ ▓██░▓██ ░▄█ ▒░ ▓██▄ ▒▓█ ▄ ▒██ ▀█▄ ▓██ ▀█ ██▒\n ▒██ ▒██ ▒██▀▀█▄ ▒ ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▓██▒ ▐▌██▒\n ▒██▒ ░██▒░██▓ ▒██▒▒██████▒▒▒ ▓███▀ ░ ▓█ ▓██▒▒██░ ▓██░\n ░ ▒░ ░ ░░ ▒▓ ░▒▓░▒ ▒▓▒ ▒ ░░ ░▒ ▒ ░ ▒▒ ▓▒█░░ ▒░ ▒ ▒\n ░ ░ ░ ░▒ ░ ▒░░ ░▒ ░ ░ ░ ▒ ▒ ▒▒ ░░ ░░ ░ ▒░\n ░ ░ ░░ ░ ░ ░ ░ ░ ░ ▒ ░ ░ ░\n ░ ░ ░ ░ ░ ░ ░ ░\n ░\n \"\"\" + YELLOW + \"\"\"Version \"\"\" + str(ver) + RED + \"\"\" (By b3rt1ng)\"\"\" + '\\n')\n line()\n\ndef get_mac(ip):\n command = \"arp | grep \" + str(ip)\n r = subprocess.check_output([command], shell=True)\n r=str(r) \n r=r[35:]\n r=r[:17]\n return r\n\ndef resolveMac(mac):\n url = \"https://api.macvendors.com/\" + str(mac)\n b_str = urllib.request.urlopen(url).read()\n b_str = str(b_str, 'utf-8')\n return b_str\n\ndef ip_info(ip):\n os.system('clear')\n sys.stdout.write(BLUE + \"\"\"IP: \"\"\" + GREEN + str(ip) + '\\n')\n mac = get_mac(ip)\n sys.stdout.write(BLUE + \"\"\"Mac: \"\"\" + GREEN + str(mac) + '\\n')\n vendor = resolveMac(mac)\n sys.stdout.write(BLUE + \"\"\"Vendor: \"\"\" + GREEN + vendor + '\\n')\n stopper()\n\n\ndef ping(ip):\n try:\n subprocess.check_output([\"ping\", \"-c\", \"1\", ip])\n return True \n except subprocess.CalledProcessError:\n return False\n\ndef version():\n git=str(urllib.request.urlopen(\"https://raw.githubusercontent.com/b3rt1ng/MR_SCAN_V2/master/version\").read())\n git=git[:-3]\n git=git.replace('b', '')\n git=git.replace(\"'\", '')\n return git\ncur=version()\n\ndef ipbase():\n stop = 3\n size = len(ip_str)\n ip_resized = ip_str[:-2]\n return str(ip_resized)\n\n\ndef scan():\n fisrt = 0\n ip = ipbase()\n sub = 0\n full_ip = ip + str(sub)\n while sub <= max_range:\n try:\n full_ip = ip + str(sub)\n if ping(full_ip) is True:\n if fisrt == 0:\n os.system('clear')\n fisrt = fisrt + 1\n discovered_ip.append(full_ip)\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + GREEN + \"\"\"+\"\"\" + MAGENTA + \"\"\"] \"\"\"); sys.stdout.write(YELLOW + full_ip); sys.stdout.write(WHITE + ' is reachable' + '\\n')\n else:\n if fisrt == 0:\n os.system('clear')\n fisrt = fisrt + 1\n if no_reach==True:\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + RED + \"\"\"-\"\"\" + MAGENTA + \"\"\"] \"\"\"); sys.stdout.write(YELLOW + full_ip); sys.stdout.write(WHITE + ' is not reachable' + '\\n')\n sub = sub + 1\n except:\n os.system('clear')\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + RED + \"\"\"!\"\"\" + MAGENTA + \"\"\"]\"\"\" + WHITE + ' interrupted.' + '\\n')\n time.sleep(1)\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + RED + \"\"\"?\"\"\" + MAGENTA + \"\"\"]\"\"\" + WHITE + ' returning to main menu.' + '\\n')\n time.sleep(1)\n menu()\n \ndef stopper():\n try:\n sys.stdout.write('\\n' + MAGENTA + \"\"\"[\"\"\" + RED + \"\"\"?\"\"\" + MAGENTA + \"\"\"]\"\"\" + YELLOW + ' Hit CTRL + C to get back on the menu.' + '\\n')\n time.sleep(6200)\n except:\n menu()\n\ndef show_disip():\n i=0\n while i < len(discovered_ip):\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + RED + str(i) + MAGENTA + \"\"\"] \"\"\" + YELLOW + str(discovered_ip[i]) + '\\n')\n i = i + 1\n stopper()\n\ndef empty():\n sys.stdout.write(MAGENTA + \"\"\"[\"\"\" + RED + \"\"\"?