None:\\r\\n s = self.match(self.break_exp)\\r\\n if self.tok and isinstance(self.tok[-1], Break):\\r\\n self.tok[-1].string += self.pre_whitespace + s\\r\\n self.i += len(s)\\r\\n self.line += 1\\r\\n self.pre_whitespace = ''\\r\\n elif self.ignore_break():\\r\\n self.add_pre_whitespace(s)\\r\\n self.line += 1\\r\\n else:\\r\\n self.add_token(Break(s))\\r\\n self.line += 1\\r\\n self.lex_indent()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7adbf39a1f1882512a9d2ae421201edb\",\n \"score\": \"0.44268492\",\n \"text\": \"def get_transcript_coordinate_cds_stop(self):\\n return self.chromosome_coordinate_to_transcript(self.thick_stop)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7029a3b97aedd692fc82f66bc8654768\",\n \"score\": \"0.4422325\",\n \"text\": \"def visit_Break(self, node): #pylint: disable=invalid-name\\n if self.label is None:\\n self.label = self.id_generator.get_unique_id()\\n return Goto(self.label, coord=node.coord)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a286a58c897a49bdf9c3db9b7b9af9\",\n \"score\": \"0.44195637\",\n \"text\": \"def get_in_ph(self):\\n return self.confidence_relation_ph, self.confidence_entity_ph, self.entity_bb_ph, self.word_embed_relations_ph, self.word_embed_entities_ph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064ea3a8c76427bf44a1c7e886bca74e\",\n \"score\": \"0.44195384\",\n \"text\": \"def lf(self):\\n return self.__STRING_LF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27e49267bce5ff002329dbfc360f1c7d\",\n \"score\": \"0.4419428\",\n \"text\": \"def _get_evidence_text(self, event_tag):\\n par_id = event_tag.attrib.get('paragraph')\\n uttnum = event_tag.attrib.get('uttnum')\\n event_text = event_tag.find('text')\\n if self.sentences is not None and uttnum is not None:\\n sentence = self.sentences[uttnum]\\n elif event_text is not None:\\n sentence = event_text.text\\n else:\\n sentence = None\\n return sentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe6de8e5ced42c906a0601f20ad34589\",\n \"score\": \"0.44069108\",\n \"text\": \"def get_total_sent(self):\\n return self.details['total_sent']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd05839947bb7b8445033fee09d87730\",\n \"score\": \"0.43951234\",\n \"text\": \"def _get_stop(self):\\n return self.__stop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180cfbddba701e9d823f64efbdc073ac\",\n \"score\": \"0.43941587\",\n \"text\": \"def _get_word(self):\\n global PUNCTUATION\\n READING_STATE = 'READING'\\n INIT_STATE = 'INIT STATE'\\n state = INIT_STATE\\n char_buffer = \\\"\\\"\\n non_word_delimeters = [\\\"-\\\", \\\"'\\\"]\\n sentence_delimeters = [\\\".\\\", \\\";\\\", \\\":\\\"]\\n while(True):\\n char = self.text_stream.get_character()\\n \\n if char is not None:\\n if state == INIT_STATE:\\n # Skip whitespace and non-alphanumeric characters.\\n if char.isspace() or not char.isalnum():\\n continue\\n else:\\n state = READING_STATE\\n char_buffer += char\\n elif state == READING_STATE:\\n # Read until we hit whitespace character or \\n # non-alpha numeric character with the exeption of\\n # some chars which are not considered to mark end of a word. \\n \\n if (not char in non_word_delimeters and \\n (char.isspace() or not char.isalnum())):\\n # If character is a sentence terminator/delimeter\\n # include it, so that downstream clients know this\\n # was the last word. \\n if char in sentence_delimeters:\\n char_buffer += char\\n \\n return char_buffer\\n else:\\n char_buffer += char\\n \\n else: # End of stream\\n return char_buffer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe18d686216c69a7e9c442a217a54d0\",\n \"score\": \"0.43880925\",\n \"text\": \"def __getitem__ (self, index):\\n if len(self.__sentence)-1 < index:\\n raise IndexError ( \\\"Invalid index \\\" + str(index) + \\\" for list of \\\" + str(len(self.__sentence)) + \\\" sentences\\\" )\\n else:\\n return self.__sentence[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540dcf031c038d8f9804be396264c3f7\",\n \"score\": \"0.43855217\",\n \"text\": \"def get_conversation2(self):\\r\\n return self.conversation2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49786a0584f7b35e1283fa040a035a8d\",\n \"score\": \"0.43799558\",\n \"text\": \"def get_whole(self):\\n\\t\\treturn self.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7a5f3bb419ef509b668f40454ec896\",\n \"score\": \"0.43748432\",\n \"text\": \"def get_word(self):\\n return self.word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bc4a8f2b6ec3e234490bffe60efb36\",\n \"score\": \"0.43717855\",\n \"text\": \"def getNewLine():\\n return \\\"<br>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9cb7736d1d5f5f59d38f3aa58440bd8\",\n \"score\": \"0.4369878\",\n \"text\": \"def get_sent_from(self):\\n return self.sent_from\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2850c1f19832681dee9a4eeb312ba0e5\",\n \"score\": \"0.4365979\",\n \"text\": \"def getENDL_CS_ENDF_MT( self ) :\\n\\n from fudge.legacy.converting import endf_endl\\n\\n MT = self.ENDF_MT\\n C, S = endf_endl.getCSFromMT( MT )\\n return( { 'C' : C, 'S' : S, 'MT' : MT } )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e386fdf4231374e981023caff5465e\",\n \"score\": \"0.43596554\",\n \"text\": \"def segment(self):\\n return self._current_line.split()[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f6dd2c6c63e3fbd73bbeff79a9e6d3\",\n \"score\": \"0.4355152\",\n \"text\": \"def comment(self):\\n return self.properties.get('comment', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"948dc51fb0d4a8e004a1bae4ac720e64\",\n \"score\": \"0.4347383\",\n \"text\": \"def get_weedline_padding(self):\\n return self._properties['weedline_padding']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f6ca7e53a4eba41946b1b4bf4fb3659\",\n \"score\": \"0.4339661\",\n \"text\": \"def bent(cls):\\n return cls._namespace_SIO('SIO_001094')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d74b4fb7b0c1836f88e56f66314885\",\n \"score\": \"0.43366507\",\n \"text\": \"def get_note_text(self):\\n if self.note:\\n return self.note.note # pylint: disable=no-member\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26f9d2bbf35cb9b395bf19d6f9404bf\",\n \"score\": \"0.43271786\",\n \"text\": \"def get_text(self):\\n\\t\\treturn self.text.get('1.0', END+'-1c')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71cc681a155f30d8fbfc7a683ce57a91\",\n \"score\": \"0.4326446\",\n \"text\": \"def ssltotencsw(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._ssltotencsw\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c158c285e16492360bc81fe73fd74845\",\n \"score\": \"0.43218237\",\n \"text\": \"def ssltotenc(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._ssltotenc\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d05dfcaaef46f8e10c8ab28c773e85e\",\n \"score\": \"0.43179157\",\n \"text\": \"def get_word(self):\\n new_word = self._get_word()\\n\\n if not new_word:\\n # Update the {prev,curr}_word varibles \\n # to indicate end of stream. \\n self.prev_word = self.curr_word\\n self.curr_word = None\\n return None\\n \\n new_word = self.normalize_word(new_word)\\n self._update_prev_word(new_word)\\n return new_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104dd9ff25d83e567be507091ea13d51\",\n \"score\": \"0.43143\",\n \"text\": \"def getSentences(self):\\n doc = self.doc\\n if doc is None:\\n console(\\\"No results available from the NLP pipeline\\\")\\n return []\\n\\n if not self.canSentence:\\n console(\\\"No sentence results available from the NLP pipeline\\\")\\n return []\\n\\n result = []\\n\\n whiteRe = re.compile(r\\\"^[.?!\\\\s]*$\\\", re.S)\\n spuriousNlBefore = re.compile(r\\\"\\\\n+(\\\\W)\\\")\\n spuriousNlAfter = re.compile(r\\\"(\\\\W)\\\\n+\\\")\\n\\n for s in doc.sents:\\n text = s.text.strip(\\\"\\\\n\\\")\\n if whiteRe.match(text):\\n continue\\n\\n tokenStart = doc[s.start]\\n tokenEnd = doc[s.end - 1]\\n sentStart = tokenStart.idx\\n sentEnd = tokenEnd.idx + len(tokenEnd.text)\\n\\n text = spuriousNlBefore.sub(r\\\"\\\\1\\\", text)\\n text = spuriousNlAfter.sub(r\\\"\\\\1\\\", text)\\n result.append((sentStart, sentEnd, text))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b366943e7a180d0f6ddb368b0bf890\",\n \"score\": \"0.4312276\",\n \"text\": \"def _get_tweet_text(data, numb):\\n try:\\n return data['statuses'][numb]['text']\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec391a7d50892b5cb8e9ec921758d4a8\",\n \"score\": \"0.4308536\",\n \"text\": \"def get_sail(self):\\n return self.__sail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffa07a5bac6bc7344b1545be9727c15\",\n \"score\": \"0.43067375\",\n \"text\": \"def get_sent_to_object(self, o):\\n return self.__sent.get(o)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725c1e0e64e10503ec7a6c3cf9d03adf\",\n \"score\": \"0.4305617\",\n \"text\": \"def LmTxStep(self):\\n return self._get_attribute('lmTxStep')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6f3716e7eaa4018a3224d1bf79c695\",\n \"score\": \"0.4305445\",\n \"text\": \"def getStartText(self):\\n return self.importedText[:self.startOffset] #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030c80d553c03db15e22c2d5b2947962\",\n \"score\": \"0.43020508\",\n \"text\": \"def msrc_number(self):\\n if self._msrc_number is None:\\n details = self._get_details()\\n raw_info = details.find(id='ScopedViewHandler_labelSecurityBulliten_Separator')\\n self._msrc_number = list(raw_info.next_siblings)[0].strip()\\n\\n return self._msrc_number\",\n \"title\": \"\"\n },\n {\n \"docid\": \"033a79acc62347d1af16636ed6c82735\",\n \"score\": \"0.43005148\",\n \"text\": \"def get_message(self):\\n rv = self.message\\n return(rv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c562c8ed9a700ffddff03cbe57a928\",\n \"score\": \"0.4296138\",\n \"text\": \"def get(self):\\n\\n return self.buffer[0:self.bufferEnd]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1f985628e24f850663a3ca1cd2ea47\",\n \"score\": \"0.42938897\",\n \"text\": \"def GetProperties(self):\\n return [FOLD_COMMENT]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1a9e12dc29fc86eda8b12c24efa28a\",\n \"score\": \"0.42921555\",\n \"text\": \"def flowFromSpacing(self):\\n \\n return self._flowFromSpacing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf25730a5cde34eb201865e1ebaf7ce\",\n \"score\": \"0.42839044\",\n \"text\": \"def getSpVerbosity(self):\\n return self._spVerbosity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88fd12591d7d837587189919002806c7\",\n \"score\": \"0.42823526\",\n \"text\": \"def get_symbol(self):\\n if self.team == \\\"white\\\":\\n return 'wp '\\n elif self.team == \\\"black\\\":\\n return 'bp '\\n else:\\n raise ValueError(\\\"this team:\\\" +\\n self.team +\\n \\\" doesn't exist in the realm of this game\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee18652086f3ab7a77f2092b8fac112\",\n \"score\": \"0.42818782\",\n \"text\": \"def get_linewidth(self):\\n return self._linewidth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8964d89dc907103d0b4782e9c8f1d556\",\n \"score\": \"0.4276719\",\n \"text\": \"def break_in_syllables(word):\\n if 'ss' in word or 'rr' in word:\\n return PortugueseTextualProcessing().PT_DICT.inserted(word).split('-')\\n return [s for s in PortugueseTextualProcessing().SILVA_SYLLABLE_SEPARATOR.separate(word) if s != '']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c303ab27c0527dcac9a76d5259316b\",\n \"score\": \"0.42751512\",\n \"text\": \"def get_mail_text(self):\\n return self.mail_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed30bb698a429e1cdff360eae116101e\",\n \"score\": \"0.42744875\",\n \"text\": \"def GetLineSpacing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6041801e8f78fd2fe900ca998076325\",\n \"score\": \"0.42722967\",\n \"text\": \"def blurb(self):\\n return self._blurb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c52020109db9b0c7a0928dc113c584\",\n \"score\": \"0.42718378\",\n \"text\": \"def getline(self):\\n return self.getpos()[0]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994279,"cells":{"query_id":{"kind":"string","value":"bc12ac2bd81b6cd878534a3d4f323653"},"query":{"kind":"string","value":"Provides the user with a list of options to search the csv file and calls the appropriate search functions"},"positive_passages":{"kind":"list like","value":[{"docid":"4d768fac5d7955f410d296ac111496db","score":"0.587037","text":"def search_entry():\n clear_screen()\n print(\"Search for an Existing Entry:\")\n print('''\n [A] Search by Date\n [B] Amount of Time\n [C] Exact Search\n [D] Regex Pattern\n [E] Return to Main Menu\n ''')\n\n while True:\n rows = open_file()\n user_input = input(\"How Do You Want To Search? \")\n\n if user_input.upper() == \"A\": # Search by date\n clear_screen()\n print(\"All Existing Dates:\")\n for row in rows:\n print(row[\"Date\"])\n\n while True:\n search_date = input(\"Enter a Date from the List Above: \")\n if re.search(r'(\\d\\d/\\d\\d/\\d\\d\\d\\d)', search_date):\n break\n print(\"Please, Enter a valid date!\")\n search_by_date(search_date, rows)\n new_search()\n\n elif user_input.upper() == \"B\": # search by time\n clear_screen()\n print(\"All Logged Time in Minutes:\")\n for row in rows:\n print(row[\"Time\"])\n\n while True:\n amount_of_time = input(\"Enter Time from the List Above: \")\n try:\n int(amount_of_time)\n except ValueError:\n print(\"Please enter a valid number!\")\n else:\n search_by_time(amount_of_time, rows)\n new_search()\n\n elif user_input.upper() == \"C\": # search by exact search\n clear_screen()\n try:\n user_input = \\\n str(input(\"Enter a String for Exact Search: \"))\n except ValueError:\n print(\"Please, Enter a String of Characters\")\n else:\n exact_search(user_input, rows)\n new_search()\n\n elif user_input.upper() == \"D\": # search by regex pattern\n clear_screen()\n print(\"Enter a Regex Pattern! Example: \\d{3}\\s\\d{3}\")\n regex_input = input(\"Enter Pattern: \")\n regex_search(regex_input, rows)\n new_search()\n\n elif user_input.upper() == \"E\": # return to main menu\n clear_screen()\n start_screen()\n\n else:\n print(\"Please, Enter a Valid Choice!\")\n pass","title":""}],"string":"[\n {\n \"docid\": \"4d768fac5d7955f410d296ac111496db\",\n \"score\": \"0.587037\",\n \"text\": \"def search_entry():\\n clear_screen()\\n print(\\\"Search for an Existing Entry:\\\")\\n print('''\\n [A] Search by Date\\n [B] Amount of Time\\n [C] Exact Search\\n [D] Regex Pattern\\n [E] Return to Main Menu\\n ''')\\n\\n while True:\\n rows = open_file()\\n user_input = input(\\\"How Do You Want To Search? \\\")\\n\\n if user_input.upper() == \\\"A\\\": # Search by date\\n clear_screen()\\n print(\\\"All Existing Dates:\\\")\\n for row in rows:\\n print(row[\\\"Date\\\"])\\n\\n while True:\\n search_date = input(\\\"Enter a Date from the List Above: \\\")\\n if re.search(r'(\\\\d\\\\d/\\\\d\\\\d/\\\\d\\\\d\\\\d\\\\d)', search_date):\\n break\\n print(\\\"Please, Enter a valid date!\\\")\\n search_by_date(search_date, rows)\\n new_search()\\n\\n elif user_input.upper() == \\\"B\\\": # search by time\\n clear_screen()\\n print(\\\"All Logged Time in Minutes:\\\")\\n for row in rows:\\n print(row[\\\"Time\\\"])\\n\\n while True:\\n amount_of_time = input(\\\"Enter Time from the List Above: \\\")\\n try:\\n int(amount_of_time)\\n except ValueError:\\n print(\\\"Please enter a valid number!\\\")\\n else:\\n search_by_time(amount_of_time, rows)\\n new_search()\\n\\n elif user_input.upper() == \\\"C\\\": # search by exact search\\n clear_screen()\\n try:\\n user_input = \\\\\\n str(input(\\\"Enter a String for Exact Search: \\\"))\\n except ValueError:\\n print(\\\"Please, Enter a String of Characters\\\")\\n else:\\n exact_search(user_input, rows)\\n new_search()\\n\\n elif user_input.upper() == \\\"D\\\": # search by regex pattern\\n clear_screen()\\n print(\\\"Enter a Regex Pattern! Example: \\\\d{3}\\\\s\\\\d{3}\\\")\\n regex_input = input(\\\"Enter Pattern: \\\")\\n regex_search(regex_input, rows)\\n new_search()\\n\\n elif user_input.upper() == \\\"E\\\": # return to main menu\\n clear_screen()\\n start_screen()\\n\\n else:\\n print(\\\"Please, Enter a Valid Choice!\\\")\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0f8e592987aa93a7d33fabc7963d8d61","score":"0.64782333","text":"def search(bot, update, args):\n csv_file = read_csv(file)\n user_string = update.message.text.split()\n result = str()\n if len(user_string) == 2:\n for i in range(len(csv_file)):\n csv_file[i] = csv_file[i].replace(' | ', ',')\n old_string = csv_file[i]\n csv_file[i] = csv_file[i].split(',')[:2]\n elem = ','.join(csv_file[i])\n if user_string[1] in elem:\n result += old_string.replace(',', ' | ')\n else:\n continue\n if result != str():\n update.message.reply_text(\n 'The book(-s) you searched for are: \\n' + result)\n else:\n update.message.reply_text('No books found on this result')\n else: # exception\n update.message.reply_text('Check the correction of your input!')","title":""},{"docid":"78ef49b733b4128effd829c026a08b7d","score":"0.6278241","text":"def search(self):\n print(\"\"\" Search Menu\n(1) Search by date.\n(2) Search by task\n(3) Search by duration\n(4) Search by comment\n(exit) type end to program at any time\n(menu) to return to main menu\n\"\"\")\n option = input(\"please select an option 1, 2 or exit: \")\n self.keywords(option)\n if option == \"1\" or option == \"(1)\":\n clear_screen()\n self.search_date()\n\n elif option == \"2\" or option == \"(2)\":\n clear_screen()\n self.search_string('task')\n elif option == \"3\" or option == \"(3)\":\n clear_screen()\n self.search_duration()\n elif option == \"4\" or option == \"(4)\":\n clear_screen()\n self.search_string('comments')\n else:\n clear_screen()\n \"please enter one of the options in brakets\"\n self.keywords(option)","title":""},{"docid":"c816c821a33bc0006176b4a6ca87ccb8","score":"0.6217566","text":"def main():\n\tcommand_line_arguments = get_command_line_arguments()\n\n\tif need_help(command_line_arguments): \n\t\tprint_usage_txt()\n\telif invalid_year_format(command_line_arguments):\n\t\tprint_invalid_year_format_error_message()\n\telse: \n\t\tdictionary_of_search_commands = turn_arguments_into_dictionary(command_line_arguments)\n\n\t\tdictionary_of_search_results = search_csv_into_dictionary(dictionary_of_search_commands)\n\n\t\tprint_search_results(dictionary_of_search_commands, dictionary_of_search_results)","title":""},{"docid":"68abca9d2a3bb2e04b6dd0d1327c4d54","score":"0.61331815","text":"def main() -> None:\n args: Dict[str, str] = read_args()\n search_file(args[\"FILE\"], args[\"COLUMN\"], args[\"OPERATION\"])","title":""},{"docid":"20a64a8d53764a70b4d7f2abd1e5e1c8","score":"0.59435296","text":"def search(self, *args):\n\n with open('all_melk.csv') as csv_file:\n # reading the csv file using DictReader\n csv_reader = csv.DictReader(csv_file)\n\n # converting the file to dictionary\n # by first converting to list\n # and then converting the list to dict\n dict_from_csv = dict(list(csv_reader)[0])\n\n print(dict_from_csv['area'])\n # it's defective","title":""},{"docid":"e0363100064403e37eec83df49e25618","score":"0.5862175","text":"def load_search():\n city = ['FI', 'MI', 'PA', 'NA', 'BO', 'TO', 'VE', 'CA']\n prefix = ['055', '02', '091', '081', '051', '011', '041', '070']\n\n # search is a control parameter.\n # For search == 'GO' the search continues\n # For search == 'ALT' the search stops\n search = 'GO'\n\n while search == 'GO':\n name = raw_input('data to search for: ')\n search2(city, prefix, name)\n search = raw_input('continue? ')","title":""},{"docid":"f0a45f1d8a11b6ea7a621fb881b98a66","score":"0.5856241","text":"def main(argv):\n try:\n opts, args = getopt.getopt(argv,\"hf:s:c:\",[\"support_threshold=\",\"confidence_threshold=\"])\n except getopt.GetoptError:\n print ('file.csv -s <support_threshold> -c <confidence_threshold>')\n sys.exit(2)\n\n for opt, arg in opts:\n if opt == '-h':\n print ('file.csv -s <support> -c <confidence>')\n sys.exit()\n elif opt in (\"-f\", \"--file\"):\n infile = arg\n elif opt in (\"-s\", \"--support\"):\n support_threshold = float(arg)\n elif opt in (\"-c\", \"--confidence\"):\n confidence_threshold = float(arg)\n\n data = read_data(infile)\n\n final_results = [\"{} => {} s = {:0.2f}, c = {:0.2f}\".format(\n item_set, item, support_frequency(data[1:], item_set.union({item})),\n confidence(data[1:], item_set, {item})) for item_set, item in apriori(\n data[1:], support_threshold, confidence_threshold)]\n for result in final_results:\n print(result)","title":""},{"docid":"772d062664fbc8104f8491d668016bbb","score":"0.5852055","text":"def do_search(self,args):\n self.ctlr.search_cmd(args)","title":""},{"docid":"9f201e401d6e09f08b6a7d5b7d75a7b4","score":"0.5843696","text":"def search_data():","title":""},{"docid":"531d71d3f9ba6114859b85b53b7de356","score":"0.5745585","text":"def search(self, **kwargs):\n with open(self.filename, 'rb') as f:\n reader = csv.DictReader(f)\n self.schema = reader.next()\n\n # Make kwargs into QueryParameter objects\n query = self.__prepare_query(kwargs)\n\n return filter(lambda row: self.__matches_row(row, query=query), reader)","title":""},{"docid":"a32139c46ead12b6105705984b4e6568","score":"0.5744557","text":"def open_file(self):\n self.column_checkboxes = []\n self.selected_column_targets = []\n file_name, filter = QFileDialog.getOpenFileName(\n self, 'Open CSV', os.getenv('HOME'), 'CSV(*.csv)')\n if file_name:\n self.load_file(file_name)","title":""},{"docid":"702980fe874a3a33652ddda3f1152015","score":"0.5732689","text":"def search():\n try:\n corp = filter_corpus()\n print('Finished filtering')\n term = search_term.get()\n is_sensitive = sensitive.get()\n write_to_file = to_file.get()\n if term == '':\n messagebox.showerror('Error', 'No search term was entered. Please try again.')\n return\n if not is_regex.get():\n term = words_to_regex(term)\n if output_type.get() == 'concordance':\n concordance_lines = corp.get_concordances(term, is_sensitive, context_len.get())\n if write_to_file:\n concordance_to_file(concordance_lines)\n else:\n show_concordances(concordance_lines, term)\n elif output_type.get() == 'total frequency':\n raw = corp.get_total_raw_count(term, is_sensitive)\n norm = corp.get_normalized_count(raw)\n if write_to_file:\n counts_to_file(raw, norm)\n else:\n show_counts(raw, norm)\n else:\n cat = category.get()\n freq_type = frequency_type.get()\n print('Getting counts by category')\n counts_by_category = corp.get_counts_by_category(term, is_sensitive, cat, freq_type)\n if write_to_file:\n categories_to_file(counts_by_category, cat, freq_type)\n else:\n show_by_category(counts_by_category, cat, freq_type)\n except Exception as e:\n messagebox.showerror('Error', e)","title":""},{"docid":"6b7d60c294eb9b73ae68b7e06a13f0dc","score":"0.56831276","text":"def main(option, args):\n print 'option:', option\n print 'args:', args\n if option.csv:\n print 'csv:', option.csv","title":""},{"docid":"55718bb0245ef685dd7ae1c11816d88e","score":"0.5680772","text":"def test_stock_search(self):\n\n rows = read_csv_file(os.path.dirname(os.path.abspath(__file__)) + \"/sample.csv\")\n stock_search = StockSearch()\n\n for row in rows:\n stock_search.insert(row[0])\n\n self.assertEqual(stock_search.search(\"AICIXE\"), (True, \"AICIXE\"))\n self.assertEqual(stock_search.search(\"AICIE\"), (False, \"AICIXE\"))\n self.assertEqual(stock_search.search(\"AM\"), (False, \"AMBKP\"))","title":""},{"docid":"ab3ce96af31d810f8a37399f1afd4b62","score":"0.566289","text":"def _cmd_search(self, options, *args):\n found = []\n for pattern in args:\n entries = self.phonebook.search(pattern, options.ignore_case,\n options.regexp, options.fields)\n # add all entries which aren't already in found. This avoids\n # printing entries twice, which are matched by more than one\n # pattern\n new = filter(lambda entry: entry not in found, entries)\n found += new\n self.print_table(found, options.output, options.sortby,\n options.descending)","title":""},{"docid":"d69be548affe14f8302baa37e20b37f4","score":"0.5660264","text":"def handle(self, *params, **options):\n if len(params) != 2:\n raise CommandError('a single .csv file amd type_slug are required as input.')\n\n region_only = ''\n while region_only != 'Y' and region_only != 'N':\n region_only = raw_input('Does the covariate data file have region column and no iso3 column? Y|N ')\n\n # read the csv file into a list of dicts\n fname = params[0]\n type_slug = params[1]\n csv_f = csv.DictReader(open(fname))\n cov_data = [d for d in csv_f]\n \n # process the data\n if cov_data and type_slug:\n # make an iso3 list\n iso3_data = [x[1:] for x in csv.reader(open(CSV_PATH + 'country_region.csv'))]\n iso3_list = []\n for r in iso3_data:\n iso3_list += r\n\n # make a region list\n region_list = [x[0] for x in csv.reader(open(CSV_PATH + 'country_region.csv'))]\n\n # make a region_country_dict\n region_country_dict = {}\n for x in csv.reader(open(CSV_PATH + 'country_region.csv')):\n region_country_dict[x[0]] = x[1:]\n\n # check the cov data content\n for ii, d in enumerate(cov_data):\n try:\n d['value'] = float(d[type_slug])\n except KeyError:\n print('Could not find column %s (is it spelled correctly?)' % type_slug)\n return\n except ValueError:\n print('Could not interpret value for %s in line %d' % (type_slug, ii+2))\n return\n\n if d.has_key('year'):\n try:\n d['year'] = int(d['year'])\n except ValueError:\n print('Could not interpret year in line %d' % (ii+2))\n return\n else:\n d['year'] = gbd.fields.ALL_YEARS\n \n d['sex'] = d.get('sex', '')\n if not d['sex'] in ['male', 'female', 'total', '']:\n print('Could not interpret sex in line %d' % (ii+2))\n return\n\n if region_only == 'Y' and not d.has_key('region'):\n print('Could not find column region (is it spelled correctly?)')\n return\n\n if not d.has_key('iso3') and not d.has_key('region'):\n print('Could not find either column iso3 or column region (is it spelled correctly?)')\n return\n\n if d.has_key('iso3') and not d['iso3'] in iso3_list:\n print('Could not interpret iso3 in line %d' % (ii+2))\n return\n\n if d.has_key('region') and not d['region'] in region_list:\n print('Could not interpret region in line %d' % (ii+2))\n return\n\n if d.has_key('iso3') and d.has_key('region') and d['iso3'] not in region_country_dict[d['region']]:\n print('The iso3 and the region are inconsistent in line %d' % (ii+2))\n return\n \n if d.has_key('age'):\n try:\n int(d['age'])\n except ValueError:\n print('Could not interpret age in line %d' % (ii+2))\n return\n\n # get or create the CovariateType object\n cov_type, is_new = CovariateType.objects.get_or_create(slug=type_slug, defaults={'year_start': 0, 'year_end': 0})\n\n # get infomation\n uploader = ''\n while uploader == '':\n uploader = raw_input('Please enter your DisMod username. ')\n cov_type.uploader = uploader\n\n source = ''\n while source == '':\n source = raw_input('Where did you get this covariate data file? ')\n cov_type.source = source\n\n description = ''\n while description == '':\n description = raw_input('Please enter a description, how the data was created, and how the missing values were filled in. ')\n cov_type.description = description\n\n year_start = ''\n while year_start == '':\n year_start = raw_input('Please enter the starting year of the covariate. ')\n try:\n int(year_start)\n except ValueError:\n year_start = ''\n cov_type.year_start = year_start\n\n year_end = ''\n while year_end == '':\n year_end = raw_input('Please enter the ending year of the covariate. ')\n try:\n int(year_end)\n except ValueError:\n year_end = ''\n cov_type.year_end = year_end\n\n if region_only == 'Y':\n cov_type.region_only = True\n else:\n cov_type.region_only = False\n\n # calculate mean and variance\n vals = [d['value'] for d in cov_data]\n cov_type.mean = pl.mean(vals)\n scale = pl.std(vals)\n cov_type.variance = scale * scale\n\n cov_type.last_modified_time = datetime.now()\n\n # save covariate type \n cov_type.save()\n\n # add cov data to cov objects\n print \"Adding covariate data from %s\" % fname\n save_covariates(cov_type, cov_data)","title":""},{"docid":"d03b31a3d5febd9191f22f7528214ce8","score":"0.5650011","text":"def main():\n\n opts = _get_parser() # get options from command line\n\n if opts.i[-4:] != '.csv':\n raise IOError('Input file is not a csv')\n\n if opts.o != 'none':\n if opts.o[-4:] != '.csv':\n raise IOError('Output file is not a csv')\n\n process_csv(opts.i, opts.o, opts.s)","title":""},{"docid":"d88e4a0f32b900db51d3551c2c1522bb","score":"0.56272006","text":"def search_and_process_data( data ):\n target= search_place( data )\n if target is not None:\n print( target )\n print()\n print( \" (1) SELECT this listing for Great Circle calculation\" )\n print( \" (2) EDIT Listing\" )\n print( \" (3) DELETE Listing\" )\n print()\n print( \" (0) RETURN to menu\" )\n z= input( \"CHOICE? \" )\n if z == \"1\":\n pass # Need to switch to pathfind with this city in tow.\n elif z == '2':\n remove_data( target, data )\n add_data( data )\n elif z == '3':\n confirm= ''\n while confirm not in ('y', 'n'):\n confirm= input( \" Are you SURE you want to delete this place? (y/n) \" )\n if confirm == 'y':\n remove_data( target, data )","title":""},{"docid":"d69db5f3f94c45aa1ee489b10278fee2","score":"0.5622514","text":"def readCsv(self, typ: str, names: List[str]) -> str:\n settings = QSettings()\n if settings.contains('/QSWATPlus/LastInputPath'):\n path = str(settings.value('/QSWATPlus/LastInputPath'))\n else:\n path = ''\n if typ == 'plant' or typ == 'urban' or typ == 'usersoil':\n caption = 'Choose {0} csv file'.format(typ)\n else:\n caption = 'Choose {0} lookup csv file'.format(typ)\n caption = QSWATUtils.trans(caption)\n filtr = FileTypes.filter(FileTypes._CSV)\n csvFile, _ = QFileDialog.getOpenFileName(None, caption, path, filtr)\n if csvFile is not None and os.path.isfile(csvFile):\n settings.setValue('/QSWATPlus/LastInputPath', os.path.dirname(str(csvFile)))\n return self.readCsvFile(csvFile, typ, names)\n else:\n return '';","title":""},{"docid":"7836e79c00121b27fc9f7fac216ffa4a","score":"0.557361","text":"def get_batch_searches_csv(app: Flask, batch_searches: dict) -> dict:\n # read csv + prep for search tests\n filename = app.config['FILE_NAME']\n with open(f'csvs/{filename}', 'r') as csvfile:\n reader = csv.DictReader(csvfile)\n # organize searches by time/criteria + split into exact/similar results\n batch_searches = parse_results(batch_searches, reader, False)\n return batch_searches","title":""},{"docid":"24eccf8bd2faa27b6fcbf9310371f3eb","score":"0.5570384","text":"def test_csv_file_selection(self):","title":""},{"docid":"cbb4d4d2f54f6c0584d53aaf11b3fe79","score":"0.5563974","text":"def search():\n\n data = None\n with open(INDEX_FILE_DIR, 'rb') as f:\n data = pickle.load(f)\n\n while True:\n query = input('入力：')\n\n if query == '':\n break\n\n print('出力')\n results = query_data(search_term=query, data=data)\n for row in results:\n print(','.join(['\"{}\"'.format(value) for value in row]))","title":""},{"docid":"dd07bedf6243c0b846998b86d1cdb752","score":"0.5519876","text":"def main(csv_file_path):\n\n obp_logger.info(\"Start Main\")\n obp_logger.debug(\"csv_file_path is: %s\" % csv_file_path)\n obp_logger.debug(\"Check that csv_file_path is valid path\")\n check_for_existing_csv(csv_file_path)\n\n obp_logger.debug(\"Start parse_row_of_csv\")\n parse_row_of_csv(csv_file_path)","title":""},{"docid":"e319c858fd77da5bd5bb3ea0660bd35d","score":"0.55111814","text":"def search():\n parent = request.args.get(\"number\").replace(\".\", \"\")\n results = []\n\n # If no input, automatically search by section\n if len(parent) == 0:\n with open(\"HTS_Sections.csv\", newline = '', encoding = \"latin-1\") as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n results.append('<button type=\"button\" class=\"list-group-item list-group-item-action\"'\n + 'onClick=\"sectionSearch(' + \"'\" + row[\"hts_number\"] + \"'\" + ')\"' + '>'\n + row[\"hts_number\"] + \" - \" + row[\"Parsed Description\"] + '</button>')\n\n # If not complete code, search by chapter\n elif len(parent) < 2:\n with open(\"HTS_Chapters.csv\", newline = '', encoding = \"latin-1\") as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n if row[\"hts_number\"][:len(parent)] == parent:\n results.append(constructButton(row))\n\n # Otherwise search the master code sheet\n else:\n with open(\"HTS_Data.csv\", newline = '', encoding = \"latin-1\") as csvfile:\n reader = csv.DictReader(csvfile)\n\n if len(parent) < 4:\n for row in reader:\n if row[\"Indent\"] == \"0\" and parent == str(row[\"hts_number\"])[:len(parent)]:\n results.append(constructButton(row))\n else:\n # Detect if code is complete (minus parents)\n if len(parent) % 2 == 0:\n # If so, find all whose direct parent is code\n for row in reader:\n if row[\"Direct Parent\"].replace(\".\", \"\") == parent:\n # Account only for non-blank codes\n if row[\"hts_number\"] != \"\":\n results.append(constructButton(row))\n # If not, return all codes whose direct parent is 1 digit longer using hts_number\n else:\n for row in reader:\n # First check if direct parent starts the same\n tempCode = row[\"hts_number\"].replace(\".\", \"\")\n if parent == tempCode[:len(parent)]:\n # Then check if direct parent is correct length\n if len(tempCode) == len(parent) + 1:\n # Account only for non-blank codes\n if row[\"hts_number\"] != \"\":\n results.append(constructButton(row))\n return jsonify(results)","title":""},{"docid":"af8b48df93467c00da86d60f65fb1964","score":"0.54899895","text":"def csv_organisations(csv_path: click.Path):\n pass","title":""},{"docid":"a5602209aabac3144470a89858b27be8","score":"0.5460069","text":"def main(q=\"\"):\n\n search = q.decode('utf-8')\n num_delims = search.count(alfred_delim)\n searchsplit = search.split(alfred_delim)\n\n # If the user hasn't typed anything, give some instructions and the\n # option to open the settings menu.\n if search.strip() == \"\":\n result = [\n alp.Item(\n title = \"Search INSPIRE\",\n subtitle = \"Begin typing to search INSPIRE\",\n valid = \"no\",\n autocomplete = \"\"\n ),\n alp.Item(\n title = \"Settings\",\n subtitle = \"Change ainspire's settings\",\n valid = \"no\",\n autocomplete = \"settings\" + alfred_delim\n )\n ]\n # Settings menu.\n elif searchsplit[0] == \"settings\":\n result = settings_menu(searchsplit[1:])\n # If the search has no delimiters the user is still typing the query:\n elif num_delims == 0:\n result = typing_menu(searchsplit[0])\n # Has the string one delimiter? Then perform a regular Inspire search.\n elif num_delims == 1:\n result = inspire_search(searchsplit[0].strip())\n # Are there two delimiters? Then it's a context menu.\n elif num_delims == 2:\n result = context_menu(searchsplit[1],searchsplit[0])\n # Three delimiters? Then it's an author search menu.\n elif num_delims == 3:\n result = author_menu(searchsplit[2])\n\n return alp.feedback(result)","title":""},{"docid":"c0d17bb59afa237cbfc8a78d2628b8df","score":"0.5416616","text":"def handle(self, *args, **options):\n path = args[0]\n term_filter = ['userid','ipaddress','dateadded',\n 'datelastupdated','sectionid', 'active','complete',\n 'adminEmailSent','stamp','useridlastupdated']\n self.port_table(term_filter, '/home/pykun/4560/dbs/term.txt')","title":""},{"docid":"80aa1c49d73d3b24b59645992ddd30bb","score":"0.53945","text":"def read_files():\n if len(sys.argv) >= 4:\n # read output file name\n global out_csv\n out_csv = sys.argv[3]\n # read text file\n valid_titles = pd.read_csv(sys.argv[1], header=None, names=[\"title\"], delimiter = '\\n')\n # read csv file\n ratings = pd.read_csv(sys.argv[2])\n return valid_titles, ratings\n else:\n print(\"There must be three arguments: 2 input file names (txt and csv) and 1 output file name (csv)\")\n return None, None","title":""},{"docid":"122887e2964bdfde4f21b4dffbab157e","score":"0.5367485","text":"def evaluate_positions(self):\n \n if self.use_csv:\n if not self.csv_file:\n self.txt.setText(\"No csv file specified\")\n else:\n self.txt.setText('')\n self.run_csv_input()\n else:\n self.run_GUI_input()","title":""},{"docid":"35e0cab6058474e325508e831856a532","score":"0.5366659","text":"def search_entries(some_entries):\n search_menu = {1: \"date\",\n 2: \"date range\",\n 3: \"time spent\",\n 4: \"search title or comments\",\n 5: \"regex pattern (advanced)\",\n 6: \"main menu\"}\n print(\"Here are your search options:\")\n for item in search_menu:\n print(\"{}. {}\".format(item, search_menu[item]))\n search_type = verify_int(\"Please enter the number of your chosen search type: \")\n\n choosing = True\n while choosing:\n if search_type == 1:\n choosing = False\n dates = []\n for entry in some_entries:\n if entry.date not in dates:\n dates.append(entry.date)\n print(\"Here are the dates with entries: \")\n for index, date in enumerate(dates, 1):\n print(\"{}. {}\".format(index, datetime.datetime.strftime(date, DATE_FORMAT)))\n selection = verify_int(\"Please enter the number of the date you would like to view entries for: \")\n results = search_by_date(some_entries, dates[selection - 1])\n\n page_entries(results, some_entries)\n\n elif search_type == 2:\n choosing = False\n date_1 = verify_date(\"Please enter the beginning of your desired date range: \", DATE_FORMAT)\n date_2 = verify_date(\"Please enter the end of your desired date range: \", DATE_FORMAT)\n results = []\n for entry in some_entries:\n if date_2 >= entry.date >= date_1:\n results.append(entry)\n\n page_entries(results, some_entries)\n\n elif search_type == 3:\n choosing = False\n time_query = verify_int(\"Please enter the time spent you would like to search entries for: \")\n results = []\n for entry in some_entries:\n if entry.time == time_query:\n results.append(entry)\n\n page_entries(results, some_entries)\n\n elif search_type == 4:\n choosing = False\n search_phrase = input(\"Please enter the phrase you would like to search with: \")\n results = []\n for entry in some_entries:\n if search_phrase in entry.work_done or search_phrase in entry.comments:\n results.append(entry)\n\n page_entries(results, some_entries)\n\n elif search_type == 5:\n choosing = False\n proper_regex = False\n results = []\n while not proper_regex:\n regex = input(\"Please enter the regex pattern you would like to search by: \")\n results = []\n try:\n for entry in some_entries:\n if re.search(regex, entry.work_done) or re.search(regex, entry.comments):\n results.append(entry)\n except re.error:\n print(\"Improper regex. Please check your regex and try again.\")\n else:\n proper_regex = True\n\n page_entries(results, some_entries)\n\n elif search_type == 6:\n choosing = False\n\n else:\n search_type = verify_int(\"Please select a proper menu selection (1 - 6): \")","title":""},{"docid":"fb8d8e0ff0a28ed2bbe84415767ce448","score":"0.5357014","text":"def search(self):\n e = simpledialog.askstring(title=\"Data Crawled\",\n prompt=\"What do you want to search?:\")\n self.refresh()\n x = self.db.search(e)\n self.update(x)","title":""},{"docid":"87e98505c792fca73d517400780745e1","score":"0.5352421","text":"def sectionSearch():\n parent = request.args.get(\"number\")\n results = []\n with open(\"HTS_Chapters.csv\", newline = '', encoding = \"latin-1\") as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n if row[\"Section\"] == parent:\n results.append(constructButton(row))\n return jsonify(results)","title":""},{"docid":"3f3c9d9f00f89c32576e50261490a37a","score":"0.5338798","text":"def function(choose):\n if choose == 0:\n '''Create CSV templates'''\n template()\n elif choose == 1:\n '''Create Normalization CSV File'''\n data_normalization()\n elif choose == 2:\n '''Create Combinatorial CSV File'''\n combinatorial_data()\n else:\n print('Invalid option')\n sys.exit()","title":""},{"docid":"015a648e1562d0178b13c1441ddb9d03","score":"0.5333438","text":"def search_csv_into_dictionary(dictionary_of_search_commands):\n\tdictionary_of_search_results = defaultdict(list)\n\twith open('books.csv', newline=\"\\n\") as csvbooks:\n\t\t\treader = csv.reader(csvbooks, delimiter=\",\", quotechar=\"\\\"\")\n\t\t\tfor csv_row in reader:\n\t\t\t\tfor search_category in dictionary_of_search_commands:\n\t\t\t\t\tfor search_string in dictionary_of_search_commands[search_category]:\n\t\t\t\t\t\tif search_string_is_in_row(search_category, search_string, csv_row):\n\t\t\t\t\t\t\tdictionary_of_search_results[search_string].append(csv_row)\n\treturn dictionary_of_search_results","title":""},{"docid":"33ac297c868abe94f36c05f302adb1de","score":"0.53113925","text":"def search(self):\n\n if self.combo_selectManufacture.get() == '' and self.combo_selectModel.get() == '':\n mb.showwarning('Message from system', 'Select at least one model or manufacture')\n\n else:\n # call the controller class method and get the result as list.\n searchList = OfficeStaff().searchCar(self.combo_selectModel.get(), self.combo_selectManufacture.get())\n\n # Create a temporary window to show the result as table\n tempWindow = Tk()\n tempWindow.title(\"Search Result\")\n\n # code for creating table\n # find total number of rows and columns in list\n for i in range(len(searchList)):\n for j in range(len(searchList[0])):\n tempEntry = Entry(tempWindow, width=20, fg='blue',\n font=('Arial', 16, 'bold'))\n tempEntry.grid(row=i, column=j)\n tempEntry.insert(END, searchList[i][j])\n tempWindow.mainloop()","title":""},{"docid":"d56915d71af34b5c23a0e08f014146c6","score":"0.53112775","text":"def pbn_search_clicked(self):\r\n # 获取 UI 数据\r\n file_path = self.__ln_file_path.text()\r\n file_name = self.__ln_file_name.text()\r\n\r\n # 检查参数\r\n if file_path == '':\r\n QMessageBox(QMessageBox.Warning, '缺少参数！', '请输入搜索路径！', QMessageBox.Ok, self).exec_()\r\n return\r\n if file_name == '':\r\n QMessageBox(QMessageBox.Warning, '缺少参数！', '请输入匹配条件！', QMessageBox.Ok, self).exec_()\r\n return\r\n\r\n # 判断搜索模式\r\n mode = self.__search_mode['fuzzy']\r\n if self.__rbn_reg.isChecked():\r\n mode = self.__search_mode['reg']\r\n elif self.__rbn_fuzzy.isChecked():\r\n mode = self.__search_mode['fuzzy']\r\n elif self.__rbn_precise.isChecked():\r\n mode = self.__search_mode['precise']\r\n\r\n # 大小写敏感标记\r\n I = True\r\n if self.__rbn_reg_Ino.isChecked():\r\n I = False\r\n\r\n self.__browser_result.clear()\r\n self.__browser_error.clear()\r\n self.__tabView.setTabText(0, '匹配结果(0)')\r\n self.__tabView.setTabText(1, '错误结果(0)')\r\n self.__searching = True\r\n\r\n # 开启子线程，后台深度遍历\r\n self.__thread_killer = False\r\n if self.__rbn_search_file.isChecked():\r\n self.__lab_state.setText('正在搜索......已搜索到 0 个文件')\r\n self.__sub_thread_search = Thread(target=self.search_from_filename, args=(file_path, file_name, mode, I))\r\n self.__sub_thread_search.start()\r\n else:\r\n self.__lab_state.setText('正在搜索......')\r\n self.__sub_thread_search = Thread(target=self.search_from_content, args=(file_path, file_name, mode, I))\r\n self.__sub_thread_search.start()\r\n\r\n # 开启子线程，显示搜索结果\r\n self.__sub_thread_show_result = Thread(target=self.show_search_result)\r\n self.__sub_thread_show_result.start()\r\n\r\n # 开启子线程，显示错误结果\r\n self.__sub_thread_show_error = Thread(target=self.show_error_result)\r\n self.__sub_thread_show_error.start()\r\n\r\n # self.__pbn_search_file.setEnable(False)\r\n # self.__pbn_search_content.setEnable(False)\r","title":""},{"docid":"e4d8a5c4fa815ac6924fee274aebff9d","score":"0.5302967","text":"def Search(self, path):\n pass","title":""},{"docid":"828569f7af402570194a8e43926b92d7","score":"0.53010553","text":"def search(snippet, filename):\n logging.info(\n \"Searching {} for a snippet matching '{}'\".format(filename, snippet))\n logging.debug(\"Opening file\")\n with open(filename, \"r\") as f:\n reader = csv.reader(f, delimiter=\",\")\n for row in reader:\n if row[1] == snippet:\n return row[0], snippet","title":""},{"docid":"371168d735acc07f1969c5eabb19d846","score":"0.52941626","text":"def read_csv_input(self):\n logging.info('INICIANDO: leitura do arquivo csv com os resultados e resultados esperados')\n expected_results = {}\n results = {}\n with open(\"result/resultados_esperados.csv\", newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n if int(row['QueryNumber']) in expected_results:\n expected_results[int(row['QueryNumber'])].append((int(row['DocNumber']), int(row['DocVotes'])))\n else:\n expected_results[int(row['QueryNumber'])] = [(int(row['DocNumber']), int(row['DocVotes']))]\n with open(self.results_file, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader: \n doc_ranking = row['RankingDoc'].replace('[', '').replace(']', '').split(\",\")\n ranking = int(doc_ranking [1])\n score = float(doc_ranking[2])\n if int(row['QueryNumber']) in results:\n results[int(row['QueryNumber'])].append((ranking, score))\n else:\n results[int(row['QueryNumber'])]=[(ranking, score)] \n logging.info('FINALIZADO: leitura do arquivo csv com os resultados e resultados esperados')\n return expected_results, results","title":""},{"docid":"eb03fd908a375f3846a6abc96d03082a","score":"0.5293505","text":"def main():\n message = input('Please enter your query:\\n')\n where = which_where(message)\n select = select_message(message)\n squeal.print_csv(select)\n \n while message != '': \n message = input('Please enter your query:\\n')\n if query != '':\n where = which_where(message)\n select = select_message(message)\n squeal.print_csv(select)\n else:\n pass","title":""},{"docid":"6d5ce88d12685550956742c4d35a1b25","score":"0.5281954","text":"def searchFile(self):\n \"\"\"Update text box with path value\"\"\"\n def test(string, expression):\n test = False\n if string in expression:\n test = True\n return test \n validFormat = \"xls\"\n file = QtGui.QFileDialog.getOpenFileName(None, 'Open file')\n \"\"\"Valid file format\"\"\"\n isValid = test(validFormat, file)\n if not isValid or isValid == \"\" :\n file = \"Please, select valid file !\"\n \"\"\"Update text box with path value\"\"\"\n return self.dlg.pathTpl.setText(file)","title":""},{"docid":"33b02a49f74bd139e6d35f98b6bf4f59","score":"0.5273229","text":"def get_filters():\r\n print('Hello! Let\\'s explore some US bikeshare data! \\n')\r\n # Get user input for city (chicago, new york city, washington).\r\n # Collecting only a valid answer for city input using while loop, lower case answers acceptable\r\n city = input('Choose a city from: Chicago, New York City or Washington: ')\r\n city = city.lower();\r\n # Validate input to match file name with while loop\r\n while city not in ['chicago', 'new york city', 'washington']:\r\n print ('You may have a typo or an invalid city, please try again.')\r\n city = input('Choose a city from: Chicago, New York City or Washington: ')\r\n city = city.lower()\r\n\r\n # Get user input for month (all, january, february, ... , june)\r\n # Collecting only a valid answer for month input using while loop, lower case answers acceptable\r\n month = input('Choose a month from: January, February, March, April, May, June or choose All: ')\r\n month = month.lower();\r\n # Validate input to match file name with while loop\r\n while month not in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:\r\n print ('You may have a typo or an invalid month, please try again.')\r\n month = input('Choose a month from: January, February, March, April, May, June or choose All: ')\r\n month = month.lower()\r\n\r\n # Get user input for day of week (all, monday, tuesday, ... sunday)\r\n # Collecting only a valid answer for day input using while loop, lower case answers acceptable\r\n day = input('Choose a day of the week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or choose All: ')\r\n day = day.lower();\r\n # Validate input to match file name with while loop\r\n while day not in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']:\r\n print ('You may have a typo or an invalid selection, please try again.')\r\n day = input('Choose a day of the week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or choose All: ')\r\n day = day.lower()\r\n # Printing the selection made by the user\r\n print(\"\\nGreat, thanks for choosing this combination, City: {}, Month: {} and Day: {}!\".format(city.title(),month.title(),day.title()))\r\n\r\n print('-'*40)\r\n return city, month, day","title":""},{"docid":"a18212da8e11bf73fc86ad9fbc923bd6","score":"0.5271173","text":"def read_file():\n # usage statement, with optional flags for header and delimiter \n # (limited to common ones for csv)\n usage = (\"usage: \" + sys.argv[0] + \" csvfile [-h] [-d <p | s | sc | t>)]\"\n \"\\n\\t-h: csv file contains header (default is no header)\"\n \"\\n\\t-d p | s | sc | t: delimiter as p (pipe), s (space), sc (semicolon) or t (tab)\"\n \"\\n\\t(default delimiter is comma)\")\n\n # Checks if file is provided. If not, program is aborted.\n if len(sys.argv) < 2:\n raise(SystemExit)(f\"csv file must be provided in the command line.\"\n f\"\\n{usage}\")\n\n # Checks if file exists and is csv. If not, program is aborted.\n try:\n filename = sys.argv[1]\n if filename.endswith('.csv'):\n csv_file = open(filename, 'r', encoding = 'utf-8')\n\n # Process optional flags in command line for header and/or delimiter\n opts = [opt for opt in sys.argv[1:] if opt.startswith(\"-\")]\n args = [arg for arg in sys.argv[1:] if not arg.startswith(\"-\")]\n\n header = ('y' if \"-h\" in opts else 'n')\n\n try:\n # Process argument for flag -d\n if \"-d\" in opts:\n if args[1] == 'p':\n separator = '|'\n elif args[1] == 's':\n separator = ' '\n elif args[1] == 'sc':\n separator = ';'\n elif args[1] == 't':\n separator = '\\t'\n else:\n separator = ','\n except:\n # Program is aborted if flag -d was not followed with valid delimiter\n raise(SystemExit)(f\"-d: No valid delimiter provided\"\n f\"\\n{usage}\")\n else:\n raise(SystemExit)(f\"{filename}: No csv file\"\n f\"\\n{usage}\")\n except FileNotFoundError:\n raise(SystemExit)(f\"{filename}: No such file or directory\"\n f\"\\n{usage}\")\n return csv_file, separator, header","title":""},{"docid":"7f41a2e5fe7846f176d0ecb7c1c8d0bc","score":"0.5250878","text":"def onSelectResultsFile(self):\n if not self.resultsFileDialog:\n self.resultsFileDialog = qt.QFileDialog(self.parent)\n self.resultsFileDialog.options = self.resultsFileDialog.DontUseNativeDialog\n self.resultsFileDialog.acceptMode = self.resultsFileDialog.AcceptOpen\n self.resultsFileDialog.defaultSuffix = \"csv\"\n self.resultsFileDialog.setNameFilter(\"Comma Separated Values (*.csv)\")\n self.resultsFileDialog.connect(\"fileSelected(QString)\", self.onResultsFileSelected)\n self.resultsFileDialog.show()","title":""},{"docid":"416d40f96ec7f6dddabc82c7c44291e8","score":"0.52392995","text":"def tc_search(self, args, range):\n # Save the current explorer for restoration when the searcher finish\n self.expSave = self.explorers[self.selectedExplorer]\n # Replace the current explorer with a searcher and borrow its buffer\n se = searcher(self.nvim, self.expSave.buffer, self.expSave.cwd)\n se.window = self.expSave.window\n # Perfor the search with the correct parameters\n dir = self.expSave.cwd\n filePattern = args[1]\n if(len(args) > 2):\n inputPattern = args[2]\n else:\n inputPattern = ''\n se.search(dir, filePattern, inputPattern)\n self.explorers[self.selectedExplorer] = se\n self.explorers[self.selectedExplorer].draw()\n self.nvim.command('startinsert')\n self.nvim.command('normal! $')","title":""},{"docid":"47f6d4f8feeb04f339b2cda8ce0270ec","score":"0.5225744","text":"def pandas_search(team,phone_number):\n\n file,search,display = load_team_information(team)\n if os.path.exists(file):\n data = []\n dest_head = []\n df = pd.read_csv(r'%s' % file, dtype='str', sep='|', low_memory=True)\n dest_head = get_column(display, df.columns)\n m = 5 #Max 5 rows are matched with the phone_number\n n = 0\n for target_column in search:\n df_mobile = df[target_column].values.tolist()\n i = 0\n for E164_number in df_mobile:\n if str(E164_number).replace(' ', '').upper() == phone_number.upper():\n record = list(df.iloc[i, ])\n data.append(record)\n n += 1\n i += 1\n if n == m:\n return data, dest_head\n else:\n i += 1\n if n == m:\n return data, dest_head\n\n if data:\n return data, dest_head\n else:\n return [], []\n else:\n return 'The CSV path %s is not correct!' %file, []","title":""},{"docid":"30b7049ecf1057c16bc1abd860e3decc","score":"0.522262","text":"def csvConsumer(self, filename):\n\n # Open the csv file\n f = self.openWriteHandle(filename)\n delim = \",\"\n\n if self.options.control is None:\n\n # Columns for in-silico control\n if self.options.methylFraction:\n cols = [\"refName\", \"tpl\", \"strand\", \"base\", \"score\", \"tMean\", \"tErr\", \"modelPrediction\", \"ipdRatio\", \"coverage\", FRAC, FRAClow, FRACup]\n else:\n if self.options.useLDA:\n # FIXME: For testing LDA model, to look at LDA scores in csv output (run without --methylFraction or --control):\n cols = [\"refName\", \"tpl\", \"strand\", \"base\", \"score\", \"tMean\", \"tErr\", \"modelPrediction\", \"ipdRatio\", \"coverage\", \"Ca5C\"]\n else:\n cols = [\"refName\", \"tpl\", \"strand\", \"base\", \"score\", \"tMean\", \"tErr\", \"modelPrediction\", \"ipdRatio\", \"coverage\"]\n\n else:\n # Columns for case-control\n if self.options.methylFraction:\n cols = [\"refName\", \"tpl\", \"strand\", \"base\", \"score\", \"pvalue\", \"caseMean\", \"controlMean\", \"caseStd\", \"controlStd\", \"ipdRatio\", \"testStatistic\", \"coverage\", \"controlCoverage\", \"caseCoverage\", FRAC, FRAClow, FRACup]\n else:\n cols = [\"refName\", \"tpl\", \"strand\", \"base\", \"score\", \"pvalue\", \"caseMean\", \"controlMean\", \"caseStd\", \"controlStd\", \"ipdRatio\", \"testStatistic\", \"coverage\", \"controlCoverage\", \"caseCoverage\"]\n\n # Special cases for formatting columns of the csv\n handlers = dict()\n threeF = lambda x: \"%.3f\" % x\n\n handlers[\"refName\"] = lambda x: \"\\\"%s\\\"\" % x\n\n handlers[\"tpl\"] = lambda x: str(x.item() + 1)\n handlers[\"score\"] = lambda x: \"%d\" % x\n\n handlers[\"tMean\"] = threeF\n handlers[\"modelPrediction\"] = threeF\n handlers[\"caseMean\"] = threeF\n handlers[\"controlMean\"] = threeF\n handlers[\"ipdRatio\"] = threeF\n handlers[\"pvalue\"] = lambda x: \"%.3e\" % x\n\n handlers[\"controlStd\"] = threeF\n handlers[\"controlStd\"] = threeF\n handlers[\"tErr\"] = threeF\n\n # FIXME: remove this line later:\n handlers[\"Ca5C\"] = threeF\n\n handlers[FRAC] = threeF\n handlers[FRAClow] = threeF\n handlers[FRACup] = threeF\n\n print >>f, delim.join(cols)\n\n def fmt(rowData, colName):\n if not rowData.has_key(colName):\n return \"\"\n\n if handlers.has_key(colName):\n return handlers[colName](rowData[colName])\n else:\n return str(rowData[colName])\n\n try:\n while True:\n # Pull a list of record in from the producer\n itemList = (yield)\n\n for item in itemList:\n values = [fmt(item, col) for col in cols]\n print >>f, delim.join(values)\n\n except GeneratorExit:\n f.close()\n return\n except Exception as e:\n print e","title":""},{"docid":"64d43479791909b770278ecf76dac402","score":"0.5217452","text":"def csv_file(self) -> None:\r\n self.create_parameters(self.type_checker(self.file[self.nft]))","title":""},{"docid":"658ac8d51245fb54784a5ed0ada1435b","score":"0.5216351","text":"def run(self):\n # show the dialog\n self.dlg.show()\n \"\"\"\"To connect event to gui elements\"\"\"\n cbGC = self.dlg.comboGC\n cbSynthese = self.dlg.comboSynthese\n cbGcId = self.dlg.idGC\n cbSyntheseId = self.dlg.idSynthese\n cbState = self.dlg.cbState\n cbOpp = self.dlg.cbOpp\n # init combo\n self.initCb(cbGC, cbGcId,cbState)\n self.initCb(cbSynthese, cbSyntheseId,cbState)\n # buttons\n self.dlg.buttonFile.clicked.connect(self.searchFile) \n self.dlg.buttonFolder.clicked.connect(self.searchFolder) \n\n '''here we need to load opportunity list wehen user select id field to get opp values''' \n for el in [cbGcId, cbSyntheseId, cbState] :\n el.currentIndexChanged.connect(lambda: self.oppFiltering(cbGcId, cbSyntheseId, cbGC, cbSynthese, cbState, cbOpp))\n self.state = [] \n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containing pass and\n self.createFile()\n # substitute with your code.\n pass","title":""},{"docid":"755e06b90dfbd3a3aad1e35b7f555325","score":"0.5212298","text":"def linear_search(files, terms):","title":""},{"docid":"101ffd77e8750a0ee3025095cfea19b0","score":"0.52047545","text":"def handle(self, *args, **options):\n path = args[0]\n student_filter = ['userid','ipaddress','dateadded','datelastupdated',\n 'sectionid', 'useridLastUpdated', \n 'adminEmailSent','stamp',\n 'Comments_length','Comments_textIsHTML']\n self.port_table(student_filter, '/home/pykun/4560/dbs/student.txt')","title":""},{"docid":"2cd861f8d7103818e500dc82533ac376","score":"0.51907563","text":"def search_and_facet_compute_resources(cbc, menu, parser):\n print('\\n' + '-' * 21)\n print('Available criteria filters:\\n')\n print(*('appliance_uuid', 'eligibility', 'cluster_name', 'name',\n 'ip_address', 'installation_status', 'uuid', 'os_type',\n 'os_architecture'), sep='\\n')\n print('-' * 21)\n\n query = cbc.select(ComputeResource)\n\n use_filter = input('\\nWould you like to use criteria filters?: Y/n\\n')\n if use_filter.lower() in ['y', 'yes']:\n print('Each criteria accepts one or more space separated values.',\n 'Exmaple: --name ABCD --appliance_uuid 1234 5678')\n args = parser.parse_args(input('Enter criteria filters: ').split())\n if args.name:\n query.set_name(args.name)\n if args.os_type:\n query.set_os_type(args.os_type)\n if args.appliance_uuid:\n query.set_appliance_uuid(args.appliance_uuid)\n if args.cluster_name:\n query.set_cluster_name(args.cluster_name)\n if args.ip_address:\n query.set_ip_address(args.ip_address)\n if args.installation_status:\n query.set_installation_status(args.installation_status)\n if args.uuid:\n query.set_uuid(args.uuid)\n if args.os_architecture:\n query.set_os_architecture(args.os_architecture)\n if args.eligibility:\n query.set_eligibility(args.eligibility)\n\n print(*query)\n new_query = input('\\nMake anothery query? Y/n\\n')\n if new_query.lower() in ['y', 'yes']:\n search_and_facet_compute_resources(cbc, menu, parser)\n else:\n menu['0']['function_call']()","title":""},{"docid":"5e41dd2b77ab201d995f7f7a6135ec1f","score":"0.51882935","text":"def main(args):\n\n results = []\n\n # Load the provided personal finance dataset\n\n if len(args) is 1:\n # Default to preformatted.csv if no file is provided as an argument\n project_root = Path(__file__).resolve().parent.parent\n default_location = project_root/\"datasets/preformatted.csv\"\n results = process_file(default_location)\n\n elif len(args) is 2:\n location = Path(args[1])\n if location.is_file():\n results = process_file(location)\n elif location.is_dir():\n print(\"Folder provided. Please provide a file.\")\n exit()\n else:\n print(\"Please provide a file as an argument.\")\n exit()\n\n else:\n print(\"Too many arguments provided!\")\n exit()","title":""},{"docid":"59d206adf74dd17ae352ef1f4620817b","score":"0.5187672","text":"def main():\n filename = \"data.csv\"\n read(filename)","title":""},{"docid":"a3167ad26bef4b20f61cfd72a35285f6","score":"0.5180637","text":"def nameCSV():\r\n while True:\r\n # get title and strip spaces\r\n title = input('What would you like to name your CSV file?: ').strip()\r\n # check if user filled .csv (otherwise, make sure to add it)\r\n title = title.rstrip('.csv') + '.csv'\r\n # double check with file name\r\n while True:\r\n response = input(f\"Are you sure that you want to name your file '{title}'?[Y/N]\")\r\n if response.upper() == 'Y':\r\n # check for unacceptable characters\r\n check = unacceptable_naming(title)\r\n if check[1]:\r\n print(f'Your file name contained illegal characters and has been renamed to the following: '\r\n f'{check[0]}')\r\n # notify before overriding\r\n if os.path.exists(check[0]):\r\n while True:\r\n cont = input(\r\n 'The file that you are about to create will override an existing file. Are you sure that'\r\n ' you would like to continue?[Y/N]:')\r\n if cont.upper() == 'Y':\r\n return check[0]\r\n elif cont.upper() == 'N':\r\n break\r\n else:\r\n print(f\"'{response}' is an invalid input. Please type Y or N.\")\r\n break\r\n else:\r\n return check[0]\r\n elif response.upper() == 'N':\r\n break\r\n else:\r\n print(f\"'{response}' is an invalid input. Please type Y or N.\")\r\n pass","title":""},{"docid":"8a3bd342393b81fa1ff75c476a418986","score":"0.517995","text":"def cli(ctx, type, view, text):\n try:\n data = ctx.tp.search(type, text)\n except Exception as ex:\n ctx.logerr(ex)\n sys.exit(1)\n else:\n if view == 'table':\n ctx.log(ctx.tp.print_result_table(data))\n elif view == 'list':\n ctx.log(ctx.tp.print_result_list(type, data))","title":""},{"docid":"d8c341690084cbeda050a32e7c2d790e","score":"0.51750344","text":"def handlerSelectLandUseLookup(self):\n file = unicode(QtGui.QFileDialog.getOpenFileName(\n self, 'Select Land Use Lookup Table', QtCore.QDir.homePath(), 'Land Use Lookup Table (*{0})'.format(self.main.appSettings['selectCsvfileExt'])))\n \n if file:\n self.lineEditLandUseLookup.setText(file)\n \n logging.getLogger(type(self).__name__).info('select file: %s', file)","title":""},{"docid":"0f31d5874ca39018df19d19f7a68c894","score":"0.5160416","text":"def main():\n file_names = [\"nysdaq.csv\", \"nyse.csv\", \"amex.csv\"]\n for file_name in file_names:\n open_file(file_name)","title":""},{"docid":"4b73293125bb4672717d399ad30af864","score":"0.5154706","text":"def search(self):\n # get the searchfiles (now a single walkthrough)\n searchfiles = self.get_search_files()\n if self.settings.verbose:\n searchdirs = sorted(list({sf.path for sf in searchfiles}))\n log('\\nDirectories to be searched ({0}):'.format(len(searchdirs)))\n for d in searchdirs:\n log(d)\n log('\\n\\nFiles to be searched ({0}):'.format(len(searchfiles)))\n for f in searchfiles:\n log(str(f))\n log(\"\")\n\n # TODO: concurrent.futures.ProcessPoolExecutor, e.g.\n # with concurrent.futures.ProcessPoolExecutor() as executor:\n # futures = [executor.submit(<func_name>, <arg>) for _ in range(10)]\n # for f in concurrent.futures.as_completed(futures):\n # print(f.result())\n # - OR -\n # nums = list(range(10))\n # # runs in parallel but returns results in list order\n # results = executor.map(<func_name>, nums)\n for sf in searchfiles:\n self.search_file(sf)","title":""},{"docid":"b5261494d530faf65df7d2da7e74361a","score":"0.51534235","text":"def handle(self, *args, **options):\n csv_file = options['csv_file']\n\n locations_added = Location.objects.add_from_csv(csv_file)\n self.stdout.write('Process finished, {0} locations added'.format(locations_added))","title":""},{"docid":"fda667108c788e0286f0213092816b5d","score":"0.51533115","text":"def read_csv(self):\n try:\n self.csvName + 'hello'\n except TypeError:\n print('Input file name must be a String type')\n logging.warning('Input file entered was not a String type')\n raise TypeError('Input file entered was not a String type')\n return None\n\n try:\n pd.read_csv(self.csvName)\n except FileNotFoundError:\n print('No file with given filename found')\n logging.debug('No file with given filename found')\n raise FileNotFoundError('No file with given filename found')\n return None\n\n headers = ['Time', 'Voltage']\n df = pd.read_csv(self.csvName, names=headers)\n self.csvDf = df","title":""},{"docid":"174b902d4c0c48749bf6d71c360217a8","score":"0.514914","text":"def get_list_from_csv(csv_file, search_key, for_values, get_key):\n result = []\n with open(csv_file) as c_fh:\n reader = csv.DictReader(c_fh)\n for row in reader:\n if row[search_key] in for_values:\n result.append(row[get_key])\n return result","title":""},{"docid":"cad76e315ab9dafcbb10e154278753a0","score":"0.5129369","text":"def search(self):\n raise NotImplementedError","title":""},{"docid":"1f721358650e921b91407ed4dd468f83","score":"0.51280946","text":"def goto():\n i = int(input('which question you want to go: '))\n os.system(\"cls\" if os.name == \"nt\" else \"clear\")\n with open(r'./quiz_wise_questions/'+quiz_number+'.csv', 'r') as file:\n reader = csv.reader(file)\n row = list(reader)\n print(\"Roll No- \", rollno)\n print(\"Name- \", Name)\n print(\"Goto Question: (Press - Ctrl+Alt+g)\")\n print(\"Final Submit: (Press Ctrl+Alt+f)\")\n print(\"Export Database Into CSV: (Press Ctrl+Alt+e)\")\n print(\"Ques No \", row[i][0], sep=':-')\n print(row[i][1], \" ?\")\n print(\"Option 1) \", row[i][2])\n print(\"Option 2) \", row[i][3])\n print(\"Option 3) \", row[i][4])\n print(\"Option 4) \", row[i][5])\n print(\"Credits if correct option: \", row[i][7])\n print(\"Negative Marking: \", row[i][8])\n print(\"Is Compulsory: \", row[i][9])\n print(\"Press esc to continue!!\")\n pass","title":""},{"docid":"8edaed5bfbe9b490ddb606bcb5d56605","score":"0.51276356","text":"def readCsv():\n # Read the job offers CSV file and fill a list with all its content\n jobOfferList = [] # Full list of offers\n with open(CSV_ROOT + 'technical-test-jobs.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=DELIMITER)\n header = True\n for row in csv_reader:\n if header: # Do not read the header\n header = False\n else:\n job = jobOffer.JobOffer(row[0], row[1], row[2], row[3], row[4]) # Create a JobOffer object\n jobOfferList += [job] # Add it to the full list\n \n # Read the job types CSV file and fill a list with all its content\n professionList = []\n with open(CSV_ROOT + 'technical-test-professions.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=DELIMITER)\n header = True\n for row in csv_reader:\n if header: # Do not read the header\n header = False\n else:\n job = profession.Profession(row[0], row[1], row[2]) # Create a JobType object\n professionList += [job] # Add it to the list\n \n return ([jobOfferList, professionList])","title":""},{"docid":"74b7e82c8fdc54a5d8a86c29e1799919","score":"0.51267004","text":"def search_string_is_in_row(search_category, search_string, csv_row):\n\tcsv_title_index = 0; csv_year_index = 1; csv_author_index = 2\n\tif search_category == csv_title_index: \n\t\treturn title_is_in_row(search_string, csv_row[csv_title_index])\n\telif search_category == csv_year_index:\n\t\treturn row_year_is_in_bounds(search_string, csv_row[csv_year_index])\t\n\telif search_category == csv_author_index:\n\t\treturn author_is_in_row(search_string, csv_row[csv_author_index])\n\treturn False","title":""},{"docid":"425a8238f68f3ef4ef571060498c52b9","score":"0.5123759","text":"def get_filters():\n\tprint('Hello! Let\\'s explore some US bikeshare data!\\n')\n\n\tcity = input('Please choose a city to analyze\\n')\n\tcity = city.lower()\n\twhile city != 'chicago' and city != 'washington' and city != 'new york city':\n\t\tcity = input('Please choose a valid city: either chicago, washington or new york city\\n')\n\t\tcity = city.lower()\n\telse:\n\t\tprint('Okay great! We\\'re going to look at stats for', city.capitalize(), '\\n')\n\n\t# next input month by name and we'll do the indexing in the load_data section\n\n\tmonth = input('Please enter a month from January to June to analyze, or type \"all\" to view all months.\\n')\n\tlist_of_valid_months = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\n\tmonth = month.lower()\n\twhile month not in list_of_valid_months:\n\t\tmonth = input('Please choose a valid month, January though June, or \\'all\\'\\n')\n\t\tmonth = month.lower()\n\t\tif month == 'all':\n\t\t\tprint('Great, you\\'ve selected all months\\n')\n\t\telif month in list_of_valid_months:\n\t\t\tprint('Great, you\\'ve selected', month.capitalize())\n\t\t\tbreak\n\telse:\n\t\tprint('Great, you\\'ve selected', month.capitalize())\n\n\tday = input('Please choose a day of the week to analyze or type \"all\"\\n')\n\tlist_of_valid_days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\n\tday = day.lower()\n\twhile day not in list_of_valid_days:\n\t\tday = input('Please choose a valid day of the week or type \\'all\\'\\n')\n\t\tday = day.lower()\n\t\tif day == 'all':\n\t\t\tprint('Great you have selected all days of the week')\n\t\telif day in list_of_valid_days:\n\t\t\tprint('Great, you\\'ve selected', day.capitalize())\n\telse:\n\t\tprint('\\n')\n\t\tprint('Great, you\\'ve selected', day.capitalize())\n\n\tprint('-'*40)\n\treturn city, month, day","title":""},{"docid":"8a468da6925ca97fb05a71ecc7c0ce1f","score":"0.5113391","text":"def extract_info(self, csv_file, request):\r\n data = pd.read_csv(csv_file)\r\n # Remove \"dontcare\" coded inputs\r\n request = {k:v for k,v in request.items() if v != 'dontcare'}\r\n options = {}\r\n for pref_type in request:\r\n # All the different unique options in a column\r\n options[pref_type] = data[pref_type].dropna().unique()\r\n for pref_type in request:\r\n data = self.__modify_data(data, pref_type, options, request[pref_type])\r\n # Shuffle the data\r\n data = data.sample(frac=1)\r\n # Prettify\r\n capt = lambda m: \" \".join([word.capitalize() for word in m.split(\" \")])\r\n data[\"restaurantname\"] = data[\"restaurantname\"].apply(capt)\r\n data[\"phone\"] = data[\"phone\"].apply(self.__fix_phone_number)\r\n data[\"addr\"] = data[\"addr\"].apply(self.__add_comma)\r\n # Can't capitalize a float\r\n data[\"addr\"] = data[\"addr\"].apply(lambda m: capt(m) if isinstance(m, str) else np.nan)\r\n return data","title":""},{"docid":"2c25e42fde20d90e918af13728b72488","score":"0.51049495","text":"def search(self, entry, types):\n # Clearing the results dictionary\n self.__results = {}\n # Iterating through the types in the passed variable 'types'\n for atype in types:\n IDList = []\n for record in self.__entities[atype]:\n # If the user did not input any text in the search bar then return all records of atype\n if entry == \"\":\n IDList.append(record)\n else:\n # Get the list of search related attributes from 'record'\n searchList = self.__entities[atype][record].getSearch()\n # Convert this list to a string with each item separated by a comma\n key = ' '.join(searchList)\n \"\"\"If the lowercase version of the passed variable 'entry' is in the search related attributes, \n append the ID of that record to IDList\"\"\"\n if entry.lower() in key.lower():\n IDList.append(record)\n \"\"\"Create an entry in the results dictionary with key of 'atype' and value of the list of matching record \n IDs for that type\"\"\"\n self.__results[atype] = IDList\n return self.__results","title":""},{"docid":"9a30bb24c9df73e306f337d6ed0255da","score":"0.5104087","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs","title":""},{"docid":"9a30bb24c9df73e306f337d6ed0255da","score":"0.5104087","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs","title":""},{"docid":"9a30bb24c9df73e306f337d6ed0255da","score":"0.5104087","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs","title":""},{"docid":"154c872f60c6fa1b1c966eafd40f8c77","score":"0.51007843","text":"def slsearch(tablename='', outfile='', freqrange=[84, 90], species=[''], reconly=False, chemnames=[''], qns=[''], intensity=[-1], smu2=[-1], loga=[-1], el=[-1], eu=[-1], rrlinclude=True, rrlonly=False, verbose=False, logfile='\"\"', append=False):\n if type(freqrange)==float: freqrange=[freqrange]\n if type(species)==str: species=[species]\n if type(chemnames)==str: chemnames=[chemnames]\n if type(qns)==str: qns=[qns]\n if type(intensity)==float: intensity=[intensity]\n if type(smu2)==float: smu2=[smu2]\n if type(loga)==float: loga=[loga]\n if type(el)==float: el=[el]\n if type(eu)==float: eu=[eu]\n\n#\n# The following is work around to avoid a bug with current python translation\n#\n mytmp = {}\n\n mytmp['tablename'] = tablename\n mytmp['outfile'] = outfile\n mytmp['freqrange'] = freqrange\n mytmp['species'] = species\n mytmp['reconly'] = reconly\n mytmp['chemnames'] = chemnames\n mytmp['qns'] = qns\n mytmp['intensity'] = intensity\n mytmp['smu2'] = smu2\n mytmp['loga'] = loga\n mytmp['el'] = el\n mytmp['eu'] = eu\n mytmp['rrlinclude'] = rrlinclude\n mytmp['rrlonly'] = rrlonly\n mytmp['verbose'] = verbose\n mytmp['logfile'] = logfile\n mytmp['append'] = append\n pathname='file://' + xmlpath( ) + '/'\n trec = casac.utils().torecord(pathname+'slsearch.xml')\n\n casalog.origin('slsearch')\n if trec.has_key('slsearch') and casac.utils().verify(mytmp, trec['slsearch']) :\n result = task_slsearch.slsearch(tablename, outfile, freqrange, species, reconly, chemnames, qns, intensity, smu2, loga, el, eu, rrlinclude, rrlonly, verbose, logfile, append)\n\n else :\n result = False\n return result","title":""},{"docid":"7ccd4b63b38d3b33db89749cab4a6aac","score":"0.51005465","text":"def search():\n filename = request.args.get('filename')\n if filename is None:\n return render_template('search.html', filename=\"\")\n\n return render_template('search.html', filename=filename)","title":""},{"docid":"fb2235c5b08f4503ba17ddb667e14125","score":"0.5095346","text":"def run_csv_input(self):\n self.txt.setText(\"\") # clear the display\n with open(self.csv_file) as csvfile:\n # The index of each field in the CSV file\n lat_idx, lon_idx, speed_idx = None, None, None\n \n reader = csv.reader(csvfile)\n headers = next(reader, None) \n \n for field_idx in range(len(headers)): \n if 'lat' in headers[field_idx].lower(): \n lat_idx = field_idx\n elif 'lon' in headers[field_idx].lower(): \n lon_idx = field_idx\n elif 'speed' in headers[field_idx].lower():\n speed_idx = field_idx\n \n # if we can't find lat or lon in the header, exit\n if lat_idx is None or lon_idx is None:\n self.txt.setText(\"cannot find lat/lon header fields in {}\".format(self.csv_file))\n return\n \n # read the CSV input sequentially. After the first line of coordinates has been read \n # we can start calculating distances between the current line and the previous.\n past_first = False\n # all of our lists for storing information...\n lats, lons, ms, nmis, bearings, speeds, travel_times, types, errors = [], [], [], [], [], [], [], [], []\n for row in reader: \n lats.append(convert_to_dd(row[lat_idx]))\n lons.append(convert_to_dd(row[lon_idx]))\n \n if speed_idx:\n try:\n speeds.append(float(row[speed_idx]))\n except Exception as err:\n print(\"could not parse speed from row: {}\".format(row))\n speeds.append(None)\n print(err)\n \n if past_first:\n start_lat, start_lon, stop_lat, stop_lon = lats[-2], lons[-2], lats[-1], lons[-1]\n if speed_idx:\n speed = speeds[-1]\n else:\n speed = None\n distance, nmi, bearing, system, error, travel_time = self.process(start_lat, start_lon, stop_lat, stop_lon, speed)\n ms.append(distance)\n nmis.append(nmi)\n bearings.append(bearings)\n types.append(system)\n travel_times.append(travel_time)\n errors.append(error)\n else:\n past_first = True\n\n self.txt.append(f\"\\nTotal distance: {sum(nmis):.3f} nmi\")\n if travel_times:\n self.txt.append(f\"Total travel time: {get_travel_time(sum(travel_times))}\")\n \n if self.output_file:\n with open(self.output_file, 'w') as outfile:\n output_header = \"start_latitude, start_longitude, end_latitude, end_longitude, type, distance (m), distance (nmi), bearing (degrees)\"\n if speed_idx:\n output_header += \", travel time\"\n outfile.write(output_header + \"\\n\")\n for i in range(len(ms)):\n output_csv_line = f\"{lats[i]}, {lons[i]}, {lats[i+1]}, {lons[i+1]}, {types[i]}, {ms[i]:.1f}, {nmis[i]:.3f}, {bearings[i]:.2f}\"\n if speed_idx:\n output_csv_line += f\", {get_travel_time(travel_times[i])}\"\n\n outfile.write(output_csv_line + \"\\n\")","title":""},{"docid":"af1a292091f0c1a45a370eea0f573382","score":"0.5093255","text":"def search_string(self, field):\n self.display_list = []\n\n search_match = input(\"do you want to search {} for an exact match Y/[N]: \".format(field))\n if search_match.lower() == 'y':\n match = True\n else:\n match = False\n\n find = input(\"enter your search terms: \")\n\n for entry in self.active_log:\n if match:\n if re.match(find, entry[field], flags=re.IGNORECASE):\n self.display_list.append(entry)\n else:\n if re.search(find, entry[field], flags=re.IGNORECASE):\n self.display_list.append(entry)\n\n self.display()","title":""},{"docid":"1120b36df7074597a5ecd5c2aec94083","score":"0.5090677","text":"def search(self):\r\n\t\t# there should be a grammar defined and some lexer/parser.\r\n\t\t# instead of this quick-and-dirty implementation.\r\n\r\n\t\tsafeEnvDict = {\r\n\t\t\t'freeSearch': self.freeSearch,\r\n\t\t\t'extentSearch': self.extentSearch,\r\n\t\t\t'indexSearch': self.indexSearch\r\n\r\n\t\t}\r\n\t\tfor col in self._dataFrame.columns:\r\n\t\t\tsafeEnvDict[col] = self._dataFrame[col]\r\n\r\n\t\ttry:\r\n\t\t\tsearchIndex = eval(self._filterString, {\r\n\t\t\t\t\t\t\t '__builtins__': None}, safeEnvDict)\r\n\t\texcept NameError as err:\r\n\t\t\treturn [], False\r\n\t\texcept SyntaxError as err:\r\n\t\t\treturn [], False\r\n\t\texcept ValueError as err:\r\n\t\t\t# the use of 'and'/'or' is not valid, need to use binary operators.\r\n\t\t\treturn [], False\r\n\t\texcept TypeError as err:\r\n\t\t\t# argument must be string or compiled pattern\r\n\t\t\treturn [], False\r\n\t\treturn searchIndex, True","title":""},{"docid":"2a87f9fbd87860933bf8b0e9f4dc4384","score":"0.50863165","text":"def on_ok_click(self):\r\n # get line edit text\r\n export_csv_dir = self.export_csv_dir_entry.text().replace(\"\\\\\", \"/\")\r\n export_csv_name_entry_text = self.export_csv_name_entry.text()\r\n csv_ext = \".csv\" if not export_csv_name_entry_text.lower().endswith(\".csv\") else \"\"\r\n export_csv_path = os.path.join(export_csv_dir, export_csv_name_entry_text + csv_ext).replace(\"\\\\\", \"/\")\r\n\r\n dir_path_obj = Path(export_csv_dir)\r\n csv_path_obj = Path(export_csv_path)\r\n\r\n dir_exists = dir_path_obj.exists()\r\n dir_is_dir = dir_path_obj.is_dir()\r\n\r\n csv_exists = csv_path_obj.exists() and csv_path_obj.is_file()\r\n csv_format_valid = export_csv_path.lower().endswith(\".csv\") and not re.match(r'\\S*\\/ *.csv', export_csv_path)\r\n\r\n dir_valid = dir_exists and dir_is_dir\r\n csv_valid = csv_exists and csv_format_valid\r\n\r\n valid = dir_valid and csv_valid\r\n\r\n if valid:\r\n self.export_csv_dir = export_csv_dir\r\n self.export_csv_path = export_csv_path\r\n self.refresh_UI()\r\n if csv_exists:\r\n valid = display_yes_no_message(self, \"Provided .csv file already exists. Replace?\")\r\n if valid: # still\r\n if self.export_to_csv(self.export_csv_path):\r\n self.close()\r\n\r\n if not dir_valid:\r\n if not dir_exists:\r\n display_warning_message(self, \"Provided directory does not exist.\")\r\n elif not dir_is_dir:\r\n display_warning_message(self, \"Provided directory format is invalid.\")\r\n self.export_csv_dir = \"\"\r\n if not csv_valid:\r\n if dir_valid and not csv_exists and csv_format_valid and \\\r\n display_yes_no_message(self, \"Create file at \" + export_csv_path + \"?\"):\r\n try:\r\n csv_file = open(export_csv_path, \"w+\")\r\n csv_file.close()\r\n except IOError as error:\r\n display_warning_message(self, \"Failed to create provided .csv file: \" + export_csv_path)\r\n else:\r\n self.export_csv_dir = export_csv_dir\r\n self.export_csv_path = export_csv_path\r\n self.refresh_UI()\r\n if self.export_to_csv(self.export_csv_path):\r\n self.close()\r\n elif not csv_format_valid:\r\n display_warning_message(self, \"Be sure to specify a name for the .csv file.\")\r\n self.export_csv_path = \"\"\r\n\r\n self.refresh_UI()","title":""},{"docid":"a3e7e96f6c627f440d7d370dbab4b3e6","score":"0.50795966","text":"def get_filters():\n\n # static strings to be used as prompt when request for input\n city_prompt = \"\\nPlease choose city(ies) (separated by comma) would you like to see data for: Chicago, New York City, Washington\\n>> \"\n month_prompt = \"\\nPlease choose which month(s) from January to June (separated by comma) or 'all' you would like to see data for:\\n>> \"\n dow_prompt = \"\\nPlease choose the day(s) of the week from Sunday to Saturday (separated by commaa) or 'all' you would like to see data for:\\n>> \"\n\n print(\"\\n\\n\", '*' * 20, \"Hello! Let's explore some US cities (Chicago, NYC, and Washington DC)bikeshare data!\", '*' * 20, \"\\n\")\n print(\"\\n>>>> Type 'end' at any time if you would like to exit the program. <<<<\\n\")\n print('=' * 80)\n\n # Collect user input for city\n while True:\n city = str(input(city_prompt))\n city = [i.strip().lower() for i in city.split(',')]\n\n # if input is end, terminate the program\n if 'end' in city:\n print('*' * 20, \"Thank you, goodbye.\", '-' * 20)\n raise SystemExit\n else:\n if list(filter(lambda x: x in CITY_DATA.keys(), city)) == city:\n user_confirmation = validate(city, 'city(ies)')\n if user_confirmation == 'Y':\n break\n else:\n print(\"\\nLet's try this again!\")\n else:\n print(\"\\n --- Please enter valid option. ---- \\n\\n\\n\")\n\n # Collect user input for Month\n print('=' * 80)\n while True:\n month = str(input(month_prompt))\n month = [i.strip().lower() for i in month.split(',')]\n # if input is end, terminate the program\n if 'end' in month:\n print('*' * 20, \"Thank you, goodbye.\", '-' * 20)\n raise SystemExit\n elif 'all' in month:\n month = months\n user_confirmation = validate(month, 'Month')\n if user_confirmation == 'Y':\n break\n else:\n print(\"\\nLet's try this again!\")\n else:\n if list(filter(lambda x: x in months, month)) == month:\n user_confirmation = validate(month, 'Month')\n if user_confirmation == 'Y':\n break\n else:\n print(\"\\nLet's try this again!\")\n else:\n print(\"\\n --- Please enter valid option. ---- \\n\\n\\n\")\n\n # Collect user input for Day(s) of the week\n print('=' * 80)\n while True:\n day = str(input(dow_prompt))\n day = [i.strip().lower() for i in day.split(',')]\n\n # if input is end, terminate the program\n if 'end' in day:\n print('*' * 20, \"Thank you, goodbye.\", '-' * 20)\n raise SystemExit\n elif 'all' in day:\n day = weekdays\n user_confirmation = validate(day, 'Day of the Week')\n if user_confirmation == 'Y':\n break\n else:\n print(\"\\nLet's try this again!\")\n else:\n if list(filter(lambda x: x in weekdays, day)) == day:\n user_confirmation = validate(day, 'Day of the Week')\n if user_confirmation == 'Y':\n break\n else:\n print(\"\\nLet's try this again!\")\n else:\n print(\"\\n --- Please enter valid option. ---- \\n\")\n\n # Print Summary\n print('=' * 80)\n print(\"\\n\\n\", '=' * 40, \"Summary of your selection:\", '=' * 40, \"\\n\")\n print(\"City(ies): \", city, \"\\n\")\n print(\"Month(s): \", month, \"\\n\")\n print(\"Day(s) of the week: \", day, \"\\n\")\n print('=' * 100)\n return city, month, day","title":""},{"docid":"76074f2b71145444004d7e469f2629a5","score":"0.50766665","text":"def gui_ask_save_csv(output_data: list):\n while True:\n try:\n # Build a dialogue box and make sure the window comes to the front of the desktop\n root = tk.Tk()\n root.withdraw()\n root.attributes(\"-topmost\",True)\n # Ask the user for the file name using a dialogue box\n output_file = tk.filedialog.asksaveasfilename(defaultextension=\".csv\", filetypes=( (\"Comma Separated\", \"*.csv\"),(\"All Files\", \"*.*\") ))\n # Kill the tk window\n root.destroy()\n\n # Open the file and dump the contents of result_table into it\n with open(output_file, \"w\", newline=\"\") as out_file: # newline=\"\" is required with python3 to avoid a \"double newline\"\n writer = csv.writer(out_file, delimiter=\",\") \n for row in output_data:\n writer.writerow(row)\n except Exception as error:\n print(error)\n print(\"\\n :: Choose a new location :: \")\n else:\n break\n\n print(\"\\n\\nOutput file can be found here:\\n \",output_file)\n\n print(\"\\n\\n\\nPress ENTER to continue.\")\n input()","title":""},{"docid":"e98d88d90e3e66a93316614535a17d09","score":"0.5076338","text":"def quicksearch_options(button, event):\n if event.type != gtk.gdk.BUTTON_PRESS:\n return False\n menu=gtk.Menu()\n item=gtk.MenuItem(_(\"Launch search\"))\n item.connect('activate', self.do_quicksearch)\n if not self.quicksearch_entry.get_text():\n item.set_sensitive(False)\n menu.append(item)\n item=gtk.CheckMenuItem(_(\"Ignore case\"))\n item.set_active(config.data.preferences['quicksearch-ignore-case'])\n item.connect('toggled', lambda i: config.data.preferences.__setitem__('quicksearch-ignore-case', i.get_active()))\n menu.append(item)\n\n item=gtk.MenuItem(_(\"Searched elements\"))\n item.connect('activate', update_quicksearch_sources)\n menu.append(item)\n\n menu.show_all()\n menu.popup(None, None, None, 0, gtk.get_current_event_time())\n return True","title":""},{"docid":"626cefd2c8d123a8ac032f4665b02d96","score":"0.5059322","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n \n check = False \n\n while True:\n city = str(input(\"\\nChoose which cities you want to explore (chicago, new york city, washington): \").strip().lower())\n if city not in (\"chicago\", \"new york city\", \"washington\"):\n print(\"\\nSorry!That\\'s not an option. Please try again\")\n continue\n else:\n print(\"\\nGood choice! It looks like you want to see data for: '{}' \".format(city.title()))\n check_option()\n break\n # get user input for month (all, january, february, ... , june)\n \n while True:\n month = str(input(\"From JANUARY to JUNE!Type the name of the month you want to filter ? (Use commas to list more than one):\").strip().lower())\n \n if month not in (\"january\", \"february\", \"march\", \"april\", \"may\", \"june\", \"all\"):\n print(\"\\nSorry! That\\'s not an option. Please type in month name(or \\\"all\\\" to select all of them)\")\n continue\n else:\n print(\"\\nOK! Confirm that you have chosen to filter by: '{} \".format(month.title()))\n check_option()\n break\n \n while True:\n day = str(input(\"\\nChoose a day of the week and write to filter by:\").strip().lower())\n \n if day not in (\"monday\", \"tuesday\", \"wednesday\", \"thursday\", \"friday\", \"saturday\" , \"sunday\", \"all\"):\n print(\"Sorry. Please type in valid day (i.e. Saturday) or \\\"all\\\" of them to select everyday:\")\n continue \n else:\n print(\"\\nOK! Confirm that you have chosen to filter by: '{}' \".format(day.title()))\n check_option()\n break\n \n \n # get user input for day of week (all, monday, tuesday, ... sunday)\n print(\"\\nYou selected '{}' as city, '{}' as month, and '{}' as day. \\nFiltering by your choices....\".format(city.title(), month.title(), day.title()))\n print()\n print('-'*40)\n return city, month, day","title":""},{"docid":"a534c18be9d806605c037dcb3fe228d9","score":"0.50479585","text":"def search(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"7756cb6df47aacde18ce9581d95a47f4","score":"0.5045136","text":"def take_file_inputs(inPointsCSV,inStatesCSV):\n \n#check file type: https://docs.python.org/2/library/mimetypes.html\n #print \"Ingesting CSV files\"\n \n with open(WORKDIR+INSTATESCSV) as file:\n state_reader(file)\n #print \"States complete\"\n poly_valid(statesList)\n\n with open(WORKDIR+INPOINTSCSV) as file:\n point_reader(file)\n #print \"Points complete\"\n point_valid(pointsList)","title":""},{"docid":"d622273c1baec899bbc3b0e9ecad8532","score":"0.50368327","text":"def generate_search(self, req_dict):\n\n\t\tsearches = {}\n\n\t\tfor arg in self.required_get_args_choose_one:\n\t\t\tif arg in req_dict:\n\t\t\t\t(key, suffix) = self.split_arg(arg)\n\n\t\t\t\tif key in searches:\n\t\t\t\t\traise RestClientError(\"You can pass only one variation of %s in a request!\" % key)\n\n\t\t\t\tif key == 'street_number': searches[key] = StreetNumberSearch(req_dict[key + suffix])\n\t\t\t\telif key == 'postal_code': searches[key] = AccountPaySearch(key, 'POSTAL_ZIP_CODE', req_dict[key + suffix], False)\n\t\t\t\telif key == 'state_province': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\n\t\t\t\telif key == 'city': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\n\t\t\t\telif key == 'country': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\n\t\t\t\telif key == 'affiliate_name': searches[key] = AffiliateNameSearch(req_dict[key + suffix], suffix)\n\t\t\t\telif key == 'username': searches[key] = UserNameSearch(key, 'ACCOUNT_USER', 'au', 'USER_NAME', req_dict[key + suffix], suffix, True)\n\t\t\t\telif key == 'account_number': searches[key] = AccountNumberSearch(key, 'ACCOUNT', 'a', key.upper(), req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'account_did': searches[key] = DefaultSearch(key, 'DID_NUMBERS', 'dn', 'PHONE_NUMBER', req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'contact_telephone': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'email_address': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\n\t\t\t\telif key == 'affiliate_refer_code': searches[key] = DefaultSearch(key, 'ACCOUNT_SUBSCRIBE_INFO', 'asi', key, req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'mac_address': searches[key] = DefaultSearch(key, 'DEVICE_UNIT', 'du', 'DEVICE_NAME', req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'order_number': searches[key] = DefaultSearch(key, 'PRODUCT_ORDERED', 'po', key, req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'last_name': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\n\t\t\t\telif key == 'account_name': searches[key] = DefaultSearch(key, 'ACCOUNT', 'a', key, req_dict[key + suffix], suffix, 'none')\n\t\t\t\telif key == 'first_name': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\n\t\t\t\telif key == 'partner_id': searches[key] = DefaultSearch(key, 'ACCOUNT', 'a', key, req_dict[key + suffix], suffix, 'none')\n\t\t\t\telse: raise RestClientError(\"Encountered a key, %s, where the search function is undefined!\" % key)\n\n\t\tsearch_criteria = SearchCriteria()\n\n\t\tfor key in searches:\n\t\t\tsearches[key].process(search_criteria)\n\n\t\t(search_criteria.lower_bound, search_criteria.upper_bound) = self.get_bounds(req_dict[\"offset\"], req_dict[\"limit\"])\n\n\t\treturn search_criteria","title":""},{"docid":"1ee01c2f4515f3ec7a112c4bb628bbe0","score":"0.50233895","text":"def run():\r\n # running function introduction\r\n introduction()\r\n\r\n # running function web_scraper\r\n # retrieving filename\r\n filename = web_scraper()\r\n\r\n print(\"Search finished! Thank you for using the newegg.com web-scraper. \\nYour results have been written to a CSV file! \\nYour CSV file is called \" + str(filename) + \"! Your file directory is: \\n\" + os.getcwd() + \"\\\\products.csv\")","title":""},{"docid":"fa80fa408ebe8cb462f131dd6be92cb1","score":"0.50124","text":"def search(filename, term, exact, display, max, species, verbose):\n configure_logging(verbose)\n LOG = get_logger()\n LOG.info(\"Search database: {}\".format(filename))\n LOG.debug(\"Term: {}\".format(term))\n LOG.debug(\"Exact: {}\".format(exact))\n LOG.debug(\"Format: {}\".format(display))\n LOG.debug(\"Max: {}\".format(max))\n LOG.debug(\"Species: {}\".format(species))\n\n search_database.DATABASE = filename\n\n maximum = max if max >= 0 else None\n\n tstart = time.time()\n result, status = search_database.search(filename, term, species, exact, False, maximum)\n tend = time.time()\n\n LOG.debug(\"Num Results: {}\".format(result.num_results))\n count = 0\n\n if status.error:\n print(\"Error occurred: {}\".format(status.message))\n sys.exit(-1)\n\n if len(result.matches) == 0:\n print(\"No results found\")\n sys.exit()\n\n headers = [\"ID\", \"SYMBOL\", \"POSITION\", \"MATCH_REASON\", \"MATCH_VALUE\"]\n\n if display in ('tab', 'csv'):\n delim = '\\t' if display == 'tab' else ','\n print(delim.join(headers))\n for match in result.matches:\n line = list()\n line.append(match.ensembl_gene_id)\n line.append(match.symbol)\n line.append(\"{}:{}-{}\".format(match.chromosome, match.position_start, match.position_end))\n line.append(match.match_reason)\n line.append(match.match_value)\n print(delim.join(map(str, line)))\n count += 1\n if count >= max > 0:\n break\n elif display == 'json':\n tbl = []\n for match in result.matches:\n line = list()\n line.append(match.ensembl_gene_id)\n line.append(match.symbol if match.symbol else '')\n line.append(\"{}:{}-{}\".format(match.chromosome, match.position_start, match.position_end))\n line.append(match.match_reason)\n line.append(match.match_value)\n tbl.append(dict(zip(headers, line)))\n count += 1\n if count >= max > 0:\n break\n print(json.dumps({'data': tbl}, indent=4))\n else:\n tbl = []\n for match in result.matches:\n line = list()\n line.append(match.ensembl_gene_id)\n line.append(match.symbol)\n line.append(\"{}:{}-{}\".format(match.chromosome, match.position_start, match.position_end))\n line.append(match.match_reason)\n line.append(match.match_value)\n tbl.append(line)\n count += 1\n if count >= max > 0:\n break\n print(tabulate(tbl, headers))\n\n LOG.info(\"Search time: {}\".format(format_time(tstart, tend)))","title":""},{"docid":"247cbcfefc0cb91cee241d45a56958e6","score":"0.5011512","text":"def test_get_query_taxon_from_csv(): # ***Incomplete test\n ##########################\n # Arrange.\n query_filename = \"query_filename\"\n\n ##########################\n # Act.\n #x = get_query_taxon_from_csv(query_filename)\n\n ##########################\n # Assert.\n assert True == True # ***Temporary.","title":""},{"docid":"583aa1e30a2b1820e7a89a9346c83333","score":"0.5007091","text":"def get_filters():\n print('\\nHello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington)\n city_index = get_user_input('city', CITY_OPTIONS, range(1, len(CITY_OPTIONS)))\n\n # get user input for month (all, january, february, ... , june)\n month_index = get_user_input('month', MONTH_OPTIONS, range(len(MONTH_OPTIONS)))\n\n # get user input for day of week (all, monday, tuesday, ... sunday)\n day_index = get_user_input('day of week', DAY_OPTIONS, range(len(DAY_OPTIONS)))\n\n print(LINE)\n return city_index, month_index, day_index","title":""},{"docid":"2138909e7ab26b1357c522f8ecf8fed1","score":"0.5002233","text":"def openResults(filename,unusedInCsv=False):\n if(not unusedInCsv):\n # Opens the requested file and extracts the data\n with open(filename, \"r\") as file:\n reader = csv.reader(file)\n dividedParcels = list(reader)\n\n spaceList = dividedParcels[0]\n dividedParcels = dividedParcels[1:]\n return spaceList, dividedParcels\n else:\n # Opens the requested file and extracts the data\n with open(filename, \"r\") as file:\n reader = csv.reader(file)\n dividedParcels = list(reader)\n\n unusedParcels = dividedParcels[-1]\n dividedParcels = dividedParcels[1:-1]\n return unusedParcels, dividedParcels","title":""},{"docid":"c661d52e114b4b288082c9d15e93428c","score":"0.50004387","text":"def main():\n if len(sys.argv) == 1:\n run(get_Rdt_jokes())\n elif len(sys.argv) == 2:\n format = sys.argv[1].split(\".\", 1)\n if len(format) != 2 or (len(format) == 2 and format[1] != \"csv\"):\n print(\"Jokes not provided in CSV file. \")\n sys.exit()\n else:\n run(read_csv(sys.argv[1]))\n else:\n print(\"Too many command line arguments. \")\n sys.exit()","title":""},{"docid":"7cb38dbf27e9598ebf6ccee295e4fa64","score":"0.49946225","text":"def search(self, caseno=\"\", court=\"\", repno=\"\", supplier=\"\"):\n driver = self.driver\n #Click search tab\n ##driver.find_element_by_css_selector(\"span\").click()\n self.ccr_tabs(click_link=\"Search For Claims\")\n\n #Complete search fields\n if caseno:\n driver.find_element_by_id(\"caseNumber\").clear()\n driver.find_element_by_id(\"caseNumber\").send_keys(caseno)#\"T20132011\"\n if court:\n Select(driver.find_element_by_id(\"court\")).select_by_visible_text(court)#\"Basildon (461)\"\n if repno:\n driver.find_element_by_id(\"representationOrderNumber\").clear()\n driver.find_element_by_id(\"representationOrderNumber\").send_keys(repno)\n if supplier:\n driver.find_element_by_id(\"supplierID\").clear()\n driver.find_element_by_id(\"supplierID\").send_keys(supplier)\n\n #Click the search button\n driver.find_element_by_xpath(\"//input[@value='Search']\").click()\n\n #Wait for sarch results\n WebDriverWait(driver,10).until(lambda driver: \"Search Results\" in driver.page_source\n or \"No claims found\" in driver.page_source,driver)\n\n #If we've results examine them\n if \"Search Results\" in driver.page_source:\n print \"Search has results as follows:\"\n #Loop to move through any multi-page results\n keep_going = True\n while keep_going:\n #Examine dispalyed results\n self.search_results()\n #Is there a \"Next\" button\n next_buttons = [e for e in driver.find_elements_by_class_name(\"button\") if e.get_attribute(\"value\")==u\"Next\"]\n ##print \"nb\", next_buttons\n #If next button click it to advance to next page, otherwise exit loop\n if next_buttons:\n next_buttons[0].click()\n else:\n keep_going = False","title":""},{"docid":"c1837316011e29e7a87aca61cc72704a","score":"0.49846646","text":"def search_place( data ):\n target= None\n print( \" < 1 > City, Town, Province, State or Country\" )\n print( \" < 2 > Latitude and Longitude\" )\n z= input( \"CHOICE? \" )\n if z == \"1\":\n target= search_name( data )\n elif z == \"2\":\n target= search_lat_lon( data )\n return target","title":""},{"docid":"03c3c16ccc66ab0df633a71a66f9df52","score":"0.49843073","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n while True:\n try:","title":""},{"docid":"dd0e961ac28d3ced5c803803af276d90","score":"0.4975195","text":"def search_by_url(self) -> None:\n url = input(Colors.p.value + \"Please enter URL:\\n\" + Colors.end.value)\n # check if the given url is valid\n if not check_valid_url(url):\n print_err(\"Incorrect url - check yourself!\")\n self.search_by_url()\n else:\n # present play options for the chosen series\n self.__player.goto(series_num=None, url=url)\n self.play_options()","title":""},{"docid":"fcc2c4bd8878affe4a91e9048e3db511","score":"0.4974744","text":"def main():\n # takes a directory name as its command line argument\n dir_name = sys.argv[1]\n\n # creates stop_word hash table\n stop_table = HashTable()\n stop_table = import_stopwords(\"stop_words.txt\", stop_table)\n\n # create an instance of SearchEngine by passing the directory name\n search_engine = SearchEngine(dir_name, stop_table)\n\n # enter an infinite loop\n print(\"Enter 'q' to exit program\")\n print(\"Enter 's:{query}' to search\")\n while True:\n\n # prompt user for input\n raw_query = str(input(\"Enter query: \"))\n\n # if input is \"q\"\n if raw_query == \"q\":\n break\n\n elif raw_query[0:2] == \"s:\":\n search_engine.search(raw_query[2:])","title":""},{"docid":"02f766fcbfb0df68ae94fd292860cecb","score":"0.49710763","text":"def execute_search():\n tw = twitter.Twitter(CATALOG_FILENAME, STOP_WORDS_FILENAME) \n logging.info(\"[Main] Initializing ...\")\n tw.load_tweets_and_load_index()\n logging.info(\"[Main] Initialized. %s docs loaded.\", \"{:,}\".format(tw.tweets_count()))\n\n query = None\n while True:\n query = input(QUERY_INPUT_MESSAGE)\n if query == '':\n continue\n if query == 'quit':\n break\n\n search_results = tw.search_tweets(query)\n print(search_results)","title":""},{"docid":"62b78e4ea54af89a4907ffdec719aaea","score":"0.49678212","text":"def get_file_list(dir_path=\"data/simulation/sim_data/\",\n str_has=['sim'], str_inc=['0001', '0002'], ftype='.csv',\n interact=True):\n import os.path\n from os import listdir\n import tkinter\n from tkinter.filedialog import askdirectory\n root = tkinter.Tk() # this will help control file dialog boxes!\n\n # Check if the path specified includes at least 1 file of the file type\n success, err_msg = check_dir_path(dir_path, [ftype], 1, False)\n\n while not success and len(dir_path):\n # If the \"default\" directory failed the check,\n # Either raise that error, or ask for a different directory!\n # (Will exit If you X out of the dialog, to avoid getting stuck)\n if interact:\n root.lift()\n root.focus_force()\n dir_path = askdirectory(parent=root, title=err_msg,\n initialdir=dir_path)\n success, err_msg = check_dir_path(dir_path, [ftype], 1, False)\n else:\n root.destroy\n raise AssertionError(err_msg)\n if not len(dir_path):\n root.destroy()\n raise AssertionError(\"You Closed the Dialog Window Without a Folder!\")\n root.destroy()\n\n \"\"\"\n If we've gotten this far, we found files!\n So now, we will filter the list based on the parameters given, and return\n the result as a file list to open.\n \"\"\"\n full_dir = listdir(dir_path)\n files_wanted = []\n for file in full_dir:\n # For each file, decide if it passes\n for str_AND in str_has:\n # IF any of these fail, ignore the file.\n if str_AND in file:\n pass\n else:\n break\n else:\n # Only does this if all \"Required\" strings pass\n for str_OR in str_inc:\n # If ANY string is found in the file,\n # Add it to the list and then go to next file\n if str_OR in file:\n files_wanted.append(os.path.join(dir_path, file))\n break\n else:\n pass\n return tuple(files_wanted), dir_path","title":""},{"docid":"8c2478d8324046e70ce705ab562bcfd0","score":"0.49670327","text":"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')\n\n #The following loop will get the user's choice of which city to display results.\n while True:\n #New Edit gives the user more choices to pick the city to ease typing\n city = input('Would you like to see data for Chicago/Ch, New York City/NYC, or Washington/WA: ').lower()\n if city not in CITY_DATA.keys() and city not in ['ch', 'nyc', 'wa']:\n print('this city is not available, please enter one of the three provided cities')\n continue\n else:\n if city == 'ch':\n city = 'chicago'\n elif city == 'nyc':\n city = 'new york city'\n elif city == 'wa':\n city = 'washington'\n city = CITY_DATA[city]\n break\n\n #The following loop will get the user choice whether to use filters or display unfiltered results.\n while True:\n filter = input('Would you like to filter the data by month or day, or not at all? \\nPlease chose(yes/no): ').lower()\n if filter == 'yes':\n filter = True\n elif filter == 'no':\n filter = False\n else:\n print('Please enter a valid answer!')\n continue\n break\n\n\n #the following loop will get the user choice for filters: whether by month, by day, or include both.\n while True:\n if filter:\n choice = input('What filter do you want to apply? please choose (month/day/both) ').lower()\n if choice not in ['month', 'day', 'both']:\n print('Please Enter a valid answer!')\n continue\n if choice == 'month':\n month = input('Which month - January, February, March, April, May, or June? ')\n if month not in months.keys():\n print('This month is invalid, Please Try again.')\n continue\n else:\n month = months[month]\n day = days\n break\n elif choice == 'day':\n day = input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? ').lower()\n if day not in days.keys():\n print('This day is invalid. Please try again.')\n continue\n else:\n day = days[day]\n month = months\n break\n elif choice == 'both':\n month = input('Which month - January, February, March, April, May, or June? ').lower()\n if month not in months.keys():\n print('This month is invalid. Please try again')\n continue\n month = months[month]\n day = input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? ').lower()\n if day not in days.keys():\n print('This day is invalid. Please try again')\n continue\n day = days[day]\n break\n else:\n day = days\n month = months\n break\n\n #Print Separator\n print('-'*40)\n #Return chosen values\n return city, month, day","title":""},{"docid":"80ff6a6a9716a89fc825f4b27bcdf4c0","score":"0.495757","text":"def _run(self, *args, url=None):\n\n with Provider_Customers_Table() as _:\n pass\n\n url = url if url else self._get_urls()[0]\n Rel_File(self.path, self.csv_dir, url).parse_file()\n utils.delete_paths([self.csv_dir, self.path])\n\n # Fills these rov++ specific tables\n with ASes_Table(clear=True) as _as_table:\n _as_table.clear_table()\n _as_table.fill_table()\n # creates and closes table\n with AS_Connectivity_Table(clear=True) as _conn_table:\n _conn_table.fill_table()\n\n with Relationships_Table(clear=True) as db:\n db.fill_table()","title":""}],"string":"[\n {\n \"docid\": \"0f8e592987aa93a7d33fabc7963d8d61\",\n \"score\": \"0.64782333\",\n \"text\": \"def search(bot, update, args):\\n csv_file = read_csv(file)\\n user_string = update.message.text.split()\\n result = str()\\n if len(user_string) == 2:\\n for i in range(len(csv_file)):\\n csv_file[i] = csv_file[i].replace(' | ', ',')\\n old_string = csv_file[i]\\n csv_file[i] = csv_file[i].split(',')[:2]\\n elem = ','.join(csv_file[i])\\n if user_string[1] in elem:\\n result += old_string.replace(',', ' | ')\\n else:\\n continue\\n if result != str():\\n update.message.reply_text(\\n 'The book(-s) you searched for are: \\\\n' + result)\\n else:\\n update.message.reply_text('No books found on this result')\\n else: # exception\\n update.message.reply_text('Check the correction of your input!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ef49b733b4128effd829c026a08b7d\",\n \"score\": \"0.6278241\",\n \"text\": \"def search(self):\\n print(\\\"\\\"\\\" Search Menu\\n(1) Search by date.\\n(2) Search by task\\n(3) Search by duration\\n(4) Search by comment\\n(exit) type end to program at any time\\n(menu) to return to main menu\\n\\\"\\\"\\\")\\n option = input(\\\"please select an option 1, 2 or exit: \\\")\\n self.keywords(option)\\n if option == \\\"1\\\" or option == \\\"(1)\\\":\\n clear_screen()\\n self.search_date()\\n\\n elif option == \\\"2\\\" or option == \\\"(2)\\\":\\n clear_screen()\\n self.search_string('task')\\n elif option == \\\"3\\\" or option == \\\"(3)\\\":\\n clear_screen()\\n self.search_duration()\\n elif option == \\\"4\\\" or option == \\\"(4)\\\":\\n clear_screen()\\n self.search_string('comments')\\n else:\\n clear_screen()\\n \\\"please enter one of the options in brakets\\\"\\n self.keywords(option)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c816c821a33bc0006176b4a6ca87ccb8\",\n \"score\": \"0.6217566\",\n \"text\": \"def main():\\n\\tcommand_line_arguments = get_command_line_arguments()\\n\\n\\tif need_help(command_line_arguments): \\n\\t\\tprint_usage_txt()\\n\\telif invalid_year_format(command_line_arguments):\\n\\t\\tprint_invalid_year_format_error_message()\\n\\telse: \\n\\t\\tdictionary_of_search_commands = turn_arguments_into_dictionary(command_line_arguments)\\n\\n\\t\\tdictionary_of_search_results = search_csv_into_dictionary(dictionary_of_search_commands)\\n\\n\\t\\tprint_search_results(dictionary_of_search_commands, dictionary_of_search_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68abca9d2a3bb2e04b6dd0d1327c4d54\",\n \"score\": \"0.61331815\",\n \"text\": \"def main() -> None:\\n args: Dict[str, str] = read_args()\\n search_file(args[\\\"FILE\\\"], args[\\\"COLUMN\\\"], args[\\\"OPERATION\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a64a8d53764a70b4d7f2abd1e5e1c8\",\n \"score\": \"0.59435296\",\n \"text\": \"def search(self, *args):\\n\\n with open('all_melk.csv') as csv_file:\\n # reading the csv file using DictReader\\n csv_reader = csv.DictReader(csv_file)\\n\\n # converting the file to dictionary\\n # by first converting to list\\n # and then converting the list to dict\\n dict_from_csv = dict(list(csv_reader)[0])\\n\\n print(dict_from_csv['area'])\\n # it's defective\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0363100064403e37eec83df49e25618\",\n \"score\": \"0.5862175\",\n \"text\": \"def load_search():\\n city = ['FI', 'MI', 'PA', 'NA', 'BO', 'TO', 'VE', 'CA']\\n prefix = ['055', '02', '091', '081', '051', '011', '041', '070']\\n\\n # search is a control parameter.\\n # For search == 'GO' the search continues\\n # For search == 'ALT' the search stops\\n search = 'GO'\\n\\n while search == 'GO':\\n name = raw_input('data to search for: ')\\n search2(city, prefix, name)\\n search = raw_input('continue? ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a45f1d8a11b6ea7a621fb881b98a66\",\n \"score\": \"0.5856241\",\n \"text\": \"def main(argv):\\n try:\\n opts, args = getopt.getopt(argv,\\\"hf:s:c:\\\",[\\\"support_threshold=\\\",\\\"confidence_threshold=\\\"])\\n except getopt.GetoptError:\\n print ('file.csv -s <support_threshold> -c <confidence_threshold>')\\n sys.exit(2)\\n\\n for opt, arg in opts:\\n if opt == '-h':\\n print ('file.csv -s <support> -c <confidence>')\\n sys.exit()\\n elif opt in (\\\"-f\\\", \\\"--file\\\"):\\n infile = arg\\n elif opt in (\\\"-s\\\", \\\"--support\\\"):\\n support_threshold = float(arg)\\n elif opt in (\\\"-c\\\", \\\"--confidence\\\"):\\n confidence_threshold = float(arg)\\n\\n data = read_data(infile)\\n\\n final_results = [\\\"{} => {} s = {:0.2f}, c = {:0.2f}\\\".format(\\n item_set, item, support_frequency(data[1:], item_set.union({item})),\\n confidence(data[1:], item_set, {item})) for item_set, item in apriori(\\n data[1:], support_threshold, confidence_threshold)]\\n for result in final_results:\\n print(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772d062664fbc8104f8491d668016bbb\",\n \"score\": \"0.5852055\",\n \"text\": \"def do_search(self,args):\\n self.ctlr.search_cmd(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f201e401d6e09f08b6a7d5b7d75a7b4\",\n \"score\": \"0.5843696\",\n \"text\": \"def search_data():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"531d71d3f9ba6114859b85b53b7de356\",\n \"score\": \"0.5745585\",\n \"text\": \"def search(self, **kwargs):\\n with open(self.filename, 'rb') as f:\\n reader = csv.DictReader(f)\\n self.schema = reader.next()\\n\\n # Make kwargs into QueryParameter objects\\n query = self.__prepare_query(kwargs)\\n\\n return filter(lambda row: self.__matches_row(row, query=query), reader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32139c46ead12b6105705984b4e6568\",\n \"score\": \"0.5744557\",\n \"text\": \"def open_file(self):\\n self.column_checkboxes = []\\n self.selected_column_targets = []\\n file_name, filter = QFileDialog.getOpenFileName(\\n self, 'Open CSV', os.getenv('HOME'), 'CSV(*.csv)')\\n if file_name:\\n self.load_file(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702980fe874a3a33652ddda3f1152015\",\n \"score\": \"0.5732689\",\n \"text\": \"def search():\\n try:\\n corp = filter_corpus()\\n print('Finished filtering')\\n term = search_term.get()\\n is_sensitive = sensitive.get()\\n write_to_file = to_file.get()\\n if term == '':\\n messagebox.showerror('Error', 'No search term was entered. Please try again.')\\n return\\n if not is_regex.get():\\n term = words_to_regex(term)\\n if output_type.get() == 'concordance':\\n concordance_lines = corp.get_concordances(term, is_sensitive, context_len.get())\\n if write_to_file:\\n concordance_to_file(concordance_lines)\\n else:\\n show_concordances(concordance_lines, term)\\n elif output_type.get() == 'total frequency':\\n raw = corp.get_total_raw_count(term, is_sensitive)\\n norm = corp.get_normalized_count(raw)\\n if write_to_file:\\n counts_to_file(raw, norm)\\n else:\\n show_counts(raw, norm)\\n else:\\n cat = category.get()\\n freq_type = frequency_type.get()\\n print('Getting counts by category')\\n counts_by_category = corp.get_counts_by_category(term, is_sensitive, cat, freq_type)\\n if write_to_file:\\n categories_to_file(counts_by_category, cat, freq_type)\\n else:\\n show_by_category(counts_by_category, cat, freq_type)\\n except Exception as e:\\n messagebox.showerror('Error', e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7d60c294eb9b73ae68b7e06a13f0dc\",\n \"score\": \"0.56831276\",\n \"text\": \"def main(option, args):\\n print 'option:', option\\n print 'args:', args\\n if option.csv:\\n print 'csv:', option.csv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55718bb0245ef685dd7ae1c11816d88e\",\n \"score\": \"0.5680772\",\n \"text\": \"def test_stock_search(self):\\n\\n rows = read_csv_file(os.path.dirname(os.path.abspath(__file__)) + \\\"/sample.csv\\\")\\n stock_search = StockSearch()\\n\\n for row in rows:\\n stock_search.insert(row[0])\\n\\n self.assertEqual(stock_search.search(\\\"AICIXE\\\"), (True, \\\"AICIXE\\\"))\\n self.assertEqual(stock_search.search(\\\"AICIE\\\"), (False, \\\"AICIXE\\\"))\\n self.assertEqual(stock_search.search(\\\"AM\\\"), (False, \\\"AMBKP\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3ce96af31d810f8a37399f1afd4b62\",\n \"score\": \"0.566289\",\n \"text\": \"def _cmd_search(self, options, *args):\\n found = []\\n for pattern in args:\\n entries = self.phonebook.search(pattern, options.ignore_case,\\n options.regexp, options.fields)\\n # add all entries which aren't already in found. This avoids\\n # printing entries twice, which are matched by more than one\\n # pattern\\n new = filter(lambda entry: entry not in found, entries)\\n found += new\\n self.print_table(found, options.output, options.sortby,\\n options.descending)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69be548affe14f8302baa37e20b37f4\",\n \"score\": \"0.5660264\",\n \"text\": \"def handle(self, *params, **options):\\n if len(params) != 2:\\n raise CommandError('a single .csv file amd type_slug are required as input.')\\n\\n region_only = ''\\n while region_only != 'Y' and region_only != 'N':\\n region_only = raw_input('Does the covariate data file have region column and no iso3 column? Y|N ')\\n\\n # read the csv file into a list of dicts\\n fname = params[0]\\n type_slug = params[1]\\n csv_f = csv.DictReader(open(fname))\\n cov_data = [d for d in csv_f]\\n \\n # process the data\\n if cov_data and type_slug:\\n # make an iso3 list\\n iso3_data = [x[1:] for x in csv.reader(open(CSV_PATH + 'country_region.csv'))]\\n iso3_list = []\\n for r in iso3_data:\\n iso3_list += r\\n\\n # make a region list\\n region_list = [x[0] for x in csv.reader(open(CSV_PATH + 'country_region.csv'))]\\n\\n # make a region_country_dict\\n region_country_dict = {}\\n for x in csv.reader(open(CSV_PATH + 'country_region.csv')):\\n region_country_dict[x[0]] = x[1:]\\n\\n # check the cov data content\\n for ii, d in enumerate(cov_data):\\n try:\\n d['value'] = float(d[type_slug])\\n except KeyError:\\n print('Could not find column %s (is it spelled correctly?)' % type_slug)\\n return\\n except ValueError:\\n print('Could not interpret value for %s in line %d' % (type_slug, ii+2))\\n return\\n\\n if d.has_key('year'):\\n try:\\n d['year'] = int(d['year'])\\n except ValueError:\\n print('Could not interpret year in line %d' % (ii+2))\\n return\\n else:\\n d['year'] = gbd.fields.ALL_YEARS\\n \\n d['sex'] = d.get('sex', '')\\n if not d['sex'] in ['male', 'female', 'total', '']:\\n print('Could not interpret sex in line %d' % (ii+2))\\n return\\n\\n if region_only == 'Y' and not d.has_key('region'):\\n print('Could not find column region (is it spelled correctly?)')\\n return\\n\\n if not d.has_key('iso3') and not d.has_key('region'):\\n print('Could not find either column iso3 or column region (is it spelled correctly?)')\\n return\\n\\n if d.has_key('iso3') and not d['iso3'] in iso3_list:\\n print('Could not interpret iso3 in line %d' % (ii+2))\\n return\\n\\n if d.has_key('region') and not d['region'] in region_list:\\n print('Could not interpret region in line %d' % (ii+2))\\n return\\n\\n if d.has_key('iso3') and d.has_key('region') and d['iso3'] not in region_country_dict[d['region']]:\\n print('The iso3 and the region are inconsistent in line %d' % (ii+2))\\n return\\n \\n if d.has_key('age'):\\n try:\\n int(d['age'])\\n except ValueError:\\n print('Could not interpret age in line %d' % (ii+2))\\n return\\n\\n # get or create the CovariateType object\\n cov_type, is_new = CovariateType.objects.get_or_create(slug=type_slug, defaults={'year_start': 0, 'year_end': 0})\\n\\n # get infomation\\n uploader = ''\\n while uploader == '':\\n uploader = raw_input('Please enter your DisMod username. ')\\n cov_type.uploader = uploader\\n\\n source = ''\\n while source == '':\\n source = raw_input('Where did you get this covariate data file? ')\\n cov_type.source = source\\n\\n description = ''\\n while description == '':\\n description = raw_input('Please enter a description, how the data was created, and how the missing values were filled in. ')\\n cov_type.description = description\\n\\n year_start = ''\\n while year_start == '':\\n year_start = raw_input('Please enter the starting year of the covariate. ')\\n try:\\n int(year_start)\\n except ValueError:\\n year_start = ''\\n cov_type.year_start = year_start\\n\\n year_end = ''\\n while year_end == '':\\n year_end = raw_input('Please enter the ending year of the covariate. ')\\n try:\\n int(year_end)\\n except ValueError:\\n year_end = ''\\n cov_type.year_end = year_end\\n\\n if region_only == 'Y':\\n cov_type.region_only = True\\n else:\\n cov_type.region_only = False\\n\\n # calculate mean and variance\\n vals = [d['value'] for d in cov_data]\\n cov_type.mean = pl.mean(vals)\\n scale = pl.std(vals)\\n cov_type.variance = scale * scale\\n\\n cov_type.last_modified_time = datetime.now()\\n\\n # save covariate type \\n cov_type.save()\\n\\n # add cov data to cov objects\\n print \\\"Adding covariate data from %s\\\" % fname\\n save_covariates(cov_type, cov_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03b31a3d5febd9191f22f7528214ce8\",\n \"score\": \"0.5650011\",\n \"text\": \"def main():\\n\\n opts = _get_parser() # get options from command line\\n\\n if opts.i[-4:] != '.csv':\\n raise IOError('Input file is not a csv')\\n\\n if opts.o != 'none':\\n if opts.o[-4:] != '.csv':\\n raise IOError('Output file is not a csv')\\n\\n process_csv(opts.i, opts.o, opts.s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88e4a0f32b900db51d3551c2c1522bb\",\n \"score\": \"0.56272006\",\n \"text\": \"def search_and_process_data( data ):\\n target= search_place( data )\\n if target is not None:\\n print( target )\\n print()\\n print( \\\" (1) SELECT this listing for Great Circle calculation\\\" )\\n print( \\\" (2) EDIT Listing\\\" )\\n print( \\\" (3) DELETE Listing\\\" )\\n print()\\n print( \\\" (0) RETURN to menu\\\" )\\n z= input( \\\"CHOICE? \\\" )\\n if z == \\\"1\\\":\\n pass # Need to switch to pathfind with this city in tow.\\n elif z == '2':\\n remove_data( target, data )\\n add_data( data )\\n elif z == '3':\\n confirm= ''\\n while confirm not in ('y', 'n'):\\n confirm= input( \\\" Are you SURE you want to delete this place? (y/n) \\\" )\\n if confirm == 'y':\\n remove_data( target, data )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69db5f3f94c45aa1ee489b10278fee2\",\n \"score\": \"0.5622514\",\n \"text\": \"def readCsv(self, typ: str, names: List[str]) -> str:\\n settings = QSettings()\\n if settings.contains('/QSWATPlus/LastInputPath'):\\n path = str(settings.value('/QSWATPlus/LastInputPath'))\\n else:\\n path = ''\\n if typ == 'plant' or typ == 'urban' or typ == 'usersoil':\\n caption = 'Choose {0} csv file'.format(typ)\\n else:\\n caption = 'Choose {0} lookup csv file'.format(typ)\\n caption = QSWATUtils.trans(caption)\\n filtr = FileTypes.filter(FileTypes._CSV)\\n csvFile, _ = QFileDialog.getOpenFileName(None, caption, path, filtr)\\n if csvFile is not None and os.path.isfile(csvFile):\\n settings.setValue('/QSWATPlus/LastInputPath', os.path.dirname(str(csvFile)))\\n return self.readCsvFile(csvFile, typ, names)\\n else:\\n return '';\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7836e79c00121b27fc9f7fac216ffa4a\",\n \"score\": \"0.557361\",\n \"text\": \"def get_batch_searches_csv(app: Flask, batch_searches: dict) -> dict:\\n # read csv + prep for search tests\\n filename = app.config['FILE_NAME']\\n with open(f'csvs/{filename}', 'r') as csvfile:\\n reader = csv.DictReader(csvfile)\\n # organize searches by time/criteria + split into exact/similar results\\n batch_searches = parse_results(batch_searches, reader, False)\\n return batch_searches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24eccf8bd2faa27b6fcbf9310371f3eb\",\n \"score\": \"0.5570384\",\n \"text\": \"def test_csv_file_selection(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb4d4d2f54f6c0584d53aaf11b3fe79\",\n \"score\": \"0.5563974\",\n \"text\": \"def search():\\n\\n data = None\\n with open(INDEX_FILE_DIR, 'rb') as f:\\n data = pickle.load(f)\\n\\n while True:\\n query = input('入力：')\\n\\n if query == '':\\n break\\n\\n print('出力')\\n results = query_data(search_term=query, data=data)\\n for row in results:\\n print(','.join(['\\\"{}\\\"'.format(value) for value in row]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd07bedf6243c0b846998b86d1cdb752\",\n \"score\": \"0.5519876\",\n \"text\": \"def main(csv_file_path):\\n\\n obp_logger.info(\\\"Start Main\\\")\\n obp_logger.debug(\\\"csv_file_path is: %s\\\" % csv_file_path)\\n obp_logger.debug(\\\"Check that csv_file_path is valid path\\\")\\n check_for_existing_csv(csv_file_path)\\n\\n obp_logger.debug(\\\"Start parse_row_of_csv\\\")\\n parse_row_of_csv(csv_file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e319c858fd77da5bd5bb3ea0660bd35d\",\n \"score\": \"0.55111814\",\n \"text\": \"def search():\\n parent = request.args.get(\\\"number\\\").replace(\\\".\\\", \\\"\\\")\\n results = []\\n\\n # If no input, automatically search by section\\n if len(parent) == 0:\\n with open(\\\"HTS_Sections.csv\\\", newline = '', encoding = \\\"latin-1\\\") as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n results.append('<button type=\\\"button\\\" class=\\\"list-group-item list-group-item-action\\\"'\\n + 'onClick=\\\"sectionSearch(' + \\\"'\\\" + row[\\\"hts_number\\\"] + \\\"'\\\" + ')\\\"' + '>'\\n + row[\\\"hts_number\\\"] + \\\" - \\\" + row[\\\"Parsed Description\\\"] + '</button>')\\n\\n # If not complete code, search by chapter\\n elif len(parent) < 2:\\n with open(\\\"HTS_Chapters.csv\\\", newline = '', encoding = \\\"latin-1\\\") as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n if row[\\\"hts_number\\\"][:len(parent)] == parent:\\n results.append(constructButton(row))\\n\\n # Otherwise search the master code sheet\\n else:\\n with open(\\\"HTS_Data.csv\\\", newline = '', encoding = \\\"latin-1\\\") as csvfile:\\n reader = csv.DictReader(csvfile)\\n\\n if len(parent) < 4:\\n for row in reader:\\n if row[\\\"Indent\\\"] == \\\"0\\\" and parent == str(row[\\\"hts_number\\\"])[:len(parent)]:\\n results.append(constructButton(row))\\n else:\\n # Detect if code is complete (minus parents)\\n if len(parent) % 2 == 0:\\n # If so, find all whose direct parent is code\\n for row in reader:\\n if row[\\\"Direct Parent\\\"].replace(\\\".\\\", \\\"\\\") == parent:\\n # Account only for non-blank codes\\n if row[\\\"hts_number\\\"] != \\\"\\\":\\n results.append(constructButton(row))\\n # If not, return all codes whose direct parent is 1 digit longer using hts_number\\n else:\\n for row in reader:\\n # First check if direct parent starts the same\\n tempCode = row[\\\"hts_number\\\"].replace(\\\".\\\", \\\"\\\")\\n if parent == tempCode[:len(parent)]:\\n # Then check if direct parent is correct length\\n if len(tempCode) == len(parent) + 1:\\n # Account only for non-blank codes\\n if row[\\\"hts_number\\\"] != \\\"\\\":\\n results.append(constructButton(row))\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8b48df93467c00da86d60f65fb1964\",\n \"score\": \"0.54899895\",\n \"text\": \"def csv_organisations(csv_path: click.Path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5602209aabac3144470a89858b27be8\",\n \"score\": \"0.5460069\",\n \"text\": \"def main(q=\\\"\\\"):\\n\\n search = q.decode('utf-8')\\n num_delims = search.count(alfred_delim)\\n searchsplit = search.split(alfred_delim)\\n\\n # If the user hasn't typed anything, give some instructions and the\\n # option to open the settings menu.\\n if search.strip() == \\\"\\\":\\n result = [\\n alp.Item(\\n title = \\\"Search INSPIRE\\\",\\n subtitle = \\\"Begin typing to search INSPIRE\\\",\\n valid = \\\"no\\\",\\n autocomplete = \\\"\\\"\\n ),\\n alp.Item(\\n title = \\\"Settings\\\",\\n subtitle = \\\"Change ainspire's settings\\\",\\n valid = \\\"no\\\",\\n autocomplete = \\\"settings\\\" + alfred_delim\\n )\\n ]\\n # Settings menu.\\n elif searchsplit[0] == \\\"settings\\\":\\n result = settings_menu(searchsplit[1:])\\n # If the search has no delimiters the user is still typing the query:\\n elif num_delims == 0:\\n result = typing_menu(searchsplit[0])\\n # Has the string one delimiter? Then perform a regular Inspire search.\\n elif num_delims == 1:\\n result = inspire_search(searchsplit[0].strip())\\n # Are there two delimiters? Then it's a context menu.\\n elif num_delims == 2:\\n result = context_menu(searchsplit[1],searchsplit[0])\\n # Three delimiters? Then it's an author search menu.\\n elif num_delims == 3:\\n result = author_menu(searchsplit[2])\\n\\n return alp.feedback(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0d17bb59afa237cbfc8a78d2628b8df\",\n \"score\": \"0.5416616\",\n \"text\": \"def handle(self, *args, **options):\\n path = args[0]\\n term_filter = ['userid','ipaddress','dateadded',\\n 'datelastupdated','sectionid', 'active','complete',\\n 'adminEmailSent','stamp','useridlastupdated']\\n self.port_table(term_filter, '/home/pykun/4560/dbs/term.txt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80aa1c49d73d3b24b59645992ddd30bb\",\n \"score\": \"0.53945\",\n \"text\": \"def read_files():\\n if len(sys.argv) >= 4:\\n # read output file name\\n global out_csv\\n out_csv = sys.argv[3]\\n # read text file\\n valid_titles = pd.read_csv(sys.argv[1], header=None, names=[\\\"title\\\"], delimiter = '\\\\n')\\n # read csv file\\n ratings = pd.read_csv(sys.argv[2])\\n return valid_titles, ratings\\n else:\\n print(\\\"There must be three arguments: 2 input file names (txt and csv) and 1 output file name (csv)\\\")\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122887e2964bdfde4f21b4dffbab157e\",\n \"score\": \"0.5367485\",\n \"text\": \"def evaluate_positions(self):\\n \\n if self.use_csv:\\n if not self.csv_file:\\n self.txt.setText(\\\"No csv file specified\\\")\\n else:\\n self.txt.setText('')\\n self.run_csv_input()\\n else:\\n self.run_GUI_input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e0cab6058474e325508e831856a532\",\n \"score\": \"0.5366659\",\n \"text\": \"def search_entries(some_entries):\\n search_menu = {1: \\\"date\\\",\\n 2: \\\"date range\\\",\\n 3: \\\"time spent\\\",\\n 4: \\\"search title or comments\\\",\\n 5: \\\"regex pattern (advanced)\\\",\\n 6: \\\"main menu\\\"}\\n print(\\\"Here are your search options:\\\")\\n for item in search_menu:\\n print(\\\"{}. {}\\\".format(item, search_menu[item]))\\n search_type = verify_int(\\\"Please enter the number of your chosen search type: \\\")\\n\\n choosing = True\\n while choosing:\\n if search_type == 1:\\n choosing = False\\n dates = []\\n for entry in some_entries:\\n if entry.date not in dates:\\n dates.append(entry.date)\\n print(\\\"Here are the dates with entries: \\\")\\n for index, date in enumerate(dates, 1):\\n print(\\\"{}. {}\\\".format(index, datetime.datetime.strftime(date, DATE_FORMAT)))\\n selection = verify_int(\\\"Please enter the number of the date you would like to view entries for: \\\")\\n results = search_by_date(some_entries, dates[selection - 1])\\n\\n page_entries(results, some_entries)\\n\\n elif search_type == 2:\\n choosing = False\\n date_1 = verify_date(\\\"Please enter the beginning of your desired date range: \\\", DATE_FORMAT)\\n date_2 = verify_date(\\\"Please enter the end of your desired date range: \\\", DATE_FORMAT)\\n results = []\\n for entry in some_entries:\\n if date_2 >= entry.date >= date_1:\\n results.append(entry)\\n\\n page_entries(results, some_entries)\\n\\n elif search_type == 3:\\n choosing = False\\n time_query = verify_int(\\\"Please enter the time spent you would like to search entries for: \\\")\\n results = []\\n for entry in some_entries:\\n if entry.time == time_query:\\n results.append(entry)\\n\\n page_entries(results, some_entries)\\n\\n elif search_type == 4:\\n choosing = False\\n search_phrase = input(\\\"Please enter the phrase you would like to search with: \\\")\\n results = []\\n for entry in some_entries:\\n if search_phrase in entry.work_done or search_phrase in entry.comments:\\n results.append(entry)\\n\\n page_entries(results, some_entries)\\n\\n elif search_type == 5:\\n choosing = False\\n proper_regex = False\\n results = []\\n while not proper_regex:\\n regex = input(\\\"Please enter the regex pattern you would like to search by: \\\")\\n results = []\\n try:\\n for entry in some_entries:\\n if re.search(regex, entry.work_done) or re.search(regex, entry.comments):\\n results.append(entry)\\n except re.error:\\n print(\\\"Improper regex. Please check your regex and try again.\\\")\\n else:\\n proper_regex = True\\n\\n page_entries(results, some_entries)\\n\\n elif search_type == 6:\\n choosing = False\\n\\n else:\\n search_type = verify_int(\\\"Please select a proper menu selection (1 - 6): \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8d8e0ff0a28ed2bbe84415767ce448\",\n \"score\": \"0.5357014\",\n \"text\": \"def search(self):\\n e = simpledialog.askstring(title=\\\"Data Crawled\\\",\\n prompt=\\\"What do you want to search?:\\\")\\n self.refresh()\\n x = self.db.search(e)\\n self.update(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e98505c792fca73d517400780745e1\",\n \"score\": \"0.5352421\",\n \"text\": \"def sectionSearch():\\n parent = request.args.get(\\\"number\\\")\\n results = []\\n with open(\\\"HTS_Chapters.csv\\\", newline = '', encoding = \\\"latin-1\\\") as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n if row[\\\"Section\\\"] == parent:\\n results.append(constructButton(row))\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3c9d9f00f89c32576e50261490a37a\",\n \"score\": \"0.5338798\",\n \"text\": \"def function(choose):\\n if choose == 0:\\n '''Create CSV templates'''\\n template()\\n elif choose == 1:\\n '''Create Normalization CSV File'''\\n data_normalization()\\n elif choose == 2:\\n '''Create Combinatorial CSV File'''\\n combinatorial_data()\\n else:\\n print('Invalid option')\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015a648e1562d0178b13c1441ddb9d03\",\n \"score\": \"0.5333438\",\n \"text\": \"def search_csv_into_dictionary(dictionary_of_search_commands):\\n\\tdictionary_of_search_results = defaultdict(list)\\n\\twith open('books.csv', newline=\\\"\\\\n\\\") as csvbooks:\\n\\t\\t\\treader = csv.reader(csvbooks, delimiter=\\\",\\\", quotechar=\\\"\\\\\\\"\\\")\\n\\t\\t\\tfor csv_row in reader:\\n\\t\\t\\t\\tfor search_category in dictionary_of_search_commands:\\n\\t\\t\\t\\t\\tfor search_string in dictionary_of_search_commands[search_category]:\\n\\t\\t\\t\\t\\t\\tif search_string_is_in_row(search_category, search_string, csv_row):\\n\\t\\t\\t\\t\\t\\t\\tdictionary_of_search_results[search_string].append(csv_row)\\n\\treturn dictionary_of_search_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ac297c868abe94f36c05f302adb1de\",\n \"score\": \"0.53113925\",\n \"text\": \"def search(self):\\n\\n if self.combo_selectManufacture.get() == '' and self.combo_selectModel.get() == '':\\n mb.showwarning('Message from system', 'Select at least one model or manufacture')\\n\\n else:\\n # call the controller class method and get the result as list.\\n searchList = OfficeStaff().searchCar(self.combo_selectModel.get(), self.combo_selectManufacture.get())\\n\\n # Create a temporary window to show the result as table\\n tempWindow = Tk()\\n tempWindow.title(\\\"Search Result\\\")\\n\\n # code for creating table\\n # find total number of rows and columns in list\\n for i in range(len(searchList)):\\n for j in range(len(searchList[0])):\\n tempEntry = Entry(tempWindow, width=20, fg='blue',\\n font=('Arial', 16, 'bold'))\\n tempEntry.grid(row=i, column=j)\\n tempEntry.insert(END, searchList[i][j])\\n tempWindow.mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56915d71af34b5c23a0e08f014146c6\",\n \"score\": \"0.53112775\",\n \"text\": \"def pbn_search_clicked(self):\\r\\n # 获取 UI 数据\\r\\n file_path = self.__ln_file_path.text()\\r\\n file_name = self.__ln_file_name.text()\\r\\n\\r\\n # 检查参数\\r\\n if file_path == '':\\r\\n QMessageBox(QMessageBox.Warning, '缺少参数！', '请输入搜索路径！', QMessageBox.Ok, self).exec_()\\r\\n return\\r\\n if file_name == '':\\r\\n QMessageBox(QMessageBox.Warning, '缺少参数！', '请输入匹配条件！', QMessageBox.Ok, self).exec_()\\r\\n return\\r\\n\\r\\n # 判断搜索模式\\r\\n mode = self.__search_mode['fuzzy']\\r\\n if self.__rbn_reg.isChecked():\\r\\n mode = self.__search_mode['reg']\\r\\n elif self.__rbn_fuzzy.isChecked():\\r\\n mode = self.__search_mode['fuzzy']\\r\\n elif self.__rbn_precise.isChecked():\\r\\n mode = self.__search_mode['precise']\\r\\n\\r\\n # 大小写敏感标记\\r\\n I = True\\r\\n if self.__rbn_reg_Ino.isChecked():\\r\\n I = False\\r\\n\\r\\n self.__browser_result.clear()\\r\\n self.__browser_error.clear()\\r\\n self.__tabView.setTabText(0, '匹配结果(0)')\\r\\n self.__tabView.setTabText(1, '错误结果(0)')\\r\\n self.__searching = True\\r\\n\\r\\n # 开启子线程，后台深度遍历\\r\\n self.__thread_killer = False\\r\\n if self.__rbn_search_file.isChecked():\\r\\n self.__lab_state.setText('正在搜索......已搜索到 0 个文件')\\r\\n self.__sub_thread_search = Thread(target=self.search_from_filename, args=(file_path, file_name, mode, I))\\r\\n self.__sub_thread_search.start()\\r\\n else:\\r\\n self.__lab_state.setText('正在搜索......')\\r\\n self.__sub_thread_search = Thread(target=self.search_from_content, args=(file_path, file_name, mode, I))\\r\\n self.__sub_thread_search.start()\\r\\n\\r\\n # 开启子线程，显示搜索结果\\r\\n self.__sub_thread_show_result = Thread(target=self.show_search_result)\\r\\n self.__sub_thread_show_result.start()\\r\\n\\r\\n # 开启子线程，显示错误结果\\r\\n self.__sub_thread_show_error = Thread(target=self.show_error_result)\\r\\n self.__sub_thread_show_error.start()\\r\\n\\r\\n # self.__pbn_search_file.setEnable(False)\\r\\n # self.__pbn_search_content.setEnable(False)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d8a5c4fa815ac6924fee274aebff9d\",\n \"score\": \"0.5302967\",\n \"text\": \"def Search(self, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828569f7af402570194a8e43926b92d7\",\n \"score\": \"0.53010553\",\n \"text\": \"def search(snippet, filename):\\n logging.info(\\n \\\"Searching {} for a snippet matching '{}'\\\".format(filename, snippet))\\n logging.debug(\\\"Opening file\\\")\\n with open(filename, \\\"r\\\") as f:\\n reader = csv.reader(f, delimiter=\\\",\\\")\\n for row in reader:\\n if row[1] == snippet:\\n return row[0], snippet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"371168d735acc07f1969c5eabb19d846\",\n \"score\": \"0.52941626\",\n \"text\": \"def read_csv_input(self):\\n logging.info('INICIANDO: leitura do arquivo csv com os resultados e resultados esperados')\\n expected_results = {}\\n results = {}\\n with open(\\\"result/resultados_esperados.csv\\\", newline='') as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n if int(row['QueryNumber']) in expected_results:\\n expected_results[int(row['QueryNumber'])].append((int(row['DocNumber']), int(row['DocVotes'])))\\n else:\\n expected_results[int(row['QueryNumber'])] = [(int(row['DocNumber']), int(row['DocVotes']))]\\n with open(self.results_file, newline='') as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader: \\n doc_ranking = row['RankingDoc'].replace('[', '').replace(']', '').split(\\\",\\\")\\n ranking = int(doc_ranking [1])\\n score = float(doc_ranking[2])\\n if int(row['QueryNumber']) in results:\\n results[int(row['QueryNumber'])].append((ranking, score))\\n else:\\n results[int(row['QueryNumber'])]=[(ranking, score)] \\n logging.info('FINALIZADO: leitura do arquivo csv com os resultados e resultados esperados')\\n return expected_results, results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb03fd908a375f3846a6abc96d03082a\",\n \"score\": \"0.5293505\",\n \"text\": \"def main():\\n message = input('Please enter your query:\\\\n')\\n where = which_where(message)\\n select = select_message(message)\\n squeal.print_csv(select)\\n \\n while message != '': \\n message = input('Please enter your query:\\\\n')\\n if query != '':\\n where = which_where(message)\\n select = select_message(message)\\n squeal.print_csv(select)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d5ce88d12685550956742c4d35a1b25\",\n \"score\": \"0.5281954\",\n \"text\": \"def searchFile(self):\\n \\\"\\\"\\\"Update text box with path value\\\"\\\"\\\"\\n def test(string, expression):\\n test = False\\n if string in expression:\\n test = True\\n return test \\n validFormat = \\\"xls\\\"\\n file = QtGui.QFileDialog.getOpenFileName(None, 'Open file')\\n \\\"\\\"\\\"Valid file format\\\"\\\"\\\"\\n isValid = test(validFormat, file)\\n if not isValid or isValid == \\\"\\\" :\\n file = \\\"Please, select valid file !\\\"\\n \\\"\\\"\\\"Update text box with path value\\\"\\\"\\\"\\n return self.dlg.pathTpl.setText(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b02a49f74bd139e6d35f98b6bf4f59\",\n \"score\": \"0.5273229\",\n \"text\": \"def get_filters():\\r\\n print('Hello! Let\\\\'s explore some US bikeshare data! \\\\n')\\r\\n # Get user input for city (chicago, new york city, washington).\\r\\n # Collecting only a valid answer for city input using while loop, lower case answers acceptable\\r\\n city = input('Choose a city from: Chicago, New York City or Washington: ')\\r\\n city = city.lower();\\r\\n # Validate input to match file name with while loop\\r\\n while city not in ['chicago', 'new york city', 'washington']:\\r\\n print ('You may have a typo or an invalid city, please try again.')\\r\\n city = input('Choose a city from: Chicago, New York City or Washington: ')\\r\\n city = city.lower()\\r\\n\\r\\n # Get user input for month (all, january, february, ... , june)\\r\\n # Collecting only a valid answer for month input using while loop, lower case answers acceptable\\r\\n month = input('Choose a month from: January, February, March, April, May, June or choose All: ')\\r\\n month = month.lower();\\r\\n # Validate input to match file name with while loop\\r\\n while month not in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:\\r\\n print ('You may have a typo or an invalid month, please try again.')\\r\\n month = input('Choose a month from: January, February, March, April, May, June or choose All: ')\\r\\n month = month.lower()\\r\\n\\r\\n # Get user input for day of week (all, monday, tuesday, ... sunday)\\r\\n # Collecting only a valid answer for day input using while loop, lower case answers acceptable\\r\\n day = input('Choose a day of the week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or choose All: ')\\r\\n day = day.lower();\\r\\n # Validate input to match file name with while loop\\r\\n while day not in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']:\\r\\n print ('You may have a typo or an invalid selection, please try again.')\\r\\n day = input('Choose a day of the week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or choose All: ')\\r\\n day = day.lower()\\r\\n # Printing the selection made by the user\\r\\n print(\\\"\\\\nGreat, thanks for choosing this combination, City: {}, Month: {} and Day: {}!\\\".format(city.title(),month.title(),day.title()))\\r\\n\\r\\n print('-'*40)\\r\\n return city, month, day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a18212da8e11bf73fc86ad9fbc923bd6\",\n \"score\": \"0.5271173\",\n \"text\": \"def read_file():\\n # usage statement, with optional flags for header and delimiter \\n # (limited to common ones for csv)\\n usage = (\\\"usage: \\\" + sys.argv[0] + \\\" csvfile [-h] [-d <p | s | sc | t>)]\\\"\\n \\\"\\\\n\\\\t-h: csv file contains header (default is no header)\\\"\\n \\\"\\\\n\\\\t-d p | s | sc | t: delimiter as p (pipe), s (space), sc (semicolon) or t (tab)\\\"\\n \\\"\\\\n\\\\t(default delimiter is comma)\\\")\\n\\n # Checks if file is provided. If not, program is aborted.\\n if len(sys.argv) < 2:\\n raise(SystemExit)(f\\\"csv file must be provided in the command line.\\\"\\n f\\\"\\\\n{usage}\\\")\\n\\n # Checks if file exists and is csv. If not, program is aborted.\\n try:\\n filename = sys.argv[1]\\n if filename.endswith('.csv'):\\n csv_file = open(filename, 'r', encoding = 'utf-8')\\n\\n # Process optional flags in command line for header and/or delimiter\\n opts = [opt for opt in sys.argv[1:] if opt.startswith(\\\"-\\\")]\\n args = [arg for arg in sys.argv[1:] if not arg.startswith(\\\"-\\\")]\\n\\n header = ('y' if \\\"-h\\\" in opts else 'n')\\n\\n try:\\n # Process argument for flag -d\\n if \\\"-d\\\" in opts:\\n if args[1] == 'p':\\n separator = '|'\\n elif args[1] == 's':\\n separator = ' '\\n elif args[1] == 'sc':\\n separator = ';'\\n elif args[1] == 't':\\n separator = '\\\\t'\\n else:\\n separator = ','\\n except:\\n # Program is aborted if flag -d was not followed with valid delimiter\\n raise(SystemExit)(f\\\"-d: No valid delimiter provided\\\"\\n f\\\"\\\\n{usage}\\\")\\n else:\\n raise(SystemExit)(f\\\"{filename}: No csv file\\\"\\n f\\\"\\\\n{usage}\\\")\\n except FileNotFoundError:\\n raise(SystemExit)(f\\\"{filename}: No such file or directory\\\"\\n f\\\"\\\\n{usage}\\\")\\n return csv_file, separator, header\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f41a2e5fe7846f176d0ecb7c1c8d0bc\",\n \"score\": \"0.5250878\",\n \"text\": \"def onSelectResultsFile(self):\\n if not self.resultsFileDialog:\\n self.resultsFileDialog = qt.QFileDialog(self.parent)\\n self.resultsFileDialog.options = self.resultsFileDialog.DontUseNativeDialog\\n self.resultsFileDialog.acceptMode = self.resultsFileDialog.AcceptOpen\\n self.resultsFileDialog.defaultSuffix = \\\"csv\\\"\\n self.resultsFileDialog.setNameFilter(\\\"Comma Separated Values (*.csv)\\\")\\n self.resultsFileDialog.connect(\\\"fileSelected(QString)\\\", self.onResultsFileSelected)\\n self.resultsFileDialog.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416d40f96ec7f6dddabc82c7c44291e8\",\n \"score\": \"0.52392995\",\n \"text\": \"def tc_search(self, args, range):\\n # Save the current explorer for restoration when the searcher finish\\n self.expSave = self.explorers[self.selectedExplorer]\\n # Replace the current explorer with a searcher and borrow its buffer\\n se = searcher(self.nvim, self.expSave.buffer, self.expSave.cwd)\\n se.window = self.expSave.window\\n # Perfor the search with the correct parameters\\n dir = self.expSave.cwd\\n filePattern = args[1]\\n if(len(args) > 2):\\n inputPattern = args[2]\\n else:\\n inputPattern = ''\\n se.search(dir, filePattern, inputPattern)\\n self.explorers[self.selectedExplorer] = se\\n self.explorers[self.selectedExplorer].draw()\\n self.nvim.command('startinsert')\\n self.nvim.command('normal! $')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f6d4f8feeb04f339b2cda8ce0270ec\",\n \"score\": \"0.5225744\",\n \"text\": \"def pandas_search(team,phone_number):\\n\\n file,search,display = load_team_information(team)\\n if os.path.exists(file):\\n data = []\\n dest_head = []\\n df = pd.read_csv(r'%s' % file, dtype='str', sep='|', low_memory=True)\\n dest_head = get_column(display, df.columns)\\n m = 5 #Max 5 rows are matched with the phone_number\\n n = 0\\n for target_column in search:\\n df_mobile = df[target_column].values.tolist()\\n i = 0\\n for E164_number in df_mobile:\\n if str(E164_number).replace(' ', '').upper() == phone_number.upper():\\n record = list(df.iloc[i, ])\\n data.append(record)\\n n += 1\\n i += 1\\n if n == m:\\n return data, dest_head\\n else:\\n i += 1\\n if n == m:\\n return data, dest_head\\n\\n if data:\\n return data, dest_head\\n else:\\n return [], []\\n else:\\n return 'The CSV path %s is not correct!' %file, []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b7049ecf1057c16bc1abd860e3decc\",\n \"score\": \"0.522262\",\n \"text\": \"def csvConsumer(self, filename):\\n\\n # Open the csv file\\n f = self.openWriteHandle(filename)\\n delim = \\\",\\\"\\n\\n if self.options.control is None:\\n\\n # Columns for in-silico control\\n if self.options.methylFraction:\\n cols = [\\\"refName\\\", \\\"tpl\\\", \\\"strand\\\", \\\"base\\\", \\\"score\\\", \\\"tMean\\\", \\\"tErr\\\", \\\"modelPrediction\\\", \\\"ipdRatio\\\", \\\"coverage\\\", FRAC, FRAClow, FRACup]\\n else:\\n if self.options.useLDA:\\n # FIXME: For testing LDA model, to look at LDA scores in csv output (run without --methylFraction or --control):\\n cols = [\\\"refName\\\", \\\"tpl\\\", \\\"strand\\\", \\\"base\\\", \\\"score\\\", \\\"tMean\\\", \\\"tErr\\\", \\\"modelPrediction\\\", \\\"ipdRatio\\\", \\\"coverage\\\", \\\"Ca5C\\\"]\\n else:\\n cols = [\\\"refName\\\", \\\"tpl\\\", \\\"strand\\\", \\\"base\\\", \\\"score\\\", \\\"tMean\\\", \\\"tErr\\\", \\\"modelPrediction\\\", \\\"ipdRatio\\\", \\\"coverage\\\"]\\n\\n else:\\n # Columns for case-control\\n if self.options.methylFraction:\\n cols = [\\\"refName\\\", \\\"tpl\\\", \\\"strand\\\", \\\"base\\\", \\\"score\\\", \\\"pvalue\\\", \\\"caseMean\\\", \\\"controlMean\\\", \\\"caseStd\\\", \\\"controlStd\\\", \\\"ipdRatio\\\", \\\"testStatistic\\\", \\\"coverage\\\", \\\"controlCoverage\\\", \\\"caseCoverage\\\", FRAC, FRAClow, FRACup]\\n else:\\n cols = [\\\"refName\\\", \\\"tpl\\\", \\\"strand\\\", \\\"base\\\", \\\"score\\\", \\\"pvalue\\\", \\\"caseMean\\\", \\\"controlMean\\\", \\\"caseStd\\\", \\\"controlStd\\\", \\\"ipdRatio\\\", \\\"testStatistic\\\", \\\"coverage\\\", \\\"controlCoverage\\\", \\\"caseCoverage\\\"]\\n\\n # Special cases for formatting columns of the csv\\n handlers = dict()\\n threeF = lambda x: \\\"%.3f\\\" % x\\n\\n handlers[\\\"refName\\\"] = lambda x: \\\"\\\\\\\"%s\\\\\\\"\\\" % x\\n\\n handlers[\\\"tpl\\\"] = lambda x: str(x.item() + 1)\\n handlers[\\\"score\\\"] = lambda x: \\\"%d\\\" % x\\n\\n handlers[\\\"tMean\\\"] = threeF\\n handlers[\\\"modelPrediction\\\"] = threeF\\n handlers[\\\"caseMean\\\"] = threeF\\n handlers[\\\"controlMean\\\"] = threeF\\n handlers[\\\"ipdRatio\\\"] = threeF\\n handlers[\\\"pvalue\\\"] = lambda x: \\\"%.3e\\\" % x\\n\\n handlers[\\\"controlStd\\\"] = threeF\\n handlers[\\\"controlStd\\\"] = threeF\\n handlers[\\\"tErr\\\"] = threeF\\n\\n # FIXME: remove this line later:\\n handlers[\\\"Ca5C\\\"] = threeF\\n\\n handlers[FRAC] = threeF\\n handlers[FRAClow] = threeF\\n handlers[FRACup] = threeF\\n\\n print >>f, delim.join(cols)\\n\\n def fmt(rowData, colName):\\n if not rowData.has_key(colName):\\n return \\\"\\\"\\n\\n if handlers.has_key(colName):\\n return handlers[colName](rowData[colName])\\n else:\\n return str(rowData[colName])\\n\\n try:\\n while True:\\n # Pull a list of record in from the producer\\n itemList = (yield)\\n\\n for item in itemList:\\n values = [fmt(item, col) for col in cols]\\n print >>f, delim.join(values)\\n\\n except GeneratorExit:\\n f.close()\\n return\\n except Exception as e:\\n print e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d43479791909b770278ecf76dac402\",\n \"score\": \"0.5217452\",\n \"text\": \"def csv_file(self) -> None:\\r\\n self.create_parameters(self.type_checker(self.file[self.nft]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658ac8d51245fb54784a5ed0ada1435b\",\n \"score\": \"0.5216351\",\n \"text\": \"def run(self):\\n # show the dialog\\n self.dlg.show()\\n \\\"\\\"\\\"\\\"To connect event to gui elements\\\"\\\"\\\"\\n cbGC = self.dlg.comboGC\\n cbSynthese = self.dlg.comboSynthese\\n cbGcId = self.dlg.idGC\\n cbSyntheseId = self.dlg.idSynthese\\n cbState = self.dlg.cbState\\n cbOpp = self.dlg.cbOpp\\n # init combo\\n self.initCb(cbGC, cbGcId,cbState)\\n self.initCb(cbSynthese, cbSyntheseId,cbState)\\n # buttons\\n self.dlg.buttonFile.clicked.connect(self.searchFile) \\n self.dlg.buttonFolder.clicked.connect(self.searchFolder) \\n\\n '''here we need to load opportunity list wehen user select id field to get opp values''' \\n for el in [cbGcId, cbSyntheseId, cbState] :\\n el.currentIndexChanged.connect(lambda: self.oppFiltering(cbGcId, cbSyntheseId, cbGC, cbSynthese, cbState, cbOpp))\\n self.state = [] \\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containing pass and\\n self.createFile()\\n # substitute with your code.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755e06b90dfbd3a3aad1e35b7f555325\",\n \"score\": \"0.5212298\",\n \"text\": \"def linear_search(files, terms):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101ffd77e8750a0ee3025095cfea19b0\",\n \"score\": \"0.52047545\",\n \"text\": \"def handle(self, *args, **options):\\n path = args[0]\\n student_filter = ['userid','ipaddress','dateadded','datelastupdated',\\n 'sectionid', 'useridLastUpdated', \\n 'adminEmailSent','stamp',\\n 'Comments_length','Comments_textIsHTML']\\n self.port_table(student_filter, '/home/pykun/4560/dbs/student.txt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd861f8d7103818e500dc82533ac376\",\n \"score\": \"0.51907563\",\n \"text\": \"def search_and_facet_compute_resources(cbc, menu, parser):\\n print('\\\\n' + '-' * 21)\\n print('Available criteria filters:\\\\n')\\n print(*('appliance_uuid', 'eligibility', 'cluster_name', 'name',\\n 'ip_address', 'installation_status', 'uuid', 'os_type',\\n 'os_architecture'), sep='\\\\n')\\n print('-' * 21)\\n\\n query = cbc.select(ComputeResource)\\n\\n use_filter = input('\\\\nWould you like to use criteria filters?: Y/n\\\\n')\\n if use_filter.lower() in ['y', 'yes']:\\n print('Each criteria accepts one or more space separated values.',\\n 'Exmaple: --name ABCD --appliance_uuid 1234 5678')\\n args = parser.parse_args(input('Enter criteria filters: ').split())\\n if args.name:\\n query.set_name(args.name)\\n if args.os_type:\\n query.set_os_type(args.os_type)\\n if args.appliance_uuid:\\n query.set_appliance_uuid(args.appliance_uuid)\\n if args.cluster_name:\\n query.set_cluster_name(args.cluster_name)\\n if args.ip_address:\\n query.set_ip_address(args.ip_address)\\n if args.installation_status:\\n query.set_installation_status(args.installation_status)\\n if args.uuid:\\n query.set_uuid(args.uuid)\\n if args.os_architecture:\\n query.set_os_architecture(args.os_architecture)\\n if args.eligibility:\\n query.set_eligibility(args.eligibility)\\n\\n print(*query)\\n new_query = input('\\\\nMake anothery query? Y/n\\\\n')\\n if new_query.lower() in ['y', 'yes']:\\n search_and_facet_compute_resources(cbc, menu, parser)\\n else:\\n menu['0']['function_call']()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e41dd2b77ab201d995f7f7a6135ec1f\",\n \"score\": \"0.51882935\",\n \"text\": \"def main(args):\\n\\n results = []\\n\\n # Load the provided personal finance dataset\\n\\n if len(args) is 1:\\n # Default to preformatted.csv if no file is provided as an argument\\n project_root = Path(__file__).resolve().parent.parent\\n default_location = project_root/\\\"datasets/preformatted.csv\\\"\\n results = process_file(default_location)\\n\\n elif len(args) is 2:\\n location = Path(args[1])\\n if location.is_file():\\n results = process_file(location)\\n elif location.is_dir():\\n print(\\\"Folder provided. Please provide a file.\\\")\\n exit()\\n else:\\n print(\\\"Please provide a file as an argument.\\\")\\n exit()\\n\\n else:\\n print(\\\"Too many arguments provided!\\\")\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d206adf74dd17ae352ef1f4620817b\",\n \"score\": \"0.5187672\",\n \"text\": \"def main():\\n filename = \\\"data.csv\\\"\\n read(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3167ad26bef4b20f61cfd72a35285f6\",\n \"score\": \"0.5180637\",\n \"text\": \"def nameCSV():\\r\\n while True:\\r\\n # get title and strip spaces\\r\\n title = input('What would you like to name your CSV file?: ').strip()\\r\\n # check if user filled .csv (otherwise, make sure to add it)\\r\\n title = title.rstrip('.csv') + '.csv'\\r\\n # double check with file name\\r\\n while True:\\r\\n response = input(f\\\"Are you sure that you want to name your file '{title}'?[Y/N]\\\")\\r\\n if response.upper() == 'Y':\\r\\n # check for unacceptable characters\\r\\n check = unacceptable_naming(title)\\r\\n if check[1]:\\r\\n print(f'Your file name contained illegal characters and has been renamed to the following: '\\r\\n f'{check[0]}')\\r\\n # notify before overriding\\r\\n if os.path.exists(check[0]):\\r\\n while True:\\r\\n cont = input(\\r\\n 'The file that you are about to create will override an existing file. Are you sure that'\\r\\n ' you would like to continue?[Y/N]:')\\r\\n if cont.upper() == 'Y':\\r\\n return check[0]\\r\\n elif cont.upper() == 'N':\\r\\n break\\r\\n else:\\r\\n print(f\\\"'{response}' is an invalid input. Please type Y or N.\\\")\\r\\n break\\r\\n else:\\r\\n return check[0]\\r\\n elif response.upper() == 'N':\\r\\n break\\r\\n else:\\r\\n print(f\\\"'{response}' is an invalid input. Please type Y or N.\\\")\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a3bd342393b81fa1ff75c476a418986\",\n \"score\": \"0.517995\",\n \"text\": \"def cli(ctx, type, view, text):\\n try:\\n data = ctx.tp.search(type, text)\\n except Exception as ex:\\n ctx.logerr(ex)\\n sys.exit(1)\\n else:\\n if view == 'table':\\n ctx.log(ctx.tp.print_result_table(data))\\n elif view == 'list':\\n ctx.log(ctx.tp.print_result_list(type, data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c341690084cbeda050a32e7c2d790e\",\n \"score\": \"0.51750344\",\n \"text\": \"def handlerSelectLandUseLookup(self):\\n file = unicode(QtGui.QFileDialog.getOpenFileName(\\n self, 'Select Land Use Lookup Table', QtCore.QDir.homePath(), 'Land Use Lookup Table (*{0})'.format(self.main.appSettings['selectCsvfileExt'])))\\n \\n if file:\\n self.lineEditLandUseLookup.setText(file)\\n \\n logging.getLogger(type(self).__name__).info('select file: %s', file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f31d5874ca39018df19d19f7a68c894\",\n \"score\": \"0.5160416\",\n \"text\": \"def main():\\n file_names = [\\\"nysdaq.csv\\\", \\\"nyse.csv\\\", \\\"amex.csv\\\"]\\n for file_name in file_names:\\n open_file(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b73293125bb4672717d399ad30af864\",\n \"score\": \"0.5154706\",\n \"text\": \"def search(self):\\n # get the searchfiles (now a single walkthrough)\\n searchfiles = self.get_search_files()\\n if self.settings.verbose:\\n searchdirs = sorted(list({sf.path for sf in searchfiles}))\\n log('\\\\nDirectories to be searched ({0}):'.format(len(searchdirs)))\\n for d in searchdirs:\\n log(d)\\n log('\\\\n\\\\nFiles to be searched ({0}):'.format(len(searchfiles)))\\n for f in searchfiles:\\n log(str(f))\\n log(\\\"\\\")\\n\\n # TODO: concurrent.futures.ProcessPoolExecutor, e.g.\\n # with concurrent.futures.ProcessPoolExecutor() as executor:\\n # futures = [executor.submit(<func_name>, <arg>) for _ in range(10)]\\n # for f in concurrent.futures.as_completed(futures):\\n # print(f.result())\\n # - OR -\\n # nums = list(range(10))\\n # # runs in parallel but returns results in list order\\n # results = executor.map(<func_name>, nums)\\n for sf in searchfiles:\\n self.search_file(sf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5261494d530faf65df7d2da7e74361a\",\n \"score\": \"0.51534235\",\n \"text\": \"def handle(self, *args, **options):\\n csv_file = options['csv_file']\\n\\n locations_added = Location.objects.add_from_csv(csv_file)\\n self.stdout.write('Process finished, {0} locations added'.format(locations_added))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda667108c788e0286f0213092816b5d\",\n \"score\": \"0.51533115\",\n \"text\": \"def read_csv(self):\\n try:\\n self.csvName + 'hello'\\n except TypeError:\\n print('Input file name must be a String type')\\n logging.warning('Input file entered was not a String type')\\n raise TypeError('Input file entered was not a String type')\\n return None\\n\\n try:\\n pd.read_csv(self.csvName)\\n except FileNotFoundError:\\n print('No file with given filename found')\\n logging.debug('No file with given filename found')\\n raise FileNotFoundError('No file with given filename found')\\n return None\\n\\n headers = ['Time', 'Voltage']\\n df = pd.read_csv(self.csvName, names=headers)\\n self.csvDf = df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"174b902d4c0c48749bf6d71c360217a8\",\n \"score\": \"0.514914\",\n \"text\": \"def get_list_from_csv(csv_file, search_key, for_values, get_key):\\n result = []\\n with open(csv_file) as c_fh:\\n reader = csv.DictReader(c_fh)\\n for row in reader:\\n if row[search_key] in for_values:\\n result.append(row[get_key])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad76e315ab9dafcbb10e154278753a0\",\n \"score\": \"0.5129369\",\n \"text\": \"def search(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f721358650e921b91407ed4dd468f83\",\n \"score\": \"0.51280946\",\n \"text\": \"def goto():\\n i = int(input('which question you want to go: '))\\n os.system(\\\"cls\\\" if os.name == \\\"nt\\\" else \\\"clear\\\")\\n with open(r'./quiz_wise_questions/'+quiz_number+'.csv', 'r') as file:\\n reader = csv.reader(file)\\n row = list(reader)\\n print(\\\"Roll No- \\\", rollno)\\n print(\\\"Name- \\\", Name)\\n print(\\\"Goto Question: (Press - Ctrl+Alt+g)\\\")\\n print(\\\"Final Submit: (Press Ctrl+Alt+f)\\\")\\n print(\\\"Export Database Into CSV: (Press Ctrl+Alt+e)\\\")\\n print(\\\"Ques No \\\", row[i][0], sep=':-')\\n print(row[i][1], \\\" ?\\\")\\n print(\\\"Option 1) \\\", row[i][2])\\n print(\\\"Option 2) \\\", row[i][3])\\n print(\\\"Option 3) \\\", row[i][4])\\n print(\\\"Option 4) \\\", row[i][5])\\n print(\\\"Credits if correct option: \\\", row[i][7])\\n print(\\\"Negative Marking: \\\", row[i][8])\\n print(\\\"Is Compulsory: \\\", row[i][9])\\n print(\\\"Press esc to continue!!\\\")\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edaed5bfbe9b490ddb606bcb5d56605\",\n \"score\": \"0.51276356\",\n \"text\": \"def readCsv():\\n # Read the job offers CSV file and fill a list with all its content\\n jobOfferList = [] # Full list of offers\\n with open(CSV_ROOT + 'technical-test-jobs.csv') as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=DELIMITER)\\n header = True\\n for row in csv_reader:\\n if header: # Do not read the header\\n header = False\\n else:\\n job = jobOffer.JobOffer(row[0], row[1], row[2], row[3], row[4]) # Create a JobOffer object\\n jobOfferList += [job] # Add it to the full list\\n \\n # Read the job types CSV file and fill a list with all its content\\n professionList = []\\n with open(CSV_ROOT + 'technical-test-professions.csv') as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=DELIMITER)\\n header = True\\n for row in csv_reader:\\n if header: # Do not read the header\\n header = False\\n else:\\n job = profession.Profession(row[0], row[1], row[2]) # Create a JobType object\\n professionList += [job] # Add it to the list\\n \\n return ([jobOfferList, professionList])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b7e82c8fdc54a5d8a86c29e1799919\",\n \"score\": \"0.51267004\",\n \"text\": \"def search_string_is_in_row(search_category, search_string, csv_row):\\n\\tcsv_title_index = 0; csv_year_index = 1; csv_author_index = 2\\n\\tif search_category == csv_title_index: \\n\\t\\treturn title_is_in_row(search_string, csv_row[csv_title_index])\\n\\telif search_category == csv_year_index:\\n\\t\\treturn row_year_is_in_bounds(search_string, csv_row[csv_year_index])\\t\\n\\telif search_category == csv_author_index:\\n\\t\\treturn author_is_in_row(search_string, csv_row[csv_author_index])\\n\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425a8238f68f3ef4ef571060498c52b9\",\n \"score\": \"0.5123759\",\n \"text\": \"def get_filters():\\n\\tprint('Hello! Let\\\\'s explore some US bikeshare data!\\\\n')\\n\\n\\tcity = input('Please choose a city to analyze\\\\n')\\n\\tcity = city.lower()\\n\\twhile city != 'chicago' and city != 'washington' and city != 'new york city':\\n\\t\\tcity = input('Please choose a valid city: either chicago, washington or new york city\\\\n')\\n\\t\\tcity = city.lower()\\n\\telse:\\n\\t\\tprint('Okay great! We\\\\'re going to look at stats for', city.capitalize(), '\\\\n')\\n\\n\\t# next input month by name and we'll do the indexing in the load_data section\\n\\n\\tmonth = input('Please enter a month from January to June to analyze, or type \\\"all\\\" to view all months.\\\\n')\\n\\tlist_of_valid_months = ['january', 'february', 'march', 'april', 'may', 'june', 'all']\\n\\tmonth = month.lower()\\n\\twhile month not in list_of_valid_months:\\n\\t\\tmonth = input('Please choose a valid month, January though June, or \\\\'all\\\\'\\\\n')\\n\\t\\tmonth = month.lower()\\n\\t\\tif month == 'all':\\n\\t\\t\\tprint('Great, you\\\\'ve selected all months\\\\n')\\n\\t\\telif month in list_of_valid_months:\\n\\t\\t\\tprint('Great, you\\\\'ve selected', month.capitalize())\\n\\t\\t\\tbreak\\n\\telse:\\n\\t\\tprint('Great, you\\\\'ve selected', month.capitalize())\\n\\n\\tday = input('Please choose a day of the week to analyze or type \\\"all\\\"\\\\n')\\n\\tlist_of_valid_days = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']\\n\\tday = day.lower()\\n\\twhile day not in list_of_valid_days:\\n\\t\\tday = input('Please choose a valid day of the week or type \\\\'all\\\\'\\\\n')\\n\\t\\tday = day.lower()\\n\\t\\tif day == 'all':\\n\\t\\t\\tprint('Great you have selected all days of the week')\\n\\t\\telif day in list_of_valid_days:\\n\\t\\t\\tprint('Great, you\\\\'ve selected', day.capitalize())\\n\\telse:\\n\\t\\tprint('\\\\n')\\n\\t\\tprint('Great, you\\\\'ve selected', day.capitalize())\\n\\n\\tprint('-'*40)\\n\\treturn city, month, day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a468da6925ca97fb05a71ecc7c0ce1f\",\n \"score\": \"0.5113391\",\n \"text\": \"def extract_info(self, csv_file, request):\\r\\n data = pd.read_csv(csv_file)\\r\\n # Remove \\\"dontcare\\\" coded inputs\\r\\n request = {k:v for k,v in request.items() if v != 'dontcare'}\\r\\n options = {}\\r\\n for pref_type in request:\\r\\n # All the different unique options in a column\\r\\n options[pref_type] = data[pref_type].dropna().unique()\\r\\n for pref_type in request:\\r\\n data = self.__modify_data(data, pref_type, options, request[pref_type])\\r\\n # Shuffle the data\\r\\n data = data.sample(frac=1)\\r\\n # Prettify\\r\\n capt = lambda m: \\\" \\\".join([word.capitalize() for word in m.split(\\\" \\\")])\\r\\n data[\\\"restaurantname\\\"] = data[\\\"restaurantname\\\"].apply(capt)\\r\\n data[\\\"phone\\\"] = data[\\\"phone\\\"].apply(self.__fix_phone_number)\\r\\n data[\\\"addr\\\"] = data[\\\"addr\\\"].apply(self.__add_comma)\\r\\n # Can't capitalize a float\\r\\n data[\\\"addr\\\"] = data[\\\"addr\\\"].apply(lambda m: capt(m) if isinstance(m, str) else np.nan)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c25e42fde20d90e918af13728b72488\",\n \"score\": \"0.51049495\",\n \"text\": \"def search(self, entry, types):\\n # Clearing the results dictionary\\n self.__results = {}\\n # Iterating through the types in the passed variable 'types'\\n for atype in types:\\n IDList = []\\n for record in self.__entities[atype]:\\n # If the user did not input any text in the search bar then return all records of atype\\n if entry == \\\"\\\":\\n IDList.append(record)\\n else:\\n # Get the list of search related attributes from 'record'\\n searchList = self.__entities[atype][record].getSearch()\\n # Convert this list to a string with each item separated by a comma\\n key = ' '.join(searchList)\\n \\\"\\\"\\\"If the lowercase version of the passed variable 'entry' is in the search related attributes, \\n append the ID of that record to IDList\\\"\\\"\\\"\\n if entry.lower() in key.lower():\\n IDList.append(record)\\n \\\"\\\"\\\"Create an entry in the results dictionary with key of 'atype' and value of the list of matching record \\n IDs for that type\\\"\\\"\\\"\\n self.__results[atype] = IDList\\n return self.__results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a30bb24c9df73e306f337d6ed0255da\",\n \"score\": \"0.5104087\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a30bb24c9df73e306f337d6ed0255da\",\n \"score\": \"0.5104087\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a30bb24c9df73e306f337d6ed0255da\",\n \"score\": \"0.5104087\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154c872f60c6fa1b1c966eafd40f8c77\",\n \"score\": \"0.51007843\",\n \"text\": \"def slsearch(tablename='', outfile='', freqrange=[84, 90], species=[''], reconly=False, chemnames=[''], qns=[''], intensity=[-1], smu2=[-1], loga=[-1], el=[-1], eu=[-1], rrlinclude=True, rrlonly=False, verbose=False, logfile='\\\"\\\"', append=False):\\n if type(freqrange)==float: freqrange=[freqrange]\\n if type(species)==str: species=[species]\\n if type(chemnames)==str: chemnames=[chemnames]\\n if type(qns)==str: qns=[qns]\\n if type(intensity)==float: intensity=[intensity]\\n if type(smu2)==float: smu2=[smu2]\\n if type(loga)==float: loga=[loga]\\n if type(el)==float: el=[el]\\n if type(eu)==float: eu=[eu]\\n\\n#\\n# The following is work around to avoid a bug with current python translation\\n#\\n mytmp = {}\\n\\n mytmp['tablename'] = tablename\\n mytmp['outfile'] = outfile\\n mytmp['freqrange'] = freqrange\\n mytmp['species'] = species\\n mytmp['reconly'] = reconly\\n mytmp['chemnames'] = chemnames\\n mytmp['qns'] = qns\\n mytmp['intensity'] = intensity\\n mytmp['smu2'] = smu2\\n mytmp['loga'] = loga\\n mytmp['el'] = el\\n mytmp['eu'] = eu\\n mytmp['rrlinclude'] = rrlinclude\\n mytmp['rrlonly'] = rrlonly\\n mytmp['verbose'] = verbose\\n mytmp['logfile'] = logfile\\n mytmp['append'] = append\\n pathname='file://' + xmlpath( ) + '/'\\n trec = casac.utils().torecord(pathname+'slsearch.xml')\\n\\n casalog.origin('slsearch')\\n if trec.has_key('slsearch') and casac.utils().verify(mytmp, trec['slsearch']) :\\n result = task_slsearch.slsearch(tablename, outfile, freqrange, species, reconly, chemnames, qns, intensity, smu2, loga, el, eu, rrlinclude, rrlonly, verbose, logfile, append)\\n\\n else :\\n result = False\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ccd4b63b38d3b33db89749cab4a6aac\",\n \"score\": \"0.51005465\",\n \"text\": \"def search():\\n filename = request.args.get('filename')\\n if filename is None:\\n return render_template('search.html', filename=\\\"\\\")\\n\\n return render_template('search.html', filename=filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2235c5b08f4503ba17ddb667e14125\",\n \"score\": \"0.5095346\",\n \"text\": \"def run_csv_input(self):\\n self.txt.setText(\\\"\\\") # clear the display\\n with open(self.csv_file) as csvfile:\\n # The index of each field in the CSV file\\n lat_idx, lon_idx, speed_idx = None, None, None\\n \\n reader = csv.reader(csvfile)\\n headers = next(reader, None) \\n \\n for field_idx in range(len(headers)): \\n if 'lat' in headers[field_idx].lower(): \\n lat_idx = field_idx\\n elif 'lon' in headers[field_idx].lower(): \\n lon_idx = field_idx\\n elif 'speed' in headers[field_idx].lower():\\n speed_idx = field_idx\\n \\n # if we can't find lat or lon in the header, exit\\n if lat_idx is None or lon_idx is None:\\n self.txt.setText(\\\"cannot find lat/lon header fields in {}\\\".format(self.csv_file))\\n return\\n \\n # read the CSV input sequentially. After the first line of coordinates has been read \\n # we can start calculating distances between the current line and the previous.\\n past_first = False\\n # all of our lists for storing information...\\n lats, lons, ms, nmis, bearings, speeds, travel_times, types, errors = [], [], [], [], [], [], [], [], []\\n for row in reader: \\n lats.append(convert_to_dd(row[lat_idx]))\\n lons.append(convert_to_dd(row[lon_idx]))\\n \\n if speed_idx:\\n try:\\n speeds.append(float(row[speed_idx]))\\n except Exception as err:\\n print(\\\"could not parse speed from row: {}\\\".format(row))\\n speeds.append(None)\\n print(err)\\n \\n if past_first:\\n start_lat, start_lon, stop_lat, stop_lon = lats[-2], lons[-2], lats[-1], lons[-1]\\n if speed_idx:\\n speed = speeds[-1]\\n else:\\n speed = None\\n distance, nmi, bearing, system, error, travel_time = self.process(start_lat, start_lon, stop_lat, stop_lon, speed)\\n ms.append(distance)\\n nmis.append(nmi)\\n bearings.append(bearings)\\n types.append(system)\\n travel_times.append(travel_time)\\n errors.append(error)\\n else:\\n past_first = True\\n\\n self.txt.append(f\\\"\\\\nTotal distance: {sum(nmis):.3f} nmi\\\")\\n if travel_times:\\n self.txt.append(f\\\"Total travel time: {get_travel_time(sum(travel_times))}\\\")\\n \\n if self.output_file:\\n with open(self.output_file, 'w') as outfile:\\n output_header = \\\"start_latitude, start_longitude, end_latitude, end_longitude, type, distance (m), distance (nmi), bearing (degrees)\\\"\\n if speed_idx:\\n output_header += \\\", travel time\\\"\\n outfile.write(output_header + \\\"\\\\n\\\")\\n for i in range(len(ms)):\\n output_csv_line = f\\\"{lats[i]}, {lons[i]}, {lats[i+1]}, {lons[i+1]}, {types[i]}, {ms[i]:.1f}, {nmis[i]:.3f}, {bearings[i]:.2f}\\\"\\n if speed_idx:\\n output_csv_line += f\\\", {get_travel_time(travel_times[i])}\\\"\\n\\n outfile.write(output_csv_line + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1a292091f0c1a45a370eea0f573382\",\n \"score\": \"0.5093255\",\n \"text\": \"def search_string(self, field):\\n self.display_list = []\\n\\n search_match = input(\\\"do you want to search {} for an exact match Y/[N]: \\\".format(field))\\n if search_match.lower() == 'y':\\n match = True\\n else:\\n match = False\\n\\n find = input(\\\"enter your search terms: \\\")\\n\\n for entry in self.active_log:\\n if match:\\n if re.match(find, entry[field], flags=re.IGNORECASE):\\n self.display_list.append(entry)\\n else:\\n if re.search(find, entry[field], flags=re.IGNORECASE):\\n self.display_list.append(entry)\\n\\n self.display()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1120b36df7074597a5ecd5c2aec94083\",\n \"score\": \"0.5090677\",\n \"text\": \"def search(self):\\r\\n\\t\\t# there should be a grammar defined and some lexer/parser.\\r\\n\\t\\t# instead of this quick-and-dirty implementation.\\r\\n\\r\\n\\t\\tsafeEnvDict = {\\r\\n\\t\\t\\t'freeSearch': self.freeSearch,\\r\\n\\t\\t\\t'extentSearch': self.extentSearch,\\r\\n\\t\\t\\t'indexSearch': self.indexSearch\\r\\n\\r\\n\\t\\t}\\r\\n\\t\\tfor col in self._dataFrame.columns:\\r\\n\\t\\t\\tsafeEnvDict[col] = self._dataFrame[col]\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tsearchIndex = eval(self._filterString, {\\r\\n\\t\\t\\t\\t\\t\\t\\t '__builtins__': None}, safeEnvDict)\\r\\n\\t\\texcept NameError as err:\\r\\n\\t\\t\\treturn [], False\\r\\n\\t\\texcept SyntaxError as err:\\r\\n\\t\\t\\treturn [], False\\r\\n\\t\\texcept ValueError as err:\\r\\n\\t\\t\\t# the use of 'and'/'or' is not valid, need to use binary operators.\\r\\n\\t\\t\\treturn [], False\\r\\n\\t\\texcept TypeError as err:\\r\\n\\t\\t\\t# argument must be string or compiled pattern\\r\\n\\t\\t\\treturn [], False\\r\\n\\t\\treturn searchIndex, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a87f9fbd87860933bf8b0e9f4dc4384\",\n \"score\": \"0.50863165\",\n \"text\": \"def on_ok_click(self):\\r\\n # get line edit text\\r\\n export_csv_dir = self.export_csv_dir_entry.text().replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n export_csv_name_entry_text = self.export_csv_name_entry.text()\\r\\n csv_ext = \\\".csv\\\" if not export_csv_name_entry_text.lower().endswith(\\\".csv\\\") else \\\"\\\"\\r\\n export_csv_path = os.path.join(export_csv_dir, export_csv_name_entry_text + csv_ext).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\r\\n dir_path_obj = Path(export_csv_dir)\\r\\n csv_path_obj = Path(export_csv_path)\\r\\n\\r\\n dir_exists = dir_path_obj.exists()\\r\\n dir_is_dir = dir_path_obj.is_dir()\\r\\n\\r\\n csv_exists = csv_path_obj.exists() and csv_path_obj.is_file()\\r\\n csv_format_valid = export_csv_path.lower().endswith(\\\".csv\\\") and not re.match(r'\\\\S*\\\\/ *.csv', export_csv_path)\\r\\n\\r\\n dir_valid = dir_exists and dir_is_dir\\r\\n csv_valid = csv_exists and csv_format_valid\\r\\n\\r\\n valid = dir_valid and csv_valid\\r\\n\\r\\n if valid:\\r\\n self.export_csv_dir = export_csv_dir\\r\\n self.export_csv_path = export_csv_path\\r\\n self.refresh_UI()\\r\\n if csv_exists:\\r\\n valid = display_yes_no_message(self, \\\"Provided .csv file already exists. Replace?\\\")\\r\\n if valid: # still\\r\\n if self.export_to_csv(self.export_csv_path):\\r\\n self.close()\\r\\n\\r\\n if not dir_valid:\\r\\n if not dir_exists:\\r\\n display_warning_message(self, \\\"Provided directory does not exist.\\\")\\r\\n elif not dir_is_dir:\\r\\n display_warning_message(self, \\\"Provided directory format is invalid.\\\")\\r\\n self.export_csv_dir = \\\"\\\"\\r\\n if not csv_valid:\\r\\n if dir_valid and not csv_exists and csv_format_valid and \\\\\\r\\n display_yes_no_message(self, \\\"Create file at \\\" + export_csv_path + \\\"?\\\"):\\r\\n try:\\r\\n csv_file = open(export_csv_path, \\\"w+\\\")\\r\\n csv_file.close()\\r\\n except IOError as error:\\r\\n display_warning_message(self, \\\"Failed to create provided .csv file: \\\" + export_csv_path)\\r\\n else:\\r\\n self.export_csv_dir = export_csv_dir\\r\\n self.export_csv_path = export_csv_path\\r\\n self.refresh_UI()\\r\\n if self.export_to_csv(self.export_csv_path):\\r\\n self.close()\\r\\n elif not csv_format_valid:\\r\\n display_warning_message(self, \\\"Be sure to specify a name for the .csv file.\\\")\\r\\n self.export_csv_path = \\\"\\\"\\r\\n\\r\\n self.refresh_UI()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e7e96f6c627f440d7d370dbab4b3e6\",\n \"score\": \"0.50795966\",\n \"text\": \"def get_filters():\\n\\n # static strings to be used as prompt when request for input\\n city_prompt = \\\"\\\\nPlease choose city(ies) (separated by comma) would you like to see data for: Chicago, New York City, Washington\\\\n>> \\\"\\n month_prompt = \\\"\\\\nPlease choose which month(s) from January to June (separated by comma) or 'all' you would like to see data for:\\\\n>> \\\"\\n dow_prompt = \\\"\\\\nPlease choose the day(s) of the week from Sunday to Saturday (separated by commaa) or 'all' you would like to see data for:\\\\n>> \\\"\\n\\n print(\\\"\\\\n\\\\n\\\", '*' * 20, \\\"Hello! Let's explore some US cities (Chicago, NYC, and Washington DC)bikeshare data!\\\", '*' * 20, \\\"\\\\n\\\")\\n print(\\\"\\\\n>>>> Type 'end' at any time if you would like to exit the program. <<<<\\\\n\\\")\\n print('=' * 80)\\n\\n # Collect user input for city\\n while True:\\n city = str(input(city_prompt))\\n city = [i.strip().lower() for i in city.split(',')]\\n\\n # if input is end, terminate the program\\n if 'end' in city:\\n print('*' * 20, \\\"Thank you, goodbye.\\\", '-' * 20)\\n raise SystemExit\\n else:\\n if list(filter(lambda x: x in CITY_DATA.keys(), city)) == city:\\n user_confirmation = validate(city, 'city(ies)')\\n if user_confirmation == 'Y':\\n break\\n else:\\n print(\\\"\\\\nLet's try this again!\\\")\\n else:\\n print(\\\"\\\\n --- Please enter valid option. ---- \\\\n\\\\n\\\\n\\\")\\n\\n # Collect user input for Month\\n print('=' * 80)\\n while True:\\n month = str(input(month_prompt))\\n month = [i.strip().lower() for i in month.split(',')]\\n # if input is end, terminate the program\\n if 'end' in month:\\n print('*' * 20, \\\"Thank you, goodbye.\\\", '-' * 20)\\n raise SystemExit\\n elif 'all' in month:\\n month = months\\n user_confirmation = validate(month, 'Month')\\n if user_confirmation == 'Y':\\n break\\n else:\\n print(\\\"\\\\nLet's try this again!\\\")\\n else:\\n if list(filter(lambda x: x in months, month)) == month:\\n user_confirmation = validate(month, 'Month')\\n if user_confirmation == 'Y':\\n break\\n else:\\n print(\\\"\\\\nLet's try this again!\\\")\\n else:\\n print(\\\"\\\\n --- Please enter valid option. ---- \\\\n\\\\n\\\\n\\\")\\n\\n # Collect user input for Day(s) of the week\\n print('=' * 80)\\n while True:\\n day = str(input(dow_prompt))\\n day = [i.strip().lower() for i in day.split(',')]\\n\\n # if input is end, terminate the program\\n if 'end' in day:\\n print('*' * 20, \\\"Thank you, goodbye.\\\", '-' * 20)\\n raise SystemExit\\n elif 'all' in day:\\n day = weekdays\\n user_confirmation = validate(day, 'Day of the Week')\\n if user_confirmation == 'Y':\\n break\\n else:\\n print(\\\"\\\\nLet's try this again!\\\")\\n else:\\n if list(filter(lambda x: x in weekdays, day)) == day:\\n user_confirmation = validate(day, 'Day of the Week')\\n if user_confirmation == 'Y':\\n break\\n else:\\n print(\\\"\\\\nLet's try this again!\\\")\\n else:\\n print(\\\"\\\\n --- Please enter valid option. ---- \\\\n\\\")\\n\\n # Print Summary\\n print('=' * 80)\\n print(\\\"\\\\n\\\\n\\\", '=' * 40, \\\"Summary of your selection:\\\", '=' * 40, \\\"\\\\n\\\")\\n print(\\\"City(ies): \\\", city, \\\"\\\\n\\\")\\n print(\\\"Month(s): \\\", month, \\\"\\\\n\\\")\\n print(\\\"Day(s) of the week: \\\", day, \\\"\\\\n\\\")\\n print('=' * 100)\\n return city, month, day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76074f2b71145444004d7e469f2629a5\",\n \"score\": \"0.50766665\",\n \"text\": \"def gui_ask_save_csv(output_data: list):\\n while True:\\n try:\\n # Build a dialogue box and make sure the window comes to the front of the desktop\\n root = tk.Tk()\\n root.withdraw()\\n root.attributes(\\\"-topmost\\\",True)\\n # Ask the user for the file name using a dialogue box\\n output_file = tk.filedialog.asksaveasfilename(defaultextension=\\\".csv\\\", filetypes=( (\\\"Comma Separated\\\", \\\"*.csv\\\"),(\\\"All Files\\\", \\\"*.*\\\") ))\\n # Kill the tk window\\n root.destroy()\\n\\n # Open the file and dump the contents of result_table into it\\n with open(output_file, \\\"w\\\", newline=\\\"\\\") as out_file: # newline=\\\"\\\" is required with python3 to avoid a \\\"double newline\\\"\\n writer = csv.writer(out_file, delimiter=\\\",\\\") \\n for row in output_data:\\n writer.writerow(row)\\n except Exception as error:\\n print(error)\\n print(\\\"\\\\n :: Choose a new location :: \\\")\\n else:\\n break\\n\\n print(\\\"\\\\n\\\\nOutput file can be found here:\\\\n \\\",output_file)\\n\\n print(\\\"\\\\n\\\\n\\\\nPress ENTER to continue.