\"\"\" + MAGENTA + \"\"\"]\"\"\" + YELLOW + ' The list is currently empty.' + '\\n')\n stopper()\n\ndef result():\n os.system('clear')\n if len(discovered_ip)==0:\n empty()\n else:\n show_disip()\n\ndef param():\n os.system('clear')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"*\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Maximum range: \"\"\" + GREEN)\n print(max_range)\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"*\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Your IP: \"\"\" + GREEN)\n print(ip_str)\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"*\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Show unreachable IP: \"\"\" + GREEN)\n print(no_reach)\n stopper()\n\n\ndef adv_ip():\n os.system('clear')\n print(YELLOW, discovered_ip)\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"*\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Type in an IP: \"\"\" + GREEN)\n IP = input()\n ip_info(IP)\n\ndef menu():\n os.system('clear')\n banner()\n sys.stdout.write('\\n')\n if cur != ver:\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"!\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Another version is avariable on github\"\"\" + '\\n')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"1\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Start Scan\"\"\" + '\\n')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"2\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Display discovered IP\"\"\" + '\\n')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"3\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Advanced IP informations\"\"\" + '\\n')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"4\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Settings\"\"\" + '\\n')\n sys.stdout.write( RED + \"\"\"[\"\"\" + BLUE + \"\"\"5\"\"\" + RED + \"\"\"]\"\"\" + YELLOW + \"\"\" Exit\"\"\" + '\\n')\n sys.stdout.write('\\n')\n sys.stdout.write( RED + \"\"\"Mr\"\"\" + YELLOW + \"\"\"_\"\"\" + BLUE + \"\"\"Scan\"\"\" + MAGENTA + \"\"\":\"\"\" + GREEN + \"\"\" \"\"\")\n choice = input()\n if choice==\"1\":\n scan()\n elif choice==\"2\":\n result()\n elif choice==\"3\":\n adv_ip()\n elif choice==\"4\":\n param()\n elif choice==\"5\":\n os.system('clear')\n exit()\n\n\n\nmenu()\n","sub_path":"MR_SCAN_V2.py","file_name":"MR_SCAN_V2.py","file_ext":"py","file_size_in_byte":7147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"540625166","text":"# Расчет результатов\n# Создайте функцию, которая подсчитывать расстояние, что проедет человек.\n\n# Функция принимает два параметра: время и скорость. \n\n# При выводе результат используйте функцию lambda для вывода корректной информации:\n\n# выводите строку: \"Вы проедете: 1 километр\", если результат был равен единице\n# выводите строку: \"Вы проедете: цифра километров\", если результат был большим за единицу\n\n# Solution: \n\ndef distance (speed, time = 1):\n dist = time * speed\n return dist\n\ndist = distance(int(input(\"Укажите скорость: \")), int((input(\"Укажите время: \"))))\n\nended = str((lambda a: \"километр.\" if dist == 1 else \"километров.