\\\")\\n input()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98d88d90e3e66a93316614535a17d09\",\n \"score\": \"0.5076338\",\n \"text\": \"def quicksearch_options(button, event):\\n if event.type != gtk.gdk.BUTTON_PRESS:\\n return False\\n menu=gtk.Menu()\\n item=gtk.MenuItem(_(\\\"Launch search\\\"))\\n item.connect('activate', self.do_quicksearch)\\n if not self.quicksearch_entry.get_text():\\n item.set_sensitive(False)\\n menu.append(item)\\n item=gtk.CheckMenuItem(_(\\\"Ignore case\\\"))\\n item.set_active(config.data.preferences['quicksearch-ignore-case'])\\n item.connect('toggled', lambda i: config.data.preferences.__setitem__('quicksearch-ignore-case', i.get_active()))\\n menu.append(item)\\n\\n item=gtk.MenuItem(_(\\\"Searched elements\\\"))\\n item.connect('activate', update_quicksearch_sources)\\n menu.append(item)\\n\\n menu.show_all()\\n menu.popup(None, None, None, 0, gtk.get_current_event_time())\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626cefd2c8d123a8ac032f4665b02d96\",\n \"score\": \"0.5059322\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n \\n check = False \\n\\n while True:\\n city = str(input(\\\"\\\\nChoose which cities you want to explore (chicago, new york city, washington): \\\").strip().lower())\\n if city not in (\\\"chicago\\\", \\\"new york city\\\", \\\"washington\\\"):\\n print(\\\"\\\\nSorry!That\\\\'s not an option. Please try again\\\")\\n continue\\n else:\\n print(\\\"\\\\nGood choice! It looks like you want to see data for: '{}' \\\".format(city.title()))\\n check_option()\\n break\\n # get user input for month (all, january, february, ... , june)\\n \\n while True:\\n month = str(input(\\\"From JANUARY to JUNE!Type the name of the month you want to filter ? (Use commas to list more than one):\\\").strip().lower())\\n \\n if month not in (\\\"january\\\", \\\"february\\\", \\\"march\\\", \\\"april\\\", \\\"may\\\", \\\"june\\\", \\\"all\\\"):\\n print(\\\"\\\\nSorry! That\\\\'s not an option. Please type in month name(or \\\\\\\"all\\\\\\\" to select all of them)\\\")\\n continue\\n else:\\n print(\\\"\\\\nOK! Confirm that you have chosen to filter by: '{} \\\".format(month.title()))\\n check_option()\\n break\\n \\n while True:\\n day = str(input(\\\"\\\\nChoose a day of the week and write to filter by:\\\").strip().lower())\\n \\n if day not in (\\\"monday\\\", \\\"tuesday\\\", \\\"wednesday\\\", \\\"thursday\\\", \\\"friday\\\", \\\"saturday\\\" , \\\"sunday\\\", \\\"all\\\"):\\n print(\\\"Sorry. Please type in valid day (i.e. Saturday) or \\\\\\\"all\\\\\\\" of them to select everyday:\\\")\\n continue \\n else:\\n print(\\\"\\\\nOK! Confirm that you have chosen to filter by: '{}' \\\".format(day.title()))\\n check_option()\\n break\\n \\n \\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n print(\\\"\\\\nYou selected '{}' as city, '{}' as month, and '{}' as day. \\\\nFiltering by your choices....\\\".format(city.title(), month.title(), day.title()))\\n print()\\n print('-'*40)\\n return city, month, day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a534c18be9d806605c037dcb3fe228d9\",\n \"score\": \"0.50479585\",\n \"text\": \"def search(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7756cb6df47aacde18ce9581d95a47f4\",\n \"score\": \"0.5045136\",\n \"text\": \"def take_file_inputs(inPointsCSV,inStatesCSV):\\n \\n#check file type: https://docs.python.org/2/library/mimetypes.html\\n #print \\\"Ingesting CSV files\\\"\\n \\n with open(WORKDIR+INSTATESCSV) as file:\\n state_reader(file)\\n #print \\\"States complete\\\"\\n poly_valid(statesList)\\n\\n with open(WORKDIR+INPOINTSCSV) as file:\\n point_reader(file)\\n #print \\\"Points complete\\\"\\n point_valid(pointsList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d622273c1baec899bbc3b0e9ecad8532\",\n \"score\": \"0.50368327\",\n \"text\": \"def generate_search(self, req_dict):\\n\\n\\t\\tsearches = {}\\n\\n\\t\\tfor arg in self.required_get_args_choose_one:\\n\\t\\t\\tif arg in req_dict:\\n\\t\\t\\t\\t(key, suffix) = self.split_arg(arg)\\n\\n\\t\\t\\t\\tif key in searches:\\n\\t\\t\\t\\t\\traise RestClientError(\\\"You can pass only one variation of %s in a request!\\\" % key)\\n\\n\\t\\t\\t\\tif key == 'street_number': searches[key] = StreetNumberSearch(req_dict[key + suffix])\\n\\t\\t\\t\\telif key == 'postal_code': searches[key] = AccountPaySearch(key, 'POSTAL_ZIP_CODE', req_dict[key + suffix], False)\\n\\t\\t\\t\\telif key == 'state_province': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\\n\\t\\t\\t\\telif key == 'city': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\\n\\t\\t\\t\\telif key == 'country': searches[key] = AccountPaySearch(key, key.upper(), req_dict[key + suffix], True)\\n\\t\\t\\t\\telif key == 'affiliate_name': searches[key] = AffiliateNameSearch(req_dict[key + suffix], suffix)\\n\\t\\t\\t\\telif key == 'username': searches[key] = UserNameSearch(key, 'ACCOUNT_USER', 'au', 'USER_NAME', req_dict[key + suffix], suffix, True)\\n\\t\\t\\t\\telif key == 'account_number': searches[key] = AccountNumberSearch(key, 'ACCOUNT', 'a', key.upper(), req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'account_did': searches[key] = DefaultSearch(key, 'DID_NUMBERS', 'dn', 'PHONE_NUMBER', req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'contact_telephone': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'email_address': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\\n\\t\\t\\t\\telif key == 'affiliate_refer_code': searches[key] = DefaultSearch(key, 'ACCOUNT_SUBSCRIBE_INFO', 'asi', key, req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'mac_address': searches[key] = DefaultSearch(key, 'DEVICE_UNIT', 'du', 'DEVICE_NAME', req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'order_number': searches[key] = DefaultSearch(key, 'PRODUCT_ORDERED', 'po', key, req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'last_name': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\\n\\t\\t\\t\\telif key == 'account_name': searches[key] = DefaultSearch(key, 'ACCOUNT', 'a', key, req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telif key == 'first_name': searches[key] = DefaultSearch(key, 'ACCOUNT_USER', 'au', key, req_dict[key + suffix], suffix, 'lower')\\n\\t\\t\\t\\telif key == 'partner_id': searches[key] = DefaultSearch(key, 'ACCOUNT', 'a', key, req_dict[key + suffix], suffix, 'none')\\n\\t\\t\\t\\telse: raise RestClientError(\\\"Encountered a key, %s, where the search function is undefined!\\\" % key)\\n\\n\\t\\tsearch_criteria = SearchCriteria()\\n\\n\\t\\tfor key in searches:\\n\\t\\t\\tsearches[key].process(search_criteria)\\n\\n\\t\\t(search_criteria.lower_bound, search_criteria.upper_bound) = self.get_bounds(req_dict[\\\"offset\\\"], req_dict[\\\"limit\\\"])\\n\\n\\t\\treturn search_criteria\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ee01c2f4515f3ec7a112c4bb628bbe0\",\n \"score\": \"0.50233895\",\n \"text\": \"def run():\\r\\n # running function introduction\\r\\n introduction()\\r\\n\\r\\n # running function web_scraper\\r\\n # retrieving filename\\r\\n filename = web_scraper()\\r\\n\\r\\n print(\\\"Search finished! Thank you for using the newegg.com web-scraper. \\\\nYour results have been written to a CSV file! \\\\nYour CSV file is called \\\" + str(filename) + \\\"! Your file directory is: \\\\n\\\" + os.getcwd() + \\\"\\\\\\\\products.csv\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa80fa408ebe8cb462f131dd6be92cb1\",\n \"score\": \"0.50124\",\n \"text\": \"def search(filename, term, exact, display, max, species, verbose):\\n configure_logging(verbose)\\n LOG = get_logger()\\n LOG.info(\\\"Search database: {}\\\".format(filename))\\n LOG.debug(\\\"Term: {}\\\".format(term))\\n LOG.debug(\\\"Exact: {}\\\".format(exact))\\n LOG.debug(\\\"Format: {}\\\".format(display))\\n LOG.debug(\\\"Max: {}\\\".format(max))\\n LOG.debug(\\\"Species: {}\\\".format(species))\\n\\n search_database.DATABASE = filename\\n\\n maximum = max if max >= 0 else None\\n\\n tstart = time.time()\\n result, status = search_database.search(filename, term, species, exact, False, maximum)\\n tend = time.time()\\n\\n LOG.debug(\\\"Num Results: {}\\\".format(result.num_results))\\n count = 0\\n\\n if status.error:\\n print(\\\"Error occurred: {}\\\".format(status.message))\\n sys.exit(-1)\\n\\n if len(result.matches) == 0:\\n print(\\\"No results found\\\")\\n sys.exit()\\n\\n headers = [\\\"ID\\\", \\\"SYMBOL\\\", \\\"POSITION\\\", \\\"MATCH_REASON\\\", \\\"MATCH_VALUE\\\"]\\n\\n if display in ('tab', 'csv'):\\n delim = '\\\\t' if display == 'tab' else ','\\n print(delim.join(headers))\\n for match in result.matches:\\n line = list()\\n line.append(match.ensembl_gene_id)\\n line.append(match.symbol)\\n line.append(\\\"{}:{}-{}\\\".format(match.chromosome, match.position_start, match.position_end))\\n line.append(match.match_reason)\\n line.append(match.match_value)\\n print(delim.join(map(str, line)))\\n count += 1\\n if count >= max > 0:\\n break\\n elif display == 'json':\\n tbl = []\\n for match in result.matches:\\n line = list()\\n line.append(match.ensembl_gene_id)\\n line.append(match.symbol if match.symbol else '')\\n line.append(\\\"{}:{}-{}\\\".format(match.chromosome, match.position_start, match.position_end))\\n line.append(match.match_reason)\\n line.append(match.match_value)\\n tbl.append(dict(zip(headers, line)))\\n count += 1\\n if count >= max > 0:\\n break\\n print(json.dumps({'data': tbl}, indent=4))\\n else:\\n tbl = []\\n for match in result.matches:\\n line = list()\\n line.append(match.ensembl_gene_id)\\n line.append(match.symbol)\\n line.append(\\\"{}:{}-{}\\\".format(match.chromosome, match.position_start, match.position_end))\\n line.append(match.match_reason)\\n line.append(match.match_value)\\n tbl.append(line)\\n count += 1\\n if count >= max > 0:\\n break\\n print(tabulate(tbl, headers))\\n\\n LOG.info(\\\"Search time: {}\\\".format(format_time(tstart, tend)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"247cbcfefc0cb91cee241d45a56958e6\",\n \"score\": \"0.5011512\",\n \"text\": \"def test_get_query_taxon_from_csv(): # ***Incomplete test\\n ##########################\\n # Arrange.\\n query_filename = \\\"query_filename\\\"\\n\\n ##########################\\n # Act.\\n #x = get_query_taxon_from_csv(query_filename)\\n\\n ##########################\\n # Assert.\\n assert True == True # ***Temporary.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"583aa1e30a2b1820e7a89a9346c83333\",\n \"score\": \"0.5007091\",\n \"text\": \"def get_filters():\\n print('\\\\nHello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington)\\n city_index = get_user_input('city', CITY_OPTIONS, range(1, len(CITY_OPTIONS)))\\n\\n # get user input for month (all, january, february, ... , june)\\n month_index = get_user_input('month', MONTH_OPTIONS, range(len(MONTH_OPTIONS)))\\n\\n # get user input for day of week (all, monday, tuesday, ... sunday)\\n day_index = get_user_input('day of week', DAY_OPTIONS, range(len(DAY_OPTIONS)))\\n\\n print(LINE)\\n return city_index, month_index, day_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2138909e7ab26b1357c522f8ecf8fed1\",\n \"score\": \"0.5002233\",\n \"text\": \"def openResults(filename,unusedInCsv=False):\\n if(not unusedInCsv):\\n # Opens the requested file and extracts the data\\n with open(filename, \\\"r\\\") as file:\\n reader = csv.reader(file)\\n dividedParcels = list(reader)\\n\\n spaceList = dividedParcels[0]\\n dividedParcels = dividedParcels[1:]\\n return spaceList, dividedParcels\\n else:\\n # Opens the requested file and extracts the data\\n with open(filename, \\\"r\\\") as file:\\n reader = csv.reader(file)\\n dividedParcels = list(reader)\\n\\n unusedParcels = dividedParcels[-1]\\n dividedParcels = dividedParcels[1:-1]\\n return unusedParcels, dividedParcels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c661d52e114b4b288082c9d15e93428c\",\n \"score\": \"0.50004387\",\n \"text\": \"def main():\\n if len(sys.argv) == 1:\\n run(get_Rdt_jokes())\\n elif len(sys.argv) == 2:\\n format = sys.argv[1].split(\\\".\\\", 1)\\n if len(format) != 2 or (len(format) == 2 and format[1] != \\\"csv\\\"):\\n print(\\\"Jokes not provided in CSV file. \\\")\\n sys.exit()\\n else:\\n run(read_csv(sys.argv[1]))\\n else:\\n print(\\\"Too many command line arguments. \\\")\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb38dbf27e9598ebf6ccee295e4fa64\",\n \"score\": \"0.49946225\",\n \"text\": \"def search(self, caseno=\\\"\\\", court=\\\"\\\", repno=\\\"\\\", supplier=\\\"\\\"):\\n driver = self.driver\\n #Click search tab\\n ##driver.find_element_by_css_selector(\\\"span\\\").click()\\n self.ccr_tabs(click_link=\\\"Search For Claims\\\")\\n\\n #Complete search fields\\n if caseno:\\n driver.find_element_by_id(\\\"caseNumber\\\").clear()\\n driver.find_element_by_id(\\\"caseNumber\\\").send_keys(caseno)#\\\"T20132011\\\"\\n if court:\\n Select(driver.find_element_by_id(\\\"court\\\")).select_by_visible_text(court)#\\\"Basildon (461)\\\"\\n if repno:\\n driver.find_element_by_id(\\\"representationOrderNumber\\\").clear()\\n driver.find_element_by_id(\\\"representationOrderNumber\\\").send_keys(repno)\\n if supplier:\\n driver.find_element_by_id(\\\"supplierID\\\").clear()\\n driver.find_element_by_id(\\\"supplierID\\\").send_keys(supplier)\\n\\n #Click the search button\\n driver.find_element_by_xpath(\\\"//input[@value='Search']\\\").click()\\n\\n #Wait for sarch results\\n WebDriverWait(driver,10).until(lambda driver: \\\"Search Results\\\" in driver.page_source\\n or \\\"No claims found\\\" in driver.page_source,driver)\\n\\n #If we've results examine them\\n if \\\"Search Results\\\" in driver.page_source:\\n print \\\"Search has results as follows:\\\"\\n #Loop to move through any multi-page results\\n keep_going = True\\n while keep_going:\\n #Examine dispalyed results\\n self.search_results()\\n #Is there a \\\"Next\\\" button\\n next_buttons = [e for e in driver.find_elements_by_class_name(\\\"button\\\") if e.get_attribute(\\\"value\\\")==u\\\"Next\\\"]\\n ##print \\\"nb\\\", next_buttons\\n #If next button click it to advance to next page, otherwise exit loop\\n if next_buttons:\\n next_buttons[0].click()\\n else:\\n keep_going = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1837316011e29e7a87aca61cc72704a\",\n \"score\": \"0.49846646\",\n \"text\": \"def search_place( data ):\\n target= None\\n print( \\\" < 1 > City, Town, Province, State or Country\\\" )\\n print( \\\" < 2 > Latitude and Longitude\\\" )\\n z= input( \\\"CHOICE? \\\" )\\n if z == \\\"1\\\":\\n target= search_name( data )\\n elif z == \\\"2\\\":\\n target= search_lat_lon( data )\\n return target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c3c16ccc66ab0df633a71a66f9df52\",\n \"score\": \"0.49843073\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\\n while True:\\n try:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0e961ac28d3ced5c803803af276d90\",\n \"score\": \"0.4975195\",\n \"text\": \"def search_by_url(self) -> None:\\n url = input(Colors.p.value + \\\"Please enter URL:\\\\n\\\" + Colors.end.value)\\n # check if the given url is valid\\n if not check_valid_url(url):\\n print_err(\\\"Incorrect url - check yourself!\\\")\\n self.search_by_url()\\n else:\\n # present play options for the chosen series\\n self.__player.goto(series_num=None, url=url)\\n self.play_options()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc2c4bd8878affe4a91e9048e3db511\",\n \"score\": \"0.4974744\",\n \"text\": \"def main():\\n # takes a directory name as its command line argument\\n dir_name = sys.argv[1]\\n\\n # creates stop_word hash table\\n stop_table = HashTable()\\n stop_table = import_stopwords(\\\"stop_words.txt\\\", stop_table)\\n\\n # create an instance of SearchEngine by passing the directory name\\n search_engine = SearchEngine(dir_name, stop_table)\\n\\n # enter an infinite loop\\n print(\\\"Enter 'q' to exit program\\\")\\n print(\\\"Enter 's:{query}' to search\\\")\\n while True:\\n\\n # prompt user for input\\n raw_query = str(input(\\\"Enter query: \\\"))\\n\\n # if input is \\\"q\\\"\\n if raw_query == \\\"q\\\":\\n break\\n\\n elif raw_query[0:2] == \\\"s:\\\":\\n search_engine.search(raw_query[2:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f766fcbfb0df68ae94fd292860cecb\",\n \"score\": \"0.49710763\",\n \"text\": \"def execute_search():\\n tw = twitter.Twitter(CATALOG_FILENAME, STOP_WORDS_FILENAME) \\n logging.info(\\\"[Main] Initializing ...\\\")\\n tw.load_tweets_and_load_index()\\n logging.info(\\\"[Main] Initialized. %s docs loaded.\\\", \\\"{:,}\\\".format(tw.tweets_count()))\\n\\n query = None\\n while True:\\n query = input(QUERY_INPUT_MESSAGE)\\n if query == '':\\n continue\\n if query == 'quit':\\n break\\n\\n search_results = tw.search_tweets(query)\\n print(search_results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62b78e4ea54af89a4907ffdec719aaea\",\n \"score\": \"0.49678212\",\n \"text\": \"def get_file_list(dir_path=\\\"data/simulation/sim_data/\\\",\\n str_has=['sim'], str_inc=['0001', '0002'], ftype='.csv',\\n interact=True):\\n import os.path\\n from os import listdir\\n import tkinter\\n from tkinter.filedialog import askdirectory\\n root = tkinter.Tk() # this will help control file dialog boxes!\\n\\n # Check if the path specified includes at least 1 file of the file type\\n success, err_msg = check_dir_path(dir_path, [ftype], 1, False)\\n\\n while not success and len(dir_path):\\n # If the \\\"default\\\" directory failed the check,\\n # Either raise that error, or ask for a different directory!\\n # (Will exit If you X out of the dialog, to avoid getting stuck)\\n if interact:\\n root.lift()\\n root.focus_force()\\n dir_path = askdirectory(parent=root, title=err_msg,\\n initialdir=dir_path)\\n success, err_msg = check_dir_path(dir_path, [ftype], 1, False)\\n else:\\n root.destroy\\n raise AssertionError(err_msg)\\n if not len(dir_path):\\n root.destroy()\\n raise AssertionError(\\\"You Closed the Dialog Window Without a Folder!\\\")\\n root.destroy()\\n\\n \\\"\\\"\\\"\\n If we've gotten this far, we found files!\\n So now, we will filter the list based on the parameters given, and return\\n the result as a file list to open.\\n \\\"\\\"\\\"\\n full_dir = listdir(dir_path)\\n files_wanted = []\\n for file in full_dir:\\n # For each file, decide if it passes\\n for str_AND in str_has:\\n # IF any of these fail, ignore the file.\\n if str_AND in file:\\n pass\\n else:\\n break\\n else:\\n # Only does this if all \\\"Required\\\" strings pass\\n for str_OR in str_inc:\\n # If ANY string is found in the file,\\n # Add it to the list and then go to next file\\n if str_OR in file:\\n files_wanted.append(os.path.join(dir_path, file))\\n break\\n else:\\n pass\\n return tuple(files_wanted), dir_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c2478d8324046e70ce705ab562bcfd0\",\n \"score\": \"0.49670327\",\n \"text\": \"def get_filters():\\n print('Hello! Let\\\\'s explore some US bikeshare data!')\\n\\n #The following loop will get the user's choice of which city to display results.\\n while True:\\n #New Edit gives the user more choices to pick the city to ease typing\\n city = input('Would you like to see data for Chicago/Ch, New York City/NYC, or Washington/WA: ').lower()\\n if city not in CITY_DATA.keys() and city not in ['ch', 'nyc', 'wa']:\\n print('this city is not available, please enter one of the three provided cities')\\n continue\\n else:\\n if city == 'ch':\\n city = 'chicago'\\n elif city == 'nyc':\\n city = 'new york city'\\n elif city == 'wa':\\n city = 'washington'\\n city = CITY_DATA[city]\\n break\\n\\n #The following loop will get the user choice whether to use filters or display unfiltered results.\\n while True:\\n filter = input('Would you like to filter the data by month or day, or not at all? \\\\nPlease chose(yes/no): ').lower()\\n if filter == 'yes':\\n filter = True\\n elif filter == 'no':\\n filter = False\\n else:\\n print('Please enter a valid answer!')\\n continue\\n break\\n\\n\\n #the following loop will get the user choice for filters: whether by month, by day, or include both.\\n while True:\\n if filter:\\n choice = input('What filter do you want to apply? please choose (month/day/both) ').lower()\\n if choice not in ['month', 'day', 'both']:\\n print('Please Enter a valid answer!')\\n continue\\n if choice == 'month':\\n month = input('Which month - January, February, March, April, May, or June? ')\\n if month not in months.keys():\\n print('This month is invalid, Please Try again.')\\n continue\\n else:\\n month = months[month]\\n day = days\\n break\\n elif choice == 'day':\\n day = input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? ').lower()\\n if day not in days.keys():\\n print('This day is invalid. Please try again.')\\n continue\\n else:\\n day = days[day]\\n month = months\\n break\\n elif choice == 'both':\\n month = input('Which month - January, February, March, April, May, or June? ').lower()\\n if month not in months.keys():\\n print('This month is invalid. Please try again')\\n continue\\n month = months[month]\\n day = input('Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? ').lower()\\n if day not in days.keys():\\n print('This day is invalid. Please try again')\\n continue\\n day = days[day]\\n break\\n else:\\n day = days\\n month = months\\n break\\n\\n #Print Separator\\n print('-'*40)\\n #Return chosen values\\n return city, month, day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ff6a6a9716a89fc825f4b27bcdf4c0\",\n \"score\": \"0.495757\",\n \"text\": \"def _run(self, *args, url=None):\\n\\n with Provider_Customers_Table() as _:\\n pass\\n\\n url = url if url else self._get_urls()[0]\\n Rel_File(self.path, self.csv_dir, url).parse_file()\\n utils.delete_paths([self.csv_dir, self.path])\\n\\n # Fills these rov++ specific tables\\n with ASes_Table(clear=True) as _as_table:\\n _as_table.clear_table()\\n _as_table.fill_table()\\n # creates and closes table\\n with AS_Connectivity_Table(clear=True) as _conn_table:\\n _conn_table.fill_table()\\n\\n with Relationships_Table(clear=True) as db:\\n db.fill_table()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994280,"cells":{"query_id":{"kind":"string","value":"d1a63422da033794916a714b5fd0b99a"},"query":{"kind":"string","value":"Given the following, generate a list of dict results split by fiscal years/quarters/months"},"positive_passages":{"kind":"list like","value":[{"docid":"68bc9b80b22032ac74bf4c74d2f56c9a","score":"0.49230993","text":"def bolster_missing_time_periods(filter_time_periods, queryset, date_range_type, columns):\n min_date, max_date = min_and_max_from_date_ranges(filter_time_periods)\n results = create_full_time_periods(min_date, max_date, date_range_type, columns)\n\n for row in queryset:\n for item in results:\n same_year = str(item[\"time_period\"][\"fy\"]) == str(row[\"fy\"])\n same_period = str(item[\"time_period\"][date_range_type]) == str(row[date_range_type])\n if same_year and same_period:\n for column_name, column_in_queryset in columns.items():\n item[column_name] = row[column_in_queryset]\n\n for result in results:\n result[\"time_period\"][\"fiscal_year\"] = result[\"time_period\"][\"fy\"]\n del result[\"time_period\"][\"fy\"]\n return results","title":""}],"string":"[\n {\n \"docid\": \"68bc9b80b22032ac74bf4c74d2f56c9a\",\n \"score\": \"0.49230993\",\n \"text\": \"def bolster_missing_time_periods(filter_time_periods, queryset, date_range_type, columns):\\n min_date, max_date = min_and_max_from_date_ranges(filter_time_periods)\\n results = create_full_time_periods(min_date, max_date, date_range_type, columns)\\n\\n for row in queryset:\\n for item in results:\\n same_year = str(item[\\\"time_period\\\"][\\\"fy\\\"]) == str(row[\\\"fy\\\"])\\n same_period = str(item[\\\"time_period\\\"][date_range_type]) == str(row[date_range_type])\\n if same_year and same_period:\\n for column_name, column_in_queryset in columns.items():\\n item[column_name] = row[column_in_queryset]\\n\\n for result in results:\\n result[\\\"time_period\\\"][\\\"fiscal_year\\\"] = result[\\\"time_period\\\"][\\\"fy\\\"]\\n del result[\\\"time_period\\\"][\\\"fy\\\"]\\n return results\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d277920cd02ddd135c01e630b1d38afa","score":"0.6372415","text":"def get_fiscalyear_revenue_timeseris_data(client_id:str)->List[Dict]:\n df = load_process_data(client_id)\n fiscalyears = df['date'].dt.to_period('A-MAR').astype(str).astype(int).unique().tolist()\n fiscalyears = sorted(fiscalyears, reverse=True)\n fy_names = list(map(lambda y: f'{y-1}-{y%1000}', fiscalyears))\n \n # return [get_yearly_quterly_montly_weekly_revenue(fy, df) for fy in fiscalyears]\n return {fy_names[i]:get_yearly_quterly_montly_weekly_revenue(fy, df) for i, fy in enumerate(fiscalyears)}","title":""},{"docid":"f427af50c15fe673a67eea1e85ca63b3","score":"0.61781615","text":"def generate_dictionary_for_quarterwise_data(stock, columnName):\n result = {}\n stock.Date = pd.to_datetime(stock.Date)\n for index, row in stock.iterrows():\n try:\n q = (row.Date.month-1)//3 + 1\n year = row.Date.year\n month = row.Date.month\n res = result.get(year, {})\n # amount = re.findall(r\"\\d+.?\\d*\",row[\"Revenue\"])[0]\n amount = row[columnName]\n q = \"1q\" if 1 <= month <= 3 else \"2q\" if 4 <= month <= 6 else \"3q\" if 6 <= month <= 9 else \"4q\"\n val = res.get(q, [])\n val.append(float(amount))\n res[q] = val\n result[year] = res\n except:\n continue\n return result","title":""},{"docid":"178ea43450a559fb8638aea5a018f058","score":"0.60559785","text":"def get_yearly_quterly_montly_weekly_revenue(fiscalyear, df):\n df2 = df[df['date'].dt.to_period('A-MAR')==fiscalyear].copy()\n yearly_revenue = df2['debit'].sum().round()\n fy_name = f'{fiscalyear-1}-{fiscalyear%1000}'\n \n quarterly_agg = df2.groupby([df2.date.dt.quarter], sort=False).agg({'debit' :sum}). \\\n rename(columns={'debit':'revenue'}).reset_index()\n quarterly_agg['quarter'] = list(range(1,5))\n quterly_revenue = quarterly_agg[['quarter', 'revenue']].round().to_dict(orient='records')\n \n monthly_agg = df2.groupby(df.date.dt.strftime('%b-%Y'), sort=False).agg({'debit' :sum}). \\\n reset_index(). \\\n rename(columns={'date':'month','debit':'monthly_revenue'})\n monthly_revenue = monthly_agg.round().to_dict(orient='records')\n \n weekly_agg = df2.groupby([df2.date.dt.week], sort=False).agg({'debit' :sum}). \\\n rename(columns={'debit':'weekly_revenue'}).reset_index()\n weekly_agg['week'] = list(range(1,53))\n weekly_revenue = weekly_agg[['week', 'weekly_revenue']].round().to_dict(orient='records')\n \n # return {'fiscalyear': fy_name,\n # 'data': {'annual_revenue': yearly_revenue,\n # 'quarterly_revenue': quterly_revenue,\n # 'monthly_revenue': monthly_revenue,\n # 'weekly_revenue': weekly_revenue}\n # }\n \n return {'weekly': weekly_revenue, \n 'monthly': monthly_revenue, \n 'quarterly': quterly_revenue}","title":""},{"docid":"00802fde5706e77a6bab67351fd50154","score":"0.60281336","text":"def generate_dictionary_for_quarterwise_growthrate_data(data):\n gr_dic = {}\n keys = list(data.keys())\n array = [''] * (len(keys)*4)\n array_index = 0\n for key in data:\n lists = data.get(key)\n array_index += 4 - len(lists.keys())\n for lis in lists:\n if math.isnan(lists.get(lis)[0]):\n array[array_index] = ''\n else:\n array[array_index] = lists.get(lis)[0]\n array_index = array_index + 1\n if (array.count('')) > ((len(keys) * 4) / 2):\n return gr_dic\n\n for i in range(4, len(keys)*4, 4):\n res = [array[i], array[i+1], array[i+2], array[i+3]]\n avg = np.mean(list(filter(lambda i: isinstance(i, float), res)))\n if np.isnan(avg):\n pass\n else:\n array[i] = avg\n\n gr_array = [''] * (len(keys)*4)\n for i in range(0, len(keys)*4-1):\n x = array[i]\n y = array[i+1]\n if x == '' and y == '':\n continue\n if y == '' or y == 0:\n continue\n if x == '':\n gr_array[i] = 1\n else:\n gr_array[i] = (x - y) / y\n index = 0\n for key in data:\n gr_dic[key] = [gr_array[index], gr_array[index+1],\n gr_array[index+2], gr_array[index+3]]\n index = index + 4\n return gr_dic","title":""},{"docid":"26a91c7045deb0a63f30a553d4bf294c","score":"0.5766984","text":"def of_year(cls, year):\n start = datetime(year, 1, 1)\n start_quarter = list(\n rrule(MONTHLY, interval=3, dtstart=start, count=4)\n )\n end_quarter = [\n date + relativedelta(months=3, days=-1) for date in start_quarter\n ]\n return [cls(*item) for item in list(zip(start_quarter, end_quarter))]","title":""},{"docid":"ebc762741203c66ae9778ad90e4a8eea","score":"0.5722563","text":"def decode_periods(temporal_schema, start_date, end_date, time_step):\n requested_periods = {}\n if start_date is None:\n return requested_periods\n if isinstance(start_date, datetime.date):\n start_date = start_date.strftime('%Y-%m-%d')\n\n td_time_step = datetime.timedelta(days=time_step)\n steps_per_period = int(round(365./time_step))\n\n if end_date is None:\n end_date = datetime.datetime.now().strftime('%Y-%m-%d')\n if isinstance(end_date, datetime.date):\n end_date = end_date.strftime('%Y-%m-%d')\n\n if temporal_schema is None:\n periodkey = start_date + '_' + start_date + '_' + end_date\n requested_period = list()\n requested_period.append(periodkey)\n requested_periods[start_date] = requested_period\n return requested_periods\n\n if temporal_schema == 'M':\n start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')\n end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')\n delta = relativedelta(months=time_step)\n requested_period = []\n while start_date <= end_date:\n next_date = start_date + delta\n periodkey = str(start_date)[:10] + '_' + str(start_date)[:10] + '_' + str(next_date - relativedelta(days=1))[:10]\n requested_period.append(periodkey)\n requested_periods[start_date] = requested_period\n start_date = next_date\n return requested_periods\n\n # Find the exact start_date based on periods that start on yyyy-01-01\n firstyear = start_date.split('-')[0]\n new_start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')\n if temporal_schema == 'A':\n dbase = datetime.datetime.strptime(firstyear+'-01-01', '%Y-%m-%d')\n while dbase < new_start_date:\n dbase += td_time_step\n if dbase > new_start_date:\n dbase -= td_time_step\n start_date = dbase.strftime('%Y-%m-%d')\n new_start_date = dbase\n\n # Find the exact end_date based on periods that start on yyyy-01-01\n lastyear = end_date.split('-')[0]\n new_end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')\n if temporal_schema == 'A':\n dbase = datetime.datetime.strptime(lastyear+'-12-31', '%Y-%m-%d')\n while dbase > new_end_date:\n dbase -= td_time_step\n end_date = dbase\n if end_date == start_date:\n end_date += td_time_step - datetime.timedelta(days=1)\n end_date = end_date.strftime('%Y-%m-%d')\n\n # For annual periods\n if temporal_schema == 'A':\n dbase = new_start_date\n yearold = dbase.year\n count = 0\n requested_period = []\n while dbase < new_end_date:\n if yearold != dbase.year:\n dbase = datetime.datetime(dbase.year,1,1)\n yearold = dbase.year\n dstart = dbase\n dend = dbase + td_time_step - datetime.timedelta(days=1)\n dend = min(datetime.datetime(dbase.year, 12, 31), dend)\n basedate = dbase.strftime('%Y-%m-%d')\n start_date = dstart.strftime('%Y-%m-%d')\n end_date = dend.strftime('%Y-%m-%d')\n periodkey = basedate + '_' + start_date + '_' + end_date\n if count % steps_per_period == 0:\n count = 0\n requested_period = []\n requested_periods[basedate] = requested_period\n requested_period.append(periodkey)\n count += 1\n dbase += td_time_step\n if len(requested_periods) == 0 and count > 0:\n requested_periods[basedate].append(requested_period)\n else:\n yeari = start_date.year\n yearf = end_date.year\n monthi = start_date.month\n monthf = end_date.month\n dayi = start_date.day\n dayf = end_date.day\n for year in range(yeari,yearf+1):\n dbase = datetime.datetime(year,monthi,dayi)\n if monthi <= monthf:\n dbasen = datetime.datetime(year,monthf,dayf)\n else:\n dbasen = datetime.datetime(year+1,monthf,dayf)\n while dbase < dbasen:\n dstart = dbase\n dend = dbase + td_time_step - datetime.timedelta(days=1)\n basedate = dbase.strftime('%Y-%m-%d')\n start_date = dstart.strftime('%Y-%m-%d')\n end_date = dend.strftime('%Y-%m-%d')\n periodkey = basedate + '_' + start_date + '_' + end_date\n requested_period = []\n requested_periods[basedate] = requested_period\n requested_periods[basedate].append(periodkey)\n dbase += td_time_step\n return requested_periods","title":""},{"docid":"9e94a8540c8064bbc321eabd2a95885c","score":"0.5685768","text":"def new_dictionary():\r\n years = {str(i) for i in range(1888, 2018)}\r\n future_dict = file_num()\r\n countries = countries_all()\r\n set1 = set()\r\n for i in countries:\r\n set1.add(str(\" \".join(i)))\r\n dict1 = dict()\r\n for element in future_dict:\r\n values = set()\r\n for j in element:\r\n if j in set1:\r\n values.add(j)\r\n if len(j) == 6:\r\n if str(j[1] + j[2] + j[3] + j[4]) in years:\r\n key = str(j[1] + j[2] + j[3] + j[4])\r\n if key in dict1:\r\n dict1[key].update(values)\r\n else:\r\n dict1[key] = values\r\n return dict1","title":""},{"docid":"4614734baee919465ee37430332517a5","score":"0.56512946","text":"def names():\n\n year_extract = '201'\n avg_sql = (f'SELECT city, substr(start_date, INSTR(start_date, {year_extract}), 4) as yr, ' +\n 'avg(duration) as avg_trip ' +\n 'FROM trip ' +\n 'JOIN station ON trip.start_station_id = station.id ' +\n 'GROUP BY city, yr'\n )\n\n avg_city = pd.read_sql(avg_sql, engine2.connect())\n avg_city['avg_trip'] = avg_city['avg_trip']/60\n avg_city['avg_trip'] = avg_city['avg_trip'].round()\n avg_list = avg_city.to_dict('records')\n\n yr_2013 = []\n yr_2014 = []\n yr_2015 = []\n\n for item in avg_list:\n data = {}\n for k,v in item.items():\n if(k=='city'):\n data['axis'] = v\n if(k=='avg_trip'):\n data['value'] = v\n if(k=='yr'and v=='2013'):\n data['name'] = v\n yr_2013.append(data)\n elif(k=='yr'and v=='2014'):\n data['name'] = v\n yr_2014.append(data)\n elif(k=='yr'and v=='2015'):\n data['name'] = v\n yr_2015.append(data)\n \n all_yr = [yr_2015, yr_2014, yr_2013]\n\n\n return jsonify(all_yr)","title":""},{"docid":"13401000b7a8392ec00dfa1233dc031f","score":"0.56424075","text":"def precipitation():\n# * Query for the dates and precipitation observations from the last year.\n# * Convert the query results to a Dictionary using `date` as the key and `prcp` as the value.\n# * Return the json representation of your dictionary.\n end_date_str = session.query(Measurement.date).order_by(Measurement.date.desc()).first().date\n dt_year = int(end_date_str[:4])\n dt_month = int(end_date_str[5:7])\n dt_day = int(end_date_str[8:10])\n end_date = dt.date(dt_year, dt_month, dt_day)\n start_date = end_date - dt.timedelta(days=365)\n\n#Select only the date and prcp values\n measurement_year = session.query(Measurement.date, Measurement.prcp).\\\n filter(Measurement.date >= start_date).\\\n filter(Measurement.date <= end_date).all()\n\n year_totals = []\n for result in measurement_year:\n year = {}\n year[\"date\"] = result[0]\n year[\"prcp\"] = result[1]\n year_totals.append(year)\n\n return jsonify(year_totals)","title":""},{"docid":"0236d9ce3ec3917df7b8e2ccc7e430f6","score":"0.5638206","text":"def create_dividend(stock, corporate):\n corporate['Ex Date'] = pd.to_datetime(\n corporate['Ex Date'], errors='coerce')\n stock['Date'] = pd.to_datetime(stock['Date'], errors='coerce')\n\n dividend = corporate[corporate['Purpose'].str.contains(\"Dividend\")]\n result = {}\n for index, row in dividend.iterrows():\n try:\n year = row[\"Ex Date\"].year\n month = row[\"Ex Date\"].month\n amount = re.findall(r\"\\d+.?\\d*\", row[\"Purpose\"])[0]\n res = result.get(year, {})\n q = \"1q\" if 1 <= month <= 3 else \"2q\" if 4 <= month <= 6 else \"3q\" if 6 <= month <= 9 else \"4q\"\n val = res.get(q, [])\n val.append(float(amount))\n res[q] = val\n result[year] = res\n except:\n pass\n for year, quaters in result.items():\n for q, a in quaters.items():\n try:\n quaters[q] = sum(a)/len(a)\n except:\n pass\n result[year] = quaters\n divList = list()\n for index, row in stock.iterrows():\n try:\n year = row[\"Date\"].year\n month = row[\"Date\"].month\n q = \"1q\" if 1 <= month <= 3 else \"2q\" if 4 <= month <= 6 else \"3q\" if 6 <= month <= 9 else \"4q\"\n if result.get(year) != None:\n if result.get(year).get(q) != None:\n divList.append(result.get(year).get(q))\n else:\n divList.append(0)\n else:\n divList.append(0)\n except:\n pass\n stock[\"Dividend Value\"] = divList\n return stock","title":""},{"docid":"ccce1eca2c258f31a2441f1ddf72cafa","score":"0.5582218","text":"def search_years(content: dict, start=0, step=0) -> dict:\n search_year = content['value']\n years_list = __get_years_(search_year, start, step)\n years = get_years_list(years_list)\n return years","title":""},{"docid":"a46c3841d2c3dde7cd82f5d9bc7d700d","score":"0.55810326","text":"def precip_records():\n session = Session(engine)\n date = dt.datetime(2017, 8, 23) - dt.timedelta(days=365)\n results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date > date).all()\n session.close()\n year_precip = []\n for date, prcp in results:\n precip_dict = {}\n precip_dict[\"Date\"] = date\n precip_dict[\"Precipitation\"] = prcp\n year_precip.append(precip_dict)\n\n return jsonify(year_precip)","title":""},{"docid":"4cd8735069f5206b1317466d442f7585","score":"0.55157673","text":"def grouping(data, separator='day'):\n output = []\n results = dict(sorted(data.items()))\n\n if separator == 'year':\n\n for years in results:\n\n months = dict(sorted(results[years].items()))\n year_mean = []\n first_day_date = None\n first_month = None\n\n for month in months:\n if first_day_date is None:\n first_day_date = list(months[month][::-1][0].items())[0][0]\n first_month = month\n year_mean += [float(day.popitem()[1]) for day in months[month]]\n\n date = '{}/{}/{}'.format(first_day_date, first_month, years)\n output.append((date, '{0:.2f}'.format(statistics.mean(year_mean))))\n\n elif separator == 'month':\n\n for years in results:\n months = dict(sorted(results[years].items()))\n\n for month in months:\n first_day_date = list(months[month][::-1][0].items())[0][0]\n mean = \"{0:.2f}\".format(statistics.mean([float(day.popitem()[1]) for day in months[month]]))\n date ='{}/{}/{}'.format(first_day_date,month,years)\n output.append((date,mean))\n\n elif separator == 'day':\n for years in results:\n months = dict(sorted(results[years].items()))\n for month in months:\n for day_data in months[month][::-1]:\n day = day_data.popitem()\n date = '{}/{}/{}'.format(day[0],month,years)\n output.append((date,day[1]))\n return output, separator","title":""},{"docid":"d2d209866c3a74514aa3bc378d98b1fb","score":"0.55133194","text":"def get_quarterly_new_members(quarters, members):\n response = {}\n for pair in quarters:\n year = pair[0]\n quarter = pair[1]\n quarter_desc = '{}-Q{}'.format(year, quarter)\n date_range = REPORT_QUARTERS[quarter-1]\n start = '{}-{}'.format(year, date_range[0])\n if quarter == 4:\n year += 1\n end = '{}-{}'.format(year, date_range[1])\n response[quarter_desc] = members.count_new_households(start, end)\n return response","title":""},{"docid":"893ec5a6517aeda76dae8cf830c8e8c6","score":"0.54815346","text":"def gen_financials():\n net_worth = random.randrange(5, 33) * 10000\n liquid_assets = net_worth / random.randrange(1, 10)\n annual_income = random.randrange(7, 42) * 5000\n\n financials = {'net_worth' : net_worth, 'liquid_assets' : liquid_assets,\n 'annual_income' : annual_income}\n\n return financials","title":""},{"docid":"7f498f103f77b92e8475de8e5010918d","score":"0.54718643","text":"def construct_years_dict(hurricanes):\n years_dict = {}\n for hurricane in hurricanes:\n current_year = hurricanes[hurricane]['Year']\n if current_year not in years_dict:\n years_dict[current_year] = [hurricanes[hurricane]]\n else:\n years_dict[current_year].append(hurricanes[hurricane])\n return years_dict","title":""},{"docid":"eda4726c2b968a7e6e4a9350cab631a4","score":"0.5427099","text":"def get_years(xlsx_files, filename_company):\n years_dict = {}\n\n for xl_path, page_ref in xlsx_files.items(): # loop through all xlsx files\n path, filename = os.path.split(xl_path) # split file and path\n work_book = openpyxl.load_workbook(xl_path) # load workbook\n work_sheet = work_book[page_ref] # take sheet reference\n flg = 1\n count = 0\n years = []\n for j in range(1, 10):\n\n \n for i in range(1, 10):\n if work_sheet.cell(row=j, column=i).value is not None:\n match_obj = re.search(\"^2\\d{3}$|^2\\d{3}\\s2\\d{3}\\s2\\d{3}$|^2\\d{3}\\s2\\d{3}$|^\\s2\\d{3}$|(^2\\d{3}){1}|[^,]\\d{3}$\", str(work_sheet.cell(row=j, column=i).value))\n \n \n if match_obj:\n match_obj_space=str(match_obj.group(0))\n \n if \" \" in match_obj_space: \n \n years.append(str.split(work_sheet.cell(row=j, column=i).value)) \n years= [val for sublist in years for val in sublist] \n flg=0\n break\n else:\n count += 1\n years.append(match_obj.group(0))\n\n if count == 3: \n flg=0\n break\n years.sort(reverse = True)\n years_dict[filename_company[filename]] = years\n if flg == 0:\n break\n op.writelines(str(years_dict))\n return years_dict","title":""},{"docid":"dccb29b8d5a25577623024f45c7b8f73","score":"0.54231036","text":"def _aggregate_by_year(historical_data_dict: dict):\n\n converted_response = {}\n\n for datapoint in historical_data_dict:\n converted_response[datapoint['date'].year] = datapoint['value']\n\n return converted_response","title":""},{"docid":"b5dd786d07e8395bc784dacf81e3396a","score":"0.54199255","text":"def get_yearly_report(self):\n ans = dict()\n\n for year in range(Settings.CF_OPENED_YEAR, date.today().year+1):\n current_year_stats = self._get_insights_for_filtered(lambda entity: entity.get_time().year == year)\n if current_year_stats:\n ans[year] = current_year_stats\n\n return ans","title":""},{"docid":"9df85e2d186917920e58f893c23c2126","score":"0.54189634","text":"def _transform_financial_stmt(std_financials_list: list, tag_filter_list: list):\n results = {}\n\n for financial in std_financials_list:\n\n if (tag_filter_list is None or\n financial.data_tag.tag in tag_filter_list):\n results[financial.data_tag.tag] = financial.value\n\n return results","title":""},{"docid":"d45fd795c4a8638d9679fef4b2d4e512","score":"0.5417625","text":"def parse_flight_data() -> Dict[str, List[Tuple[str, str, int]]]:\n data_paths = {\n 'test': 'data/test_flight_data.csv',\n '11-19': 'data/Nov2019_flight_data.csv',\n '12-19': 'data/Dec2019_flight_data.csv',\n '01-20': 'data/Jan2020_flight_data.csv',\n '02-19': 'data/Feb2019_flight_data.csv',\n '03-19': 'data/Mar2019_flight_data.csv',\n '04-19': 'data/Apr2019_flight_data.csv',\n '05-19': 'data/May2019_flight_data.csv',\n '06-19': 'data/Jun2019_flight_data.csv',\n '07-19': 'data/Jul2019_flight_data.csv',\n '08-19': 'data/Aug2019_flight_data.csv',\n }\n flight_data = dict()\n for month_year, data_path in data_paths.items():\n flight_data[month_year] = list()\n with open(data_path) as f:\n lines_to_skip = 1\n i = 0\n for line in f:\n if i < lines_to_skip:\n i += 1\n continue\n split_line = line.rstrip().split(',')\n try:\n int(split_line[11])\n except:\n raise Exception('Bad line: {}'.format(line))\n flight_data[month_year].append((split_line[4], split_line[8], int(split_line[11])))\n return flight_data","title":""},{"docid":"19adcc20050b657c0d3e4e7a9dc0f423","score":"0.5409015","text":"def generate_dates():\n current_year = datetime.now().year\n current_date = datetime.now().strftime('%m%d')\n years = range(2015, current_year)\n quarters = [\"0331\", \"0630\", \"0930\", \"1231\"]\n all_dates = []\n for r in itertools.product(years, quarters):\n all_dates.append(str(r[0]) + r[1])\n for q in quarters:\n if q < current_date:\n all_dates.append(str(current_year) + q)\n logging.info(\"dates %s\", all_dates)\n return all_dates","title":""},{"docid":"511bcca5f08f87abd6a82f7e3d6b5f45","score":"0.54009223","text":"def pullcalldates(self):\n datelist = cal.Calendar()\n dlist = datelist.yeardatescalendar(2020)\n itr = 0\n self.d = {}\n for i in dlist[:]:\n for j in i:\n for k in j:\n for u in k:\n itr += 1\n self.d[itr] = u\n return self.d","title":""},{"docid":"b91547afd334dea2da6b47dcb529830f","score":"0.53689575","text":"def year_frequency_dict(year_dict, start, end):\n year_freq_dict = dict()\n for year in range(start, end+1):\n if year in year_dict:\n year_freq_dict[year] = year_dict[year][1]/year_dict[year][0]\n return year_freq_dict","title":""},{"docid":"4f179399ff9f3a698f32b03553e41b1f","score":"0.53654516","text":"def _returnCommonYears(arrayFNameS, lngFromYear, lngToYear):\n # arrayDays=[]\n arrayIncr = []\n blnSpecialDates = 0\n\n for arrayFName in arrayFNameS:\n # strTempDates=''\n # if (arrayFName[\"shape_dates\"]!=\"\"):\n # strTempDates=arrayFName[\"shape_dates\"]\n\n strTemporalType = arrayFName[\"interval\"]\n\n if ((strTemporalType == \"10d\") or (strTemporalType == \"16d\") or (strTemporalType == \"15d\") or (strTemporalType == \"1d\") or (strTemporalType == \"1m\") or (strTemporalType == \"1y\")):\n arrayIncr.append(1)\n else:\n if (strTemporalType == \"10y\"):\n arrayIncr.append(10)\n else:\n blnSpecialDates = 1\n arrayIncr.append(-1)\n\n lngStepYear = 0\n arrayReturn = []\n if ((blnSpecialDates == 1) and (len(arrayIncr) == 1)):\n\n arrayYears = arrayFName[\"fixed\"].split('_')\n arrayReturn = []\n for strTemp in arrayYears:\n arrayReturn.append(int(strTemp[:4]))\n else:\n lngStepYear = 1\n arrayReturn = range(int(lngFromYear), int(lngToYear) + 1, lngStepYear)\n\n return arrayReturn","title":""},{"docid":"436e894c98e7982a82491977a93e0158","score":"0.5351873","text":"def base_data(code, begin='0000-01-01', end='9999-12-31'):\n\n accesses = self.ratchet.general(code)\n\n del accesses['total']\n del accesses['code']\n\n if len(begin) == 4:\n begin += '-01' \n\n if len(end) == 4:\n end += '-12' \n\n empty_months_range = {'%02d' % x: None for x in range(1, 13)}\n data = {}\n for key, value in accesses.items():\n if key in ['html', 'abstract', 'pdf']:\n del value['total']\n for year, months in value.items():\n del months['total']\n if year[1:] >= begin[0:4] and year[1:] <= end[0:4]:\n ye = data.setdefault(year[1:], copy.copy(empty_months_range))\n for month, days in months.items():\n if year[1:]+'-'+month[1:] >= begin and year[1:]+'-'+month[1:] <= end:\n if ye[month[1:]] == None:\n ye[month[1:]] = 0\n ye[month[1:]] += days['total']\n\n return data","title":""},{"docid":"a47d7468eaaa3a6899b851b2e33ffee2","score":"0.5343377","text":"def get_quarter(y, m):\n \n result = []\n quarter = calc_quarter(y, m)\n for i in quarter:\n result.append(get_month(i[0], i[1]))\n \n return result","title":""},{"docid":"592fdd4156ea98e22baa8b18cea62c67","score":"0.5334084","text":"def process_2019_raw_into_monthly() -> List[float]:\n this_file_path = Path(os.path.realpath(__file__))\n this_file_dir = this_file_path.parent\n usage_2019_file = this_file_dir / 'resources' / 'electricity2019.json'\n with usage_2019_file.open() as f:\n usage_2019_data = json.loads(f.read())\n flat_kwh_per_day = []\n for raw_month in usage_2019_data:\n start_date = datetime.strptime(raw_month['StartDate'], '%m/%d/%Y')\n end_date = datetime.strptime(raw_month['EndDate'], '%m/%d/%Y')\n days_in_range = (end_date - start_date).days\n for i in range(days_in_range + 1):\n this_date = start_date + timedelta(i)\n if this_date.year == 2019:\n flat_kwh_per_day.append(raw_month['kWh'])\n day_index = -1\n month_usages = []\n for month in range(1, 13):\n num_days = monthrange(2019, month)[1]\n month_sum = 0\n for day in range(num_days):\n day_index += 1\n month_sum += flat_kwh_per_day[day_index]\n month_usages.append(round(month_sum / num_days, 2))\n return month_usages","title":""},{"docid":"11c90bea257ebbb063743758609bc40c","score":"0.5325994","text":"def _span(yf: YearData, yt: YearData, pf: int, pt: int = None) -> Dict[YearData, Tuple[int, int]]:\n years = (x for x in YearData.__subclasses__() if yf.year <= x.year <= yt.year)\n return {year: (pf, pt) if pt else (pf, pf) for year in years}","title":""},{"docid":"877309f7053e125a74df922899646848","score":"0.53181916","text":"def make_prices_vecs(data):\n\n\toutput = []\n\n\t# We have the same set of dates (keys) for all companies,\n\t# so we have to sort keys only once (keys from whatever element).\n\n\tsome_el = data[0]\n\tsorted_keys = some_el[1].keys()\n\tsorted_keys.sort()\n\n\tfor elem in data:\n\t\tdate_price_dict = elem[1]\n\t\toutput.append([date_price_dict[key] for key in sorted_keys])\n\t\n\treturn output;","title":""},{"docid":"6cde7cb2a1e98f7a4b0cf0d650725f9e","score":"0.5299571","text":"def create_years_genre(sql_data: tuple, years: dict):\n # MB i need to rewrite that, but this is work well =)\n for sql_line in sql_data:\n year = sql_line[0]\n years[year]['count'] = sql_line[1]\n return years","title":""},{"docid":"8476d5e2bfc0abf3d9ce2d0ba3e9a1e1","score":"0.5297891","text":"def __get_years_(search_year, start, step) -> list:\n sql_request = _sql_request_search_years(search_year)\n years = get_ids_by_request(sql_request, start, step)\n return years","title":""},{"docid":"93f20cfe72eb4951826084a4b4fe0082","score":"0.5296002","text":"def _aggregate_by_year_month(historical_data: dict):\n if historical_data is None:\n return {}\n\n converted_response = {}\n\n # first pass assemble the basic return value\n for datapoint in historical_data:\n year = datapoint['date'].year\n month = datapoint['date'].month\n\n if year not in converted_response:\n converted_response[year] = {}\n if month not in converted_response[year]:\n converted_response[year][month] = []\n\n converted_response[year][month].append(datapoint['value'])\n\n # second pass calculate averages\n for year in converted_response.keys():\n for month in converted_response[year]:\n converted_response[year][month] = sum(\n converted_response[year][month]) / len(converted_response[year][month])\n\n return converted_response","title":""},{"docid":"312ef24c9f0408eab7b1778d9c94782d","score":"0.5292689","text":"def query_infos(self, fromYear, toYear):\n connection = sqlite3.connect(\"weather.sqlite\")\n cur = connection.cursor()\n toYear = int(toYear) + 1\n dictOuter = {}\n for row in cur.execute(\"select * from samples where \\\n sample_date between ? and ?\",\n (str(fromYear)+'%', str(toYear)+'%')):\n print(f\"row {row}\")\n myMonth = datetime.datetime.strptime(row[1], '%Y/%m/%d').month\n dictOuter.setdefault(myMonth, []).append(row[5])\n print(dictOuter)\n return dictOuter\n connection.commit()\n connection.close()","title":""},{"docid":"50cb5ad1bc6e9a24d490050a2073dbb7","score":"0.528686","text":"def special_by_dd(keys):\n def expand_year(df, dd_name):\n \"\"\" For jan1989 - sep1995 they wrote the year as a SINGLE DIGIT\"\"\"\n if 'HRYEAR' in df.columns:\n k = 'HRYEAR'\n else:\n k = k = 'HdYEAR'\n last_digit = df[k].dropna().unique()[0]\n if last_digit >= 10:\n last_digit = last_digit % 10\n base_year = int(dd_name[-4:-1]) * 10\n df[\"HRYEAR4\"] = base_year + last_digit\n df = df.drop(k, axis=1)\n return df\n\n def combine_age(df, dd_name):\n \"\"\"For jan89 and jan92 they split the age over two fields.\"\"\"\n df[\"PRTAGE\"] = df[\"AdAGEDG1\"] * 10 + df[\"AdAGEDG2\"]\n df = df.drop([\"AdAGEDG1\", \"AdAGEDG2\"], axis=1)\n return df\n\n def align_lfsr(df, dd_name):\n \"\"\"Jan1989 and Jan1999. LFSR (labor focrce status recode)\n had\n 1 = WORKING\n 2 = WITH JOB,NOT AT WORK\n 3 = UNEMPLOYED, LOOKING FOR WORK\n 4 = UNEMPLOYED, ON LAYOFF\n 5 = NILF - WORKING W/O PAY < 15 HRS;\n TEMP ABSENT FROM W/O PAY JOB\n 6 = NILF - UNAVAILABLE\n 7 = OTHER NILF\n newer ones have\n 1 EMPLOYED-AT WORK\n 2 EMPLOYED-ABSENT\n 3 UNEMPLOYED-ON LAYOFF\n 4 UNEMPLOYED-LOOKING\n 5 NOT IN LABOR FORCE-RETIRED\n 6 NOT IN LABOR FORCE-DISABLED\n 7 NOT IN LABOR FORCE-OTHER\n this func does several things:\n 1. Change 3 -> 4 and 4 -> 3 in the old ones.\n 2. Change 5 and 6 to 7.\n 2. Read retired from AhNLFREA == 4 and set to 5.\n 3. Read ill/disabled from AhNLFREA == 2 and set to 6.\n Group 7 kind of loses meaning now.\n \"\"\"\n # 1. realign 3 & 3\n status = df[\"AhLFSR\"]\n # status = status.replace({3: 4, 4: 3}) # chcek on ordering\n\n status_ = status.copy()\n status_[status == 3] = 4\n status_[status == 4] = 3\n status = status_\n\n # 2. Add 5 and 6 to 7\n status = status.replace({5: 7, 6: 7})\n\n # 3. ill/disabled -> 6\n status[df['AhNLFREA'] == 2] = 6\n\n df['PEMLR'] = status\n df = df.drop([\"AhLFSR\", \"AhNLFREA\"], axis=1)\n return df\n\n def expand_hours(df, dd_name):\n \"\"\"\n 89 and 92 have a question for hours and bins. I goto midpoint of bin.\n\n Roughly corresponds to PEERNHRO\n\n A-EMPHRS CHARACTER*002 . (0357:0358) LFSR=1 OR 2\n REASONS NOT AT WORK OR HOURS AT WORK\n -1 = NOT IN UNIVERSE\n WITH A JOB, BUT NOT AT WORK\n 01 = ILLNESS\n 02 = VACATION\n 03 = BAD WEATHER\n 04 = LABOR DISPUTE\n 05 = ALL OTHER\n AT WORK\n 06 = 1-4 HOURS\n 07 = 5-14 HOURS\n 08 = 15-21 HOURS\n 09 = 22-29 HOURS\n 10 = 30-34 HOURS\n 11 = 35-39 HOURS\n 12 = 40 HOURS\n 13 = 41-47 HOURS\n 14 = 48 HOURS\n 15 = 49-59 HOURS\n 16 = 60 HOURS OR MORE\n \"\"\"\n hours = df['AhEMPHRS']\n hours_dic = {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan,\n 6: 2, 7: 9.5, 8: 18, 9: 25.5, 10: 32, 11: 37, 13: 44,\n 15: 54}\n hours = hours.replace(hours_dic)\n df['PEERNHRO'] = hours\n df.drop(\"AhEMPHRS\", axis=1)\n return df\n\n def combine_hours(df, dd_name):\n \"\"\"\n For 89 and 92; \"AdHRS1\", \"AdHRS2\" combine to form \"PEHRACTT\"\n \"\"\"\n fst = df['AdHRS1']\n snd = df['AdHRS2']\n df['PEHRACTT'] = fst * 10 + snd\n df = df.drop([\"AdHRS1\", \"AdHRS2\"], axis=1)\n return df\n\n func_dict = {\"expand_year\": expand_year, \"combine_age\": combine_age,\n \"expand_hours\": expand_hours, \"align_lfsr\": align_lfsr,\n \"combine_hours\": combine_hours}\n to_apply = filter(lambda x: x in keys, func_dict)\n filtered = {k: func_dict[k] for k in to_apply}\n return filtered","title":""},{"docid":"75f0165efd1a3dc3ddea0b2eed32f263","score":"0.52836514","text":"def get_spread(quotes, dates, contract):\r\n\r\n # for quarter1, quarter2 in zip(list(dates.keys()), list(dates.keys()[1:])):\r\n\r\n new_quotes = {}\r\n new_dates = {}\r\n\r\n dates_keys = list(dates.keys())\r\n\r\n step = 2 if contract == \"Quarters\" else 1\r\n\r\n for index in range(0, len(dates_keys) - 1, step):\r\n current_key = dates_keys[index] # Q1 key\r\n key_after = dates_keys[index + 1] # Q2 key\r\n\r\n first_date = dates[key_after][0] # Get the first date of the Q2 time series\r\n\r\n try:\r\n index_first_date = dates[current_key].index(first_date)\r\n key_spread = \"{}x{}\".format(current_key, key_after)\r\n\r\n new_dates[key_spread] = []\r\n new_quotes[key_spread] = []\r\n\r\n offset_1 = 0\r\n offset_2 = 0\r\n\r\n # we go through all the days in the Q2 days list and add the common days and spread into the new lists\r\n for index_days in range(len(dates[key_after])):\r\n if dates[current_key][index_first_date + index_days + offset_1] == dates[key_after][\r\n index_days + offset_2]:\r\n new_dates[key_spread].append(dates[key_after][index_days + offset_1])\r\n new_quotes[key_spread].append(\r\n quotes[current_key][index_first_date + index_days + offset_1] - quotes[key_after][\r\n index_days + offset_2])\r\n\r\n else:\r\n date_1 = dt.datetime.strptime(dates[current_key][index_first_date + offset_1], \"%Y-%m-%dT00:00:00Z\")\r\n date_2 = dt.datetime.strptime(dates[key_after][index_first_date + offset_2], \"%Y-%m-%dT00:00:00Z\")\r\n\r\n while date_1 != date_2 and offset_1 < 10 and offset_2 < 10:\r\n if date_1 > date_2:\r\n offset_2 += 1\r\n else:\r\n offset_1 += 1\r\n\r\n if date_1 != date_2:\r\n continue\r\n\r\n new_dates[key_spread].append(dates[key_after][index_days + offset_1])\r\n new_quotes[key_spread].append(\r\n quotes[current_key][index_first_date + index_days + offset_1] - quotes[key_after][\r\n index_days + offset_2])\r\n\r\n # check_date(new_dates,new_quotes,quotes,dates)\r\n except IndexError:\r\n continue\r\n except Exception as e:\r\n print(\"Exception : {}\".format(e.args))\r\n print(\"No overlap for {} and {}\".format(current_key, key_after))\r\n continue\r\n\r\n return new_dates, new_quotes","title":""},{"docid":"34e1565e03681cddf71dfa83444c53bd","score":"0.5273918","text":"def filter_by_year(data: dict, year: int) -> dict:\n filtered_data = data | {\"places\": []}\n\n for place in data[\"places\"]:\n dataframes = []\n\n for dataframe in place[\"data\"]:\n if dataframe[\"startYear\"] <= year <= dataframe[\"endYear\"]:\n dataframes.append(dataframe)\n\n if dataframes:\n filtered_data[\"places\"].append(\n place | {\"data\": dataframes}\n )\n\n return filtered_data","title":""},{"docid":"fac1df3ff8bc107aee7b78b500a92e76","score":"0.5269259","text":"def createDictionaries(data):\n buildDict(data, ['office', 'office_name', 'level'], 1, 0)\n buildDict(data, ['level', 'level_name'], 1, 0)\n buildDict(data, ['area', 'area_name'], 1, 0)\n buildDict(data, ['source', 'source_name'], 1, 0)\n buildDict(data, ['financier', 'financier_name'], 1, 0)\n buildDict(data, ['year', 'office', 'unit', 'unit_name'], 3, 2)\n buildDict(data, ['year', 'office', 'unit', 'line', 'line_name'], 4, 3)","title":""},{"docid":"9cd48b904c3b346bea1e0db7e901623d","score":"0.52553475","text":"def get_aggregates(self, monattag: str = \"\") -> dict:\n aggregates = {}\n for e in [\"TXK\", \"TNK\", \"RSK\", \"SDK\", \"PM\", \"UPM\"]:\n d = {}\n if monattag == \"\":\n list_ = [self.create_timeline(e, t) for t in self.tagreihe]\n s = sum(list_) / len(list_)\n else:\n s = self.create_timeline(e, monattag)\n if s.count() == 0:\n d = {\n \"first_year\": \"\",\n \"last_year\": \"\",\n \"mean\": \"\",\n \"mean2010\": \"\",\n \"count\": 0,\n \"std\": \"\",\n \"median\": \"\",\n \"max\": \"\",\n \"max_year\": \"\",\n \"min_year\": \"\",\n \"min\": \"\",\n \"zerorain\": \"\",\n \"zerosun\": \"\",\n \"mitteldruck\": \"\",\n \"depressiondays\": \"\"\n }\n else:\n if \"2010\" in s.index:\n index_2010 = s.index.to_list().index(\"2010\")\n d[\"mean2010\"] = round(s[index_2010:][~s.isnull()].mean(), 0)\n else:\n d[\"mean2010\"]=\"\"\n d[\"first_year\"] = s[~s.isnull()].index[0]\n d[\"last_year\"] = s[~s.isnull()].index[-1]\n d[\"mean\"] = round(s[~s.isnull()].mean(), 0)\n \n d[\"count\"] = s[~s.isnull()].count()\n d[\"std\"] = round(s[~s.isnull()].std(), 0)\n d[\"median\"] = s[~s.isnull()].median()\n d[\"max\"] = round(s.max(), 1)\n d[\"max_year\"] = s.index[np.where(s == np.max(s))[0][0]]\n d[\"min_year\"] = s.index[np.where(s == np.min(s))[0][0]]\n d[\"min\"] = round(s.min(), 1)\n if e == \"RSK\":\n d[\"zerorain\"] = s[s == 0.0].count()\n if e == \"SDK\":\n d[\"zerosun\"] = s[s == 0.0].count()\n if e == \"PM\":\n mittelhoehe = sum([x.stationshoehe for x in self.stations]) / len(self.stations)\n d[\"mean_pressure\"] = round(1013.25 * (1 - (0.0065 * mittelhoehe) / 288.15)**5.255,0) #Barometric formula\n d[\"depression_days\"] = s[s < d[\"mean_pressure\"]].count()\n aggregates[e] = d.copy()\n self.aggregates = aggregates.copy()\n return aggregates","title":""},{"docid":"b2a2bb7a48a10f4bc819a4e58cbffbc1","score":"0.5249546","text":"def calculate_years(self):\n\n\n for node in self.nodes.values():\n node.min_year = int(cfg.cfgfile.get('case', 'current_year'))\n attributes = vars(node) \n for att in attributes:\n obj = getattr(node, att)\n if inspect.isclass(type(obj)) and hasattr(obj, '__dict__') and hasattr(obj, 'data') and obj.data is True:\n try:\n min_year = min(obj.raw_values.index.get_level_values('year'))\n except:\n min_year = min(obj.raw_values.index.get_level_values('vintage'))\n if min_year < node.min_year:\n node.min_year = min_year \n if hasattr(node,'technologies'):\n for technology in node.technologies.values():\n for reference_sales in technology.reference_sales.values():\n try:\n min_year = min(reference_sales.raw_values.index.levels[util.position_in_index(reference_sales.raw_values, 'vintage')])\n except:\n min_year = node.min_year\n if min_year < node.min_year:\n node.min_year = min_year\n for sales in technology.sales.values():\n try:\n min_year = min(sales.raw_values.index.get_level_values( 'vintage'))\n except:\n min_year = node.min_year\n if min_year < node.min_year:\n node.min_year = min_year \n if hasattr(node,'stock') and node.stock.data is True:\n try:\n min_year = min(node.stock.raw_values.index.levels[util.position_in_index(node.stock.raw_values, 'year')])\n except:\n min_year = node.min_year\n if min_year < node.min_year:\n node.min_year = min_year \n \n node.years = range(node.min_year,\n int(cfg.cfgfile.get('case', 'end_year')) + 1,\n int(cfg.cfgfile.get('case', 'year_step')))\n node.vintages = copy.deepcopy(node.years)\n self.years = cfg.cfgfile.get('case','supply_years')","title":""},{"docid":"48cd10b1f1140e4f5e91588c554e7e36","score":"0.52380484","text":"def get_data(airport, start, end):\n\n data = {\"Max Temperature\": {},\n \"Min Temperature\": {},\n \"Mean Temperature\": {},\n \"Dew Point\": {}}\n\n for k in data:\n for i in range(1, 13):\n m = str(i)\n data[k][m] = {\"max\": [], \"avg\": [], \"min\": []}\n\n for year in range(start, end+1):\n for i in range(1, 13):\n m = str(i)\n html = download(airport, year, i, 1)\n for k in data:\n parse(html, data[k][m], k)\n\n return data","title":""},{"docid":"401263491e91c4244ae2ee0f2abad561","score":"0.5211393","text":"def generate_fiscal_date_range(min_date: datetime, max_date: datetime, frequency: str) -> list:\n if frequency == \"fiscal_year\":\n interval = 12\n elif frequency == \"quarter\":\n interval = 3\n else: # month\n interval = 1\n\n date_range = []\n current_date = min_date\n while current_date <= max_date:\n date_range.append(\n {\n \"fiscal_year\": generate_fiscal_year(current_date),\n \"fiscal_quarter\": generate_fiscal_quarter(current_date),\n \"fiscal_month\": generate_fiscal_month(current_date),\n }\n )\n current_date = current_date + relativedelta(months=interval)\n\n return date_range","title":""},{"docid":"a1864749a750557ed32fd54e73955375","score":"0.5210616","text":"def extract_financials_dataset(\n work_dict,\n scrub_mode='sort-by-date'):\n return extract_dataset('financials', work_dict, scrub_mode=scrub_mode)","title":""},{"docid":"e622fa0b8c59c8b851268303230e03db","score":"0.52005756","text":"def extract_box_office_data(quarter='q1'):\n box_URL = 'https://www.boxofficemojo.com/quarter/' + quarter + '/?grossesOption=calendarGrosses'\n\n # Load the cache, save in global variable\n CACHE_DICT = load_cache()\n url_text = make_url_request_using_cache(box_URL, CACHE_DICT)\n soup = BeautifulSoup(url_text, 'html.parser')\n listing_divs = soup.find_all('tr')\n listing_divs.remove(listing_divs[0])\n\n data_in_rows = []\n print('-' * 50) # separator\n print(quarter.upper() + ' Historical Box Office')\n print('-' * 50) # separator\n for listing_div in listing_divs:\n alist = listing_div.find_all('a')\n year = alist[0].string\n name = alist[1].string.title()\n rank_of_year_path = alist[0]['href']\n rank_of_year_link = \"https://www.boxofficemojo.com\" + rank_of_year_path\n print(\"The #1 release of \" + year + \" \" + quarter + \" is \" + name)\n\n money_list = listing_div.find_all('td', class_=\"a-text-right mojo-field-type-money\")\n cumulative_gross = int(money_list[0].string.replace(\"$\", \"\").replace(',', ''))\n per_release_average_gross = int(money_list[1].string.replace(\"$\", \"\").replace(',', ''))\n data_in_rows.append([year, quarter, name, cumulative_gross, per_release_average_gross, rank_of_year_link])\n\n return data_in_rows","title":""},{"docid":"330ea92fecffd906f1700281dee552db","score":"0.51993227","text":"def dates(self):\n base = dateutil.parser.parse(\"19000101\")\n with Dataset(self.index_path) as data:\n min_date = base + dt.timedelta(days=data[\"time\"][:].min())\n max_date = base + dt.timedelta(days=data[\"time\"][:].max())\n dates = {}\n dates[\"max_year\"] = pd.Timestamp(max_date).year\n dates[\"min_year\"] = pd.Timestamp(min_date).year + 1 # for 12 month indices\n dates[\"max_month\"] = pd.Timestamp(max_date).month\n dates[\"years\"] = list(range(dates[\"min_year\"], dates[\"max_year\"] + 1))\n return dates","title":""},{"docid":"37313b5e818111fb7f6c028c26edf36c","score":"0.5198772","text":"def parse_billing_data(billing_data):\n user_dict = collections.defaultdict(dict)\n currency = ''\n month = ''\n year = ''\n\n for row in billing_data:\n if len(row) < 4:\n continue\n if row[3] == 'AccountTotal':\n if not currency:\n currency = row[23]\n\n if not month or not year:\n date = row[6]\n month = date[5:7]\n year = date[0:4]\n\n acct_num = row[2]\n user_dict[acct_num]['name'] = row[9]\n user_dict[acct_num]['total'] = float(row[24])\n user_dict[acct_num]['currency'] = row[23]\n\n return user_dict","title":""},{"docid":"cecc5fcf08bd8fb8fb2e9fc7f817cea5","score":"0.51934886","text":"def get_quarterly_event_counts(quarters, event_manager):\n response = {}\n for pair in quarters:\n year = pair[0]\n quarter = pair[1]\n quarter_desc = '{}-Q{}'.format(year, quarter)\n date_range = REPORT_QUARTERS[quarter-1]\n start = '{}-{}'.format(year, date_range[0])\n if quarter == 4:\n year += 1\n end = '{}-{}'.format(year, date_range[1])\n response[quarter_desc] = event_manager.event_group_counts(start, end)\n return response","title":""},{"docid":"a08fcf10f5fb5f4e36fcf75a0417594a","score":"0.51927936","text":"def aggregate_over_year(self) -> dict:\n oy = {\n \"monattag\": [],\n \"TXK\": {\n \"mean\": [],\n \"std\": []\n },\n \"TNK\": {\n \"mean\": [],\n \"std\": []\n },\n \"RSK\": {\n \"mean\": [],\n \"std\": []\n },\n \"SDK\": {\n \"mean\": [],\n \"std\": []\n },\n \"PM\": {\n \"mean\": [],\n \"std\": []\n },\n \"UPM\": {\n \"mean\": [],\n \"std\": []\n }\n }\n for mt in self.monattag_generator():\n oy[\"monattag\"].append(mt)\n aggregates = {}\n aggregates = self.get_aggregates(mt)\n for e in [\"TXK\", \"TNK\", \"RSK\", \"SDK\", \"PM\", \"UPM\"]:\n oy[e][\"mean\"].append(aggregates[e][\"mean\"])\n oy[e][\"std\"].append(aggregates[e][\"std\"])\n return oy","title":""},{"docid":"385415e3bcdab55d2af71624e297effb","score":"0.51850444","text":"def collect_table(worksheet: Worksheet, year: int, fund_name, row: int, column: int) -> object:\n\n data = {}\n\n for iterated_row in range(row, row + 21):\n row_label: str = worksheet.cell(row=iterated_row, column=column).value\n\n if not row_label:\n # Some of the years has less than 21 fields which mean we'll fail\n # with older files. Skipping on an empty row label.\n continue\n\n row_data = {}\n for iterated_column in range(column + 1, column + 1 + 24):\n # Getting the matching month of the current cell and resetting\n # the cell value.\n month_index = int(iterated_column / 2)\n\n if month_index not in months:\n continue\n\n month_name = months[month_index]\n\n if month_name not in row_data:\n row_data[month_name] = {\n 'התרומה לתשואה': '',\n 'שיעור מסך הנכסים': '',\n 'year': year,\n 'fund': fund_name,\n }\n\n cell_kwargs = {'row': iterated_row, 'column': iterated_column}\n cell_value = worksheet.cell(**cell_kwargs).value\n\n if cell_value:\n if type(cell_value) is not str:\n cell_value = format(cell_value * 100, '.2f')\n\n # Values in cell with even index number have a label and odd\n # indexed-cell have a different label.\n if iterated_column % 2 == 0:\n key = 'התרומה לתשואה'\n else:\n key = 'שיעור מסך הנכסים'\n row_data[month_name][key] = cell_value\n\n data[row_label.strip()] = row_data\n\n return data","title":""},{"docid":"4409b92c616a592f7c726c76e48493cd","score":"0.51814896","text":"def quarter_dict_key(freq_start):\n return str(freq_start.year) + quarter_dict[freq_start.month]","title":""},{"docid":"a0d836b487720e59e09858bf10db052a","score":"0.5180538","text":"def _get_data_pre2007(date): \r\n \r\n # build the url based on year\r\n url = '{}/Environmental_Data_{}.txt'.format(BASE_URL, date.year)\r\n print('Fetching online data for {} (full year)'.format(date.year))\r\n \r\n try:\r\n year_data = request.urlopen(url).read().decode(encoding='utf_8').split('\\n') \r\n except:\r\n raise ValueError(date) # error accessing website\r\n else:\r\n year_data.pop(0) # remove first item which contain column header info\r\n \r\n for line in year_data:\r\n \r\n elements = line.split()\r\n yield dict(Date = elements[0],\r\n Time = elements[1],\r\n Status = 'COMPLETE', # all data from pre2007 will be complete\r\n Air_Temp = elements[5],\r\n Barometric_Press = elements[7],\r\n Wind_Speed = elements[2])","title":""},{"docid":"9651f20d619d3a1c3dbe76f2283bd8e8","score":"0.51773274","text":"def stations():\n\n year_extract = '201'\n avg_station_sql = (f'SELECT city, name, substr(start_date, INSTR(start_date, {year_extract}), 4) as yr, ' +\n 'avg(duration) as avg_trip ' +\n 'FROM trip ' +\n 'JOIN station ON trip.start_station_id = station.id ' +\n 'GROUP BY city, name, yr'\n )\n\n avg_station = pd.read_sql(avg_station_sql, engine2.connect())\n\n avg_station['avg_trip'] = avg_station['avg_trip']/60\n avg_station['avg_trip'] = avg_station['avg_trip'].round()\n avg_station_list = avg_station.to_dict('records')\n\n yrs_2013 = []\n yrs_2014 = []\n yrs_2015 = []\n\n for item in avg_station_list:\n data = {}\n for k,v in item.items():\n if(k=='name'):\n data['axis'] = v\n if(k=='avg_trip'):\n data['value'] = v\n if(k=='city'):\n data['city'] = v\n if(k=='yr'and v=='2013'):\n data['name'] = v\n yrs_2013.append(data)\n elif(k=='yr'and v=='2014'):\n data['name'] = v\n yrs_2014.append(data)\n elif(k=='yr'and v=='2015'):\n data['name'] = v\n yrs_2015.append(data)\n \n all_yr_station = [yrs_2015, yrs_2014, yrs_2013]\n\n\n return jsonify(all_yr_station)","title":""},{"docid":"55f8534937949b37e823653ef59c45bb","score":"0.5148466","text":"def ageGroups4():\n name = \"ageGroups4\"\n groups = {\n AGE: {\n \"Under 4 years\" : list(range(0, 4)),\n \"4 to 7 years\" : list(range(4, 8)),\n \"8 to 11 years\": list(range(8, 12)),\n \"12 to 15 years\": list(range(12, 16)),\n \"16 to 19 years\": list(range(16, 20)),\n \"20 to 23 years\": list(range(20, 24)),\n \"24 to 27 years\": list(range(24, 28)),\n \"28 to 31 years\": list(range(28, 32)),\n \"32 to 35 years\": list(range(32, 36)),\n \"36 to 39 years\": list(range(36, 40)),\n \"40 to 43 years\": list(range(40, 44)),\n \"44 to 47 years\": list(range(44, 48)),\n \"48 to 51 years\": list(range(48, 52)),\n \"52 to 55 years\": list(range(52, 56)),\n \"56 to 59 years\": list(range(56, 60)),\n \"60 to 63 years\": list(range(60, 64)),\n \"64 to 67 years\": list(range(64, 68)),\n \"68 to 71 years\": list(range(68, 72)),\n \"72 to 75 years\": list(range(72, 76)),\n \"76 to 79 years\": list(range(76, 80)),\n \"80 to 83 years\": list(range(80, 84)),\n \"84 to 87 years\": list(range(84, 88)),\n \"88 to 91 years\": list(range(88, 92)),\n \"92 to 95 years\": list(range(92, 96)),\n \"96 to 99 years\": list(range(96, 100)),\n \"100 to 103 years\": list(range(100, 104)),\n \"104 to 107 years\": list(range(104, 108)),\n \"108 to 111 years\": list(range(108, 112)),\n \"112 to 115 years\": list(range(112, 116)),\n }\n }\n return name, groups","title":""},{"docid":"aa947001fa1f5b68b4ececc83462919d","score":"0.51367325","text":"def get_relevant_text_bodies(subreddit_list, start_year, start_month, end_month, base_path):\n\n text_bodies = defaultdict(lambda: [])\n\n #this really should be done using bash brace expansion...\n\n base_path_full_non_top_level = base_path + \"non_top_level_comments/\"\n valid_file_paths_non_top_level = extract_pairs.list_file_appropriate_data_range(start_year,\n start_month, end_month,\n base_path_full_non_top_level)\n\n for file_path in valid_file_paths_non_top_level:\n with open(file_path, 'rb') as fop:\n # because that's what this file and others had for output\n csvreadr = csv.reader(fop, delimiter=',', quotechar='|')\n csvreadr.next()\n for line in csvreadr:\n try:\n comm = json.loads(line[0])\n except ValueError:\n print \"BADLY FORMATTED\", line\n continue\n if any (subreddit in comm[\"subreddit\"] for subreddit in subreddit_list):\n #body = re.search('\\\"body\\\":\\\"(.+?)\\\"(,\\\")|(})', line).group(1)\n body = comm[\"body\"] #these two options appear to the be the same speed.\n if body == \"[deleted]\" or body == \"[removed]\":\n continue\n\n subreddit = comm[\"subreddit\"]\n\n text_bodies[subreddit].append(body)\n\n\n # TODO: parameterize the end month here. cause what if it's two months or whatever\n base_path_full_top_level = base_path + \"top_level_comments/\"\n valid_file_paths_top_level = extract_pairs.list_file_appropriate_data_range(start_year,\n start_month, end_month-1,base_path_full_top_level)\n\n for file_path in valid_file_paths_top_level:\n with open(file_path, 'rb') as fop:\n # because that's what this file and others had for output\n csvreadr = csv.reader(fop, delimiter=',', quotechar='|')\n csvreadr.next()\n for line in csvreadr:\n try:\n parent = json.loads(line[0])\n child = json.loads(line[1])\n except ValueError:\n print \"BADLY FORMATTED\", line\n continue\n\n for comm in [parent, child]:\n if any (subreddit in comm[\"subreddit\"] for subreddit in subreddit_list):\n #body = re.search('\\\"body\\\":\\\"(.+?)\\\"(,\\\")|(})', line).group(1)\n body = comm[\"body\"] #these two options appear to the be the same speed.\n if body == \"[deleted]\" or body == \"[removed]\":\n continue\n\n subreddit = comm[\"subreddit\"]\n\n text_bodies[subreddit].append(body)\n return text_bodies","title":""},{"docid":"4729b851910250e8d78e057da7e00078","score":"0.512572","text":"def get_story(cls, x):\n\n r = []\n t20100101 = datetime.datetime(2010, 1, 1, tzinfo=pytz.utc)\n t20120101 = datetime.datetime(2012, 1, 1, tzinfo=pytz.utc)\n t20130101 = datetime.datetime(2013, 1, 1, tzinfo=pytz.utc)\n t20140101 = datetime.datetime(2014, 1, 1, tzinfo=pytz.utc)\n t20141001 = datetime.datetime(2014, 10, 1, tzinfo=pytz.utc)\n t20150101 = datetime.datetime(2015, 1, 1, tzinfo=pytz.utc)\n # M5 2015/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.M5.value,\n test_start_at=t20150101,\n ai_id=x.ai_id)]\n # H1 2010/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.H1.value,\n test_start_at=t20100101,\n ai_id=x.ai_id)]\n # H1 2012/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.H1.value,\n test_start_at=t20120101,\n ai_id=x.ai_id)]\n # H1 2013/10/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.H1.value,\n test_start_at=t20130101,\n ai_id=x.ai_id)]\n # H1 2014/10/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.H1.value,\n test_start_at=t20140101,\n ai_id=x.ai_id)]\n # H1 2015/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.H1.value,\n test_start_at=t20150101,\n ai_id=x.ai_id)]\n # M5 2013/1/1〜\n # r += [cls(name=x.name,\n # genetic_id=x.id,\n # currency_pair=x.currency_pair,\n # span=Granularity.M5.value,\n # test_start_at=t20130101,\n # ai_id=x.ai_id)]\n # M5 2014/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.M5.value,\n test_start_at=t20140101,\n ai_id=x.ai_id)]\n # M1 2014/10/1〜\n # r += [cls(name=x.name,\n # genetic_id=x.id,\n # currency_pair=x.currency_pair,\n # span=Granularity.M1.value,\n # test_start_at=t20141001,\n # ai_id=x.ai_id)]\n # M1 2015/1/1〜\n r += [cls(name=x.name,\n genetic_id=x.id,\n currency_pair=x.currency_pair,\n span=Granularity.M1.value,\n test_start_at=t20150101,\n ai_id=x.ai_id)]\n return r","title":""},{"docid":"bc9a53143ebcb7e3ac64b6e0fa303102","score":"0.51230866","text":"def make_quarters_request():\n args = {'command': 'quarters'}\n res = _make_ww_request(args)\n _check_ww_response(res)\n\n df = pd.DataFrame(res.json()['quarters'])\n df['filing_period'] = pd.to_datetime(df['filing_period'])\n return df.set_index('filing_period')","title":""},{"docid":"ba1f8f5dca779bccb3c164a7808f39e4","score":"0.5121964","text":"def months(q):\n try:\n if q == 1:\n return ['jan', 'feb', 'mar']\n if q == 2:\n return ['apr', 'may', 'jun']\n if q == 3:\n return ['jul', 'aug', 'sep']\n if q == 4:\n return ['oct', 'nov', 'dec']\n else:\n raise\n except:\n print('Not a valid quarter. Enter 1, 2, 3, or 4')\n raise","title":""},{"docid":"2a65fba93a8c3111ed54ea7b47cf12e7","score":"0.51124316","text":"def retrievecarbondata(periodstart, periodend, geometrycode):\n\n # Calculate base parameters from all area data\n # This uses numeric equations to calculate linear trends\n now = datetime.datetime.now()\n areadata = Data.objects.filter(geometrycode=geometrycode).order_by('year')\n if areadata.exists() is False: return {}\n baseparameters = calculatebaseparameters(areadata)\n\n # Create blank array for all years in requested range\n data = {}\n for year in range(int(periodstart), 1 + int(periodend)):\n year = str(year)\n data[year] = {'electricity': 0, 'gas': 0}\n\n # Populate blank array with existing data where it exists\n maxyear = 0\n for areadataitem in areadata:\n year = areadataitem.year\n if int(year) > maxyear: maxyear = int(year)\n if year in data:\n if areadataitem.type == 0: \n key = 'electricity'\n conversionfactor = converter_kg_electricity[year] / 1000\n if areadataitem.type == 1: \n key = 'gas'\n conversionfactor = converter_kg_gas[year] / 1000\n data[year][key] = int(conversionfactor * float(areadataitem.value))\n\n # For years outside provided data, estimate using base parameters\n if maxyear < int(periodend):\n extrastart = maxyear + 1\n if extrastart < int(periodstart): extrastart = int(periodstart)\n for year in range(extrastart, 1 + int(periodend)):\n predictionarray = makeprediction(baseparameters, year)\n data[year] = calculateemissions(predictionarray)\n \n return data","title":""},{"docid":"2c019c1a3945dea060f8eec8d47b89d2","score":"0.5101044","text":"def get_years(type_: str, value_: str, page: int, step: int):\n years = factory.get_elem_list(Year, type_, value_, page, step)\n return years","title":""},{"docid":"b8e48962e2d993cfb722569c1799f802","score":"0.5093737","text":"def barclay_hfi():\n df = pd.read_csv(\"data_parser/Barclay_HFI.csv\")\n df.columns = [\"Year\"] + [j+1 for j in range(12)] \n # df = df.drop([\"Temp\"], axis = 1)\n df.set_index(\"Year\", inplace = True)\n\n first_year = int(df.index[0])\n target_ret_dict = {}\n for i in range(df.shape[0]):\n for j in range(df.shape[1]):\n dt = datetime.datetime(first_year + i, 1+j, 1) + relativedelta(months = 1) - datetime.timedelta(days = 1) \n raw_val = df.iloc[i,j]\n if not df.isna().iloc[i,j]:\n ret = float(raw_val.replace(\"%\", \"\"))/100\n target_ret_dict[dt] = ret\n tdf = pd.Series(target_ret_dict)\n tdf = pd.DataFrame(tdf)\n tdf.columns = [\"Barclay_HFI\"]\n return target_ret_dict, tdf","title":""},{"docid":"5dbd307b16cad6c073027873a54be501","score":"0.5087606","text":"def construct_all_holdings(self):\n\n\t\td = dict((k, v) for k, v in [(s, self.current_positions[s]) for s in self.symbol_list])\n\n\t\td['datetime'] = self.start_date\n\t\td['cash'] = self.initial_capital\n\t\tprint(d)\n\t\td['commission'] = 0.0 ## todo why do i need this\n\t\td['total'] = self.initial_capital\n\t\treturn [d]","title":""},{"docid":"2a559e0283fc1f14ee9a0f3e3e9438ca","score":"0.50796884","text":"def precipitation():\n # Query all dates and thier temperature\n results = session.query(Measurement.date,Measurement.tobs).\\\n filter(func.strftime(\"%y\", Measurement.date) == \"2017\").all()\n\n # Create a dictionary from the row data and append to a list of last_year_tobs\n last_year_tobs = []\n for tobs in results:\n date_tobs_dict = {}\n date_tobs_dict[\"date\"] = Measurement.date\n date_tobs_dict[\"tobs\"] = Measurement.tobs\n \n last_year_tobs.append(date_tobs_dict)\n\n return jsonify(last_year_tobs)","title":""},{"docid":"b3ee76d82e5b449f7e9d124e383fe59b","score":"0.5077868","text":"def get_birthday_structure_to_check(got_data):\n presents = dict()\n for month in got_data:\n for citizen in got_data[month]:\n key = str((citizen['citizen_id'], month))\n presents[key] = citizen['presents']\n\n return presents","title":""},{"docid":"fa3f071e9c2e0db528ee1c9bb6db569e","score":"0.5074117","text":"def collect_year_growth(markup) -> List[Tuple[float, str]]:\n\n results = []\n rows = BeautifulSoup(\n markup,\n \"html.parser\",\n parse_only=SoupStrainer(\"table\", class_=\"table table-small\"),\n ).find_all(\"tr\")[1:]\n for row in islice(rows, 1, None):\n columns = row.find_all(\"td\")\n name = columns[0].text.strip()\n growth = float(columns[-2].find_all(\"span\")[-1].string.replace(\"%\", \"\"))\n results.append((growth, name))\n return results","title":""},{"docid":"f44897ac90470cc73c81bca4b74acddf","score":"0.50718236","text":"def report_dates(start_date, end_date):\n # iterate over every year within date range\n for year in range(start_date.year, end_date.year + 1):\n # find the month range for the year\n month_range = range(1, 13)\n # start and end year cases\n if year == start_date.year:\n month_range = range(start_date.month, 13)\n elif year == end_date.year:\n month_range = range(1, end_date.month + 1)\n # single year case\n if start_date.year == end_date.year:\n month_range = range(start_date.month, end_date.month + 1)\n # iterate over every month in the year\n for month in month_range:\n # find the day range for the year\n day_range = (1, days_in_month(year, month))\n # start and end month cases\n if year == start_date.year and month == start_date.month:\n day_range = (start_date.day, days_in_month(year, month))\n elif year == end_date.year and month == end_date.month:\n day_range = (1, end_date.day)\n # single month case\n if start_date.year == end_date.year and start_date.month == end_date.month:\n day_range = (start_date.day, end_date.day)\n # create the sub reports\n yield (date(year, month, day_range[0]), date(year, month, day_range[1]))","title":""},{"docid":"7b62385b35ba067a0d288bb5d337e9cd","score":"0.5071464","text":"def precipitation():\n session = Session(engine)\n last_date = str(session.query(Measurement.date).order_by(Measurement.date.desc()).first())\n last_date_dt = dt.datetime.strptime(last_date, \"('%Y-%m-%d',)\")\n year_ago = last_date_dt - dt.timedelta(days=365)\n precip_results = session.query(Measurement.date, Measurement.prcp).\\\n filter(func.DATE(Measurement.date) > year_ago).all()\n session.close()\n\n date = [result[0] for result in precip_results]\n precip = [result[1] for result in precip_results]\n precip_dict = dict(zip(date, precip))\n\n return jsonify(precip_dict)","title":""},{"docid":"29ef7164fe7b57b60772278d03f49913","score":"0.50678146","text":"def _parse_entries(raw_lines):\n\n # First two lines are field names\n entries = []\n\n for group in _get_groups(raw_lines):\n # Refactor date lines onto start of following line and split tabs\n entry = {}\n first_values = group[0].split(\" \")\n entry[\"Transaction Status\"] = \" \".join(first_values[:2])\n entry[\"Date\"] = \" \".join(first_values[2:])\n\n entry[\"Description\"] = group[1]\n if \" \" in group[2]:\n entry[\"Amount\"], entry[\"Balance\"] = group[2].split(\" \")\n else:\n # No balance\n entry[\"Amount\"], entry[\"Balance\"] = group[2], \"\"\n\n for field in entry:\n entry[field] = format_value(entry[field], field, DATE_FORMAT)\n\n entries.append(entry)\n\n return entries","title":""},{"docid":"8dcdea2f892615694a846a3ab194b2e2","score":"0.5064139","text":"def generate_demo_quarter_data(requests_in_quarter: pd.DataFrame, output_dir: str) -> None:\n def generate_json_demo_data(data_in_range: pd.DataFrame, range_: str, interval_: str) -> None:\n # write json file with demand and wait_time for given range and interval\n demand = compute_metric_on_intervals(data_in_range, interval_, 'demand').to_dict()\n wait_time = compute_metric_on_intervals(data_in_range, interval_, 'wait_time').to_dict()\n\n # convert all entries to strings, and round integers by two\n demand_key, wait_time_key = list(demand.keys())[0], list(wait_time.keys())[0]\n data = {\n list(demand.keys())[0]:\n {str(key): str(demand[demand_key][key])\n for key in demand[demand_key]},\n list(wait_time.keys())[0]:\n {str(key): str(round(wait_time[wait_time_key][key], 2))\n for key in wait_time[wait_time_key]}\n }\n interval_ = 'week' if interval_ == 'week_in_quarter' else interval_\n file_name = f'time_range={range_}&interval={interval_}.json'\n io_lib.create_json_file(file_path=os_lib.join_path(output_dir, file_name), contents=data)\n\n if len(set(requests_in_quarter['quarter'])) != 1:\n raise ValueError('Given data must contain only one quarter type (eg: \\'Fall 2015\\').')\n\n # single file (since single quarter) generated containing daily stats over a quarter\n quarter_term, quarter_year = requests_in_quarter['quarter'].iloc[0].split()\n generate_json_demo_data(data_in_range=requests_in_quarter,\n range_=f'quarter+{quarter_term}_{quarter_year}', interval_='week_in_quarter')\n\n # for each week in quarter, generate a file containing daily stats over a week range\n all_weeks_in_qtr = requests_in_quarter['week_in_quarter'].unique()\n for week_num in all_weeks_in_qtr:\n single_week_data = requests_in_quarter[requests_in_quarter['week_in_quarter'] == week_num]\n generate_json_demo_data(data_in_range=single_week_data,\n range_=f'week+{week_num}', interval_='day')\n\n # for each day in quarter, generate a file containing hourly stats over a day (24 hour) range\n all_recorded_datetimes = pd.Series(data=requests_in_quarter.index)\n dates_in_qtr = all_recorded_datetimes.apply(func=lambda dt: str(dt).split()[0]).unique()\n for date in dates_in_qtr:\n generate_json_demo_data(data_in_range=requests_in_quarter[date],\n range_=f'day+{date}', interval_='hour')","title":""},{"docid":"a614f453fc6605d950c3ecebf58b44fa","score":"0.50499946","text":"def calc_quarter(y, m):\n \n # Previous / Next month's year number and month number\n prev_y = y\n prev_m = m - 1\n next_y = y\n next_m = m + 1 \n \n if m == 1:\n prev_m = 12\n prev_y = y - 1\n elif m == 12:\n next_m = 1\n next_y = y + 1\n \n return [(prev_y, prev_m), (y, m), (next_y, next_m)]","title":""},{"docid":"e693893afae45446640c8f3283215749","score":"0.503061","text":"def get_buildtime(in_list, start_year, path_list):\n buildtime_dict = {}\n for index, row in in_list.iterrows():\n comm_date = date.parse(row['Grid Date'])\n start_date = [comm_date.year, comm_date.month, comm_date.day]\n delta = ((start_date[0] - int(start_year)) * 12 +\n (start_date[1]) +\n round(start_date[2] / (365.0 / 12)))\n if delta < 0:\n delta = 1\n for index, reactor in enumerate(path_list):\n name = row['Unit'].replace(' ', '_')\n country = row['Country']\n file_name = (reactor.replace(\n os.path.dirname(path_list[index]), '')).replace('/', '')\n if (name + '.xml' == file_name):\n buildtime_dict.update({name: (country, delta)})\n return buildtime_dict","title":""},{"docid":"9db1e84c97594469c918acac53ca5f06","score":"0.50304216","text":"def list_qtr():\n\tqtr = ['Q1','Q2','Q3','Q4']\n\tfor y in range(2015,2025):\n\t\tfor q in qtr:\n\t\t\tif y == 2015 and q !='Q4':\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tyield(str(y)+\"-\"+q)","title":""},{"docid":"bddad394471640c6fa6737765bb3c79a","score":"0.502508","text":"def process_query(query: str, dataset: dict) -> dict:\n start_year, end_year, top_n = map(int, query.strip().split(','))\n logger.debug('got query \"%s,%s,%s\"', start_year, end_year, top_n)\n answer = defaultdict(int)\n for key, values in dataset.items():\n for value in values:\n if start_year <= value[0] <= end_year:\n answer[key] += value[1]\n\n answer = sorted(answer.items(), key=lambda x: (-x[1], x[0]))\n if len(answer) < top_n:\n logger.warning(\n 'not enough data to answer, found %s words out of %s for period \"%s,%s\"',\n len(answer), top_n, start_year, end_year\n )\n answer = [list(x) for x in answer[:top_n]]\n answer = {\"start\": start_year, \"end\": end_year, \"top\": answer}\n return answer","title":""},{"docid":"2b4c277dcad4b9dd57edebe05fd3aed2","score":"0.50178164","text":"def map_ints_to_dmy(int_list):\n if int_list[1] > 31 or int_list[1] <= 0:\n return None\n\n over_12 = 0\n over_31 = 0\n under_1 = 0\n for i in int_list:\n if 99 < i < DATE_MIN_YEAR or i > DATE_MAX_YEAR:\n return None\n\n if i > 31:\n over_31 += 1\n if i > 12:\n over_12 += 1\n if i <= 0:\n under_1 += 1\n\n if over_31 >= 2 or over_12 == 3 or under_1 >= 2:\n return None\n\n possible_year_splits = [\n [int_list[2], int_list[:2]], # year last\n [int_list[0], int_list[1:]] # year first\n ]\n for [y, rest] in possible_year_splits:\n if DATE_MIN_YEAR <= y <= DATE_MAX_YEAR:\n dm = map_ints_to_dm(rest)\n if dm is not None:\n return {\n \"year\": y,\n \"month\": dm[\"month\"],\n \"day\": dm[\"day\"]\n }\n else:\n return None\n\n for [y, rest] in possible_year_splits:\n dm = map_ints_to_dm(rest)\n if dm is not None:\n y = two_to_four_digit_year(y)\n return {\n \"year\": y,\n \"month\": dm[\"month\"],\n \"day\": dm[\"day\"]\n }","title":""},{"docid":"08d2960f1a0db429fa3eb84cab1e4eaf","score":"0.501417","text":"def get_months(self, language):\n # done via naive way as django's having tough time while aggregating on date fields\n entries = self.filter_by_language(language)\n dates = entries.values_list('publication_start', flat=True)\n dates = [(x.year, x.month) for x in dates]\n date_counter = Counter(dates)\n dates = set(dates)\n dates = sorted(dates, reverse=True)\n return [{'date': datetime.date(year=year, month=month, day=1),\n 'count': date_counter[year, month]} for year, month in dates]","title":""},{"docid":"497d7f84b560524327016254f3f0a251","score":"0.50086653","text":"def dictify(dataframe):\n data = defaultdict(dict)\n data7 = defaultdict(dict)\n companies = set()\n invests = set()\n\n for idx, row in dataframe.iterrows():\n #Date filter\n try:\n dt = datetime.strptime(row['日期(time)'], \"%Y.%m.%d\")\n except ValueError as ve:\n print(ve, row['日期(time)'])\n\n company = row['公司(company)']\n companies.add(company)\n round_ = row['融资轮数(round)']\n invests_ = eval(row['投资机构(invests)'])\n assert (isinstance(invests_, list))\n\n if '投资方未透露' in invests_:\n invests_ = []\n\n if dt.year < 2017:\n if round_ not in data[company]:\n data[company][round_] = invests_\n invests = invests.union(invests_)\n else:\n if round_ not in data[company]:\n data7[company][round_] = invests_\n invests = invests.union(invests_)\n\n return data, data7, companies, invests","title":""},{"docid":"d67cd23dd40cad97ec57b9061b10e1fc","score":"0.49964508","text":"def yearly_dates():\n start_date = datetime.date.today()\n return rr.rrule(\n rr.YEARLY,\n dtstart=start_date,\n count=1,\n byweekday=(rr.FR(4)),\n bymonth=(12)\n )","title":""},{"docid":"86815376808e43e44b73c14a1be75480","score":"0.49936873","text":"def man2df(mandict: dict, year1: int = 1) -> pd.DataFrame:\n rows = []\n baseyear = year1 # roundabout\n for iofe in range(mandict[\"iofe\"]):\n for iyear in range(mandict[\"inyr\"]):\n year = iyear + baseyear\n scenyr = mandict[\"rotations\"][iyear][iofe][\"yearindex\"]\n ncrop = mandict[\"scens\"][scenyr - 1][\"ntype\"]\n tilseq = mandict[\"scens\"][scenyr - 1][\"tilseq\"]\n plant_date = None\n if mandict[\"surfeffects\"]:\n for surfeff in mandict[\"surfeffects\"][tilseq - 1][\"tills\"]:\n op = surfeff[\"op\"]\n if (\n mandict[\"operations\"][op - 1][\"scecomment\"].find(\n \"Planter\"\n )\n > -1\n ):\n doy = surfeff[\"mdate\"]\n plant_date = datetime.date(\n year, 1, 1\n ) + datetime.timedelta(days=doy - 1)\n rows.append(\n {\n \"year\": year,\n \"ofe\": iofe + 1,\n \"plant_date\": plant_date,\n \"crop_name\": mandict[\"crops\"][ncrop - 1][\"crpnam\"],\n }\n )\n return pd.DataFrame(rows)","title":""},{"docid":"19e01f68ce527c424dc544f4eb2223ee","score":"0.49803594","text":"def fundings(bank):\n conn = engine.connect()\n loanPie_df = pd.read_sql(\"select ApprovalYear as FundingYear, sum(grossApproval) as TotalApproval from bank_data2 group by 1,2\", conn)\n all_fundings = loanPie_df.to_json(orient='records')\n\n return all_fundings","title":""},{"docid":"05bc421beb0d0b0d9b1be964237322ea","score":"0.49758768","text":"def month_dict_constructor(line, grouped, list_group):\n id_, length, start, end = grouped.groups()\n ids, lengths, starts, ends = list_group\n ids.append(id_)\n lengths.append(int(length))\n starts.append(int(start))\n ends.append(int(end))\n return list_group","title":""},{"docid":"dfca2bae9f227ee16be273afc25ce323","score":"0.49752215","text":"def scale_series(numerator, denominator):\n data = {}\n for date, value in numerator.items():\n if date in denominator:\n if denominator[date] > 0:\n data[date] = value / denominator[date]\n else:\n data[date] = 0\n else:\n try:\n numerator_year = get_year(date)\n for i in range(0, MAX_DENOMINATOR_BACK_YEAR + 1):\n year = str(numerator_year - i)\n if year in denominator:\n if denominator[year] > 0:\n data[date] = value / denominator[year]\n else:\n data[date] = 0\n break\n except ValueError:\n return {}\n return data","title":""},{"docid":"480976a0fa9ccd7bc9d17fd3803e162f","score":"0.49749124","text":"def get_yearly_profit(self):\n\n data = (TradingDay.objects\n .filter(user=self.user)\n .filter(account=self.account)\n .annotate(year=Year(\"date_created\"))\n .values(\"year\")\n .annotate(total=Sum(\"profit\"))\n .order_by(\"year\"))\n\n data_dict = {}\n for year in data:\n try:\n total = round(float(year[\"total\"]), 2)\n except:\n total = 0\n data_dict[year[\"year\"]] = total\n\n return data_dict","title":""},{"docid":"95350e455ff7fe7b6d35dcfded94f458","score":"0.49613392","text":"def range_year(some_data):\n print(\"Unique Year is {} \".format(some_data.FiscalYear.unique()))","title":""},{"docid":"d3efc4ea4f829dfa9597ddccdfc0861d","score":"0.49568775","text":"def getSeasonalProbabilities(probability_collection, year, band_names, reduce_method='median', season_list = [['winter',-1,12,1,0,2,'end'],['spring',0,3,1,0,5,'end'],['summer',0,6,1,0,8,'end'],['fall',0,9,1,0,11,'end']], include_difference=True, year_difference=1, image_name='season_probs_{}'):\n season_changes = []\n year = int(year)\n for season_definition in season_list:\n season_name = season_definition[0]\n season_name = season_name.lower()\n \n season_start_year_position = season_definition[1]\n season_start_month = season_definition[2]\n season_start_day = season_definition[3]\n season_end_year_position = season_definition[4]\n season_end_month = season_definition[5]\n season_end_day = season_definition[6]\n \n season_start_year_firstYear = year+season_start_year_position\n season_end_year_firstYear = year+season_end_year_position\n \n if include_difference:\n season_start_year_secondYear = year+season_start_year_position+year_difference\n season_end_year_secondYear = year+season_end_year_position+year_difference\n \n if season_start_day == 'end':\n season_firstYear_start_day = calendar.monthrange(season_start_year_firstYear, int(season_start_month))[1]\n if include_difference:\n season_secondYear_start_day = calendar.monthrange(season_end_year_firstYear, int(season_start_month))[1]\n \n else:\n season_firstYear_start_day = season_start_day\n if include_difference:\n season_secondYear_start_day = season_start_day\n \n if season_end_day == 'end':\n season_firstYear_end_day = calendar.monthrange(season_end_year_firstYear, int(season_end_month))[1]\n if include_difference:\n season_secondYear_end_day = calendar.monthrange(season_start_year_secondYear, int(season_end_month))[1]\n \n else:\n season_firstYear_end_day = season_end_day\n if include_difference:\n season_secondYear_end_day = season_end_day\n \n season_firstYear_start = '{}-{}-{}'.format(season_start_year_firstYear, season_start_month, season_firstYear_start_day)\n season_firstYear_end = '{}-{}-{}'.format(season_end_year_firstYear, season_end_month, season_firstYear_end_day)\n \n if include_difference:\n season_secondYear_start = '{}-{}-{}'.format(season_start_year_secondYear, season_start_month, season_secondYear_start_day)\n season_secondYear_end = '{}-{}-{}'.format(season_end_year_secondYear, season_end_month, season_secondYear_end_day) \n \n if reduce_method=='mean':\n season_image = probability_collection.filterDate(season_firstYear_start,season_firstYear_end).reduce(ee.Reducer.mean()).rename(band_names)\n if include_difference:\n diff_image = getTemporalProbabilityDifference(probability_collection, season_firstYear_start, \n season_firstYear_end, season_secondYear_start, season_secondYear_end, reduce_method='mean').rename(band_names)\n else:\n season_image = probability_collection.filterDate(season_firstYear_start,season_firstYear_end).reduce(ee.Reducer.median()).rename(band_names)\n if include_difference:\n diff_image = getTemporalProbabilityDifference(probability_collection, season_firstYear_start, \n season_firstYear_end, season_secondYear_start, season_secondYear_end, reduce_method='median').rename(band_names)\n \n season_image = season_image.set('system:index','{}_start'.format(season_name))\n \n season_changes.append(season_image)\n \n if include_difference:\n diff_image = diff_image.set('system:index','{}_difference'.format(season_name))\n season_changes.append(diff_image) \n \n season_changes = ee.ImageCollection(season_changes) \n season_changes = season_changes.toBands()\n season_changes = season_changes.set('system:index',image_name.format(year))\n season_changes = season_changes.set('system:time_start',ee.Date(season_firstYear_start))\n season_changes = season_changes.set('system:time_end',ee.Date(season_firstYear_end))\n return season_changes","title":""},{"docid":"14e65b1e68af69135b958ef514ca8f1d","score":"0.49498585","text":"def get_contracts_report_structure():\n return [\n {\n 'header': 'Contract Name',\n 'field': 'contract__description',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Contract Number',\n 'field': 'contract__number',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Contract Type',\n 'field': 'contract__type', # choicefield\n 'type': 'choice',\n 'value': 'CONTRACT_TYPES'\n },\n {\n 'header': 'Contract Start',\n 'field': 'contract__start_date',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Contract End',\n 'field': 'contract__end_date',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Product ID',\n 'field': 'item__ndc',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Product Type',\n 'field': 'created_by',\n 'type': 'static',\n 'value': 'NDC'\n },\n {\n 'header': 'Product Name',\n 'field': 'item__description',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Unit Measure',\n 'field': 'updated_by',\n 'type': 'static',\n 'value': 'EA'\n },\n {\n 'header': 'Unit Contract Price',\n 'field': 'price',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Product Start',\n 'field': 'start_date',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Product End',\n 'field': 'end_date',\n 'type': '',\n 'value': ''\n },\n {\n 'header': 'Status',\n 'field': 'status', # choicefield\n 'type': 'choice',\n 'value': 'STATUSES'\n }\n ]","title":""},{"docid":"9131645c75deb9557b11be33a04a010f","score":"0.4946772","text":"def parse_encost(fpath: Path) -> Tuple[List[dict], int]:\n\n fname = fpath.with_suffix(\"\").name\n year = int(f\"20{fname.replace('encost', '')}\")\n print(f\"Start parsing {fpath=}, {year=}\")\n\n with open(fpath, \"r\") as f:\n content = f.read()\n lines = [x.strip() for x in content.splitlines()]\n\n region = None\n years = None\n escalation_start_year = None\n fuel = None\n prices = None\n results = []\n\n for i, line in enumerate(lines):\n if not line:\n region = None\n years = None\n # print(\"Reset.\\n\")\n continue\n\n if region is None:\n region = line\n # print(f\"{region=}\")\n continue\n\n if years is None:\n years = [int(x) for x in line.split(\" \")]\n if len(years) != NUMBER_YEARS:\n print(\n f\"Warning: expected {NUMBER_YEARS} years, \"\n f\"got {len(years)}: {years}\"\n )\n if years[0] != year:\n raise ValueError(\n f\"Given the name of the file {fpath.name=}, we assumed \"\n f\"we would find {year=} but got {years[0]}\"\n )\n if escalation_start_year is not None:\n if escalation_start_year != years[1]:\n raise ValueError(\"Inconsistent escalation start year\")\n\n escalation_start_year = years[1]\n continue\n\n if fuel is None:\n fuel = line\n prices = None\n continue\n\n if prices is None:\n prices = [float(x) for x in line.split(\" \")]\n if len(prices) != NUMBER_YEARS:\n print(\n f\"Warning: expected {NUMBER_YEARS} years, \"\n f\"got {len(prices)} prices: {prices}\"\n )\n\n escalations = [x / prices[0] for x in prices[1:]]\n this_dict = {\n \"region\": region,\n \"fuel\": fuel,\n \"escalations\": dict(zip(years[1:], escalations)),\n # 'prices': dict(zip(years, prices)),\n }\n results.append(this_dict)\n fuel = None\n\n return results, escalation_start_year","title":""},{"docid":"231a11796b5774bef7ec6e1244036093","score":"0.4946276","text":"def yearly_return(stockList):\n for stock in stockList: # Iterate through list of stock dictionaries\n current_date = datetime.strptime(today,\"%m/%d/%Y\") # Create datetome object from string of current date\n purchase_date = datetime.strptime(stock['purchaseDate'],\"%m/%d/%Y\") # Create datetime object from string of sotck purchase data.\n no_days = (current_date-purchase_date).days # Calc number of days between two datetime objects. \n yearlyVal = 365.2425 # Constant year value\n yearlyReturn = (stock['percentage_yield']/(no_days/yearlyVal)) * 100 # Calculate the perctnage of yearly loss/earnings for each stock\n stock['yearly_return'] = yearlyReturn # Append new value tp yearly_return in stock dictionary.","title":""},{"docid":"4faf88c15e5a3aec669db1e795f16bde","score":"0.49412447","text":"def precipitation():\n last_day = session.query(Measurement.date, Measurement.prcp).order_by(Measurement.date.desc()).first()[0]\n last_year = str(dt.datetime.strptime(last_day, \"%Y-%m-%d\") - dt.timedelta(days=366))\n\n precipitation = session.query(Measurement.date, Measurement.prcp).\\\n filter(Measurement.date >= last_year, Measurement.date <= last_day).\\\n order_by(Measurement.date).all()\n prcp_dict = {date: prcp for date, prcp in precipitation}\n return jsonify(prcp_dict)","title":""},{"docid":"6c21ca33f32ed18b643781b52773f115","score":"0.49346516","text":"def create_country_yearly_data(country_data: List[CountryTemperature],\r\n nation: str, year: int, end_year: int) -> List[CountryTemperature]:\r\n country_only_data = [row for row in country_data if row.country == nation]\r\n data_so_far = []\r\n for i in range(year, end_year):\r\n sum_temperature = []\r\n for row in country_only_data:\r\n if int(row.date.strftime(\"%Y\")) < i:\r\n pass\r\n elif int(row.date.strftime(\"%Y\")) == i:\r\n sum_temperature.append(row.temperature)\r\n elif int(row.date.strftime(\"%Y\")) >= i:\r\n if isinstance(sum_temperature[0], float) is True:\r\n data_so_far.append(CountryTemperature(datetime.date(i, 1, 1),\r\n statistics.mean(sum_temperature), nation))\r\n break\r\n return data_so_far","title":""},{"docid":"5069fe1c54a9e14d982862a8b5ea1c52","score":"0.49336904","text":"def get_date_endpoints(q=None, year=2018, kind='year'):\n if kind=='year':\n return (dt.date(year, 1, 1), dt.date(year, 12, 31))\n elif kind=='quarter':\n return get_quarter_start_end(q,year=year)","title":""},{"docid":"d337754dc0ebdddf6127a503ddd7ddec","score":"0.49279833","text":"def fetch_subset(start_yr, end_yr):\r\n for yr in range(start_yr, end_yr+1):\r\n fetch_single(yr)","title":""},{"docid":"a9f48b9e08c801f08b97784d2d300ae5","score":"0.49251616","text":"def get_crime_at_year(year, data):\n return {i: data[i] for i in data if str(year) in i}","title":""},{"docid":"8c013745577336b557c09e90112490b4","score":"0.49234694","text":"def processGroup(data):\n fillAges(data)\n titles = findTitles(data)\n names = findLastNames(data)\n\n return {'titles': titles, 'names': names}","title":""},{"docid":"ee19dfb7f1f4afbcc3a75e19ea1540d7","score":"0.49151674","text":"def get_all_records():\n season_records = {}\n round_records = {}\n\n round_scores = Tip.objects \\\n .exclude(afl_fixture__round__status='Scheduled') \\\n .exclude(afl_fixture__round__is_finals=True) \\\n .filter(afl_fixture__round__season=2014) \\\n .values('afl_fixture__round__season', 'afl_fixture__round', 'club') \\\n .annotate(\n round_total=Sum('total'),\n round_winners=Sum('winner_score'),\n round_margins=Sum('margin_score'),\n round_crowds=Sum('crowd_score'),\n round_bogs=Sum('bog_score')\n ) \\\n .order_by('afl_fixture__round__season', 'club', 'afl_fixture__round')\n\n # Get round records\n def _season_key(row):\n return (row['afl_fixture__round__season'], row['club'])\n\n# def _max(group, key):\n# '''\n# Return the maximum score and the round it was scored in the group\n# '''\n# sorted_group = sorted(group, key=)\n#\n# return (row['afl_fixture__round__season'], row['club'])\n#\n# def _season_min_key(row):\n# return (row['afl_fixture__round__season'], row['club'])\n\n for attr in ('total', 'winners', 'margins', 'crowds', 'bogs'):\n attr_key = 'round_%s' % attr\n scores = round_scores.order_by(\n 'afl_fixture__round__season',\n 'club',\n attr_key,\n 'afl_fixture__round'\n )\n for key, group in groupby(scores, key=_season_key):\n club_records = {}\n\n group = list(group)\n season = key[0]\n club = Club.objects.get(id=key[1])\n if club in club_records:\n club_records[club].update(\n {\n 'total_%s' % attr: sum(g[attr_key] for g in group),\n 'max_%s' % attr: max(g[attr_key] for g in group),\n 'min_%s' % attr: min(g[attr_key] for g in group),\n 'avg_%s' % attr: avg(g[attr_key] for g in group),\n }\n )\n else:\n club_records[club] = {\n 'total_%s' % attr: sum(g[attr_key] for g in group),\n 'max_%s' % attr: max(g[attr_key] for g in group),\n 'min_%s' % attr: min(g[attr_key] for g in group),\n 'avg_%s' % attr: avg(g[attr_key] for g in group),\n }\n# 'total_winners': sum(g['round_winners'] for g in group),\n# 'max_winners': max(g['round_winners'] for g in group),\n# 'min_winners': min(g['round_winners'] for g in group),\n# 'avg_winners': avg(g['round_winners'] for g in group),\n# 'total_margins': sum(g['round_margins'] for g in group),\n# 'max_margins': max(g['round_margins'] for g in group),\n# 'min_margins': min(g['round_margins'] for g in group),\n# 'avg_margins': avg(g['round_margins'] for g in group),\n# 'total_crowds': sum(g['round_crowds'] for g in group),\n# 'max_crowds': max(g['round_crowds'] for g in group),\n# 'min_crowds': min(g['round_crowds'] for g in group),\n# 'avg_crowds': avg(g['round_crowds'] for g in group),\n# 'total_bogs': sum(g['round_bogs'] for g in group),\n# 'max_bogs': max(g['round_bogs'] for g in group),\n# 'min_bogs': min(g['round_bogs'] for g in group),\n# 'avg_bogs': avg(g['round_bogs'] for g in group)\n\n # Winners bonus isn't included in tips scores from 2012 so adjust\n # total scores\n if attr == 'total':\n for g in group:\n if g['afl_fixture__round__season'] >= 2012:\n if g['round_winners'] == 54:\n club_records[club]['total_total'] += 10\n\n round_records[season] = club_records","title":""},{"docid":"81d74af9fff9fb759fd7673c30a2799a","score":"0.49082693","text":"def stock_performance(start_year,end_year,stocks):\n \n # obtaining the TICKER symbols of the stocks\n stock = stocks\n \n # create a list to obtain all the returns of the stock\n all_returns = []\n\n # obtaining Quarterly returns using quarterly_returns() function\n stock_data = quarterly_returns(start_year,end_year,stocks)\n \n\n # for each TICKER symbol in stock \n for abbv in stock:\n data = stock_data[abbv]\n\n \n # creating pyfolio tearsheet\n# pf.create_returns_tear_sheet(data)\n\n # changing into numpy array for calculation\n data = np.array(data)\n \n # creating a list to remove the NaN and make it a list of float values \n val = []\n for i in data:\n if np.isnan(i):\n i = float(0)\n val.append(i)\n else:\n i = float(i)\n val.append(i)\n \n # normalising to 100\n norm = 100\n for i in range(len(val)):\n push = (1+val[i])*norm\n val[i] = push\n norm = push\n\n # adding the normalised returns of all stocks to the all_returns[] list\n all_returns.append(val)\n\n return all_returns","title":""},{"docid":"d6fbcc994afc0988968438af3e806076","score":"0.49082536","text":"def get_year(self, year):\n return [item\n for month in range(1, 13)\n for item in self.get_month(year, month)]","title":""},{"docid":"c091f15c8d7769426b1f979cbcb84098","score":"0.4901673","text":"def get_years(self):\n \n items = []\n\n # Get a datetime object\n now = datetime.datetime.now()\n currentYear = now.year\n startYear = 1950\n \n for eachYear in range(startYear, (currentYear + 5), 1):\n items.append( str(eachYear) ) \n\n return items","title":""},{"docid":"c77ace9ed99b3f301ce91e0ba078e42f","score":"0.48991683","text":"def get_quarters(n=3):\n now = datetime.datetime.now()\n year = now.year\n quarter = pd.Timestamp(now).quarter\n quarters = [(year, quarter)]\n for i in range(n):\n if quarter == 1:\n quarter = 4\n year -= 1\n else:\n quarter -= 1\n quarters.append((year, quarter))\n quarters.reverse()\n return quarters","title":""},{"docid":"d13f2926f48c21cdb18b6be729d2e9b4","score":"0.4897494","text":"def construct_all_holdings(self):\r\n d=dict((k,v) for k,v in [(s,0.0) for s in self.symbol_list])\r\n d['datetime']=self.start_date\r\n d['cash']=self.initial_capital\r\n d['commisson']=0.0\r\n d['total']=self.initial_capital\r\n \r\n return [d]","title":""},{"docid":"6ac6b82e1ffe05a04c3fafb32c5465b0","score":"0.48928243","text":"def agg_by_state(fuel_codes, df_jan_to_nov, df_dec):\n\n # output of function = makes dictionary of states\n fuel_per_state = { }\n\n # note -- the retrieved row in only a copy, not the original. Dataframes are good for data manipulation (e.g. pivot table), but are not very mutable.\n for idx,row in enumerate(df_jan_to_nov.values):\n\n # get all the data, and insert into nested dict\n plant_name, state, fuel_type, mwh_gen = row[0], row[1], row[2], row[3:]\n # convert fuel code, to actual fuel name\n fuel_type_name = fuel_codes[fuel_type]\n\n starting_totals = [0,0,0,0,0,0,0,0,0,0,0,0]\n\n # only add to dict, if not \"State Incremental Fuel Level\":\n if plant_name != \"State-Fuel Level Increment\":\n\n # add to dict, summing the list per month:\n if state not in fuel_per_state:\n fuel_per_state[state] = {\n \"gas\":starting_totals,\n \"coal\":starting_totals,\n \"solar\":starting_totals,\n \"wind\":starting_totals,\n \"nuclear\": starting_totals,\n \"hydro\":starting_totals,\n \"other\":starting_totals\n }\n\n already_in_dict = fuel_per_state[state][fuel_type_name]\n replace_in_dict = [ (mwh_gen[i]+int(already_in_dict[i]) ) for i in range(len(mwh_gen)) ]\n\n fuel_per_state[state][fuel_type_name] = replace_in_dict\n\n # add the december fuel data. make sure to add the 12th month\n for idx,row in enumerate(df_dec.values):\n plant_name = row[0] # for each row, get the plant name\n\n # make sure we know the state, before we take the data\n if plant_name != \"State-Fuel Level Increment\":\n\n # get all the data, and insert into nested dict\n plant_name, state, fuel_type, mwh_gen = row[0], row[1], row[2], row[3:]\n # convert fuel code, to actual fuel name\n fuel_type_name = fuel_codes[fuel_type]\n\n # add to dict, as the 12th month in the list:\n in_dict = fuel_per_state[state][fuel_type_name]\n if len(in_dict) < 12:\n in_dict.append(int(mwh_gen))\n else:\n in_dict[11] += int(mwh_gen)\n fuel_per_state[state][fuel_type_name] = in_dict\n\n return fuel_per_state","title":""},{"docid":"190d34f53bce60cd1b7f18050c4a0da7","score":"0.4891378","text":"def get_dated_items(self, year, month):\r\n date_field = self.get_date_field()\r\n date = _date_from_string(year, '%Y', month, self.get_month_format())\r\n\r\n # Construct a date-range lookup.\r\n first_day, last_day = _month_bounds(date)\r\n lookup_kwargs = {\r\n '%s__gte' % date_field: first_day,\r\n '%s__lt' % date_field: last_day,\r\n }\r\n\r\n allow_future = self.get_allow_future()\r\n qs = self.get_dated_queryset(allow_future=allow_future, **lookup_kwargs)\r\n date_list = self.get_date_list(qs, 'day')\r\n\r\n return (date_list, qs, {\r\n 'month': date,\r\n 'next_month': self.get_next_month(date),\r\n 'previous_month': self.get_previous_month(date),\r\n })","title":""}],"string":"[\n {\n \"docid\": \"d277920cd02ddd135c01e630b1d38afa\",\n \"score\": \"0.6372415\",\n \"text\": \"def get_fiscalyear_revenue_timeseris_data(client_id:str)->List[Dict]:\\n df = load_process_data(client_id)\\n fiscalyears = df['date'].dt.to_period('A-MAR').astype(str).astype(int).unique().tolist()\\n fiscalyears = sorted(fiscalyears, reverse=True)\\n fy_names = list(map(lambda y: f'{y-1}-{y%1000}', fiscalyears))\\n \\n # return [get_yearly_quterly_montly_weekly_revenue(fy, df) for fy in fiscalyears]\\n return {fy_names[i]:get_yearly_quterly_montly_weekly_revenue(fy, df) for i, fy in enumerate(fiscalyears)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f427af50c15fe673a67eea1e85ca63b3\",\n \"score\": \"0.61781615\",\n \"text\": \"def generate_dictionary_for_quarterwise_data(stock, columnName):\\n result = {}\\n stock.Date = pd.to_datetime(stock.Date)\\n for index, row in stock.iterrows():\\n try:\\n q = (row.Date.month-1)//3 + 1\\n year = row.Date.year\\n month = row.Date.month\\n res = result.get(year, {})\\n # amount = re.findall(r\\\"\\\\d+.?\\\\d*\\\",row[\\\"Revenue\\\"])[0]\\n amount = row[columnName]\\n q = \\\"1q\\\" if 1 <= month <= 3 else \\\"2q\\\" if 4 <= month <= 6 else \\\"3q\\\" if 6 <= month <= 9 else \\\"4q\\\"\\n val = res.get(q, [])\\n val.append(float(amount))\\n res[q] = val\\n result[year] = res\\n except:\\n continue\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178ea43450a559fb8638aea5a018f058\",\n \"score\": \"0.60559785\",\n \"text\": \"def get_yearly_quterly_montly_weekly_revenue(fiscalyear, df):\\n df2 = df[df['date'].dt.to_period('A-MAR')==fiscalyear].copy()\\n yearly_revenue = df2['debit'].sum().round()\\n fy_name = f'{fiscalyear-1}-{fiscalyear%1000}'\\n \\n quarterly_agg = df2.groupby([df2.date.dt.quarter], sort=False).agg({'debit' :sum}). \\\\\\n rename(columns={'debit':'revenue'}).reset_index()\\n quarterly_agg['quarter'] = list(range(1,5))\\n quterly_revenue = quarterly_agg[['quarter', 'revenue']].round().to_dict(orient='records')\\n \\n monthly_agg = df2.groupby(df.date.dt.strftime('%b-%Y'), sort=False).agg({'debit' :sum}). \\\\\\n reset_index(). \\\\\\n rename(columns={'date':'month','debit':'monthly_revenue'})\\n monthly_revenue = monthly_agg.round().to_dict(orient='records')\\n \\n weekly_agg = df2.groupby([df2.date.dt.week], sort=False).agg({'debit' :sum}). \\\\\\n rename(columns={'debit':'weekly_revenue'}).reset_index()\\n weekly_agg['week'] = list(range(1,53))\\n weekly_revenue = weekly_agg[['week', 'weekly_revenue']].round().to_dict(orient='records')\\n \\n # return {'fiscalyear': fy_name,\\n # 'data': {'annual_revenue': yearly_revenue,\\n # 'quarterly_revenue': quterly_revenue,\\n # 'monthly_revenue': monthly_revenue,\\n # 'weekly_revenue': weekly_revenue}\\n # }\\n \\n return {'weekly': weekly_revenue, \\n 'monthly': monthly_revenue, \\n 'quarterly': quterly_revenue}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00802fde5706e77a6bab67351fd50154\",\n \"score\": \"0.60281336\",\n \"text\": \"def generate_dictionary_for_quarterwise_growthrate_data(data):\\n gr_dic = {}\\n keys = list(data.keys())\\n array = [''] * (len(keys)*4)\\n array_index = 0\\n for key in data:\\n lists = data.get(key)\\n array_index += 4 - len(lists.keys())\\n for lis in lists:\\n if math.isnan(lists.get(lis)[0]):\\n array[array_index] = ''\\n else:\\n array[array_index] = lists.get(lis)[0]\\n array_index = array_index + 1\\n if (array.count('')) > ((len(keys) * 4) / 2):\\n return gr_dic\\n\\n for i in range(4, len(keys)*4, 4):\\n res = [array[i], array[i+1], array[i+2], array[i+3]]\\n avg = np.mean(list(filter(lambda i: isinstance(i, float), res)))\\n if np.isnan(avg):\\n pass\\n else:\\n array[i] = avg\\n\\n gr_array = [''] * (len(keys)*4)\\n for i in range(0, len(keys)*4-1):\\n x = array[i]\\n y = array[i+1]\\n if x == '' and y == '':\\n continue\\n if y == '' or y == 0:\\n continue\\n if x == '':\\n gr_array[i] = 1\\n else:\\n gr_array[i] = (x - y) / y\\n index = 0\\n for key in data:\\n gr_dic[key] = [gr_array[index], gr_array[index+1],\\n gr_array[index+2], gr_array[index+3]]\\n index = index + 4\\n return gr_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26a91c7045deb0a63f30a553d4bf294c\",\n \"score\": \"0.5766984\",\n \"text\": \"def of_year(cls, year):\\n start = datetime(year, 1, 1)\\n start_quarter = list(\\n rrule(MONTHLY, interval=3, dtstart=start, count=4)\\n )\\n end_quarter = [\\n date + relativedelta(months=3, days=-1) for date in start_quarter\\n ]\\n return [cls(*item) for item in list(zip(start_quarter, end_quarter))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc762741203c66ae9778ad90e4a8eea\",\n \"score\": \"0.5722563\",\n \"text\": \"def decode_periods(temporal_schema, start_date, end_date, time_step):\\n requested_periods = {}\\n if start_date is None:\\n return requested_periods\\n if isinstance(start_date, datetime.date):\\n start_date = start_date.strftime('%Y-%m-%d')\\n\\n td_time_step = datetime.timedelta(days=time_step)\\n steps_per_period = int(round(365./time_step))\\n\\n if end_date is None:\\n end_date = datetime.datetime.now().strftime('%Y-%m-%d')\\n if isinstance(end_date, datetime.date):\\n end_date = end_date.strftime('%Y-%m-%d')\\n\\n if temporal_schema is None:\\n periodkey = start_date + '_' + start_date + '_' + end_date\\n requested_period = list()\\n requested_period.append(periodkey)\\n requested_periods[start_date] = requested_period\\n return requested_periods\\n\\n if temporal_schema == 'M':\\n start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')\\n end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')\\n delta = relativedelta(months=time_step)\\n requested_period = []\\n while start_date <= end_date:\\n next_date = start_date + delta\\n periodkey = str(start_date)[:10] + '_' + str(start_date)[:10] + '_' + str(next_date - relativedelta(days=1))[:10]\\n requested_period.append(periodkey)\\n requested_periods[start_date] = requested_period\\n start_date = next_date\\n return requested_periods\\n\\n # Find the exact start_date based on periods that start on yyyy-01-01\\n firstyear = start_date.split('-')[0]\\n new_start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')\\n if temporal_schema == 'A':\\n dbase = datetime.datetime.strptime(firstyear+'-01-01', '%Y-%m-%d')\\n while dbase < new_start_date:\\n dbase += td_time_step\\n if dbase > new_start_date:\\n dbase -= td_time_step\\n start_date = dbase.strftime('%Y-%m-%d')\\n new_start_date = dbase\\n\\n # Find the exact end_date based on periods that start on yyyy-01-01\\n lastyear = end_date.split('-')[0]\\n new_end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')\\n if temporal_schema == 'A':\\n dbase = datetime.datetime.strptime(lastyear+'-12-31', '%Y-%m-%d')\\n while dbase > new_end_date:\\n dbase -= td_time_step\\n end_date = dbase\\n if end_date == start_date:\\n end_date += td_time_step - datetime.timedelta(days=1)\\n end_date = end_date.strftime('%Y-%m-%d')\\n\\n # For annual periods\\n if temporal_schema == 'A':\\n dbase = new_start_date\\n yearold = dbase.year\\n count = 0\\n requested_period = []\\n while dbase < new_end_date:\\n if yearold != dbase.year:\\n dbase = datetime.datetime(dbase.year,1,1)\\n yearold = dbase.year\\n dstart = dbase\\n dend = dbase + td_time_step - datetime.timedelta(days=1)\\n dend = min(datetime.datetime(dbase.year, 12, 31), dend)\\n basedate = dbase.strftime('%Y-%m-%d')\\n start_date = dstart.strftime('%Y-%m-%d')\\n end_date = dend.strftime('%Y-%m-%d')\\n periodkey = basedate + '_' + start_date + '_' + end_date\\n if count % steps_per_period == 0:\\n count = 0\\n requested_period = []\\n requested_periods[basedate] = requested_period\\n requested_period.append(periodkey)\\n count += 1\\n dbase += td_time_step\\n if len(requested_periods) == 0 and count > 0:\\n requested_periods[basedate].append(requested_period)\\n else:\\n yeari = start_date.year\\n yearf = end_date.year\\n monthi = start_date.month\\n monthf = end_date.month\\n dayi = start_date.day\\n dayf = end_date.day\\n for year in range(yeari,yearf+1):\\n dbase = datetime.datetime(year,monthi,dayi)\\n if monthi <= monthf:\\n dbasen = datetime.datetime(year,monthf,dayf)\\n else:\\n dbasen = datetime.datetime(year+1,monthf,dayf)\\n while dbase < dbasen:\\n dstart = dbase\\n dend = dbase + td_time_step - datetime.timedelta(days=1)\\n basedate = dbase.strftime('%Y-%m-%d')\\n start_date = dstart.strftime('%Y-%m-%d')\\n end_date = dend.strftime('%Y-%m-%d')\\n periodkey = basedate + '_' + start_date + '_' + end_date\\n requested_period = []\\n requested_periods[basedate] = requested_period\\n requested_periods[basedate].append(periodkey)\\n dbase += td_time_step\\n return requested_periods\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e94a8540c8064bbc321eabd2a95885c\",\n \"score\": \"0.5685768\",\n \"text\": \"def new_dictionary():\\r\\n years = {str(i) for i in range(1888, 2018)}\\r\\n future_dict = file_num()\\r\\n countries = countries_all()\\r\\n set1 = set()\\r\\n for i in countries:\\r\\n set1.add(str(\\\" \\\".join(i)))\\r\\n dict1 = dict()\\r\\n for element in future_dict:\\r\\n values = set()\\r\\n for j in element:\\r\\n if j in set1:\\r\\n values.add(j)\\r\\n if len(j) == 6:\\r\\n if str(j[1] + j[2] + j[3] + j[4]) in years:\\r\\n key = str(j[1] + j[2] + j[3] + j[4])\\r\\n if key in dict1:\\r\\n dict1[key].update(values)\\r\\n else:\\r\\n dict1[key] = values\\r\\n return dict1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4614734baee919465ee37430332517a5\",\n \"score\": \"0.56512946\",\n \"text\": \"def names():\\n\\n year_extract = '201'\\n avg_sql = (f'SELECT city, substr(start_date, INSTR(start_date, {year_extract}), 4) as yr, ' +\\n 'avg(duration) as avg_trip ' +\\n 'FROM trip ' +\\n 'JOIN station ON trip.start_station_id = station.id ' +\\n 'GROUP BY city, yr'\\n )\\n\\n avg_city = pd.read_sql(avg_sql, engine2.connect())\\n avg_city['avg_trip'] = avg_city['avg_trip']/60\\n avg_city['avg_trip'] = avg_city['avg_trip'].round()\\n avg_list = avg_city.to_dict('records')\\n\\n yr_2013 = []\\n yr_2014 = []\\n yr_2015 = []\\n\\n for item in avg_list:\\n data = {}\\n for k,v in item.items():\\n if(k=='city'):\\n data['axis'] = v\\n if(k=='avg_trip'):\\n data['value'] = v\\n if(k=='yr'and v=='2013'):\\n data['name'] = v\\n yr_2013.append(data)\\n elif(k=='yr'and v=='2014'):\\n data['name'] = v\\n yr_2014.append(data)\\n elif(k=='yr'and v=='2015'):\\n data['name'] = v\\n yr_2015.append(data)\\n \\n all_yr = [yr_2015, yr_2014, yr_2013]\\n\\n\\n return jsonify(all_yr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13401000b7a8392ec00dfa1233dc031f\",\n \"score\": \"0.56424075\",\n \"text\": \"def precipitation():\\n# * Query for the dates and precipitation observations from the last year.\\n# * Convert the query results to a Dictionary using `date` as the key and `prcp` as the value.\\n# * Return the json representation of your dictionary.\\n end_date_str = session.query(Measurement.date).order_by(Measurement.date.desc()).first().date\\n dt_year = int(end_date_str[:4])\\n dt_month = int(end_date_str[5:7])\\n dt_day = int(end_date_str[8:10])\\n end_date = dt.date(dt_year, dt_month, dt_day)\\n start_date = end_date - dt.timedelta(days=365)\\n\\n#Select only the date and prcp values\\n measurement_year = session.query(Measurement.date, Measurement.prcp).\\\\\\n filter(Measurement.date >= start_date).\\\\\\n filter(Measurement.date <= end_date).all()\\n\\n year_totals = []\\n for result in measurement_year:\\n year = {}\\n year[\\\"date\\\"] = result[0]\\n year[\\\"prcp\\\"] = result[1]\\n year_totals.append(year)\\n\\n return jsonify(year_totals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0236d9ce3ec3917df7b8e2ccc7e430f6\",\n \"score\": \"0.5638206\",\n \"text\": \"def create_dividend(stock, corporate):\\n corporate['Ex Date'] = pd.to_datetime(\\n corporate['Ex Date'], errors='coerce')\\n stock['Date'] = pd.to_datetime(stock['Date'], errors='coerce')\\n\\n dividend = corporate[corporate['Purpose'].str.contains(\\\"Dividend\\\")]\\n result = {}\\n for index, row in dividend.iterrows():\\n try:\\n year = row[\\\"Ex Date\\\"].year\\n month = row[\\\"Ex Date\\\"].month\\n amount = re.findall(r\\\"\\\\d+.?\\\\d*\\\", row[\\\"Purpose\\\"])[0]\\n res = result.get(year, {})\\n q = \\\"1q\\\" if 1 <= month <= 3 else \\\"2q\\\" if 4 <= month <= 6 else \\\"3q\\\" if 6 <= month <= 9 else \\\"4q\\\"\\n val = res.get(q, [])\\n val.append(float(amount))\\n res[q] = val\\n result[year] = res\\n except:\\n pass\\n for year, quaters in result.items():\\n for q, a in quaters.items():\\n try:\\n quaters[q] = sum(a)/len(a)\\n except:\\n pass\\n result[year] = quaters\\n divList = list()\\n for index, row in stock.iterrows():\\n try:\\n year = row[\\\"Date\\\"].year\\n month = row[\\\"Date\\\"].month\\n q = \\\"1q\\\" if 1 <= month <= 3 else \\\"2q\\\" if 4 <= month <= 6 else \\\"3q\\\" if 6 <= month <= 9 else \\\"4q\\\"\\n if result.get(year) != None:\\n if result.get(year).get(q) != None:\\n divList.append(result.get(year).get(q))\\n else:\\n divList.append(0)\\n else:\\n divList.append(0)\\n except:\\n pass\\n stock[\\\"Dividend Value\\\"] = divList\\n return stock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccce1eca2c258f31a2441f1ddf72cafa\",\n \"score\": \"0.5582218\",\n \"text\": \"def search_years(content: dict, start=0, step=0) -> dict:\\n search_year = content['value']\\n years_list = __get_years_(search_year, start, step)\\n years = get_years_list(years_list)\\n return years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46c3841d2c3dde7cd82f5d9bc7d700d\",\n \"score\": \"0.55810326\",\n \"text\": \"def precip_records():\\n session = Session(engine)\\n date = dt.datetime(2017, 8, 23) - dt.timedelta(days=365)\\n results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date > date).all()\\n session.close()\\n year_precip = []\\n for date, prcp in results:\\n precip_dict = {}\\n precip_dict[\\\"Date\\\"] = date\\n precip_dict[\\\"Precipitation\\\"] = prcp\\n year_precip.append(precip_dict)\\n\\n return jsonify(year_precip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd8735069f5206b1317466d442f7585\",\n \"score\": \"0.55157673\",\n \"text\": \"def grouping(data, separator='day'):\\n output = []\\n results = dict(sorted(data.items()))\\n\\n if separator == 'year':\\n\\n for years in results:\\n\\n months = dict(sorted(results[years].items()))\\n year_mean = []\\n first_day_date = None\\n first_month = None\\n\\n for month in months:\\n if first_day_date is None:\\n first_day_date = list(months[month][::-1][0].items())[0][0]\\n first_month = month\\n year_mean += [float(day.popitem()[1]) for day in months[month]]\\n\\n date = '{}/{}/{}'.format(first_day_date, first_month, years)\\n output.append((date, '{0:.2f}'.format(statistics.mean(year_mean))))\\n\\n elif separator == 'month':\\n\\n for years in results:\\n months = dict(sorted(results[years].items()))\\n\\n for month in months:\\n first_day_date = list(months[month][::-1][0].items())[0][0]\\n mean = \\\"{0:.2f}\\\".format(statistics.mean([float(day.popitem()[1]) for day in months[month]]))\\n date ='{}/{}/{}'.format(first_day_date,month,years)\\n output.append((date,mean))\\n\\n elif separator == 'day':\\n for years in results:\\n months = dict(sorted(results[years].items()))\\n for month in months:\\n for day_data in months[month][::-1]:\\n day = day_data.popitem()\\n date = '{}/{}/{}'.format(day[0],month,years)\\n output.append((date,day[1]))\\n return output, separator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d209866c3a74514aa3bc378d98b1fb\",\n \"score\": \"0.55133194\",\n \"text\": \"def get_quarterly_new_members(quarters, members):\\n response = {}\\n for pair in quarters:\\n year = pair[0]\\n quarter = pair[1]\\n quarter_desc = '{}-Q{}'.format(year, quarter)\\n date_range = REPORT_QUARTERS[quarter-1]\\n start = '{}-{}'.format(year, date_range[0])\\n if quarter == 4:\\n year += 1\\n end = '{}-{}'.format(year, date_range[1])\\n response[quarter_desc] = members.count_new_households(start, end)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"893ec5a6517aeda76dae8cf830c8e8c6\",\n \"score\": \"0.54815346\",\n \"text\": \"def gen_financials():\\n net_worth = random.randrange(5, 33) * 10000\\n liquid_assets = net_worth / random.randrange(1, 10)\\n annual_income = random.randrange(7, 42) * 5000\\n\\n financials = {'net_worth' : net_worth, 'liquid_assets' : liquid_assets,\\n 'annual_income' : annual_income}\\n\\n return financials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f498f103f77b92e8475de8e5010918d\",\n \"score\": \"0.54718643\",\n \"text\": \"def construct_years_dict(hurricanes):\\n years_dict = {}\\n for hurricane in hurricanes:\\n current_year = hurricanes[hurricane]['Year']\\n if current_year not in years_dict:\\n years_dict[current_year] = [hurricanes[hurricane]]\\n else:\\n years_dict[current_year].append(hurricanes[hurricane])\\n return years_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda4726c2b968a7e6e4a9350cab631a4\",\n \"score\": \"0.5427099\",\n \"text\": \"def get_years(xlsx_files, filename_company):\\n years_dict = {}\\n\\n for xl_path, page_ref in xlsx_files.items(): # loop through all xlsx files\\n path, filename = os.path.split(xl_path) # split file and path\\n work_book = openpyxl.load_workbook(xl_path) # load workbook\\n work_sheet = work_book[page_ref] # take sheet reference\\n flg = 1\\n count = 0\\n years = []\\n for j in range(1, 10):\\n\\n \\n for i in range(1, 10):\\n if work_sheet.cell(row=j, column=i).value is not None:\\n match_obj = re.search(\\\"^2\\\\d{3}$|^2\\\\d{3}\\\\s2\\\\d{3}\\\\s2\\\\d{3}$|^2\\\\d{3}\\\\s2\\\\d{3}$|^\\\\s2\\\\d{3}$|(^2\\\\d{3}){1}|[^,]\\\\d{3}$\\\", str(work_sheet.cell(row=j, column=i).value))\\n \\n \\n if match_obj:\\n match_obj_space=str(match_obj.group(0))\\n \\n if \\\" \\\" in match_obj_space: \\n \\n years.append(str.split(work_sheet.cell(row=j, column=i).value)) \\n years= [val for sublist in years for val in sublist] \\n flg=0\\n break\\n else:\\n count += 1\\n years.append(match_obj.group(0))\\n\\n if count == 3: \\n flg=0\\n break\\n years.sort(reverse = True)\\n years_dict[filename_company[filename]] = years\\n if flg == 0:\\n break\\n op.writelines(str(years_dict))\\n return years_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccb29b8d5a25577623024f45c7b8f73\",\n \"score\": \"0.54231036\",\n \"text\": \"def _aggregate_by_year(historical_data_dict: dict):\\n\\n converted_response = {}\\n\\n for datapoint in historical_data_dict:\\n converted_response[datapoint['date'].year] = datapoint['value']\\n\\n return converted_response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5dd786d07e8395bc784dacf81e3396a\",\n \"score\": \"0.54199255\",\n \"text\": \"def get_yearly_report(self):\\n ans = dict()\\n\\n for year in range(Settings.CF_OPENED_YEAR, date.today().year+1):\\n current_year_stats = self._get_insights_for_filtered(lambda entity: entity.get_time().year == year)\\n if current_year_stats:\\n ans[year] = current_year_stats\\n\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df85e2d186917920e58f893c23c2126\",\n \"score\": \"0.54189634\",\n \"text\": \"def _transform_financial_stmt(std_financials_list: list, tag_filter_list: list):\\n results = {}\\n\\n for financial in std_financials_list:\\n\\n if (tag_filter_list is None or\\n financial.data_tag.tag in tag_filter_list):\\n results[financial.data_tag.tag] = financial.value\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45fd795c4a8638d9679fef4b2d4e512\",\n \"score\": \"0.5417625\",\n \"text\": \"def parse_flight_data() -> Dict[str, List[Tuple[str, str, int]]]:\\n data_paths = {\\n 'test': 'data/test_flight_data.csv',\\n '11-19': 'data/Nov2019_flight_data.csv',\\n '12-19': 'data/Dec2019_flight_data.csv',\\n '01-20': 'data/Jan2020_flight_data.csv',\\n '02-19': 'data/Feb2019_flight_data.csv',\\n '03-19': 'data/Mar2019_flight_data.csv',\\n '04-19': 'data/Apr2019_flight_data.csv',\\n '05-19': 'data/May2019_flight_data.csv',\\n '06-19': 'data/Jun2019_flight_data.csv',\\n '07-19': 'data/Jul2019_flight_data.csv',\\n '08-19': 'data/Aug2019_flight_data.csv',\\n }\\n flight_data = dict()\\n for month_year, data_path in data_paths.items():\\n flight_data[month_year] = list()\\n with open(data_path) as f:\\n lines_to_skip = 1\\n i = 0\\n for line in f:\\n if i < lines_to_skip:\\n i += 1\\n continue\\n split_line = line.rstrip().split(',')\\n try:\\n int(split_line[11])\\n except:\\n raise Exception('Bad line: {}'.format(line))\\n flight_data[month_year].append((split_line[4], split_line[8], int(split_line[11])))\\n return flight_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19adcc20050b657c0d3e4e7a9dc0f423\",\n \"score\": \"0.5409015\",\n \"text\": \"def generate_dates():\\n current_year = datetime.now().year\\n current_date = datetime.now().strftime('%m%d')\\n years = range(2015, current_year)\\n quarters = [\\\"0331\\\", \\\"0630\\\", \\\"0930\\\", \\\"1231\\\"]\\n all_dates = []\\n for r in itertools.product(years, quarters):\\n all_dates.append(str(r[0]) + r[1])\\n for q in quarters:\\n if q < current_date:\\n all_dates.append(str(current_year) + q)\\n logging.info(\\\"dates %s\\\", all_dates)\\n return all_dates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"511bcca5f08f87abd6a82f7e3d6b5f45\",\n \"score\": \"0.54009223\",\n \"text\": \"def pullcalldates(self):\\n datelist = cal.Calendar()\\n dlist = datelist.yeardatescalendar(2020)\\n itr = 0\\n self.d = {}\\n for i in dlist[:]:\\n for j in i:\\n for k in j:\\n for u in k:\\n itr += 1\\n self.d[itr] = u\\n return self.d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91547afd334dea2da6b47dcb529830f\",\n \"score\": \"0.53689575\",\n \"text\": \"def year_frequency_dict(year_dict, start, end):\\n year_freq_dict = dict()\\n for year in range(start, end+1):\\n if year in year_dict:\\n year_freq_dict[year] = year_dict[year][1]/year_dict[year][0]\\n return year_freq_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f179399ff9f3a698f32b03553e41b1f\",\n \"score\": \"0.53654516\",\n \"text\": \"def _returnCommonYears(arrayFNameS, lngFromYear, lngToYear):\\n # arrayDays=[]\\n arrayIncr = []\\n blnSpecialDates = 0\\n\\n for arrayFName in arrayFNameS:\\n # strTempDates=''\\n # if (arrayFName[\\\"shape_dates\\\"]!=\\\"\\\"):\\n # strTempDates=arrayFName[\\\"shape_dates\\\"]\\n\\n strTemporalType = arrayFName[\\\"interval\\\"]\\n\\n if ((strTemporalType == \\\"10d\\\") or (strTemporalType == \\\"16d\\\") or (strTemporalType == \\\"15d\\\") or (strTemporalType == \\\"1d\\\") or (strTemporalType == \\\"1m\\\") or (strTemporalType == \\\"1y\\\")):\\n arrayIncr.append(1)\\n else:\\n if (strTemporalType == \\\"10y\\\"):\\n arrayIncr.append(10)\\n else:\\n blnSpecialDates = 1\\n arrayIncr.append(-1)\\n\\n lngStepYear = 0\\n arrayReturn = []\\n if ((blnSpecialDates == 1) and (len(arrayIncr) == 1)):\\n\\n arrayYears = arrayFName[\\\"fixed\\\"].split('_')\\n arrayReturn = []\\n for strTemp in arrayYears:\\n arrayReturn.append(int(strTemp[:4]))\\n else:\\n lngStepYear = 1\\n arrayReturn = range(int(lngFromYear), int(lngToYear) + 1, lngStepYear)\\n\\n return arrayReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"436e894c98e7982a82491977a93e0158\",\n \"score\": \"0.5351873\",\n \"text\": \"def base_data(code, begin='0000-01-01', end='9999-12-31'):\\n\\n accesses = self.ratchet.general(code)\\n\\n del accesses['total']\\n del accesses['code']\\n\\n if len(begin) == 4:\\n begin += '-01' \\n\\n if len(end) == 4:\\n end += '-12' \\n\\n empty_months_range = {'%02d' % x: None for x in range(1, 13)}\\n data = {}\\n for key, value in accesses.items():\\n if key in ['html', 'abstract', 'pdf']:\\n del value['total']\\n for year, months in value.items():\\n del months['total']\\n if year[1:] >= begin[0:4] and year[1:] <= end[0:4]:\\n ye = data.setdefault(year[1:], copy.copy(empty_months_range))\\n for month, days in months.items():\\n if year[1:]+'-'+month[1:] >= begin and year[1:]+'-'+month[1:] <= end:\\n if ye[month[1:]] == None:\\n ye[month[1:]] = 0\\n ye[month[1:]] += days['total']\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47d7468eaaa3a6899b851b2e33ffee2\",\n \"score\": \"0.5343377\",\n \"text\": \"def get_quarter(y, m):\\n \\n result = []\\n quarter = calc_quarter(y, m)\\n for i in quarter:\\n result.append(get_month(i[0], i[1]))\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592fdd4156ea98e22baa8b18cea62c67\",\n \"score\": \"0.5334084\",\n \"text\": \"def process_2019_raw_into_monthly() -> List[float]:\\n this_file_path = Path(os.path.realpath(__file__))\\n this_file_dir = this_file_path.parent\\n usage_2019_file = this_file_dir / 'resources' / 'electricity2019.json'\\n with usage_2019_file.open() as f:\\n usage_2019_data = json.loads(f.read())\\n flat_kwh_per_day = []\\n for raw_month in usage_2019_data:\\n start_date = datetime.strptime(raw_month['StartDate'], '%m/%d/%Y')\\n end_date = datetime.strptime(raw_month['EndDate'], '%m/%d/%Y')\\n days_in_range = (end_date - start_date).days\\n for i in range(days_in_range + 1):\\n this_date = start_date + timedelta(i)\\n if this_date.year == 2019:\\n flat_kwh_per_day.append(raw_month['kWh'])\\n day_index = -1\\n month_usages = []\\n for month in range(1, 13):\\n num_days = monthrange(2019, month)[1]\\n month_sum = 0\\n for day in range(num_days):\\n day_index += 1\\n month_sum += flat_kwh_per_day[day_index]\\n month_usages.append(round(month_sum / num_days, 2))\\n return month_usages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c90bea257ebbb063743758609bc40c\",\n \"score\": \"0.5325994\",\n \"text\": \"def _span(yf: YearData, yt: YearData, pf: int, pt: int = None) -> Dict[YearData, Tuple[int, int]]:\\n years = (x for x in YearData.__subclasses__() if yf.year <= x.year <= yt.year)\\n return {year: (pf, pt) if pt else (pf, pf) for year in years}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877309f7053e125a74df922899646848\",\n \"score\": \"0.53181916\",\n \"text\": \"def make_prices_vecs(data):\\n\\n\\toutput = []\\n\\n\\t# We have the same set of dates (keys) for all companies,\\n\\t# so we have to sort keys only once (keys from whatever element).\\n\\n\\tsome_el = data[0]\\n\\tsorted_keys = some_el[1].keys()\\n\\tsorted_keys.sort()\\n\\n\\tfor elem in data:\\n\\t\\tdate_price_dict = elem[1]\\n\\t\\toutput.append([date_price_dict[key] for key in sorted_keys])\\n\\t\\n\\treturn output;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cde7cb2a1e98f7a4b0cf0d650725f9e\",\n \"score\": \"0.5299571\",\n \"text\": \"def create_years_genre(sql_data: tuple, years: dict):\\n # MB i need to rewrite that, but this is work well =)\\n for sql_line in sql_data:\\n year = sql_line[0]\\n years[year]['count'] = sql_line[1]\\n return years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8476d5e2bfc0abf3d9ce2d0ba3e9a1e1\",\n \"score\": \"0.5297891\",\n \"text\": \"def __get_years_(search_year, start, step) -> list:\\n sql_request = _sql_request_search_years(search_year)\\n years = get_ids_by_request(sql_request, start, step)\\n return years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f20cfe72eb4951826084a4b4fe0082\",\n \"score\": \"0.5296002\",\n \"text\": \"def _aggregate_by_year_month(historical_data: dict):\\n if historical_data is None:\\n return {}\\n\\n converted_response = {}\\n\\n # first pass assemble the basic return value\\n for datapoint in historical_data:\\n year = datapoint['date'].year\\n month = datapoint['date'].month\\n\\n if year not in converted_response:\\n converted_response[year] = {}\\n if month not in converted_response[year]:\\n converted_response[year][month] = []\\n\\n converted_response[year][month].append(datapoint['value'])\\n\\n # second pass calculate averages\\n for year in converted_response.keys():\\n for month in converted_response[year]:\\n converted_response[year][month] = sum(\\n converted_response[year][month]) / len(converted_response[year][month])\\n\\n return converted_response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"312ef24c9f0408eab7b1778d9c94782d\",\n \"score\": \"0.5292689\",\n \"text\": \"def query_infos(self, fromYear, toYear):\\n connection = sqlite3.connect(\\\"weather.sqlite\\\")\\n cur = connection.cursor()\\n toYear = int(toYear) + 1\\n dictOuter = {}\\n for row in cur.execute(\\\"select * from samples where \\\\\\n sample_date between ? and ?\\\",\\n (str(fromYear)+'%', str(toYear)+'%')):\\n print(f\\\"row {row}\\\")\\n myMonth = datetime.datetime.strptime(row[1], '%Y/%m/%d').month\\n dictOuter.setdefault(myMonth, []).append(row[5])\\n print(dictOuter)\\n return dictOuter\\n connection.commit()\\n connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50cb5ad1bc6e9a24d490050a2073dbb7\",\n \"score\": \"0.528686\",\n \"text\": \"def special_by_dd(keys):\\n def expand_year(df, dd_name):\\n \\\"\\\"\\\" For jan1989 - sep1995 they wrote the year as a SINGLE DIGIT\\\"\\\"\\\"\\n if 'HRYEAR' in df.columns:\\n k = 'HRYEAR'\\n else:\\n k = k = 'HdYEAR'\\n last_digit = df[k].dropna().unique()[0]\\n if last_digit >= 10:\\n last_digit = last_digit % 10\\n base_year = int(dd_name[-4:-1]) * 10\\n df[\\\"HRYEAR4\\\"] = base_year + last_digit\\n df = df.drop(k, axis=1)\\n return df\\n\\n def combine_age(df, dd_name):\\n \\\"\\\"\\\"For jan89 and jan92 they split the age over two fields.\\\"\\\"\\\"\\n df[\\\"PRTAGE\\\"] = df[\\\"AdAGEDG1\\\"] * 10 + df[\\\"AdAGEDG2\\\"]\\n df = df.drop([\\\"AdAGEDG1\\\", \\\"AdAGEDG2\\\"], axis=1)\\n return df\\n\\n def align_lfsr(df, dd_name):\\n \\\"\\\"\\\"Jan1989 and Jan1999. LFSR (labor focrce status recode)\\n had\\n 1 = WORKING\\n 2 = WITH JOB,NOT AT WORK\\n 3 = UNEMPLOYED, LOOKING FOR WORK\\n 4 = UNEMPLOYED, ON LAYOFF\\n 5 = NILF - WORKING W/O PAY < 15 HRS;\\n TEMP ABSENT FROM W/O PAY JOB\\n 6 = NILF - UNAVAILABLE\\n 7 = OTHER NILF\\n newer ones have\\n 1 EMPLOYED-AT WORK\\n 2 EMPLOYED-ABSENT\\n 3 UNEMPLOYED-ON LAYOFF\\n 4 UNEMPLOYED-LOOKING\\n 5 NOT IN LABOR FORCE-RETIRED\\n 6 NOT IN LABOR FORCE-DISABLED\\n 7 NOT IN LABOR FORCE-OTHER\\n this func does several things:\\n 1. Change 3 -> 4 and 4 -> 3 in the old ones.\\n 2. Change 5 and 6 to 7.\\n 2. Read retired from AhNLFREA == 4 and set to 5.\\n 3. Read ill/disabled from AhNLFREA == 2 and set to 6.\\n Group 7 kind of loses meaning now.\\n \\\"\\\"\\\"\\n # 1. realign 3 & 3\\n status = df[\\\"AhLFSR\\\"]\\n # status = status.replace({3: 4, 4: 3}) # chcek on ordering\\n\\n status_ = status.copy()\\n status_[status == 3] = 4\\n status_[status == 4] = 3\\n status = status_\\n\\n # 2. Add 5 and 6 to 7\\n status = status.replace({5: 7, 6: 7})\\n\\n # 3. ill/disabled -> 6\\n status[df['AhNLFREA'] == 2] = 6\\n\\n df['PEMLR'] = status\\n df = df.drop([\\\"AhLFSR\\\", \\\"AhNLFREA\\\"], axis=1)\\n return df\\n\\n def expand_hours(df, dd_name):\\n \\\"\\\"\\\"\\n 89 and 92 have a question for hours and bins. I goto midpoint of bin.\\n\\n Roughly corresponds to PEERNHRO\\n\\n A-EMPHRS CHARACTER*002 . (0357:0358) LFSR=1 OR 2\\n REASONS NOT AT WORK OR HOURS AT WORK\\n -1 = NOT IN UNIVERSE\\n WITH A JOB, BUT NOT AT WORK\\n 01 = ILLNESS\\n 02 = VACATION\\n 03 = BAD WEATHER\\n 04 = LABOR DISPUTE\\n 05 = ALL OTHER\\n AT WORK\\n 06 = 1-4 HOURS\\n 07 = 5-14 HOURS\\n 08 = 15-21 HOURS\\n 09 = 22-29 HOURS\\n 10 = 30-34 HOURS\\n 11 = 35-39 HOURS\\n 12 = 40 HOURS\\n 13 = 41-47 HOURS\\n 14 = 48 HOURS\\n 15 = 49-59 HOURS\\n 16 = 60 HOURS OR MORE\\n \\\"\\\"\\\"\\n hours = df['AhEMPHRS']\\n hours_dic = {1: np.nan, 2: np.nan, 3: np.nan, 4: np.nan, 5: np.nan,\\n 6: 2, 7: 9.5, 8: 18, 9: 25.5, 10: 32, 11: 37, 13: 44,\\n 15: 54}\\n hours = hours.replace(hours_dic)\\n df['PEERNHRO'] = hours\\n df.drop(\\\"AhEMPHRS\\\", axis=1)\\n return df\\n\\n def combine_hours(df, dd_name):\\n \\\"\\\"\\\"\\n For 89 and 92; \\\"AdHRS1\\\", \\\"AdHRS2\\\" combine to form \\\"PEHRACTT\\\"\\n \\\"\\\"\\\"\\n fst = df['AdHRS1']\\n snd = df['AdHRS2']\\n df['PEHRACTT'] = fst * 10 + snd\\n df = df.drop([\\\"AdHRS1\\\", \\\"AdHRS2\\\"], axis=1)\\n return df\\n\\n func_dict = {\\\"expand_year\\\": expand_year, \\\"combine_age\\\": combine_age,\\n \\\"expand_hours\\\": expand_hours, \\\"align_lfsr\\\": align_lfsr,\\n \\\"combine_hours\\\": combine_hours}\\n to_apply = filter(lambda x: x in keys, func_dict)\\n filtered = {k: func_dict[k] for k in to_apply}\\n return filtered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f0165efd1a3dc3ddea0b2eed32f263\",\n \"score\": \"0.52836514\",\n \"text\": \"def get_spread(quotes, dates, contract):\\r\\n\\r\\n # for quarter1, quarter2 in zip(list(dates.keys()), list(dates.keys()[1:])):\\r\\n\\r\\n new_quotes = {}\\r\\n new_dates = {}\\r\\n\\r\\n dates_keys = list(dates.keys())\\r\\n\\r\\n step = 2 if contract == \\\"Quarters\\\" else 1\\r\\n\\r\\n for index in range(0, len(dates_keys) - 1, step):\\r\\n current_key = dates_keys[index] # Q1 key\\r\\n key_after = dates_keys[index + 1] # Q2 key\\r\\n\\r\\n first_date = dates[key_after][0] # Get the first date of the Q2 time series\\r\\n\\r\\n try:\\r\\n index_first_date = dates[current_key].index(first_date)\\r\\n key_spread = \\\"{}x{}\\\".format(current_key, key_after)\\r\\n\\r\\n new_dates[key_spread] = []\\r\\n new_quotes[key_spread] = []\\r\\n\\r\\n offset_1 = 0\\r\\n offset_2 = 0\\r\\n\\r\\n # we go through all the days in the Q2 days list and add the common days and spread into the new lists\\r\\n for index_days in range(len(dates[key_after])):\\r\\n if dates[current_key][index_first_date + index_days + offset_1] == dates[key_after][\\r\\n index_days + offset_2]:\\r\\n new_dates[key_spread].append(dates[key_after][index_days + offset_1])\\r\\n new_quotes[key_spread].append(\\r\\n quotes[current_key][index_first_date + index_days + offset_1] - quotes[key_after][\\r\\n index_days + offset_2])\\r\\n\\r\\n else:\\r\\n date_1 = dt.datetime.strptime(dates[current_key][index_first_date + offset_1], \\\"%Y-%m-%dT00:00:00Z\\\")\\r\\n date_2 = dt.datetime.strptime(dates[key_after][index_first_date + offset_2], \\\"%Y-%m-%dT00:00:00Z\\\")\\r\\n\\r\\n while date_1 != date_2 and offset_1 < 10 and offset_2 < 10:\\r\\n if date_1 > date_2:\\r\\n offset_2 += 1\\r\\n else:\\r\\n offset_1 += 1\\r\\n\\r\\n if date_1 != date_2:\\r\\n continue\\r\\n\\r\\n new_dates[key_spread].append(dates[key_after][index_days + offset_1])\\r\\n new_quotes[key_spread].append(\\r\\n quotes[current_key][index_first_date + index_days + offset_1] - quotes[key_after][\\r\\n index_days + offset_2])\\r\\n\\r\\n # check_date(new_dates,new_quotes,quotes,dates)\\r\\n except IndexError:\\r\\n continue\\r\\n except Exception as e:\\r\\n print(\\\"Exception : {}\\\".format(e.args))\\r\\n print(\\\"No overlap for {} and {}\\\".format(current_key, key_after))\\r\\n continue\\r\\n\\r\\n return new_dates, new_quotes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e1565e03681cddf71dfa83444c53bd\",\n \"score\": \"0.5273918\",\n \"text\": \"def filter_by_year(data: dict, year: int) -> dict:\\n filtered_data = data | {\\\"places\\\": []}\\n\\n for place in data[\\\"places\\\"]:\\n dataframes = []\\n\\n for dataframe in place[\\\"data\\\"]:\\n if dataframe[\\\"startYear\\\"] <= year <= dataframe[\\\"endYear\\\"]:\\n dataframes.append(dataframe)\\n\\n if dataframes:\\n filtered_data[\\\"places\\\"].append(\\n place | {\\\"data\\\": dataframes}\\n )\\n\\n return filtered_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac1df3ff8bc107aee7b78b500a92e76\",\n \"score\": \"0.5269259\",\n \"text\": \"def createDictionaries(data):\\n buildDict(data, ['office', 'office_name', 'level'], 1, 0)\\n buildDict(data, ['level', 'level_name'], 1, 0)\\n buildDict(data, ['area', 'area_name'], 1, 0)\\n buildDict(data, ['source', 'source_name'], 1, 0)\\n buildDict(data, ['financier', 'financier_name'], 1, 0)\\n buildDict(data, ['year', 'office', 'unit', 'unit_name'], 3, 2)\\n buildDict(data, ['year', 'office', 'unit', 'line', 'line_name'], 4, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd48b904c3b346bea1e0db7e901623d\",\n \"score\": \"0.52553475\",\n \"text\": \"def get_aggregates(self, monattag: str = \\\"\\\") -> dict:\\n aggregates = {}\\n for e in [\\\"TXK\\\", \\\"TNK\\\", \\\"RSK\\\", \\\"SDK\\\", \\\"PM\\\", \\\"UPM\\\"]:\\n d = {}\\n if monattag == \\\"\\\":\\n list_ = [self.create_timeline(e, t) for t in self.tagreihe]\\n s = sum(list_) / len(list_)\\n else:\\n s = self.create_timeline(e, monattag)\\n if s.count() == 0:\\n d = {\\n \\\"first_year\\\": \\\"\\\",\\n \\\"last_year\\\": \\\"\\\",\\n \\\"mean\\\": \\\"\\\",\\n \\\"mean2010\\\": \\\"\\\",\\n \\\"count\\\": 0,\\n \\\"std\\\": \\\"\\\",\\n \\\"median\\\": \\\"\\\",\\n \\\"max\\\": \\\"\\\",\\n \\\"max_year\\\": \\\"\\\",\\n \\\"min_year\\\": \\\"\\\",\\n \\\"min\\\": \\\"\\\",\\n \\\"zerorain\\\": \\\"\\\",\\n \\\"zerosun\\\": \\\"\\\",\\n \\\"mitteldruck\\\": \\\"\\\",\\n \\\"depressiondays\\\": \\\"\\\"\\n }\\n else:\\n if \\\"2010\\\" in s.index:\\n index_2010 = s.index.to_list().index(\\\"2010\\\")\\n d[\\\"mean2010\\\"] = round(s[index_2010:][~s.isnull()].mean(), 0)\\n else:\\n d[\\\"mean2010\\\"]=\\\"\\\"\\n d[\\\"first_year\\\"] = s[~s.isnull()].index[0]\\n d[\\\"last_year\\\"] = s[~s.isnull()].index[-1]\\n d[\\\"mean\\\"] = round(s[~s.isnull()].mean(), 0)\\n \\n d[\\\"count\\\"] = s[~s.isnull()].count()\\n d[\\\"std\\\"] = round(s[~s.isnull()].std(), 0)\\n d[\\\"median\\\"] = s[~s.isnull()].median()\\n d[\\\"max\\\"] = round(s.max(), 1)\\n d[\\\"max_year\\\"] = s.index[np.where(s == np.max(s))[0][0]]\\n d[\\\"min_year\\\"] = s.index[np.where(s == np.min(s))[0][0]]\\n d[\\\"min\\\"] = round(s.min(), 1)\\n if e == \\\"RSK\\\":\\n d[\\\"zerorain\\\"] = s[s == 0.0].count()\\n if e == \\\"SDK\\\":\\n d[\\\"zerosun\\\"] = s[s == 0.0].count()\\n if e == \\\"PM\\\":\\n mittelhoehe = sum([x.stationshoehe for x in self.stations]) / len(self.stations)\\n d[\\\"mean_pressure\\\"] = round(1013.25 * (1 - (0.0065 * mittelhoehe) / 288.15)**5.255,0) #Barometric formula\\n d[\\\"depression_days\\\"] = s[s < d[\\\"mean_pressure\\\"]].count()\\n aggregates[e] = d.copy()\\n self.aggregates = aggregates.copy()\\n return aggregates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a2bb7a48a10f4bc819a4e58cbffbc1\",\n \"score\": \"0.5249546\",\n \"text\": \"def calculate_years(self):\\n\\n\\n for node in self.nodes.values():\\n node.min_year = int(cfg.cfgfile.get('case', 'current_year'))\\n attributes = vars(node) \\n for att in attributes:\\n obj = getattr(node, att)\\n if inspect.isclass(type(obj)) and hasattr(obj, '__dict__') and hasattr(obj, 'data') and obj.data is True:\\n try:\\n min_year = min(obj.raw_values.index.get_level_values('year'))\\n except:\\n min_year = min(obj.raw_values.index.get_level_values('vintage'))\\n if min_year < node.min_year:\\n node.min_year = min_year \\n if hasattr(node,'technologies'):\\n for technology in node.technologies.values():\\n for reference_sales in technology.reference_sales.values():\\n try:\\n min_year = min(reference_sales.raw_values.index.levels[util.position_in_index(reference_sales.raw_values, 'vintage')])\\n except:\\n min_year = node.min_year\\n if min_year < node.min_year:\\n node.min_year = min_year\\n for sales in technology.sales.values():\\n try:\\n min_year = min(sales.raw_values.index.get_level_values( 'vintage'))\\n except:\\n min_year = node.min_year\\n if min_year < node.min_year:\\n node.min_year = min_year \\n if hasattr(node,'stock') and node.stock.data is True:\\n try:\\n min_year = min(node.stock.raw_values.index.levels[util.position_in_index(node.stock.raw_values, 'year')])\\n except:\\n min_year = node.min_year\\n if min_year < node.min_year:\\n node.min_year = min_year \\n \\n node.years = range(node.min_year,\\n int(cfg.cfgfile.get('case', 'end_year')) + 1,\\n int(cfg.cfgfile.get('case', 'year_step')))\\n node.vintages = copy.deepcopy(node.years)\\n self.years = cfg.cfgfile.get('case','supply_years')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48cd10b1f1140e4f5e91588c554e7e36\",\n \"score\": \"0.52380484\",\n \"text\": \"def get_data(airport, start, end):\\n\\n data = {\\\"Max Temperature\\\": {},\\n \\\"Min Temperature\\\": {},\\n \\\"Mean Temperature\\\": {},\\n \\\"Dew Point\\\": {}}\\n\\n for k in data:\\n for i in range(1, 13):\\n m = str(i)\\n data[k][m] = {\\\"max\\\": [], \\\"avg\\\": [], \\\"min\\\": []}\\n\\n for year in range(start, end+1):\\n for i in range(1, 13):\\n m = str(i)\\n html = download(airport, year, i, 1)\\n for k in data:\\n parse(html, data[k][m], k)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401263491e91c4244ae2ee0f2abad561\",\n \"score\": \"0.5211393\",\n \"text\": \"def generate_fiscal_date_range(min_date: datetime, max_date: datetime, frequency: str) -> list:\\n if frequency == \\\"fiscal_year\\\":\\n interval = 12\\n elif frequency == \\\"quarter\\\":\\n interval = 3\\n else: # month\\n interval = 1\\n\\n date_range = []\\n current_date = min_date\\n while current_date <= max_date:\\n date_range.append(\\n {\\n \\\"fiscal_year\\\": generate_fiscal_year(current_date),\\n \\\"fiscal_quarter\\\": generate_fiscal_quarter(current_date),\\n \\\"fiscal_month\\\": generate_fiscal_month(current_date),\\n }\\n )\\n current_date = current_date + relativedelta(months=interval)\\n\\n return date_range\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1864749a750557ed32fd54e73955375\",\n \"score\": \"0.5210616\",\n \"text\": \"def extract_financials_dataset(\\n work_dict,\\n scrub_mode='sort-by-date'):\\n return extract_dataset('financials', work_dict, scrub_mode=scrub_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e622fa0b8c59c8b851268303230e03db\",\n \"score\": \"0.52005756\",\n \"text\": \"def extract_box_office_data(quarter='q1'):\\n box_URL = 'https://www.boxofficemojo.com/quarter/' + quarter + '/?grossesOption=calendarGrosses'\\n\\n # Load the cache, save in global variable\\n CACHE_DICT = load_cache()\\n url_text = make_url_request_using_cache(box_URL, CACHE_DICT)\\n soup = BeautifulSoup(url_text, 'html.parser')\\n listing_divs = soup.find_all('tr')\\n listing_divs.remove(listing_divs[0])\\n\\n data_in_rows = []\\n print('-' * 50) # separator\\n print(quarter.upper() + ' Historical Box Office')\\n print('-' * 50) # separator\\n for listing_div in listing_divs:\\n alist = listing_div.find_all('a')\\n year = alist[0].string\\n name = alist[1].string.title()\\n rank_of_year_path = alist[0]['href']\\n rank_of_year_link = \\\"https://www.boxofficemojo.com\\\" + rank_of_year_path\\n print(\\\"The #1 release of \\\" + year + \\\" \\\" + quarter + \\\" is \\\" + name)\\n\\n money_list = listing_div.find_all('td', class_=\\\"a-text-right mojo-field-type-money\\\")\\n cumulative_gross = int(money_list[0].string.replace(\\\"$\\\", \\\"\\\").replace(',', ''))\\n per_release_average_gross = int(money_list[1].string.replace(\\\"$\\\", \\\"\\\").replace(',', ''))\\n data_in_rows.append([year, quarter, name, cumulative_gross, per_release_average_gross, rank_of_year_link])\\n\\n return data_in_rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330ea92fecffd906f1700281dee552db\",\n \"score\": \"0.51993227\",\n \"text\": \"def dates(self):\\n base = dateutil.parser.parse(\\\"19000101\\\")\\n with Dataset(self.index_path) as data:\\n min_date = base + dt.timedelta(days=data[\\\"time\\\"][:].min())\\n max_date = base + dt.timedelta(days=data[\\\"time\\\"][:].max())\\n dates = {}\\n dates[\\\"max_year\\\"] = pd.Timestamp(max_date).year\\n dates[\\\"min_year\\\"] = pd.Timestamp(min_date).year + 1 # for 12 month indices\\n dates[\\\"max_month\\\"] = pd.Timestamp(max_date).month\\n dates[\\\"years\\\"] = list(range(dates[\\\"min_year\\\"], dates[\\\"max_year\\\"] + 1))\\n return dates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37313b5e818111fb7f6c028c26edf36c\",\n \"score\": \"0.5198772\",\n \"text\": \"def parse_billing_data(billing_data):\\n user_dict = collections.defaultdict(dict)\\n currency = ''\\n month = ''\\n year = ''\\n\\n for row in billing_data:\\n if len(row) < 4:\\n continue\\n if row[3] == 'AccountTotal':\\n if not currency:\\n currency = row[23]\\n\\n if not month or not year:\\n date = row[6]\\n month = date[5:7]\\n year = date[0:4]\\n\\n acct_num = row[2]\\n user_dict[acct_num]['name'] = row[9]\\n user_dict[acct_num]['total'] = float(row[24])\\n user_dict[acct_num]['currency'] = row[23]\\n\\n return user_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cecc5fcf08bd8fb8fb2e9fc7f817cea5\",\n \"score\": \"0.51934886\",\n \"text\": \"def get_quarterly_event_counts(quarters, event_manager):\\n response = {}\\n for pair in quarters:\\n year = pair[0]\\n quarter = pair[1]\\n quarter_desc = '{}-Q{}'.format(year, quarter)\\n date_range = REPORT_QUARTERS[quarter-1]\\n start = '{}-{}'.format(year, date_range[0])\\n if quarter == 4:\\n year += 1\\n end = '{}-{}'.format(year, date_range[1])\\n response[quarter_desc] = event_manager.event_group_counts(start, end)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08fcf10f5fb5f4e36fcf75a0417594a\",\n \"score\": \"0.51927936\",\n \"text\": \"def aggregate_over_year(self) -> dict:\\n oy = {\\n \\\"monattag\\\": [],\\n \\\"TXK\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n },\\n \\\"TNK\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n },\\n \\\"RSK\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n },\\n \\\"SDK\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n },\\n \\\"PM\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n },\\n \\\"UPM\\\": {\\n \\\"mean\\\": [],\\n \\\"std\\\": []\\n }\\n }\\n for mt in self.monattag_generator():\\n oy[\\\"monattag\\\"].append(mt)\\n aggregates = {}\\n aggregates = self.get_aggregates(mt)\\n for e in [\\\"TXK\\\", \\\"TNK\\\", \\\"RSK\\\", \\\"SDK\\\", \\\"PM\\\", \\\"UPM\\\"]:\\n oy[e][\\\"mean\\\"].append(aggregates[e][\\\"mean\\\"])\\n oy[e][\\\"std\\\"].append(aggregates[e][\\\"std\\\"])\\n return oy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385415e3bcdab55d2af71624e297effb\",\n \"score\": \"0.51850444\",\n \"text\": \"def collect_table(worksheet: Worksheet, year: int, fund_name, row: int, column: int) -> object:\\n\\n data = {}\\n\\n for iterated_row in range(row, row + 21):\\n row_label: str = worksheet.cell(row=iterated_row, column=column).value\\n\\n if not row_label:\\n # Some of the years has less than 21 fields which mean we'll fail\\n # with older files. Skipping on an empty row label.\\n continue\\n\\n row_data = {}\\n for iterated_column in range(column + 1, column + 1 + 24):\\n # Getting the matching month of the current cell and resetting\\n # the cell value.\\n month_index = int(iterated_column / 2)\\n\\n if month_index not in months:\\n continue\\n\\n month_name = months[month_index]\\n\\n if month_name not in row_data:\\n row_data[month_name] = {\\n 'התרומה לתשואה': '',\\n 'שיעור מסך הנכסים': '',\\n 'year': year,\\n 'fund': fund_name,\\n }\\n\\n cell_kwargs = {'row': iterated_row, 'column': iterated_column}\\n cell_value = worksheet.cell(**cell_kwargs).value\\n\\n if cell_value:\\n if type(cell_value) is not str:\\n cell_value = format(cell_value * 100, '.2f')\\n\\n # Values in cell with even index number have a label and odd\\n # indexed-cell have a different label.\\n if iterated_column % 2 == 0:\\n key = 'התרומה לתשואה'\\n else:\\n key = 'שיעור מסך הנכסים'\\n row_data[month_name][key] = cell_value\\n\\n data[row_label.strip()] = row_data\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4409b92c616a592f7c726c76e48493cd\",\n \"score\": \"0.51814896\",\n \"text\": \"def quarter_dict_key(freq_start):\\n return str(freq_start.year) + quarter_dict[freq_start.month]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d836b487720e59e09858bf10db052a\",\n \"score\": \"0.5180538\",\n \"text\": \"def _get_data_pre2007(date): \\r\\n \\r\\n # build the url based on year\\r\\n url = '{}/Environmental_Data_{}.txt'.format(BASE_URL, date.year)\\r\\n print('Fetching online data for {} (full year)'.format(date.year))\\r\\n \\r\\n try:\\r\\n year_data = request.urlopen(url).read().decode(encoding='utf_8').split('\\\\n') \\r\\n except:\\r\\n raise ValueError(date) # error accessing website\\r\\n else:\\r\\n year_data.pop(0) # remove first item which contain column header info\\r\\n \\r\\n for line in year_data:\\r\\n \\r\\n elements = line.split()\\r\\n yield dict(Date = elements[0],\\r\\n Time = elements[1],\\r\\n Status = 'COMPLETE', # all data from pre2007 will be complete\\r\\n Air_Temp = elements[5],\\r\\n Barometric_Press = elements[7],\\r\\n Wind_Speed = elements[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9651f20d619d3a1c3dbe76f2283bd8e8\",\n \"score\": \"0.51773274\",\n \"text\": \"def stations():\\n\\n year_extract = '201'\\n avg_station_sql = (f'SELECT city, name, substr(start_date, INSTR(start_date, {year_extract}), 4) as yr, ' +\\n 'avg(duration) as avg_trip ' +\\n 'FROM trip ' +\\n 'JOIN station ON trip.start_station_id = station.id ' +\\n 'GROUP BY city, name, yr'\\n )\\n\\n avg_station = pd.read_sql(avg_station_sql, engine2.connect())\\n\\n avg_station['avg_trip'] = avg_station['avg_trip']/60\\n avg_station['avg_trip'] = avg_station['avg_trip'].round()\\n avg_station_list = avg_station.to_dict('records')\\n\\n yrs_2013 = []\\n yrs_2014 = []\\n yrs_2015 = []\\n\\n for item in avg_station_list:\\n data = {}\\n for k,v in item.items():\\n if(k=='name'):\\n data['axis'] = v\\n if(k=='avg_trip'):\\n data['value'] = v\\n if(k=='city'):\\n data['city'] = v\\n if(k=='yr'and v=='2013'):\\n data['name'] = v\\n yrs_2013.append(data)\\n elif(k=='yr'and v=='2014'):\\n data['name'] = v\\n yrs_2014.append(data)\\n elif(k=='yr'and v=='2015'):\\n data['name'] = v\\n yrs_2015.append(data)\\n \\n all_yr_station = [yrs_2015, yrs_2014, yrs_2013]\\n\\n\\n return jsonify(all_yr_station)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f8534937949b37e823653ef59c45bb\",\n \"score\": \"0.5148466\",\n \"text\": \"def ageGroups4():\\n name = \\\"ageGroups4\\\"\\n groups = {\\n AGE: {\\n \\\"Under 4 years\\\" : list(range(0, 4)),\\n \\\"4 to 7 years\\\" : list(range(4, 8)),\\n \\\"8 to 11 years\\\": list(range(8, 12)),\\n \\\"12 to 15 years\\\": list(range(12, 16)),\\n \\\"16 to 19 years\\\": list(range(16, 20)),\\n \\\"20 to 23 years\\\": list(range(20, 24)),\\n \\\"24 to 27 years\\\": list(range(24, 28)),\\n \\\"28 to 31 years\\\": list(range(28, 32)),\\n \\\"32 to 35 years\\\": list(range(32, 36)),\\n \\\"36 to 39 years\\\": list(range(36, 40)),\\n \\\"40 to 43 years\\\": list(range(40, 44)),\\n \\\"44 to 47 years\\\": list(range(44, 48)),\\n \\\"48 to 51 years\\\": list(range(48, 52)),\\n \\\"52 to 55 years\\\": list(range(52, 56)),\\n \\\"56 to 59 years\\\": list(range(56, 60)),\\n \\\"60 to 63 years\\\": list(range(60, 64)),\\n \\\"64 to 67 years\\\": list(range(64, 68)),\\n \\\"68 to 71 years\\\": list(range(68, 72)),\\n \\\"72 to 75 years\\\": list(range(72, 76)),\\n \\\"76 to 79 years\\\": list(range(76, 80)),\\n \\\"80 to 83 years\\\": list(range(80, 84)),\\n \\\"84 to 87 years\\\": list(range(84, 88)),\\n \\\"88 to 91 years\\\": list(range(88, 92)),\\n \\\"92 to 95 years\\\": list(range(92, 96)),\\n \\\"96 to 99 years\\\": list(range(96, 100)),\\n \\\"100 to 103 years\\\": list(range(100, 104)),\\n \\\"104 to 107 years\\\": list(range(104, 108)),\\n \\\"108 to 111 years\\\": list(range(108, 112)),\\n \\\"112 to 115 years\\\": list(range(112, 116)),\\n }\\n }\\n return name, groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa947001fa1f5b68b4ececc83462919d\",\n \"score\": \"0.51367325\",\n \"text\": \"def get_relevant_text_bodies(subreddit_list, start_year, start_month, end_month, base_path):\\n\\n text_bodies = defaultdict(lambda: [])\\n\\n #this really should be done using bash brace expansion...\\n\\n base_path_full_non_top_level = base_path + \\\"non_top_level_comments/\\\"\\n valid_file_paths_non_top_level = extract_pairs.list_file_appropriate_data_range(start_year,\\n start_month, end_month,\\n base_path_full_non_top_level)\\n\\n for file_path in valid_file_paths_non_top_level:\\n with open(file_path, 'rb') as fop:\\n # because that's what this file and others had for output\\n csvreadr = csv.reader(fop, delimiter=',', quotechar='|')\\n csvreadr.next()\\n for line in csvreadr:\\n try:\\n comm = json.loads(line[0])\\n except ValueError:\\n print \\\"BADLY FORMATTED\\\", line\\n continue\\n if any (subreddit in comm[\\\"subreddit\\\"] for subreddit in subreddit_list):\\n #body = re.search('\\\\\\\"body\\\\\\\":\\\\\\\"(.+?)\\\\\\\"(,\\\\\\\")|(})', line).group(1)\\n body = comm[\\\"body\\\"] #these two options appear to the be the same speed.\\n if body == \\\"[deleted]\\\" or body == \\\"[removed]\\\":\\n continue\\n\\n subreddit = comm[\\\"subreddit\\\"]\\n\\n text_bodies[subreddit].append(body)\\n\\n\\n # TODO: parameterize the end month here. cause what if it's two months or whatever\\n base_path_full_top_level = base_path + \\\"top_level_comments/\\\"\\n valid_file_paths_top_level = extract_pairs.list_file_appropriate_data_range(start_year,\\n start_month, end_month-1,base_path_full_top_level)\\n\\n for file_path in valid_file_paths_top_level:\\n with open(file_path, 'rb') as fop:\\n # because that's what this file and others had for output\\n csvreadr = csv.reader(fop, delimiter=',', quotechar='|')\\n csvreadr.next()\\n for line in csvreadr:\\n try:\\n parent = json.loads(line[0])\\n child = json.loads(line[1])\\n except ValueError:\\n print \\\"BADLY FORMATTED\\\", line\\n continue\\n\\n for comm in [parent, child]:\\n if any (subreddit in comm[\\\"subreddit\\\"] for subreddit in subreddit_list):\\n #body = re.search('\\\\\\\"body\\\\\\\":\\\\\\\"(.+?)\\\\\\\"(,\\\\\\\")|(})', line).group(1)\\n body = comm[\\\"body\\\"] #these two options appear to the be the same speed.\\n if body == \\\"[deleted]\\\" or body == \\\"[removed]\\\":\\n continue\\n\\n subreddit = comm[\\\"subreddit\\\"]\\n\\n text_bodies[subreddit].append(body)\\n return text_bodies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4729b851910250e8d78e057da7e00078\",\n \"score\": \"0.512572\",\n \"text\": \"def get_story(cls, x):\\n\\n r = []\\n t20100101 = datetime.datetime(2010, 1, 1, tzinfo=pytz.utc)\\n t20120101 = datetime.datetime(2012, 1, 1, tzinfo=pytz.utc)\\n t20130101 = datetime.datetime(2013, 1, 1, tzinfo=pytz.utc)\\n t20140101 = datetime.datetime(2014, 1, 1, tzinfo=pytz.utc)\\n t20141001 = datetime.datetime(2014, 10, 1, tzinfo=pytz.utc)\\n t20150101 = datetime.datetime(2015, 1, 1, tzinfo=pytz.utc)\\n # M5 2015/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.M5.value,\\n test_start_at=t20150101,\\n ai_id=x.ai_id)]\\n # H1 2010/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.H1.value,\\n test_start_at=t20100101,\\n ai_id=x.ai_id)]\\n # H1 2012/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.H1.value,\\n test_start_at=t20120101,\\n ai_id=x.ai_id)]\\n # H1 2013/10/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.H1.value,\\n test_start_at=t20130101,\\n ai_id=x.ai_id)]\\n # H1 2014/10/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.H1.value,\\n test_start_at=t20140101,\\n ai_id=x.ai_id)]\\n # H1 2015/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.H1.value,\\n test_start_at=t20150101,\\n ai_id=x.ai_id)]\\n # M5 2013/1/1〜\\n # r += [cls(name=x.name,\\n # genetic_id=x.id,\\n # currency_pair=x.currency_pair,\\n # span=Granularity.M5.value,\\n # test_start_at=t20130101,\\n # ai_id=x.ai_id)]\\n # M5 2014/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.M5.value,\\n test_start_at=t20140101,\\n ai_id=x.ai_id)]\\n # M1 2014/10/1〜\\n # r += [cls(name=x.name,\\n # genetic_id=x.id,\\n # currency_pair=x.currency_pair,\\n # span=Granularity.M1.value,\\n # test_start_at=t20141001,\\n # ai_id=x.ai_id)]\\n # M1 2015/1/1〜\\n r += [cls(name=x.name,\\n genetic_id=x.id,\\n currency_pair=x.currency_pair,\\n span=Granularity.M1.value,\\n test_start_at=t20150101,\\n ai_id=x.ai_id)]\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9a53143ebcb7e3ac64b6e0fa303102\",\n \"score\": \"0.51230866\",\n \"text\": \"def make_quarters_request():\\n args = {'command': 'quarters'}\\n res = _make_ww_request(args)\\n _check_ww_response(res)\\n\\n df = pd.DataFrame(res.json()['quarters'])\\n df['filing_period'] = pd.to_datetime(df['filing_period'])\\n return df.set_index('filing_period')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1f8f5dca779bccb3c164a7808f39e4\",\n \"score\": \"0.5121964\",\n \"text\": \"def months(q):\\n try:\\n if q == 1:\\n return ['jan', 'feb', 'mar']\\n if q == 2:\\n return ['apr', 'may', 'jun']\\n if q == 3:\\n return ['jul', 'aug', 'sep']\\n if q == 4:\\n return ['oct', 'nov', 'dec']\\n else:\\n raise\\n except:\\n print('Not a valid quarter. Enter 1, 2, 3, or 4')\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a65fba93a8c3111ed54ea7b47cf12e7\",\n \"score\": \"0.51124316\",\n \"text\": \"def retrievecarbondata(periodstart, periodend, geometrycode):\\n\\n # Calculate base parameters from all area data\\n # This uses numeric equations to calculate linear trends\\n now = datetime.datetime.now()\\n areadata = Data.objects.filter(geometrycode=geometrycode).order_by('year')\\n if areadata.exists() is False: return {}\\n baseparameters = calculatebaseparameters(areadata)\\n\\n # Create blank array for all years in requested range\\n data = {}\\n for year in range(int(periodstart), 1 + int(periodend)):\\n year = str(year)\\n data[year] = {'electricity': 0, 'gas': 0}\\n\\n # Populate blank array with existing data where it exists\\n maxyear = 0\\n for areadataitem in areadata:\\n year = areadataitem.year\\n if int(year) > maxyear: maxyear = int(year)\\n if year in data:\\n if areadataitem.type == 0: \\n key = 'electricity'\\n conversionfactor = converter_kg_electricity[year] / 1000\\n if areadataitem.type == 1: \\n key = 'gas'\\n conversionfactor = converter_kg_gas[year] / 1000\\n data[year][key] = int(conversionfactor * float(areadataitem.value))\\n\\n # For years outside provided data, estimate using base parameters\\n if maxyear < int(periodend):\\n extrastart = maxyear + 1\\n if extrastart < int(periodstart): extrastart = int(periodstart)\\n for year in range(extrastart, 1 + int(periodend)):\\n predictionarray = makeprediction(baseparameters, year)\\n data[year] = calculateemissions(predictionarray)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c019c1a3945dea060f8eec8d47b89d2\",\n \"score\": \"0.5101044\",\n \"text\": \"def get_years(type_: str, value_: str, page: int, step: int):\\n years = factory.get_elem_list(Year, type_, value_, page, step)\\n return years\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e48962e2d993cfb722569c1799f802\",\n \"score\": \"0.5093737\",\n \"text\": \"def barclay_hfi():\\n df = pd.read_csv(\\\"data_parser/Barclay_HFI.csv\\\")\\n df.columns = [\\\"Year\\\"] + [j+1 for j in range(12)] \\n # df = df.drop([\\\"Temp\\\"], axis = 1)\\n df.set_index(\\\"Year\\\", inplace = True)\\n\\n first_year = int(df.index[0])\\n target_ret_dict = {}\\n for i in range(df.shape[0]):\\n for j in range(df.shape[1]):\\n dt = datetime.datetime(first_year + i, 1+j, 1) + relativedelta(months = 1) - datetime.timedelta(days = 1) \\n raw_val = df.iloc[i,j]\\n if not df.isna().iloc[i,j]:\\n ret = float(raw_val.replace(\\\"%\\\", \\\"\\\"))/100\\n target_ret_dict[dt] = ret\\n tdf = pd.Series(target_ret_dict)\\n tdf = pd.DataFrame(tdf)\\n tdf.columns = [\\\"Barclay_HFI\\\"]\\n return target_ret_dict, tdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dbd307b16cad6c073027873a54be501\",\n \"score\": \"0.5087606\",\n \"text\": \"def construct_all_holdings(self):\\n\\n\\t\\td = dict((k, v) for k, v in [(s, self.current_positions[s]) for s in self.symbol_list])\\n\\n\\t\\td['datetime'] = self.start_date\\n\\t\\td['cash'] = self.initial_capital\\n\\t\\tprint(d)\\n\\t\\td['commission'] = 0.0 ## todo why do i need this\\n\\t\\td['total'] = self.initial_capital\\n\\t\\treturn [d]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a559e0283fc1f14ee9a0f3e3e9438ca\",\n \"score\": \"0.50796884\",\n \"text\": \"def precipitation():\\n # Query all dates and thier temperature\\n results = session.query(Measurement.date,Measurement.tobs).\\\\\\n filter(func.strftime(\\\"%y\\\", Measurement.date) == \\\"2017\\\").all()\\n\\n # Create a dictionary from the row data and append to a list of last_year_tobs\\n last_year_tobs = []\\n for tobs in results:\\n date_tobs_dict = {}\\n date_tobs_dict[\\\"date\\\"] = Measurement.date\\n date_tobs_dict[\\\"tobs\\\"] = Measurement.tobs\\n \\n last_year_tobs.append(date_tobs_dict)\\n\\n return jsonify(last_year_tobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ee76d82e5b449f7e9d124e383fe59b\",\n \"score\": \"0.5077868\",\n \"text\": \"def get_birthday_structure_to_check(got_data):\\n presents = dict()\\n for month in got_data:\\n for citizen in got_data[month]:\\n key = str((citizen['citizen_id'], month))\\n presents[key] = citizen['presents']\\n\\n return presents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3f071e9c2e0db528ee1c9bb6db569e\",\n \"score\": \"0.5074117\",\n \"text\": \"def collect_year_growth(markup) -> List[Tuple[float, str]]:\\n\\n results = []\\n rows = BeautifulSoup(\\n markup,\\n \\\"html.parser\\\",\\n parse_only=SoupStrainer(\\\"table\\\", class_=\\\"table table-small\\\"),\\n ).find_all(\\\"tr\\\")[1:]\\n for row in islice(rows, 1, None):\\n columns = row.find_all(\\\"td\\\")\\n name = columns[0].text.strip()\\n growth = float(columns[-2].find_all(\\\"span\\\")[-1].string.replace(\\\"%\\\", \\\"\\\"))\\n results.append((growth, name))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f44897ac90470cc73c81bca4b74acddf\",\n \"score\": \"0.50718236\",\n \"text\": \"def report_dates(start_date, end_date):\\n # iterate over every year within date range\\n for year in range(start_date.year, end_date.year + 1):\\n # find the month range for the year\\n month_range = range(1, 13)\\n # start and end year cases\\n if year == start_date.year:\\n month_range = range(start_date.month, 13)\\n elif year == end_date.year:\\n month_range = range(1, end_date.month + 1)\\n # single year case\\n if start_date.year == end_date.year:\\n month_range = range(start_date.month, end_date.month + 1)\\n # iterate over every month in the year\\n for month in month_range:\\n # find the day range for the year\\n day_range = (1, days_in_month(year, month))\\n # start and end month cases\\n if year == start_date.year and month == start_date.month:\\n day_range = (start_date.day, days_in_month(year, month))\\n elif year == end_date.year and month == end_date.month:\\n day_range = (1, end_date.day)\\n # single month case\\n if start_date.year == end_date.year and start_date.month == end_date.month:\\n day_range = (start_date.day, end_date.day)\\n # create the sub reports\\n yield (date(year, month, day_range[0]), date(year, month, day_range[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b62385b35ba067a0d288bb5d337e9cd\",\n \"score\": \"0.5071464\",\n \"text\": \"def precipitation():\\n session = Session(engine)\\n last_date = str(session.query(Measurement.date).order_by(Measurement.date.desc()).first())\\n last_date_dt = dt.datetime.strptime(last_date, \\\"('%Y-%m-%d',)\\\")\\n year_ago = last_date_dt - dt.timedelta(days=365)\\n precip_results = session.query(Measurement.date, Measurement.prcp).\\\\\\n filter(func.DATE(Measurement.date) > year_ago).all()\\n session.close()\\n\\n date = [result[0] for result in precip_results]\\n precip = [result[1] for result in precip_results]\\n precip_dict = dict(zip(date, precip))\\n\\n return jsonify(precip_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ef7164fe7b57b60772278d03f49913\",\n \"score\": \"0.50678146\",\n \"text\": \"def _parse_entries(raw_lines):\\n\\n # First two lines are field names\\n entries = []\\n\\n for group in _get_groups(raw_lines):\\n # Refactor date lines onto start of following line and split tabs\\n entry = {}\\n first_values = group[0].split(\\\" \\\")\\n entry[\\\"Transaction Status\\\"] = \\\" \\\".join(first_values[:2])\\n entry[\\\"Date\\\"] = \\\" \\\".join(first_values[2:])\\n\\n entry[\\\"Description\\\"] = group[1]\\n if \\\" \\\" in group[2]:\\n entry[\\\"Amount\\\"], entry[\\\"Balance\\\"] = group[2].split(\\\" \\\")\\n else:\\n # No balance\\n entry[\\\"Amount\\\"], entry[\\\"Balance\\\"] = group[2], \\\"\\\"\\n\\n for field in entry:\\n entry[field] = format_value(entry[field], field, DATE_FORMAT)\\n\\n entries.append(entry)\\n\\n return entries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcdea2f892615694a846a3ab194b2e2\",\n \"score\": \"0.5064139\",\n \"text\": \"def generate_demo_quarter_data(requests_in_quarter: pd.DataFrame, output_dir: str) -> None:\\n def generate_json_demo_data(data_in_range: pd.DataFrame, range_: str, interval_: str) -> None:\\n # write json file with demand and wait_time for given range and interval\\n demand = compute_metric_on_intervals(data_in_range, interval_, 'demand').to_dict()\\n wait_time = compute_metric_on_intervals(data_in_range, interval_, 'wait_time').to_dict()\\n\\n # convert all entries to strings, and round integers by two\\n demand_key, wait_time_key = list(demand.keys())[0], list(wait_time.keys())[0]\\n data = {\\n list(demand.keys())[0]:\\n {str(key): str(demand[demand_key][key])\\n for key in demand[demand_key]},\\n list(wait_time.keys())[0]:\\n {str(key): str(round(wait_time[wait_time_key][key], 2))\\n for key in wait_time[wait_time_key]}\\n }\\n interval_ = 'week' if interval_ == 'week_in_quarter' else interval_\\n file_name = f'time_range={range_}&interval={interval_}.json'\\n io_lib.create_json_file(file_path=os_lib.join_path(output_dir, file_name), contents=data)\\n\\n if len(set(requests_in_quarter['quarter'])) != 1:\\n raise ValueError('Given data must contain only one quarter type (eg: \\\\'Fall 2015\\\\').')