\")(dist))\n\nprint (\"Вы проедете:\", dist, ended)\n","sub_path":"lesson12/exercise1.py","file_name":"exercise1.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"448957584","text":"import pyautogui as MI\r\nimport groupsFF as group\r\nimport time\r\nimport math\r\nMI.PAUSE = 0.001\r\n\r\n\r\n\r\ndef main():\r\n loops = 0\r\n while(True):\r\n loops = 0\r\n while(loops < 70):\r\n print(loops)\r\n ##Selina\r\n MI.click(1832,482)\r\n time.sleep(0.5)\r\n\r\n ##Shop\r\n MI.click(1106,326)\r\n time.sleep(0.5)\r\n\r\n ##Select Item\r\n MI.click(1258,742)\r\n time.sleep(0.5)\r\n\r\n ##Purchase Item\r\n MI.click(1430,535)\r\n time.sleep(0.5)\r\n\r\n ##Exit\r\n MI.click(1907, 242)\r\n time.sleep(0.5)\r\n\r\n loops = loops + 1\r\n\r\n ##Exit Again\r\n MI.click(1907, 242)\r\n time.sleep(0.5)\r\n \r\n ##Open Inventory\r\n inventory = MI.locateCenterOnScreen('images/skills/inventory.png', confidence = 0.85, region = (1640, 800, 40, 50))\r\n while(not inventory):\r\n inventory = MI.locateCenterOnScreen('images/skills/inventory.png', confidence = 0.85, region = (1640, 800, 40, 50))\r\n\r\n ##Click Inventory\r\n MI.click(inventory)\r\n time.sleep(1)\r\n ##Close Inventory\r\n MI.click(1911,248)\r\n time.sleep(1)\r\n \r\n\r\nmain()","sub_path":"itemPurchase.py","file_name":"itemPurchase.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"404401669","text":"\"\"\"\n\"\"\"\nimport requests\nimport datetime\nfrom locust import events\n\n\nclass DataBuffer:\n \"\"\"\n \"\"\"\n\n def __init__(self, hostname, *args, buffer_limit=20, **kwargs):\n \"\"\"\n Creates and starts a DataBuffer that stores request data\n so that it can be sent in batches to the server.\n Data is uploaded when the buffer_limit is reached,\n or the test completes\n \"\"\"\n print(\"Nile: Initializing Data Buffer\")\n self.hostname = hostname\n self.buffer_limit = buffer_limit\n\n self.data_endpoint = f'http://{hostname}/api/v1/requests'\n self.buffer = list()\n\n events.request_success += self.request_success\n events.request_failure += self.request_failure\n events.quitting += self.on_quitting\n\n def request_success(self, request_type, name,\n response_time, response_length, **kwargs):\n\n self._on_request_data(request_type, name, response_time,\n response_length, True, None)\n\n def request_failure(self, request_type, name, response_time,\n response_length, exception, **kwargs):\n\n self._on_request_data(request_type, name, response_time,\n response_length, False, exception)\n\n def _on_request_data(self, request_type, name, response_time,\n response_length, success, exception, **kwargs):\n\n data = {\n 'request_method': request_type,\n 'name': name,\n 'response_time': response_time,\n 'response_length': response_length,\n 'success': success,\n 'exception': exception}\n\n if 'request_timestamp' in kwargs:\n data['request_timestamp'] = kwargs['request_timestamp']\n else:\n request_time = datetime.datetime.now() \\\n - datetime.timedelta(milliseconds=response_time)\n data['request_timestamp'] = request_time.isoformat()\n\n if 'request_length' in kwargs:\n data['request_length'] = kwargs['request_length']\n else:\n