\\n\\n # single file (since single quarter) generated containing daily stats over a quarter\\n quarter_term, quarter_year = requests_in_quarter['quarter'].iloc[0].split()\\n generate_json_demo_data(data_in_range=requests_in_quarter,\\n range_=f'quarter+{quarter_term}_{quarter_year}', interval_='week_in_quarter')\\n\\n # for each week in quarter, generate a file containing daily stats over a week range\\n all_weeks_in_qtr = requests_in_quarter['week_in_quarter'].unique()\\n for week_num in all_weeks_in_qtr:\\n single_week_data = requests_in_quarter[requests_in_quarter['week_in_quarter'] == week_num]\\n generate_json_demo_data(data_in_range=single_week_data,\\n range_=f'week+{week_num}', interval_='day')\\n\\n # for each day in quarter, generate a file containing hourly stats over a day (24 hour) range\\n all_recorded_datetimes = pd.Series(data=requests_in_quarter.index)\\n dates_in_qtr = all_recorded_datetimes.apply(func=lambda dt: str(dt).split()[0]).unique()\\n for date in dates_in_qtr:\\n generate_json_demo_data(data_in_range=requests_in_quarter[date],\\n range_=f'day+{date}', interval_='hour')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a614f453fc6605d950c3ecebf58b44fa\",\n \"score\": \"0.50499946\",\n \"text\": \"def calc_quarter(y, m):\\n \\n # Previous / Next month's year number and month number\\n prev_y = y\\n prev_m = m - 1\\n next_y = y\\n next_m = m + 1 \\n \\n if m == 1:\\n prev_m = 12\\n prev_y = y - 1\\n elif m == 12:\\n next_m = 1\\n next_y = y + 1\\n \\n return [(prev_y, prev_m), (y, m), (next_y, next_m)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e693893afae45446640c8f3283215749\",\n \"score\": \"0.503061\",\n \"text\": \"def get_buildtime(in_list, start_year, path_list):\\n buildtime_dict = {}\\n for index, row in in_list.iterrows():\\n comm_date = date.parse(row['Grid Date'])\\n start_date = [comm_date.year, comm_date.month, comm_date.day]\\n delta = ((start_date[0] - int(start_year)) * 12 +\\n (start_date[1]) +\\n round(start_date[2] / (365.0 / 12)))\\n if delta < 0:\\n delta = 1\\n for index, reactor in enumerate(path_list):\\n name = row['Unit'].replace(' ', '_')\\n country = row['Country']\\n file_name = (reactor.replace(\\n os.path.dirname(path_list[index]), '')).replace('/', '')\\n if (name + '.xml' == file_name):\\n buildtime_dict.update({name: (country, delta)})\\n return buildtime_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db1e84c97594469c918acac53ca5f06\",\n \"score\": \"0.50304216\",\n \"text\": \"def list_qtr():\\n\\tqtr = ['Q1','Q2','Q3','Q4']\\n\\tfor y in range(2015,2025):\\n\\t\\tfor q in qtr:\\n\\t\\t\\tif y == 2015 and q !='Q4':\\n\\t\\t\\t\\tpass\\n\\t\\t\\telse:\\n\\t\\t\\t\\tyield(str(y)+\\\"-\\\"+q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bddad394471640c6fa6737765bb3c79a\",\n \"score\": \"0.502508\",\n \"text\": \"def process_query(query: str, dataset: dict) -> dict:\\n start_year, end_year, top_n = map(int, query.strip().split(','))\\n logger.debug('got query \\\"%s,%s,%s\\\"', start_year, end_year, top_n)\\n answer = defaultdict(int)\\n for key, values in dataset.items():\\n for value in values:\\n if start_year <= value[0] <= end_year:\\n answer[key] += value[1]\\n\\n answer = sorted(answer.items(), key=lambda x: (-x[1], x[0]))\\n if len(answer) < top_n:\\n logger.warning(\\n 'not enough data to answer, found %s words out of %s for period \\\"%s,%s\\\"',\\n len(answer), top_n, start_year, end_year\\n )\\n answer = [list(x) for x in answer[:top_n]]\\n answer = {\\\"start\\\": start_year, \\\"end\\\": end_year, \\\"top\\\": answer}\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b4c277dcad4b9dd57edebe05fd3aed2\",\n \"score\": \"0.50178164\",\n \"text\": \"def map_ints_to_dmy(int_list):\\n if int_list[1] > 31 or int_list[1] <= 0:\\n return None\\n\\n over_12 = 0\\n over_31 = 0\\n under_1 = 0\\n for i in int_list:\\n if 99 < i < DATE_MIN_YEAR or i > DATE_MAX_YEAR:\\n return None\\n\\n if i > 31:\\n over_31 += 1\\n if i > 12:\\n over_12 += 1\\n if i <= 0:\\n under_1 += 1\\n\\n if over_31 >= 2 or over_12 == 3 or under_1 >= 2:\\n return None\\n\\n possible_year_splits = [\\n [int_list[2], int_list[:2]], # year last\\n [int_list[0], int_list[1:]] # year first\\n ]\\n for [y, rest] in possible_year_splits:\\n if DATE_MIN_YEAR <= y <= DATE_MAX_YEAR:\\n dm = map_ints_to_dm(rest)\\n if dm is not None:\\n return {\\n \\\"year\\\": y,\\n \\\"month\\\": dm[\\\"month\\\"],\\n \\\"day\\\": dm[\\\"day\\\"]\\n }\\n else:\\n return None\\n\\n for [y, rest] in possible_year_splits:\\n dm = map_ints_to_dm(rest)\\n if dm is not None:\\n y = two_to_four_digit_year(y)\\n return {\\n \\\"year\\\": y,\\n \\\"month\\\": dm[\\\"month\\\"],\\n \\\"day\\\": dm[\\\"day\\\"]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d2960f1a0db429fa3eb84cab1e4eaf\",\n \"score\": \"0.501417\",\n \"text\": \"def get_months(self, language):\\n # done via naive way as django's having tough time while aggregating on date fields\\n entries = self.filter_by_language(language)\\n dates = entries.values_list('publication_start', flat=True)\\n dates = [(x.year, x.month) for x in dates]\\n date_counter = Counter(dates)\\n dates = set(dates)\\n dates = sorted(dates, reverse=True)\\n return [{'date': datetime.date(year=year, month=month, day=1),\\n 'count': date_counter[year, month]} for year, month in dates]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497d7f84b560524327016254f3f0a251\",\n \"score\": \"0.50086653\",\n \"text\": \"def dictify(dataframe):\\n data = defaultdict(dict)\\n data7 = defaultdict(dict)\\n companies = set()\\n invests = set()\\n\\n for idx, row in dataframe.iterrows():\\n #Date filter\\n try:\\n dt = datetime.strptime(row['日期(time)'], \\\"%Y.%m.%d\\\")\\n except ValueError as ve:\\n print(ve, row['日期(time)'])\\n\\n company = row['公司(company)']\\n companies.add(company)\\n round_ = row['融资轮数(round)']\\n invests_ = eval(row['投资机构(invests)'])\\n assert (isinstance(invests_, list))\\n\\n if '投资方未透露' in invests_:\\n invests_ = []\\n\\n if dt.year < 2017:\\n if round_ not in data[company]:\\n data[company][round_] = invests_\\n invests = invests.union(invests_)\\n else:\\n if round_ not in data[company]:\\n data7[company][round_] = invests_\\n invests = invests.union(invests_)\\n\\n return data, data7, companies, invests\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d67cd23dd40cad97ec57b9061b10e1fc\",\n \"score\": \"0.49964508\",\n \"text\": \"def yearly_dates():\\n start_date = datetime.date.today()\\n return rr.rrule(\\n rr.YEARLY,\\n dtstart=start_date,\\n count=1,\\n byweekday=(rr.FR(4)),\\n bymonth=(12)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86815376808e43e44b73c14a1be75480\",\n \"score\": \"0.49936873\",\n \"text\": \"def man2df(mandict: dict, year1: int = 1) -> pd.DataFrame:\\n rows = []\\n baseyear = year1 # roundabout\\n for iofe in range(mandict[\\\"iofe\\\"]):\\n for iyear in range(mandict[\\\"inyr\\\"]):\\n year = iyear + baseyear\\n scenyr = mandict[\\\"rotations\\\"][iyear][iofe][\\\"yearindex\\\"]\\n ncrop = mandict[\\\"scens\\\"][scenyr - 1][\\\"ntype\\\"]\\n tilseq = mandict[\\\"scens\\\"][scenyr - 1][\\\"tilseq\\\"]\\n plant_date = None\\n if mandict[\\\"surfeffects\\\"]:\\n for surfeff in mandict[\\\"surfeffects\\\"][tilseq - 1][\\\"tills\\\"]:\\n op = surfeff[\\\"op\\\"]\\n if (\\n mandict[\\\"operations\\\"][op - 1][\\\"scecomment\\\"].find(\\n \\\"Planter\\\"\\n )\\n > -1\\n ):\\n doy = surfeff[\\\"mdate\\\"]\\n plant_date = datetime.date(\\n year, 1, 1\\n ) + datetime.timedelta(days=doy - 1)\\n rows.append(\\n {\\n \\\"year\\\": year,\\n \\\"ofe\\\": iofe + 1,\\n \\\"plant_date\\\": plant_date,\\n \\\"crop_name\\\": mandict[\\\"crops\\\"][ncrop - 1][\\\"crpnam\\\"],\\n }\\n )\\n return pd.DataFrame(rows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e01f68ce527c424dc544f4eb2223ee\",\n \"score\": \"0.49803594\",\n \"text\": \"def fundings(bank):\\n conn = engine.connect()\\n loanPie_df = pd.read_sql(\\\"select ApprovalYear as FundingYear, sum(grossApproval) as TotalApproval from bank_data2 group by 1,2\\\", conn)\\n all_fundings = loanPie_df.to_json(orient='records')\\n\\n return all_fundings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05bc421beb0d0b0d9b1be964237322ea\",\n \"score\": \"0.49758768\",\n \"text\": \"def month_dict_constructor(line, grouped, list_group):\\n id_, length, start, end = grouped.groups()\\n ids, lengths, starts, ends = list_group\\n ids.append(id_)\\n lengths.append(int(length))\\n starts.append(int(start))\\n ends.append(int(end))\\n return list_group\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfca2bae9f227ee16be273afc25ce323\",\n \"score\": \"0.49752215\",\n \"text\": \"def scale_series(numerator, denominator):\\n data = {}\\n for date, value in numerator.items():\\n if date in denominator:\\n if denominator[date] > 0:\\n data[date] = value / denominator[date]\\n else:\\n data[date] = 0\\n else:\\n try:\\n numerator_year = get_year(date)\\n for i in range(0, MAX_DENOMINATOR_BACK_YEAR + 1):\\n year = str(numerator_year - i)\\n if year in denominator:\\n if denominator[year] > 0:\\n data[date] = value / denominator[year]\\n else:\\n data[date] = 0\\n break\\n except ValueError:\\n return {}\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480976a0fa9ccd7bc9d17fd3803e162f\",\n \"score\": \"0.49749124\",\n \"text\": \"def get_yearly_profit(self):\\n\\n data = (TradingDay.objects\\n .filter(user=self.user)\\n .filter(account=self.account)\\n .annotate(year=Year(\\\"date_created\\\"))\\n .values(\\\"year\\\")\\n .annotate(total=Sum(\\\"profit\\\"))\\n .order_by(\\\"year\\\"))\\n\\n data_dict = {}\\n for year in data:\\n try:\\n total = round(float(year[\\\"total\\\"]), 2)\\n except:\\n total = 0\\n data_dict[year[\\\"year\\\"]] = total\\n\\n return data_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95350e455ff7fe7b6d35dcfded94f458\",\n \"score\": \"0.49613392\",\n \"text\": \"def range_year(some_data):\\n print(\\\"Unique Year is {} \\\".format(some_data.FiscalYear.unique()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3efc4ea4f829dfa9597ddccdfc0861d\",\n \"score\": \"0.49568775\",\n \"text\": \"def getSeasonalProbabilities(probability_collection, year, band_names, reduce_method='median', season_list = [['winter',-1,12,1,0,2,'end'],['spring',0,3,1,0,5,'end'],['summer',0,6,1,0,8,'end'],['fall',0,9,1,0,11,'end']], include_difference=True, year_difference=1, image_name='season_probs_{}'):\\n season_changes = []\\n year = int(year)\\n for season_definition in season_list:\\n season_name = season_definition[0]\\n season_name = season_name.lower()\\n \\n season_start_year_position = season_definition[1]\\n season_start_month = season_definition[2]\\n season_start_day = season_definition[3]\\n season_end_year_position = season_definition[4]\\n season_end_month = season_definition[5]\\n season_end_day = season_definition[6]\\n \\n season_start_year_firstYear = year+season_start_year_position\\n season_end_year_firstYear = year+season_end_year_position\\n \\n if include_difference:\\n season_start_year_secondYear = year+season_start_year_position+year_difference\\n season_end_year_secondYear = year+season_end_year_position+year_difference\\n \\n if season_start_day == 'end':\\n season_firstYear_start_day = calendar.monthrange(season_start_year_firstYear, int(season_start_month))[1]\\n if include_difference:\\n season_secondYear_start_day = calendar.monthrange(season_end_year_firstYear, int(season_start_month))[1]\\n \\n else:\\n season_firstYear_start_day = season_start_day\\n if include_difference:\\n season_secondYear_start_day = season_start_day\\n \\n if season_end_day == 'end':\\n season_firstYear_end_day = calendar.monthrange(season_end_year_firstYear, int(season_end_month))[1]\\n if include_difference:\\n season_secondYear_end_day = calendar.monthrange(season_start_year_secondYear, int(season_end_month))[1]\\n \\n else:\\n season_firstYear_end_day = season_end_day\\n if include_difference:\\n season_secondYear_end_day = season_end_day\\n \\n season_firstYear_start = '{}-{}-{}'.format(season_start_year_firstYear, season_start_month, season_firstYear_start_day)\\n season_firstYear_end = '{}-{}-{}'.format(season_end_year_firstYear, season_end_month, season_firstYear_end_day)\\n \\n if include_difference:\\n season_secondYear_start = '{}-{}-{}'.format(season_start_year_secondYear, season_start_month, season_secondYear_start_day)\\n season_secondYear_end = '{}-{}-{}'.format(season_end_year_secondYear, season_end_month, season_secondYear_end_day) \\n \\n if reduce_method=='mean':\\n season_image = probability_collection.filterDate(season_firstYear_start,season_firstYear_end).reduce(ee.Reducer.mean()).rename(band_names)\\n if include_difference:\\n diff_image = getTemporalProbabilityDifference(probability_collection, season_firstYear_start, \\n season_firstYear_end, season_secondYear_start, season_secondYear_end, reduce_method='mean').rename(band_names)\\n else:\\n season_image = probability_collection.filterDate(season_firstYear_start,season_firstYear_end).reduce(ee.Reducer.median()).rename(band_names)\\n if include_difference:\\n diff_image = getTemporalProbabilityDifference(probability_collection, season_firstYear_start, \\n season_firstYear_end, season_secondYear_start, season_secondYear_end, reduce_method='median').rename(band_names)\\n \\n season_image = season_image.set('system:index','{}_start'.format(season_name))\\n \\n season_changes.append(season_image)\\n \\n if include_difference:\\n diff_image = diff_image.set('system:index','{}_difference'.format(season_name))\\n season_changes.append(diff_image) \\n \\n season_changes = ee.ImageCollection(season_changes) \\n season_changes = season_changes.toBands()\\n season_changes = season_changes.set('system:index',image_name.format(year))\\n season_changes = season_changes.set('system:time_start',ee.Date(season_firstYear_start))\\n season_changes = season_changes.set('system:time_end',ee.Date(season_firstYear_end))\\n return season_changes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e65b1e68af69135b958ef514ca8f1d\",\n \"score\": \"0.49498585\",\n \"text\": \"def get_contracts_report_structure():\\n return [\\n {\\n 'header': 'Contract Name',\\n 'field': 'contract__description',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Contract Number',\\n 'field': 'contract__number',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Contract Type',\\n 'field': 'contract__type', # choicefield\\n 'type': 'choice',\\n 'value': 'CONTRACT_TYPES'\\n },\\n {\\n 'header': 'Contract Start',\\n 'field': 'contract__start_date',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Contract End',\\n 'field': 'contract__end_date',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Product ID',\\n 'field': 'item__ndc',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Product Type',\\n 'field': 'created_by',\\n 'type': 'static',\\n 'value': 'NDC'\\n },\\n {\\n 'header': 'Product Name',\\n 'field': 'item__description',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Unit Measure',\\n 'field': 'updated_by',\\n 'type': 'static',\\n 'value': 'EA'\\n },\\n {\\n 'header': 'Unit Contract Price',\\n 'field': 'price',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Product Start',\\n 'field': 'start_date',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Product End',\\n 'field': 'end_date',\\n 'type': '',\\n 'value': ''\\n },\\n {\\n 'header': 'Status',\\n 'field': 'status', # choicefield\\n 'type': 'choice',\\n 'value': 'STATUSES'\\n }\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9131645c75deb9557b11be33a04a010f\",\n \"score\": \"0.4946772\",\n \"text\": \"def parse_encost(fpath: Path) -> Tuple[List[dict], int]:\\n\\n fname = fpath.with_suffix(\\\"\\\").name\\n year = int(f\\\"20{fname.replace('encost', '')}\\\")\\n print(f\\\"Start parsing {fpath=}, {year=}\\\")\\n\\n with open(fpath, \\\"r\\\") as f:\\n content = f.read()\\n lines = [x.strip() for x in content.splitlines()]\\n\\n region = None\\n years = None\\n escalation_start_year = None\\n fuel = None\\n prices = None\\n results = []\\n\\n for i, line in enumerate(lines):\\n if not line:\\n region = None\\n years = None\\n # print(\\\"Reset.\\\\n\\\")\\n continue\\n\\n if region is None:\\n region = line\\n # print(f\\\"{region=}\\\")\\n continue\\n\\n if years is None:\\n years = [int(x) for x in line.split(\\\" \\\")]\\n if len(years) != NUMBER_YEARS:\\n print(\\n f\\\"Warning: expected {NUMBER_YEARS} years, \\\"\\n f\\\"got {len(years)}: {years}\\\"\\n )\\n if years[0] != year:\\n raise ValueError(\\n f\\\"Given the name of the file {fpath.name=}, we assumed \\\"\\n f\\\"we would find {year=} but got {years[0]}\\\"\\n )\\n if escalation_start_year is not None:\\n if escalation_start_year != years[1]:\\n raise ValueError(\\\"Inconsistent escalation start year\\\")\\n\\n escalation_start_year = years[1]\\n continue\\n\\n if fuel is None:\\n fuel = line\\n prices = None\\n continue\\n\\n if prices is None:\\n prices = [float(x) for x in line.split(\\\" \\\")]\\n if len(prices) != NUMBER_YEARS:\\n print(\\n f\\\"Warning: expected {NUMBER_YEARS} years, \\\"\\n f\\\"got {len(prices)} prices: {prices}\\\"\\n )\\n\\n escalations = [x / prices[0] for x in prices[1:]]\\n this_dict = {\\n \\\"region\\\": region,\\n \\\"fuel\\\": fuel,\\n \\\"escalations\\\": dict(zip(years[1:], escalations)),\\n # 'prices': dict(zip(years, prices)),\\n }\\n results.append(this_dict)\\n fuel = None\\n\\n return results, escalation_start_year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"231a11796b5774bef7ec6e1244036093\",\n \"score\": \"0.4946276\",\n \"text\": \"def yearly_return(stockList):\\n for stock in stockList: # Iterate through list of stock dictionaries\\n current_date = datetime.strptime(today,\\\"%m/%d/%Y\\\") # Create datetome object from string of current date\\n purchase_date = datetime.strptime(stock['purchaseDate'],\\\"%m/%d/%Y\\\") # Create datetime object from string of sotck purchase data.\\n no_days = (current_date-purchase_date).days # Calc number of days between two datetime objects. \\n yearlyVal = 365.2425 # Constant year value\\n yearlyReturn = (stock['percentage_yield']/(no_days/yearlyVal)) * 100 # Calculate the perctnage of yearly loss/earnings for each stock\\n stock['yearly_return'] = yearlyReturn # Append new value tp yearly_return in stock dictionary.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4faf88c15e5a3aec669db1e795f16bde\",\n \"score\": \"0.49412447\",\n \"text\": \"def precipitation():\\n last_day = session.query(Measurement.date, Measurement.prcp).order_by(Measurement.date.desc()).first()[0]\\n last_year = str(dt.datetime.strptime(last_day, \\\"%Y-%m-%d\\\") - dt.timedelta(days=366))\\n\\n precipitation = session.query(Measurement.date, Measurement.prcp).\\\\\\n filter(Measurement.date >= last_year, Measurement.date <= last_day).\\\\\\n order_by(Measurement.date).all()\\n prcp_dict = {date: prcp for date, prcp in precipitation}\\n return jsonify(prcp_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c21ca33f32ed18b643781b52773f115\",\n \"score\": \"0.49346516\",\n \"text\": \"def create_country_yearly_data(country_data: List[CountryTemperature],\\r\\n nation: str, year: int, end_year: int) -> List[CountryTemperature]:\\r\\n country_only_data = [row for row in country_data if row.country == nation]\\r\\n data_so_far = []\\r\\n for i in range(year, end_year):\\r\\n sum_temperature = []\\r\\n for row in country_only_data:\\r\\n if int(row.date.strftime(\\\"%Y\\\")) < i:\\r\\n pass\\r\\n elif int(row.date.strftime(\\\"%Y\\\")) == i:\\r\\n sum_temperature.append(row.temperature)\\r\\n elif int(row.date.strftime(\\\"%Y\\\")) >= i:\\r\\n if isinstance(sum_temperature[0], float) is True:\\r\\n data_so_far.append(CountryTemperature(datetime.date(i, 1, 1),\\r\\n statistics.mean(sum_temperature), nation))\\r\\n break\\r\\n return data_so_far\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5069fe1c54a9e14d982862a8b5ea1c52\",\n \"score\": \"0.49336904\",\n \"text\": \"def get_date_endpoints(q=None, year=2018, kind='year'):\\n if kind=='year':\\n return (dt.date(year, 1, 1), dt.date(year, 12, 31))\\n elif kind=='quarter':\\n return get_quarter_start_end(q,year=year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d337754dc0ebdddf6127a503ddd7ddec\",\n \"score\": \"0.49279833\",\n \"text\": \"def fetch_subset(start_yr, end_yr):\\r\\n for yr in range(start_yr, end_yr+1):\\r\\n fetch_single(yr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f48b9e08c801f08b97784d2d300ae5\",\n \"score\": \"0.49251616\",\n \"text\": \"def get_crime_at_year(year, data):\\n return {i: data[i] for i in data if str(year) in i}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c013745577336b557c09e90112490b4\",\n \"score\": \"0.49234694\",\n \"text\": \"def processGroup(data):\\n fillAges(data)\\n titles = findTitles(data)\\n names = findLastNames(data)\\n\\n return {'titles': titles, 'names': names}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee19dfb7f1f4afbcc3a75e19ea1540d7\",\n \"score\": \"0.49151674\",\n \"text\": \"def get_all_records():\\n season_records = {}\\n round_records = {}\\n\\n round_scores = Tip.objects \\\\\\n .exclude(afl_fixture__round__status='Scheduled') \\\\\\n .exclude(afl_fixture__round__is_finals=True) \\\\\\n .filter(afl_fixture__round__season=2014) \\\\\\n .values('afl_fixture__round__season', 'afl_fixture__round', 'club') \\\\\\n .annotate(\\n round_total=Sum('total'),\\n round_winners=Sum('winner_score'),\\n round_margins=Sum('margin_score'),\\n round_crowds=Sum('crowd_score'),\\n round_bogs=Sum('bog_score')\\n ) \\\\\\n .order_by('afl_fixture__round__season', 'club', 'afl_fixture__round')\\n\\n # Get round records\\n def _season_key(row):\\n return (row['afl_fixture__round__season'], row['club'])\\n\\n# def _max(group, key):\\n# '''\\n# Return the maximum score and the round it was scored in the group\\n# '''\\n# sorted_group = sorted(group, key=)\\n#\\n# return (row['afl_fixture__round__season'], row['club'])\\n#\\n# def _season_min_key(row):\\n# return (row['afl_fixture__round__season'], row['club'])\\n\\n for attr in ('total', 'winners', 'margins', 'crowds', 'bogs'):\\n attr_key = 'round_%s' % attr\\n scores = round_scores.order_by(\\n 'afl_fixture__round__season',\\n 'club',\\n attr_key,\\n 'afl_fixture__round'\\n )\\n for key, group in groupby(scores, key=_season_key):\\n club_records = {}\\n\\n group = list(group)\\n season = key[0]\\n club = Club.objects.get(id=key[1])\\n if club in club_records:\\n club_records[club].update(\\n {\\n 'total_%s' % attr: sum(g[attr_key] for g in group),\\n 'max_%s' % attr: max(g[attr_key] for g in group),\\n 'min_%s' % attr: min(g[attr_key] for g in group),\\n 'avg_%s' % attr: avg(g[attr_key] for g in group),\\n }\\n )\\n else:\\n club_records[club] = {\\n 'total_%s' % attr: sum(g[attr_key] for g in group),\\n 'max_%s' % attr: max(g[attr_key] for g in group),\\n 'min_%s' % attr: min(g[attr_key] for g in group),\\n 'avg_%s' % attr: avg(g[attr_key] for g in group),\\n }\\n# 'total_winners': sum(g['round_winners'] for g in group),\\n# 'max_winners': max(g['round_winners'] for g in group),\\n# 'min_winners': min(g['round_winners'] for g in group),\\n# 'avg_winners': avg(g['round_winners'] for g in group),\\n# 'total_margins': sum(g['round_margins'] for g in group),\\n# 'max_margins': max(g['round_margins'] for g in group),\\n# 'min_margins': min(g['round_margins'] for g in group),\\n# 'avg_margins': avg(g['round_margins'] for g in group),\\n# 'total_crowds': sum(g['round_crowds'] for g in group),\\n# 'max_crowds': max(g['round_crowds'] for g in group),\\n# 'min_crowds': min(g['round_crowds'] for g in group),\\n# 'avg_crowds': avg(g['round_crowds'] for g in group),\\n# 'total_bogs': sum(g['round_bogs'] for g in group),\\n# 'max_bogs': max(g['round_bogs'] for g in group),\\n# 'min_bogs': min(g['round_bogs'] for g in group),\\n# 'avg_bogs': avg(g['round_bogs'] for g in group)\\n\\n # Winners bonus isn't included in tips scores from 2012 so adjust\\n # total scores\\n if attr == 'total':\\n for g in group:\\n if g['afl_fixture__round__season'] >= 2012:\\n if g['round_winners'] == 54:\\n club_records[club]['total_total'] += 10\\n\\n round_records[season] = club_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d74af9fff9fb759fd7673c30a2799a\",\n \"score\": \"0.49082693\",\n \"text\": \"def stock_performance(start_year,end_year,stocks):\\n \\n # obtaining the TICKER symbols of the stocks\\n stock = stocks\\n \\n # create a list to obtain all the returns of the stock\\n all_returns = []\\n\\n # obtaining Quarterly returns using quarterly_returns() function\\n stock_data = quarterly_returns(start_year,end_year,stocks)\\n \\n\\n # for each TICKER symbol in stock \\n for abbv in stock:\\n data = stock_data[abbv]\\n\\n \\n # creating pyfolio tearsheet\\n# pf.create_returns_tear_sheet(data)\\n\\n # changing into numpy array for calculation\\n data = np.array(data)\\n \\n # creating a list to remove the NaN and make it a list of float values \\n val = []\\n for i in data:\\n if np.isnan(i):\\n i = float(0)\\n val.append(i)\\n else:\\n i = float(i)\\n val.append(i)\\n \\n # normalising to 100\\n norm = 100\\n for i in range(len(val)):\\n push = (1+val[i])*norm\\n val[i] = push\\n norm = push\\n\\n # adding the normalised returns of all stocks to the all_returns[] list\\n all_returns.append(val)\\n\\n return all_returns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6fbcc994afc0988968438af3e806076\",\n \"score\": \"0.49082536\",\n \"text\": \"def get_year(self, year):\\n return [item\\n for month in range(1, 13)\\n for item in self.get_month(year, month)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c091f15c8d7769426b1f979cbcb84098\",\n \"score\": \"0.4901673\",\n \"text\": \"def get_years(self):\\n \\n items = []\\n\\n # Get a datetime object\\n now = datetime.datetime.now()\\n currentYear = now.year\\n startYear = 1950\\n \\n for eachYear in range(startYear, (currentYear + 5), 1):\\n items.append( str(eachYear) ) \\n\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77ace9ed99b3f301ce91e0ba078e42f\",\n \"score\": \"0.48991683\",\n \"text\": \"def get_quarters(n=3):\\n now = datetime.datetime.now()\\n year = now.year\\n quarter = pd.Timestamp(now).quarter\\n quarters = [(year, quarter)]\\n for i in range(n):\\n if quarter == 1:\\n quarter = 4\\n year -= 1\\n else:\\n quarter -= 1\\n quarters.append((year, quarter))\\n quarters.reverse()\\n return quarters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13f2926f48c21cdb18b6be729d2e9b4\",\n \"score\": \"0.4897494\",\n \"text\": \"def construct_all_holdings(self):\\r\\n d=dict((k,v) for k,v in [(s,0.0) for s in self.symbol_list])\\r\\n d['datetime']=self.start_date\\r\\n d['cash']=self.initial_capital\\r\\n d['commisson']=0.0\\r\\n d['total']=self.initial_capital\\r\\n \\r\\n return [d]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac6b82e1ffe05a04c3fafb32c5465b0\",\n \"score\": \"0.48928243\",\n \"text\": \"def agg_by_state(fuel_codes, df_jan_to_nov, df_dec):\\n\\n # output of function = makes dictionary of states\\n fuel_per_state = { }\\n\\n # note -- the retrieved row in only a copy, not the original. Dataframes are good for data manipulation (e.g. pivot table), but are not very mutable.\\n for idx,row in enumerate(df_jan_to_nov.values):\\n\\n # get all the data, and insert into nested dict\\n plant_name, state, fuel_type, mwh_gen = row[0], row[1], row[2], row[3:]\\n # convert fuel code, to actual fuel name\\n fuel_type_name = fuel_codes[fuel_type]\\n\\n starting_totals = [0,0,0,0,0,0,0,0,0,0,0,0]\\n\\n # only add to dict, if not \\\"State Incremental Fuel Level\\\":\\n if plant_name != \\\"State-Fuel Level Increment\\\":\\n\\n # add to dict, summing the list per month:\\n if state not in fuel_per_state:\\n fuel_per_state[state] = {\\n \\\"gas\\\":starting_totals,\\n \\\"coal\\\":starting_totals,\\n \\\"solar\\\":starting_totals,\\n \\\"wind\\\":starting_totals,\\n \\\"nuclear\\\": starting_totals,\\n \\\"hydro\\\":starting_totals,\\n \\\"other\\\":starting_totals\\n }\\n\\n already_in_dict = fuel_per_state[state][fuel_type_name]\\n replace_in_dict = [ (mwh_gen[i]+int(already_in_dict[i]) ) for i in range(len(mwh_gen)) ]\\n\\n fuel_per_state[state][fuel_type_name] = replace_in_dict\\n\\n # add the december fuel data. make sure to add the 12th month\\n for idx,row in enumerate(df_dec.values):\\n plant_name = row[0] # for each row, get the plant name\\n\\n # make sure we know the state, before we take the data\\n if plant_name != \\\"State-Fuel Level Increment\\\":\\n\\n # get all the data, and insert into nested dict\\n plant_name, state, fuel_type, mwh_gen = row[0], row[1], row[2], row[3:]\\n # convert fuel code, to actual fuel name\\n fuel_type_name = fuel_codes[fuel_type]\\n\\n # add to dict, as the 12th month in the list:\\n in_dict = fuel_per_state[state][fuel_type_name]\\n if len(in_dict) < 12:\\n in_dict.append(int(mwh_gen))\\n else:\\n in_dict[11] += int(mwh_gen)\\n fuel_per_state[state][fuel_type_name] = in_dict\\n\\n return fuel_per_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"190d34f53bce60cd1b7f18050c4a0da7\",\n \"score\": \"0.4891378\",\n \"text\": \"def get_dated_items(self, year, month):\\r\\n date_field = self.get_date_field()\\r\\n date = _date_from_string(year, '%Y', month, self.get_month_format())\\r\\n\\r\\n # Construct a date-range lookup.\\r\\n first_day, last_day = _month_bounds(date)\\r\\n lookup_kwargs = {\\r\\n '%s__gte' % date_field: first_day,\\r\\n '%s__lt' % date_field: last_day,\\r\\n }\\r\\n\\r\\n allow_future = self.get_allow_future()\\r\\n qs = self.get_dated_queryset(allow_future=allow_future, **lookup_kwargs)\\r\\n date_list = self.get_date_list(qs, 'day')\\r\\n\\r\\n return (date_list, qs, {\\r\\n 'month': date,\\r\\n 'next_month': self.get_next_month(date),\\r\\n 'previous_month': self.get_previous_month(date),\\r\\n })\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994281,"cells":{"query_id":{"kind":"string","value":"3d6d60c4d0a2893d1edeb7e1161b1596"},"query":{"kind":"string","value":"Pull ungoogledchromium repositories, run scripts and apply patches."},"positive_passages":{"kind":"list like","value":[{"docid":"706f0331272d80f78e074f013adb38fa","score":"0.6706228","text":"def prepare(config):\n # Checkout ungoogled-chromium\n uc_git_origin = 'https://github.com/Eloston/ungoogled-chromium.git'\\\n if ungoogled_chromium_origin is None else ungoogled_chromium_origin\n git_maybe_checkout(\n uc_git_origin,\n 'ungoogled-chromium',\n branch=ungoogled_chromium_version, reset=True)\n if config.target_os == 'android':\n git_maybe_checkout(\n 'https://github.com/ungoogled-software/ungoogled-chromium-android.git',\n 'ungoogled-chromium-android',\n branch=ungoogled_chromium_android_version, reset=True)\n sp.check_call(['patch', '-p1', '--ignore-whitespace', '-i',\n os.path.join('ungoogled-chromium-android', 'patches', 'Other', 'ungoogled-main-repo-fix.patch'),\n '--no-backup-if-mismatch'])\n\n domain_substitution_cache_file = \"domsubcache.tar.gz\"\n if os.path.exists(domain_substitution_cache_file):\n os.remove(domain_substitution_cache_file)\n\n # ungoogled-chromium scripts\n # Do not check here because prune script return non-zero for non-existing files\n cwd = SRC_DIR\n uc_dir = 'ungoogled-chromium'\n utils_dir = os.path.join(uc_dir, 'utils')\n sp.run([os.path.join(utils_dir, 'prune_binaries.py'),\n SRC_DIR, filter_list_file(\n uc_dir, 'pruning.list',\n excludes=['buildtools/linux64/gn'])])\n sp.check_call([os.path.join(utils_dir, 'patches.py'),\n 'apply', 'src', os.path.join(uc_dir, 'patches')])\n sp.check_call([os.path.join(utils_dir, 'domain_substitution.py'),\n 'apply', '-r', os.path.join(uc_dir, 'domain_regex.list'),\n '-f', filter_list_file(uc_dir, 'domain_substitution.list'),\n '-c', domain_substitution_cache_file, 'src'])\n\n # ungoogled-chromium-android scripts\n if config.target_os == 'android':\n if os.path.exists(domain_substitution_cache_file):\n os.remove(domain_substitution_cache_file)\n\n uca_dir = 'ungoogled-chromium-android'\n sp.run([os.path.join(utils_dir, 'prune_binaries.py'),\n 'src', filter_list_file(uca_dir, 'pruning_2.list')])\n sp.check_call([os.path.join(utils_dir, 'patches.py'),\n 'apply', 'src', os.path.join(uca_dir, 'patches')])\n sp.check_call([os.path.join(utils_dir, 'domain_substitution.py'),\n 'apply', '-r', os.path.join(uc_dir, 'domain_regex.list'),\n '-f', filter_list_file(uca_dir, 'domain_sub_2.list'),\n '-c', domain_substitution_cache_file, 'src'])","title":""}],"string":"[\n {\n \"docid\": \"706f0331272d80f78e074f013adb38fa\",\n \"score\": \"0.6706228\",\n \"text\": \"def prepare(config):\\n # Checkout ungoogled-chromium\\n uc_git_origin = 'https://github.com/Eloston/ungoogled-chromium.git'\\\\\\n if ungoogled_chromium_origin is None else ungoogled_chromium_origin\\n git_maybe_checkout(\\n uc_git_origin,\\n 'ungoogled-chromium',\\n branch=ungoogled_chromium_version, reset=True)\\n if config.target_os == 'android':\\n git_maybe_checkout(\\n 'https://github.com/ungoogled-software/ungoogled-chromium-android.git',\\n 'ungoogled-chromium-android',\\n branch=ungoogled_chromium_android_version, reset=True)\\n sp.check_call(['patch', '-p1', '--ignore-whitespace', '-i',\\n os.path.join('ungoogled-chromium-android', 'patches', 'Other', 'ungoogled-main-repo-fix.patch'),\\n '--no-backup-if-mismatch'])\\n\\n domain_substitution_cache_file = \\\"domsubcache.tar.gz\\\"\\n if os.path.exists(domain_substitution_cache_file):\\n os.remove(domain_substitution_cache_file)\\n\\n # ungoogled-chromium scripts\\n # Do not check here because prune script return non-zero for non-existing files\\n cwd = SRC_DIR\\n uc_dir = 'ungoogled-chromium'\\n utils_dir = os.path.join(uc_dir, 'utils')\\n sp.run([os.path.join(utils_dir, 'prune_binaries.py'),\\n SRC_DIR, filter_list_file(\\n uc_dir, 'pruning.list',\\n excludes=['buildtools/linux64/gn'])])\\n sp.check_call([os.path.join(utils_dir, 'patches.py'),\\n 'apply', 'src', os.path.join(uc_dir, 'patches')])\\n sp.check_call([os.path.join(utils_dir, 'domain_substitution.py'),\\n 'apply', '-r', os.path.join(uc_dir, 'domain_regex.list'),\\n '-f', filter_list_file(uc_dir, 'domain_substitution.list'),\\n '-c', domain_substitution_cache_file, 'src'])\\n\\n # ungoogled-chromium-android scripts\\n if config.target_os == 'android':\\n if os.path.exists(domain_substitution_cache_file):\\n os.remove(domain_substitution_cache_file)\\n\\n uca_dir = 'ungoogled-chromium-android'\\n sp.run([os.path.join(utils_dir, 'prune_binaries.py'),\\n 'src', filter_list_file(uca_dir, 'pruning_2.list')])\\n sp.check_call([os.path.join(utils_dir, 'patches.py'),\\n 'apply', 'src', os.path.join(uca_dir, 'patches')])\\n sp.check_call([os.path.join(utils_dir, 'domain_substitution.py'),\\n 'apply', '-r', os.path.join(uc_dir, 'domain_regex.list'),\\n '-f', filter_list_file(uca_dir, 'domain_sub_2.list'),\\n '-c', domain_substitution_cache_file, 'src'])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ecac29780f2dec0c3d136abb6f000c6c","score":"0.57570237","text":"def exec_main():\n\n try_get_lock()\n\n # Allow user to force a SHA1 from env, mostly for testing purpose without\n # putting pressure on Github API (anon is rate-limited to 60 reqs/hr from the\n # same IP address.\n try:\n sha_to_run = os.environ.get('ds_automation_force_sha').split(',')\n except:\n sha_to_run = [ ]\n\n # ensure we have a real dir name\n assert len(DEEPSPEECH_CLONE_PATH) > 3\n assert os.path.isabs(DEEPSPEECH_CLONE_PATH)\n\n # Creating holding directories if needed\n if not os.path.isdir(CACHE_DIR):\n os.makedirs(CACHE_DIR)\n\n if not os.path.isdir(DATA_DIR):\n os.makedirs(DATA_DIR)\n\n if len(sha_to_run) == 0:\n current = get_last_sha1()\n if len(current) == 40:\n print('Existing SHA1:', current, 'fetching changes')\n sha_to_run, rt = get_new_commits(current)\n if sha_to_run is not None and len(sha_to_run) is 0:\n print(\"No new SHA1, got HTTP status:\", rt)\n sys_exit_safe()\n elif sha_to_run is None:\n print(\"Something went badly wrong, unable to use Github compare\")\n sys_exit_safe()\n else:\n # Ok, we do not have an existing SHA1, let us get one\n print('No pre-existing SHA1, fetching refs')\n sha1, rt = get_current_sha1()\n if sha1 is None:\n print(\"No SHA1, got HTTP status:\", rt)\n sys_exit_safe()\n sha_to_run = [ sha1 ]\n else:\n print(\"Using forced SHA1 from env\")\n\n print(\"Will execute for\", sha_to_run)\n\n for sha in sha_to_run:\n if not ensure_git_clone(sha):\n print(\"Error with git repo handling.\")\n sys_exit_safe()\n\n print(\"Ready for\", sha)\n\n print(\"Let us place ourselves into the git clone directory ...\")\n root_dir = os.getcwd()\n os.chdir(DEEPSPEECH_CLONE_PATH)\n\n print(\"Starting GPU nvidia-smi monitoring\")\n gpu_usage_csv, gpu_usage_charts = ensure_gpu_usage(root_dir)\n gu = GPUUsage(csvfile=gpu_usage_csv)\n gu.start()\n\n print(\"Do the training for getting WER computation\")\n exec_wer_run()\n\n print(\"Producing GPU monitoring charts\")\n gu.stop()\n GPUUsageChart(source=gpu_usage_csv, basename=gpu_usage_charts)\n\n print(\"Save progress\")\n write_last_sha1(sha)\n\n print(\"Let us place back to the previous directory %s ...\" % root_dir)\n os.chdir(root_dir)\n\n print(\"Getting rid of git clone\")\n wipe_git_clone()\n\n release_lock()","title":""},{"docid":"3c5be54f5511e7bd55288895dd7d66b4","score":"0.57431793","text":"def update_code():\n with cd('/srv/directedstudies'):\n sudo('git pull')","title":""},{"docid":"dc8f6b8ddb8d77ac432c93cf3b8061a7","score":"0.57357633","text":"def _update(self):\n self._system.execute_command(\"git\", [\"pull\"], cwd=self.get_install_path(), verbose=True)\n self._install()","title":""},{"docid":"dc8f6b8ddb8d77ac432c93cf3b8061a7","score":"0.57357633","text":"def _update(self):\n self._system.execute_command(\"git\", [\"pull\"], cwd=self.get_install_path(), verbose=True)\n self._install()","title":""},{"docid":"894e9e90d2a206ddfafb9ffc4a6440a6","score":"0.5680683","text":"def site_updaterepos():\n cmd = [\"git\", \"submodule\", \"foreach\", \"--recursive\", \"git\", \"pull\", \"origin\", \"main\"]\n with Popen(cmd, env=os.environ) as proc:\n proc.wait()","title":""},{"docid":"2159d9b3b5047cd35571d00d483fe1b5","score":"0.5612964","text":"def fetchRepo():\n # get covid19 data\n if os.path.isdir(COVID_19_DATA_DIR):\n os.system(f\"cd {COVID_19_DATA_DIR} & git pull\")\n else:\n os.system(f\"git clone {COVID_19_DATA_URL} {COVID_19_DATA_DIR}\")\n \n # get populaion data\n if os.path.isdir(POPULATION_DATA_DIR):\n os.system(f\"cd {POPULATION_DATA_DIR} & git pull\")\n else:\n os.system(f\"git clone {POPULATION_DATA_URL} {POPULATION_DATA_DIR}\")\n \n # get country to region map\n if os.path.isdir(COUNTRY_TO_REGION_MAPPING_DIR):\n os.system(f\"cd {COUNTRY_TO_REGION_MAPPING_DIR} & git pull\")\n else:\n os.system(f\"git clone {COUNTRY_TO_REGION_MAPPING_URL} {COUNTRY_TO_REGION_MAPPING_DIR}\")","title":""},{"docid":"fff4fcb582800e47841f1ace3214240c","score":"0.55779135","text":"def update():\n with cd(site_path):\n run('git pull --all')\n reload()","title":""},{"docid":"bbf80f8ecc65941a6ab5d8e192f0befc","score":"0.5454635","text":"def main():\n #gotoMYChrome()\n #returndockspot()\n gotochrome()\n copyurl()\n getsearchbar()\n pasteurl()","title":""},{"docid":"a95012a42bda14b7c48c1c4af2e35a48","score":"0.5423222","text":"def _post_install():\n import subprocess\n from distutils import log\n log.set_verbosity(log.DEBUG)\n\n try:\n print(\"Downloading most resent gitignores from Github.com...\")\n pass\n except:\n log.warn(\"Post Install Failed.\")\n pass","title":""},{"docid":"04ec5ac856415af6ead54d602f1f5c75","score":"0.53963065","text":"def dip_upgrade(names):\n for name in names:\n with settings.getapp(name) as app:\n try:\n app.repo.pull()\n except AttributeError:\n pass","title":""},{"docid":"d6661b592ad5f70a24d38779f5047838","score":"0.53792614","text":"def puller(options):\n \n executor([\"git\", \"pull\"] + options)","title":""},{"docid":"67db9a8df4e58978cd77cabcdd5b3e14","score":"0.53673506","text":"def perform_update():\n if os.path.islink(\"./data\"):\n os.rename(\"./data\", \"./data_updating\")\n try:\n os.makedirs(\"./data\")\n except OSError as e:\n print(\"System update error \", e)\n\n command = u\"git config core.filemode true\"\n subprocess.call(command.split())\n \n command = u\"git pull\"\n subprocess.call(command.split())\n \n if os.path.isdir(\"./data_updating\"):\n try:\n shutil.rmtree(\"./data\")\n os.rename(\"./data_updating\", \"./data\")\n except OSError as e:\n print(\"System update error \", e)","title":""},{"docid":"e01b1e7c6a0334d936addaf94797a622","score":"0.5357921","text":"async def source(self, ctx, *, command: str = None):\r\n #chrome_options = Options()\r\n #chrome_options.add_argument(\"--headless\")\r\n #chrome_options.add_argument('--no-sandbox')\r\n #driver = webdriver.Chrome('/usr/lib/chromium-browser/chromedriver', options=chrome_options)\r\n #driver2 = webdriver.Chrome('/usr/lib/chromium-browser/chromedriver', options=chrome_options)\r\n source_url = 'https://github.com/proguy914629bot/LyricMaster'\r\n branch = '1.0.0'\r\n\r\n if command is None:\r\n return await ctx.send(source_url + f'\\n\\nHint: You can specify a specific command to view the source of that command, Like `lm?source ping`!')\r\n\r\n obj = self.bot.get_command(command.replace('.', ' '))\r\n if obj is None:\r\n return await ctx.send(f\"Command Named {command} Not Found!\")\r\n\r\n src = obj.callback.__code__\r\n module = obj.callback.__module__\r\n filename = src.co_filename\r\n\r\n lines, firstlineno = inspect.getsourcelines(src)\r\n location = os.path.realpath(filename).replace(\"\\\\\", '/')\r\n\r\n if \"home/gilb\" in location:\r\n await ctx.send(\"Command Found but Not Updated/Found in Github Repo. Here is the main link! {}!\".format(source_url))\r\n return\r\n\r\n #if \"/home/gilb/.local\" in location:\r\n # async with ctx.message.channel.typing():\r\n # finallocation = location.replace(\"/home/gilb/.local/lib/python3.8/site-packages/\", \"\")\r\n # finallocation2 = finallocation.replace(\" \", \"+\")\r\n # driver2.get(f\"https://www.google.com/search?q=pip+install+{finallocation2}\")\r\n #\r\n # pypipage = driver2.find_elements_by_css_selector(\"yuRUbf > a\").__getattribute__('href')\r\n #\r\n # print(pypipage)\r\n #\r\n # driver.get(f'{str(pypipage)}')\r\n #\r\n # pipcmd = driver.find_element_by_xpath('.//span[@class = \"package-header__pip-instructions\"]')[0]\r\n #\r\n # print(pipcmd)\r\n\r\n # finallocation = location.replace(\"/home/gilb/.local/lib/python3.8/site-packages/\", \"\")\r\n # finallocation2 = finallocation.replace(\" \", \"+\")\r\n # req = Request(f\"https://www.google.com/search?q=pip+install+{finallocation2}\", headers={'User-Agent': 'Mozilla/5.0'})\r\n # webpage = urlopen(req).read()\r\n # soup = BeautifulSoup(webpage, \"html.parser\")\r\n # for link in soup.findAll('a', attrs={'href', re.compile(\"^https://pypi.com/\")}):\r\n # href = print(link.get('href'))\r\n\r\n #async with ClientSession() as session:\r\n # async with session.get(f'{source_url}/blob/{branch}/{location}#L{firstlineno - 1}-L{firstlineno + len(lines) - 2}') as response:\r\n # if response.status != 200:\r\n # await ctx.send(\"Cannot connect to GitHub Servers. Try Again Later! Maybe check the Github Status. If not here is the main link! {}\".format(source_url))\r\n # return\r\n # else:\r\n # pass\r\n\r\n final_url = f'{source_url}/blob/{branch}/{location}#L{firstlineno - 1}-L{firstlineno + len(lines) - 2}'\r\n finalurl2 = final_url.replace('//home/gilb/LyricMaster/', '/')\r\n await ctx.send(finalurl2)","title":""},{"docid":"ba6b9a072b5b886a8adcf7c78728baed","score":"0.53354615","text":"def sync(config):\n # Fetch & Sync Chromium\n # Copy PATH from current process and add depot_tools to it\n depot_tools_path = os.path.join(os.getcwd(), 'depot_tools')\n if not os.path.exists(depot_tools_path) or not os.path.isdir(depot_tools_path):\n raise FileNotFoundError(\"Cannot find depot_tools!\")\n _env = os.environ.copy()\n _env[\"PATH\"] = depot_tools_path + \":\" + _env[\"PATH\"]\n\n # Get chromium ref\n # Set src HEAD to version\n chromium_ref = set_revision(config)\n\n # Create .gclient file\n with open('.gclient', 'w', encoding='utf-8') as f:\n f.write(GCLIENT_CONFIG.replace(\"@@TARGET_OS@@\", \"'{}'\".format(config.target_os)))\n\n # Run gclient sync without hooks\n extra_args = []\n if config.reset:\n extra_args += ['--revision', 'src@' + chromium_ref, '--force', '--upstream', '--reset']\n if config.shallow:\n # There is a bug with --no-history when syncing third_party/wayland. See\n # https://bugs.chromium.org/p/chromium/issues/detail?id=1226496\n extra_args += ['--shallow']\n else:\n extra_args += ['--with_tags', '--with_branch_heads']\n\n sp.check_call(['gclient', 'sync', '--nohooks'] + extra_args, env=_env)\n\n # Run hooks\n sp.check_call(['gclient', 'runhooks'], env=_env)\n\n # If Debian/Ubuntu and install_deps, then run the script.\n # Note: requires sudo\n if config.install_build_deps:\n if config.target_os == 'android':\n script = 'install-build-deps-android.sh'\n else:\n script = 'install-build-deps.sh'\n distro_name = distro.linux_distribution(full_distribution_name=False)[0].lower()\n if distro_name == 'debian' or distro_name == 'ubuntu':\n warnings.warn(\"Note: installing dependencies requires root privilege!\",\n RuntimeWarning)\n sp.check_call(['sudo', os.path.join(SRC_DIR, 'build', script)])\n else:\n warnings.warn(\"Installing dependencies only works on Debian based systems, skipping.\",\n RuntimeWarning)","title":""},{"docid":"f59fcbb73b07ac726f380a869c275510","score":"0.5229842","text":"def update():\n for install_type in VSCode:\n checkout = checkout_directory(install_type)\n if not checkout.exists():\n continue\n print(f\"UPDATING {checkout}\")\n print(\"Deleting files downloaded by the extension ...\")\n cleanup(checkout)\n print(\"Updating clone ...\")\n update_checkout(checkout)\n build(checkout)","title":""},{"docid":"38deecf9c422fd2526e63f854a2dc97a","score":"0.5225225","text":"def _pull_and_resolve_conflicts(self):\n\n logging.info('Starting pull from {}'.format(self.git_url))\n\n yield from execute_cmd(['git', 'checkout', self.branch_name], cwd=self.repo_dir)\n yield from execute_cmd(['git', 'fetch'], cwd=self.repo_dir)\n yield from execute_cmd(['git', 'merge', '-Xours', 'origin/{}'.format(self.branch_name)], cwd=self.repo_dir)\n\n logging.info('Pulled from {}'.format(self.git_url))","title":""},{"docid":"e523b739a907ac07930f9068581966b2","score":"0.519865","text":"def main(pr_ids, output_dir, upstream):\n check_for_cargo_benchcmp()\n check_github_api_env_vars()\n\n temp_dir = None\n if output_dir is None:\n temp_dir = TemporaryDirectory()\n output_dir = temp_dir.name\n else:\n os.makedirs(output_dir, exist_ok=True)\n\n if upstream is None:\n check_environment_variable('RBU_UPSTREAM_URL', 'if --upstream is not')\n upstream = os.environ['RBU_UPSTREAM_URL']\n\n for pr_id in pr_ids:\n benchmark_github_pr(pr_id, upstream, output_dir)\n\n # Cleanup if it's a temp dir\n if temp_dir is not None:\n temp_dir.cleanup()","title":""},{"docid":"0d07a072b9a2a84f23e5275a9202a910","score":"0.5186958","text":"def update_fetch(self):\n Popen([\"mount\", \"-t\", \"devfs\", \"devfs\",\n \"{}/releases/{}/root/dev\".format(self.iocroot,\n self.release)]).communicate()\n copy(\"/etc/resolv.conf\",\n \"{}/releases/{}/root/etc/resolv.conf\".format(self.iocroot,\n self.release))\n\n # TODO: Check for STABLE/PRERELEASE/CURRENT/BETA if we support those.\n # TODO: Fancier.\n self.lgr.info(\"\\n* Updating {} to the latest patch level... \".format(\n self.release))\n\n os.environ[\"UNAME_r\"] = self.release\n os.environ[\"PAGER\"] = \"/bin/cat\"\n new_root = \"{}/releases/{}/root\".format(self.iocroot, self.release)\n if os.path.isfile(\"{}/etc/freebsd-update.conf\".format(new_root)):\n # 10.1-RELEASE and under have a interactive check\n if float(self.release.partition(\"-\")[0][:5]) <= 10.1:\n with NamedTemporaryFile(delete=False) as tmp_conf:\n conf = \"{}/usr/sbin/freebsd-update\".format(new_root)\n with open(conf) as update_conf:\n for line in update_conf:\n tmp_conf.write(re.sub(\"\\[ ! -t 0 \\]\", \"false\",\n line))\n\n os.chmod(tmp_conf.name, 0o755)\n Popen([tmp_conf.name, \"-b\", new_root, \"-d\",\n \"{}/var/db/freebsd-update/\".format(new_root), \"-f\",\n \"{}/etc/freebsd-update.conf\".format(new_root),\n \"fetch\"], stdout=PIPE, stderr=PIPE).communicate()\n os.remove(tmp_conf.name)\n else:\n Popen([\"freebsd-update\", \"-b\", new_root, \"-d\",\n \"{}/var/db/freebsd-update/\".format(new_root), \"-f\",\n \"{}/etc/freebsd-update.conf\".format(new_root),\n \"fetch\"], stdout=PIPE, stderr=PIPE).communicate()\n\n Popen([\"freebsd-update\", \"-b\", new_root, \"-d\",\n \"{}/var/db/freebsd-update/\".format(new_root), \"-f\",\n \"{}/etc/freebsd-update.conf\".format(new_root),\n \"install\"], stdout=PIPE, stderr=PIPE).communicate()\n\n try:\n # Why this sometimes doesn't exist, we may never know.\n os.remove(\"{}/releases/{}/root/etc/resolv.conf\".format(\n self.iocroot, self.release))\n except OSError:\n pass\n\n Popen([\"umount\", \"{}/releases/{}/root/dev\".format(\n self.iocroot, self.release)]).communicate()","title":""},{"docid":"9f36ab8a04e43304346ad9e5a6e485e1","score":"0.51862854","text":"def DP():\n url = \"https://github.com/W1l50n2208\"\n os.startfile(url)","title":""},{"docid":"dd0a3541ad9ec2a22e2c2a2e8af51117","score":"0.51758665","text":"def pull():\n local('git pull origin master')","title":""},{"docid":"22a13d27f39ad2b52f407c1ae7dfdffe","score":"0.5153826","text":"def refresh_external_libs():\n for url, alias in settings.EXTERNAL_LIBRARIES:\n path = os.path.join(settings.EXTERNAL_LIBRARIES_ROOT, alias)\n if os.path.exists(path):\n pull(alias, url)\n logger.info(f\"Library '{alias}' updated from '{url}.\")\n else:\n clone(alias, url)\n logger.info(f\"Library '{alias}' cloned from '{url}.\")","title":""},{"docid":"1e1d0ab50f4462c010f3c71831e3b69e","score":"0.51401055","text":"def repo_update():\n with cd(env.base_dir):\n run('git fetch --all && git reset --hard origin/master')","title":""},{"docid":"dab8d08a20148424d77e201e82fb42b5","score":"0.51230353","text":"def pull(self):\n\n Program.runprogram(shlex.split(\"git pull\"), workdir=self.directory, use_sudo=self.use_sudo, user=self.sudo_user)","title":""},{"docid":"21253266bc44016b41b6209492b502af","score":"0.50979227","text":"def pull(env, opts, args):\n remotes = remote.get_remotes(env)\n if args:\n filt = lambda rmt: rmt.name in args\n else:\n filt = lambda rmt: True\n for entry in filter(filt, remotes):\n external.rsync(entry.url, env.directory, backup_dir=env.backup)","title":""},{"docid":"ae36b5c1a49ae955ce4fc10c854f840d","score":"0.50876707","text":"def soft_deploy():\n clear_compiled_python_files()\n git_pull()\n collectstatic()\n reload_http()","title":""},{"docid":"11db07804dc60e5b5dd91eac8b424071","score":"0.5043245","text":"def main(repo_dirs, linewise, loop, delay, repos):\n\n if repo_dirs is not None:\n repos = repo_dirs.read().splitlines()\n if not repos:\n return\n\n while True:\n dirs = get_git_repos(repos)\n\n codes = []\n for d in dirs:\n chdir(d)\n\n status = get_git_status()\n if status == \"-\":\n continue\n\n code = get_git_code(d, status)\n if code:\n codes.append(code)\n\n if linewise:\n codes = \"\\n\".join(codes)\n else:\n codes = \" \".join(codes)\n click.echo(codes)\n\n if not loop:\n break\n time.sleep(delay)","title":""},{"docid":"31b6c0e338a09af69a5cd589959b966d","score":"0.5024227","text":"async def pull(self, ctx):\n await ctx.typing()\n try:\n output = subprocess.check_output(\n ['git', 'pull']).decode()\n await ctx.send('```git\\n' + output + '\\n```')\n except Exception as e:\n return await ctx.send(str(e))\n\n _cogs = [f'cogs.{i}' for i in self.cog_re.findall(output)]\n active_cogs = [i for i in _cogs if i in self.client.extensions]\n if active_cogs:\n for cog_name in active_cogs:\n await ctx.invoke(self.client.get_command('reload'), cog_name)","title":""},{"docid":"79de43382848d91c9f1f995cdc6d6672","score":"0.5023246","text":"def CM():\n url = \"https://github.com/Chrism1c\"\n os.startfile(url)","title":""},{"docid":"0254aab07f47c30f36709b63bba55011","score":"0.50232244","text":"def main():\n last_url = \"\"\n config_file = read_config()\n while True:\n try:\n url = get_latest_url()\n if url != last_url: # update detected\n print(f\"Update detected!\")\n\n last_url = url\n data_location = config_file[\"downloaded_data_path\"]\n\n create_data_folder(data_location)\n download_file(url, data_location)\n\n print(\"Extraction completed.\")\n except Exception as e:\n print(f\"Error! {e}\")\n continue\n return 0","title":""},{"docid":"e9d33f77a9c32d40eec20f4ffcb2b751","score":"0.50189006","text":"def downloadUpdateClick():\n lcd.clear()\n lcd.message(\"Downloading...\")\n lcd.setCursor(14, 0)\n lcd.ToggleBlink()\n os.system(\"mount -o rw,remount /\")\n try:\n response = subprocess.check_output([\"git\", \"pull\"])\n except:\n response = \"Update Error:\\nCheck Internet\"\n os.system(\"mount -o ro,remount /\")\n if response.strip() == \"Already up-to-date.\":\n message = \"No Update Found\"\n else:\n message = response.strip()\n lcd.ToggleBlink()\n lcdPrint(message, 2)","title":""},{"docid":"db6ae95bae35a1c71e16eb21c9af1113","score":"0.50038403","text":"def main():\n setup_and_install_dependencies()\n run_lighthouse_checks()\n delete_reports()","title":""},{"docid":"ad3c2780c7ddfe0aa19187141b4b6807","score":"0.49998796","text":"def pull(c, only_latest=False):\n images = [\n ('6.0', 'latest')\n ]\n if not only_latest:\n images.extend([\n ('5.0', 'stable'),\n ('7.0', 'testing'),\n ])\n for image, tag in images:\n c.run(f'docker pull readthedocs/build:{image}', pty=True)\n c.run(f'docker tag readthedocs/build:{image} readthedocs/build:{tag}', pty=True)","title":""},{"docid":"7c88df71b7982d7dcd77552162f97020","score":"0.4989056","text":"def update(self, options):\n\n self._repo.ensure_lines(\".gitignore\", [\n \"workspace/\",\n \"*.egg-info/\",\n \"dist/*\",\n \"__pycache__\",\n \"*.pyc\",\n \".pytest_cache/\",\n \"build/*\"\n ])\n\n self._repo.ensure_template(\".editorconfig\", template=\"editorconfig\")\n self._repo.ensure_lines(\"requirements_build.txt\", [\n \"requests ~= 2.20\",\n \"twine ~= 1.12\",\n \"pycryptodome ~= 3.7\",\n \"pytest ~= 4.0\",\n \"wheel >= 0.32\"\n ], [\n r\"^requests\",\n r\"^twine\",\n r\"^pycryptodome\",\n r\"^pytest\",\n r\"^wheel\"\n ], multi=True)\n\n variables = {\n 'options': options,\n 'components': self._repo.components\n }\n\n self._repo.ensure_directory(self._repo.SCRIPT_DIR)\n self._repo.ensure_template(os.path.join(self._repo.MULTIPACKAGE_DIR, \"components.txt\"), template=\"components.txt\", overwrite=False)\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \"release_by_name.py\"), \"release_by_name.py.tpl\", variables)\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \"test_by_name.py\"), \"test_by_name.py.tpl\", variables)\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \"components.py\"), \"components.py.tpl\", variables)\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \"tag_release.py\"), \"tag_release.py.tpl\", variables)\n\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \"shared_errors.py\"), \"shared_errors.py\")\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \"release_notes.py\"), \"release_notes.py\")\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \"release_component.py\"), \"release_component.py\")","title":""},{"docid":"40bdeaeaa7a49a5dc2de9a4cb21c18ea","score":"0.49812675","text":"def pull(url, revision=None):\r\n do_bzr_cmd(cmd_pull, location=url, revision=revision)","title":""},{"docid":"335ef2d96c80e15a44077504e3fa2e36","score":"0.49721912","text":"def git_f():\r\n webbrowser.open(\"https://github.com/Katerunner/Flights-Course-Work\")","title":""},{"docid":"0f299d209b5897cbd5861ab8d8334063","score":"0.49692765","text":"async def pull(ctx, pip=None):\n dev = ctx.message.author\n if bot.botdev_role in dev.roles or bot.owner_role in dev.roles:\n await ctx.send(\"`Pulling changes...`\")\n call([\"git\", \"stash\", \"save\"])\n call([\"git\", \"pull\"])\n call([\"git\", \"stash\", \"clear\"])\n pip_text = \"\"\n if pip == \"-p\" or pip == \"--pip\" or pip == \"-Syu\":\n await ctx.send(\"`Updating python dependencies...`\")\n call([\"python3.6\", \"-m\", \"pip\", \"install\", \"--user\", \"--upgrade\", \"-r\",\n \"requirements.txt\"])\n pip_text = \" and updated python dependencies\"\n await ctx.send(\"Pulled changes{}! Restarting...\".format(pip_text))\n execv(\"./Brick.py\", argv)\n else:\n if \"pacman\" in ctx.message.content:\n await ctx.send(\"`{} is not in the sudoers file. This incident will be reported.`\".format(ctx.message.author.display_name))\n else:\n await ctx.send(\"Only bot devs and / or owners can use this command\")","title":""},{"docid":"f0ef4a1a4bc0bf3eee043955cc29e5e7","score":"0.49266657","text":"def waitUncommentableSourceIfNecessary(self):\n url = 'http://download.csdn.net/my/downloads/1'\n maxMinutes = 11\n for i in range(0, maxMinutes):\n html = self.getUrlContent(self.sess, url)\n if html is None:\n break\n soup = BeautifulSoup(html)\n sourcelist = soup.findAll('span', attrs={'class' : 'btn-comment'})\n if sourcelist is None or len(sourcelist) == 0:\n print('None uncommentable source now!')\n break\n print('Waiting for uncommentable source count down %d minutes.' % (maxMinutes-i))\n time.sleep(60)","title":""},{"docid":"22c97af99de78a03c3ea5e7da122ce6d","score":"0.49146226","text":"def main(self):\r\n req = urllib2.Request(raw_input(\"Enter a URL! \\n\"), headers={ 'User-Agent': 'Mozilla/5.0' })\r\n target = urllib2.urlopen(req) #getting the source code\r\n inter = target.read()\r\n inter = inter.decode(\"utf-8\") #decode unicode entities\r\n inter = inter.replace(\"<style type\", \"<s><style>\") #replace problematic tags\r\n inter = inter.replace(\"<!DOCTYPE\", \"<DOCTYP><\")\r\n inter = inter.replace(\"<!doctype\", \"<doctyp><\")\r\n inter = inter.replace(\"<![\", \"<[[\")\r\n inter = inter.replace(\"<!]\", \"<]]\")\r\n text = list(inter) #make a list containing every character\r\n logging.info(\"\\nPLEASE WAIT\\n\")\r\n logging.info(\"removing self.comments...\\n\" )\r\n self.removeComments(text) #comment removal\r\n logging.info(\"removing self.scripts...\\n\")\r\n self.removeScripts(text) #script removal\r\n logging.info(\"removing self.styles...\\n\")\r\n self.removeStyles(text) #style removal\r\n logging.info(\"extracting html and non-html characters...\\n\" )\r\n self.htmlSeparate(text) #separating the remaining parts\r\n logging.info(\"FINISHED! EVERY FILE IS IN THE SAME FOLDER AS THIS SCRIPT.\")","title":""},{"docid":"d0c82e5a83232ad6d0e4609b19bbc277","score":"0.49099478","text":"def update_dependencies(dep_dict: dict, open_pull_requests: dict):\n file_queue = Queue(config.MAXIMUM_DEPENDENCIES)\n for dep_file in glob.glob('{}/**/ivy.xml'.format(config.REPO_PATH.replace('/', '')), recursive=True):\n file_queue.put(dep_file)\n threads = []\n for i in range(config.NETWORKING_THREADS_GITHUB):\n t = threading.Thread(\n target=update_run, name='th-{}'.format(i),\n kwargs={'file_queue': file_queue, 'dep_dict': dep_dict, 'open_pull_requests': open_pull_requests})\n threads.append(t)\n for t in threads:\n t.start()\n for t in threads:\n t.join()","title":""},{"docid":"f8fd9b8fd0c8e07a9bc1024daaa3e478","score":"0.48900807","text":"def cli(from_url: str, force: bool, noleanup: bool, python_debug: bool) -> None:\n global cache_url, force_download, lean_upgrade, debug\n cache_url = from_url\n force_download = force\n lean_upgrade = not noleanup\n debug = python_debug","title":""},{"docid":"7d854b9b8c5c46517fdc2bc891136328","score":"0.48873034","text":"def update():\n local('git pull origin master')\n local('pip install -r requirements.txt')\n\n kwarg = _check_env()\n with shell_env(**kwarg):\n local('python pillbox-engine/manage.py migrate')\n local('python pillbox-engine/manage.py collectstatic --noinput')\n local('python pillbox-engine/manage.py makeusers')","title":""},{"docid":"c7ea35b864619c46883aca7fd214a868","score":"0.48757318","text":"def main():\n args = docopt(__doc__, version=VERSION)\n\n config = ConfigParser(interpolation=ExtendedInterpolation())\n # Set defaults for all sections\n config.read_dict(copy.copy(DEFAULT_OPTIONS))\n # Load configuration from a file. NOTE: ConfigParser doesn't warn if user\n # sets a filename which doesn't exist, in this case defaults will be used.\n try:\n config.read(args['--file'])\n except ParsingError as exc:\n sys.exit(str(exc))\n\n if args['--print']:\n for section in sorted(DEFAULT_OPTIONS):\n if section == 'pull' or section == 'DEFAULT':\n print(\"[{}]\".format(section))\n for key, value in sorted(DEFAULT_OPTIONS[section].items()):\n print(\"{k} = {v}\".format(k=key, v=value))\n print()\n sys.exit(0)\n if args['--print-conf']:\n for section in sorted(config):\n if section == 'pull' or section == 'DEFAULT':\n print(\"[{}]\".format(section))\n for key, value in sorted(config[section].items()):\n print(\"{k} = {v}\".format(k=key, v=value))\n print()\n sys.exit(0)\n\n try:\n configuration_check(config, 'pull')\n except ValueError as exc:\n sys.exit(str(exc))\n\n loglevel = (config.get('pull', 'loglevel') # pylint: disable=no-member\n .upper())\n log.setLevel(getattr(logging, loglevel, None))\n\n log.info('haproxystats-pull %s version started', VERSION)\n # Setup our event loop\n loop = asyncio.get_event_loop()\n executor = ThreadPoolExecutor(max_workers=config.getint('pull',\n 'workers'))\n # Register shutdown to signals\n\n def shutdown(signalname):\n \"\"\"Perform a clean shutdown.\n\n Arguments:\n signalname (str): Signal name\n \"\"\"\n tasks_running = False\n log.info('received %s', signalname)\n\n for task in asyncio.Task.all_tasks():\n if not task.done():\n tasks_running = True\n log.info('cancelling %s task', task)\n task.cancel()\n\n if not tasks_running:\n log.info('no tasks were running when %s signal received', signal)\n log.info('waiting for threads to finish any pending IO tasks')\n executor.shutdown(wait=True)\n sys.exit(0)\n\n loop.add_signal_handler(signal.SIGHUP, partial(shutdown, 'SIGHUP'))\n loop.add_signal_handler(signal.SIGTERM, partial(shutdown, 'SIGTERM'))\n\n # a temporary directory to store fetched data\n tmp_dst_dir = config['pull']['tmp-dst-dir']\n # a permanent directory to move data from the temporary directory. Data are\n # picked up by the process daemon from that directory.\n dst_dir = config['pull']['dst-dir']\n for directory in dst_dir, tmp_dst_dir:\n try:\n os.makedirs(directory)\n except OSError as exc:\n # errno 17 => file exists\n if exc.errno != 17:\n sys.exit(\"failed to make directory {d}:{e}\"\n .format(d=directory, e=exc))\n supervisor(loop, config, executor)","title":""},{"docid":"d97a1759419d9157e06a0870ab250ba3","score":"0.48682088","text":"def git_pull(options):\n check_call([\"git\", \"pull\"])","title":""},{"docid":"bd0e90d91aff718268b0a1d54259680d","score":"0.4868125","text":"def scrape_github():\n githubs = [\"/bitshares/bitshares-ui/master/app/api/apiConfig.js\"]\n if not GITHUB_MASTER:\n githubs += Nodes.github()\n # scrape from github\n urls = []\n http = \"https://\"\n raw = \"raw.githubusercontent.com\"\n if PROXY_GITHUB: # text proxy\n uri = http + PROXY + raw\n else:\n uri = http + raw\n for _, git in enumerate(githubs):\n url = uri + git\n urls.append(url)\n print(\"scraping github for Bitshares nodes...\")\n validated = []\n repos = []\n for url in urls:\n attempts = 3\n while attempts > 0:\n try:\n raw = requests.get(url, timeout=(6, 30)).text\n ret = validate(parse(clean(raw)))\n if ret:\n repos.append(url)\n del raw\n repo = url.replace(uri, \"\")\n repo = \"/\".join(repo.split(\"/\", 3)[:3])\n print((\"found %s nodes at %s\" % (len(ret), repo)))\n validated += ret\n attempts = 0\n except BaseException:\n print((\"failed to connect to %s\" % url))\n attempts -= 1\n return validated, repos","title":""},{"docid":"328613caf0318b587caf1cd286245d83","score":"0.48634103","text":"def main():\n err_print(\"Booting up web driver...\")\n beige_books = webdriver.Chrome(WEBDRIVER_PATH) # Webdriver loaded from folder in case PATH not configured\n err_print(\"Loading main archive page...\")\n beige_books.get(ARCHIVE_URL)\n err_print(\"Loading XPATH and ID identifiers...\")\n search_button = beige_books.find_element_by_xpath(SEARCH_BUTTON_XPATH) # Easy button pressing\n selects = Select(beige_books.find_element_by_name(SELECT_ID)).options # Grab all available options\n err_print(\"Selecting all possible years...\\n\")\n reports = []\n curr_year = CURRENT_YEAR\n\n while curr_year >= END_YEAR: # While loop used becuase 'selects' change on every iteration and useful for indexing\n year = selects[index_from_year(curr_year)] # Loads the year into memory\n err_print(\"Grabbing all reports for \" + str(curr_year) + \"...\")\n year.click()\n search_button.click()\n links = beige_books.find_elements_by_xpath(HTML_LINK_XPATH) # Grabs all hyperlinks\n\n for link in links:\n link_html = link.get_attribute(\"href\") # Converts Selenium object to string URLs\n if contains(URL_IDENTIFIER + str(curr_year), link_html) and link_html not in reports: # Checks for dupes\n err_print(\"Grabbed \" + link_html + \"!\")\n reports.append(link_html)\n\n err_print(\"Finished \" + str(curr_year) + \"!\")\n curr_year -= 1 # Since the current year is the first item on the select, deincrement after every loop\n err_print(\"Refreshing parameters for \" + str(curr_year) + \"...\\n\")\n search_button = beige_books.find_element_by_xpath(SEARCH_BUTTON_XPATH) # Refresh needed because site changed\n selects = Select(beige_books.find_element_by_name(SELECT_ID)).options\n\n err_print(\"Closing web driver...\")\n beige_books.close() # Exits out of the website\n err_print(\"End of scrape!\")\n\n # START OF PARSING #\n\n err_print(\"Instantiating new data table...\")\n final_table = new_table() # Creates a new table to store data\n err_print(\"Parsing reports to table...\\n\")\n for report in reports: # Reads the 'text content' of all the scraped files and parses them into the final table\n err_print(\"Extracting data from \" + report + \"...\")\n site = soupify(report)\n final_table = parse(site, final_table)\n save(final_table) # Saves the table into a CSV\n err_print(\"End of parse!\")","title":""},{"docid":"2dfd07bb8af2396a3d18e3aacc82fb84","score":"0.48553193","text":"def api_github_message():\n if rq.headers['Content-Type'] == 'application/json':\n my_info = json.dumps(rq.json)\n payload = json.loads(my_info)\n if not payload['action'] == 'closed':\n url = payload['pull_request']['url']\n url = url + '/files'\n comment_url = payload['pull_request']['comments_url']\n r = requests.get(url)\n a = r.json()\n #print(a)\n print(\"Received Pull Request for %d Changed Files\"%(len(a)))\n print(\"Initializing Linting Process\")\n i = 0\n x = []\n filelist = []\n for i in range(len(a)):\n raw = (a[i]['raw_url'])\n #print(raw)\n filename = (a[i]['filename']).split(\"/\")\n file = filename[-1]\n\n re = urllib.request.urlopen('%s' % raw)\n # returned_value = os.system(\"start \\\"\\\" %s\" %raw)\n data = re.read()\n dst = open(\"%s\" % file, \"wb\")\n dst.write(data)\n dst = open(\"%s\" % file, \"r\")\n print(\"Checking syntax errors for File: %d. %s\" %(i+1, file))\n\n file_ext = (file).split(\".\")\n print(\"File extension of the file: %s\" % file_ext[-1])\n if file_ext[-1] == 'py':\n print(\"Cloned file: %s deleted\" % file)\n print(\"Linting file : %s\" % file)\n cmd = 'pycodestyle %s' % file\n process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE).communicate(0)\n syntax_check = str(process)[2:-1]\n # p = str(p)\n # p = p[2:-1]\n if process == \"b''\":\n q = \"No syntax error found\"\n print(q)\n\n else:\n print(\"Syntax error found: %s\" % syntax_check)\n q = \"Syntax error found\"\n filelist.append(file)\n\n dst.close()\n os.remove(\"%s\" % file)\n s = \"%s %s\" % (q, syntax_check)\n else:\n print(\"No linter found for %s extension\" % file_ext[-1])\n s = \"No linter found for %s extension\" % file_ext[-1]\n x.append(s)\n i = i + 1\n if x is None:\n comment_body = \"Git Assist Prediction : No Syntax Errors Found.\"\n else:\n filelist = list( dict.fromkeys(filelist) )\n filelist = ', '.join(filelist)\n comment_body = \"Git Assist Prediction: To Be Rejected. Syntax Errors found in the following files: %s.\"%filelist\n\n headers = {'Content-Type': 'application/json'}\n myurl = '%s' % comment_url\n post_comment_data = '{\\\"body\\\":\\\"%s\\\"}' % comment_body\n post_comment_json = json.loads(post_comment_data)\n response = requests.post(myurl, auth=(\"iamthebj\", \"Kj9158852858#\"), headers=headers,\n json=post_comment_json)\n if response.status_code == 201:\n print(\"Successfully posted a comment\")\n else:\n print(\"Failed to post a response with http status code : \", response.status_code)\n return \"%s\" % x\n else:\n print(\"Closed pull request\")\n return \"Closed pull request\"","title":""},{"docid":"2e1c079cc7c16bd70c74ca9e25b4115f","score":"0.48452666","text":"def post_pr_merge_sync(context):\n context.run(\"git switch main\")\n context.run(\"git pull\")","title":""},{"docid":"aedbd6deb7f263ed35508c635b58f685","score":"0.4842096","text":"def _cleanup_and_commit(self, updates):\n self._clean_up_web_root()\n self._git_apply_changes(updates)","title":""},{"docid":"fbbf83dd83765c46feafc7adae9a7847","score":"0.48368776","text":"def main():\n _, repo_root = commands.getstatusoutput('git rev-parse --show-toplevel')\n checks = imp.load_source('run_checks',\n repo_root + \"/devtools/run_checks.py\")\n exit(checks.main())","title":""},{"docid":"d73ce5dde2baa9c8c010fc38457ea73f","score":"0.4835393","text":"def main():\n current_build_number = os.environ.get(\"BUILDKITE_BUILD_NUMBER\", None)\n if not current_build_number:\n raise Exception(\"Not running inside Buildkite\")\n current_build_number = int(current_build_number)\n\n for _ in range(5):\n latest_generation, latest_build_number = latest_generation_and_build_number()\n\n if current_build_number <= latest_build_number:\n eprint(\n (\n \"Current build '{0}' is not newer than latest published '{1}'. \"\n + \"Skipping publishing of binaries.\"\n ).format(current_build_number, latest_build_number)\n )\n break\n\n try:\n try_publish_binaries(current_build_number, latest_generation)\n except BinaryUploadRaceException:\n # Retry.\n continue\n\n eprint(\n \"Successfully updated '{0}' to binaries from build {1}.\".format(\n bazelci_builds_metadata_url(), current_build_number\n )\n )\n break\n else:\n raise Exception(\"Could not publish binaries, ran out of attempts.\")","title":""},{"docid":"2929748d00c944809dadcc070aad7fb9","score":"0.48351246","text":"def hunt(urls, threads, exclude_flags, include_flags, interesting_extensions, interesting_files, interesting_keywords,\n stdout_flags, progress_enabled, timeout, max_depth, not_follow_subdomains, exclude_sources, proxies, delay,\n not_allow_redirects, limit, to_file, user_agent, cookies, headers):\n if exclude_flags and include_flags:\n raise BadOptionUsage('--exclude-flags and --include-flags are mutually exclusive.')\n welcome()\n urls = flat_list(urls)\n proxies = multiplier_args(proxies)\n if not urls:\n click.echo('•_•) OOPS! Add urls to analyze.\\nFor example: dirhunt http://domain/path\\n\\n'\n 'Need help? Then use dirhunt --help', err=True)\n return\n exclude_flags, include_flags = flags_range(exclude_flags), flags_range(include_flags)\n progress_enabled = (sys.stdout.isatty() or sys.stderr.isatty()) if progress_enabled is None else progress_enabled\n crawler = Crawler(max_workers=threads, interesting_extensions=interesting_extensions,\n interesting_files=interesting_files, interesting_keywords=interesting_keywords,\n std=sys.stdout if sys.stdout.isatty() else sys.stderr,\n progress_enabled=progress_enabled, timeout=timeout, depth=max_depth,\n not_follow_subdomains=not_follow_subdomains, exclude_sources=exclude_sources,\n not_allow_redirects=not_allow_redirects, proxies=proxies, delay=delay, limit=limit,\n to_file=to_file, user_agent=user_agent, cookies=cookies, headers=headers)\n crawler.add_init_urls(*urls)\n if os.path.exists(crawler.get_resume_file()):\n click.echo('Resuming the previous program execution...')\n try:\n crawler.resume(crawler.get_resume_file())\n except IncompatibleVersionError as e:\n click.echo(e)\n while True:\n choice = catch_keyboard_interrupt_choices(crawler.print_results, ['abort', 'continue', 'results'], 'a') \\\n (set(exclude_flags), set(include_flags))\n if choice == 'a':\n crawler.close(True)\n click.echo('Created resume file \"{}\". Run again using the same parameters to resume.'.format(\n crawler.get_resume_file())\n )\n return\n elif choice == 'c':\n crawler.restart()\n continue\n else:\n break\n crawler.print_urls_info()\n if not sys.stdout.isatty():\n output_urls(crawler, stdout_flags)\n if to_file:\n crawler.create_report(to_file)\n if not to_file and os.path.exists(crawler.get_resume_file()):\n # The resume file exists. Deleting...\n os.remove(crawler.get_resume_file())","title":""},{"docid":"3f155df21b78df33d691f99311b89722","score":"0.4811949","text":"def main():\n logging.basicConfig(\n format='[%(asctime)s] %(levelname)s -- %(message)s',\n level=logging.DEBUG)\n\n parser = argparse.ArgumentParser(description='Synchronizes a github repository with a local repository.')\n parser.add_argument('--git-url', help='Url of the repo to sync', required=True)\n parser.add_argument('--branch-name', default='master', help='Branch of repo to sync')\n parser.add_argument('--repo-dir', default='./', help='Path to sync to')\n args = parser.parse_args()\n\n for line in GitAutoSync(\n args.git_url,\n args.branch_name,\n args.repo_dir\n ).pull_from_remote():\n print(line)","title":""},{"docid":"68507aa3d67f277ccaab4c7150eec540","score":"0.48108003","text":"def install_core():\n commands = \"\"\"\nyum -y update\nyum -y groupinstall \"Development Tools\"\nyum install -y gvim xterm\nyum install -y python-pip python-virtualenv\npip install --upgrade pip\npip install virtualenvwrapper\npip install --upgrade google-api-python-client oauth2client pyasn1 --ignore-installed requests\npip install --no-cache-dir -U crcmod\n \"\"\".strip().split('\\n')\n run_commands(commands)","title":""},{"docid":"4db17b04aa500fbb22d7910028433174","score":"0.48068625","text":"def setUp(self):\n self.driver = webdriver.Chrome(executable_path=\"E:\\chrome driver\\chromedriver_win32\\chromedriver.exe\")\n self.driver.maximize_window()\n self.base_url = \"https://github.com/\"","title":""},{"docid":"61912d4944a8c6a13c140669b93d7114","score":"0.48045424","text":"def main(local):\n\n rewrite_baseurl(local)\n rewrite_manifest(local)","title":""},{"docid":"357f5d3b03b1e434c6e649ebd2e1572a","score":"0.4802946","text":"def start():\n\n pr_builder = PullRequestBuilder()\n events.dispatcher.register_target(PullRequestListener(pr_builder))\n events.dispatcher.register_target(ManualPullRequestListener(pr_builder))\n events.dispatcher.register_target(IRCRebuildListener(pr_builder))\n utils.DaemonThread(target=pr_builder.run).start()\n\n collector = BuildStatusCollector()\n events.dispatcher.register_target(BBHookListener(collector))\n utils.DaemonThread(target=collector.run).start()","title":""},{"docid":"86f1f4f5cfe273d4088f62ee618ed9a0","score":"0.4802121","text":"def fetch(args):\n\n # figure out which repos/revs we're hoping to update.\n # None is our internal, temp keyword representing the LATEST possible\n # rev.\n user_repo_revs = {} # repo -> version\n repo_paths = map(lambda x: x.local_path, REPOS)\n args_queue = collections.deque(args[:])\n\n while len(args_queue) > 0:\n current_arg = args_queue.popleft()\n\n # If the user provides repo revisions, it MUST be a specific repo.\n if current_arg in repo_paths:\n # the user might provide a revision.\n # It's a rev if it's not a repo.\n try:\n possible_rev = args_queue.popleft()\n except IndexError:\n # When no other args after the repo\n user_repo_revs[current_arg] = None\n continue\n\n if possible_rev in repo_paths:\n # then it's not a revision, it's a repo. put it back.\n # Also, assume user wants the repo we're currently working with\n # to be updated to the tip OR whatever.\n user_repo_revs[current_arg] = None\n args_queue.appendleft(possible_rev)\n continue\n elif possible_rev in ['-r', '--rev']:\n requested_rev = args_queue.popleft()\n user_repo_revs[current_arg] = requested_rev\n else:\n print \"ERROR: unclear arg %s\" % possible_rev\n return\n\n # determine which groupings the user wants to operate on.\n # example: `src` would represent all repos under src/\n # example: `data` would represent all repos under data/\n # example: `src/pygeoprocessing` would represent the pygeoprocessing repo\n repos = set([])\n for argument in args:\n if not argument.startswith('-'):\n repos.add(argument)\n\n def _user_requested_repo(local_repo_path):\n \"\"\"\n Check if the user requested this repository.\n Does so by checking prefixes provided by the user.\n\n Arguments:\n local_repo_path (string): the path to the local repository\n relative to the CWD. (example: src/pygeoprocessing)\n\n Returns:\n Boolean: Whether the user did request this repo.\n \"\"\"\n # check that the user wants to update this repo\n for user_arg_prefix in repos:\n if local_repo_path.startswith(user_arg_prefix):\n return True\n return False\n\n for repo in REPOS:\n LOGGER.debug('Checking %s', repo.local_path)\n\n # If the user did not request this repo AND the user didn't want to\n # update everything (by specifying no positional args), skip this repo.\n if not _user_requested_repo(repo.local_path) and len(repos) > 0:\n continue\n\n # does repo exist? If not, clone it.\n if not repo.ischeckedout():\n repo.clone()\n else:\n LOGGER.debug('Repository %s exists', repo.local_path)\n\n # is repo up-to-date? If not, update it.\n # If the user specified a target revision, use that instead.\n try:\n target_rev = user_repo_revs[repo.local_path]\n if target_rev is None:\n raise KeyError\n except KeyError:\n try:\n target_rev = repo.tracked_version()\n except KeyError:\n print 'ERROR: repo not tracked in versions.json: %s' % repo.local_path\n return 1\n\n repo.pull()\n repo.update(target_rev)","title":""},{"docid":"a4a68f02b159b73a970b71c3bc12ac51","score":"0.4798461","text":"def test_pull():","title":""},{"docid":"d28ddac3c4d5c5cc0337f9f01ad8552c","score":"0.4795856","text":"def needs_pull(self):\n for remote in self.repo.remotes:\n remote.fetch()\n\n for _ in self.repo.iter_commits('master..origin/master'):\n logger.info(\"Repo %s needs update\", self.repo_name)\n return True\n return False","title":""},{"docid":"fbfd37f030eace6d5d07ff861498329b","score":"0.47957903","text":"def run_updates(self):\n updates = {}\n try:\n sites = self.get_sites_to_update()\n except DrupdatesError as update_status_error:\n raise DrupdatesUpdateError(20, update_status_error)\n if not sites['count']:\n return updates\n else:\n sites.pop('count')\n # Note: call Drush.call() without site alias as alias comes after dd argument.\n drush_dd = Drush.call(['dd', '@drupdates.' + self._site_name])\n self.site_web_root = drush_dd[0]\n # Create seperate commits for each project (ie module/theme)\n one_commit_per_project = self.settings.get('oneCommitPerProject')\n # Iterate through the site/sub-sites and perform updates, update files etc...\n sites_copy = copy.copy(sites)\n for site, data in sites.items():\n if 'modules' not in data:\n sites_copy.pop(site)\n continue\n modules = copy.copy(data['modules'])\n x = 0\n for project, descriptions in data['modules'].items():\n if self.settings.get('useMakeFile'):\n self.update_make_file(project, descriptions['current'], descriptions['candidate'])\n if one_commit_per_project:\n if x:\n build = Sitebuild(self._site_name, self.ssh, self.working_dir)\n build.build()\n self._update_code(site, [project])\n modules.pop(project)\n updates = self._build_commit_message(sites_copy, site, project)\n self._cleanup_and_commit(updates)\n x += 1\n if self.settings.get('buildSource') == 'make' and self.settings.get('useMakeFile'):\n self.utilities.make_site(self._site_name, self.site_dir)\n elif len(modules):\n self._update_code(site, modules.keys())\n if not one_commit_per_project:\n updates = self._build_commit_message(sites_copy)\n self._cleanup_and_commit(updates)\n return updates","title":""},{"docid":"b2e9340ff3341af2f0de2b8361ab1366","score":"0.47924095","text":"def github_list_pull_requests(urls, numbers_only=False):\n pulls = github_get_pull_request_all(urls)\n formatted_pulls = []\n print \"Total pull count\", len(pulls)\n sys.stdout.write(\"Processing pulls...\")\n for pull in pulls:\n n = pull[\"number\"]\n sys.stdout.write(\" %d\" % n)\n sys.stdout.flush()\n pull_info = github_get_pull_request(urls, n)\n if not pull_info:\n # Pull request is an issue\n continue\n mergeable = pull_info[\"mergeable\"]\n if pull[\"head\"][\"repo\"]:\n repo = pull[\"head\"][\"repo\"][\"html_url\"]\n else:\n repo = None\n branch = pull[\"head\"][\"ref\"]\n created_at = pull[\"created_at\"]\n created_at = time.strptime(created_at, \"%Y-%m-%dT%H:%M:%SZ\")\n created_at = time.mktime(created_at)\n username = pull[\"user\"][\"login\"]\n user_info = github_get_user_info(urls, username)\n author = \"\\\"%s\\\" <%s>\" % (user_info.get(\"name\", \"unknown\"),\n user_info.get(\"email\", \"\"))\n branch_against = pull[\"base\"][\"ref\"]\n formatted_pulls.append({\n 'created_at': created_at,\n 'n': n,\n 'repo': repo,\n 'branch': branch,\n 'author': author,\n 'mergeable': mergeable,\n 'branch_against': branch_against,\n })\n formatted_pulls.sort(key=lambda x: x['created_at'])\n print \"\\nPatches that cannot be merged without conflicts:\"\n nonmergeable = []\n for pull in formatted_pulls:\n if pull['mergeable']:\n continue\n nonmergeable.append(int(pull['n']))\n if numbers_only:\n print pull['n'],\n else:\n print \"#%03d: %s %s (against %s)\" % (pull['n'], pull['repo'], pull['branch'], pull['branch_against'])\n print unicode(\" Author : %s\" % pull['author']).encode('utf8')\n print \" Date : %s\" % time.ctime(pull['created_at'])\n if numbers_only:\n print\n print\n print \"-\"*80\n print \"Patches that can be merged without conflicts:\"\n mergeable_list = []\n for pull in formatted_pulls:\n if not pull['mergeable']:\n continue\n mergeable_list.append(int(pull['n']))\n if numbers_only:\n print pull['n'],\n else:\n print \"#%03d: %s %s (against %s)\" % (pull['n'], pull['repo'], pull['branch'], pull['branch_against'])\n print unicode(\" Author : %s\" % pull['author']).encode('utf8')\n print \" Date : %s\" % time.ctime(pull['created_at'])\n if numbers_only:\n print\n print\n return nonmergeable, mergeable_list","title":""},{"docid":"f149b4633005e2971d1cf63158e5e81c","score":"0.47882986","text":"def update_uwsgitool():\n url = 'https://github.com/kyan001/PyMyApps/raw/master/UwsgiTool/uwsgiTool.py'\n if cct.update_file(__file__, url):\n cct.run_cmd('{py} \"{f}\"'.format(py=cct.get_py_cmd(), f=__file__))\n cit.bye(0)","title":""},{"docid":"de90c722d9dab0c46969c302e5d83152","score":"0.4787962","text":"def update():\n update_all_hometpl()\n update_ssh()\n update_debian()\n update_crontab()","title":""},{"docid":"1d0c4a84678b5cf973e2b74fe99fd3f1","score":"0.47873887","text":"def download_old_versions(d):\n\n content_url = d.getVar('SWUPD_CONTENT_BUILD_URL', True)\n version_url = d.getVar('SWUPD_VERSION_BUILD_URL', True)\n current_format = int(d.getVar('SWUPD_FORMAT', True))\n deploy_dir = d.getVar('DEPLOY_DIR_SWUPD', True)\n www_dir = os.path.join(deploy_dir, 'www')\n\n if not content_url or not version_url:\n bb.warn('SWUPD_CONTENT_BUILD_URL and/or SWUPD_VERSION_BUILD_URL not set, skipping download of old versions for the initial build of a swupd update stream.')\n return\n\n # Avoid double // in path. At least twisted is sensitive to that.\n content_url = content_url.rstrip('/')\n version_url = version_url.rstrip('/')\n\n # Set up env variables with proxy information for use in urllib.\n export_proxies(d)\n\n # Find latest version for each of the older formats.\n # For now we ignore the released milestones and go\n # directly to the URL with all builds. The information\n # about milestones may be relevant for determining\n # how format changes need to be handled.\n latest_versions = {}\n for format in range(3, current_format + 1):\n try:\n url = '%s/version/format%d/latest' % (version_url, format)\n response = urllib.request.urlopen(url)\n version = int(response.read())\n latest_versions[format] = version\n formatdir = os.path.join(www_dir, 'version', 'format%d' % format)\n bb.utils.mkdirhier(formatdir)\n with open(os.path.join(formatdir, 'latest'), 'w') as latest:\n latest.write(str(version))\n except urllib.error.HTTPError as http_error:\n if http_error.code == 404:\n bb.debug(1, '%s does not exist, skipping that format' % url)\n else:\n raise\n except urllib.error.URLError as url_error:\n # Happens for file:// URLs.\n if isinstance(url_error.reason, OSError) and url_error.reason.errno == errno.ENOENT:\n bb.debug(1, '%s does not exist, skipping that format' % url)\n else:\n raise\n\n # Now get the Manifests of the latest versions and the\n # versions we are supposed to provide a delta for, as a starting point.\n # In addition, we also need Manifests that provide files reused by\n # these initial set of Manifests or get referenced by them.\n #\n # There's no integrity checking for the files. bsdtar is\n # expected to detect corrupted archives and https is expected\n # to protect against man-in-the-middle attacks.\n pending_versions = set(latest_versions.values())\n pending_versions.update([int(x) for x in d.getVar('SWUPD_DELTAPACK_VERSIONS', True).split()])\n fetched_versions = set([0])\n while pending_versions:\n version = pending_versions.pop()\n sub_versions = set()\n sub_versions.update(download_manifests(content_url, version,\n 'MoM',\n os.path.join(www_dir, str(version))))\n sub_versions.update(download_manifests(content_url, version,\n 'full',\n os.path.join(www_dir, str(version))))\n fetched_versions.add(version)\n pending_versions.update(sub_versions.difference(fetched_versions))\n\n latest_version_file = os.path.join(deploy_dir, 'image', 'latest.version')\n if not os.path.exists(latest_version_file):\n # We located information about latest version from online www update repo.\n # Now use that to determine what we are updating from. Doing this here\n # instead of swupd-image.bbclass has the advantage that we can do some\n # sanity checking very early in a build.\n #\n # Building a proper update makes swupd_create_fullfiles\n # a lot faster because it allows reusing existing, unmodified files.\n # Saves a lot of space, too, because the new Manifest files then merely\n # point to the older version (no entry in ${DEPLOY_DIR_SWUPD}/www/${OS_VERSION}/files,\n # not even a link).\n if not latest_versions:\n bb.fatal(\"%s does not exist and no information was found under SWUPD_CONTENT_BUILD_URL %s, cannot proceed without information about the previous build. When building the initial version, unset SWUPD_VERSION_BUILD_URL and SWUPD_CONTENT_BUILD_URL to proceed.\" % (latest_version_file, content_url))\n latest = sorted(latest_versions.values())[-1]\n bb.debug(2, \"Setting %d in latest.version file\" % latest)\n with open(latest_version_file, 'w') as f:\n f.write(str(latest))","title":""},{"docid":"46cf41a81a9d1dc11b2cb58237a7aa27","score":"0.47735083","text":"async def update(self, ctx):\n\t\tlog.info(emb(f'Git pull called, Caller:[{ctx.author.id}][{ctx.author.name}]'))\n\t\tbash_cmd = \"git pull\"\n\t\tprocess = subprocess.Popen(bash_cmd.split(), stdout=subprocess.PIPE)\n\t\toutput, error = process.communicate()\n\t\toutput = output.decode(\"utf-8\")\n\t\tlog.info(output)\n\t\tawait ctx.send(emb(output))\n\n\t\tif error is not None:\n\t\t\tlog.error(error)","title":""},{"docid":"66a998ae0a42f49efc4cc345bfaf3982","score":"0.4769368","text":"def graball(self):\n\n\t\tif self.debug:\n\t\t\tif self.debounce():\n\t\t\t\tfor uri in self.urilist:\n\t\t\t\t\tself.grabone(uri)\n\t\t\t\t\t\n\t\t\telse:\n\t\t\t\tprint('Not fetching because we fetched within the last second.')\n\n\t\t\tprint('Waiting...')\n\n\t\telse:\n\t\t\tif self.debounce():\n\t\t\t\tname = str(time.time()).split('.')[0] + '.html'\n\t\t\t\tfor uri in self.urilist:\n\t\t\t\t\tbasepath = op.normpath(self.outdirname + '/' + WebGetter.pathfromuri(uri) + '/')\n\n\t\t\t\t\treq = self.http.req(uri)\n\t\t\t\t\tflat = Inliner(req, uri, self.http).get()\n\n\t\t\t\t\tif self.checkhash(basepath, flat):\n\t\t\t\t\t\turidir = WebGetter.pathfromuri(uri)\n\t\t\t\t\t\tpath = self.outdirname + '/' + uridir + '/' + name\n\t\t\t\t\t\tfhandle = open(op.normpath(path), 'wb')\n\t\t\t\t\t\tfhandle.write(flat.encode('utf-8'))\n\t\t\t\t\t\tfhandle.close()","title":""},{"docid":"74700b3ecfc9dd64c909eb44a17d2948","score":"0.47403857","text":"def main():\n with open(os.getenv('GITHUB_EVENT_PATH')) as f:\n event_info = json.loads(f.read())\n\n repo = setup_git(event_info['pull_request']['merge_commit_sha'])\n\n comment_body = ''\n new_tag = None\n if len(repo.tags) == 0:\n new_tag = 'v1.0.0'\n else:\n try:\n new_tag = semver_bump(repo)\n except ValueError:\n comment_body = 'latest tag does not conform to semver ([v]?MAJOR.MINOR.PATCH), failed to bump version'\n\n if new_tag is not None:\n create_and_push_tag(repo, event_info['pull_request']['merge_commit_sha'], new_tag)\n comment_body = f\"This PR has now been tagged as [{new_tag}](https://github.com/{os.getenv('GITHUB_REPOSITORY')}/releases/tag/{new_tag})\"\n\n comment_on_pr(event_info['number'], comment_body)","title":""},{"docid":"e277e38259195fe86b0f884d8c056053","score":"0.47390124","text":"def run(self) -> None:\n try:\n resp = requests.get(self.url, auth=self.auth, timeout=self.timeout)\n if not resp.ok:\n logger.info(\n f\"{self.prog_name} : unable to fetch {self.url} : {resp.text}\"\n ) # pylint: disable=logging-fstring-interpolation\n return\n\n parser = MyParser()\n parser.feed(resp.text)\n if not parser.output_list:\n logger.info(\n f\"{self.prog_name} : no packages links detected in {resp.text}\"\n ) # pylint: disable=logging-fstring-interpolation\n return\n\n last_link = parser.output_list[-1]\n last_version_matches = re.search(r\"(?:(\\d+\\.[.\\d]*\\d+))\", last_link)\n if not last_version_matches:\n logger.info(\n f\"{self.prog_name} : no version found in string {last_link}\"\n ) # pylint: disable=logging-fstring-interpolation\n return\n\n last_version = last_version_matches.group(1)\n\n last_version_info = Version.parse(last_version)\n current_version_info = Version.parse(self.current_version)\n if current_version_info < last_version_info:\n pip_extra_index_url = \"\"\n pipx_extra_index_url = \"\"\n if self.domain != DEFAULT_PYPI:\n pip_extra_index_url = f\"--extra-index-url=https://{self.domain} \"\n pipx_extra_index_url = f\"--index-url=https://{self.domain} \"\n self.new_version_warning = click.style(\n f\"\"\"\n{self.prog_name} : new version {last_version} available (current version: {self.current_version})\nupgrade command :\n pip3 install -U {pip_extra_index_url}{self.prog_name}\nOR pipx upgrade {pipx_extra_index_url}{self.prog_name} (preferred)\"\"\",\n fg=\"bright_blue\",\n )\n except Exception as error: # pylint: disable=broad-except\n logger.exception(error)","title":""},{"docid":"440da2e765acc69b4bac536335f4576a","score":"0.47359017","text":"def loadRoutine2():\n subprocess.call([\"notify-send\", \"i-am-bored\", \"Enjoy Terence Tao's blog today.\"])\n subprocess.call(\"xdg-open https://terrytao.wordpress.com/\", shell=True)","title":""},{"docid":"6b70dbbf50edc573e553f11b9ece7e7b","score":"0.47322673","text":"def update():\n result = run(\"yum check-update --disablerepo='*artifactory' %s\" % (env.excludes), pty=True)\n if result.return_code == 100:\n \"\"\"Run yum update with exclusions\"\"\"\n print \"<font color=yellow>%s needs updating.</font>\" % env.host\n print (\"These are the excludes: %s\") % (env.excludes)\n sudo(\"yum -y update --disablerepo='*artifactory' %s\" % (env.excludes), pty=True)\n elif result.return_code == 0:\n print \"<font color=red>%s does not seem to need any updates, skipping...</font>\" % env.host\n elif result.return_code == 1:\n print \"<font color=red>%s returned an error</font>\" % env.host","title":""},{"docid":"32c4eaddd5f9cef8039f530288385ffa","score":"0.47188002","text":"def hooks() -> None:\n proj().setup_git_hooks()","title":""},{"docid":"aecca12f0a06a1025270e346d051060f","score":"0.4714615","text":"def run(self):\r\n if self.demands.refresh_metadata:\r\n self.base.cleanCli('expire-cache')\r\n if self.demands.sack_activation:\r\n lar = self.demands.available_repos\r\n self.base.fill_sack(load_system_repo='auto',\r\n load_available_repos=lar)\r\n self.base.plugins.run_sack()\r\n return self.command.run(self.base.extcmds)","title":""},{"docid":"07c0d3e63e045d105b0319685d9360df","score":"0.47138634","text":"def main():\n clear_api()\n build()","title":""},{"docid":"ac8878f32ef860b2ae5525c62ce21b59","score":"0.4711336","text":"def run():\n _rundocker('dev', env.docker_run_cmd)","title":""},{"docid":"af26926769b9981117de6d540f6de3d9","score":"0.46989152","text":"def patch():\n\tCLIENT_PATH = VENV_PATH + 'src/grimoirelab-perceval/perceval/client.py'\n\tclient_patch(CLIENT_PATH).patch()\n\t\n\tINDEX_LOCK_PATH = VENV_PATH + 'src/grimoirelab-elk/grimoire_elk/elastic.py'\n\tindex_lock_patch(INDEX_LOCK_PATH).patch()\n\t\n\tGITHUB_PATH = VENV_PATH + 'src/grimoirelab-perceval/perceval/backends/core/github.py'\n\tgithub_patch(GITHUB_PATH).patch()","title":""},{"docid":"0b2957d228db6b33144b45872f405ab4","score":"0.4696051","text":"def _pull_site_action(self, event):\n self._fetch_site()\n event.set_results({\"result\": \"site pulled\"})","title":""},{"docid":"4f48ce82039e338adfac5ef776ec719d","score":"0.46897972","text":"def pull_code(local_folder, urlrepo=\"https://github.com/di-unipi-socc/DockerFinder.git\", branch=\"dfcustom\" ):\n Repo.clone_from(urlrepo, local_folder , branch=branch, depth=1)","title":""},{"docid":"1b789dc76edef85501a6ba94a1737963","score":"0.4687412","text":"def _build_and_run_tests(self, cfg, update_step, bot_db, revision_hash,\n **kwargs):\n with_patch = 'With Patch' in kwargs.get('name') # pragma: no cover\n\n # We don't need to do a checkout if there's a patch applied, since that will\n # overwrite the local changes and potentially change the test results.\n if not with_patch: # pragma: no cover\n update_step = self._checkout_revision(update_step, bot_db, revision_hash)\n if not revision_hash:\n if update_step.presentation.properties:\n revision_hash = update_step.presentation.properties['got_revision']\n revision = build_state.BuildState(self, revision_hash, with_patch)\n # request build and wait for it only when the build is nonexistent\n if with_patch or not self._gsutil_file_exists(revision.build_file_path):\n revision.request_build()\n revision.wait_for()\n revision.download_build(update_step, bot_db)\n if self.m.chromium.c.TARGET_PLATFORM == 'android':\n self.m.chromium_android.adb_install_apk('ChromePublic.apk')\n\n return self._run_test(cfg, **kwargs)","title":""},{"docid":"4d428df469aebae71ed5bb779804e6fb","score":"0.46746722","text":"def github_get_pull_request_all():\n return json.load(urlopen(format_repo('http://github.com/api/v2/json/pulls/{repo}')))","title":""},{"docid":"6e84f59223e1d74711c47d74c4839735","score":"0.4663502","text":"def hotfix(url,products_dir='products'):\n with api.cd('%s/%s'%(api.env.path,products_dir)):\n with asbuildoutuser():\n #api.run(\"curl %s /tmp/hotfix.zip\"%url)\n #api.run(\"python -c \\\"import urllib; f=open('/tmp/hotfix.zip','w'); f.write(urllib.urlopen('%s').read()); f.close()\\\"\"%url)\n filename = os.path.basename(url)\n tmp = '/tmp/%s'%filename\n if not os.path.exists(tmp):\n f=open(tmp,'w')\n f.write(urllib.urlopen(url).read())\n f.close()\n api.put(tmp, tmp)\n try:\n api.run(\"unzip -o %s\"%tmp)\n except:\n api.run(\"\"\"python -c \"import zipfile;import urllib;import StringIO; zipfile.ZipFile(StringIO.StringIO(urllib.urlopen('%s').read())).extractall()\" \"\"\"%url)\n\n group = api.env['buildout-group']\n api.run(\"chgrp -R %s .\"%(group))\n api.run('rm %s'%tmp)","title":""},{"docid":"15ed65a9571e5c34eacf4adbba65e157","score":"0.46541414","text":"def run_downloader(self):\n cmd = '''\n source import-stx\n stx -d shell --no-tty -c \"downloader -b -s -B std,rt\"\n '''\n ret = run_cmd(cmd)\n log.info(\"Downloader return code %s\", ret.returncode)\n if ret.returncode != 0:\n raise Exception(\"Error while downloading dependencies\")","title":""},{"docid":"284b42169720f0c88e4a289815c5f73a","score":"0.46497148","text":"def git_pull():\n repo.git.pull('origin')","title":""},{"docid":"72ed090ea9933838a34b9b64766667f2","score":"0.46468872","text":"def main() -> None:\n parser = argparse.ArgumentParser(description=main.__doc__)\n parser.add_argument(\"url\")\n args = parser.parse_args()\n\n if platform.system() == \"Darwin\":\n subprocess.call((\"open\", args.url))\n else:\n subprocess.call((\"chromium\", \"--new-window\", args.url))","title":""},{"docid":"a182863158c0a8e227478cecf7a97071","score":"0.46411553","text":"def tools_upgrade(auth, ignore_apps=False, ignore_packages=False):\n from yunohost.app import app_upgrade\n\n failure = False\n\n # Retrieve interface\n is_api = True if msettings.get('interface') == 'api' else False\n\n if not ignore_packages:\n cache = apt.Cache()\n cache.open(None)\n cache.upgrade(True)\n\n # If API call\n if is_api:\n critical_packages = (\"moulinette\", \"moulinette-yunohost\",\n \"yunohost-admin\", \"yunohost-config-nginx\", \"ssowat\", \"python\")\n critical_upgrades = set()\n\n for pkg in cache.get_changes():\n if pkg.name in critical_packages:\n critical_upgrades.add(pkg.name)\n # Temporarily keep package ...\n pkg.mark_keep()\n # ... and set a hourly cron up to upgrade critical packages\n if critical_upgrades:\n msignals.display(m18n.n('packages_upgrade_critical_later',\n ', '.join(critical_upgrades)))\n with open('/etc/cron.d/yunohost-upgrade', 'w+') as f:\n f.write('00 * * * * root PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin apt-get install %s -y && rm -f /etc/cron.d/yunohost-upgrade\\n' % ' '.join(critical_upgrades))\n\n if cache.get_changes():\n msignals.display(m18n.n('upgrading_packages'))\n try:\n # Apply APT changes\n # TODO: Logs output for the API\n cache.commit(apt.progress.text.AcquireProgress(),\n apt.progress.base.InstallProgress())\n except Exception as e:\n failure = True\n logging.warning('unable to upgrade packages: %s' % str(e))\n msignals.display(m18n.n('packages_upgrade_failed'), 'error')\n else:\n msignals.display(m18n.n('done'))\n else:\n msignals.display(m18n.n('packages_no_upgrade'))\n\n if not ignore_apps:\n try:\n app_upgrade(auth)\n except Exception as e:\n failure = True\n logging.warning('unable to upgrade apps: %s' % str(e))\n msignals.display(m18n.n('app_upgrade_failed'), 'error')\n\n if not failure:\n msignals.display(m18n.n('system_upgraded'), 'success')\n\n # Return API logs if it is an API call\n if is_api:\n from yunohost.service import service_log\n return { \"log\": service_log('yunohost-api', number=\"100\").values()[0] }","title":""},{"docid":"bdcfd24289cc3e52c462f3c51bb4a95f","score":"0.46410316","text":"def release(repo, tag, site_uri, sync_uri=None):\n build(repo, tag, site_uri, None, False)\n if (sync_uri):\n sync_site(sync_uri, site_uri)\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)\n local('php /var/aegir/drush/drush.php @%s features-list' % site_uri)\n local('php /var/aegir/drush/drush.php --yes @%s features-revert-all' % site_uri)\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)","title":""},{"docid":"185f862b7ef72826b0a3aa296d6c3a6b","score":"0.46341094","text":"def update():\n local('sudo git pull')\n local('sudo pyclean .')\n\n local('sudo ../.env/bin/pip install -q -r requirements.txt')\n local('sudo ../.env/bin/yoyo-migrate -b apply migrations/')\n\n local('sudo service uwsgi restart')","title":""},{"docid":"3b3a297cc1961080794f9d33ab7ca04c","score":"0.46320468","text":"def fetch(ui, url, path, *args, **opts):\n if url.startswith(\"hg+ssh://\"):\n url = url[8:]\n else:\n url = url.replace(\"hg://\", \"http://\")\n try:\n cmd = locate_program(\"hg\", raise_error=True)\n except ValueError, e:\n raise VendorError(e)\n \n cmd += \" clone %s %s\" % (url, path)\n\n # exec cmd\n (child_stdin, child_stdout, child_stderr) = popen3(cmd)\n err = child_stderr.read()\n if ui.verbose >=2:\n ui.logger.info(child_stdout.read())\n if err:\n raise VendorError(str(err)) \n return","title":""},{"docid":"e1e484ccfccc8043c2262ab705e6cfd0","score":"0.4624194","text":"def deploy():\n clear_compiled_python_files()\n git_pull()\n update_requirements()\n run_migrations()\n collectstatic()\n reload_http()","title":""},{"docid":"e026b032939e72a71dba0fad5883f72a","score":"0.46217918","text":"def first_deploy():\n with cd(APPS_DIR):\n run(\"git clone %s\" % GITHUB_REPO)\n restart_apache()","title":""},{"docid":"4181664b821ae34410ae8654c86d22d7","score":"0.46207875","text":"def fetch_runner(pkgnames, preprocessed=False):\n abspkgs = []\n aurpkgs = []\n allpkgs = []\n try:\n if preprocessed:\n allpkgs = pkgnames\n pkgnames = [p.name for p in allpkgs]\n else:\n print(':: ' + _('Fetching package information...'))\n for pkgname in pkgnames:\n pkg = None\n try:\n pkg = pkgbuilder.utils.info([pkgname])[0]\n except IndexError:\n try:\n DS.log.info('{0} not found in the AUR, checking in '\n 'repositories'.format(pkgname))\n syncpkgs = []\n for j in [i.pkgcache for i in DS.pyc.get_syncdbs()]:\n syncpkgs.append(j)\n syncpkgs = functools.reduce(lambda x, y: x + y,\n syncpkgs)\n abspkg = pyalpm.find_satisfier(syncpkgs, pkgname)\n pkg = pkgbuilder.package.ABSPackage.from_pyalpm(abspkg)\n\n except AttributeError:\n pass\n allpkgs.append(pkg)\n if not pkg:\n raise pkgbuilder.exceptions.PackageNotFoundError(\n pkgname, 'fetch')\n\n for pkg in allpkgs:\n if pkg.is_abs:\n abspkgs.append(pkg)\n else:\n aurpkgs.append(pkg)\n\n if abspkgs:\n print(_(':: Retrieving packages from asp...'))\n pm = pkgbuilder.ui.Progress(len(abspkgs))\n for pkg in abspkgs:\n pm.msg(_('retrieving {0}').format(pkg.name), True)\n rc = asp_export(pkg)\n if rc > 0:\n raise pkgbuilder.exceptions.NetworkError(\n _('Failed to retieve {0} (from ASP).').format(\n pkg.name), source='asp', pkg=pkg, retcode=rc)\n\n if aurpkgs:\n print(_(':: Retrieving packages from aur...'))\n pm = pkgbuilder.ui.Progress(len(aurpkgs))\n for pkg in aurpkgs:\n pm.msg(_('cloning {0}').format(pkg.packagebase), True)\n clone(pkg.packagebase)\n\n print(_('Successfully fetched: ') + ' '.join(pkgnames))\n except pkgbuilder.exceptions.PBException as e:\n print(':: ERROR: ' + str(e.msg))\n exit(1)","title":""},{"docid":"bfa7b45ed98917204fc73742fbebb4b4","score":"0.461377","text":"def pull(remote: str = '') -> None:\n proj().pull(remote)","title":""},{"docid":"fb2783b935a8b5d9cf3b638aabb0f2fb","score":"0.46110207","text":"def download_dependencies(projects, dependencies, branch, project_root):\n for dependency in dependencies:\n project = find_project_for_name(projects, dependency)\n if project is None:\n print(f'Skipping unknown dependency {dependency}')\n continue\n directory = project_root + '/' + project['path']\n run_command(f\"git fetch {project['remote']['name']} {branch}\",\n directory)","title":""},{"docid":"0e4778835fa86fde2ce78706c57145f8","score":"0.46105772","text":"def setup_pristine_repositories():\n\n # these directories don't exist out of the box, so we may have to create them\n if not os.path.exists(main.general_wc_dir):\n os.makedirs(main.general_wc_dir)\n\n if not os.path.exists(main.general_repo_dir):\n os.makedirs(main.general_repo_dir) # this also creates all the intermediate dirs\n\n if not os.path.exists(main.other_dav_root_dir):\n os.makedirs(main.other_dav_root_dir)\n if not os.path.exists(main.non_dav_root_dir):\n os.makedirs(main.non_dav_root_dir)\n\n _setup_pristine_repo(main.greek_state,\n main.pristine_greek_repos_dir,\n main.greek_dump_dir,\n main.pristine_greek_repos_url)\n\n # NOTE: We don't use precooked trojan repositories.\n _setup_pristine_repo(main.trojan_state,\n main.pristine_trojan_repos_dir,\n main.trojan_dump_dir,\n main.pristine_trojan_repos_url,\n use_precooked=False)","title":""},{"docid":"409bcfa9d0c2d1018b31d578567188f6","score":"0.46065253","text":"def github_get_pull_request_all(urls):\n return keep_trying(lambda: _query(urls.pull_list_url), urllib2.URLError,\n \"get list of all pull requests\")","title":""},{"docid":"6523cd565db9f75ebfe8a2eb9b9e96cb","score":"0.4598307","text":"def sync(args=\"\"):\n\n spawn.process(f\"{config.aur_helper} --noconfirm -Sua {args}\")\n log.write(\"Synced AUR packages\")","title":""},{"docid":"55399e63a9c7850c3a7286935e807a07","score":"0.45883447","text":"def _download_chrome_drivers():\n # Mac OS X\n if platform.system() == 'Darwin':\n remote_file = driver_urls['chrome_os_x']\n elif platform.system() == 'Linux':\n remote_file = driver_urls['chrome_ubuntu']\n elif platform.system() == 'Windows':\n remote_file = driver_urls['chrome_windows_10']\n else:\n raise ValueError('Unsupported OS specified: %s' % (platform.system()))\n _download_temp_file(remote_file['url'], remote_file['file_name'])","title":""},{"docid":"d02629142812d403cf7a5c691f4768d0","score":"0.45816785","text":"def pull(keys, targets='all'):","title":""},{"docid":"edfca4955955386a5f47f52a26256f3f","score":"0.45725054","text":"def _pull_predefined_dockerimages(self):\n dc = DockerClient()\n for url in list(self.remote_docker_image_urls.values()):\n # only pull if not present (speedup for development)\n if not FORCE_PULL:\n if len(dc.images.list(name=url)) > 0:\n LOG.debug(\"Image %r present. Skipping pull.\" % url)\n continue\n LOG.info(\"Pulling image: %r\" % url)\n # this seems to fail with latest docker api version 2.0.2\n # dc.images.pull(url,\n # insecure_registry=True)\n # using docker cli instead\n cmd = [\"docker\",\n \"pull\",\n url,\n ]\n Popen(cmd).wait()","title":""},{"docid":"c4d5d13a4143133833bef60d61df731b","score":"0.45721","text":"def MP():\n url = \"https://github.com/m3ttiw\"\n os.startfile(url)","title":""},{"docid":"ff6e80c163ea5c9b03631b43cae7ba1e","score":"0.45707574","text":"def DownloadChromium(channel):\n # Get the version for the current channel from omahaproxy\n platform_data = PLATFORM_MAPPING[sys.platform]\n omaha_platform = platform_data['omaha']\n version_lookup_url = VERSION_LOOKUP_URL % (omaha_platform, channel)\n response = urllib2.urlopen(version_lookup_url)\n version = response.readlines()[1].split(',')[2]\n\n # Get the base position for that version from omahaproxy\n base_pos_lookup_url = BASE_POS_LOOKUP_URL % version\n response = urllib2.urlopen(base_pos_lookup_url)\n base_pos = json.load(response)['chromium_base_position']\n\n # Find the build from that base position in cloud storage. If it's not found,\n # decrement base position until one is found.\n cloud_storage_lookup_url = CLOUD_STORAGE_LOOKUP_URL % (\n platform_data['prefix'], base_pos)\n download_url = None\n while not download_url:\n response = urllib2.urlopen(cloud_storage_lookup_url)\n prefixes = json.load(response).get('prefixes')\n if prefixes:\n download_url = CLOUD_STORAGE_DOWNLOAD_URL % (\n platform_data['prefix'], base_pos, platform_data['zip_prefix'])\n break\n base_pos = int(base_pos) - 1\n cloud_storage_lookup_url = CLOUD_STORAGE_LOOKUP_URL % (\n platform_data['prefix'], base_pos)\n\n print 'Approximating Chrome %s with chromium from base position %s.' % (\n version, base_pos)\n print 'Downloading from %s' % download_url\n\n tmpdir = tempfile.mkdtemp()\n zip_path = os.path.join(tmpdir, 'chrome.zip')\n with open(zip_path, 'wb') as local_file:\n local_file.write(urllib2.urlopen(download_url).read())\n zf = zipfile.ZipFile(zip_path)\n zf.extractall(path=tmpdir)\n return tmpdir, version","title":""}],"string":"[\n {\n \"docid\": \"ecac29780f2dec0c3d136abb6f000c6c\",\n \"score\": \"0.57570237\",\n \"text\": \"def exec_main():\\n\\n try_get_lock()\\n\\n # Allow user to force a SHA1 from env, mostly for testing purpose without\\n # putting pressure on Github API (anon is rate-limited to 60 reqs/hr from the\\n # same IP address.\\n try:\\n sha_to_run = os.environ.get('ds_automation_force_sha').split(',')\\n except:\\n sha_to_run = [ ]\\n\\n # ensure we have a real dir name\\n assert len(DEEPSPEECH_CLONE_PATH) > 3\\n assert os.path.isabs(DEEPSPEECH_CLONE_PATH)\\n\\n # Creating holding directories if needed\\n if not os.path.isdir(CACHE_DIR):\\n os.makedirs(CACHE_DIR)\\n\\n if not os.path.isdir(DATA_DIR):\\n os.makedirs(DATA_DIR)\\n\\n if len(sha_to_run) == 0:\\n current = get_last_sha1()\\n if len(current) == 40:\\n print('Existing SHA1:', current, 'fetching changes')\\n sha_to_run, rt = get_new_commits(current)\\n if sha_to_run is not None and len(sha_to_run) is 0:\\n print(\\\"No new SHA1, got HTTP status:\\\", rt)\\n sys_exit_safe()\\n elif sha_to_run is None:\\n print(\\\"Something went badly wrong, unable to use Github compare\\\")\\n sys_exit_safe()\\n else:\\n # Ok, we do not have an existing SHA1, let us get one\\n print('No pre-existing SHA1, fetching refs')\\n sha1, rt = get_current_sha1()\\n if sha1 is None:\\n print(\\\"No SHA1, got HTTP status:\\\", rt)\\n sys_exit_safe()\\n sha_to_run = [ sha1 ]\\n else:\\n print(\\\"Using forced SHA1 from env\\\")\\n\\n print(\\\"Will execute for\\\", sha_to_run)\\n\\n for sha in sha_to_run:\\n if not ensure_git_clone(sha):\\n print(\\\"Error with git repo handling.\\\")\\n sys_exit_safe()\\n\\n print(\\\"Ready for\\\", sha)\\n\\n print(\\\"Let us place ourselves into the git clone directory ...\\\")\\n root_dir = os.getcwd()\\n os.chdir(DEEPSPEECH_CLONE_PATH)\\n\\n print(\\\"Starting GPU nvidia-smi monitoring\\\")\\n gpu_usage_csv, gpu_usage_charts = ensure_gpu_usage(root_dir)\\n gu = GPUUsage(csvfile=gpu_usage_csv)\\n gu.start()\\n\\n print(\\\"Do the training for getting WER computation\\\")\\n exec_wer_run()\\n\\n print(\\\"Producing GPU monitoring charts\\\")\\n gu.stop()\\n GPUUsageChart(source=gpu_usage_csv, basename=gpu_usage_charts)\\n\\n print(\\\"Save progress\\\")\\n write_last_sha1(sha)\\n\\n print(\\\"Let us place back to the previous directory %s ...\\\" % root_dir)\\n os.chdir(root_dir)\\n\\n print(\\\"Getting rid of git clone\\\")\\n wipe_git_clone()\\n\\n release_lock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5be54f5511e7bd55288895dd7d66b4\",\n \"score\": \"0.57431793\",\n \"text\": \"def update_code():\\n with cd('/srv/directedstudies'):\\n sudo('git pull')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8f6b8ddb8d77ac432c93cf3b8061a7\",\n \"score\": \"0.57357633\",\n \"text\": \"def _update(self):\\n self._system.execute_command(\\\"git\\\", [\\\"pull\\\"], cwd=self.get_install_path(), verbose=True)\\n self._install()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8f6b8ddb8d77ac432c93cf3b8061a7\",\n \"score\": \"0.57357633\",\n \"text\": \"def _update(self):\\n self._system.execute_command(\\\"git\\\", [\\\"pull\\\"], cwd=self.get_install_path(), verbose=True)\\n self._install()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894e9e90d2a206ddfafb9ffc4a6440a6\",\n \"score\": \"0.5680683\",\n \"text\": \"def site_updaterepos():\\n cmd = [\\\"git\\\", \\\"submodule\\\", \\\"foreach\\\", \\\"--recursive\\\", \\\"git\\\", \\\"pull\\\", \\\"origin\\\", \\\"main\\\"]\\n with Popen(cmd, env=os.environ) as proc:\\n proc.wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2159d9b3b5047cd35571d00d483fe1b5\",\n \"score\": \"0.5612964\",\n \"text\": \"def fetchRepo():\\n # get covid19 data\\n if os.path.isdir(COVID_19_DATA_DIR):\\n os.system(f\\\"cd {COVID_19_DATA_DIR} & git pull\\\")\\n else:\\n os.system(f\\\"git clone {COVID_19_DATA_URL} {COVID_19_DATA_DIR}\\\")\\n \\n # get populaion data\\n if os.path.isdir(POPULATION_DATA_DIR):\\n os.system(f\\\"cd {POPULATION_DATA_DIR} & git pull\\\")\\n else:\\n os.system(f\\\"git clone {POPULATION_DATA_URL} {POPULATION_DATA_DIR}\\\")\\n \\n # get country to region map\\n if os.path.isdir(COUNTRY_TO_REGION_MAPPING_DIR):\\n os.system(f\\\"cd {COUNTRY_TO_REGION_MAPPING_DIR} & git pull\\\")\\n else:\\n os.system(f\\\"git clone {COUNTRY_TO_REGION_MAPPING_URL} {COUNTRY_TO_REGION_MAPPING_DIR}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff4fcb582800e47841f1ace3214240c\",\n \"score\": \"0.55779135\",\n \"text\": \"def update():\\n with cd(site_path):\\n run('git pull --all')\\n reload()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf80f8ecc65941a6ab5d8e192f0befc\",\n \"score\": \"0.5454635\",\n \"text\": \"def main():\\n #gotoMYChrome()\\n #returndockspot()\\n gotochrome()\\n copyurl()\\n getsearchbar()\\n pasteurl()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95012a42bda14b7c48c1c4af2e35a48\",\n \"score\": \"0.5423222\",\n \"text\": \"def _post_install():\\n import subprocess\\n from distutils import log\\n log.set_verbosity(log.DEBUG)\\n\\n try:\\n print(\\\"Downloading most resent gitignores from Github.com...\\\")\\n pass\\n except:\\n log.warn(\\\"Post Install Failed.\\\")\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ec5ac856415af6ead54d602f1f5c75\",\n \"score\": \"0.53963065\",\n \"text\": \"def dip_upgrade(names):\\n for name in names:\\n with settings.getapp(name) as app:\\n try:\\n app.repo.pull()\\n except AttributeError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6661b592ad5f70a24d38779f5047838\",\n \"score\": \"0.53792614\",\n \"text\": \"def puller(options):\\n \\n executor([\\\"git\\\", \\\"pull\\\"] + options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67db9a8df4e58978cd77cabcdd5b3e14\",\n \"score\": \"0.53673506\",\n \"text\": \"def perform_update():\\n if os.path.islink(\\\"./data\\\"):\\n os.rename(\\\"./data\\\", \\\"./data_updating\\\")\\n try:\\n os.makedirs(\\\"./data\\\")\\n except OSError as e:\\n print(\\\"System update error \\\", e)\\n\\n command = u\\\"git config core.filemode true\\\"\\n subprocess.call(command.split())\\n \\n command = u\\\"git pull\\\"\\n subprocess.call(command.split())\\n \\n if os.path.isdir(\\\"./data_updating\\\"):\\n try:\\n shutil.rmtree(\\\"./data\\\")\\n os.rename(\\\"./data_updating\\\", \\\"./data\\\")\\n except OSError as e:\\n print(\\\"System update error \\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01b1e7c6a0334d936addaf94797a622\",\n \"score\": \"0.5357921\",\n \"text\": \"async def source(self, ctx, *, command: str = None):\\r\\n #chrome_options = Options()\\r\\n #chrome_options.add_argument(\\\"--headless\\\")\\r\\n #chrome_options.add_argument('--no-sandbox')\\r\\n #driver = webdriver.Chrome('/usr/lib/chromium-browser/chromedriver', options=chrome_options)\\r\\n #driver2 = webdriver.Chrome('/usr/lib/chromium-browser/chromedriver', options=chrome_options)\\r\\n source_url = 'https://github.com/proguy914629bot/LyricMaster'\\r\\n branch = '1.0.0'\\r\\n\\r\\n if command is None:\\r\\n return await ctx.send(source_url + f'\\\\n\\\\nHint: You can specify a specific command to view the source of that command, Like `lm?source ping`!')\\r\\n\\r\\n obj = self.bot.get_command(command.replace('.', ' '))\\r\\n if obj is None:\\r\\n return await ctx.send(f\\\"Command Named {command} Not Found!\\\")\\r\\n\\r\\n src = obj.callback.__code__\\r\\n module = obj.callback.__module__\\r\\n filename = src.co_filename\\r\\n\\r\\n lines, firstlineno = inspect.getsourcelines(src)\\r\\n location = os.path.realpath(filename).replace(\\\"\\\\\\\\\\\", '/')\\r\\n\\r\\n if \\\"home/gilb\\\" in location:\\r\\n await ctx.send(\\\"Command Found but Not Updated/Found in Github Repo. Here is the main link! {}!\\\".format(source_url))\\r\\n return\\r\\n\\r\\n #if \\\"/home/gilb/.local\\\" in location:\\r\\n # async with ctx.message.channel.typing():\\r\\n # finallocation = location.replace(\\\"/home/gilb/.local/lib/python3.8/site-packages/\\\", \\\"\\\")\\r\\n # finallocation2 = finallocation.replace(\\\" \\\", \\\"+\\\")\\r\\n # driver2.get(f\\\"https://www.google.com/search?q=pip+install+{finallocation2}\\\")\\r\\n #\\r\\n # pypipage = driver2.find_elements_by_css_selector(\\\"yuRUbf > a\\\").__getattribute__('href')\\r\\n #\\r\\n # print(pypipage)\\r\\n #\\r\\n # driver.get(f'{str(pypipage)}')\\r\\n #\\r\\n # pipcmd = driver.find_element_by_xpath('.//span[@class = \\\"package-header__pip-instructions\\\"]')[0]\\r\\n #\\r\\n # print(pipcmd)\\r\\n\\r\\n # finallocation = location.replace(\\\"/home/gilb/.local/lib/python3.8/site-packages/\\\", \\\"\\\")\\r\\n # finallocation2 = finallocation.replace(\\\" \\\", \\\"+\\\")\\r\\n # req = Request(f\\\"https://www.google.com/search?q=pip+install+{finallocation2}\\\", headers={'User-Agent': 'Mozilla/5.0'})\\r\\n # webpage = urlopen(req).read()\\r\\n # soup = BeautifulSoup(webpage, \\\"html.parser\\\")\\r\\n # for link in soup.findAll('a', attrs={'href', re.compile(\\\"^https://pypi.com/\\\")}):\\r\\n # href = print(link.get('href'))\\r\\n\\r\\n #async with ClientSession() as session:\\r\\n # async with session.get(f'{source_url}/blob/{branch}/{location}#L{firstlineno - 1}-L{firstlineno + len(lines) - 2}') as response:\\r\\n # if response.status != 200:\\r\\n # await ctx.send(\\\"Cannot connect to GitHub Servers. Try Again Later! Maybe check the Github Status. If not here is the main link! {}\\\".format(source_url))\\r\\n # return\\r\\n # else:\\r\\n # pass\\r\\n\\r\\n final_url = f'{source_url}/blob/{branch}/{location}#L{firstlineno - 1}-L{firstlineno + len(lines) - 2}'\\r\\n finalurl2 = final_url.replace('//home/gilb/LyricMaster/', '/')\\r\\n await ctx.send(finalurl2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6b9a072b5b886a8adcf7c78728baed\",\n \"score\": \"0.53354615\",\n \"text\": \"def sync(config):\\n # Fetch & Sync Chromium\\n # Copy PATH from current process and add depot_tools to it\\n depot_tools_path = os.path.join(os.getcwd(), 'depot_tools')\\n if not os.path.exists(depot_tools_path) or not os.path.isdir(depot_tools_path):\\n raise FileNotFoundError(\\\"Cannot find depot_tools!\\\")\\n _env = os.environ.copy()\\n _env[\\\"PATH\\\"] = depot_tools_path + \\\":\\\" + _env[\\\"PATH\\\"]\\n\\n # Get chromium ref\\n # Set src HEAD to version\\n chromium_ref = set_revision(config)\\n\\n # Create .gclient file\\n with open('.gclient', 'w', encoding='utf-8') as f:\\n f.write(GCLIENT_CONFIG.replace(\\\"@@TARGET_OS@@\\\", \\\"'{}'\\\".format(config.target_os)))\\n\\n # Run gclient sync without hooks\\n extra_args = []\\n if config.reset:\\n extra_args += ['--revision', 'src@' + chromium_ref, '--force', '--upstream', '--reset']\\n if config.shallow:\\n # There is a bug with --no-history when syncing third_party/wayland. See\\n # https://bugs.chromium.org/p/chromium/issues/detail?id=1226496\\n extra_args += ['--shallow']\\n else:\\n extra_args += ['--with_tags', '--with_branch_heads']\\n\\n sp.check_call(['gclient', 'sync', '--nohooks'] + extra_args, env=_env)\\n\\n # Run hooks\\n sp.check_call(['gclient', 'runhooks'], env=_env)\\n\\n # If Debian/Ubuntu and install_deps, then run the script.\\n # Note: requires sudo\\n if config.install_build_deps:\\n if config.target_os == 'android':\\n script = 'install-build-deps-android.sh'\\n else:\\n script = 'install-build-deps.sh'\\n distro_name = distro.linux_distribution(full_distribution_name=False)[0].lower()\\n if distro_name == 'debian' or distro_name == 'ubuntu':\\n warnings.warn(\\\"Note: installing dependencies requires root privilege!\\\",\\n RuntimeWarning)\\n sp.check_call(['sudo', os.path.join(SRC_DIR, 'build', script)])\\n else:\\n warnings.warn(\\\"Installing dependencies only works on Debian based systems, skipping.\\\",\\n RuntimeWarning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f59fcbb73b07ac726f380a869c275510\",\n \"score\": \"0.5229842\",\n \"text\": \"def update():\\n for install_type in VSCode:\\n checkout = checkout_directory(install_type)\\n if not checkout.exists():\\n continue\\n print(f\\\"UPDATING {checkout}\\\")\\n print(\\\"Deleting files downloaded by the extension ...\\\")\\n cleanup(checkout)\\n print(\\\"Updating clone ...\\\")\\n update_checkout(checkout)\\n build(checkout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38deecf9c422fd2526e63f854a2dc97a\",\n \"score\": \"0.5225225\",\n \"text\": \"def _pull_and_resolve_conflicts(self):\\n\\n logging.info('Starting pull from {}'.format(self.git_url))\\n\\n yield from execute_cmd(['git', 'checkout', self.branch_name], cwd=self.repo_dir)\\n yield from execute_cmd(['git', 'fetch'], cwd=self.repo_dir)\\n yield from execute_cmd(['git', 'merge', '-Xours', 'origin/{}'.format(self.branch_name)], cwd=self.repo_dir)\\n\\n logging.info('Pulled from {}'.format(self.git_url))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e523b739a907ac07930f9068581966b2\",\n \"score\": \"0.519865\",\n \"text\": \"def main(pr_ids, output_dir, upstream):\\n check_for_cargo_benchcmp()\\n check_github_api_env_vars()\\n\\n temp_dir = None\\n if output_dir is None:\\n temp_dir = TemporaryDirectory()\\n output_dir = temp_dir.name\\n else:\\n os.makedirs(output_dir, exist_ok=True)\\n\\n if upstream is None:\\n check_environment_variable('RBU_UPSTREAM_URL', 'if --upstream is not')\\n upstream = os.environ['RBU_UPSTREAM_URL']\\n\\n for pr_id in pr_ids:\\n benchmark_github_pr(pr_id, upstream, output_dir)\\n\\n # Cleanup if it's a temp dir\\n if temp_dir is not None:\\n temp_dir.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d07a072b9a2a84f23e5275a9202a910\",\n \"score\": \"0.5186958\",\n \"text\": \"def update_fetch(self):\\n Popen([\\\"mount\\\", \\\"-t\\\", \\\"devfs\\\", \\\"devfs\\\",\\n \\\"{}/releases/{}/root/dev\\\".format(self.iocroot,\\n self.release)]).communicate()\\n copy(\\\"/etc/resolv.conf\\\",\\n \\\"{}/releases/{}/root/etc/resolv.conf\\\".format(self.iocroot,\\n self.release))\\n\\n # TODO: Check for STABLE/PRERELEASE/CURRENT/BETA if we support those.\\n # TODO: Fancier.\\n self.lgr.info(\\\"\\\\n* Updating {} to the latest patch level... \\\".format(\\n self.release))\\n\\n os.environ[\\\"UNAME_r\\\"] = self.release\\n os.environ[\\\"PAGER\\\"] = \\\"/bin/cat\\\"\\n new_root = \\\"{}/releases/{}/root\\\".format(self.iocroot, self.release)\\n if os.path.isfile(\\\"{}/etc/freebsd-update.conf\\\".format(new_root)):\\n # 10.1-RELEASE and under have a interactive check\\n if float(self.release.partition(\\\"-\\\")[0][:5]) <= 10.1:\\n with NamedTemporaryFile(delete=False) as tmp_conf:\\n conf = \\\"{}/usr/sbin/freebsd-update\\\".format(new_root)\\n with open(conf) as update_conf:\\n for line in update_conf:\\n tmp_conf.write(re.sub(\\\"\\\\[ ! -t 0 \\\\]\\\", \\\"false\\\",\\n line))\\n\\n os.chmod(tmp_conf.name, 0o755)\\n Popen([tmp_conf.name, \\\"-b\\\", new_root, \\\"-d\\\",\\n \\\"{}/var/db/freebsd-update/\\\".format(new_root), \\\"-f\\\",\\n \\\"{}/etc/freebsd-update.conf\\\".format(new_root),\\n \\\"fetch\\\"], stdout=PIPE, stderr=PIPE).communicate()\\n os.remove(tmp_conf.name)\\n else:\\n Popen([\\\"freebsd-update\\\", \\\"-b\\\", new_root, \\\"-d\\\",\\n \\\"{}/var/db/freebsd-update/\\\".format(new_root), \\\"-f\\\",\\n \\\"{}/etc/freebsd-update.conf\\\".format(new_root),\\n \\\"fetch\\\"], stdout=PIPE, stderr=PIPE).communicate()\\n\\n Popen([\\\"freebsd-update\\\", \\\"-b\\\", new_root, \\\"-d\\\",\\n \\\"{}/var/db/freebsd-update/\\\".format(new_root), \\\"-f\\\",\\n \\\"{}/etc/freebsd-update.conf\\\".format(new_root),\\n \\\"install\\\"], stdout=PIPE, stderr=PIPE).communicate()\\n\\n try:\\n # Why this sometimes doesn't exist, we may never know.\\n os.remove(\\\"{}/releases/{}/root/etc/resolv.conf\\\".format(\\n self.iocroot, self.release))\\n except OSError:\\n pass\\n\\n Popen([\\\"umount\\\", \\\"{}/releases/{}/root/dev\\\".format(\\n self.iocroot, self.release)]).communicate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f36ab8a04e43304346ad9e5a6e485e1\",\n \"score\": \"0.51862854\",\n \"text\": \"def DP():\\n url = \\\"https://github.com/W1l50n2208\\\"\\n os.startfile(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0a3541ad9ec2a22e2c2a2e8af51117\",\n \"score\": \"0.51758665\",\n \"text\": \"def pull():\\n local('git pull origin master')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a13d27f39ad2b52f407c1ae7dfdffe\",\n \"score\": \"0.5153826\",\n \"text\": \"def refresh_external_libs():\\n for url, alias in settings.EXTERNAL_LIBRARIES:\\n path = os.path.join(settings.EXTERNAL_LIBRARIES_ROOT, alias)\\n if os.path.exists(path):\\n pull(alias, url)\\n logger.info(f\\\"Library '{alias}' updated from '{url}.\\\")\\n else:\\n clone(alias, url)\\n logger.info(f\\\"Library '{alias}' cloned from '{url}.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1d0ab50f4462c010f3c71831e3b69e\",\n \"score\": \"0.51401055\",\n \"text\": \"def repo_update():\\n with cd(env.base_dir):\\n run('git fetch --all && git reset --hard origin/master')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab8d08a20148424d77e201e82fb42b5\",\n \"score\": \"0.51230353\",\n \"text\": \"def pull(self):\\n\\n Program.runprogram(shlex.split(\\\"git pull\\\"), workdir=self.directory, use_sudo=self.use_sudo, user=self.sudo_user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21253266bc44016b41b6209492b502af\",\n \"score\": \"0.50979227\",\n \"text\": \"def pull(env, opts, args):\\n remotes = remote.get_remotes(env)\\n if args:\\n filt = lambda rmt: rmt.name in args\\n else:\\n filt = lambda rmt: True\\n for entry in filter(filt, remotes):\\n external.rsync(entry.url, env.directory, backup_dir=env.backup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae36b5c1a49ae955ce4fc10c854f840d\",\n \"score\": \"0.50876707\",\n \"text\": \"def soft_deploy():\\n clear_compiled_python_files()\\n git_pull()\\n collectstatic()\\n reload_http()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11db07804dc60e5b5dd91eac8b424071\",\n \"score\": \"0.5043245\",\n \"text\": \"def main(repo_dirs, linewise, loop, delay, repos):\\n\\n if repo_dirs is not None:\\n repos = repo_dirs.read().splitlines()\\n if not repos:\\n return\\n\\n while True:\\n dirs = get_git_repos(repos)\\n\\n codes = []\\n for d in dirs:\\n chdir(d)\\n\\n status = get_git_status()\\n if status == \\\"-\\\":\\n continue\\n\\n code = get_git_code(d, status)\\n if code:\\n codes.append(code)\\n\\n if linewise:\\n codes = \\\"\\\\n\\\".join(codes)\\n else:\\n codes = \\\" \\\".join(codes)\\n click.echo(codes)\\n\\n if not loop:\\n break\\n time.sleep(delay)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b6c0e338a09af69a5cd589959b966d\",\n \"score\": \"0.5024227\",\n \"text\": \"async def pull(self, ctx):\\n await ctx.typing()\\n try:\\n output = subprocess.check_output(\\n ['git', 'pull']).decode()\\n await ctx.send('```git\\\\n' + output + '\\\\n```')\\n except Exception as e:\\n return await ctx.send(str(e))\\n\\n _cogs = [f'cogs.{i}' for i in self.cog_re.findall(output)]\\n active_cogs = [i for i in _cogs if i in self.client.extensions]\\n if active_cogs:\\n for cog_name in active_cogs:\\n await ctx.invoke(self.client.get_command('reload'), cog_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79de43382848d91c9f1f995cdc6d6672\",\n \"score\": \"0.5023246\",\n \"text\": \"def CM():\\n url = \\\"https://github.com/Chrism1c\\\"\\n os.startfile(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0254aab07f47c30f36709b63bba55011\",\n \"score\": \"0.50232244\",\n \"text\": \"def main():\\n last_url = \\\"\\\"\\n config_file = read_config()\\n while True:\\n try:\\n url = get_latest_url()\\n if url != last_url: # update detected\\n print(f\\\"Update detected!\\\")\\n\\n last_url = url\\n data_location = config_file[\\\"downloaded_data_path\\\"]\\n\\n create_data_folder(data_location)\\n download_file(url, data_location)\\n\\n print(\\\"Extraction completed.\\\")\\n except Exception as e:\\n print(f\\\"Error! {e}\\\")\\n continue\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d33f77a9c32d40eec20f4ffcb2b751\",\n \"score\": \"0.50189006\",\n \"text\": \"def downloadUpdateClick():\\n lcd.clear()\\n lcd.message(\\\"Downloading...\\\")\\n lcd.setCursor(14, 0)\\n lcd.ToggleBlink()\\n os.system(\\\"mount -o rw,remount /\\\")\\n try:\\n response = subprocess.check_output([\\\"git\\\", \\\"pull\\\"])\\n except:\\n response = \\\"Update Error:\\\\nCheck Internet\\\"\\n os.system(\\\"mount -o ro,remount /\\\")\\n if response.strip() == \\\"Already up-to-date.\\\":\\n message = \\\"No Update Found\\\"\\n else:\\n message = response.strip()\\n lcd.ToggleBlink()\\n lcdPrint(message, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db6ae95bae35a1c71e16eb21c9af1113\",\n \"score\": \"0.50038403\",\n \"text\": \"def main():\\n setup_and_install_dependencies()\\n run_lighthouse_checks()\\n delete_reports()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad3c2780c7ddfe0aa19187141b4b6807\",\n \"score\": \"0.49998796\",\n \"text\": \"def pull(c, only_latest=False):\\n images = [\\n ('6.0', 'latest')\\n ]\\n if not only_latest:\\n images.extend([\\n ('5.0', 'stable'),\\n ('7.0', 'testing'),\\n ])\\n for image, tag in images:\\n c.run(f'docker pull readthedocs/build:{image}', pty=True)\\n c.run(f'docker tag readthedocs/build:{image} readthedocs/build:{tag}', pty=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c88df71b7982d7dcd77552162f97020\",\n \"score\": \"0.4989056\",\n \"text\": \"def update(self, options):\\n\\n self._repo.ensure_lines(\\\".gitignore\\\", [\\n \\\"workspace/\\\",\\n \\\"*.egg-info/\\\",\\n \\\"dist/*\\\",\\n \\\"__pycache__\\\",\\n \\\"*.pyc\\\",\\n \\\".pytest_cache/\\\",\\n \\\"build/*\\\"\\n ])\\n\\n self._repo.ensure_template(\\\".editorconfig\\\", template=\\\"editorconfig\\\")\\n self._repo.ensure_lines(\\\"requirements_build.txt\\\", [\\n \\\"requests ~= 2.20\\\",\\n \\\"twine ~= 1.12\\\",\\n \\\"pycryptodome ~= 3.7\\\",\\n \\\"pytest ~= 4.0\\\",\\n \\\"wheel >= 0.32\\\"\\n ], [\\n r\\\"^requests\\\",\\n r\\\"^twine\\\",\\n r\\\"^pycryptodome\\\",\\n r\\\"^pytest\\\",\\n r\\\"^wheel\\\"\\n ], multi=True)\\n\\n variables = {\\n 'options': options,\\n 'components': self._repo.components\\n }\\n\\n self._repo.ensure_directory(self._repo.SCRIPT_DIR)\\n self._repo.ensure_template(os.path.join(self._repo.MULTIPACKAGE_DIR, \\\"components.txt\\\"), template=\\\"components.txt\\\", overwrite=False)\\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \\\"release_by_name.py\\\"), \\\"release_by_name.py.tpl\\\", variables)\\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \\\"test_by_name.py\\\"), \\\"test_by_name.py.tpl\\\", variables)\\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \\\"components.py\\\"), \\\"components.py.tpl\\\", variables)\\n self._repo.ensure_template(os.path.join(self._repo.SCRIPT_DIR, \\\"tag_release.py\\\"), \\\"tag_release.py.tpl\\\", variables)\\n\\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \\\"shared_errors.py\\\"), \\\"shared_errors.py\\\")\\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \\\"release_notes.py\\\"), \\\"release_notes.py\\\")\\n self._repo.ensure_script(os.path.join(self._repo.SCRIPT_DIR, \\\"release_component.py\\\"), \\\"release_component.py\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40bdeaeaa7a49a5dc2de9a4cb21c18ea\",\n \"score\": \"0.49812675\",\n \"text\": \"def pull(url, revision=None):\\r\\n do_bzr_cmd(cmd_pull, location=url, revision=revision)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335ef2d96c80e15a44077504e3fa2e36\",\n \"score\": \"0.49721912\",\n \"text\": \"def git_f():\\r\\n webbrowser.open(\\\"https://github.com/Katerunner/Flights-Course-Work\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f299d209b5897cbd5861ab8d8334063\",\n \"score\": \"0.49692765\",\n \"text\": \"async def pull(ctx, pip=None):\\n dev = ctx.message.author\\n if bot.botdev_role in dev.roles or bot.owner_role in dev.roles:\\n await ctx.send(\\\"`Pulling changes...`\\\")\\n call([\\\"git\\\", \\\"stash\\\", \\\"save\\\"])\\n call([\\\"git\\\", \\\"pull\\\"])\\n call([\\\"git\\\", \\\"stash\\\", \\\"clear\\\"])\\n pip_text = \\\"\\\"\\n if pip == \\\"-p\\\" or pip == \\\"--pip\\\" or pip == \\\"-Syu\\\":\\n await ctx.send(\\\"`Updating python dependencies...`\\\")\\n call([\\\"python3.6\\\", \\\"-m\\\", \\\"pip\\\", \\\"install\\\", \\\"--user\\\", \\\"--upgrade\\\", \\\"-r\\\",\\n \\\"requirements.txt\\\"])\\n pip_text = \\\" and updated python dependencies\\\"\\n await ctx.send(\\\"Pulled changes{}! Restarting...\\\".format(pip_text))\\n execv(\\\"./Brick.py\\\", argv)\\n else:\\n if \\\"pacman\\\" in ctx.message.content:\\n await ctx.send(\\\"`{} is not in the sudoers file. This incident will be reported.`\\\".format(ctx.message.author.display_name))\\n else:\\n await ctx.send(\\\"Only bot devs and / or owners can use this command\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ef4a1a4bc0bf3eee043955cc29e5e7\",\n \"score\": \"0.49266657\",\n \"text\": \"def waitUncommentableSourceIfNecessary(self):\\n url = 'http://download.csdn.net/my/downloads/1'\\n maxMinutes = 11\\n for i in range(0, maxMinutes):\\n html = self.getUrlContent(self.sess, url)\\n if html is None:\\n break\\n soup = BeautifulSoup(html)\\n sourcelist = soup.findAll('span', attrs={'class' : 'btn-comment'})\\n if sourcelist is None or len(sourcelist) == 0:\\n print('None uncommentable source now!')\\n break\\n print('Waiting for uncommentable source count down %d minutes.' % (maxMinutes-i))\\n time.sleep(60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22c97af99de78a03c3ea5e7da122ce6d\",\n \"score\": \"0.49146226\",\n \"text\": \"def main(self):\\r\\n req = urllib2.Request(raw_input(\\\"Enter a URL! \\\\n\\\"), headers={ 'User-Agent': 'Mozilla/5.0' })\\r\\n target = urllib2.urlopen(req) #getting the source code\\r\\n inter = target.read()\\r\\n inter = inter.decode(\\\"utf-8\\\") #decode unicode entities\\r\\n inter = inter.replace(\\\"<style type\\\", \\\"<s><style>\\\") #replace problematic tags\\r\\n inter = inter.replace(\\\"<!DOCTYPE\\\", \\\"<DOCTYP><\\\")\\r\\n inter = inter.replace(\\\"<!doctype\\\", \\\"<doctyp><\\\")\\r\\n inter = inter.replace(\\\"<![\\\", \\\"<[[\\\")\\r\\n inter = inter.replace(\\\"<!]\\\", \\\"<]]\\\")\\r\\n text = list(inter) #make a list containing every character\\r\\n logging.info(\\\"\\\\nPLEASE WAIT\\\\n\\\")\\r\\n logging.info(\\\"removing self.comments...\\\\n\\\" )\\r\\n self.removeComments(text) #comment removal\\r\\n logging.info(\\\"removing self.scripts...\\\\n\\\")\\r\\n self.removeScripts(text) #script removal\\r\\n logging.info(\\\"removing self.styles...\\\\n\\\")\\r\\n self.removeStyles(text) #style removal\\r\\n logging.info(\\\"extracting html and non-html characters...\\\\n\\\" )\\r\\n self.htmlSeparate(text) #separating the remaining parts\\r\\n logging.info(\\\"FINISHED! EVERY FILE IS IN THE SAME FOLDER AS THIS SCRIPT.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0c82e5a83232ad6d0e4609b19bbc277\",\n \"score\": \"0.49099478\",\n \"text\": \"def update_dependencies(dep_dict: dict, open_pull_requests: dict):\\n file_queue = Queue(config.MAXIMUM_DEPENDENCIES)\\n for dep_file in glob.glob('{}/**/ivy.xml'.format(config.REPO_PATH.replace('/', '')), recursive=True):\\n file_queue.put(dep_file)\\n threads = []\\n for i in range(config.NETWORKING_THREADS_GITHUB):\\n t = threading.Thread(\\n target=update_run, name='th-{}'.format(i),\\n kwargs={'file_queue': file_queue, 'dep_dict': dep_dict, 'open_pull_requests': open_pull_requests})\\n threads.append(t)\\n for t in threads:\\n t.start()\\n for t in threads:\\n t.join()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fd9b8fd0c8e07a9bc1024daaa3e478\",\n \"score\": \"0.48900807\",\n \"text\": \"def cli(from_url: str, force: bool, noleanup: bool, python_debug: bool) -> None:\\n global cache_url, force_download, lean_upgrade, debug\\n cache_url = from_url\\n force_download = force\\n lean_upgrade = not noleanup\\n debug = python_debug\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d854b9b8c5c46517fdc2bc891136328\",\n \"score\": \"0.48873034\",\n \"text\": \"def update():\\n local('git pull origin master')\\n local('pip install -r requirements.txt')\\n\\n kwarg = _check_env()\\n with shell_env(**kwarg):\\n local('python pillbox-engine/manage.py migrate')\\n local('python pillbox-engine/manage.py collectstatic --noinput')\\n local('python pillbox-engine/manage.py makeusers')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ea35b864619c46883aca7fd214a868\",\n \"score\": \"0.48757318\",\n \"text\": \"def main():\\n args = docopt(__doc__, version=VERSION)\\n\\n config = ConfigParser(interpolation=ExtendedInterpolation())\\n # Set defaults for all sections\\n config.read_dict(copy.copy(DEFAULT_OPTIONS))\\n # Load configuration from a file. NOTE: ConfigParser doesn't warn if user\\n # sets a filename which doesn't exist, in this case defaults will be used.\\n try:\\n config.read(args['--file'])\\n except ParsingError as exc:\\n sys.exit(str(exc))\\n\\n if args['--print']:\\n for section in sorted(DEFAULT_OPTIONS):\\n if section == 'pull' or section == 'DEFAULT':\\n print(\\\"[{}]\\\".format(section))\\n for key, value in sorted(DEFAULT_OPTIONS[section].items()):\\n print(\\\"{k} = {v}\\\".format(k=key, v=value))\\n print()\\n sys.exit(0)\\n if args['--print-conf']:\\n for section in sorted(config):\\n if section == 'pull' or section == 'DEFAULT':\\n print(\\\"[{}]\\\".format(section))\\n for key, value in sorted(config[section].items()):\\n print(\\\"{k} = {v}\\\".format(k=key, v=value))\\n print()\\n sys.exit(0)\\n\\n try:\\n configuration_check(config, 'pull')\\n except ValueError as exc:\\n sys.exit(str(exc))\\n\\n loglevel = (config.get('pull', 'loglevel') # pylint: disable=no-member\\n .upper())\\n log.setLevel(getattr(logging, loglevel, None))\\n\\n log.info('haproxystats-pull %s version started', VERSION)\\n # Setup our event loop\\n loop = asyncio.get_event_loop()\\n executor = ThreadPoolExecutor(max_workers=config.getint('pull',\\n 'workers'))\\n # Register shutdown to signals\\n\\n def shutdown(signalname):\\n \\\"\\\"\\\"Perform a clean shutdown.\\n\\n Arguments:\\n signalname (str): Signal name\\n \\\"\\\"\\\"\\n tasks_running = False\\n log.info('received %s', signalname)\\n\\n for task in asyncio.Task.all_tasks():\\n if not task.done():\\n tasks_running = True\\n log.info('cancelling %s task', task)\\n task.cancel()\\n\\n if not tasks_running:\\n log.info('no tasks were running when %s signal received', signal)\\n log.info('waiting for threads to finish any pending IO tasks')\\n executor.shutdown(wait=True)\\n sys.exit(0)\\n\\n loop.add_signal_handler(signal.SIGHUP, partial(shutdown, 'SIGHUP'))\\n loop.add_signal_handler(signal.SIGTERM, partial(shutdown, 'SIGTERM'))\\n\\n # a temporary directory to store fetched data\\n tmp_dst_dir = config['pull']['tmp-dst-dir']\\n # a permanent directory to move data from the temporary directory. Data are\\n # picked up by the process daemon from that directory.\\n dst_dir = config['pull']['dst-dir']\\n for directory in dst_dir, tmp_dst_dir:\\n try:\\n os.makedirs(directory)\\n except OSError as exc:\\n # errno 17 => file exists\\n if exc.errno != 17:\\n sys.exit(\\\"failed to make directory {d}:{e}\\\"\\n .format(d=directory, e=exc))\\n supervisor(loop, config, executor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97a1759419d9157e06a0870ab250ba3\",\n \"score\": \"0.48682088\",\n \"text\": \"def git_pull(options):\\n check_call([\\\"git\\\", \\\"pull\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd0e90d91aff718268b0a1d54259680d\",\n \"score\": \"0.4868125\",\n \"text\": \"def scrape_github():\\n githubs = [\\\"/bitshares/bitshares-ui/master/app/api/apiConfig.js\\\"]\\n if not GITHUB_MASTER:\\n githubs += Nodes.github()\\n # scrape from github\\n urls = []\\n http = \\\"https://\\\"\\n raw = \\\"raw.githubusercontent.com\\\"\\n if PROXY_GITHUB: # text proxy\\n uri = http + PROXY + raw\\n else:\\n uri = http + raw\\n for _, git in enumerate(githubs):\\n url = uri + git\\n urls.append(url)\\n print(\\\"scraping github for Bitshares nodes...\\\")\\n validated = []\\n repos = []\\n for url in urls:\\n attempts = 3\\n while attempts > 0:\\n try:\\n raw = requests.get(url, timeout=(6, 30)).text\\n ret = validate(parse(clean(raw)))\\n if ret:\\n repos.append(url)\\n del raw\\n repo = url.replace(uri, \\\"\\\")\\n repo = \\\"/\\\".join(repo.split(\\\"/\\\", 3)[:3])\\n print((\\\"found %s nodes at %s\\\" % (len(ret), repo)))\\n validated += ret\\n attempts = 0\\n except BaseException:\\n print((\\\"failed to connect to %s\\\" % url))\\n attempts -= 1\\n return validated, repos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328613caf0318b587caf1cd286245d83\",\n \"score\": \"0.48634103\",\n \"text\": \"def main():\\n err_print(\\\"Booting up web driver...\\\")\\n beige_books = webdriver.Chrome(WEBDRIVER_PATH) # Webdriver loaded from folder in case PATH not configured\\n err_print(\\\"Loading main archive page...\\\")\\n beige_books.get(ARCHIVE_URL)\\n err_print(\\\"Loading XPATH and ID identifiers...\\\")\\n search_button = beige_books.find_element_by_xpath(SEARCH_BUTTON_XPATH) # Easy button pressing\\n selects = Select(beige_books.find_element_by_name(SELECT_ID)).options # Grab all available options\\n err_print(\\\"Selecting all possible years...\\\\n\\\")\\n reports = []\\n curr_year = CURRENT_YEAR\\n\\n while curr_year >= END_YEAR: # While loop used becuase 'selects' change on every iteration and useful for indexing\\n year = selects[index_from_year(curr_year)] # Loads the year into memory\\n err_print(\\\"Grabbing all reports for \\\" + str(curr_year) + \\\"...\\\")\\n year.click()\\n search_button.click()\\n links = beige_books.find_elements_by_xpath(HTML_LINK_XPATH) # Grabs all hyperlinks\\n\\n for link in links:\\n link_html = link.get_attribute(\\\"href\\\") # Converts Selenium object to string URLs\\n if contains(URL_IDENTIFIER + str(curr_year), link_html) and link_html not in reports: # Checks for dupes\\n err_print(\\\"Grabbed \\\" + link_html + \\\"!\\\")\\n reports.append(link_html)\\n\\n err_print(\\\"Finished \\\" + str(curr_year) + \\\"!\\\")\\n curr_year -= 1 # Since the current year is the first item on the select, deincrement after every loop\\n err_print(\\\"Refreshing parameters for \\\" + str(curr_year) + \\\"...\\\\n\\\")\\n search_button = beige_books.find_element_by_xpath(SEARCH_BUTTON_XPATH) # Refresh needed because site changed\\n selects = Select(beige_books.find_element_by_name(SELECT_ID)).options\\n\\n err_print(\\\"Closing web driver...\\\")\\n beige_books.close() # Exits out of the website\\n err_print(\\\"End of scrape!\\\")\\n\\n # START OF PARSING #\\n\\n err_print(\\\"Instantiating new data table...\\\")\\n final_table = new_table() # Creates a new table to store data\\n err_print(\\\"Parsing reports to table...\\\\n\\\")\\n for report in reports: # Reads the 'text content' of all the scraped files and parses them into the final table\\n err_print(\\\"Extracting data from \\\" + report + \\\"...\\\")\\n site = soupify(report)\\n final_table = parse(site, final_table)\\n save(final_table) # Saves the table into a CSV\\n err_print(\\\"End of parse!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dfd07bb8af2396a3d18e3aacc82fb84\",\n \"score\": \"0.48553193\",\n \"text\": \"def api_github_message():\\n if rq.headers['Content-Type'] == 'application/json':\\n my_info = json.dumps(rq.json)\\n payload = json.loads(my_info)\\n if not payload['action'] == 'closed':\\n url = payload['pull_request']['url']\\n url = url + '/files'\\n comment_url = payload['pull_request']['comments_url']\\n r = requests.get(url)\\n a = r.json()\\n #print(a)\\n print(\\\"Received Pull Request for %d Changed Files\\\"%(len(a)))\\n print(\\\"Initializing Linting Process\\\")\\n i = 0\\n x = []\\n filelist = []\\n for i in range(len(a)):\\n raw = (a[i]['raw_url'])\\n #print(raw)\\n filename = (a[i]['filename']).split(\\\"/\\\")\\n file = filename[-1]\\n\\n re = urllib.request.urlopen('%s' % raw)\\n # returned_value = os.system(\\\"start \\\\\\\"\\\\\\\" %s\\\" %raw)\\n data = re.read()\\n dst = open(\\\"%s\\\" % file, \\\"wb\\\")\\n dst.write(data)\\n dst = open(\\\"%s\\\" % file, \\\"r\\\")\\n print(\\\"Checking syntax errors for File: %d. %s\\\" %(i+1, file))\\n\\n file_ext = (file).split(\\\".\\\")\\n print(\\\"File extension of the file: %s\\\" % file_ext[-1])\\n if file_ext[-1] == 'py':\\n print(\\\"Cloned file: %s deleted\\\" % file)\\n print(\\\"Linting file : %s\\\" % file)\\n cmd = 'pycodestyle %s' % file\\n process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE).communicate(0)\\n syntax_check = str(process)[2:-1]\\n # p = str(p)\\n # p = p[2:-1]\\n if process == \\\"b''\\\":\\n q = \\\"No syntax error found\\\"\\n print(q)\\n\\n else:\\n print(\\\"Syntax error found: %s\\\" % syntax_check)\\n q = \\\"Syntax error found\\\"\\n filelist.append(file)\\n\\n dst.close()\\n os.remove(\\\"%s\\\" % file)\\n s = \\\"%s %s\\\" % (q, syntax_check)\\n else:\\n print(\\\"No linter found for %s extension\\\" % file_ext[-1])\\n s = \\\"No linter found for %s extension\\\" % file_ext[-1]\\n x.append(s)\\n i = i + 1\\n if x is None:\\n comment_body = \\\"Git Assist Prediction : No Syntax Errors Found.\\\"\\n else:\\n filelist = list( dict.fromkeys(filelist) )\\n filelist = ', '.join(filelist)\\n comment_body = \\\"Git Assist Prediction: To Be Rejected. Syntax Errors found in the following files: %s.\\\"%filelist\\n\\n headers = {'Content-Type': 'application/json'}\\n myurl = '%s' % comment_url\\n post_comment_data = '{\\\\\\\"body\\\\\\\":\\\\\\\"%s\\\\\\\"}' % comment_body\\n post_comment_json = json.loads(post_comment_data)\\n response = requests.post(myurl, auth=(\\\"iamthebj\\\", \\\"Kj9158852858#\\\"), headers=headers,\\n json=post_comment_json)\\n if response.status_code == 201:\\n print(\\\"Successfully posted a comment\\\")\\n else:\\n print(\\\"Failed to post a response with http status code : \\\", response.status_code)\\n return \\\"%s\\\" % x\\n else:\\n print(\\\"Closed pull request\\\")\\n return \\\"Closed pull request\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e1c079cc7c16bd70c74ca9e25b4115f\",\n \"score\": \"0.48452666\",\n \"text\": \"def post_pr_merge_sync(context):\\n context.run(\\\"git switch main\\\")\\n context.run(\\\"git pull\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aedbd6deb7f263ed35508c635b58f685\",\n \"score\": \"0.4842096\",\n \"text\": \"def _cleanup_and_commit(self, updates):\\n self._clean_up_web_root()\\n self._git_apply_changes(updates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbbf83dd83765c46feafc7adae9a7847\",\n \"score\": \"0.48368776\",\n \"text\": \"def main():\\n _, repo_root = commands.getstatusoutput('git rev-parse --show-toplevel')\\n checks = imp.load_source('run_checks',\\n repo_root + \\\"/devtools/run_checks.py\\\")\\n exit(checks.main())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73ce5dde2baa9c8c010fc38457ea73f\",\n \"score\": \"0.4835393\",\n \"text\": \"def main():\\n current_build_number = os.environ.get(\\\"BUILDKITE_BUILD_NUMBER\\\", None)\\n if not current_build_number:\\n raise Exception(\\\"Not running inside Buildkite\\\")\\n current_build_number = int(current_build_number)\\n\\n for _ in range(5):\\n latest_generation, latest_build_number = latest_generation_and_build_number()\\n\\n if current_build_number <= latest_build_number:\\n eprint(\\n (\\n \\\"Current build '{0}' is not newer than latest published '{1}'. \\\"\\n + \\\"Skipping publishing of binaries.\\\"\\n ).format(current_build_number, latest_build_number)\\n )\\n break\\n\\n try:\\n try_publish_binaries(current_build_number, latest_generation)\\n except BinaryUploadRaceException:\\n # Retry.\\n continue\\n\\n eprint(\\n \\\"Successfully updated '{0}' to binaries from build {1}.\\\".format(\\n bazelci_builds_metadata_url(), current_build_number\\n )\\n )\\n break\\n else:\\n raise Exception(\\\"Could not publish binaries, ran out of attempts.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2929748d00c944809dadcc070aad7fb9\",\n \"score\": \"0.48351246\",\n \"text\": \"def hunt(urls, threads, exclude_flags, include_flags, interesting_extensions, interesting_files, interesting_keywords,\\n stdout_flags, progress_enabled, timeout, max_depth, not_follow_subdomains, exclude_sources, proxies, delay,\\n not_allow_redirects, limit, to_file, user_agent, cookies, headers):\\n if exclude_flags and include_flags:\\n raise BadOptionUsage('--exclude-flags and --include-flags are mutually exclusive.')\\n welcome()\\n urls = flat_list(urls)\\n proxies = multiplier_args(proxies)\\n if not urls:\\n click.echo('•_•) OOPS! Add urls to analyze.\\\\nFor example: dirhunt http://domain/path\\\\n\\\\n'\\n 'Need help? Then use dirhunt --help', err=True)\\n return\\n exclude_flags, include_flags = flags_range(exclude_flags), flags_range(include_flags)\\n progress_enabled = (sys.stdout.isatty() or sys.stderr.isatty()) if progress_enabled is None else progress_enabled\\n crawler = Crawler(max_workers=threads, interesting_extensions=interesting_extensions,\\n interesting_files=interesting_files, interesting_keywords=interesting_keywords,\\n std=sys.stdout if sys.stdout.isatty() else sys.stderr,\\n progress_enabled=progress_enabled, timeout=timeout, depth=max_depth,\\n not_follow_subdomains=not_follow_subdomains, exclude_sources=exclude_sources,\\n not_allow_redirects=not_allow_redirects, proxies=proxies, delay=delay, limit=limit,\\n to_file=to_file, user_agent=user_agent, cookies=cookies, headers=headers)\\n crawler.add_init_urls(*urls)\\n if os.path.exists(crawler.get_resume_file()):\\n click.echo('Resuming the previous program execution...')\\n try:\\n crawler.resume(crawler.get_resume_file())\\n except IncompatibleVersionError as e:\\n click.echo(e)\\n while True:\\n choice = catch_keyboard_interrupt_choices(crawler.print_results, ['abort', 'continue', 'results'], 'a') \\\\\\n (set(exclude_flags), set(include_flags))\\n if choice == 'a':\\n crawler.close(True)\\n click.echo('Created resume file \\\"{}\\\". Run again using the same parameters to resume.'.format(\\n crawler.get_resume_file())\\n )\\n return\\n elif choice == 'c':\\n crawler.restart()\\n continue\\n else:\\n break\\n crawler.print_urls_info()\\n if not sys.stdout.isatty():\\n output_urls(crawler, stdout_flags)\\n if to_file:\\n crawler.create_report(to_file)\\n if not to_file and os.path.exists(crawler.get_resume_file()):\\n # The resume file exists. Deleting...\\n os.remove(crawler.get_resume_file())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f155df21b78df33d691f99311b89722\",\n \"score\": \"0.4811949\",\n \"text\": \"def main():\\n logging.basicConfig(\\n format='[%(asctime)s] %(levelname)s -- %(message)s',\\n level=logging.DEBUG)\\n\\n parser = argparse.ArgumentParser(description='Synchronizes a github repository with a local repository.')\\n parser.add_argument('--git-url', help='Url of the repo to sync', required=True)\\n parser.add_argument('--branch-name', default='master', help='Branch of repo to sync')\\n parser.add_argument('--repo-dir', default='./', help='Path to sync to')\\n args = parser.parse_args()\\n\\n for line in GitAutoSync(\\n args.git_url,\\n args.branch_name,\\n args.repo_dir\\n ).pull_from_remote():\\n print(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68507aa3d67f277ccaab4c7150eec540\",\n \"score\": \"0.48108003\",\n \"text\": \"def install_core():\\n commands = \\\"\\\"\\\"\\nyum -y update\\nyum -y groupinstall \\\"Development Tools\\\"\\nyum install -y gvim xterm\\nyum install -y python-pip python-virtualenv\\npip install --upgrade pip\\npip install virtualenvwrapper\\npip install --upgrade google-api-python-client oauth2client pyasn1 --ignore-installed requests\\npip install --no-cache-dir -U crcmod\\n \\\"\\\"\\\".strip().split('\\\\n')\\n run_commands(commands)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db17b04aa500fbb22d7910028433174\",\n \"score\": \"0.48068625\",\n \"text\": \"def setUp(self):\\n self.driver = webdriver.Chrome(executable_path=\\\"E:\\\\chrome driver\\\\chromedriver_win32\\\\chromedriver.exe\\\")\\n self.driver.maximize_window()\\n self.base_url = \\\"https://github.com/\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61912d4944a8c6a13c140669b93d7114\",\n \"score\": \"0.48045424\",\n \"text\": \"def main(local):\\n\\n rewrite_baseurl(local)\\n rewrite_manifest(local)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357f5d3b03b1e434c6e649ebd2e1572a\",\n \"score\": \"0.4802946\",\n \"text\": \"def start():\\n\\n pr_builder = PullRequestBuilder()\\n events.dispatcher.register_target(PullRequestListener(pr_builder))\\n events.dispatcher.register_target(ManualPullRequestListener(pr_builder))\\n events.dispatcher.register_target(IRCRebuildListener(pr_builder))\\n utils.DaemonThread(target=pr_builder.run).start()\\n\\n collector = BuildStatusCollector()\\n events.dispatcher.register_target(BBHookListener(collector))\\n utils.DaemonThread(target=collector.run).start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f1f4f5cfe273d4088f62ee618ed9a0\",\n \"score\": \"0.4802121\",\n \"text\": \"def fetch(args):\\n\\n # figure out which repos/revs we're hoping to update.\\n # None is our internal, temp keyword representing the LATEST possible\\n # rev.\\n user_repo_revs = {} # repo -> version\\n repo_paths = map(lambda x: x.local_path, REPOS)\\n args_queue = collections.deque(args[:])\\n\\n while len(args_queue) > 0:\\n current_arg = args_queue.popleft()\\n\\n # If the user provides repo revisions, it MUST be a specific repo.\\n if current_arg in repo_paths:\\n # the user might provide a revision.\\n # It's a rev if it's not a repo.\\n try:\\n possible_rev = args_queue.popleft()\\n except IndexError:\\n # When no other args after the repo\\n user_repo_revs[current_arg] = None\\n continue\\n\\n if possible_rev in repo_paths:\\n # then it's not a revision, it's a repo. put it back.\\n # Also, assume user wants the repo we're currently working with\\n # to be updated to the tip OR whatever.\\n user_repo_revs[current_arg] = None\\n args_queue.appendleft(possible_rev)\\n continue\\n elif possible_rev in ['-r', '--rev']:\\n requested_rev = args_queue.popleft()\\n user_repo_revs[current_arg] = requested_rev\\n else:\\n print \\\"ERROR: unclear arg %s\\\" % possible_rev\\n return\\n\\n # determine which groupings the user wants to operate on.\\n # example: `src` would represent all repos under src/\\n # example: `data` would represent all repos under data/\\n # example: `src/pygeoprocessing` would represent the pygeoprocessing repo\\n repos = set([])\\n for argument in args:\\n if not argument.startswith('-'):\\n repos.add(argument)\\n\\n def _user_requested_repo(local_repo_path):\\n \\\"\\\"\\\"\\n Check if the user requested this repository.\\n Does so by checking prefixes provided by the user.\\n\\n Arguments:\\n local_repo_path (string): the path to the local repository\\n relative to the CWD. (example: src/pygeoprocessing)\\n\\n Returns:\\n Boolean: Whether the user did request this repo.\\n \\\"\\\"\\\"\\n # check that the user wants to update this repo\\n for user_arg_prefix in repos:\\n if local_repo_path.startswith(user_arg_prefix):\\n return True\\n return False\\n\\n for repo in REPOS:\\n LOGGER.debug('Checking %s', repo.local_path)\\n\\n # If the user did not request this repo AND the user didn't want to\\n # update everything (by specifying no positional args), skip this repo.\\n if not _user_requested_repo(repo.local_path) and len(repos) > 0:\\n continue\\n\\n # does repo exist? If not, clone it.\\n if not repo.ischeckedout():\\n repo.clone()\\n else:\\n LOGGER.debug('Repository %s exists', repo.local_path)\\n\\n # is repo up-to-date? If not, update it.\\n # If the user specified a target revision, use that instead.\\n try:\\n target_rev = user_repo_revs[repo.local_path]\\n if target_rev is None:\\n raise KeyError\\n except KeyError:\\n try:\\n target_rev = repo.tracked_version()\\n except KeyError:\\n print 'ERROR: repo not tracked in versions.json: %s' % repo.local_path\\n return 1\\n\\n repo.pull()\\n repo.update(target_rev)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a68f02b159b73a970b71c3bc12ac51\",\n \"score\": \"0.4798461\",\n \"text\": \"def test_pull():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28ddac3c4d5c5cc0337f9f01ad8552c\",\n \"score\": \"0.4795856\",\n \"text\": \"def needs_pull(self):\\n for remote in self.repo.remotes:\\n remote.fetch()\\n\\n for _ in self.repo.iter_commits('master..origin/master'):\\n logger.info(\\\"Repo %s needs update\\\", self.repo_name)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfd37f030eace6d5d07ff861498329b\",\n \"score\": \"0.47957903\",\n \"text\": \"def run_updates(self):\\n updates = {}\\n try:\\n sites = self.get_sites_to_update()\\n except DrupdatesError as update_status_error:\\n raise DrupdatesUpdateError(20, update_status_error)\\n if not sites['count']:\\n return updates\\n else:\\n sites.pop('count')\\n # Note: call Drush.call() without site alias as alias comes after dd argument.\\n drush_dd = Drush.call(['dd', '@drupdates.' + self._site_name])\\n self.site_web_root = drush_dd[0]\\n # Create seperate commits for each project (ie module/theme)\\n one_commit_per_project = self.settings.get('oneCommitPerProject')\\n # Iterate through the site/sub-sites and perform updates, update files etc...\\n sites_copy = copy.copy(sites)\\n for site, data in sites.items():\\n if 'modules' not in data:\\n sites_copy.pop(site)\\n continue\\n modules = copy.copy(data['modules'])\\n x = 0\\n for project, descriptions in data['modules'].items():\\n if self.settings.get('useMakeFile'):\\n self.update_make_file(project, descriptions['current'], descriptions['candidate'])\\n if one_commit_per_project:\\n if x:\\n build = Sitebuild(self._site_name, self.ssh, self.working_dir)\\n build.build()\\n self._update_code(site, [project])\\n modules.pop(project)\\n updates = self._build_commit_message(sites_copy, site, project)\\n self._cleanup_and_commit(updates)\\n x += 1\\n if self.settings.get('buildSource') == 'make' and self.settings.get('useMakeFile'):\\n self.utilities.make_site(self._site_name, self.site_dir)\\n elif len(modules):\\n self._update_code(site, modules.keys())\\n if not one_commit_per_project:\\n updates = self._build_commit_message(sites_copy)\\n self._cleanup_and_commit(updates)\\n return updates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e9340ff3341af2f0de2b8361ab1366\",\n \"score\": \"0.47924095\",\n \"text\": \"def github_list_pull_requests(urls, numbers_only=False):\\n pulls = github_get_pull_request_all(urls)\\n formatted_pulls = []\\n print \\\"Total pull count\\\", len(pulls)\\n sys.stdout.write(\\\"Processing pulls...\\\")\\n for pull in pulls:\\n n = pull[\\\"number\\\"]\\n sys.stdout.write(\\\" %d\\\" % n)\\n sys.stdout.flush()\\n pull_info = github_get_pull_request(urls, n)\\n if not pull_info:\\n # Pull request is an issue\\n continue\\n mergeable = pull_info[\\\"mergeable\\\"]\\n if pull[\\\"head\\\"][\\\"repo\\\"]:\\n repo = pull[\\\"head\\\"][\\\"repo\\\"][\\\"html_url\\\"]\\n else:\\n repo = None\\n branch = pull[\\\"head\\\"][\\\"ref\\\"]\\n created_at = pull[\\\"created_at\\\"]\\n created_at = time.strptime(created_at, \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n created_at = time.mktime(created_at)\\n username = pull[\\\"user\\\"][\\\"login\\\"]\\n user_info = github_get_user_info(urls, username)\\n author = \\\"\\\\\\\"%s\\\\\\\" <%s>\\\" % (user_info.get(\\\"name\\\", \\\"unknown\\\"),\\n user_info.get(\\\"email\\\", \\\"\\\"))\\n branch_against = pull[\\\"base\\\"][\\\"ref\\\"]\\n formatted_pulls.append({\\n 'created_at': created_at,\\n 'n': n,\\n 'repo': repo,\\n 'branch': branch,\\n 'author': author,\\n 'mergeable': mergeable,\\n 'branch_against': branch_against,\\n })\\n formatted_pulls.sort(key=lambda x: x['created_at'])\\n print \\\"\\\\nPatches that cannot be merged without conflicts:\\\"\\n nonmergeable = []\\n for pull in formatted_pulls:\\n if pull['mergeable']:\\n continue\\n nonmergeable.append(int(pull['n']))\\n if numbers_only:\\n print pull['n'],\\n else:\\n print \\\"#%03d: %s %s (against %s)\\\" % (pull['n'], pull['repo'], pull['branch'], pull['branch_against'])\\n print unicode(\\\" Author : %s\\\" % pull['author']).encode('utf8')\\n print \\\" Date : %s\\\" % time.ctime(pull['created_at'])\\n if numbers_only:\\n print\\n print\\n print \\\"-\\\"*80\\n print \\\"Patches that can be merged without conflicts:\\\"\\n mergeable_list = []\\n for pull in formatted_pulls:\\n if not pull['mergeable']:\\n continue\\n mergeable_list.append(int(pull['n']))\\n if numbers_only:\\n print pull['n'],\\n else:\\n print \\\"#%03d: %s %s (against %s)\\\" % (pull['n'], pull['repo'], pull['branch'], pull['branch_against'])\\n print unicode(\\\" Author : %s\\\" % pull['author']).encode('utf8')\\n print \\\" Date : %s\\\" % time.ctime(pull['created_at'])\\n if numbers_only:\\n print\\n print\\n return nonmergeable, mergeable_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f149b4633005e2971d1cf63158e5e81c\",\n \"score\": \"0.47882986\",\n \"text\": \"def update_uwsgitool():\\n url = 'https://github.com/kyan001/PyMyApps/raw/master/UwsgiTool/uwsgiTool.py'\\n if cct.update_file(__file__, url):\\n cct.run_cmd('{py} \\\"{f}\\\"'.format(py=cct.get_py_cmd(), f=__file__))\\n cit.bye(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de90c722d9dab0c46969c302e5d83152\",\n \"score\": \"0.4787962\",\n \"text\": \"def update():\\n update_all_hometpl()\\n update_ssh()\\n update_debian()\\n update_crontab()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0c4a84678b5cf973e2b74fe99fd3f1\",\n \"score\": \"0.47873887\",\n \"text\": \"def download_old_versions(d):\\n\\n content_url = d.getVar('SWUPD_CONTENT_BUILD_URL', True)\\n version_url = d.getVar('SWUPD_VERSION_BUILD_URL', True)\\n current_format = int(d.getVar('SWUPD_FORMAT', True))\\n deploy_dir = d.getVar('DEPLOY_DIR_SWUPD', True)\\n www_dir = os.path.join(deploy_dir, 'www')\\n\\n if not content_url or not version_url:\\n bb.warn('SWUPD_CONTENT_BUILD_URL and/or SWUPD_VERSION_BUILD_URL not set, skipping download of old versions for the initial build of a swupd update stream.')\\n return\\n\\n # Avoid double // in path. At least twisted is sensitive to that.\\n content_url = content_url.rstrip('/')\\n version_url = version_url.rstrip('/')\\n\\n # Set up env variables with proxy information for use in urllib.\\n export_proxies(d)\\n\\n # Find latest version for each of the older formats.\\n # For now we ignore the released milestones and go\\n # directly to the URL with all builds. The information\\n # about milestones may be relevant for determining\\n # how format changes need to be handled.\\n latest_versions = {}\\n for format in range(3, current_format + 1):\\n try:\\n url = '%s/version/format%d/latest' % (version_url, format)\\n response = urllib.request.urlopen(url)\\n version = int(response.read())\\n latest_versions[format] = version\\n formatdir = os.path.join(www_dir, 'version', 'format%d' % format)\\n bb.utils.mkdirhier(formatdir)\\n with open(os.path.join(formatdir, 'latest'), 'w') as latest:\\n latest.write(str(version))\\n except urllib.error.HTTPError as http_error:\\n if http_error.code == 404:\\n bb.debug(1, '%s does not exist, skipping that format' % url)\\n else:\\n raise\\n except urllib.error.URLError as url_error:\\n # Happens for file:// URLs.\\n if isinstance(url_error.reason, OSError) and url_error.reason.errno == errno.ENOENT:\\n bb.debug(1, '%s does not exist, skipping that format' % url)\\n else:\\n raise\\n\\n # Now get the Manifests of the latest versions and the\\n # versions we are supposed to provide a delta for, as a starting point.\\n # In addition, we also need Manifests that provide files reused by\\n # these initial set of Manifests or get referenced by them.\\n #\\n # There's no integrity checking for the files. bsdtar is\\n # expected to detect corrupted archives and https is expected\\n # to protect against man-in-the-middle attacks.\\n pending_versions = set(latest_versions.values())\\n pending_versions.update([int(x) for x in d.getVar('SWUPD_DELTAPACK_VERSIONS', True).split()])\\n fetched_versions = set([0])\\n while pending_versions:\\n version = pending_versions.pop()\\n sub_versions = set()\\n sub_versions.update(download_manifests(content_url, version,\\n 'MoM',\\n os.path.join(www_dir, str(version))))\\n sub_versions.update(download_manifests(content_url, version,\\n 'full',\\n os.path.join(www_dir, str(version))))\\n fetched_versions.add(version)\\n pending_versions.update(sub_versions.difference(fetched_versions))\\n\\n latest_version_file = os.path.join(deploy_dir, 'image', 'latest.version')\\n if not os.path.exists(latest_version_file):\\n # We located information about latest version from online www update repo.\\n # Now use that to determine what we are updating from. Doing this here\\n # instead of swupd-image.bbclass has the advantage that we can do some\\n # sanity checking very early in a build.\\n #\\n # Building a proper update makes swupd_create_fullfiles\\n # a lot faster because it allows reusing existing, unmodified files.\\n # Saves a lot of space, too, because the new Manifest files then merely\\n # point to the older version (no entry in ${DEPLOY_DIR_SWUPD}/www/${OS_VERSION}/files,\\n # not even a link).\\n if not latest_versions:\\n bb.fatal(\\\"%s does not exist and no information was found under SWUPD_CONTENT_BUILD_URL %s, cannot proceed without information about the previous build. When building the initial version, unset SWUPD_VERSION_BUILD_URL and SWUPD_CONTENT_BUILD_URL to proceed.\\\" % (latest_version_file, content_url))\\n latest = sorted(latest_versions.values())[-1]\\n bb.debug(2, \\\"Setting %d in latest.version file\\\" % latest)\\n with open(latest_version_file, 'w') as f:\\n f.write(str(latest))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46cf41a81a9d1dc11b2cb58237a7aa27\",\n \"score\": \"0.47735083\",\n \"text\": \"async def update(self, ctx):\\n\\t\\tlog.info(emb(f'Git pull called, Caller:[{ctx.author.id}][{ctx.author.name}]'))\\n\\t\\tbash_cmd = \\\"git pull\\\"\\n\\t\\tprocess = subprocess.Popen(bash_cmd.split(), stdout=subprocess.PIPE)\\n\\t\\toutput, error = process.communicate()\\n\\t\\toutput = output.decode(\\\"utf-8\\\")\\n\\t\\tlog.info(output)\\n\\t\\tawait ctx.send(emb(output))\\n\\n\\t\\tif error is not None:\\n\\t\\t\\tlog.error(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a998ae0a42f49efc4cc345bfaf3982\",\n \"score\": \"0.4769368\",\n \"text\": \"def graball(self):\\n\\n\\t\\tif self.debug:\\n\\t\\t\\tif self.debounce():\\n\\t\\t\\t\\tfor uri in self.urilist:\\n\\t\\t\\t\\t\\tself.grabone(uri)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint('Not fetching because we fetched within the last second.')\\n\\n\\t\\t\\tprint('Waiting...')\\n\\n\\t\\telse:\\n\\t\\t\\tif self.debounce():\\n\\t\\t\\t\\tname = str(time.time()).split('.')[0] + '.html'\\n\\t\\t\\t\\tfor uri in self.urilist:\\n\\t\\t\\t\\t\\tbasepath = op.normpath(self.outdirname + '/' + WebGetter.pathfromuri(uri) + '/')\\n\\n\\t\\t\\t\\t\\treq = self.http.req(uri)\\n\\t\\t\\t\\t\\tflat = Inliner(req, uri, self.http).get()\\n\\n\\t\\t\\t\\t\\tif self.checkhash(basepath, flat):\\n\\t\\t\\t\\t\\t\\turidir = WebGetter.pathfromuri(uri)\\n\\t\\t\\t\\t\\t\\tpath = self.outdirname + '/' + uridir + '/' + name\\n\\t\\t\\t\\t\\t\\tfhandle = open(op.normpath(path), 'wb')\\n\\t\\t\\t\\t\\t\\tfhandle.write(flat.encode('utf-8'))\\n\\t\\t\\t\\t\\t\\tfhandle.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74700b3ecfc9dd64c909eb44a17d2948\",\n \"score\": \"0.47403857\",\n \"text\": \"def main():\\n with open(os.getenv('GITHUB_EVENT_PATH')) as f:\\n event_info = json.loads(f.read())\\n\\n repo = setup_git(event_info['pull_request']['merge_commit_sha'])\\n\\n comment_body = ''\\n new_tag = None\\n if len(repo.tags) == 0:\\n new_tag = 'v1.0.0'\\n else:\\n try:\\n new_tag = semver_bump(repo)\\n except ValueError:\\n comment_body = 'latest tag does not conform to semver ([v]?MAJOR.MINOR.PATCH), failed to bump version'\\n\\n if new_tag is not None:\\n create_and_push_tag(repo, event_info['pull_request']['merge_commit_sha'], new_tag)\\n comment_body = f\\\"This PR has now been tagged as [{new_tag}](https://github.com/{os.getenv('GITHUB_REPOSITORY')}/releases/tag/{new_tag})\\\"\\n\\n comment_on_pr(event_info['number'], comment_body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e277e38259195fe86b0f884d8c056053\",\n \"score\": \"0.47390124\",\n \"text\": \"def run(self) -> None:\\n try:\\n resp = requests.get(self.url, auth=self.auth, timeout=self.timeout)\\n if not resp.ok:\\n logger.info(\\n f\\\"{self.prog_name} : unable to fetch {self.url} : {resp.text}\\\"\\n ) # pylint: disable=logging-fstring-interpolation\\n return\\n\\n parser = MyParser()\\n parser.feed(resp.text)\\n if not parser.output_list:\\n logger.info(\\n f\\\"{self.prog_name} : no packages links detected in {resp.text}\\\"\\n ) # pylint: disable=logging-fstring-interpolation\\n return\\n\\n last_link = parser.output_list[-1]\\n last_version_matches = re.search(r\\\"(?:(\\\\d+\\\\.[.\\\\d]*\\\\d+))\\\", last_link)\\n if not last_version_matches:\\n logger.info(\\n f\\\"{self.prog_name} : no version found in string {last_link}\\\"\\n ) # pylint: disable=logging-fstring-interpolation\\n return\\n\\n last_version = last_version_matches.group(1)\\n\\n last_version_info = Version.parse(last_version)\\n current_version_info = Version.parse(self.current_version)\\n if current_version_info < last_version_info:\\n pip_extra_index_url = \\\"\\\"\\n pipx_extra_index_url = \\\"\\\"\\n if self.domain != DEFAULT_PYPI:\\n pip_extra_index_url = f\\\"--extra-index-url=https://{self.domain} \\\"\\n pipx_extra_index_url = f\\\"--index-url=https://{self.domain} \\\"\\n self.new_version_warning = click.style(\\n f\\\"\\\"\\\"\\n{self.prog_name} : new version {last_version} available (current version: {self.current_version})\\nupgrade command :\\n pip3 install -U {pip_extra_index_url}{self.prog_name}\\nOR pipx upgrade {pipx_extra_index_url}{self.prog_name} (preferred)\\\"\\\"\\\",\\n fg=\\\"bright_blue\\\",\\n )\\n except Exception as error: # pylint: disable=broad-except\\n logger.exception(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440da2e765acc69b4bac536335f4576a\",\n \"score\": \"0.47359017\",\n \"text\": \"def loadRoutine2():\\n subprocess.call([\\\"notify-send\\\", \\\"i-am-bored\\\", \\\"Enjoy Terence Tao's blog today.\\\"])\\n subprocess.call(\\\"xdg-open https://terrytao.wordpress.com/\\\", shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b70dbbf50edc573e553f11b9ece7e7b\",\n \"score\": \"0.47322673\",\n \"text\": \"def update():\\n result = run(\\\"yum check-update --disablerepo='*artifactory' %s\\\" % (env.excludes), pty=True)\\n if result.return_code == 100:\\n \\\"\\\"\\\"Run yum update with exclusions\\\"\\\"\\\"\\n print \\\"<font color=yellow>%s needs updating.</font>\\\" % env.host\\n print (\\\"These are the excludes: %s\\\") % (env.excludes)\\n sudo(\\\"yum -y update --disablerepo='*artifactory' %s\\\" % (env.excludes), pty=True)\\n elif result.return_code == 0:\\n print \\\"<font color=red>%s does not seem to need any updates, skipping...</font>\\\" % env.host\\n elif result.return_code == 1:\\n print \\\"<font color=red>%s returned an error</font>\\\" % env.host\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c4eaddd5f9cef8039f530288385ffa\",\n \"score\": \"0.47188002\",\n \"text\": \"def hooks() -> None:\\n proj().setup_git_hooks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aecca12f0a06a1025270e346d051060f\",\n \"score\": \"0.4714615\",\n \"text\": \"def run(self):\\r\\n if self.demands.refresh_metadata:\\r\\n self.base.cleanCli('expire-cache')\\r\\n if self.demands.sack_activation:\\r\\n lar = self.demands.available_repos\\r\\n self.base.fill_sack(load_system_repo='auto',\\r\\n load_available_repos=lar)\\r\\n self.base.plugins.run_sack()\\r\\n return self.command.run(self.base.extcmds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c0d3e63e045d105b0319685d9360df\",\n \"score\": \"0.47138634\",\n \"text\": \"def main():\\n clear_api()\\n build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8878f32ef860b2ae5525c62ce21b59\",\n \"score\": \"0.4711336\",\n \"text\": \"def run():\\n _rundocker('dev', env.docker_run_cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af26926769b9981117de6d540f6de3d9\",\n \"score\": \"0.46989152\",\n \"text\": \"def patch():\\n\\tCLIENT_PATH = VENV_PATH + 'src/grimoirelab-perceval/perceval/client.py'\\n\\tclient_patch(CLIENT_PATH).patch()\\n\\t\\n\\tINDEX_LOCK_PATH = VENV_PATH + 'src/grimoirelab-elk/grimoire_elk/elastic.py'\\n\\tindex_lock_patch(INDEX_LOCK_PATH).patch()\\n\\t\\n\\tGITHUB_PATH = VENV_PATH + 'src/grimoirelab-perceval/perceval/backends/core/github.py'\\n\\tgithub_patch(GITHUB_PATH).patch()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b2957d228db6b33144b45872f405ab4\",\n \"score\": \"0.4696051\",\n \"text\": \"def _pull_site_action(self, event):\\n self._fetch_site()\\n event.set_results({\\\"result\\\": \\\"site pulled\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f48ce82039e338adfac5ef776ec719d\",\n \"score\": \"0.46897972\",\n \"text\": \"def pull_code(local_folder, urlrepo=\\\"https://github.com/di-unipi-socc/DockerFinder.git\\\", branch=\\\"dfcustom\\\" ):\\n Repo.clone_from(urlrepo, local_folder , branch=branch, depth=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b789dc76edef85501a6ba94a1737963\",\n \"score\": \"0.4687412\",\n \"text\": \"def _build_and_run_tests(self, cfg, update_step, bot_db, revision_hash,\\n **kwargs):\\n with_patch = 'With Patch' in kwargs.get('name') # pragma: no cover\\n\\n # We don't need to do a checkout if there's a patch applied, since that will\\n # overwrite the local changes and potentially change the test results.\\n if not with_patch: # pragma: no cover\\n update_step = self._checkout_revision(update_step, bot_db, revision_hash)\\n if not revision_hash:\\n if update_step.presentation.properties:\\n revision_hash = update_step.presentation.properties['got_revision']\\n revision = build_state.BuildState(self, revision_hash, with_patch)\\n # request build and wait for it only when the build is nonexistent\\n if with_patch or not self._gsutil_file_exists(revision.build_file_path):\\n revision.request_build()\\n revision.wait_for()\\n revision.download_build(update_step, bot_db)\\n if self.m.chromium.c.TARGET_PLATFORM == 'android':\\n self.m.chromium_android.adb_install_apk('ChromePublic.apk')\\n\\n return self._run_test(cfg, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d428df469aebae71ed5bb779804e6fb\",\n \"score\": \"0.46746722\",\n \"text\": \"def github_get_pull_request_all():\\n return json.load(urlopen(format_repo('http://github.com/api/v2/json/pulls/{repo}')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e84f59223e1d74711c47d74c4839735\",\n \"score\": \"0.4663502\",\n \"text\": \"def hotfix(url,products_dir='products'):\\n with api.cd('%s/%s'%(api.env.path,products_dir)):\\n with asbuildoutuser():\\n #api.run(\\\"curl %s /tmp/hotfix.zip\\\"%url)\\n #api.run(\\\"python -c \\\\\\\"import urllib; f=open('/tmp/hotfix.zip','w'); f.write(urllib.urlopen('%s').read()); f.close()\\\\\\\"\\\"%url)\\n filename = os.path.basename(url)\\n tmp = '/tmp/%s'%filename\\n if not os.path.exists(tmp):\\n f=open(tmp,'w')\\n f.write(urllib.urlopen(url).read())\\n f.close()\\n api.put(tmp, tmp)\\n try:\\n api.run(\\\"unzip -o %s\\\"%tmp)\\n except:\\n api.run(\\\"\\\"\\\"python -c \\\"import zipfile;import urllib;import StringIO; zipfile.ZipFile(StringIO.StringIO(urllib.urlopen('%s').read())).extractall()\\\" \\\"\\\"\\\"%url)\\n\\n group = api.env['buildout-group']\\n api.run(\\\"chgrp -R %s .\\\"%(group))\\n api.run('rm %s'%tmp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ed65a9571e5c34eacf4adbba65e157\",\n \"score\": \"0.46541414\",\n \"text\": \"def run_downloader(self):\\n cmd = '''\\n source import-stx\\n stx -d shell --no-tty -c \\\"downloader -b -s -B std,rt\\\"\\n '''\\n ret = run_cmd(cmd)\\n log.info(\\\"Downloader return code %s\\\", ret.returncode)\\n if ret.returncode != 0:\\n raise Exception(\\\"Error while downloading dependencies\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284b42169720f0c88e4a289815c5f73a\",\n \"score\": \"0.46497148\",\n \"text\": \"def git_pull():\\n repo.git.pull('origin')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ed090ea9933838a34b9b64766667f2\",\n \"score\": \"0.46468872\",\n \"text\": \"def main() -> None:\\n parser = argparse.ArgumentParser(description=main.__doc__)\\n parser.add_argument(\\\"url\\\")\\n args = parser.parse_args()\\n\\n if platform.system() == \\\"Darwin\\\":\\n subprocess.call((\\\"open\\\", args.url))\\n else:\\n subprocess.call((\\\"chromium\\\", \\\"--new-window\\\", args.url))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a182863158c0a8e227478cecf7a97071\",\n \"score\": \"0.46411553\",\n \"text\": \"def tools_upgrade(auth, ignore_apps=False, ignore_packages=False):\\n from yunohost.app import app_upgrade\\n\\n failure = False\\n\\n # Retrieve interface\\n is_api = True if msettings.get('interface') == 'api' else False\\n\\n if not ignore_packages:\\n cache = apt.Cache()\\n cache.open(None)\\n cache.upgrade(True)\\n\\n # If API call\\n if is_api:\\n critical_packages = (\\\"moulinette\\\", \\\"moulinette-yunohost\\\",\\n \\\"yunohost-admin\\\", \\\"yunohost-config-nginx\\\", \\\"ssowat\\\", \\\"python\\\")\\n critical_upgrades = set()\\n\\n for pkg in cache.get_changes():\\n if pkg.name in critical_packages:\\n critical_upgrades.add(pkg.name)\\n # Temporarily keep package ...