data['request_length'] = None\n\n if 'status_code' in kwargs:\n data['status_code'] = kwargs['status_code']\n else:\n data['status_code'] = None\n\n self.buffer.append(data)\n if len(self.buffer) > self.buffer_limit:\n self._upload_buffer()\n\n def on_quitting(self):\n print('Nile: Handling Test Shutdown')\n self._upload_buffer()\n\n def _upload_buffer(self):\n contents = self.buffer\n self.buffer = list()\n print('Nile: Uploading Buffer with size ' + str(len(contents)))\n requests_endpoint = f'http://{self.hostname}/api/v1/requests'\n\n response = requests.post(requests_endpoint, json=contents)\n if response.status_code != 200:\n raise RuntimeError(f'Could not upload buffer after test \\\n shutdown {response}')\n","sub_path":"nile_lib/nile_test/integration/databuffer.py","file_name":"databuffer.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"646580505","text":"from numpy import sin,cos,array\r\nfrom visual import sphere,color,rate,display,arrow\r\n\r\ndef f(val,t): #vorticity vector\r\n r = val[0]\r\n th = val[1]\r\n z = val[2]\r\n dr = 3*dP*t\r\n dth = 0.01\r\n dz = v_inlet*0.01\r\n return array([dr,dth,dz],dtype=float)\r\n\r\ndef rk4(val,t):\r\n k1 = f(val,t)\r\n k2 = f(val+0.5*k1,t+0.5*h)\r\n k3 = f(val+0.5*k2,t+0.5*h)\r\n k4 = f(val+k3,t+h)\r\n dval = (k1+2*k2+2*k3+k4)/6\r\n val += dval\r\n return array([val,dval],dtype=float)\r\n \r\n#define constants and lists\r\nr = []\r\nth = []\r\nz = []\r\nvr = []\r\nvth = []\r\nvz = []\r\nt = []\r\nvelvec = {}\r\n\r\n#\r\nh = 1e-5\r\ni = 0\r\niterations = 5000\r\n\r\nv_inlet = float(input('Enter a freestream velocity (mph):')) #165 #freestream (mph) typical for airliner takeoff\r\ndP = float(input('Enter a pressure difference:'))#3 #pressure difference induced by wing (atm/atm)\r\n\r\n#initialize and set values\r\nt.append(0.0)\r\nvr.append(0.0)\r\nvth.append(0.01)\r\nvz.append(v_inlet)\r\nval = array([0.1,0.0,0.0],dtype=float)\r\ndval = array([0.0,0.0,0.0],dtype=float)\r\nout = ([val,dval])\r\n\r\n#solve for vortex ryz coordinates over time\r\nwhile(i < iterations):\r\n r.append(val[0])\r\n th.append(val[1])\r\n z.append(val[2])\r\n vr.append(dval[0])\r\n vth.append(dval[1])\r\n vz.append(dval[2])\r\n out = rk4(val,t[i])\r\n val = out[0]\r\n dval = out[1]\r\n t.append(t[i]+h)\r\n i += 1\r\n\r\n#display vortex\r\nscene = display(title='Airfoil Induced Vortex',width=1280, height=1024, center=[0,0,0], background=color.black)\r\nscene.cursor.visible = False\r\nviews = [[1,0,0],[1,-1,-1],[0,0,-1]] #side, iso, z\r\nscene.forward = views[1]\r\nvortex = sphere(pos=[0,0,0],radius=0.01,make_trail=True)\r\nvortex.color = color.blue\r\nvl = sphere(pos=[0,0,0],radius=0.01,make_trail=True)\r\nvl.trail_object.color = (0,0.8,0)\r\n\r\n#scale rate of animation and size of vectors\r\nscaleAnim = 500\r\nscaleVec = 0.05\r\n\r\n#display the position, vortex line, and velocity vectors of the vortex\r\nfor i in range(0,iterations):\r\n rate(scaleAnim)\r\n vortex.pos = [r[i]*cos(th[i]),r[i]*sin(th[i]),z[i]]\r\n vl.pos = [0,0,z[i]]\r\n pos1 = array([r[i]*cos(th[i]),r[i]*sin(th[i]),z[i]])\r\n \r\n #adjust view - ONLY for isometric view (view[1])\r\n if(i < (iterations*3/4)):\r\n scene.center = [0,0,z[i]]\r\n \r\n #show velocity vector every 75 iterations\r\n if(i%75 == 0): \r\n prev = array([r[i-1]*cos(th[i-1]),r[i-1]*sin(th[i-1]),z[i-1]])\r\n vel = (pos1-prev)/(t[i]-t[i-1])\r\n j = i/50\r\n velvec[j] = arrow(pos=pos1, axis=vel*scaleVec*t[i], shaftwidth=5*scaleVec)\r\n velvec[j].color = (0,0.75,1)\r\n \r\n#calculate initial freestream velocity and pressure difference based on final iteration\r\ndPcalc = (r[iterations-1]-r[iterations-2])/(t[iterations-1]-t[iterations-2])/t[iterations-1]/3*h\r\nprint('Initial Freestream Velocity:',(z[iterations-1]-z[iterations-2])*100,'mph')\r\nprint('Initial Pressure Difference:',dPcalc)\r\n","sub_path":"AirfoilVorticesFinal.py","file_name":"AirfoilVorticesFinal.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"}
+{"seq_id":"282397730","text":"import h5py\nimport pickle\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\n\n\n# Allow user to choose which embedding to use\nprint(\"__BLOG EMBEDDINGS__\")\nprint(\"1: Time\")\nprint(\"2: Age\")\nprint(\"3: Topic (Computer Similarity)\")\nprint(\"4: Topic (Human Similarity)\")\nchoice = input(\"To select an embedding, enter a number 0 through 4: \")\nchoice = int(choice)\nprint()\n\n# Based on which embedding the user chooses, define the necessary settings\nif choice == 1:\n file_str = 'embeddings/embeddings-time-5iter.h5'\n vocab_file_str = 'embeddings/blog_vocab_age.pkl'\n categories = [\n 'APR03', 'MAY03', 'JUN03', 'JUL03', 'AUG03', 'SEP03', 'OCT03', 'NOV03', 'DEC03',\n 'JAN04', 'FEB04', 'MAR04', 'APR04', 'MAY04', 'JUN04', 'JUL04', 'AUG04'\n ]\nelif choice == 2:\n file_str = 'embeddings/embeddings-age-5iter.h5'\n vocab_file_str = 'embeddings/blog_vocab_age.pkl'\n categories = ['13to15', '16to18', '19to21', '22to24', '25to27', '28to30',\n '31to33', '34to36', '37to39', '40to42', '42to47']\nelif choice == 3:\n file_str = 'embeddings/embeddings-topic-human-5iter.h5'\n vocab_file_str = 'embeddings/blog_vocab_topic.pkl'\n categories = ['Accounting', 'Advertising', 'Arts', 'Banking', 'BusinessServices', 'Chemicals',\n 'Communications-Media', 'Consulting', 'Education', 'Engineering', 'Fashion', 'Government', 'Internet',\n 'Law', 'Marketing', 'Non-Profit', 'Publishing', 'Religion', 'Science', 'Student', 'Technology']\nelif choice == 4:\n file_str = 'embeddings/embeddings-topic-ppmi_cosine-5iter.h5'\n vocab_file_str = 'embeddings/blog_vocab_topic.pkl'\n categories = ['Accounting', 'Advertising', 'Arts', 'Banking', 'BusinessServices', 'Chemicals',\n 'Communications-Media', 'Consulting', 'Education', 'Engineering', 'Fashion', 'Government', 'Internet',\n 'Law', 'Marketing', 'Non-Profit', 'Publishing', 'Religion', 'Science', 'Student', 'Technology']\nelse:\n print(\"The input you gave was invalid\")\n\n# Load the dataset\nprint(\"Loading dataset...\")\nfile = h5py.File(file_str, 'r')\n\n# for key in file.keys():\n# print(key)\n\nembedding = file['U']\n\nfile = h5py.File('embeddings/blog_dataset_sample.h5', 'r')\ntopics = file['label'][:, 0]\n\n# print(embedding.shape)\n# print(embedding[:, :, 1])\n\n\n# Utility script to find the nearest neighbors to the word at a given |