\\n pkg.mark_keep()\\n # ... and set a hourly cron up to upgrade critical packages\\n if critical_upgrades:\\n msignals.display(m18n.n('packages_upgrade_critical_later',\\n ', '.join(critical_upgrades)))\\n with open('/etc/cron.d/yunohost-upgrade', 'w+') as f:\\n f.write('00 * * * * root PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin apt-get install %s -y && rm -f /etc/cron.d/yunohost-upgrade\\\\n' % ' '.join(critical_upgrades))\\n\\n if cache.get_changes():\\n msignals.display(m18n.n('upgrading_packages'))\\n try:\\n # Apply APT changes\\n # TODO: Logs output for the API\\n cache.commit(apt.progress.text.AcquireProgress(),\\n apt.progress.base.InstallProgress())\\n except Exception as e:\\n failure = True\\n logging.warning('unable to upgrade packages: %s' % str(e))\\n msignals.display(m18n.n('packages_upgrade_failed'), 'error')\\n else:\\n msignals.display(m18n.n('done'))\\n else:\\n msignals.display(m18n.n('packages_no_upgrade'))\\n\\n if not ignore_apps:\\n try:\\n app_upgrade(auth)\\n except Exception as e:\\n failure = True\\n logging.warning('unable to upgrade apps: %s' % str(e))\\n msignals.display(m18n.n('app_upgrade_failed'), 'error')\\n\\n if not failure:\\n msignals.display(m18n.n('system_upgraded'), 'success')\\n\\n # Return API logs if it is an API call\\n if is_api:\\n from yunohost.service import service_log\\n return { \\\"log\\\": service_log('yunohost-api', number=\\\"100\\\").values()[0] }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdcfd24289cc3e52c462f3c51bb4a95f\",\n \"score\": \"0.46410316\",\n \"text\": \"def release(repo, tag, site_uri, sync_uri=None):\\n build(repo, tag, site_uri, None, False)\\n if (sync_uri):\\n sync_site(sync_uri, site_uri)\\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)\\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)\\n local('php /var/aegir/drush/drush.php @%s features-list' % site_uri)\\n local('php /var/aegir/drush/drush.php --yes @%s features-revert-all' % site_uri)\\n local('php /var/aegir/drush/drush.php @%s cache-clear all' % site_uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185f862b7ef72826b0a3aa296d6c3a6b\",\n \"score\": \"0.46341094\",\n \"text\": \"def update():\\n local('sudo git pull')\\n local('sudo pyclean .')\\n\\n local('sudo ../.env/bin/pip install -q -r requirements.txt')\\n local('sudo ../.env/bin/yoyo-migrate -b apply migrations/')\\n\\n local('sudo service uwsgi restart')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3a297cc1961080794f9d33ab7ca04c\",\n \"score\": \"0.46320468\",\n \"text\": \"def fetch(ui, url, path, *args, **opts):\\n if url.startswith(\\\"hg+ssh://\\\"):\\n url = url[8:]\\n else:\\n url = url.replace(\\\"hg://\\\", \\\"http://\\\")\\n try:\\n cmd = locate_program(\\\"hg\\\", raise_error=True)\\n except ValueError, e:\\n raise VendorError(e)\\n \\n cmd += \\\" clone %s %s\\\" % (url, path)\\n\\n # exec cmd\\n (child_stdin, child_stdout, child_stderr) = popen3(cmd)\\n err = child_stderr.read()\\n if ui.verbose >=2:\\n ui.logger.info(child_stdout.read())\\n if err:\\n raise VendorError(str(err)) \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e484ccfccc8043c2262ab705e6cfd0\",\n \"score\": \"0.4624194\",\n \"text\": \"def deploy():\\n clear_compiled_python_files()\\n git_pull()\\n update_requirements()\\n run_migrations()\\n collectstatic()\\n reload_http()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e026b032939e72a71dba0fad5883f72a\",\n \"score\": \"0.46217918\",\n \"text\": \"def first_deploy():\\n with cd(APPS_DIR):\\n run(\\\"git clone %s\\\" % GITHUB_REPO)\\n restart_apache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4181664b821ae34410ae8654c86d22d7\",\n \"score\": \"0.46207875\",\n \"text\": \"def fetch_runner(pkgnames, preprocessed=False):\\n abspkgs = []\\n aurpkgs = []\\n allpkgs = []\\n try:\\n if preprocessed:\\n allpkgs = pkgnames\\n pkgnames = [p.name for p in allpkgs]\\n else:\\n print(':: ' + _('Fetching package information...'))\\n for pkgname in pkgnames:\\n pkg = None\\n try:\\n pkg = pkgbuilder.utils.info([pkgname])[0]\\n except IndexError:\\n try:\\n DS.log.info('{0} not found in the AUR, checking in '\\n 'repositories'.format(pkgname))\\n syncpkgs = []\\n for j in [i.pkgcache for i in DS.pyc.get_syncdbs()]:\\n syncpkgs.append(j)\\n syncpkgs = functools.reduce(lambda x, y: x + y,\\n syncpkgs)\\n abspkg = pyalpm.find_satisfier(syncpkgs, pkgname)\\n pkg = pkgbuilder.package.ABSPackage.from_pyalpm(abspkg)\\n\\n except AttributeError:\\n pass\\n allpkgs.append(pkg)\\n if not pkg:\\n raise pkgbuilder.exceptions.PackageNotFoundError(\\n pkgname, 'fetch')\\n\\n for pkg in allpkgs:\\n if pkg.is_abs:\\n abspkgs.append(pkg)\\n else:\\n aurpkgs.append(pkg)\\n\\n if abspkgs:\\n print(_(':: Retrieving packages from asp...'))\\n pm = pkgbuilder.ui.Progress(len(abspkgs))\\n for pkg in abspkgs:\\n pm.msg(_('retrieving {0}').format(pkg.name), True)\\n rc = asp_export(pkg)\\n if rc > 0:\\n raise pkgbuilder.exceptions.NetworkError(\\n _('Failed to retieve {0} (from ASP).').format(\\n pkg.name), source='asp', pkg=pkg, retcode=rc)\\n\\n if aurpkgs:\\n print(_(':: Retrieving packages from aur...'))\\n pm = pkgbuilder.ui.Progress(len(aurpkgs))\\n for pkg in aurpkgs:\\n pm.msg(_('cloning {0}').format(pkg.packagebase), True)\\n clone(pkg.packagebase)\\n\\n print(_('Successfully fetched: ') + ' '.join(pkgnames))\\n except pkgbuilder.exceptions.PBException as e:\\n print(':: ERROR: ' + str(e.msg))\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa7b45ed98917204fc73742fbebb4b4\",\n \"score\": \"0.461377\",\n \"text\": \"def pull(remote: str = '') -> None:\\n proj().pull(remote)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2783b935a8b5d9cf3b638aabb0f2fb\",\n \"score\": \"0.46110207\",\n \"text\": \"def download_dependencies(projects, dependencies, branch, project_root):\\n for dependency in dependencies:\\n project = find_project_for_name(projects, dependency)\\n if project is None:\\n print(f'Skipping unknown dependency {dependency}')\\n continue\\n directory = project_root + '/' + project['path']\\n run_command(f\\\"git fetch {project['remote']['name']} {branch}\\\",\\n directory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4778835fa86fde2ce78706c57145f8\",\n \"score\": \"0.46105772\",\n \"text\": \"def setup_pristine_repositories():\\n\\n # these directories don't exist out of the box, so we may have to create them\\n if not os.path.exists(main.general_wc_dir):\\n os.makedirs(main.general_wc_dir)\\n\\n if not os.path.exists(main.general_repo_dir):\\n os.makedirs(main.general_repo_dir) # this also creates all the intermediate dirs\\n\\n if not os.path.exists(main.other_dav_root_dir):\\n os.makedirs(main.other_dav_root_dir)\\n if not os.path.exists(main.non_dav_root_dir):\\n os.makedirs(main.non_dav_root_dir)\\n\\n _setup_pristine_repo(main.greek_state,\\n main.pristine_greek_repos_dir,\\n main.greek_dump_dir,\\n main.pristine_greek_repos_url)\\n\\n # NOTE: We don't use precooked trojan repositories.\\n _setup_pristine_repo(main.trojan_state,\\n main.pristine_trojan_repos_dir,\\n main.trojan_dump_dir,\\n main.pristine_trojan_repos_url,\\n use_precooked=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409bcfa9d0c2d1018b31d578567188f6\",\n \"score\": \"0.46065253\",\n \"text\": \"def github_get_pull_request_all(urls):\\n return keep_trying(lambda: _query(urls.pull_list_url), urllib2.URLError,\\n \\\"get list of all pull requests\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6523cd565db9f75ebfe8a2eb9b9e96cb\",\n \"score\": \"0.4598307\",\n \"text\": \"def sync(args=\\\"\\\"):\\n\\n spawn.process(f\\\"{config.aur_helper} --noconfirm -Sua {args}\\\")\\n log.write(\\\"Synced AUR packages\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55399e63a9c7850c3a7286935e807a07\",\n \"score\": \"0.45883447\",\n \"text\": \"def _download_chrome_drivers():\\n # Mac OS X\\n if platform.system() == 'Darwin':\\n remote_file = driver_urls['chrome_os_x']\\n elif platform.system() == 'Linux':\\n remote_file = driver_urls['chrome_ubuntu']\\n elif platform.system() == 'Windows':\\n remote_file = driver_urls['chrome_windows_10']\\n else:\\n raise ValueError('Unsupported OS specified: %s' % (platform.system()))\\n _download_temp_file(remote_file['url'], remote_file['file_name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02629142812d403cf7a5c691f4768d0\",\n \"score\": \"0.45816785\",\n \"text\": \"def pull(keys, targets='all'):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edfca4955955386a5f47f52a26256f3f\",\n \"score\": \"0.45725054\",\n \"text\": \"def _pull_predefined_dockerimages(self):\\n dc = DockerClient()\\n for url in list(self.remote_docker_image_urls.values()):\\n # only pull if not present (speedup for development)\\n if not FORCE_PULL:\\n if len(dc.images.list(name=url)) > 0:\\n LOG.debug(\\\"Image %r present. Skipping pull.\\\" % url)\\n continue\\n LOG.info(\\\"Pulling image: %r\\\" % url)\\n # this seems to fail with latest docker api version 2.0.2\\n # dc.images.pull(url,\\n # insecure_registry=True)\\n # using docker cli instead\\n cmd = [\\\"docker\\\",\\n \\\"pull\\\",\\n url,\\n ]\\n Popen(cmd).wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d5d13a4143133833bef60d61df731b\",\n \"score\": \"0.45721\",\n \"text\": \"def MP():\\n url = \\\"https://github.com/m3ttiw\\\"\\n os.startfile(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6e80c163ea5c9b03631b43cae7ba1e\",\n \"score\": \"0.45707574\",\n \"text\": \"def DownloadChromium(channel):\\n # Get the version for the current channel from omahaproxy\\n platform_data = PLATFORM_MAPPING[sys.platform]\\n omaha_platform = platform_data['omaha']\\n version_lookup_url = VERSION_LOOKUP_URL % (omaha_platform, channel)\\n response = urllib2.urlopen(version_lookup_url)\\n version = response.readlines()[1].split(',')[2]\\n\\n # Get the base position for that version from omahaproxy\\n base_pos_lookup_url = BASE_POS_LOOKUP_URL % version\\n response = urllib2.urlopen(base_pos_lookup_url)\\n base_pos = json.load(response)['chromium_base_position']\\n\\n # Find the build from that base position in cloud storage. If it's not found,\\n # decrement base position until one is found.\\n cloud_storage_lookup_url = CLOUD_STORAGE_LOOKUP_URL % (\\n platform_data['prefix'], base_pos)\\n download_url = None\\n while not download_url:\\n response = urllib2.urlopen(cloud_storage_lookup_url)\\n prefixes = json.load(response).get('prefixes')\\n if prefixes:\\n download_url = CLOUD_STORAGE_DOWNLOAD_URL % (\\n platform_data['prefix'], base_pos, platform_data['zip_prefix'])\\n break\\n base_pos = int(base_pos) - 1\\n cloud_storage_lookup_url = CLOUD_STORAGE_LOOKUP_URL % (\\n platform_data['prefix'], base_pos)\\n\\n print 'Approximating Chrome %s with chromium from base position %s.' % (\\n version, base_pos)\\n print 'Downloading from %s' % download_url\\n\\n tmpdir = tempfile.mkdtemp()\\n zip_path = os.path.join(tmpdir, 'chrome.zip')\\n with open(zip_path, 'wb') as local_file:\\n local_file.write(urllib2.urlopen(download_url).read())\\n zf = zipfile.ZipFile(zip_path)\\n zf.extractall(path=tmpdir)\\n return tmpdir, version\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994282,"cells":{"query_id":{"kind":"string","value":"a5654440ae5c404efcd3140429dcd2df"},"query":{"kind":"string","value":"Excutes 'query' in textarea in respose to the 'submit' button. Either the 'status' div or the cell's output region are updated according to whether the output mode is set to 'html' or 'text'. An explict COMMIT is required to modify a database file opened in either 'r' or 'w' mode. The pseudo command \".schema\" dumps the database's schema, and '.changes' gives the total number of changes to the database in this interactive session."},"positive_passages":{"kind":"list like","value":[{"docid":"1aebb93a9022179b9fdb512f14d20a1d","score":"0.64445883","text":"def executeSQL(self, button):\n query = self.query.value\n self.history.append(query)\n self.histIdx = -1\n if (query.strip().lower().startswith('.schema')):\n table = query[7:].strip()\n if len(table):\n df = pandas.read_sql_query(self.getTable % table, self.db)\n else:\n df = pandas.read_sql_query(self.getSchema, self.db)\n if (self.output == 'html'):\n html = ''\n for i, sql in enumerate(df['sql']):\n if not (df['name'][i].startswith('sqlite_')):\n if (self.output == 'html'):\n html += self.schemaStyle % (sql.replace(' ', ' ').replace('\\n', '<br>'))\n else:\n print(sql)\n print()\n if (self.output == 'html'):\n self.status.value = '<div class=\"sqldiv\">%s</div>' % html\n self.result = None\n return\n if (query.strip().lower() == '.changes'):\n if (self.output == 'html'):\n self.status.value = self.defaultStyle % (\"SQL Total Changes = %d\" % self.db.total_changes)\n else:\n print(\"SQL Total Changes = %d\" % self.db.total_changes)\n self.result = None\n return\n elif (query.strip().lower() == 'commit'):\n self.db.commit()\n if (self.output == 'html'):\n self.status.value = self.defaultStyle % \"SQL Transaction committed\"\n else:\n print(\"SQL Transaction committed\")\n self.result = None\n return\n try:\n df = pandas.read_sql_query(query, self.db)\n if (self.output == 'html'):\n result = df.to_html(max_rows=50, classes='sqltable')\n self.status.value = self.tableStyle % result\n else:\n print(df)\n print()\n self.result = [[v for v in row.values] for i, row in df.iterrows()]\n except TypeError as error:\n if (self.output == 'html'):\n self.status.value = self.defaultStyle % \"SQL Command Succeeded\"\n else:\n print(\"SQL Command Succeeded\")\n pass\n self.result = None\n except Exception as error:\n if (self.output == 'html'):\n self.status.value = self.errorStyle % str(error)\n else:\n print(\"SQL Error: %s\" % str(error))\n pass\n self.result = None","title":""}],"string":"[\n {\n \"docid\": \"1aebb93a9022179b9fdb512f14d20a1d\",\n \"score\": \"0.64445883\",\n \"text\": \"def executeSQL(self, button):\\n query = self.query.value\\n self.history.append(query)\\n self.histIdx = -1\\n if (query.strip().lower().startswith('.schema')):\\n table = query[7:].strip()\\n if len(table):\\n df = pandas.read_sql_query(self.getTable % table, self.db)\\n else:\\n df = pandas.read_sql_query(self.getSchema, self.db)\\n if (self.output == 'html'):\\n html = ''\\n for i, sql in enumerate(df['sql']):\\n if not (df['name'][i].startswith('sqlite_')):\\n if (self.output == 'html'):\\n html += self.schemaStyle % (sql.replace(' ', ' ').replace('\\\\n', '<br>'))\\n else:\\n print(sql)\\n print()\\n if (self.output == 'html'):\\n self.status.value = '<div class=\\\"sqldiv\\\">%s</div>' % html\\n self.result = None\\n return\\n if (query.strip().lower() == '.changes'):\\n if (self.output == 'html'):\\n self.status.value = self.defaultStyle % (\\\"SQL Total Changes = %d\\\" % self.db.total_changes)\\n else:\\n print(\\\"SQL Total Changes = %d\\\" % self.db.total_changes)\\n self.result = None\\n return\\n elif (query.strip().lower() == 'commit'):\\n self.db.commit()\\n if (self.output == 'html'):\\n self.status.value = self.defaultStyle % \\\"SQL Transaction committed\\\"\\n else:\\n print(\\\"SQL Transaction committed\\\")\\n self.result = None\\n return\\n try:\\n df = pandas.read_sql_query(query, self.db)\\n if (self.output == 'html'):\\n result = df.to_html(max_rows=50, classes='sqltable')\\n self.status.value = self.tableStyle % result\\n else:\\n print(df)\\n print()\\n self.result = [[v for v in row.values] for i, row in df.iterrows()]\\n except TypeError as error:\\n if (self.output == 'html'):\\n self.status.value = self.defaultStyle % \\\"SQL Command Succeeded\\\"\\n else:\\n print(\\\"SQL Command Succeeded\\\")\\n pass\\n self.result = None\\n except Exception as error:\\n if (self.output == 'html'):\\n self.status.value = self.errorStyle % str(error)\\n else:\\n print(\\\"SQL Error: %s\\\" % str(error))\\n pass\\n self.result = None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e21bbedcfe57e4dde89c501f78053c1d","score":"0.5836899","text":"def commit(message):\n settings = _load_settings()\n status = _load_settings(\".TDB\", check=[])\n settings.update(status)\n database = settings[\"database\"]\n client, msg = _connect(settings)\n click.echo(msg)\n sys.path.append(os.getcwd())\n schema_plan = __import__(\"schema\", globals(), locals(), [], 0)\n last_item = None\n documentation = {}\n for line in schema_plan.__doc__.split(\"\\n\"):\n if \"Title:\" in line:\n documentation[\"title\"] = line[6:].strip()\n last_tiem = documentation[\"title\"]\n elif \"Description:\" in line:\n documentation[\"description\"] = line[12:].strip()\n last_tiem = documentation[\"description\"]\n elif \"Authors:\" in line:\n documentation[\"authors\"] = line[8:].strip()\n last_tiem = documentation[\"authors\"]\n elif last_item is not None:\n last_item += \"\\n\" + line.strip()\n authors = documentation.get(\"authors\")\n if authors:\n authors = documentation[\"authors\"].split(\",\")\n authors = list(map(lambda x: x.strip(), authors))\n schema_obj = WOQLSchema(\n title=documentation.get(\"title\"),\n description=documentation.get(\"description\"),\n authors=authors,\n )\n for obj_str in dir(schema_plan):\n obj = eval(f\"schema_plan.{obj_str}\") # noqa: S307\n if isinstance(obj, woqlschema.TerminusClass) or isinstance(obj, enum.EnumMeta):\n if obj_str not in [\"DocumentTemplate\", \"EnumTemplate\", \"TaggedUnion\"]:\n schema_obj.add_obj(obj.__name__, obj)\n if message is None:\n message = \"Schema updated by Python client.\"\n schema_obj.commit(client, commit_msg=message, full_replace=True)\n click.echo(f\"{database} schema updated.\")","title":""},{"docid":"20f4b7e800f85d30417dae67aa538cde","score":"0.56011313","text":"def sql_btn():\n content = None\n sql = None\n if request.method == 'POST':\n db = get_db()\n query = request.form['sql']\n query = \"\\n\".join(query.splitlines())\n #print(query)\n #pprint.pprint(query)\n sql = query.split('\\n')\n\n print(\"\")\n\n try:\n if sql[0].split(\" \")[0].upper() == \"SELECT\":\n print(\"{}\".format(\" \".join(sql)))\n content = db.execute(\" \".join(sql)).fetchall()\n else:\n print('{}'.format(\" \".join(sql)))\n db.execute(\" \".join(sql))\n db.commit()\n except BaseException as e:\n content = None\n print(str(e))\n flash(str(e))\n\n print(\"\")\n return render_template('sql.html', sql=sql, content=content)","title":""},{"docid":"b21f8b9a80ccfb1a010fef94a8f80b55","score":"0.5516456","text":"def accept(self):\n self.parent.sqlcommand = self.sqlEdit.toPlainText()\n self.parent.errormessage = \"The data was successfully consolidated,\\nthe program will now close.\"\n self.close()","title":""},{"docid":"e44149c528588baa6ceea6346e8d17cc","score":"0.5497707","text":"def handle_editor_command(self, text):\n while special.editor_command(text):\n filename = special.get_filename(text)\n query = (special.get_editor_query(text) or\n self.get_last_query())\n sql, message = special.open_external_editor(filename, sql=query)\n if message:\n # Something went wrong. Raise an exception and bail.\n raise RuntimeError(message)\n while True:\n try:\n text = self.prompt_app.prompt(default=sql)\n break\n except KeyboardInterrupt:\n sql = ''\n continue\n return text","title":""},{"docid":"903985c0d2a908c9c4ce9527b832770f","score":"0.5439685","text":"def sqlcmd(self):\n self.update_sqlcmd = \"\\n\".join([i for i in [self.update_clause, \n self.set_clause, \n self.where_clause] if i])","title":""},{"docid":"25ce078eb48a25c83fa01b4fb25a5c43","score":"0.54361415","text":"def test_execute_sql_query_selection(self):\n self.tab = self._add_new_query_tab()\n sql_query_string = 'SELECT name FROM streets LIMIT 3\\n UPDATE'\n self._prepare_query_text(sql_query_string)\n self.tab.run_query()\n self.assertTrue(self.tab.errors_panel.isVisible())\n self.assertIn('UPDATE', self.tab.errors_panel.toPlainText())\n\n # position cursor before `\\n` in the SQL query\n cur_pos = 32\n cur = self.tab.query.textCursor()\n cur.setPosition(0)\n cur.setPosition(cur_pos, QTextCursor.KeepAnchor)\n self.tab.query.setTextCursor(cur)\n self.assertEqual(self.tab.query.textCursor().selectedText(),\n sql_query_string[:cur_pos])\n\n self.tab.run_query()\n self.assertFalse(self.tab.errors_panel.isVisible())\n self.assertTrue(self.tab.table.isVisible())\n self.assertEqual(self.tab.table.table_data.number_layer_rows, 3)\n self.assertEqual(self.tab.table.table_data.columnCount(), 1)\n return","title":""},{"docid":"0003c9ea1822aada63869891dd3bc090","score":"0.53923744","text":"def menu_update_query(run_args):\n run_args['command'] = input(\"input command:\")\n run_args['error'] = input(\"input error:\")\n run_args['query'] = get_query(run_args['command'], run_args['error'])","title":""},{"docid":"50e971f298692a6fce4de3a911f7dfb3","score":"0.53099227","text":"def submitQuery(self):\n \n # Update query entries\n fpO1 = open('C:/Users/toExecute.sql',\"w\")\n data = fp2.read()\n fp2.close()\n nvlVal1 = self.queryUpdate1.get()\n nvlVal2 = self.queryUpdate2.get()\n nvlVal3 = self.queryUpdate3.get()\n nvlVal4 = nvlVal2[:4] + nvlVal2[5:7] + nvlVal2[8:10]\n print (nvlVal4)\n mydict = {'myNV1': nvlVal1, 'myNV2': nvlVal2, 'myNV3' : nvlVal3, 'myNV4' : nvlVal4}\n \n for key, value in mydict.items():\n data = data.replace(key, value) \n fpO1.write(data)\n fpO1.close()\n \n # Prepare the file and the query\n fpO1 = open(\"C:/Users/toExecute.sql\")\n outFile1 = open(\"C:/Users/query1.sql\", \"w\")\n outFile2 = open(\"C:/Users/query2.sql\", \"w\")\n outFile3 = open(\"C:/Users/query3.sql\", \"w\")\n outFile4 = open(\"C:/Users/query4.sql\", \"w\")\n buffer = []\n keepCurrentSet = True\n for line in fpO1:\n buffer.append(line)\n if line.startswith(\"/*A\"):\n #---- starts a new data set\n if keepCurrentSet:\n outFile1.write(\"\".join(buffer))\n #now reset our state\n keepCurrentSet = False\n buffer = []\n \n elif line.startswith(\"/*B\"):\n if keepCurrentSet:\n outFile2.write(\"\".join(buffer))\n keepCurrentSet = False\n buffer = []\n \n elif line.startswith(\"/*C\"):\n if keepCurrentSet:\n outFile3.write(\"\".join(buffer))\n keepCurrentSet = False\n buffer = []\n \n elif line.startswith(\"/*D\"):\n if keepCurrentSet:\n outFile4.write(\"\".join(buffer))\n keepCurrentSet = False\n buffer = []\n \n elif line.startswith(\";\"):\n keepCurrentSet = True\n \n fpO1.close()\n outFile1.close()\n outFile2.close()\n outFile3.close()\n outFile4.close()\n \n outFile1 = open(\"C:/Users/query1.sql\", \"r\")\n qury1 = outFile1.read()\n \n outFile2 = open(\"C:/Users/query2.sql\", \"r\")\n qury2 = outFile2.read()\n \n outFile3 = open(\"C:/Users/query3.sql\", \"r\")\n qury3 = outFile3.read()\n \n outFile4 = open('C:/Users/query4.sql', 'r')\n qury4 = outFile4.read()\n \n status = ' --- Connection successful --- Query successfully executed ---'\n global cursor1\n global cursor2\n global cursor3\n global cursor4\n global data1\n global data2\n global data3\n global data4\n global fields1, fields2, fields3, fields4\n global names1, names2, names3, traiames4\n \n # Select instance, Open connection, retrieve cursor and execute query\n if self.var.get() == 'IE301':\n connection = pymssql.connect(server ='FRER0973\\IO13',user = 'OMUser', password = '%Essilor1%', database = 'OrderDB')\n connection2 = pymssql.connect(server ='FRER0973\\ION3',user = 'OMUser', password = '%Essilor1%', database = 'OnRouteDB')\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n elif self.var.get() == 'IE401':\n connection = pymssql.connect(server='FRER0972\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\n connection2 = pymssql.connect(server='FRER0972\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n elif self.var.get() == 'IE402':\n connection = pymssql.connect(server='FRER1420\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\n connection2 = pymssql.connect(server='FRER1420\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n elif self.var.get() == 'IE501':\n connection = pymssql.connect(server='FRER1308\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\n connection2 = pymssql.connect(server='FRER1308\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n elif self.var.get() == 'IFF02':\n connection = pymssql.connect(server='FRER1344\\IO1F', user='OMUser', password='%Essilor1%', database='OrderDB')\n connection2 = pymssql.connect(server='FRER1344\\IONF', user='OMUser', password='%Essilor1%', database='OnRouteDB')\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n elif self.var.get() == 'New server':\n \n parser = RawConfigParser()\n parser.read(file_path2)\n myserver = parser.get('add_instance', 'server')\n myserver2 = parser.get('add_instance', 'server2')\n myuser = parser.get('add_instance', 'user')\n mypassword = parser.get('add_instance', 'password')\n mydatabase = parser.get('add_instance', 'dbname')\n \n connection = pymssql.connect(server = myserver, user = myuser, password = mypassword, database = mydatabase)\n connection2 = pymssql.connect(server = myserver2, user = myuser, password = mypassword, database = mydatabase2)\n \n cursor1 = connection.cursor()\n cursor1.execute(qury1)\n data1 = cursor1.fetchall()\n fields1 = cursor1.description # metadata from query\n names1 = [x[0] for x in cursor1.description]\n cursor1.close()\n \n cursor2 = connection.cursor()\n cursor2.execute(qury2)\n data2 = cursor2.fetchall()\n fields2 = cursor2.description # metadata from query\n names2 = [x[0] for x in cursor2.description]\n cursor2.close()\n \n cursor3 = connection.cursor()\n cursor3.execute(qury3)\n data3 = cursor3.fetchall()\n fields3 = cursor3.description # metadata from query\n names3 = [x[0] for x in cursor3.description]\n cursor3.close()\n \n cursor4 = connection2.cursor()\n cursor4.execute(qury4)\n data4 = cursor4.fetchall()\n fields4 = cursor4.description # metadata from query\n names4 = [x[0] for x in cursor4.description]\n cursor4.close()\n \n self.submitStatus.setentry(status)\n \n else:\n status = '--- Connection failed --- No query executed ---'\n self.submitStatus.setentry(status)\n \n self.master.withdraw()\n infoMessage = \"Please wait ! The export is running...\\nThe window will appear once exporting finish.\\nYou can continue to use your PC.\"\n showinfo(\"Running...\", infoMessage)\n \n \"\"\"Export query results to Excel file\"\"\" \n df = pd.DataFrame(list(data1), columns = names1)\n df2 = pd.DataFrame(list(data2), columns = names2)\n df3 = pd.DataFrame(list(data3), columns = names3)\n df4 = pd.DataFrame(list(data4), columns = names4)\n \n # Save a copy of the xls file for upload\n save_path = 'C:/temp/Results.xlsx' \n writer = pd.ExcelWriter(save_path, engine='xlsxwriter')\n list_dfs = [df, df2, df3, df4]\n for n, df in enumerate(list_dfs):\n df.to_excel(writer,'sheet%s' % n)\n writer.save()\n \n src_filename = 'C:/temp/Results.xlsx'\n dest_filename = 'C:/temp/Template_Report.xlsx'\n \n wb1 = openpyxl.load_workbook(dest_filename)\n wb2 = openpyxl.load_workbook(src_filename)\n \n ws2 = wb2.get_sheet_by_name('sheet0')\n ws22 = wb2.get_sheet_by_name('sheet1')\n ws222 = wb2.get_sheet_by_name('sheet2')\n ws2222 = wb2.get_sheet_by_name('sheet3')\n \n sh11_count = (ws222.max_row - 1)\n \n #ws0 = wb1.worksheets[0]\n ws1 = wb1.worksheets[1]\n ws11 = wb1.worksheets[2]\n ws111 = wb1.worksheets[3]\n ws1111 = wb1.worksheets[4]\n \n sh0_count = ws2.max_column\n sh1_count = ws22.max_column\n sh2_count = ws222.max_column\n sh3_count = ws2222.max_column\n \n for i in range(1, sh0_count):\n col_d = ws2.columns[i] # 0-indexing\n for idx, cell in enumerate(col_d, 1):\n ws1.cell(row = idx, column = i).value = cell.value #1-indexing\n ws1['A1'] = 'Request ID'\n ws1['B1'] = 'Order Entry'\n ws1['C1'] = 'Customer Number'\n ws1['D1'] = 'DN Number'\n \n ws1['P1'] = 'Creation Hour'\n ws1['Q1'] = 'Allocation Hour'\n ws1['R1'] = 'Diff Crea/Allo (in Second)'\n ws1['S1'] = 'Round Allocation Time (in Minute)'\n \n for j in range(1, sh1_count):\n col_d = ws22.columns[j] # 0-indexing\n for idx, cell in enumerate(col_d, 1):\n ws11.cell(row = idx, column = j).value = cell.value #1-indexing\n ws11['A1'] = 'Order Entry'\n ws11['B1'] = 'Customer Number'\n ws11['C1'] = 'Request ID'\n ws11['D1'] = 'DN Number'\n ws11['E1'] = 'Product Code'\n ws11['F1'] = 'Product Label'\n ws11['G1'] = 'Status'\n ws11['H1'] = 'Reason'\n ws11['I1'] = 'Lab'\n ws11['J1'] = 'Creation Date'\n ws11['K1'] = 'Severity'\n ws11['L1'] = 'Error Message'\n ws11['M1'] = 'Detailed Error Message'\n \n for k in range(1, sh2_count):\n col_d = ws222.columns[k] # 0-indexing\n for idx, cell in enumerate(col_d, 1):\n ws111.cell(row = idx, column = k).value = cell.value #1-indexing\n ws111['A1'] = 'Order Entry'\n ws111['B1'] = 'Customer Number'\n ws111['C1'] = 'Request ID'\n ws111['D1'] = 'DN Number'\n ws111['G1'] = 'Customer Reference'\n ws111['H1'] = 'Status'\n ws111['I1'] = 'Reason'\n ws111['J1'] = 'Lab'\n ws111['K1'] = 'Creation Date'\n \n for l in range(1, sh3_count):\n col_d = ws2222.columns[l] # 0-indexing\n for idx, cell in enumerate(col_d, 1):\n ws1111.cell(row = idx, column = l).value = cell.value #1-indexing\n ws1111['B1'] = 'Request ID'\n ws1111['C1'] = 'Error Message'\n ws1111['D1'] = 'Error Value'\n \n \n '''for rowNumP in range(2, (ws1.get_highest_row()) + 1):\n if ws1.cell(row = rowNumP, column = 10).value is not None:\n CreationDT = ws1.cell(row = rowNumP, column = 10).value\n CreationT = str(CreationDT)\n Cre = re.search(' (.+?)\\.', CreationT).group(1)\n CreT = datetime.datetime.strptime(Cre, \"%H:%M:%S\")\n CreationTime = CreT.strftime(\"%I:%M:%S %p\")\n ws1.cell(row = rowNumP, column = 16).value = CreationTime\n creTime = time.strptime(CreationTime.split(' ')[0],'%H:%M:%S')\n CrSec = datetime.timedelta(hours=creTime.tm_hour,minutes=creTime.tm_min,seconds=creTime.tm_sec).total_seconds()\n \n if ws1.cell(row = rowNumP, column = 11).value is not None:\n AllocationDT = ws1.cell(row = rowNumP, column = 11).value\n AllocationT = str(AllocationDT)\n Alo = re.search('(?<= )\\S+', AllocationT).group(0)\n OK = re.search(r'\\.', Alo)\n if OK:\n AloT = datetime.datetime.strptime(Alo, \"%H:%M:%S.%f\")\n AllocationTime = AloT.strftime(\"%I:%M:%S %p\")\n ws1.cell(row = rowNumP, column = 17).value = AllocationTime\n aloTime = time.strptime(AllocationTime.split(' ')[0],'%H:%M:%S')\n alSec = datetime.timedelta(hours=aloTime.tm_hour,minutes=aloTime.tm_min,seconds=aloTime.tm_sec).total_seconds()\n ws1.cell(row = rowNumP, column = 18).value = alSec - CrSec\n ws1.cell(row = rowNumP, column = 19).value = round(((ws1.cell(row = rowNumP, column = 18).value)/60), 1)\n \n else:\n AloT = datetime.datetime.strptime(Alo, \"%H:%M:%S\")\n AllocationTime = AloT.strftime(\"%I:%M:%S %p\")\n ws1.cell(row = rowNumP, column = 17).value = AllocationTime\n aloTime = time.strptime(AllocationTime.split(' ')[0],'%H:%M:%S')\n alSec = datetime.timedelta(hours=aloTime.tm_hour,minutes=aloTime.tm_min,seconds=aloTime.tm_sec).total_seconds()\n ws1.cell(row = rowNumP, column = 18).value = alSec - CrSec\n ws1.cell(row = rowNumP, column = 19).value = round(((ws1.cell(row = rowNumP, column = 18).value)/60), 1)\n \n \n if ws1.cell(row = rowNumP, column = 1).value is not None and ws1.cell(row = rowNumP, column = 19).value is None:\n ws1.cell(row = rowNumP, column = 19).value = 0.00'''\n \n wb1.save('C:/temp/ReportUP.xlsx')\n \n # Show the tool window again \n status = \"--- Successfully exported to the final reports file --- Look at C:\\Users ---\"\n self.submitStatus.setentry('')\n self.submitStatus.setentry(status)\n self.master.deiconify()\n infoMessage = \"Allocation rate = %d commands\" % sh11_count\n showinfo(\"Finish\", infoMessage)","title":""},{"docid":"862259078688ff55fff89a2532cf968b","score":"0.5165891","text":"def fm_edit(self):\n return self._commit(\"edit\")","title":""},{"docid":"ae85c4117e5e5a27a836b03e16888b9c","score":"0.51652116","text":"def act_query(query):\n connection = conn\n cursor = connection.cursor()\n num_affected_rows = cursor.execute(query)\n cursor.close()\n connection.commit()\n return num_affected_rows","title":""},{"docid":"6fa3c136ecd401824890d8232e62061b","score":"0.515516","text":"def _prepare_query_text(self, sql_query):\n self.tab.gdb = self.local_gdb\n sql_query_string = sql_query\n self.tab.query.setText(sql_query_string)\n return","title":""},{"docid":"0bfa1a48b30594357ad7d7c6116285e2","score":"0.5114265","text":"def _run_query(self, query):\n try:\n result = self.cursor.execute(query)\n except:\n messagebox.showerror(\"Error\",\"\"\"\nSomething went wrong executing the query.\n\nCheck if you are connected to the database.\n\nIf you are trying to manipulate data, check if you filled in the fields correctly.\n\nIf you were using delete or update you must select a register on the table before.'\"\"\")\n\n self.connection.commit()\n return result","title":""},{"docid":"516f72418c9d0c4ad35c8b4a751a446c","score":"0.5096106","text":"def real_process(text):\n prod = parser(text, utcnow=common.utcnow())\n if not common.dbwrite_enabled():\n return\n if prod.warnings:\n common.email_error(\"\\n\".join(prod.warnings), text)\n df = DBPOOL.runInteraction(prod.sql)\n df.addCallback(got_data)\n df.addErrback(common.email_error, text)","title":""},{"docid":"87b8033fa8192771d155bd96aaecfc69","score":"0.50843114","text":"def modify(self, SQL, params='', verbose=True):\n try:\n \n # Make sure the database isn't locked\n self.conn.commit()\n \n if SQL.lower().startswith('select'):\n print('Use self.query method for queries.')\n else:\n self.list(SQL, params)\n self.conn.commit()\n if verbose:\n print('Number of records modified: {}'.format(self.list(\"SELECT changes()\").fetchone()[0] or '0'))\n except:\n print(\"Could not execute: \"+SQL)","title":""},{"docid":"9d52685d54bbc8d787972983ad9064f3","score":"0.507759","text":"def docmd(self, fmt, *args):\n if not self.gdb.accepting_cmd():\n self.gdb.console_print(\n \"gdb busy: command discarded, please retry\\n\")\n return False\n\n self.gdb.gdb_busy = True\n self.result = ''\n return self.send(fmt, *args)","title":""},{"docid":"477d56fe945bb8eb90a93a42b0e61ba9","score":"0.50667006","text":"def _commit(self):\r\n self.conn.commit()\r\n self.changed = False","title":""},{"docid":"97a3289e1cb047263b16b7dd0126ca7d","score":"0.5056425","text":"def submit_query(self, query):\n return self.app.post('/query_database/', data=dict(\n query=query\n ), follow_redirects=True)","title":""},{"docid":"56989304d6f6c38df5f879e37448494d","score":"0.50355613","text":"def cmd(ctx, url, key, secret, export_format, table_format, sq, folder, create, echo):\n client = ctx.obj.start_client(url=url, key=key, secret=secret)\n\n p_grp = ctx.parent.parent.command.name\n apiobj = getattr(client, p_grp)\n\n with ctx.obj.exc_wrap(wraperror=ctx.obj.wraperror):\n data = apiobj.saved_query.update_folder(\n sq=sq, folder=folder, create=create, echo=echo, as_dataclass=True\n )\n ctx.obj.echo_ok(f\"Successfully updated Saved Query {data.name}\")\n\n click.secho(EXPORT_FORMATS[export_format](data=data, table_format=table_format))\n ctx.exit(0)","title":""},{"docid":"0a4a5faa9747af799d7a9388f7c58af2","score":"0.5008634","text":"def mysql(self, cursor, query):\n if isinstance(query, basestring):\n query = [query]\n for q in query:\n cursor.execute(q)\n q.replace('\\n', ' ')\n self.print_log('Executed: {}'.format(q))","title":""},{"docid":"f710ed780a16500ee0ba33da855f6ca2","score":"0.49840534","text":"def opModify(self, sql):\n insert_num = self.cur.execute(sql) # execute sql\n self.coon.commit()\n return insert_num","title":""},{"docid":"2be7523496aeffd2f8e5ae94f3cf4701","score":"0.49723747","text":"def prompt(skipfile, skipall, query, chunk):\n newpatches = None\n if skipall is not None:\n return skipall, skipfile, skipall, newpatches\n if skipfile is not None:\n return skipfile, skipfile, skipall, newpatches\n while True:\n resps = messages[b'help'][operation]\n # IMPORTANT: keep the last line of this prompt short (<40 english\n # chars is a good target) because of issue6158.\n r = ui.promptchoice(b\"%s\\n(enter ? for help) %s\" % (query, resps))\n ui.write(b\"\\n\")\n if r == 8: # ?\n for c, t in ui.extractchoices(resps)[1]:\n ui.write(b'%s - %s\\n' % (c, encoding.lower(t)))\n continue\n elif r == 0: # yes\n ret = True\n elif r == 1: # no\n ret = False\n elif r == 2: # Edit patch\n if chunk is None:\n ui.write(_(b'cannot edit patch for whole file'))\n ui.write(b\"\\n\")\n continue\n if chunk.header.binary():\n ui.write(_(b'cannot edit patch for binary file'))\n ui.write(b\"\\n\")\n continue\n # Patch comment based on the Git one (based on comment at end of\n # https://mercurial-scm.org/wiki/RecordExtension)\n phelp = b'---' + _(\n b\"\"\"\nTo remove '-' lines, make them ' ' lines (context).\nTo remove '+' lines, delete them.\nLines starting with # will be removed from the patch.\n\nIf the patch applies cleanly, the edited hunk will immediately be\nadded to the record list. If it does not apply cleanly, a rejects\nfile will be generated: you can use that when you try again. If\nall lines of the hunk are removed, then the edit is aborted and\nthe hunk is left unchanged.\n\"\"\"\n )\n (patchfd, patchfn) = pycompat.mkstemp(\n prefix=b\"hg-editor-\", suffix=b\".diff\"\n )\n ncpatchfp = None\n try:\n # Write the initial patch\n f = util.nativeeolwriter(os.fdopen(patchfd, 'wb'))\n chunk.header.write(f)\n chunk.write(f)\n f.write(\n b''.join(\n [b'# ' + i + b'\\n' for i in phelp.splitlines()]\n )\n )\n f.close()\n # Start the editor and wait for it to complete\n editor = ui.geteditor()\n ret = ui.system(\n b\"%s \\\"%s\\\"\" % (editor, patchfn),\n environ={b'HGUSER': ui.username()},\n blockedtag=b'filterpatch',\n )\n if ret != 0:\n ui.warn(_(b\"editor exited with exit code %d\\n\") % ret)\n continue\n # Remove comment lines\n patchfp = open(patchfn, 'rb')\n ncpatchfp = stringio()\n for line in util.iterfile(patchfp):\n line = util.fromnativeeol(line)\n if not line.startswith(b'#'):\n ncpatchfp.write(line)\n patchfp.close()\n ncpatchfp.seek(0)\n newpatches = parsepatch(ncpatchfp)\n finally:\n os.unlink(patchfn)\n del ncpatchfp\n # Signal that the chunk shouldn't be applied as-is, but\n # provide the new patch to be used instead.\n ret = False\n elif r == 3: # Skip\n ret = skipfile = False\n elif r == 4: # file (Record remaining)\n ret = skipfile = True\n elif r == 5: # done, skip remaining\n ret = skipall = False\n elif r == 6: # all\n ret = skipall = True\n elif r == 7: # quit\n raise error.CanceledError(_(b'user quit'))\n return ret, skipfile, skipall, newpatches","title":""},{"docid":"8278ced69daf481d385aafa53c4373e4","score":"0.49688333","text":"def test_sql_code_styling(self):\n self.tab = self._add_new_query_tab()\n sql_query_string = 'SELECT name FROM streets LIMIT 3'\n self._prepare_query_text(sql_query_string)\n\n cur = self.tab.query.textCursor()\n cur.setPosition(0)\n cur.setPosition(6, QTextCursor.KeepAnchor)\n self.tab.query.setTextCursor(cur)\n return","title":""},{"docid":"66ae32f923589713677394732ebbd070","score":"0.49606788","text":"def commit(self):\n self.buffer.commit()","title":""},{"docid":"f87c382a88f3d488b69292b8ed3085a2","score":"0.4956843","text":"def command_sql(self, event):\n conn = database.obj.get_conn()\n\n try:\n tbl = MessageTable(codeblock=False)\n\n with conn.cursor() as cur:\n start = time.time()\n cur.execute(event.codeblock.format(e=event))\n dur = time.time() - start\n if not cur.description:\n return event.msg.reply('_took {}ms - no result_'.format(\n int(dur * 1000)))\n tbl.set_header(*[desc[0] for desc in cur.description])\n\n for row in cur.fetchall():\n tbl.add(*row)\n\n result = tbl.compile()\n if len(result) > 1900:\n return event.msg.reply(\n '_took {}ms_'.format(int(dur * 1000)),\n attachments=[('result.txt', result)])\n\n event.msg.reply('```' + result + '```\\n_took {}ms_\\n'.format(\n int(dur * 1000)))\n except psycopg2.Error as e:\n event.msg.reply('```{}```'.format(e.pgerror))","title":""},{"docid":"b716b577b4adc31a962470803b29ae1d","score":"0.4952748","text":"def commitdata(self):\n try:\n self.db.execute(self.sqlcommand)\n self.conn.commit()\n except Exception as e:\n self.setVisible(False)\n self.errormessage = \"Error Data Commit\\n\" + \"Error committing the data for: \" + \\\n self.enstr + \" \" + self.rustr + \"\\nError: \" + str(e) + \"\\nCommand: \" + self.sqlcommand\n errorgui = ErrorDisplay(self)\n errorgui.activateWindow()\n errorgui.exec()\n self.setVisible(True)\n self.activateWindow()","title":""},{"docid":"f5f4cc0fe27c1e914fd6b04f89f3b14f","score":"0.49368232","text":"def _execute_sql(self, sql, user_gdb=None):\n self.tab = self.ui.tab_widget.currentWidget()\n if user_gdb:\n self.tab.gdb = Geodatabase(user_gdb)\n else:\n self.tab.gdb = self.local_gdb\n self.tab.query.setPlainText(sql)\n self.tab.run_query()\n return","title":""},{"docid":"6803e96604a7d4f40b6bae3f39138526","score":"0.49363384","text":"def commit(self):\r\n return self.conn.commit()","title":""},{"docid":"5e8e074b324b9c1f8a8ea2349c3c2093","score":"0.49327952","text":"def command_edit_answer(update, context):\n\n chat_data = context.chat_data\n\n text = (\n '___DESCRIPTION___: You are in command editting mode. '\n 'You can either send new messages to add them '\n 'or delete your previous messages.'\n )\n update.message.reply_text(\n text=text,\n reply_markup=keyboards.edit_asnwer_markup(),\n parse_mode='MARKDOWN',\n )\n command = chat_data['cmd_instance']\n chat_data['edit_actions_log'] = []\n chat_data['msgs_count'] = command.message_set.count()\n chat_data['msgs_last_id'] = 0\n chat_data['msgs_to_delete'] = []\n\n inform_number_of_commands(update, context)\n return states.SEND_EDIT_MESSAGE","title":""},{"docid":"729b45eda5b7b240df53452d7a76e7f7","score":"0.49326035","text":"def __init__(self, dbfilename, initialize='', mode='r', output='html'):\n if (output.lower() in ['html', 'text']):\n self.output = output.lower()\n else:\n print('Unsupported output format: \"%s\", use one of \"html\" or \"text\"' % output)\n self.result = None\n self.history = []\n self.histIdx = 0\n buttonRow = []\n self.query = ipywidgets.Textarea(description=\"SQL:\", value=initialize, \n layout=ipywidgets.Layout(width=\"600px\", height=\"100px\", font_weight=\"bold\"))\n self.query.color = \"#000040\"\n\n self.executeButton = ipywidgets.Button(description=\"Execute\")\n self.executeButton.width = \"100px\"\n self.executeButton.margin = \"10px 5px 10px 72px\"\n self.executeButton.on_click(self.executeSQL)\n buttonRow.append(self.executeButton)\n \n self.submitButton = ipywidgets.Button(description=\"Submit\")\n self.submitButton.width = \"100px\"\n self.submitButton.margin = \"10px 5px 10px 72px\"\n self.submitButton.on_click(self.executeSQL)\n buttonRow.append(self.submitButton)\n \n self.prevButton = ipywidgets.Button(description=\"prev\")\n self.prevButton.width = \"50px\"\n self.prevButton.margin = \"10px 5px\"\n self.prevButton.on_click(self.prevQuery)\n buttonRow.append(self.prevButton)\n \n self.nextButton = ipywidgets.Button(description=\"next\")\n self.nextButton.width = \"50px\"\n self.nextButton.margin = \"10px 5px\"\n self.nextButton.visible = False\n self.nextButton.on_click(self.nextQuery)\n buttonRow.append(self.nextButton)\n\n if (self.output.lower() == 'html'):\n self.status = ipywidgets.HTML()\n\n self.container = ipywidgets.VBox(children=[self.query, ipywidgets.HBox(children=buttonRow), self.status])\n if isinstance(dbfilename, sqlite3.Connection):\n self.db = dbfilename\n if (self.output == 'html'):\n self.status.value = 'Using existing database connection'\n else:\n print('Using existing database connection')\n elif ('r' in mode) and os.path.isfile(dbfilename):\n self.db = sqlite3.connect(dbfilename)\n if (self.output == 'html'):\n self.status.value = 'Connected to database: <em>"%s"</em>' % dbfilename\n else:\n print('Connected to database: \"%s\"' % dbfilename)\n elif ('w' in mode):\n if os.path.isfile(dbfilename):\n os.remove(dbfilename)\n self.db = sqlite3.connect(dbfilename)\n if (self.output == 'html'):\n self.status.value = 'Created database: <em>"%s"</em>' % dbfilename\n else:\n print('Created database: \"%s\"' % dbfilename)\n else:\n if (self.output == 'html'):\n self.status.value = self.errorStyle % (\"Database "%s" does not exist\" % dbfilename)\n else:\n print('Connected to database: \"%s\"' % dbfilename)\n self.query.close()\n self.submitButton.close()\n display(self.container)","title":""},{"docid":"1322df4aef4a5a154205979db32c3e97","score":"0.49315444","text":"def commit():","title":""},{"docid":"7389315399befd2c27903b213802e9d4","score":"0.48991475","text":"def execute(self, _, db_conn):\n\n self.create_additional_input_column(db_conn)\n\n failed = set([])\n cursor = db_conn.cursor()\n update_sql = \"UPDATE input SET %s = ? WHERE identifier = ?\" % (\n self.column_name,\n )\n\n # Read the input documents\n annotation_set = self.get_input_set(db_conn)\n\n for count, (identifier, text) in enumerate(annotation_set):\n # Annotate each input document\n value = self.produce_annotation(identifier, text, db_conn)\n if value is None:\n failed.add(identifier)\n logging.debug(\"Annotation failed: %d\", identifier)\n # Save the annotation\n cursor.execute(update_sql, (value, identifier))\n if count % 20 == 0:\n logging.debug(\"Annotation %.2f%% complete\", count * 100.0 / len(annotation_set))\n\n if len(failed) != 0:\n logging.info(\"Failed to annotate %d entries\", len(failed))\n\n logging.info(\"Committing changes...\")\n db_conn.commit()\n return True, db_conn","title":""},{"docid":"f97374807375d7034db1c5f89e01e4b5","score":"0.489561","text":"def Commit(self):\n self.cursor.commit()","title":""},{"docid":"6645904bec6fd3c25fc288abcd501e49","score":"0.4893254","text":"def submitQuery(self, query):\r\n return True","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.48835176","text":"def commit(self):","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.48835176","text":"def commit(self):","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.48835176","text":"def commit(self):","title":""},{"docid":"41ef534238e9c6f7b3718c606c670884","score":"0.4880506","text":"def GetQuery(command):\n\n QueryTemplate = {\n \"update\":\"UPDATE {table} SET {column} = {value} WHERE {condition}\",\n \"delete\":\"DELETE FROM {table} WHERE {condition}\",\n \"select\":\"SELECT {columns} FROM {table} WHERE \",\n \"select_all\":\"SELECT {columns} FROM {table}\",\n \"get_years\":\"SELECT DISTINCT DATENAME({Year_Month},{column}) AS YEARS FROM {table}\",\n \"get_months\":\"SELECT DISTINCT DATENAME({Year_Month},{column}) AS MONTHS FROM {table}\"\n }\n return QueryTemplate.get(command)","title":""},{"docid":"63943be5230060cc4bee018993c6c9b6","score":"0.48783872","text":"def commit(self):\n self.conn.commit()","title":""},{"docid":"59cf71675fb17558c89f11e70d15636c","score":"0.4872711","text":"def commit(self):\r\n return self.adaptor.commit()","title":""},{"docid":"632f218c0df7a062b4899bdec09ecf55","score":"0.48614815","text":"def commit ( self ):\n pass","title":""},{"docid":"7e82ffb53d8f55ec3b5aa781159310e6","score":"0.48404896","text":"def commit(self):\n return _xapian.WritableDatabase_commit(self)","title":""},{"docid":"2e7ba78df5c9d9e87911f4b0b3ba3c8b","score":"0.48258275","text":"def run_query(parameters):\n conn = parameters['conn'] \n cur = conn.cursor()\n cur.execute(parameters['query'])\n conn.commit();","title":""},{"docid":"f54bf55db6bb92b99ad52a7497cde8b5","score":"0.48236316","text":"def step_impl(context):\n context.tranql_query = context.text","title":""},{"docid":"c48c68ad456b8ade00ca74a67c9965ff","score":"0.48208627","text":"def checksql(self):\n self.setVisible(False)\n sql = SQLTest(self)\n sql.activateWindow()\n sql.exec()\n self.setVisible(True)\n self.activateWindow()","title":""},{"docid":"574b65737f8fb7c8a542d8a54088ef84","score":"0.48044884","text":"def format_database(self):\n qr = self.cursor.executescript(\"\"\"\ndelete from Packages;\ndelete from Gits;\ndelete from Repositories;\n\nupdate sqlite_sequence set seq=0 where name=\"Packages\";\nupdate sqlite_sequence set seq=0 where name=\"Gits\";\nupdate sqlite_sequence set seq=0 where name=\"Repositories\";\n \"\"\")\n del qr","title":""},{"docid":"dcd20e1a9f3dda73b3ab9236af24cc21","score":"0.4791975","text":"def set_query(self,query,data):\n self.cursor.execute(query,data)\n self.db.commit()","title":""},{"docid":"bfa83370a6ba0e42e17a62010866eda8","score":"0.47914323","text":"def popup_submit(self, event, columns, table, row_id, text_list): # !!!!! new code added by Jetsun\n\n db = SqliteQueries(\"database.db\")\n entry_values = []\n for i in range(len(text_list)):\n entry_values.append(text_list[i].get(0.0, \"end\"))\n db.update(table, columns, tuple(entry_values), 'userID = %s' % str(row_id))\n self.popup.destroy()\n\n # refresh results\n if self.btn_click == \"show_all_record\":\n self.show_all_record()\n elif self.btn_click == \"search_record\":\n self.search_record()\n else:\n pass","title":""},{"docid":"a4e670eb6c4863a82b334de7ad8d5033","score":"0.47911483","text":"def commit(self, a=False, m=None, amend=False):","title":""},{"docid":"6023ebc7f2d64dd69fbe952ea3982c3d","score":"0.47899255","text":"def query(self, cmd):\n data = {\n 'code': 'from google.appengine.ext import db\\n' + cmd,\n 'module_name': 'default',\n 'xsrf_token': self.xsrf_token,\n }\n return self.post('console', data, url=self.admin_url)","title":""},{"docid":"d5a629b3dbe487d4f60163d64bc31fdd","score":"0.4766869","text":"def edit(self, command, prompt):\n instruction, target = command.split(None)\n self.sock.send(\"Graphique print {}\".format(target).encode())\n size_of_file = self.sock.recv(BUFFER_SIZE).decode('utf-8')\n print(size_of_file)\n if size_of_file == \"Directory\":\n self.editor.replace(\n \"0.0\", tk.END, \"{}{} is a directory, so I've cd you into it\".format(prompt, target))\n elif size_of_file == \"AccessError\":\n self.editor.replace(\n \"0.0\", tk.END, \"{} You can't access{}\".format(prompt, target))\n else:\n content = self.sock.recv(int(size_of_file)).decode('utf-8')\n print(content)\n self.editing = True\n self.filename = target\n self.editor.replace(\n \"0.0\", tk.END, \"{}\".format(content))\n # page1.pop","title":""},{"docid":"5d712464dbce2d83a3c2a05ae4e86f4f","score":"0.47487596","text":"def commit(self):\n if self.con:\n self.con.commit()","title":""},{"docid":"3def92f430044d5807e461dfa65287dd","score":"0.4745809","text":"def commit(self) -> None:","title":""},{"docid":"68f4e98ec6aaa5f2e9288541c2437de8","score":"0.47407138","text":"def execute(self,sql):\r\n #print sql \r\n cursor = self.db.cursor() \r\n cursor.execute(sql)\r\n self.db.commit()\r\n cursor.close()","title":""},{"docid":"e060a5bae2d22bdd13d4f96c96e1024b","score":"0.47224945","text":"def insert(self):\n MAX_RESULTS = 10\n query = \"\"\n t = None\n self.detailWindow.clear() #clear detail window\n self.detailWindow.refresh()\n self.HelpWindow.clear()\n self.HelpWindow.addstr(self.INSERT_TIP)\n self.HelpWindow.refresh()\n #Insert mode\n self.modeWindow.addstr(0, 0, self.INSERT_NAME)\n self.modeWindow.refresh()\n #Clear any previous queries\n self.queryWindow.clear()\n self.queryWindow.refresh()\n while True:\n t = self.getChar()\n if t == self.ESC:\n break #dont clear results, save it for selecting through\n else:\n if t == self.BACKSPACE and len(query) > 0:\n query = query[:-1]\n elif t != self.ENTER:\n query+=t\n self.queryWindow.clear()\n self.queryWindow.addstr(query)\n\n self.searchResults = [] #empty the results list from before\n self.selectCursor = -1 #select nothing\n self.search(query) #mutate self.results\n\n self.resultsWindow.clear()\n for j in xrange(len(self.searchResults)):\n if j > MAX_RESULTS:\n break\n #add results, each on its own line\n self.resultsWindow.addstr(j, 1, self.searchResults[j].cleanName())\n self.queryWindow.refresh()\n self.resultsWindow.refresh()\n self.modeWindow.addstr(0, 0, self.SELECT_NAME)\n self.modeWindow.refresh()\n self.HelpWindow.clear()\n self.HelpWindow.refresh()","title":""},{"docid":"996d1219ea3ada3d4b362b3bb3031e48","score":"0.4721329","text":"def save(self):\r\n if self.instance is None:\r\n raise CQLEngineException(\"DML Query intance attribute is None\")\r\n assert type(self.instance) == self.model\r\n\r\n # organize data\r\n value_pairs = []\r\n values = self.instance._as_dict()\r\n\r\n # get defined fields and their column names\r\n for name, col in self.model._columns.items():\r\n val = values.get(name)\r\n if val is None:\r\n continue\r\n value_pairs += [(col.db_field_name, val)]\r\n\r\n # construct query string\r\n field_names = zip(*value_pairs)[0]\r\n field_ids = {n: uuid4().hex for n in field_names}\r\n field_values = dict(value_pairs)\r\n query_values = {field_ids[n]: field_values[n] for n in field_names}\r\n\r\n qs = []\r\n if self.instance._has_counter or self.instance._can_update():\r\n qs += [\"UPDATE {}\".format(self.column_family_name)]\r\n qs += [\"SET\"]\r\n\r\n set_statements = []\r\n # get defined fields and their column names\r\n for name, col in self.model._columns.items():\r\n if not col.is_primary_key:\r\n val = values.get(name)\r\n if val is None:\r\n continue\r\n if isinstance(col, (BaseContainerColumn, Counter)):\r\n # remove value from query values, the column will\r\n # handle it\r\n query_values.pop(field_ids.get(name), None)\r\n\r\n val_mgr = self.instance._values[name]\r\n set_statements += col.get_update_statement(\r\n val, val_mgr.previous_value, query_values)\r\n\r\n else:\r\n set_statements += [\r\n '\"{}\" = %({})s'.format(col.db_field_name, field_ids[col.db_field_name])]\r\n qs += [', '.join(set_statements)]\r\n\r\n qs += ['WHERE']\r\n\r\n where_statements = []\r\n for name, col in self.model._primary_keys.items():\r\n where_statements += ['\"{}\" = %({})s'.format(col.db_field_name,\r\n field_ids[col.db_field_name])]\r\n\r\n qs += [' AND '.join(where_statements)]\r\n\r\n # clear the qs if there are no set statements and this is not a\r\n # counter model\r\n if not set_statements and not self.instance._has_counter:\r\n qs = []\r\n\r\n else:\r\n qs += [\"INSERT INTO {}\".format(self.column_family_name)]\r\n qs += [\"({})\".format(', '.join(['\"{}\"'.format(f)\r\n for f in field_names]))]\r\n qs += ['VALUES']\r\n qs += [\"({})\".format(', '.join(['%(' + field_ids[f] + ')s' for f in field_names]))]\r\n\r\n qs = ' '.join(qs)\r\n\r\n # skip query execution if it's empty\r\n # caused by pointless update queries\r\n if qs:\r\n if self._batch:\r\n self._batch.add_query(qs, query_values)\r\n else:\r\n execute(qs, query_values)\r\n\r\n # delete nulled columns and removed map keys\r\n qs = ['DELETE']\r\n query_values = {}\r\n\r\n del_statements = []\r\n for k, v in self.instance._values.items():\r\n col = v.column\r\n if v.deleted:\r\n del_statements += ['\"{}\"'.format(col.db_field_name)]\r\n elif isinstance(col, Map):\r\n del_statements += col.get_delete_statement(\r\n v.value, v.previous_value, query_values)\r\n\r\n if del_statements:\r\n qs += [', '.join(del_statements)]\r\n\r\n qs += ['FROM {}'.format(self.column_family_name)]\r\n\r\n qs += ['WHERE']\r\n where_statements = []\r\n for name, col in self.model._primary_keys.items():\r\n field_id = uuid4().hex\r\n query_values[field_id] = field_values[name]\r\n where_statements += [\r\n '\"{}\" = %({})s'.format(col.db_field_name, field_id)]\r\n qs += [' AND '.join(where_statements)]\r\n\r\n qs = ' '.join(qs)\r\n\r\n if self._batch:\r\n self._batch.add_query(qs, query_values)\r\n else:\r\n execute(qs, query_values)","title":""},{"docid":"87d3979a3a145ed1df1502cf6c2092f7","score":"0.47211018","text":"def endsql(self):\n self.sqldata = None\n self.adjdict.clear()\n self.tagtext = ''\n self.declension = list()\n self.contpage = True\n self.adjpages = list([False, False])\n self.shortadj = True\n self.adjcommand = \"\"\n self.shortadjcommand = \"\"\n tagval = None\n tagval = self.listDisplay.selectedItems()\n if tagval:\n self.tagtext = tagval[0].text()\n self.sqldict[self.finalkey] = self.tagtext\n if self.wclass == \"noun\":\n self.setVisible(False)\n decline = DeclEntry(self)\n decline.activateWindow()\n decline.exec()\n if self.cancel or len(self.declension) == 0:\n self.setVisible(True)\n self.activateWindow()\n self.wordclasses()\n return\n basecommand = self.sqlcommand\n self.sqlcommand = \"\"\n for x in self.declension:\n cols = \"(\"\n data = \"(\"\n cols += \"variety, \"\n data += \"\\'\" + x[0] + \"\\', \"\n cols += \"type, \"\n data += \"\\'\" + x[1] + \"\\', \"\n cols += \"runame, \"\n data += \"\\'\" + x[2] + \"\\', \"\n cols += \"name, \"\n data += \"\\'\" + x[3] + \"\\', \"\n cols += \"gender, \"\n data += \"\\'\" + x[4] + \"\\', \"\n cols += \"declension, \"\n data += \"\\'\" + x[5] + \"\\', \"\n cols += \"wordcase, \"\n data += \"\\'\" + x[6] + \"\\', \"\n cols += \"animate) VALUES\"\n data += \"\\'\" + x[7] + \"\\');\\n\"\n self.sqlcommand += basecommand + cols + data\n elif self.wclass == \"adjective\":\n basecommand = self.sqlcommand\n self.sqldict[self.finalkey] = \"\\'\" + self.sqldict[self.finalkey] + \"\\'\"\n if self.tagtext == \"descriptive\":\n self.setVisible(False)\n while self.contpage:\n self.sqldict[\"variety\"] = \"\\'descriptive\\'\"\n decline = AdjDeclEntry(self)\n decline.activateWindow()\n decline.exec()\n self.setVisible(True)\n if self.cancel:\n self.activateWindow()\n self.wordclasses()\n return\n self.sqldict[\"variety\"] = \"\\'short adjective\\'\"\n adjgui = ShortAdj(self)\n adjgui.activateWindow()\n adjgui.exec()\n self.setVisible(True)\n if self.cancel:\n self.activateWindow()\n self.wordclasses()\n return\n self.sqlcommand = self.adjcommand\n if self.shortadj:\n self.sqlcommand += self.shortadjcommand\n self.activateWindow()\n elif self.tagtext == \"comparative\":\n engcomp = \"\"\n self.completed = True\n while len(engcomp) == 0 and self.completed:\n engcomp, self.completed = QInputDialog.getText(self, \"English Comparative\", \"Comparative Adjective Value:\")\n if engcomp and not engcomp[0] in self.morphs.enall:\n QMessageBox.warning(self, \"Non-English Character\", \"A non-english character was entered for an english word.\")\n engcomp = \"\"\n if self.completed:\n self.sqldict[\"name\"] = \"\\'\" + engcomp + \"\\'\"\n else:\n self.wordclasses()\n return\n rucomp = \"\"\n self.completed = True\n while len(rucomp) == 0 and self.completed:\n rucomp, self.completed = QInputDialog.getText(self, \"Russian Comparative\", \"Russian Comparative Adjective:\")\n if rucomp and not rucomp[0] in self.morphs.ruall:\n QMessageBox.warning(self, \"Non-Cyrillic Character\", \"A non-cyrillic character was entered for an russian word.\")\n rucomp = \"\"\n if self.completed:\n self.sqldict[\"declension\"] = \"\\'\" + rucomp + \"\\'\"\n else:\n self.wordclasses()\n return\n self.sqldict[\"runame\"] = \"\\'\" + self.rustr + \"\\'\"\n self.sqldict[\"gender\"] = \"\\'masculine\\'\"\n self.sqldict[\"wordcase\"] = \"\\'nominative\\'\"\n self.sqldict[\"animate\"] = \"\\'inanimate\\'\"\n self.sqldict[\"variety\"] = \"\\'comparative\\'\"\n cols = \"(\"\n data = \"(\"\n for y in self.sqldict:\n cols += y + \", \"\n data += self.sqldict[y] + \", \"\n cols = cols[:-2] + \") VALUES\"\n data = data[:-2] + \");\\n\"\n self.sqlcommand = basecommand + cols + data\n elif self.tagtext == \"superlative\":\n self.setVisible(False)\n self.sqldict[\"variety\"] = \"\\'superlative\\'\"\n decline = AdjDeclEntry(self)\n decline.activateWindow()\n decline.exec()\n self.setVisible(True)\n self.activateWindow()\n if self.cancel:\n self.wordclasses()\n return\n self.sqlcommand = self.adjcommand\n self.noexit = True\n self.finalize()\n else:\n QMessageBox.warning(self, \"No Item Selected\", \"You must select an item from the list or cancel.\")","title":""},{"docid":"12b3badc60659ea49cadcbc978c0a23d","score":"0.47151494","text":"def query(self, arg):\r\n self.cur.execute(arg)\r\n self.conn.commit()\r\n return self.cur","title":""},{"docid":"74e0af94cc91216d18fcfd76ba4b93e7","score":"0.4710647","text":"def schema(self,sql_str):\n\t\tself.cursor_db.executescript(sql_str)\n\t\tself.conn_db.commit()","title":""},{"docid":"263af540ef2b92ceb9aa2f6247a4cfa9","score":"0.4702192","text":"async def sql(self, ctx):\n await util.command_group_help(ctx)","title":""},{"docid":"24235ee1765db78e6af90d4fd98ed242","score":"0.46943846","text":"def QueryHowDoI(Query, num_answers, full_text):\n output = search_book(Query)\n print(\"You asked: \" + Query)\n print(\"_______________________________________\")\n print(output)\n exit_code = 3","title":""},{"docid":"ba52805d2ba853af5cbf75f113249a58","score":"0.46941972","text":"def executescript(self,sql):\r\n #print sql \r\n cursor = self.db.cursor() \r\n cursor.executescript(sql)\r\n self.db.commit()\r\n cursor.close()","title":""},{"docid":"94b8d1eae799d06b25aca2f018d3bcb0","score":"0.46883547","text":"def execute_query(connection, query):\n cursor = connection.cursor()\n try:\n cursor.execute(query)\n connection.commit()\n print(\"Query executed successfully\")\n except Error as e:\n print(f\"The error '{e}' occurred\")","title":""},{"docid":"9e4011a30e6abc7fdbfacbfa5c7acdc4","score":"0.46793383","text":"def query_expense():\n\n if request.method == 'POST':\n sql = request.form[\"sql\"]\n\n error = None\n\n if not sql:\n error = \"query is required\"\n\n if error is None:\n obj = sqlOperation()\n results = obj.searchQuery(sql, session['user_id'])\n table = readResults(results)\n table.border = True\n return render_template('expense/table.html', table = table)\n else:\n flash(error)\n\n return render_template('expense/sql.html')","title":""},{"docid":"327080e49104d0ff94b811b21ec72c31","score":"0.4672157","text":"def execute_query(cur, conn, query):\n if __debug__:\n print(\"Executing ================================ Query\")\n print(query)\n cur.execute(query)\n conn.commit()","title":""},{"docid":"d3cc13e77e83190eac3f815196259ad7","score":"0.46710664","text":"def make_commit(self):\n self.sat.commit()","title":""},{"docid":"47cbe5a8067ddc3bcbe3eb7e49248bf8","score":"0.46670425","text":"def commit_and_close(self):\r\n request = input('Save the database? ')\r\n if request.upper() == 'YES':\r\n self.database.commit()\r\n self.database.close()","title":""},{"docid":"90caca07874299609a6876e8682485fe","score":"0.4665845","text":"def do_query(self) -> None:\n ...","title":""},{"docid":"4a642ff48f0322ca6f2c791ce5b3c50f","score":"0.4661898","text":"def test_preserve_whitespace(self):\n query = \"DUMP\\nSCHEMA\\n\\n;\"\n for fragment in query.split(\"\\n\"):\n self.query(fragment)\n self.assertEqual(self.engine.last_query, query)","title":""},{"docid":"eff0e15b2ed4296ae907d69c80527baa","score":"0.46522677","text":"def _commit_handler(self, cmd):\n current_prompt = self.device.find_prompt().strip()\n terminating_char = current_prompt[-1]\n # Look for trailing pattern that includes '#' and '>'\n pattern1 = r\"[>#{}]\\s*$\".format(terminating_char)\n # Handle special username removal pattern\n pattern2 = r\".*all username.*confirm\"\n patterns = r\"(?:{}|{})\".format(pattern1, pattern2)\n output = self.device.send_command_expect(cmd, expect_string=patterns)\n loop_count = 50\n new_output = output\n for i in range(loop_count):\n if re.search(pattern2, new_output):\n # Send confirmation if username removal\n new_output = self.device.send_command_timing(\n \"\\n\", strip_prompt=False, strip_command=False\n )\n output += new_output\n else:\n break\n # Reset base prompt in case hostname changed\n self.device.set_base_prompt()\n return output","title":""},{"docid":"d9b156bec756de2c09ce1745f7de9acb","score":"0.46498632","text":"def handleCustomQuery(self):\n\n # Get query\n print(\"Enter query below\")\n query = input()\n # Process documents\n processedQuery = self.preprocessQueries([query])[0]\n\n # Read documents\n docs_json = json.load(open(args.dataset + \"cran_docs.json\", 'r'))[:10]\n docs = [item[\"body\"] for item in docs_json]\n # Process documents\n processedDocs = self.preprocessDocs(docs)","title":""},{"docid":"b56fcf3bc68efa86ff38670cf2ece9a5","score":"0.46482247","text":"def commit(self):\r\n self.connection.commit()","title":""},{"docid":"49237b79f48e9d644736b826c1d234be","score":"0.46452644","text":"def _update_rows(self, query):\n self.db.get_connection()\n with self.db.conn.cursor() as cur:\n cur.execute(query)\n self.db.conn.commit()\n logger.debug(f'Query is: {query}.')\n logger.info(f\"{cur.rowcount} rows affected.\")","title":""},{"docid":"d272e7c24acb4c3fde76e91e946d8640","score":"0.46423507","text":"def custom_query(self, con, cur, query):\n\n try:\n cur.execute(query)\n con.comit()\n print('\\n\\n QUERY SUCCESSFULL \\n\\n')\n except Exception as error:\n print('\\n Error by executing YOUR QUERY. '\n '\\n Server said: {}'.format(error)\n )","title":""},{"docid":"58601b82f844e31a8aa211e13ab5c275","score":"0.46373543","text":"def qsqldb( sqldb, sql_cmd, outfilename=None ):\n\t#\n\t# Create the output file if specified\n\tif outfilename:\n\t\toutfile = open(outfilename, \"wb\")\n\t\tcsvout = csv.writer(outfile, quoting=csv.QUOTE_NONNUMERIC)\n\t#\n\t# Execute SQL\n\tcurs = sqldb.cursor()\n\tcurs.execute(sql_cmd)\n\t#\n\t# Write output to file or console\n\tif outfilename:\n\t\tdatarows = curs.fetchall()\n\t\theaders = [ item[0] for item in curs.description ]\n\t\tcsvout.writerow(headers)\n\t\tfor row in datarows:\n\t\t\tcsvout.writerow(list(row))\n\t\toutfile.close()\n\telse:\n\t\tprint pp(curs)","title":""},{"docid":"011eca2001844c9633087ed0db67ddba","score":"0.4634989","text":"def color_q(query_object):\n return color_sql(compile_query(query_object))","title":""},{"docid":"5360a7de8c72610842cce277150526a9","score":"0.46349132","text":"def db_action(sql_action: str):\n conn = psycopg2.connect(db_url)\n curs = conn.cursor()\n curs.execute(sql_action)\n conn.commit()\n curs.close()\n conn.close()","title":""},{"docid":"6bc835fcbd42e91ccdded3ea90ed3b91","score":"0.46291798","text":"def _commit(self, action):\n self._dbparams_append((\"-max\", self._maxret))\n\n url = self._url % self.__dict__\n data = urlencode(self._dbdata + self._dbparams) + \"&-\" + action\n self._dbparams = []\n # use POST to submit data\n request = Request(url, bytestr(data))\n request.add_header('User-Agent', b'Fmkr.py')\n # authorization header\n auth = b\"Basic \" + encodestr(\"%s:%s\" % (self._dbuser,\n self._dbpasswd))[:-1]\n request.add_header(\"Authorization\", auth)\n\n try:\n fd = urlopen(request)\n except HTTPError as e:\n raise FMError(str(e))\n except URLError as e:\n raise FMError(\"URL Error: %s\" % str(e.reason))\n\n results = FMPXMLResult()\n results.httpinfo = fd.info()\n results.url = url + \"?\" + data\n # hide logon information\n #if self._dbuser and self._dbpasswd:\n # results.url = results.url.replace(\n # \"//\", \"//%s:%s@\" % (self._dbuser, self._dbpasswd), 1)\n doc = minidom.parse(fd)\n fd.close()\n\n # <PRODUCT BUILD=\"06/14/2006\" NAME=\"FileMaker Web Publishing Engine\"\n # VERSION=\"8.0.4.128\"/>\n try:\n attrs = doc.getElementsByTagName(\"PRODUCT\")[0].attributes\n results.product.update(attrs.items())\n except Exception:\n pass\n\n # <DATABASE DATEFORMAT=\"MM/dd/yyyy\" LAYOUT=\"data entry\" NAME=\"Test\"\n # RECORDS=\"68\" TIMEFORMAT=\"HH:mm:ss\"/>\n try:\n attrs = doc.getElementsByTagName(\"DATABASE\")[0].attributes\n results.database.update(attrs.items())\n except Exception:\n pass\n\n # <ERRORCODE>0</ERRORCODE>\n try:\n results.errorcode = int(\n doc.getElementsByTagName(\"ERRORCODE\")[0].firstChild.data)\n except Exception:\n results.errorcode = -1\n\n if results.errorcode:\n try:\n errormsg = FMError.codes[results.errorcode]\n except KeyError:\n errormsg = \"Unknown error code\"\n raise FMError(\"FileMaker Error #%s: %s\" % (\n results.errorcode, errormsg), results.errorcode)\n\n # <METADATA>\n fields = doc.getElementsByTagName(\"METADATA\")[0].childNodes\n metadata = results.metadata\n for field in fields:\n metadata.append(FMField(field.attributes))\n\n # <RESULTSET>\n try:\n resultset = doc.getElementsByTagName(\"RESULTSET\")[0]\n except Exception:\n pass\n else:\n escrslt = self._escrslt\n unctype = unicode_t\n for row in resultset.childNodes:\n d = {}\n d[\"MODID\"] = int(row.attributes[\"MODID\"].value)\n d[\"RECORDID\"] = int(row.attributes[\"RECORDID\"].value)\n for md, cn in zip(metadata, row.childNodes):\n if escrslt and md.dtype == unctype:\n convert_type = escape_unicode\n else:\n convert_type = md.dtype\n if md.maxrepeat == 1:\n de = cn.firstChild.firstChild\n d[md.name] = convert_type(de.data) if de else None\n else:\n a = []\n for c in cn.childNodes:\n value = c.firstChild\n if value:\n a.append(convert_type(value.data))\n else:\n break\n d[md.name] = a\n results.resultset.append(d)\n return results","title":""},{"docid":"f875ebf0d965df05e4a6d4969b277f21","score":"0.4614563","text":"def edit(self, query=None, **kwargs):\n pass","title":""},{"docid":"7c40551d211449ed1dec5f69af03ee40","score":"0.4592555","text":"def formatted_queries():\n\n id = get_id() # Could be a SQLi response..\n\n query1 = f\"SELECT * FROM users WHERE id = {id}\"\n\n query2 = \"SELECT * FROM users WHERE id = {0}\" % id\n\n query3 = \"SELECT * FROM users WHERE id = {0}\".format(id)\n\n query4 = f\"UPDATE users SET is_admin = 1 WHERE id = {id}\"\n\n query5 = f\"DELETE FROM users WHERE id = {id}\"\n\n query6 = f\"INSERT INTO users (id) VALUES ( id = {id} )\"\n\n query7 = f\"SELECT * FROM users WHERE id = {id}\"","title":""},{"docid":"d7f78cf09994c8f069f7146171f320bb","score":"0.4590935","text":"def commit(self):\n self.connection.commit()","title":""},{"docid":"d7f78cf09994c8f069f7146171f320bb","score":"0.4590935","text":"def commit(self):\n self.connection.commit()","title":""},{"docid":"ec13f5ddfb3792a28f440c6d6f1f53f2","score":"0.45908436","text":"def commit_rst(self):\n self.state_machine.insert_input(self.rst_lines, \"\")\n self.rst_lines = []","title":""},{"docid":"982819eb11651908f894cfcdf9739fe1","score":"0.45901954","text":"def meta_command(connection, user_input):\n user_parts = user_input.split()\n replaced = ''\n\n # .tables: print list of tables in database\n if user_parts[0].lower() == '.tables' or user_parts[0].lower() == '.table':\n replaced = 'SELECT name FROM sqlite_master WHERE type = \"table\";'\n\n # .schema: print details about a tables column\n elif user_parts[0].lower() == '.schema':\n if len(user_parts) == 1:\n replaced = 'SELECT sql FROM sqlite_master WHERE type = \"table\";'\n elif len(user_parts) == 2:\n s = 'SELECT sql FROM sqlite_master WHERE type = \"table\" AND name = \"{[1]}\";'\n replaced = s.format(user_parts)\n else:\n print('Usage: .schema [table]')\n\n # .dump: print SQL commands to recreate table\n # ToDo: http://www.sqlitetutorial.net/sqlite-dump/\n elif user_parts[0].lower() == '.dump':\n if len(user_parts) == 1:\n for line in connection.iterdump():\n print(line)\n elif len(user_parts) == 2:\n replaced = 'SELECT * from {[1]};'.format(user_parts)\n else:\n print('Usage: .dump [table]')\n\n else:\n print('That sqlite3 meta command has not been implimented')\n\n return replaced","title":""},{"docid":"fe5bb448d4e729664a53d52a337c233e","score":"0.4587876","text":"def execute_query(self, query):\n sql_blacklist = ['drop', 'delete']\n if any(word.casefold() in query.casefold() for word in sql_blacklist):\n print(\"No queries with {0} will be executed\".format([word for word in sql_blacklist if word.casefold() in query.casefold()][0]))\n return\n dbcursor = self.connection.cursor()\n dbcursor.execute(query)\n result = dbcursor.fetchall()\n return result","title":""},{"docid":"9bc2e40b428d4a5fb47134018f0c3fdf","score":"0.45863706","text":"def edit():","title":""},{"docid":"08b6eaae8567df11341309842b1e20bd","score":"0.4586093","text":"def commit(self):\n self.connect.commit()","title":""},{"docid":"d19fa45f4ed36207f1de5bd22ff54b60","score":"0.4576854","text":"def submit_textarea():\n post_content = request.form[\"content\"]\n author = request.form[\"author\"]\n\n post_object = {\n 'author': author,\n 'content': post_content,\n }\n\n # Submit a transaction\n new_tx_address = \"{}/new_transaction\".format(CONNECTED_NODE_ADDRESS)\n\n requests.post(new_tx_address,\n json=post_object,\n headers={'Content-type': 'application/json'})\n\n return redirect('/')","title":""},{"docid":"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0","score":"0.45657098","text":"def commit(self):\n pass","title":""},{"docid":"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0","score":"0.45657098","text":"def commit(self):\n pass","title":""},{"docid":"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0","score":"0.45657098","text":"def commit(self):\n pass","title":""},{"docid":"0c63c83926920827115ebe9e62d22751","score":"0.45615292","text":"def submit_textarea():\n post_object = {\n \"contents\": transaction.to_dict(),\n \"signature\": transaction.signature\n }\n\n # Submit a transaction\n if request.method == 'GET':\n new_tx_address = \"http://127.0.0.1:8000/new_transaction\"\n else:\n port_nb = request.get_json()\n new_tx_address = \"http://127.0.0.1:\" + str(port_nb) + \"/new_transaction\"\n\n requests.post(new_tx_address,\n json=post_object,\n headers={'Content-type': 'application/json'})\n\n return \"Success\", 201","title":""},{"docid":"6bf63136f22ed1813f702a3eefc3f733","score":"0.45585993","text":"def execute(self, req, data = {}):\n res = self.cursor().execute(req, data)\n if not('SELECT' in req) and self.auto_commit:\n self.commit()\n return res","title":""},{"docid":"6cfce166a7e5f5199782bd00a7aba9f4","score":"0.45553318","text":"def format_queries(queries):\n output = '-- Queries %s\\n' % ('-'*(LINE_BIG-11))\n\n for query in queries:\n output += '-- %s %s\\n' % (query, '-'*(LINE_SMALL-4-len(query)))\n output += queries[query]\n output += '\\n;%s' % ('\\n'*3)\n\n return output","title":""},{"docid":"4ae4fdd9487cc167bf18577c58d5679e","score":"0.4553439","text":"def commit(self):\n self.get_connection().commit()","title":""},{"docid":"64ff9a9219abb92fdd8c79dd0c4f78a5","score":"0.4548623","text":"def write():\n db = bottle.request.forms.get('db')\n return \"Not implemented.\"","title":""},{"docid":"f9a38c1baa64adf90927da1ddc9c6ce5","score":"0.45434672","text":"def get_sql(self, undo=False):\n return self.undo if undo else self.do","title":""},{"docid":"a9ccd280dc1c1812786911cc0667553c","score":"0.4541497","text":"def query(self, qu):\n return self.cur.execute(qu)","title":""},{"docid":"51da7ec25e38198334a0e58eaf1d0ce6","score":"0.45397788","text":"def test_query_button_clicked_query_parse_error(self, widget):\n\n widget.query.text = \"ans(study_id, term, tfidf):-neurosynth_default_mode_study_id(study_id),neurosynth_pcc_study_id(study_id),neurosynth_study_tfidf(study_id, term, tfidf\"\n widget._on_query_button_clicked(None)\n assert widget.result_viewer.layout.visibility == \"hidden\"\n assert widget.error_display.layout.visibility == \"visible\"\n assert len(widget.query.marks) == 1\n assert len(widget.query.text_marks) == 1","title":""},{"docid":"914f9be4724a27528047407f06d521f3","score":"0.45382926","text":"def commit(self) -> None:\n self.con.commit()","title":""},{"docid":"64dd4aba698f62aeb80815ea901e267d","score":"0.45381135","text":"def update_row(self,query,info):\n \n with self.conn.cursor() as cur:\n try:\n cur.execute(query,info)\n self.conn.commit()\n cur.close()\n except Exception as e:\n print(e)\n cur.execute(\"ROLLBACK\")\n self.conn.commit()\n cur.close()\n raise HTTPException(status_code=400, detail=\"Information given was invalid\")\n return \"Row updated\"","title":""}],"string":"[\n {\n \"docid\": \"e21bbedcfe57e4dde89c501f78053c1d\",\n \"score\": \"0.5836899\",\n \"text\": \"def commit(message):\\n settings = _load_settings()\\n status = _load_settings(\\\".TDB\\\", check=[])\\n settings.update(status)\\n database = settings[\\\"database\\\"]\\n client, msg = _connect(settings)\\n click.echo(msg)\\n sys.path.append(os.getcwd())\\n schema_plan = __import__(\\\"schema\\\", globals(), locals(), [], 0)\\n last_item = None\\n documentation = {}\\n for line in schema_plan.__doc__.split(\\\"\\\\n\\\"):\\n if \\\"Title:\\\" in line:\\n documentation[\\\"title\\\"] = line[6:].strip()\\n last_tiem = documentation[\\\"title\\\"]\\n elif \\\"Description:\\\" in line:\\n documentation[\\\"description\\\"] = line[12:].strip()\\n last_tiem = documentation[\\\"description\\\"]\\n elif \\\"Authors:\\\" in line:\\n documentation[\\\"authors\\\"] = line[8:].strip()\\n last_tiem = documentation[\\\"authors\\\"]\\n elif last_item is not None:\\n last_item += \\\"\\\\n\\\" + line.strip()\\n authors = documentation.get(\\\"authors\\\")\\n if authors:\\n authors = documentation[\\\"authors\\\"].split(\\\",\\\")\\n authors = list(map(lambda x: x.strip(), authors))\\n schema_obj = WOQLSchema(\\n title=documentation.get(\\\"title\\\"),\\n description=documentation.get(\\\"description\\\"),\\n authors=authors,\\n )\\n for obj_str in dir(schema_plan):\\n obj = eval(f\\\"schema_plan.{obj_str}\\\") # noqa: S307\\n if isinstance(obj, woqlschema.TerminusClass) or isinstance(obj, enum.EnumMeta):\\n if obj_str not in [\\\"DocumentTemplate\\\", \\\"EnumTemplate\\\", \\\"TaggedUnion\\\"]:\\n schema_obj.add_obj(obj.__name__, obj)\\n if message is None:\\n message = \\\"Schema updated by Python client.\\\"\\n schema_obj.commit(client, commit_msg=message, full_replace=True)\\n click.echo(f\\\"{database} schema updated.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f4b7e800f85d30417dae67aa538cde\",\n \"score\": \"0.56011313\",\n \"text\": \"def sql_btn():\\n content = None\\n sql = None\\n if request.method == 'POST':\\n db = get_db()\\n query = request.form['sql']\\n query = \\\"\\\\n\\\".join(query.splitlines())\\n #print(query)\\n #pprint.pprint(query)\\n sql = query.split('\\\\n')\\n\\n print(\\\"\\\")\\n\\n try:\\n if sql[0].split(\\\" \\\")[0].upper() == \\\"SELECT\\\":\\n print(\\\"{}\\\".format(\\\" \\\".join(sql)))\\n content = db.execute(\\\" \\\".join(sql)).fetchall()\\n else:\\n print('{}'.format(\\\" \\\".join(sql)))\\n db.execute(\\\" \\\".join(sql))\\n db.commit()\\n except BaseException as e:\\n content = None\\n print(str(e))\\n flash(str(e))\\n\\n print(\\\"\\\")\\n return render_template('sql.html', sql=sql, content=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21f8b9a80ccfb1a010fef94a8f80b55\",\n \"score\": \"0.5516456\",\n \"text\": \"def accept(self):\\n self.parent.sqlcommand = self.sqlEdit.toPlainText()\\n self.parent.errormessage = \\\"The data was successfully consolidated,\\\\nthe program will now close.\\\"\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44149c528588baa6ceea6346e8d17cc\",\n \"score\": \"0.5497707\",\n \"text\": \"def handle_editor_command(self, text):\\n while special.editor_command(text):\\n filename = special.get_filename(text)\\n query = (special.get_editor_query(text) or\\n self.get_last_query())\\n sql, message = special.open_external_editor(filename, sql=query)\\n if message:\\n # Something went wrong. Raise an exception and bail.\\n raise RuntimeError(message)\\n while True:\\n try:\\n text = self.prompt_app.prompt(default=sql)\\n break\\n except KeyboardInterrupt:\\n sql = ''\\n continue\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903985c0d2a908c9c4ce9527b832770f\",\n \"score\": \"0.5439685\",\n \"text\": \"def sqlcmd(self):\\n self.update_sqlcmd = \\\"\\\\n\\\".join([i for i in [self.update_clause, \\n self.set_clause, \\n self.where_clause] if i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ce078eb48a25c83fa01b4fb25a5c43\",\n \"score\": \"0.54361415\",\n \"text\": \"def test_execute_sql_query_selection(self):\\n self.tab = self._add_new_query_tab()\\n sql_query_string = 'SELECT name FROM streets LIMIT 3\\\\n UPDATE'\\n self._prepare_query_text(sql_query_string)\\n self.tab.run_query()\\n self.assertTrue(self.tab.errors_panel.isVisible())\\n self.assertIn('UPDATE', self.tab.errors_panel.toPlainText())\\n\\n # position cursor before `\\\\n` in the SQL query\\n cur_pos = 32\\n cur = self.tab.query.textCursor()\\n cur.setPosition(0)\\n cur.setPosition(cur_pos, QTextCursor.KeepAnchor)\\n self.tab.query.setTextCursor(cur)\\n self.assertEqual(self.tab.query.textCursor().selectedText(),\\n sql_query_string[:cur_pos])\\n\\n self.tab.run_query()\\n self.assertFalse(self.tab.errors_panel.isVisible())\\n self.assertTrue(self.tab.table.isVisible())\\n self.assertEqual(self.tab.table.table_data.number_layer_rows, 3)\\n self.assertEqual(self.tab.table.table_data.columnCount(), 1)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0003c9ea1822aada63869891dd3bc090\",\n \"score\": \"0.53923744\",\n \"text\": \"def menu_update_query(run_args):\\n run_args['command'] = input(\\\"input command:\\\")\\n run_args['error'] = input(\\\"input error:\\\")\\n run_args['query'] = get_query(run_args['command'], run_args['error'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e971f298692a6fce4de3a911f7dfb3\",\n \"score\": \"0.53099227\",\n \"text\": \"def submitQuery(self):\\n \\n # Update query entries\\n fpO1 = open('C:/Users/toExecute.sql',\\\"w\\\")\\n data = fp2.read()\\n fp2.close()\\n nvlVal1 = self.queryUpdate1.get()\\n nvlVal2 = self.queryUpdate2.get()\\n nvlVal3 = self.queryUpdate3.get()\\n nvlVal4 = nvlVal2[:4] + nvlVal2[5:7] + nvlVal2[8:10]\\n print (nvlVal4)\\n mydict = {'myNV1': nvlVal1, 'myNV2': nvlVal2, 'myNV3' : nvlVal3, 'myNV4' : nvlVal4}\\n \\n for key, value in mydict.items():\\n data = data.replace(key, value) \\n fpO1.write(data)\\n fpO1.close()\\n \\n # Prepare the file and the query\\n fpO1 = open(\\\"C:/Users/toExecute.sql\\\")\\n outFile1 = open(\\\"C:/Users/query1.sql\\\", \\\"w\\\")\\n outFile2 = open(\\\"C:/Users/query2.sql\\\", \\\"w\\\")\\n outFile3 = open(\\\"C:/Users/query3.sql\\\", \\\"w\\\")\\n outFile4 = open(\\\"C:/Users/query4.sql\\\", \\\"w\\\")\\n buffer = []\\n keepCurrentSet = True\\n for line in fpO1:\\n buffer.append(line)\\n if line.startswith(\\\"/*A\\\"):\\n #---- starts a new data set\\n if keepCurrentSet:\\n outFile1.write(\\\"\\\".join(buffer))\\n #now reset our state\\n keepCurrentSet = False\\n buffer = []\\n \\n elif line.startswith(\\\"/*B\\\"):\\n if keepCurrentSet:\\n outFile2.write(\\\"\\\".join(buffer))\\n keepCurrentSet = False\\n buffer = []\\n \\n elif line.startswith(\\\"/*C\\\"):\\n if keepCurrentSet:\\n outFile3.write(\\\"\\\".join(buffer))\\n keepCurrentSet = False\\n buffer = []\\n \\n elif line.startswith(\\\"/*D\\\"):\\n if keepCurrentSet:\\n outFile4.write(\\\"\\\".join(buffer))\\n keepCurrentSet = False\\n buffer = []\\n \\n elif line.startswith(\\\";\\\"):\\n keepCurrentSet = True\\n \\n fpO1.close()\\n outFile1.close()\\n outFile2.close()\\n outFile3.close()\\n outFile4.close()\\n \\n outFile1 = open(\\\"C:/Users/query1.sql\\\", \\\"r\\\")\\n qury1 = outFile1.read()\\n \\n outFile2 = open(\\\"C:/Users/query2.sql\\\", \\\"r\\\")\\n qury2 = outFile2.read()\\n \\n outFile3 = open(\\\"C:/Users/query3.sql\\\", \\\"r\\\")\\n qury3 = outFile3.read()\\n \\n outFile4 = open('C:/Users/query4.sql', 'r')\\n qury4 = outFile4.read()\\n \\n status = ' --- Connection successful --- Query successfully executed ---'\\n global cursor1\\n global cursor2\\n global cursor3\\n global cursor4\\n global data1\\n global data2\\n global data3\\n global data4\\n global fields1, fields2, fields3, fields4\\n global names1, names2, names3, traiames4\\n \\n # Select instance, Open connection, retrieve cursor and execute query\\n if self.var.get() == 'IE301':\\n connection = pymssql.connect(server ='FRER0973\\\\IO13',user = 'OMUser', password = '%Essilor1%', database = 'OrderDB')\\n connection2 = pymssql.connect(server ='FRER0973\\\\ION3',user = 'OMUser', password = '%Essilor1%', database = 'OnRouteDB')\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n elif self.var.get() == 'IE401':\\n connection = pymssql.connect(server='FRER0972\\\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\\n connection2 = pymssql.connect(server='FRER0972\\\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n elif self.var.get() == 'IE402':\\n connection = pymssql.connect(server='FRER1420\\\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\\n connection2 = pymssql.connect(server='FRER1420\\\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n elif self.var.get() == 'IE501':\\n connection = pymssql.connect(server='FRER1308\\\\IO14', user='OMUser', password='%Essilor1%', database='OrderDB')\\n connection2 = pymssql.connect(server='FRER1308\\\\ION4', user='OMUser', password='%Essilor1%', database='OnRouteDB')\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n elif self.var.get() == 'IFF02':\\n connection = pymssql.connect(server='FRER1344\\\\IO1F', user='OMUser', password='%Essilor1%', database='OrderDB')\\n connection2 = pymssql.connect(server='FRER1344\\\\IONF', user='OMUser', password='%Essilor1%', database='OnRouteDB')\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n elif self.var.get() == 'New server':\\n \\n parser = RawConfigParser()\\n parser.read(file_path2)\\n myserver = parser.get('add_instance', 'server')\\n myserver2 = parser.get('add_instance', 'server2')\\n myuser = parser.get('add_instance', 'user')\\n mypassword = parser.get('add_instance', 'password')\\n mydatabase = parser.get('add_instance', 'dbname')\\n \\n connection = pymssql.connect(server = myserver, user = myuser, password = mypassword, database = mydatabase)\\n connection2 = pymssql.connect(server = myserver2, user = myuser, password = mypassword, database = mydatabase2)\\n \\n cursor1 = connection.cursor()\\n cursor1.execute(qury1)\\n data1 = cursor1.fetchall()\\n fields1 = cursor1.description # metadata from query\\n names1 = [x[0] for x in cursor1.description]\\n cursor1.close()\\n \\n cursor2 = connection.cursor()\\n cursor2.execute(qury2)\\n data2 = cursor2.fetchall()\\n fields2 = cursor2.description # metadata from query\\n names2 = [x[0] for x in cursor2.description]\\n cursor2.close()\\n \\n cursor3 = connection.cursor()\\n cursor3.execute(qury3)\\n data3 = cursor3.fetchall()\\n fields3 = cursor3.description # metadata from query\\n names3 = [x[0] for x in cursor3.description]\\n cursor3.close()\\n \\n cursor4 = connection2.cursor()\\n cursor4.execute(qury4)\\n data4 = cursor4.fetchall()\\n fields4 = cursor4.description # metadata from query\\n names4 = [x[0] for x in cursor4.description]\\n cursor4.close()\\n \\n self.submitStatus.setentry(status)\\n \\n else:\\n status = '--- Connection failed --- No query executed ---'\\n self.submitStatus.setentry(status)\\n \\n self.master.withdraw()\\n infoMessage = \\\"Please wait ! The export is running...\\\\nThe window will appear once exporting finish.\\\\nYou can continue to use your PC.\\\"\\n showinfo(\\\"Running...\\\", infoMessage)\\n \\n \\\"\\\"\\\"Export query results to Excel file\\\"\\\"\\\" \\n df = pd.DataFrame(list(data1), columns = names1)\\n df2 = pd.DataFrame(list(data2), columns = names2)\\n df3 = pd.DataFrame(list(data3), columns = names3)\\n df4 = pd.DataFrame(list(data4), columns = names4)\\n \\n # Save a copy of the xls file for upload\\n save_path = 'C:/temp/Results.xlsx' \\n writer = pd.ExcelWriter(save_path, engine='xlsxwriter')\\n list_dfs = [df, df2, df3, df4]\\n for n, df in enumerate(list_dfs):\\n df.to_excel(writer,'sheet%s' % n)\\n writer.save()\\n \\n src_filename = 'C:/temp/Results.xlsx'\\n dest_filename = 'C:/temp/Template_Report.xlsx'\\n \\n wb1 = openpyxl.load_workbook(dest_filename)\\n wb2 = openpyxl.load_workbook(src_filename)\\n \\n ws2 = wb2.get_sheet_by_name('sheet0')\\n ws22 = wb2.get_sheet_by_name('sheet1')\\n ws222 = wb2.get_sheet_by_name('sheet2')\\n ws2222 = wb2.get_sheet_by_name('sheet3')\\n \\n sh11_count = (ws222.max_row - 1)\\n \\n #ws0 = wb1.worksheets[0]\\n ws1 = wb1.worksheets[1]\\n ws11 = wb1.worksheets[2]\\n ws111 = wb1.worksheets[3]\\n ws1111 = wb1.worksheets[4]\\n \\n sh0_count = ws2.max_column\\n sh1_count = ws22.max_column\\n sh2_count = ws222.max_column\\n sh3_count = ws2222.max_column\\n \\n for i in range(1, sh0_count):\\n col_d = ws2.columns[i] # 0-indexing\\n for idx, cell in enumerate(col_d, 1):\\n ws1.cell(row = idx, column = i).value = cell.value #1-indexing\\n ws1['A1'] = 'Request ID'\\n ws1['B1'] = 'Order Entry'\\n ws1['C1'] = 'Customer Number'\\n ws1['D1'] = 'DN Number'\\n \\n ws1['P1'] = 'Creation Hour'\\n ws1['Q1'] = 'Allocation Hour'\\n ws1['R1'] = 'Diff Crea/Allo (in Second)'\\n ws1['S1'] = 'Round Allocation Time (in Minute)'\\n \\n for j in range(1, sh1_count):\\n col_d = ws22.columns[j] # 0-indexing\\n for idx, cell in enumerate(col_d, 1):\\n ws11.cell(row = idx, column = j).value = cell.value #1-indexing\\n ws11['A1'] = 'Order Entry'\\n ws11['B1'] = 'Customer Number'\\n ws11['C1'] = 'Request ID'\\n ws11['D1'] = 'DN Number'\\n ws11['E1'] = 'Product Code'\\n ws11['F1'] = 'Product Label'\\n ws11['G1'] = 'Status'\\n ws11['H1'] = 'Reason'\\n ws11['I1'] = 'Lab'\\n ws11['J1'] = 'Creation Date'\\n ws11['K1'] = 'Severity'\\n ws11['L1'] = 'Error Message'\\n ws11['M1'] = 'Detailed Error Message'\\n \\n for k in range(1, sh2_count):\\n col_d = ws222.columns[k] # 0-indexing\\n for idx, cell in enumerate(col_d, 1):\\n ws111.cell(row = idx, column = k).value = cell.value #1-indexing\\n ws111['A1'] = 'Order Entry'\\n ws111['B1'] = 'Customer Number'\\n ws111['C1'] = 'Request ID'\\n ws111['D1'] = 'DN Number'\\n ws111['G1'] = 'Customer Reference'\\n ws111['H1'] = 'Status'\\n ws111['I1'] = 'Reason'\\n ws111['J1'] = 'Lab'\\n ws111['K1'] = 'Creation Date'\\n \\n for l in range(1, sh3_count):\\n col_d = ws2222.columns[l] # 0-indexing\\n for idx, cell in enumerate(col_d, 1):\\n ws1111.cell(row = idx, column = l).value = cell.value #1-indexing\\n ws1111['B1'] = 'Request ID'\\n ws1111['C1'] = 'Error Message'\\n ws1111['D1'] = 'Error Value'\\n \\n \\n '''for rowNumP in range(2, (ws1.get_highest_row()) + 1):\\n if ws1.cell(row = rowNumP, column = 10).value is not None:\\n CreationDT = ws1.cell(row = rowNumP, column = 10).value\\n CreationT = str(CreationDT)\\n Cre = re.search(' (.+?)\\\\.', CreationT).group(1)\\n CreT = datetime.datetime.strptime(Cre, \\\"%H:%M:%S\\\")\\n CreationTime = CreT.strftime(\\\"%I:%M:%S %p\\\")\\n ws1.cell(row = rowNumP, column = 16).value = CreationTime\\n creTime = time.strptime(CreationTime.split(' ')[0],'%H:%M:%S')\\n CrSec = datetime.timedelta(hours=creTime.tm_hour,minutes=creTime.tm_min,seconds=creTime.tm_sec).total_seconds()\\n \\n if ws1.cell(row = rowNumP, column = 11).value is not None:\\n AllocationDT = ws1.cell(row = rowNumP, column = 11).value\\n AllocationT = str(AllocationDT)\\n Alo = re.search('(?<= )\\\\S+', AllocationT).group(0)\\n OK = re.search(r'\\\\.', Alo)\\n if OK:\\n AloT = datetime.datetime.strptime(Alo, \\\"%H:%M:%S.%f\\\")\\n AllocationTime = AloT.strftime(\\\"%I:%M:%S %p\\\")\\n ws1.cell(row = rowNumP, column = 17).value = AllocationTime\\n aloTime = time.strptime(AllocationTime.split(' ')[0],'%H:%M:%S')\\n alSec = datetime.timedelta(hours=aloTime.tm_hour,minutes=aloTime.tm_min,seconds=aloTime.tm_sec).total_seconds()\\n ws1.cell(row = rowNumP, column = 18).value = alSec - CrSec\\n ws1.cell(row = rowNumP, column = 19).value = round(((ws1.cell(row = rowNumP, column = 18).value)/60), 1)\\n \\n else:\\n AloT = datetime.datetime.strptime(Alo, \\\"%H:%M:%S\\\")\\n AllocationTime = AloT.strftime(\\\"%I:%M:%S %p\\\")\\n ws1.cell(row = rowNumP, column = 17).value = AllocationTime\\n aloTime = time.strptime(AllocationTime.split(' ')[0],'%H:%M:%S')\\n alSec = datetime.timedelta(hours=aloTime.tm_hour,minutes=aloTime.tm_min,seconds=aloTime.tm_sec).total_seconds()\\n ws1.cell(row = rowNumP, column = 18).value = alSec - CrSec\\n ws1.cell(row = rowNumP, column = 19).value = round(((ws1.cell(row = rowNumP, column = 18).value)/60), 1)\\n \\n \\n if ws1.cell(row = rowNumP, column = 1).value is not None and ws1.cell(row = rowNumP, column = 19).value is None:\\n ws1.cell(row = rowNumP, column = 19).value = 0.00'''\\n \\n wb1.save('C:/temp/ReportUP.xlsx')\\n \\n # Show the tool window again \\n status = \\\"--- Successfully exported to the final reports file --- Look at C:\\\\Users ---\\\"\\n self.submitStatus.setentry('')\\n self.submitStatus.setentry(status)\\n self.master.deiconify()\\n infoMessage = \\\"Allocation rate = %d commands\\\" % sh11_count\\n showinfo(\\\"Finish\\\", infoMessage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862259078688ff55fff89a2532cf968b\",\n \"score\": \"0.5165891\",\n \"text\": \"def fm_edit(self):\\n return self._commit(\\\"edit\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae85c4117e5e5a27a836b03e16888b9c\",\n \"score\": \"0.51652116\",\n \"text\": \"def act_query(query):\\n connection = conn\\n cursor = connection.cursor()\\n num_affected_rows = cursor.execute(query)\\n cursor.close()\\n connection.commit()\\n return num_affected_rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa3c136ecd401824890d8232e62061b\",\n \"score\": \"0.515516\",\n \"text\": \"def _prepare_query_text(self, sql_query):\\n self.tab.gdb = self.local_gdb\\n sql_query_string = sql_query\\n self.tab.query.setText(sql_query_string)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfa1a48b30594357ad7d7c6116285e2\",\n \"score\": \"0.5114265\",\n \"text\": \"def _run_query(self, query):\\n try:\\n result = self.cursor.execute(query)\\n except:\\n messagebox.showerror(\\\"Error\\\",\\\"\\\"\\\"\\nSomething went wrong executing the query.\\n\\nCheck if you are connected to the database.\\n\\nIf you are trying to manipulate data, check if you filled in the fields correctly.\\n\\nIf you were using delete or update you must select a register on the table before.'\\\"\\\"\\\")\\n\\n self.connection.commit()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"516f72418c9d0c4ad35c8b4a751a446c\",\n \"score\": \"0.5096106\",\n \"text\": \"def real_process(text):\\n prod = parser(text, utcnow=common.utcnow())\\n if not common.dbwrite_enabled():\\n return\\n if prod.warnings:\\n common.email_error(\\\"\\\\n\\\".join(prod.warnings), text)\\n df = DBPOOL.runInteraction(prod.sql)\\n df.addCallback(got_data)\\n df.addErrback(common.email_error, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b8033fa8192771d155bd96aaecfc69\",\n \"score\": \"0.50843114\",\n \"text\": \"def modify(self, SQL, params='', verbose=True):\\n try:\\n \\n # Make sure the database isn't locked\\n self.conn.commit()\\n \\n if SQL.lower().startswith('select'):\\n print('Use self.query method for queries.')\\n else:\\n self.list(SQL, params)\\n self.conn.commit()\\n if verbose:\\n print('Number of records modified: {}'.format(self.list(\\\"SELECT changes()\\\").fetchone()[0] or '0'))\\n except:\\n print(\\\"Could not execute: \\\"+SQL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d52685d54bbc8d787972983ad9064f3\",\n \"score\": \"0.507759\",\n \"text\": \"def docmd(self, fmt, *args):\\n if not self.gdb.accepting_cmd():\\n self.gdb.console_print(\\n \\\"gdb busy: command discarded, please retry\\\\n\\\")\\n return False\\n\\n self.gdb.gdb_busy = True\\n self.result = ''\\n return self.send(fmt, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477d56fe945bb8eb90a93a42b0e61ba9\",\n \"score\": \"0.50667006\",\n \"text\": \"def _commit(self):\\r\\n self.conn.commit()\\r\\n self.changed = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97a3289e1cb047263b16b7dd0126ca7d\",\n \"score\": \"0.5056425\",\n \"text\": \"def submit_query(self, query):\\n return self.app.post('/query_database/', data=dict(\\n query=query\\n ), follow_redirects=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56989304d6f6c38df5f879e37448494d\",\n \"score\": \"0.50355613\",\n \"text\": \"def cmd(ctx, url, key, secret, export_format, table_format, sq, folder, create, echo):\\n client = ctx.obj.start_client(url=url, key=key, secret=secret)\\n\\n p_grp = ctx.parent.parent.command.name\\n apiobj = getattr(client, p_grp)\\n\\n with ctx.obj.exc_wrap(wraperror=ctx.obj.wraperror):\\n data = apiobj.saved_query.update_folder(\\n sq=sq, folder=folder, create=create, echo=echo, as_dataclass=True\\n )\\n ctx.obj.echo_ok(f\\\"Successfully updated Saved Query {data.name}\\\")\\n\\n click.secho(EXPORT_FORMATS[export_format](data=data, table_format=table_format))\\n ctx.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4a5faa9747af799d7a9388f7c58af2\",\n \"score\": \"0.5008634\",\n \"text\": \"def mysql(self, cursor, query):\\n if isinstance(query, basestring):\\n query = [query]\\n for q in query:\\n cursor.execute(q)\\n q.replace('\\\\n', ' ')\\n self.print_log('Executed: {}'.format(q))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f710ed780a16500ee0ba33da855f6ca2\",\n \"score\": \"0.49840534\",\n \"text\": \"def opModify(self, sql):\\n insert_num = self.cur.execute(sql) # execute sql\\n self.coon.commit()\\n return insert_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be7523496aeffd2f8e5ae94f3cf4701\",\n \"score\": \"0.49723747\",\n \"text\": \"def prompt(skipfile, skipall, query, chunk):\\n newpatches = None\\n if skipall is not None:\\n return skipall, skipfile, skipall, newpatches\\n if skipfile is not None:\\n return skipfile, skipfile, skipall, newpatches\\n while True:\\n resps = messages[b'help'][operation]\\n # IMPORTANT: keep the last line of this prompt short (<40 english\\n # chars is a good target) because of issue6158.\\n r = ui.promptchoice(b\\\"%s\\\\n(enter ? for help) %s\\\" % (query, resps))\\n ui.write(b\\\"\\\\n\\\")\\n if r == 8: # ?\\n for c, t in ui.extractchoices(resps)[1]:\\n ui.write(b'%s - %s\\\\n' % (c, encoding.lower(t)))\\n continue\\n elif r == 0: # yes\\n ret = True\\n elif r == 1: # no\\n ret = False\\n elif r == 2: # Edit patch\\n if chunk is None:\\n ui.write(_(b'cannot edit patch for whole file'))\\n ui.write(b\\\"\\\\n\\\")\\n continue\\n if chunk.header.binary():\\n ui.write(_(b'cannot edit patch for binary file'))\\n ui.write(b\\\"\\\\n\\\")\\n continue\\n # Patch comment based on the Git one (based on comment at end of\\n # https://mercurial-scm.org/wiki/RecordExtension)\\n phelp = b'---' + _(\\n b\\\"\\\"\\\"\\nTo remove '-' lines, make them ' ' lines (context).\\nTo remove '+' lines, delete them.\\nLines starting with # will be removed from the patch.\\n\\nIf the patch applies cleanly, the edited hunk will immediately be\\nadded to the record list. If it does not apply cleanly, a rejects\\nfile will be generated: you can use that when you try again. If\\nall lines of the hunk are removed, then the edit is aborted and\\nthe hunk is left unchanged.\\n\\\"\\\"\\\"\\n )\\n (patchfd, patchfn) = pycompat.mkstemp(\\n prefix=b\\\"hg-editor-\\\", suffix=b\\\".diff\\\"\\n )\\n ncpatchfp = None\\n try:\\n # Write the initial patch\\n f = util.nativeeolwriter(os.fdopen(patchfd, 'wb'))\\n chunk.header.write(f)\\n chunk.write(f)\\n f.write(\\n b''.join(\\n [b'# ' + i + b'\\\\n' for i in phelp.splitlines()]\\n )\\n )\\n f.close()\\n # Start the editor and wait for it to complete\\n editor = ui.geteditor()\\n ret = ui.system(\\n b\\\"%s \\\\\\\"%s\\\\\\\"\\\" % (editor, patchfn),\\n environ={b'HGUSER': ui.username()},\\n blockedtag=b'filterpatch',\\n )\\n if ret != 0:\\n ui.warn(_(b\\\"editor exited with exit code %d\\\\n\\\") % ret)\\n continue\\n # Remove comment lines\\n patchfp = open(patchfn, 'rb')\\n ncpatchfp = stringio()\\n for line in util.iterfile(patchfp):\\n line = util.fromnativeeol(line)\\n if not line.startswith(b'#'):\\n ncpatchfp.write(line)\\n patchfp.close()\\n ncpatchfp.seek(0)\\n newpatches = parsepatch(ncpatchfp)\\n finally:\\n os.unlink(patchfn)\\n del ncpatchfp\\n # Signal that the chunk shouldn't be applied as-is, but\\n # provide the new patch to be used instead.\\n ret = False\\n elif r == 3: # Skip\\n ret = skipfile = False\\n elif r == 4: # file (Record remaining)\\n ret = skipfile = True\\n elif r == 5: # done, skip remaining\\n ret = skipall = False\\n elif r == 6: # all\\n ret = skipall = True\\n elif r == 7: # quit\\n raise error.CanceledError(_(b'user quit'))\\n return ret, skipfile, skipall, newpatches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8278ced69daf481d385aafa53c4373e4\",\n \"score\": \"0.49688333\",\n \"text\": \"def test_sql_code_styling(self):\\n self.tab = self._add_new_query_tab()\\n sql_query_string = 'SELECT name FROM streets LIMIT 3'\\n self._prepare_query_text(sql_query_string)\\n\\n cur = self.tab.query.textCursor()\\n cur.setPosition(0)\\n cur.setPosition(6, QTextCursor.KeepAnchor)\\n self.tab.query.setTextCursor(cur)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66ae32f923589713677394732ebbd070\",\n \"score\": \"0.49606788\",\n \"text\": \"def commit(self):\\n self.buffer.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87c382a88f3d488b69292b8ed3085a2\",\n \"score\": \"0.4956843\",\n \"text\": \"def command_sql(self, event):\\n conn = database.obj.get_conn()\\n\\n try:\\n tbl = MessageTable(codeblock=False)\\n\\n with conn.cursor() as cur:\\n start = time.time()\\n cur.execute(event.codeblock.format(e=event))\\n dur = time.time() - start\\n if not cur.description:\\n return event.msg.reply('_took {}ms - no result_'.format(\\n int(dur * 1000)))\\n tbl.set_header(*[desc[0] for desc in cur.description])\\n\\n for row in cur.fetchall():\\n tbl.add(*row)\\n\\n result = tbl.compile()\\n if len(result) > 1900:\\n return event.msg.reply(\\n '_took {}ms_'.format(int(dur * 1000)),\\n attachments=[('result.txt', result)])\\n\\n event.msg.reply('```' + result + '```\\\\n_took {}ms_\\\\n'.format(\\n int(dur * 1000)))\\n except psycopg2.Error as e:\\n event.msg.reply('```{}```'.format(e.pgerror))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b716b577b4adc31a962470803b29ae1d\",\n \"score\": \"0.4952748\",\n \"text\": \"def commitdata(self):\\n try:\\n self.db.execute(self.sqlcommand)\\n self.conn.commit()\\n except Exception as e:\\n self.setVisible(False)\\n self.errormessage = \\\"Error Data Commit\\\\n\\\" + \\\"Error committing the data for: \\\" + \\\\\\n self.enstr + \\\" \\\" + self.rustr + \\\"\\\\nError: \\\" + str(e) + \\\"\\\\nCommand: \\\" + self.sqlcommand\\n errorgui = ErrorDisplay(self)\\n errorgui.activateWindow()\\n errorgui.exec()\\n self.setVisible(True)\\n self.activateWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f4cc0fe27c1e914fd6b04f89f3b14f\",\n \"score\": \"0.49368232\",\n \"text\": \"def _execute_sql(self, sql, user_gdb=None):\\n self.tab = self.ui.tab_widget.currentWidget()\\n if user_gdb:\\n self.tab.gdb = Geodatabase(user_gdb)\\n else:\\n self.tab.gdb = self.local_gdb\\n self.tab.query.setPlainText(sql)\\n self.tab.run_query()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6803e96604a7d4f40b6bae3f39138526\",\n \"score\": \"0.49363384\",\n \"text\": \"def commit(self):\\r\\n return self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8e074b324b9c1f8a8ea2349c3c2093\",\n \"score\": \"0.49327952\",\n \"text\": \"def command_edit_answer(update, context):\\n\\n chat_data = context.chat_data\\n\\n text = (\\n '___DESCRIPTION___: You are in command editting mode. '\\n 'You can either send new messages to add them '\\n 'or delete your previous messages.'\\n )\\n update.message.reply_text(\\n text=text,\\n reply_markup=keyboards.edit_asnwer_markup(),\\n parse_mode='MARKDOWN',\\n )\\n command = chat_data['cmd_instance']\\n chat_data['edit_actions_log'] = []\\n chat_data['msgs_count'] = command.message_set.count()\\n chat_data['msgs_last_id'] = 0\\n chat_data['msgs_to_delete'] = []\\n\\n inform_number_of_commands(update, context)\\n return states.SEND_EDIT_MESSAGE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729b45eda5b7b240df53452d7a76e7f7\",\n \"score\": \"0.49326035\",\n \"text\": \"def __init__(self, dbfilename, initialize='', mode='r', output='html'):\\n if (output.lower() in ['html', 'text']):\\n self.output = output.lower()\\n else:\\n print('Unsupported output format: \\\"%s\\\", use one of \\\"html\\\" or \\\"text\\\"' % output)\\n self.result = None\\n self.history = []\\n self.histIdx = 0\\n buttonRow = []\\n self.query = ipywidgets.Textarea(description=\\\"SQL:\\\", value=initialize, \\n layout=ipywidgets.Layout(width=\\\"600px\\\", height=\\\"100px\\\", font_weight=\\\"bold\\\"))\\n self.query.color = \\\"#000040\\\"\\n\\n self.executeButton = ipywidgets.Button(description=\\\"Execute\\\")\\n self.executeButton.width = \\\"100px\\\"\\n self.executeButton.margin = \\\"10px 5px 10px 72px\\\"\\n self.executeButton.on_click(self.executeSQL)\\n buttonRow.append(self.executeButton)\\n \\n self.submitButton = ipywidgets.Button(description=\\\"Submit\\\")\\n self.submitButton.width = \\\"100px\\\"\\n self.submitButton.margin = \\\"10px 5px 10px 72px\\\"\\n self.submitButton.on_click(self.executeSQL)\\n buttonRow.append(self.submitButton)\\n \\n self.prevButton = ipywidgets.Button(description=\\\"prev\\\")\\n self.prevButton.width = \\\"50px\\\"\\n self.prevButton.margin = \\\"10px 5px\\\"\\n self.prevButton.on_click(self.prevQuery)\\n buttonRow.append(self.prevButton)\\n \\n self.nextButton = ipywidgets.Button(description=\\\"next\\\")\\n self.nextButton.width = \\\"50px\\\"\\n self.nextButton.margin = \\\"10px 5px\\\"\\n self.nextButton.visible = False\\n self.nextButton.on_click(self.nextQuery)\\n buttonRow.append(self.nextButton)\\n\\n if (self.output.lower() == 'html'):\\n self.status = ipywidgets.HTML()\\n\\n self.container = ipywidgets.VBox(children=[self.query, ipywidgets.HBox(children=buttonRow), self.status])\\n if isinstance(dbfilename, sqlite3.Connection):\\n self.db = dbfilename\\n if (self.output == 'html'):\\n self.status.value = 'Using existing database connection'\\n else:\\n print('Using existing database connection')\\n elif ('r' in mode) and os.path.isfile(dbfilename):\\n self.db = sqlite3.connect(dbfilename)\\n if (self.output == 'html'):\\n self.status.value = 'Connected to database: <em>"%s"</em>' % dbfilename\\n else:\\n print('Connected to database: \\\"%s\\\"' % dbfilename)\\n elif ('w' in mode):\\n if os.path.isfile(dbfilename):\\n os.remove(dbfilename)\\n self.db = sqlite3.connect(dbfilename)\\n if (self.output == 'html'):\\n self.status.value = 'Created database: <em>"%s"</em>' % dbfilename\\n else:\\n print('Created database: \\\"%s\\\"' % dbfilename)\\n else:\\n if (self.output == 'html'):\\n self.status.value = self.errorStyle % (\\\"Database "%s" does not exist\\\" % dbfilename)\\n else:\\n print('Connected to database: \\\"%s\\\"' % dbfilename)\\n self.query.close()\\n self.submitButton.close()\\n display(self.container)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1322df4aef4a5a154205979db32c3e97\",\n \"score\": \"0.49315444\",\n \"text\": \"def commit():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7389315399befd2c27903b213802e9d4\",\n \"score\": \"0.48991475\",\n \"text\": \"def execute(self, _, db_conn):\\n\\n self.create_additional_input_column(db_conn)\\n\\n failed = set([])\\n cursor = db_conn.cursor()\\n update_sql = \\\"UPDATE input SET %s = ? WHERE identifier = ?\\\" % (\\n self.column_name,\\n )\\n\\n # Read the input documents\\n annotation_set = self.get_input_set(db_conn)\\n\\n for count, (identifier, text) in enumerate(annotation_set):\\n # Annotate each input document\\n value = self.produce_annotation(identifier, text, db_conn)\\n if value is None:\\n failed.add(identifier)\\n logging.debug(\\\"Annotation failed: %d\\\", identifier)\\n # Save the annotation\\n cursor.execute(update_sql, (value, identifier))\\n if count % 20 == 0:\\n logging.debug(\\\"Annotation %.2f%% complete\\\", count * 100.0 / len(annotation_set))\\n\\n if len(failed) != 0:\\n logging.info(\\\"Failed to annotate %d entries\\\", len(failed))\\n\\n logging.info(\\\"Committing changes...\\\")\\n db_conn.commit()\\n return True, db_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f97374807375d7034db1c5f89e01e4b5\",\n \"score\": \"0.489561\",\n \"text\": \"def Commit(self):\\n self.cursor.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6645904bec6fd3c25fc288abcd501e49\",\n \"score\": \"0.4893254\",\n \"text\": \"def submitQuery(self, query):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.48835176\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.48835176\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.48835176\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41ef534238e9c6f7b3718c606c670884\",\n \"score\": \"0.4880506\",\n \"text\": \"def GetQuery(command):\\n\\n QueryTemplate = {\\n \\\"update\\\":\\\"UPDATE {table} SET {column} = {value} WHERE {condition}\\\",\\n \\\"delete\\\":\\\"DELETE FROM {table} WHERE {condition}\\\",\\n \\\"select\\\":\\\"SELECT {columns} FROM {table} WHERE \\\",\\n \\\"select_all\\\":\\\"SELECT {columns} FROM {table}\\\",\\n \\\"get_years\\\":\\\"SELECT DISTINCT DATENAME({Year_Month},{column}) AS YEARS FROM {table}\\\",\\n \\\"get_months\\\":\\\"SELECT DISTINCT DATENAME({Year_Month},{column}) AS MONTHS FROM {table}\\\"\\n }\\n return QueryTemplate.get(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63943be5230060cc4bee018993c6c9b6\",\n \"score\": \"0.48783872\",\n \"text\": \"def commit(self):\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cf71675fb17558c89f11e70d15636c\",\n \"score\": \"0.4872711\",\n \"text\": \"def commit(self):\\r\\n return self.adaptor.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632f218c0df7a062b4899bdec09ecf55\",\n \"score\": \"0.48614815\",\n \"text\": \"def commit ( self ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e82ffb53d8f55ec3b5aa781159310e6\",\n \"score\": \"0.48404896\",\n \"text\": \"def commit(self):\\n return _xapian.WritableDatabase_commit(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7ba78df5c9d9e87911f4b0b3ba3c8b\",\n \"score\": \"0.48258275\",\n \"text\": \"def run_query(parameters):\\n conn = parameters['conn'] \\n cur = conn.cursor()\\n cur.execute(parameters['query'])\\n conn.commit();\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54bf55db6bb92b99ad52a7497cde8b5\",\n \"score\": \"0.48236316\",\n \"text\": \"def step_impl(context):\\n context.tranql_query = context.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48c68ad456b8ade00ca74a67c9965ff\",\n \"score\": \"0.48208627\",\n \"text\": \"def checksql(self):\\n self.setVisible(False)\\n sql = SQLTest(self)\\n sql.activateWindow()\\n sql.exec()\\n self.setVisible(True)\\n self.activateWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574b65737f8fb7c8a542d8a54088ef84\",\n \"score\": \"0.48044884\",\n \"text\": \"def format_database(self):\\n qr = self.cursor.executescript(\\\"\\\"\\\"\\ndelete from Packages;\\ndelete from Gits;\\ndelete from Repositories;\\n\\nupdate sqlite_sequence set seq=0 where name=\\\"Packages\\\";\\nupdate sqlite_sequence set seq=0 where name=\\\"Gits\\\";\\nupdate sqlite_sequence set seq=0 where name=\\\"Repositories\\\";\\n \\\"\\\"\\\")\\n del qr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd20e1a9f3dda73b3ab9236af24cc21\",\n \"score\": \"0.4791975\",\n \"text\": \"def set_query(self,query,data):\\n self.cursor.execute(query,data)\\n self.db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa83370a6ba0e42e17a62010866eda8\",\n \"score\": \"0.47914323\",\n \"text\": \"def popup_submit(self, event, columns, table, row_id, text_list): # !!!!! new code added by Jetsun\\n\\n db = SqliteQueries(\\\"database.db\\\")\\n entry_values = []\\n for i in range(len(text_list)):\\n entry_values.append(text_list[i].get(0.0, \\\"end\\\"))\\n db.update(table, columns, tuple(entry_values), 'userID = %s' % str(row_id))\\n self.popup.destroy()\\n\\n # refresh results\\n if self.btn_click == \\\"show_all_record\\\":\\n self.show_all_record()\\n elif self.btn_click == \\\"search_record\\\":\\n self.search_record()\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4e670eb6c4863a82b334de7ad8d5033\",\n \"score\": \"0.47911483\",\n \"text\": \"def commit(self, a=False, m=None, amend=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6023ebc7f2d64dd69fbe952ea3982c3d\",\n \"score\": \"0.47899255\",\n \"text\": \"def query(self, cmd):\\n data = {\\n 'code': 'from google.appengine.ext import db\\\\n' + cmd,\\n 'module_name': 'default',\\n 'xsrf_token': self.xsrf_token,\\n }\\n return self.post('console', data, url=self.admin_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a629b3dbe487d4f60163d64bc31fdd\",\n \"score\": \"0.4766869\",\n \"text\": \"def edit(self, command, prompt):\\n instruction, target = command.split(None)\\n self.sock.send(\\\"Graphique print {}\\\".format(target).encode())\\n size_of_file = self.sock.recv(BUFFER_SIZE).decode('utf-8')\\n print(size_of_file)\\n if size_of_file == \\\"Directory\\\":\\n self.editor.replace(\\n \\\"0.0\\\", tk.END, \\\"{}{} is a directory, so I've cd you into it\\\".format(prompt, target))\\n elif size_of_file == \\\"AccessError\\\":\\n self.editor.replace(\\n \\\"0.0\\\", tk.END, \\\"{} You can't access{}\\\".format(prompt, target))\\n else:\\n content = self.sock.recv(int(size_of_file)).decode('utf-8')\\n print(content)\\n self.editing = True\\n self.filename = target\\n self.editor.replace(\\n \\\"0.0\\\", tk.END, \\\"{}\\\".format(content))\\n # page1.pop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d712464dbce2d83a3c2a05ae4e86f4f\",\n \"score\": \"0.47487596\",\n \"text\": \"def commit(self):\\n if self.con:\\n self.con.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3def92f430044d5807e461dfa65287dd\",\n \"score\": \"0.4745809\",\n \"text\": \"def commit(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f4e98ec6aaa5f2e9288541c2437de8\",\n \"score\": \"0.47407138\",\n \"text\": \"def execute(self,sql):\\r\\n #print sql \\r\\n cursor = self.db.cursor() \\r\\n cursor.execute(sql)\\r\\n self.db.commit()\\r\\n cursor.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e060a5bae2d22bdd13d4f96c96e1024b\",\n \"score\": \"0.47224945\",\n \"text\": \"def insert(self):\\n MAX_RESULTS = 10\\n query = \\\"\\\"\\n t = None\\n self.detailWindow.clear() #clear detail window\\n self.detailWindow.refresh()\\n self.HelpWindow.clear()\\n self.HelpWindow.addstr(self.INSERT_TIP)\\n self.HelpWindow.refresh()\\n #Insert mode\\n self.modeWindow.addstr(0, 0, self.INSERT_NAME)\\n self.modeWindow.refresh()\\n #Clear any previous queries\\n self.queryWindow.clear()\\n self.queryWindow.refresh()\\n while True:\\n t = self.getChar()\\n if t == self.ESC:\\n break #dont clear results, save it for selecting through\\n else:\\n if t == self.BACKSPACE and len(query) > 0:\\n query = query[:-1]\\n elif t != self.ENTER:\\n query+=t\\n self.queryWindow.clear()\\n self.queryWindow.addstr(query)\\n\\n self.searchResults = [] #empty the results list from before\\n self.selectCursor = -1 #select nothing\\n self.search(query) #mutate self.results\\n\\n self.resultsWindow.clear()\\n for j in xrange(len(self.searchResults)):\\n if j > MAX_RESULTS:\\n break\\n #add results, each on its own line\\n self.resultsWindow.addstr(j, 1, self.searchResults[j].cleanName())\\n self.queryWindow.refresh()\\n self.resultsWindow.refresh()\\n self.modeWindow.addstr(0, 0, self.SELECT_NAME)\\n self.modeWindow.refresh()\\n self.HelpWindow.clear()\\n self.HelpWindow.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996d1219ea3ada3d4b362b3bb3031e48\",\n \"score\": \"0.4721329\",\n \"text\": \"def save(self):\\r\\n if self.instance is None:\\r\\n raise CQLEngineException(\\\"DML Query intance attribute is None\\\")\\r\\n assert type(self.instance) == self.model\\r\\n\\r\\n # organize data\\r\\n value_pairs = []\\r\\n values = self.instance._as_dict()\\r\\n\\r\\n # get defined fields and their column names\\r\\n for name, col in self.model._columns.items():\\r\\n val = values.get(name)\\r\\n if val is None:\\r\\n continue\\r\\n value_pairs += [(col.db_field_name, val)]\\r\\n\\r\\n # construct query string\\r\\n field_names = zip(*value_pairs)[0]\\r\\n field_ids = {n: uuid4().hex for n in field_names}\\r\\n field_values = dict(value_pairs)\\r\\n query_values = {field_ids[n]: field_values[n] for n in field_names}\\r\\n\\r\\n qs = []\\r\\n if self.instance._has_counter or self.instance._can_update():\\r\\n qs += [\\\"UPDATE {}\\\".format(self.column_family_name)]\\r\\n qs += [\\\"SET\\\"]\\r\\n\\r\\n set_statements = []\\r\\n # get defined fields and their column names\\r\\n for name, col in self.model._columns.items():\\r\\n if not col.is_primary_key:\\r\\n val = values.get(name)\\r\\n if val is None:\\r\\n continue\\r\\n if isinstance(col, (BaseContainerColumn, Counter)):\\r\\n # remove value from query values, the column will\\r\\n # handle it\\r\\n query_values.pop(field_ids.get(name), None)\\r\\n\\r\\n val_mgr = self.instance._values[name]\\r\\n set_statements += col.get_update_statement(\\r\\n val, val_mgr.previous_value, query_values)\\r\\n\\r\\n else:\\r\\n set_statements += [\\r\\n '\\\"{}\\\" = %({})s'.format(col.db_field_name, field_ids[col.db_field_name])]\\r\\n qs += [', '.join(set_statements)]\\r\\n\\r\\n qs += ['WHERE']\\r\\n\\r\\n where_statements = []\\r\\n for name, col in self.model._primary_keys.items():\\r\\n where_statements += ['\\\"{}\\\" = %({})s'.format(col.db_field_name,\\r\\n field_ids[col.db_field_name])]\\r\\n\\r\\n qs += [' AND '.join(where_statements)]\\r\\n\\r\\n # clear the qs if there are no set statements and this is not a\\r\\n # counter model\\r\\n if not set_statements and not self.instance._has_counter:\\r\\n qs = []\\r\\n\\r\\n else:\\r\\n qs += [\\\"INSERT INTO {}\\\".format(self.column_family_name)]\\r\\n qs += [\\\"({})\\\".format(', '.join(['\\\"{}\\\"'.format(f)\\r\\n for f in field_names]))]\\r\\n qs += ['VALUES']\\r\\n qs += [\\\"({})\\\".format(', '.join(['%(' + field_ids[f] + ')s' for f in field_names]))]\\r\\n\\r\\n qs = ' '.join(qs)\\r\\n\\r\\n # skip query execution if it's empty\\r\\n # caused by pointless update queries\\r\\n if qs:\\r\\n if self._batch:\\r\\n self._batch.add_query(qs, query_values)\\r\\n else:\\r\\n execute(qs, query_values)\\r\\n\\r\\n # delete nulled columns and removed map keys\\r\\n qs = ['DELETE']\\r\\n query_values = {}\\r\\n\\r\\n del_statements = []\\r\\n for k, v in self.instance._values.items():\\r\\n col = v.column\\r\\n if v.deleted:\\r\\n del_statements += ['\\\"{}\\\"'.format(col.db_field_name)]\\r\\n elif isinstance(col, Map):\\r\\n del_statements += col.get_delete_statement(\\r\\n v.value, v.previous_value, query_values)\\r\\n\\r\\n if del_statements:\\r\\n qs += [', '.join(del_statements)]\\r\\n\\r\\n qs += ['FROM {}'.format(self.column_family_name)]\\r\\n\\r\\n qs += ['WHERE']\\r\\n where_statements = []\\r\\n for name, col in self.model._primary_keys.items():\\r\\n field_id = uuid4().hex\\r\\n query_values[field_id] = field_values[name]\\r\\n where_statements += [\\r\\n '\\\"{}\\\" = %({})s'.format(col.db_field_name, field_id)]\\r\\n qs += [' AND '.join(where_statements)]\\r\\n\\r\\n qs = ' '.join(qs)\\r\\n\\r\\n if self._batch:\\r\\n self._batch.add_query(qs, query_values)\\r\\n else:\\r\\n execute(qs, query_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d3979a3a145ed1df1502cf6c2092f7\",\n \"score\": \"0.47211018\",\n \"text\": \"def endsql(self):\\n self.sqldata = None\\n self.adjdict.clear()\\n self.tagtext = ''\\n self.declension = list()\\n self.contpage = True\\n self.adjpages = list([False, False])\\n self.shortadj = True\\n self.adjcommand = \\\"\\\"\\n self.shortadjcommand = \\\"\\\"\\n tagval = None\\n tagval = self.listDisplay.selectedItems()\\n if tagval:\\n self.tagtext = tagval[0].text()\\n self.sqldict[self.finalkey] = self.tagtext\\n if self.wclass == \\\"noun\\\":\\n self.setVisible(False)\\n decline = DeclEntry(self)\\n decline.activateWindow()\\n decline.exec()\\n if self.cancel or len(self.declension) == 0:\\n self.setVisible(True)\\n self.activateWindow()\\n self.wordclasses()\\n return\\n basecommand = self.sqlcommand\\n self.sqlcommand = \\\"\\\"\\n for x in self.declension:\\n cols = \\\"(\\\"\\n data = \\\"(\\\"\\n cols += \\\"variety, \\\"\\n data += \\\"\\\\'\\\" + x[0] + \\\"\\\\', \\\"\\n cols += \\\"type, \\\"\\n data += \\\"\\\\'\\\" + x[1] + \\\"\\\\', \\\"\\n cols += \\\"runame, \\\"\\n data += \\\"\\\\'\\\" + x[2] + \\\"\\\\', \\\"\\n cols += \\\"name, \\\"\\n data += \\\"\\\\'\\\" + x[3] + \\\"\\\\', \\\"\\n cols += \\\"gender, \\\"\\n data += \\\"\\\\'\\\" + x[4] + \\\"\\\\', \\\"\\n cols += \\\"declension, \\\"\\n data += \\\"\\\\'\\\" + x[5] + \\\"\\\\', \\\"\\n cols += \\\"wordcase, \\\"\\n data += \\\"\\\\'\\\" + x[6] + \\\"\\\\', \\\"\\n cols += \\\"animate) VALUES\\\"\\n data += \\\"\\\\'\\\" + x[7] + \\\"\\\\');\\\\n\\\"\\n self.sqlcommand += basecommand + cols + data\\n elif self.wclass == \\\"adjective\\\":\\n basecommand = self.sqlcommand\\n self.sqldict[self.finalkey] = \\\"\\\\'\\\" + self.sqldict[self.finalkey] + \\\"\\\\'\\\"\\n if self.tagtext == \\\"descriptive\\\":\\n self.setVisible(False)\\n while self.contpage:\\n self.sqldict[\\\"variety\\\"] = \\\"\\\\'descriptive\\\\'\\\"\\n decline = AdjDeclEntry(self)\\n decline.activateWindow()\\n decline.exec()\\n self.setVisible(True)\\n if self.cancel:\\n self.activateWindow()\\n self.wordclasses()\\n return\\n self.sqldict[\\\"variety\\\"] = \\\"\\\\'short adjective\\\\'\\\"\\n adjgui = ShortAdj(self)\\n adjgui.activateWindow()\\n adjgui.exec()\\n self.setVisible(True)\\n if self.cancel:\\n self.activateWindow()\\n self.wordclasses()\\n return\\n self.sqlcommand = self.adjcommand\\n if self.shortadj:\\n self.sqlcommand += self.shortadjcommand\\n self.activateWindow()\\n elif self.tagtext == \\\"comparative\\\":\\n engcomp = \\\"\\\"\\n self.completed = True\\n while len(engcomp) == 0 and self.completed:\\n engcomp, self.completed = QInputDialog.getText(self, \\\"English Comparative\\\", \\\"Comparative Adjective Value:\\\")\\n if engcomp and not engcomp[0] in self.morphs.enall:\\n QMessageBox.warning(self, \\\"Non-English Character\\\", \\\"A non-english character was entered for an english word.\\\")\\n engcomp = \\\"\\\"\\n if self.completed:\\n self.sqldict[\\\"name\\\"] = \\\"\\\\'\\\" + engcomp + \\\"\\\\'\\\"\\n else:\\n self.wordclasses()\\n return\\n rucomp = \\\"\\\"\\n self.completed = True\\n while len(rucomp) == 0 and self.completed:\\n rucomp, self.completed = QInputDialog.getText(self, \\\"Russian Comparative\\\", \\\"Russian Comparative Adjective:\\\")\\n if rucomp and not rucomp[0] in self.morphs.ruall:\\n QMessageBox.warning(self, \\\"Non-Cyrillic Character\\\", \\\"A non-cyrillic character was entered for an russian word.\\\")\\n rucomp = \\\"\\\"\\n if self.completed:\\n self.sqldict[\\\"declension\\\"] = \\\"\\\\'\\\" + rucomp + \\\"\\\\'\\\"\\n else:\\n self.wordclasses()\\n return\\n self.sqldict[\\\"runame\\\"] = \\\"\\\\'\\\" + self.rustr + \\\"\\\\'\\\"\\n self.sqldict[\\\"gender\\\"] = \\\"\\\\'masculine\\\\'\\\"\\n self.sqldict[\\\"wordcase\\\"] = \\\"\\\\'nominative\\\\'\\\"\\n self.sqldict[\\\"animate\\\"] = \\\"\\\\'inanimate\\\\'\\\"\\n self.sqldict[\\\"variety\\\"] = \\\"\\\\'comparative\\\\'\\\"\\n cols = \\\"(\\\"\\n data = \\\"(\\\"\\n for y in self.sqldict:\\n cols += y + \\\", \\\"\\n data += self.sqldict[y] + \\\", \\\"\\n cols = cols[:-2] + \\\") VALUES\\\"\\n data = data[:-2] + \\\");\\\\n\\\"\\n self.sqlcommand = basecommand + cols + data\\n elif self.tagtext == \\\"superlative\\\":\\n self.setVisible(False)\\n self.sqldict[\\\"variety\\\"] = \\\"\\\\'superlative\\\\'\\\"\\n decline = AdjDeclEntry(self)\\n decline.activateWindow()\\n decline.exec()\\n self.setVisible(True)\\n self.activateWindow()\\n if self.cancel:\\n self.wordclasses()\\n return\\n self.sqlcommand = self.adjcommand\\n self.noexit = True\\n self.finalize()\\n else:\\n QMessageBox.warning(self, \\\"No Item Selected\\\", \\\"You must select an item from the list or cancel.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b3badc60659ea49cadcbc978c0a23d\",\n \"score\": \"0.47151494\",\n \"text\": \"def query(self, arg):\\r\\n self.cur.execute(arg)\\r\\n self.conn.commit()\\r\\n return self.cur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e0af94cc91216d18fcfd76ba4b93e7\",\n \"score\": \"0.4710647\",\n \"text\": \"def schema(self,sql_str):\\n\\t\\tself.cursor_db.executescript(sql_str)\\n\\t\\tself.conn_db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"263af540ef2b92ceb9aa2f6247a4cfa9\",\n \"score\": \"0.4702192\",\n \"text\": \"async def sql(self, ctx):\\n await util.command_group_help(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24235ee1765db78e6af90d4fd98ed242\",\n \"score\": \"0.46943846\",\n \"text\": \"def QueryHowDoI(Query, num_answers, full_text):\\n output = search_book(Query)\\n print(\\\"You asked: \\\" + Query)\\n print(\\\"_______________________________________\\\")\\n print(output)\\n exit_code = 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba52805d2ba853af5cbf75f113249a58\",\n \"score\": \"0.46941972\",\n \"text\": \"def executescript(self,sql):\\r\\n #print sql \\r\\n cursor = self.db.cursor() \\r\\n cursor.executescript(sql)\\r\\n self.db.commit()\\r\\n cursor.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b8d1eae799d06b25aca2f018d3bcb0\",\n \"score\": \"0.46883547\",\n \"text\": \"def execute_query(connection, query):\\n cursor = connection.cursor()\\n try:\\n cursor.execute(query)\\n connection.commit()\\n print(\\\"Query executed successfully\\\")\\n except Error as e:\\n print(f\\\"The error '{e}' occurred\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e4011a30e6abc7fdbfacbfa5c7acdc4\",\n \"score\": \"0.46793383\",\n \"text\": \"def query_expense():\\n\\n if request.method == 'POST':\\n sql = request.form[\\\"sql\\\"]\\n\\n error = None\\n\\n if not sql:\\n error = \\\"query is required\\\"\\n\\n if error is None:\\n obj = sqlOperation()\\n results = obj.searchQuery(sql, session['user_id'])\\n table = readResults(results)\\n table.border = True\\n return render_template('expense/table.html', table = table)\\n else:\\n flash(error)\\n\\n return render_template('expense/sql.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"327080e49104d0ff94b811b21ec72c31\",\n \"score\": \"0.4672157\",\n \"text\": \"def execute_query(cur, conn, query):\\n if __debug__:\\n print(\\\"Executing ================================ Query\\\")\\n print(query)\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3cc13e77e83190eac3f815196259ad7\",\n \"score\": \"0.46710664\",\n \"text\": \"def make_commit(self):\\n self.sat.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cbe5a8067ddc3bcbe3eb7e49248bf8\",\n \"score\": \"0.46670425\",\n \"text\": \"def commit_and_close(self):\\r\\n request = input('Save the database? ')\\r\\n if request.upper() == 'YES':\\r\\n self.database.commit()\\r\\n self.database.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90caca07874299609a6876e8682485fe\",\n \"score\": \"0.4665845\",\n \"text\": \"def do_query(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a642ff48f0322ca6f2c791ce5b3c50f\",\n \"score\": \"0.4661898\",\n \"text\": \"def test_preserve_whitespace(self):\\n query = \\\"DUMP\\\\nSCHEMA\\\\n\\\\n;\\\"\\n for fragment in query.split(\\\"\\\\n\\\"):\\n self.query(fragment)\\n self.assertEqual(self.engine.last_query, query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff0e15b2ed4296ae907d69c80527baa\",\n \"score\": \"0.46522677\",\n \"text\": \"def _commit_handler(self, cmd):\\n current_prompt = self.device.find_prompt().strip()\\n terminating_char = current_prompt[-1]\\n # Look for trailing pattern that includes '#' and '>'\\n pattern1 = r\\\"[>#{}]\\\\s*$\\\".format(terminating_char)\\n # Handle special username removal pattern\\n pattern2 = r\\\".*all username.*confirm\\\"\\n patterns = r\\\"(?:{}|{})\\\".format(pattern1, pattern2)\\n output = self.device.send_command_expect(cmd, expect_string=patterns)\\n loop_count = 50\\n new_output = output\\n for i in range(loop_count):\\n if re.search(pattern2, new_output):\\n # Send confirmation if username removal\\n new_output = self.device.send_command_timing(\\n \\\"\\\\n\\\", strip_prompt=False, strip_command=False\\n )\\n output += new_output\\n else:\\n break\\n # Reset base prompt in case hostname changed\\n self.device.set_base_prompt()\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b156bec756de2c09ce1745f7de9acb\",\n \"score\": \"0.46498632\",\n \"text\": \"def handleCustomQuery(self):\\n\\n # Get query\\n print(\\\"Enter query below\\\")\\n query = input()\\n # Process documents\\n processedQuery = self.preprocessQueries([query])[0]\\n\\n # Read documents\\n docs_json = json.load(open(args.dataset + \\\"cran_docs.json\\\", 'r'))[:10]\\n docs = [item[\\\"body\\\"] for item in docs_json]\\n # Process documents\\n processedDocs = self.preprocessDocs(docs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56fcf3bc68efa86ff38670cf2ece9a5\",\n \"score\": \"0.46482247\",\n \"text\": \"def commit(self):\\r\\n self.connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49237b79f48e9d644736b826c1d234be\",\n \"score\": \"0.46452644\",\n \"text\": \"def _update_rows(self, query):\\n self.db.get_connection()\\n with self.db.conn.cursor() as cur:\\n cur.execute(query)\\n self.db.conn.commit()\\n logger.debug(f'Query is: {query}.')\\n logger.info(f\\\"{cur.rowcount} rows affected.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d272e7c24acb4c3fde76e91e946d8640\",\n \"score\": \"0.46423507\",\n \"text\": \"def custom_query(self, con, cur, query):\\n\\n try:\\n cur.execute(query)\\n con.comit()\\n print('\\\\n\\\\n QUERY SUCCESSFULL \\\\n\\\\n')\\n except Exception as error:\\n print('\\\\n Error by executing YOUR QUERY. '\\n '\\\\n Server said: {}'.format(error)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58601b82f844e31a8aa211e13ab5c275\",\n \"score\": \"0.46373543\",\n \"text\": \"def qsqldb( sqldb, sql_cmd, outfilename=None ):\\n\\t#\\n\\t# Create the output file if specified\\n\\tif outfilename:\\n\\t\\toutfile = open(outfilename, \\\"wb\\\")\\n\\t\\tcsvout = csv.writer(outfile, quoting=csv.QUOTE_NONNUMERIC)\\n\\t#\\n\\t# Execute SQL\\n\\tcurs = sqldb.cursor()\\n\\tcurs.execute(sql_cmd)\\n\\t#\\n\\t# Write output to file or console\\n\\tif outfilename:\\n\\t\\tdatarows = curs.fetchall()\\n\\t\\theaders = [ item[0] for item in curs.description ]\\n\\t\\tcsvout.writerow(headers)\\n\\t\\tfor row in datarows:\\n\\t\\t\\tcsvout.writerow(list(row))\\n\\t\\toutfile.close()\\n\\telse:\\n\\t\\tprint pp(curs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011eca2001844c9633087ed0db67ddba\",\n \"score\": \"0.4634989\",\n \"text\": \"def color_q(query_object):\\n return color_sql(compile_query(query_object))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5360a7de8c72610842cce277150526a9\",\n \"score\": \"0.46349132\",\n \"text\": \"def db_action(sql_action: str):\\n conn = psycopg2.connect(db_url)\\n curs = conn.cursor()\\n curs.execute(sql_action)\\n conn.commit()\\n curs.close()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc835fcbd42e91ccdded3ea90ed3b91\",\n \"score\": \"0.46291798\",\n \"text\": \"def _commit(self, action):\\n self._dbparams_append((\\\"-max\\\", self._maxret))\\n\\n url = self._url % self.__dict__\\n data = urlencode(self._dbdata + self._dbparams) + \\\"&-\\\" + action\\n self._dbparams = []\\n # use POST to submit data\\n request = Request(url, bytestr(data))\\n request.add_header('User-Agent', b'Fmkr.py')\\n # authorization header\\n auth = b\\\"Basic \\\" + encodestr(\\\"%s:%s\\\" % (self._dbuser,\\n self._dbpasswd))[:-1]\\n request.add_header(\\\"Authorization\\\", auth)\\n\\n try:\\n fd = urlopen(request)\\n except HTTPError as e:\\n raise FMError(str(e))\\n except URLError as e:\\n raise FMError(\\\"URL Error: %s\\\" % str(e.reason))\\n\\n results = FMPXMLResult()\\n results.httpinfo = fd.info()\\n results.url = url + \\\"?\\\" + data\\n # hide logon information\\n #if self._dbuser and self._dbpasswd:\\n # results.url = results.url.replace(\\n # \\\"//\\\", \\\"//%s:%s@\\\" % (self._dbuser, self._dbpasswd), 1)\\n doc = minidom.parse(fd)\\n fd.close()\\n\\n # <PRODUCT BUILD=\\\"06/14/2006\\\" NAME=\\\"FileMaker Web Publishing Engine\\\"\\n # VERSION=\\\"8.0.4.128\\\"/>\\n try:\\n attrs = doc.getElementsByTagName(\\\"PRODUCT\\\")[0].attributes\\n results.product.update(attrs.items())\\n except Exception:\\n pass\\n\\n # <DATABASE DATEFORMAT=\\\"MM/dd/yyyy\\\" LAYOUT=\\\"data entry\\\" NAME=\\\"Test\\\"\\n # RECORDS=\\\"68\\\" TIMEFORMAT=\\\"HH:mm:ss\\\"/>\\n try:\\n attrs = doc.getElementsByTagName(\\\"DATABASE\\\")[0].attributes\\n results.database.update(attrs.items())\\n except Exception:\\n pass\\n\\n # <ERRORCODE>0</ERRORCODE>\\n try:\\n results.errorcode = int(\\n doc.getElementsByTagName(\\\"ERRORCODE\\\")[0].firstChild.data)\\n except Exception:\\n results.errorcode = -1\\n\\n if results.errorcode:\\n try:\\n errormsg = FMError.codes[results.errorcode]\\n except KeyError:\\n errormsg = \\\"Unknown error code\\\"\\n raise FMError(\\\"FileMaker Error #%s: %s\\\" % (\\n results.errorcode, errormsg), results.errorcode)\\n\\n # <METADATA>\\n fields = doc.getElementsByTagName(\\\"METADATA\\\")[0].childNodes\\n metadata = results.metadata\\n for field in fields:\\n metadata.append(FMField(field.attributes))\\n\\n # <RESULTSET>\\n try:\\n resultset = doc.getElementsByTagName(\\\"RESULTSET\\\")[0]\\n except Exception:\\n pass\\n else:\\n escrslt = self._escrslt\\n unctype = unicode_t\\n for row in resultset.childNodes:\\n d = {}\\n d[\\\"MODID\\\"] = int(row.attributes[\\\"MODID\\\"].value)\\n d[\\\"RECORDID\\\"] = int(row.attributes[\\\"RECORDID\\\"].value)\\n for md, cn in zip(metadata, row.childNodes):\\n if escrslt and md.dtype == unctype:\\n convert_type = escape_unicode\\n else:\\n convert_type = md.dtype\\n if md.maxrepeat == 1:\\n de = cn.firstChild.firstChild\\n d[md.name] = convert_type(de.data) if de else None\\n else:\\n a = []\\n for c in cn.childNodes:\\n value = c.firstChild\\n if value:\\n a.append(convert_type(value.data))\\n else:\\n break\\n d[md.name] = a\\n results.resultset.append(d)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f875ebf0d965df05e4a6d4969b277f21\",\n \"score\": \"0.4614563\",\n \"text\": \"def edit(self, query=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c40551d211449ed1dec5f69af03ee40\",\n \"score\": \"0.4592555\",\n \"text\": \"def formatted_queries():\\n\\n id = get_id() # Could be a SQLi response..\\n\\n query1 = f\\\"SELECT * FROM users WHERE id = {id}\\\"\\n\\n query2 = \\\"SELECT * FROM users WHERE id = {0}\\\" % id\\n\\n query3 = \\\"SELECT * FROM users WHERE id = {0}\\\".format(id)\\n\\n query4 = f\\\"UPDATE users SET is_admin = 1 WHERE id = {id}\\\"\\n\\n query5 = f\\\"DELETE FROM users WHERE id = {id}\\\"\\n\\n query6 = f\\\"INSERT INTO users (id) VALUES ( id = {id} )\\\"\\n\\n query7 = f\\\"SELECT * FROM users WHERE id = {id}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f78cf09994c8f069f7146171f320bb\",\n \"score\": \"0.4590935\",\n \"text\": \"def commit(self):\\n self.connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f78cf09994c8f069f7146171f320bb\",\n \"score\": \"0.4590935\",\n \"text\": \"def commit(self):\\n self.connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec13f5ddfb3792a28f440c6d6f1f53f2\",\n \"score\": \"0.45908436\",\n \"text\": \"def commit_rst(self):\\n self.state_machine.insert_input(self.rst_lines, \\\"\\\")\\n self.rst_lines = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982819eb11651908f894cfcdf9739fe1\",\n \"score\": \"0.45901954\",\n \"text\": \"def meta_command(connection, user_input):\\n user_parts = user_input.split()\\n replaced = ''\\n\\n # .tables: print list of tables in database\\n if user_parts[0].lower() == '.tables' or user_parts[0].lower() == '.table':\\n replaced = 'SELECT name FROM sqlite_master WHERE type = \\\"table\\\";'\\n\\n # .schema: print details about a tables column\\n elif user_parts[0].lower() == '.schema':\\n if len(user_parts) == 1:\\n replaced = 'SELECT sql FROM sqlite_master WHERE type = \\\"table\\\";'\\n elif len(user_parts) == 2:\\n s = 'SELECT sql FROM sqlite_master WHERE type = \\\"table\\\" AND name = \\\"{[1]}\\\";'\\n replaced = s.format(user_parts)\\n else:\\n print('Usage: .schema [table]')\\n\\n # .dump: print SQL commands to recreate table\\n # ToDo: http://www.sqlitetutorial.net/sqlite-dump/\\n elif user_parts[0].lower() == '.dump':\\n if len(user_parts) == 1:\\n for line in connection.iterdump():\\n print(line)\\n elif len(user_parts) == 2:\\n replaced = 'SELECT * from {[1]};'.format(user_parts)\\n else:\\n print('Usage: .dump [table]')\\n\\n else:\\n print('That sqlite3 meta command has not been implimented')\\n\\n return replaced\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5bb448d4e729664a53d52a337c233e\",\n \"score\": \"0.4587876\",\n \"text\": \"def execute_query(self, query):\\n sql_blacklist = ['drop', 'delete']\\n if any(word.casefold() in query.casefold() for word in sql_blacklist):\\n print(\\\"No queries with {0} will be executed\\\".format([word for word in sql_blacklist if word.casefold() in query.casefold()][0]))\\n return\\n dbcursor = self.connection.cursor()\\n dbcursor.execute(query)\\n result = dbcursor.fetchall()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc2e40b428d4a5fb47134018f0c3fdf\",\n \"score\": \"0.45863706\",\n \"text\": \"def edit():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08b6eaae8567df11341309842b1e20bd\",\n \"score\": \"0.4586093\",\n \"text\": \"def commit(self):\\n self.connect.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d19fa45f4ed36207f1de5bd22ff54b60\",\n \"score\": \"0.4576854\",\n \"text\": \"def submit_textarea():\\n post_content = request.form[\\\"content\\\"]\\n author = request.form[\\\"author\\\"]\\n\\n post_object = {\\n 'author': author,\\n 'content': post_content,\\n }\\n\\n # Submit a transaction\\n new_tx_address = \\\"{}/new_transaction\\\".format(CONNECTED_NODE_ADDRESS)\\n\\n requests.post(new_tx_address,\\n json=post_object,\\n headers={'Content-type': 'application/json'})\\n\\n return redirect('/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0\",\n \"score\": \"0.45657098\",\n \"text\": \"def commit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0\",\n \"score\": \"0.45657098\",\n \"text\": \"def commit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa8b5c5b06e5e1f7c60d8fbe04ef6d0\",\n \"score\": \"0.45657098\",\n \"text\": \"def commit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c63c83926920827115ebe9e62d22751\",\n \"score\": \"0.45615292\",\n \"text\": \"def submit_textarea():\\n post_object = {\\n \\\"contents\\\": transaction.to_dict(),\\n \\\"signature\\\": transaction.signature\\n }\\n\\n # Submit a transaction\\n if request.method == 'GET':\\n new_tx_address = \\\"http://127.0.0.1:8000/new_transaction\\\"\\n else:\\n port_nb = request.get_json()\\n new_tx_address = \\\"http://127.0.0.1:\\\" + str(port_nb) + \\\"/new_transaction\\\"\\n\\n requests.post(new_tx_address,\\n json=post_object,\\n headers={'Content-type': 'application/json'})\\n\\n return \\\"Success\\\", 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf63136f22ed1813f702a3eefc3f733\",\n \"score\": \"0.45585993\",\n \"text\": \"def execute(self, req, data = {}):\\n res = self.cursor().execute(req, data)\\n if not('SELECT' in req) and self.auto_commit:\\n self.commit()\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfce166a7e5f5199782bd00a7aba9f4\",\n \"score\": \"0.45553318\",\n \"text\": \"def format_queries(queries):\\n output = '-- Queries %s\\\\n' % ('-'*(LINE_BIG-11))\\n\\n for query in queries:\\n output += '-- %s %s\\\\n' % (query, '-'*(LINE_SMALL-4-len(query)))\\n output += queries[query]\\n output += '\\\\n;%s' % ('\\\\n'*3)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae4fdd9487cc167bf18577c58d5679e\",\n \"score\": \"0.4553439\",\n \"text\": \"def commit(self):\\n self.get_connection().commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ff9a9219abb92fdd8c79dd0c4f78a5\",\n \"score\": \"0.4548623\",\n \"text\": \"def write():\\n db = bottle.request.forms.get('db')\\n return \\\"Not implemented.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a38c1baa64adf90927da1ddc9c6ce5\",\n \"score\": \"0.45434672\",\n \"text\": \"def get_sql(self, undo=False):\\n return self.undo if undo else self.do\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ccd280dc1c1812786911cc0667553c\",\n \"score\": \"0.4541497\",\n \"text\": \"def query(self, qu):\\n return self.cur.execute(qu)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51da7ec25e38198334a0e58eaf1d0ce6\",\n \"score\": \"0.45397788\",\n \"text\": \"def test_query_button_clicked_query_parse_error(self, widget):\\n\\n widget.query.text = \\\"ans(study_id, term, tfidf):-neurosynth_default_mode_study_id(study_id),neurosynth_pcc_study_id(study_id),neurosynth_study_tfidf(study_id, term, tfidf\\\"\\n widget._on_query_button_clicked(None)\\n assert widget.result_viewer.layout.visibility == \\\"hidden\\\"\\n assert widget.error_display.layout.visibility == \\\"visible\\\"\\n assert len(widget.query.marks) == 1\\n assert len(widget.query.text_marks) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914f9be4724a27528047407f06d521f3\",\n \"score\": \"0.45382926\",\n \"text\": \"def commit(self) -> None:\\n self.con.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64dd4aba698f62aeb80815ea901e267d\",\n \"score\": \"0.45381135\",\n \"text\": \"def update_row(self,query,info):\\n \\n with self.conn.cursor() as cur:\\n try:\\n cur.execute(query,info)\\n self.conn.commit()\\n cur.close()\\n except Exception as e:\\n print(e)\\n cur.execute(\\\"ROLLBACK\\\")\\n self.conn.commit()\\n cur.close()\\n raise HTTPException(status_code=400, detail=\\\"Information given was invalid\\\")\\n return \\\"Row updated\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994283,"cells":{"query_id":{"kind":"string","value":"cab3e85aee2ae1056716a2a86c357d5f"},"query":{"kind":"string","value":"Prefect task to finalize the downscaling run."},"positive_passages":{"kind":"list like","value":[{"docid":"b68008b92ad4c8e52eef51cd0120f6e8","score":"0.0","text":"def finalize(run_parameters: RunParameters = None, **paths):\n _finalize(run_parameters, kind='runs', **paths)","title":""}],"string":"[\n {\n \"docid\": \"b68008b92ad4c8e52eef51cd0120f6e8\",\n \"score\": \"0.0\",\n \"text\": \"def finalize(run_parameters: RunParameters = None, **paths):\\n _finalize(run_parameters, kind='runs', **paths)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f1f9202e19312af090558d9300f3c976","score":"0.6260884","text":"def teardown(self, task, job_config):","title":""},{"docid":"1a1c2f4fd22c23c94eab7333d3e99aee","score":"0.616505","text":"def finalize(self):\n self.logger.info(\"Please wait while finalizing the operation.. Thank you\")\n self.save_checkpoint()\n # self.summary_writer.export_scalars_to_json(\"{}all_scalars.json\".format(self.config.summary_dir))\n # self.summary_writer.close()\n # self.dataloader.finalize()","title":""},{"docid":"f48c5d6a1e1470da3127bfc0e9aa1b28","score":"0.6017869","text":"def _post_compute(self):\n pass","title":""},{"docid":"1b8d8d660d524b84f3277c7b155d3143","score":"0.5813739","text":"def finalize_timepoint(self):\n pass","title":""},{"docid":"3c942b1700e1adfd749cbf41f64b3a53","score":"0.5796718","text":"def ExecuteFinalize(self):\n pass","title":""},{"docid":"62002f7cf997a569e1213856931a3f88","score":"0.5784241","text":"def finished_task(self):\n self.mutex.lock()\n if self.cache is not None:\n args, kwargs = self.cache\n self.algorithm.set_parameters(*args, **kwargs)\n self.cache = None\n self.clean_later = False\n if self._image is not None:\n self.algorithm.set_image(self._image)\n self._image = None\n if self._mask is not None:\n self.algorithm.set_mask(self._mask)\n self._mask = None\n if self.rerun[0]:\n self.rerun = False, QThread.InheritPriority\n super().start(self.rerun[1])\n elif self.clean_later:\n self.algorithm.clean()\n self.clean_later = False\n self.mutex.unlock()","title":""},{"docid":"830c5eb3d65b9e5e31d11a1627d080c7","score":"0.5719745","text":"def cleanup_run(self, *kargs, **kwargs):\n pass","title":""},{"docid":"2e3a5a0288c231d4c513b377e4d5fb62","score":"0.571714","text":"def finalize(self) -> None:\n ...","title":""},{"docid":"11c4e7194d1e1fb4df64103a852f6c91","score":"0.571261","text":"def finalize(self):\n pass # currently nothing to do here","title":""},{"docid":"cacc113ca7766fb5b2680238e76e50e6","score":"0.5700527","text":"def after_main_loop(self):\n for task in self.session:\n if isinstance(task,GsnowpackApplication) and task.execution.returncode == 0:\n # Only consider successfully terminated tasks\n _update_original_sno_files(task)\n \n if self.params.www and task.get_images():\n try:\n _publish_data(task, self.params.www)\n except OSError, osx:\n continue","title":""},{"docid":"362527538ef01a8b8b55ebb82dc3d379","score":"0.5681788","text":"def _cleanup(self):\n functions.DAQmxWaitUntillTaskDone(self.taskHandle, -1) # second argument needs checking\n functions.DAQmxStopTask(self.taskHandle)\n functions.DAQmxClearTask(self.taskHandle)","title":""},{"docid":"76a61d8f24431362d3da8862938ff2b9","score":"0.5672402","text":"def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")","title":""},{"docid":"76a61d8f24431362d3da8862938ff2b9","score":"0.5672402","text":"def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")","title":""},{"docid":"76a61d8f24431362d3da8862938ff2b9","score":"0.5672402","text":"def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")","title":""},{"docid":"76a61d8f24431362d3da8862938ff2b9","score":"0.5672402","text":"def postRunCleanup(self):\n self.logDesc(\"Post Run Cleanup\")","title":""},{"docid":"bf88fa429f38f638ea5e532b91157ede","score":"0.567161","text":"def _finalize(self):\n self._finalized = True","title":""},{"docid":"c8fb1c29151dffdcb6f53cf35689fb91","score":"0.5633229","text":"def finalize(self):\n # Finalize output training\n self.pca_cell.finalize()\n\n # Finalized\n self.finalized = True","title":""},{"docid":"0b87f5501b23a309b84bb80544899bf2","score":"0.5624566","text":"def _finalize(self):\n pass","title":""},{"docid":"b755d6fd430ce98ac87075348ab8f444","score":"0.56239605","text":"def finalize(self):\n pass","title":""},{"docid":"b755d6fd430ce98ac87075348ab8f444","score":"0.56239605","text":"def finalize(self):\n pass","title":""},{"docid":"b755d6fd430ce98ac87075348ab8f444","score":"0.56239605","text":"def finalize(self):\n pass","title":""},{"docid":"b755d6fd430ce98ac87075348ab8f444","score":"0.56239605","text":"def finalize(self):\n pass","title":""},{"docid":"b21a454931fb57f8c91f36e515708891","score":"0.5617566","text":"def finalize(args):\n return Update.within_job(args.metric_value, args.test)","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"86247afd98fd0af6087c5dd18db7d104","score":"0.560529","text":"def finalize(self):","title":""},{"docid":"3d64d460f2254138857b91899c2743ac","score":"0.55869454","text":"def _finalize_run(self):\n self._run_information[self.v_crun]['completed'] = 1\n while len(self._new_links):\n name_pair, child_parent_pair = self._new_links.popitem(last=False)\n parent_node, _ = child_parent_pair\n _, link = name_pair\n parent_node.f_remove_child(link)\n\n while len(self._new_nodes):\n _, child_parent_pair = self._new_nodes.popitem(last=False)\n parent, child = child_parent_pair\n child_name = child.v_name\n parent.f_remove_child(child_name, recursive=True)","title":""},{"docid":"61252ecafd86555516b9a9d5895b23a6","score":"0.55836266","text":"def finalize(self):\n raise NotImplementedError","title":""},{"docid":"2a7fec7bee8790abae08451ee7fdfce5","score":"0.5562341","text":"def finalize(self):\n DLOG.verbose(\"Default thread worker finalize called for %s.\"\n % self._name)","title":""},{"docid":"bf65c549e98de6bf12769f27ee86daef","score":"0.55509955","text":"def process_cleanup(self):","title":""},{"docid":"bd4d8d96d9e46015bc6eb726ab5550db","score":"0.5543055","text":"def finalize(self):\n\t\tutils.forgetMemoized(self)\n\t\tfor proc in itertools.chain(self.metaMakers, self.dataFunctions):\n\t\t\tutils.forgetMemoized(proc)\n\t\tutils.forgetMemoized(self.descriptorGenerator)\n\t\tif self.dataFormatter:\n\t\t\tutils.forgetMemoized(self.dataFormatter)\n\t\tself.breakCircles()\n\t\tself.run = None","title":""},{"docid":"889a8b06b7b30bcd6e4cdf26169d99e5","score":"0.5526978","text":"def finalize(self):\r\n pass","title":""},{"docid":"8fac4d8af648ac685f80e96bdd900336","score":"0.5520506","text":"def finalize(self) -> None:\n total_execution_time = time.time() - self.start_all\n print(f\"TOTAL EXECUTION TIME: {total_execution_time:.10f} sec\")\n self.merge_outputs()","title":""},{"docid":"af187d5fd35793273ce85350f4a1b426","score":"0.551647","text":"def _set_finish(self):\n\n run_info_dict = self._run_information[self.v_crun]\n timestamp_run = run_info_dict['timestamp']\n\n run_summary = self._summarize_explored_parameters()\n\n finish_timestamp_run = time.time()\n\n findatetime = datetime.datetime.fromtimestamp(finish_timestamp_run)\n startdatetime = datetime.datetime.fromtimestamp(timestamp_run)\n\n runtime_run = str(findatetime - startdatetime)\n\n run_info_dict['parameter_summary'] = run_summary\n run_info_dict['completed'] = 1\n run_info_dict['finish_timestamp'] = finish_timestamp_run\n run_info_dict['runtime'] = runtime_run","title":""},{"docid":"2e16f4b7d7f12ada9a8554e2f19c8f5f","score":"0.551429","text":"def finalize(self):\n self.classifier.finalize()","title":""},{"docid":"7689206054dd79b00f3bcb0c3541994d","score":"0.5514289","text":"def finalize():","title":""},{"docid":"7689206054dd79b00f3bcb0c3541994d","score":"0.5514289","text":"def finalize():","title":""},{"docid":"28596baa6965789722574d07e7fcc5de","score":"0.5513951","text":"def _on_task_run(self):\n # self.publish_desired_twist_power(self.twist) # change to ALlocateVelocityTask\n # self.publish_desired_twist(self.twist.linear.x,\n # self.twist.linear.y,\n # self.twist.linear.z,\n # self.twist.angular.x,\n # self.twist.angular.y,\n # self.twist.angular.z\n # )\n\n self.rotate_task.run()\n\n if not self.on_finish_segment and task_utils.at_pose(\n self.state.pose.pose, self.target_pose, float(\"inf\"), self.seg_rads / 2):\n self.current_segment += 1\n self.target_pose = task_utils.add_poses([self.target_pose, self.angle_pose])\n rospy.loginfo(\"Now on segment \" + str(self.current_segment))\n self.output[\"rads_turned\"] += self.seg_rads\n self.output[\"points_scored\"] = int(self.output[\"rads_turned\"] / (math.pi / 2)) * self.nintey_points\n\n # rospy.loginfo(\"1 clear\")\n\n if not self.on_finish_segment and self.current_segment == self.num_segments:\n self.on_finish_segment = True\n\n # rospy.loginfo(\"2 clear\")\n\n if self.on_finish_segment and task_utils.at_pose(\n self.state.pose.pose, self.target_pose, float(\"inf\"), self.seg_rads):\n rospy.loginfo(\"Turn Complete!\\n\")\n self.output[\"rads_turned\"] = self.angle\n self.output[\"points_scored\"] = int(self.output[\"rads_turned\"] / (math.pi / 2)) * self.nintey_points\n rospy.loginfo(\"Stopping turn!\")\n self.rotate_task.finish()\n self.finish()\n\n # rospy.loginfo(\"all clear, self.finished is {}\".format(self.finished))","title":""},{"docid":"06bf10d20f94cf9ff247be4bcbdec8d0","score":"0.54889214","text":"def post(self, executor):\n tasks.PauseExecutor(executor)","title":""},{"docid":"8d6efe452ecfa67fd710d9e222e40bce","score":"0.5461304","text":"def process_cleanup(self):\n pass","title":""},{"docid":"0a230a62e94fc9dfcdd364db0f8380a7","score":"0.5441202","text":"def finalize(self):\r\n pass","title":""},{"docid":"0a230a62e94fc9dfcdd364db0f8380a7","score":"0.5441202","text":"def finalize(self):\r\n pass","title":""},{"docid":"0a230a62e94fc9dfcdd364db0f8380a7","score":"0.5441202","text":"def finalize(self):\r\n pass","title":""},{"docid":"2d77d14eafa02c7ba5801786154098db","score":"0.54286355","text":"def fin():\n u_libs.testflow.teardown(\n \"Release hosts from CPU load: %s\", load_to_resources.values()\n )\n sla_helpers.stop_load_on_resources(load_to_resources.values())","title":""},{"docid":"b7ccf732c99c3ca2a038cedca21f40c6","score":"0.54172254","text":"def finalizeActualSampling(self, jobObject, model, myInput):\n self.localFinalizeActualSampling(jobObject, model, myInput)","title":""},{"docid":"0def583b5a6ab21e79adb4eaf7955145","score":"0.54120964","text":"def work(self):\n # HACK: overwrite the classes' self._do_stage() method with our own\n try:\n ds_orig = self._do_stage\n # Functools.partial() should be more jit friendly than lambda\n self._do_stage = functools.partial(self.__do_stage, _ds=ds_orig)\n super(Deinstall, self).work()\n finally:\n self._do_stage = ds_orig","title":""},{"docid":"e63a5463d9c114c73aaf0a1499e5bcda","score":"0.53938115","text":"def finalize(self):\n assert self.num_frames[\"data\"] > 0\n # Note: self.num_frames could be greater than self.data.get_num_timesteps() in case of chunking.\n for key, value in self.results.items():\n if key != \"ctc_priors\":\n self.results[key] *= self.epoch_norm_factor_for_result(key)\n self.score = dict([(key,value) for (key, value) in self.results.items() if key.startswith(\"cost:\")])\n self.error = dict([(key,value) for (key, value) in self.results.items() if key.startswith(\"error:\")])\n self.finalized = True","title":""},{"docid":"8fb2d6423fbda66b2e1512adbbdf061f","score":"0.53866833","text":"def scale_down(self, inactive_additional_hosts):\n Logger.info('Start grid engine SCALING DOWN for %s hosts.' % len(inactive_additional_hosts))\n self.scale_down_orchestrator.scale_down(inactive_additional_hosts)","title":""},{"docid":"9e44e7830810a6a48e0088572ed0f45c","score":"0.5382066","text":"def unset_pre_scale_done(self):\n key = self._qualifyKey(NodeDecorator.PRE_SCALE_DONE)\n self._ss_client.setRuntimeParameter(key, 'false')","title":""},{"docid":"ccacdce455feebe1cb069f0af4ea1d10","score":"0.5380459","text":"def re_run(self, task):\n # Basically delete all and run again.\n pass","title":""},{"docid":"a7c6dd07df633726c2cf43fb0c947083","score":"0.53783774","text":"def finalize(self):\n self._finalize.set()","title":""},{"docid":"25227499a45aa95dba795d37bba9d00c","score":"0.5373788","text":"def finalize_run(self, run_state):\n failure_message = run_state.info.get('failure_message', None)\n if 'exitcode' not in run_state.info:\n run_state.info['exitcode'] = None\n if not failure_message and run_state.is_killed:\n run_state.info['failure_message'] = run_state.info['kill_message']\n run_state.info['finalized'] = False\n return run_state._replace(\n stage=LocalRunStage.FINALIZING, info=run_state.info, run_status=\"Finalizing bundle\"\n )","title":""},{"docid":"137f67628d91a43d8c5f15a914658c0a","score":"0.53666997","text":"def end_run(self):\n # not necessary\n pass","title":""},{"docid":"89f6e94d212db843d55c683e31be1f3a","score":"0.5364355","text":"def tear_down(self):\n\n self.run_etl_job_cutoff_statement()","title":""},{"docid":"5e8d1f6d6f7c906109daf8ca24c47868","score":"0.5319805","text":"def finalize(self, loss, optimizer):\n self.loss, self.optimizer = loss, optimizer\n self.finalized = True","title":""},{"docid":"5e8d1f6d6f7c906109daf8ca24c47868","score":"0.5319805","text":"def finalize(self, loss, optimizer):\n self.loss, self.optimizer = loss, optimizer\n self.finalized = True","title":""},{"docid":"410c19aa571e63a03bd1b2ebbc7d9abe","score":"0.5315652","text":"def onComplete(self, task): #$NON-NLS-1$\r","title":""},{"docid":"ae1295a08dfffc2d01d25f836820ae5b","score":"0.53156406","text":"def end_run(\n self,\n *,\n state: gca_execution.Execution.State = gca_execution.Execution.State.COMPLETE,\n ):\n self.update_state(state)","title":""},{"docid":"e16a7f732648c81cab0445eb6ee93b03","score":"0.5315085","text":"def shutdown_worker(self):\n parallel_sampler.terminate_task(scope=self.algo.scope)","title":""},{"docid":"9c6d6a40ade20a4f828ec67fd85ec522","score":"0.53096217","text":"def _postprocess(self):\n if not super()._postprocess():\n return False\n\n try:\n torch.distributed.destroy_process_group()\n except BaseException as e:\n self._result.set_return_code(ReturnCode.DISTRIBUTED_SETTING_DESTROY_FAILURE)\n logger.error(\n 'Post process failed - benchmark: {}, mode: {}, message: {}.'.format(\n self._name, self._args.mode, str(e)\n )\n )\n return False\n\n return True","title":""},{"docid":"e900b0d994ae9ead66862686c7ff340e","score":"0.53038293","text":"def distribute_task(self, task: object):","title":""},{"docid":"324167904bb0fd9961af605d4ff719c6","score":"0.5303111","text":"def _perform_task(self):\n self._get_zeropoint_index()\n self._execute_compensation(self.parameters['zeropoint_index'])","title":""},{"docid":"4c9711549439bdce3d72d459b5c09943","score":"0.5297899","text":"def force_finalize_on_wait(self):\n self._finalize_on_wait = True","title":""},{"docid":"72fdcb6fa8ec3782a5e3b73fd8cac27a","score":"0.52747715","text":"def cleanup_resources_from_task_run(self, task_run: \"TaskRun\", server_url: str) -> None:\n raise NotImplementedError()","title":""},{"docid":"f69a7bfbe06d86b75b6c33b6afb7651e","score":"0.5271502","text":"def finalize(self, ctx, shard_state):\n return","title":""},{"docid":"57c62082184e724d28fe4637f12e3f4c","score":"0.52652365","text":"def set_pre_scale_done(self):\n key = self._qualifyKey(NodeDecorator.PRE_SCALE_DONE)\n self._ss_client.setRuntimeParameter(key, NodeDecorator.PRE_SCALE_DONE_SUCCESS)","title":""},{"docid":"e9f59b28975b092865edcd08669ae3df","score":"0.5247657","text":"def release(self) -> None:\n self.thr._task_logs.pop()","title":""},{"docid":"aba89a93093941599f217863e283975e","score":"0.5245691","text":"def clean(self):\n\n self.instance.send_event('Cleaning job..')\n result = self.instance.execute_operation('croupier.interfaces.lifecycle.cleanup',\n kwargs={\"name\": self.name})\n\n self.instance.send_event('.. job cleaned')\n\n return result.task","title":""},{"docid":"614831f18af27faa4291875dfbb04635","score":"0.5243402","text":"def signal_shutdown(self):\n self.log.info('done')\n self.metrics.set_ready(False)","title":""},{"docid":"a519d358eaa90f8e61aaf99e6edf2218","score":"0.5241345","text":"async def cleanup(self) -> None:\n ...","title":""},{"docid":"2c37034d842407eba3a2b0b2b7f115e2","score":"0.5238297","text":"def shutdown(self):\n\n logger.debug('Job done!')","title":""},{"docid":"4c32973dcd51b17ae863a713ec46839a","score":"0.5237835","text":"def unthrottle_downlad(self):","title":""},{"docid":"4c160a823daf70543e27c2a69e0ab952","score":"0.52340615","text":"def task(self):\n pass","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.52276","text":"def finish(self):\n pass","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.52276","text":"def finish(self):\n pass","title":""},{"docid":"9d60d7c941ed7102fa6c93128493449f","score":"0.52276","text":"def finish(self):\n pass","title":""},{"docid":"a5b49537b8138bc93349fe17c35c80ce","score":"0.5226661","text":"def task5(self):\n \n pass","title":""},{"docid":"e305a9594cb2ca413d43265a9fe70d46","score":"0.5226587","text":"def cog_unload(self):\n\t\tself.bg_task.cancel()\n\t\tprint(\"DNfeed cog unloaded\")","title":""},{"docid":"42e5291df190d8641277d415270e513d","score":"0.5218882","text":"def run_post_processing(self):\n self.update_post_processing_solutions()\n\n self.exports.t = self.t\n self.exports.write(self.label_to_function, self.dt)","title":""},{"docid":"cd340b26acbd6546f487de4662edef9a","score":"0.5206462","text":"def run(task):","title":""},{"docid":"92b36027dcd9d76470e8cb2c3a3874f2","score":"0.5203779","text":"def _transition_from_CLEANING_UP(self, run_state):\n bundle_uuid = run_state.bundle['uuid']\n if run_state.container_id is not None:\n while True:\n try:\n finished, _, _ = docker_utils.check_finished(run_state.container)\n if finished:\n run_state.container.remove(force=True)\n break\n except docker.errors.APIError:\n logger.error(traceback.format_exc())\n time.sleep(1)\n\n for dep in run_state.bundle['dependencies']:\n self.dependency_manager.release(bundle_uuid, (dep['parent_uuid'], dep['parent_path']))\n\n child_path = os.path.join(run_state.bundle_path, dep['child_path'])\n try:\n remove_path(child_path)\n except Exception:\n logger.error(traceback.format_exc())\n\n if run_state.has_contents:\n return run_state._replace(\n stage=LocalRunStage.UPLOADING_RESULTS,\n run_status='Uploading results',\n container=None,\n )\n else:\n return self.finalize_run(run_state)","title":""},{"docid":"3926745fcb5cb231035e4bde257b5088","score":"0.52021843","text":"def finalize(self, interrupted=False):\n pass","title":""},{"docid":"34f768bb2cb461e71485054bed5fa47b","score":"0.5201078","text":"def end(self):\n print(\"AWS Task is finished.\")","title":""},{"docid":"cfcdd31365a214fb0d91a0e1b45e9c32","score":"0.5194666","text":"def task2(self):\n\n pass","title":""},{"docid":"f6d1ab194209e043b7d54acd082a0d7f","score":"0.5188646","text":"def finish(self):\n raise NotImplementedError","title":""},{"docid":"0301c6cb545a9f4a7d8fd16ea4609f2f","score":"0.5180248","text":"def finalize(self):\n if self.output_stream:\n self.output_stream.close()\n else:\n pass","title":""},{"docid":"b8743289f2b4ce07ce5fdac530990fdd","score":"0.5179197","text":"def task4(self):\n\n pass","title":""},{"docid":"0d913aab6696403e0033cf4853d38456","score":"0.51692045","text":"def __finishRun(self):\n print('finish run called')\n os.remove(self.tempFile)\n self.tempFile = None","title":""},{"docid":"23690bd51b3e1892f54d195ecc51e190","score":"0.5165639","text":"def _on_task_run(self):\n pass","title":""},{"docid":"f1046f0ac918044bb35f75ac46bfc1be","score":"0.5162351","text":"def localFinalizeActualSampling(self, jobObject, model, myInput):","title":""},{"docid":"72662e6d3fd64cc7dbc52833f5fb03d4","score":"0.5157304","text":"def finalize(self, kwargs):\n pass","title":""},{"docid":"a4552d35dc0f2fe541672962e9f069f8","score":"0.51567674","text":"def _finalize(self, store_meta_data=True):\n self._is_run = False\n self.f_set_crun(None)\n if store_meta_data:\n self.f_store(only_init=True)","title":""},{"docid":"3e2cac8b1b8e4b0ee34bf6179e463d24","score":"0.5147796","text":"def tearDown(self):\n del self.task\n del self.scheduler","title":""},{"docid":"a3e9f0d6d9f088050d244043cac27296","score":"0.51415724","text":"def _complete_inst_progress():\n global __INST_PROGRESS__\n if not self._inst_uuid in __INST_PROGRESS__:\n LOG.error(_(\"The progress updater has not started\"\n \" for virtual machine %s.\") % self._inst_uuid)\n raise pvcex.IBMPowerVMProgressUpdateError(uuid=self._inst_uuid)\n\n # before clean up, make sure we set the progress to 100\n if __INST_PROGRESS__[self._inst_uuid] != 100:\n conductor.API().instance_update(self._context, self._inst_uuid,\n progress=round(100))\n del __INST_PROGRESS__[self._inst_uuid]","title":""},{"docid":"fa491f1b220293d322be55d6a8153bb9","score":"0.5140171","text":"def test_downscale(runner):\n src_path = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012.h5')\n truth_path = os.path.join(TESTDATADIR, 'wtk/rechunk_3hr.h5')\n var_attrs = create_var_attrs(src_path, t_chunk=(7 * 24))\n\n with tempfile.TemporaryDirectory() as td:\n rechunk_path = os.path.join(td, 'rechunk.h5')\n attrs_path = os.path.join(td, 'var_attrs.json')\n var_attrs.to_json(attrs_path)\n\n result = runner.invoke(main, ['-src', src_path,\n '-dst', rechunk_path,\n '-vap', attrs_path,\n '-res', '3h'])\n msg = ('Failed with error {}'\n .format(traceback.print_exception(*result.exc_info)))\n assert result.exit_code == 0, msg\n\n check_rechunk(truth_path, rechunk_path)\n\n LOGGERS.clear()","title":""},{"docid":"a95b86610c0cf37174d265820795e625","score":"0.5130314","text":"def finalize(self, loss, optimizer):\n self.loss, self.optimizer = loss, optimizer\n self.finalized = True","title":""},{"docid":"a95b86610c0cf37174d265820795e625","score":"0.5130314","text":"def finalize(self, loss, optimizer):\n self.loss, self.optimizer = loss, optimizer\n self.finalized = True","title":""}],"string":"[\n {\n \"docid\": \"f1f9202e19312af090558d9300f3c976\",\n \"score\": \"0.6260884\",\n \"text\": \"def teardown(self, task, job_config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1c2f4fd22c23c94eab7333d3e99aee\",\n \"score\": \"0.616505\",\n \"text\": \"def finalize(self):\\n self.logger.info(\\\"Please wait while finalizing the operation.. Thank you\\\")\\n self.save_checkpoint()\\n # self.summary_writer.export_scalars_to_json(\\\"{}all_scalars.json\\\".format(self.config.summary_dir))\\n # self.summary_writer.close()\\n # self.dataloader.finalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48c5d6a1e1470da3127bfc0e9aa1b28\",\n \"score\": \"0.6017869\",\n \"text\": \"def _post_compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8d8d660d524b84f3277c7b155d3143\",\n \"score\": \"0.5813739\",\n \"text\": \"def finalize_timepoint(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c942b1700e1adfd749cbf41f64b3a53\",\n \"score\": \"0.5796718\",\n \"text\": \"def ExecuteFinalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62002f7cf997a569e1213856931a3f88\",\n \"score\": \"0.5784241\",\n \"text\": \"def finished_task(self):\\n self.mutex.lock()\\n if self.cache is not None:\\n args, kwargs = self.cache\\n self.algorithm.set_parameters(*args, **kwargs)\\n self.cache = None\\n self.clean_later = False\\n if self._image is not None:\\n self.algorithm.set_image(self._image)\\n self._image = None\\n if self._mask is not None:\\n self.algorithm.set_mask(self._mask)\\n self._mask = None\\n if self.rerun[0]:\\n self.rerun = False, QThread.InheritPriority\\n super().start(self.rerun[1])\\n elif self.clean_later:\\n self.algorithm.clean()\\n self.clean_later = False\\n self.mutex.unlock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"830c5eb3d65b9e5e31d11a1627d080c7\",\n \"score\": \"0.5719745\",\n \"text\": \"def cleanup_run(self, *kargs, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3a5a0288c231d4c513b377e4d5fb62\",\n \"score\": \"0.571714\",\n \"text\": \"def finalize(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c4e7194d1e1fb4df64103a852f6c91\",\n \"score\": \"0.571261\",\n \"text\": \"def finalize(self):\\n pass # currently nothing to do here\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cacc113ca7766fb5b2680238e76e50e6\",\n \"score\": \"0.5700527\",\n \"text\": \"def after_main_loop(self):\\n for task in self.session:\\n if isinstance(task,GsnowpackApplication) and task.execution.returncode == 0:\\n # Only consider successfully terminated tasks\\n _update_original_sno_files(task)\\n \\n if self.params.www and task.get_images():\\n try:\\n _publish_data(task, self.params.www)\\n except OSError, osx:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362527538ef01a8b8b55ebb82dc3d379\",\n \"score\": \"0.5681788\",\n \"text\": \"def _cleanup(self):\\n functions.DAQmxWaitUntillTaskDone(self.taskHandle, -1) # second argument needs checking\\n functions.DAQmxStopTask(self.taskHandle)\\n functions.DAQmxClearTask(self.taskHandle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a61d8f24431362d3da8862938ff2b9\",\n \"score\": \"0.5672402\",\n \"text\": \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a61d8f24431362d3da8862938ff2b9\",\n \"score\": \"0.5672402\",\n \"text\": \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a61d8f24431362d3da8862938ff2b9\",\n \"score\": \"0.5672402\",\n \"text\": \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a61d8f24431362d3da8862938ff2b9\",\n \"score\": \"0.5672402\",\n \"text\": \"def postRunCleanup(self):\\n self.logDesc(\\\"Post Run Cleanup\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf88fa429f38f638ea5e532b91157ede\",\n \"score\": \"0.567161\",\n \"text\": \"def _finalize(self):\\n self._finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8fb1c29151dffdcb6f53cf35689fb91\",\n \"score\": \"0.5633229\",\n \"text\": \"def finalize(self):\\n # Finalize output training\\n self.pca_cell.finalize()\\n\\n # Finalized\\n self.finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b87f5501b23a309b84bb80544899bf2\",\n \"score\": \"0.5624566\",\n \"text\": \"def _finalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b755d6fd430ce98ac87075348ab8f444\",\n \"score\": \"0.56239605\",\n \"text\": \"def finalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b755d6fd430ce98ac87075348ab8f444\",\n \"score\": \"0.56239605\",\n \"text\": \"def finalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b755d6fd430ce98ac87075348ab8f444\",\n \"score\": \"0.56239605\",\n \"text\": \"def finalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b755d6fd430ce98ac87075348ab8f444\",\n \"score\": \"0.56239605\",\n \"text\": \"def finalize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21a454931fb57f8c91f36e515708891\",\n \"score\": \"0.5617566\",\n \"text\": \"def finalize(args):\\n return Update.within_job(args.metric_value, args.test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86247afd98fd0af6087c5dd18db7d104\",\n \"score\": \"0.560529\",\n \"text\": \"def finalize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d64d460f2254138857b91899c2743ac\",\n \"score\": \"0.55869454\",\n \"text\": \"def _finalize_run(self):\\n self._run_information[self.v_crun]['completed'] = 1\\n while len(self._new_links):\\n name_pair, child_parent_pair = self._new_links.popitem(last=False)\\n parent_node, _ = child_parent_pair\\n _, link = name_pair\\n parent_node.f_remove_child(link)\\n\\n while len(self._new_nodes):\\n _, child_parent_pair = self._new_nodes.popitem(last=False)\\n parent, child = child_parent_pair\\n child_name = child.v_name\\n parent.f_remove_child(child_name, recursive=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61252ecafd86555516b9a9d5895b23a6\",\n \"score\": \"0.55836266\",\n \"text\": \"def finalize(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7fec7bee8790abae08451ee7fdfce5\",\n \"score\": \"0.5562341\",\n \"text\": \"def finalize(self):\\n DLOG.verbose(\\\"Default thread worker finalize called for %s.\\\"\\n % self._name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf65c549e98de6bf12769f27ee86daef\",\n \"score\": \"0.55509955\",\n \"text\": \"def process_cleanup(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd4d8d96d9e46015bc6eb726ab5550db\",\n \"score\": \"0.5543055\",\n \"text\": \"def finalize(self):\\n\\t\\tutils.forgetMemoized(self)\\n\\t\\tfor proc in itertools.chain(self.metaMakers, self.dataFunctions):\\n\\t\\t\\tutils.forgetMemoized(proc)\\n\\t\\tutils.forgetMemoized(self.descriptorGenerator)\\n\\t\\tif self.dataFormatter:\\n\\t\\t\\tutils.forgetMemoized(self.dataFormatter)\\n\\t\\tself.breakCircles()\\n\\t\\tself.run = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889a8b06b7b30bcd6e4cdf26169d99e5\",\n \"score\": \"0.5526978\",\n \"text\": \"def finalize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fac4d8af648ac685f80e96bdd900336\",\n \"score\": \"0.5520506\",\n \"text\": \"def finalize(self) -> None:\\n total_execution_time = time.time() - self.start_all\\n print(f\\\"TOTAL EXECUTION TIME: {total_execution_time:.10f} sec\\\")\\n self.merge_outputs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af187d5fd35793273ce85350f4a1b426\",\n \"score\": \"0.551647\",\n \"text\": \"def _set_finish(self):\\n\\n run_info_dict = self._run_information[self.v_crun]\\n timestamp_run = run_info_dict['timestamp']\\n\\n run_summary = self._summarize_explored_parameters()\\n\\n finish_timestamp_run = time.time()\\n\\n findatetime = datetime.datetime.fromtimestamp(finish_timestamp_run)\\n startdatetime = datetime.datetime.fromtimestamp(timestamp_run)\\n\\n runtime_run = str(findatetime - startdatetime)\\n\\n run_info_dict['parameter_summary'] = run_summary\\n run_info_dict['completed'] = 1\\n run_info_dict['finish_timestamp'] = finish_timestamp_run\\n run_info_dict['runtime'] = runtime_run\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e16f4b7d7f12ada9a8554e2f19c8f5f\",\n \"score\": \"0.551429\",\n \"text\": \"def finalize(self):\\n self.classifier.finalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7689206054dd79b00f3bcb0c3541994d\",\n \"score\": \"0.5514289\",\n \"text\": \"def finalize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7689206054dd79b00f3bcb0c3541994d\",\n \"score\": \"0.5514289\",\n \"text\": \"def finalize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28596baa6965789722574d07e7fcc5de\",\n \"score\": \"0.5513951\",\n \"text\": \"def _on_task_run(self):\\n # self.publish_desired_twist_power(self.twist) # change to ALlocateVelocityTask\\n # self.publish_desired_twist(self.twist.linear.x,\\n # self.twist.linear.y,\\n # self.twist.linear.z,\\n # self.twist.angular.x,\\n # self.twist.angular.y,\\n # self.twist.angular.z\\n # )\\n\\n self.rotate_task.run()\\n\\n if not self.on_finish_segment and task_utils.at_pose(\\n self.state.pose.pose, self.target_pose, float(\\\"inf\\\"), self.seg_rads / 2):\\n self.current_segment += 1\\n self.target_pose = task_utils.add_poses([self.target_pose, self.angle_pose])\\n rospy.loginfo(\\\"Now on segment \\\" + str(self.current_segment))\\n self.output[\\\"rads_turned\\\"] += self.seg_rads\\n self.output[\\\"points_scored\\\"] = int(self.output[\\\"rads_turned\\\"] / (math.pi / 2)) * self.nintey_points\\n\\n # rospy.loginfo(\\\"1 clear\\\")\\n\\n if not self.on_finish_segment and self.current_segment == self.num_segments:\\n self.on_finish_segment = True\\n\\n # rospy.loginfo(\\\"2 clear\\\")\\n\\n if self.on_finish_segment and task_utils.at_pose(\\n self.state.pose.pose, self.target_pose, float(\\\"inf\\\"), self.seg_rads):\\n rospy.loginfo(\\\"Turn Complete!\\\\n\\\")\\n self.output[\\\"rads_turned\\\"] = self.angle\\n self.output[\\\"points_scored\\\"] = int(self.output[\\\"rads_turned\\\"] / (math.pi / 2)) * self.nintey_points\\n rospy.loginfo(\\\"Stopping turn!\\\")\\n self.rotate_task.finish()\\n self.finish()\\n\\n # rospy.loginfo(\\\"all clear, self.finished is {}\\\".format(self.finished))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06bf10d20f94cf9ff247be4bcbdec8d0\",\n \"score\": \"0.54889214\",\n \"text\": \"def post(self, executor):\\n tasks.PauseExecutor(executor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6efe452ecfa67fd710d9e222e40bce\",\n \"score\": \"0.5461304\",\n \"text\": \"def process_cleanup(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a230a62e94fc9dfcdd364db0f8380a7\",\n \"score\": \"0.5441202\",\n \"text\": \"def finalize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a230a62e94fc9dfcdd364db0f8380a7\",\n \"score\": \"0.5441202\",\n \"text\": \"def finalize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a230a62e94fc9dfcdd364db0f8380a7\",\n \"score\": \"0.5441202\",\n \"text\": \"def finalize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d77d14eafa02c7ba5801786154098db\",\n \"score\": \"0.54286355\",\n \"text\": \"def fin():\\n u_libs.testflow.teardown(\\n \\\"Release hosts from CPU load: %s\\\", load_to_resources.values()\\n )\\n sla_helpers.stop_load_on_resources(load_to_resources.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ccf732c99c3ca2a038cedca21f40c6\",\n \"score\": \"0.54172254\",\n \"text\": \"def finalizeActualSampling(self, jobObject, model, myInput):\\n self.localFinalizeActualSampling(jobObject, model, myInput)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0def583b5a6ab21e79adb4eaf7955145\",\n \"score\": \"0.54120964\",\n \"text\": \"def work(self):\\n # HACK: overwrite the classes' self._do_stage() method with our own\\n try:\\n ds_orig = self._do_stage\\n # Functools.partial() should be more jit friendly than lambda\\n self._do_stage = functools.partial(self.__do_stage, _ds=ds_orig)\\n super(Deinstall, self).work()\\n finally:\\n self._do_stage = ds_orig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63a5463d9c114c73aaf0a1499e5bcda\",\n \"score\": \"0.53938115\",\n \"text\": \"def finalize(self):\\n assert self.num_frames[\\\"data\\\"] > 0\\n # Note: self.num_frames could be greater than self.data.get_num_timesteps() in case of chunking.\\n for key, value in self.results.items():\\n if key != \\\"ctc_priors\\\":\\n self.results[key] *= self.epoch_norm_factor_for_result(key)\\n self.score = dict([(key,value) for (key, value) in self.results.items() if key.startswith(\\\"cost:\\\")])\\n self.error = dict([(key,value) for (key, value) in self.results.items() if key.startswith(\\\"error:\\\")])\\n self.finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb2d6423fbda66b2e1512adbbdf061f\",\n \"score\": \"0.53866833\",\n \"text\": \"def scale_down(self, inactive_additional_hosts):\\n Logger.info('Start grid engine SCALING DOWN for %s hosts.' % len(inactive_additional_hosts))\\n self.scale_down_orchestrator.scale_down(inactive_additional_hosts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e44e7830810a6a48e0088572ed0f45c\",\n \"score\": \"0.5382066\",\n \"text\": \"def unset_pre_scale_done(self):\\n key = self._qualifyKey(NodeDecorator.PRE_SCALE_DONE)\\n self._ss_client.setRuntimeParameter(key, 'false')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccacdce455feebe1cb069f0af4ea1d10\",\n \"score\": \"0.5380459\",\n \"text\": \"def re_run(self, task):\\n # Basically delete all and run again.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c6dd07df633726c2cf43fb0c947083\",\n \"score\": \"0.53783774\",\n \"text\": \"def finalize(self):\\n self._finalize.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25227499a45aa95dba795d37bba9d00c\",\n \"score\": \"0.5373788\",\n \"text\": \"def finalize_run(self, run_state):\\n failure_message = run_state.info.get('failure_message', None)\\n if 'exitcode' not in run_state.info:\\n run_state.info['exitcode'] = None\\n if not failure_message and run_state.is_killed:\\n run_state.info['failure_message'] = run_state.info['kill_message']\\n run_state.info['finalized'] = False\\n return run_state._replace(\\n stage=LocalRunStage.FINALIZING, info=run_state.info, run_status=\\\"Finalizing bundle\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137f67628d91a43d8c5f15a914658c0a\",\n \"score\": \"0.53666997\",\n \"text\": \"def end_run(self):\\n # not necessary\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f6e94d212db843d55c683e31be1f3a\",\n \"score\": \"0.5364355\",\n \"text\": \"def tear_down(self):\\n\\n self.run_etl_job_cutoff_statement()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8d1f6d6f7c906109daf8ca24c47868\",\n \"score\": \"0.5319805\",\n \"text\": \"def finalize(self, loss, optimizer):\\n self.loss, self.optimizer = loss, optimizer\\n self.finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8d1f6d6f7c906109daf8ca24c47868\",\n \"score\": \"0.5319805\",\n \"text\": \"def finalize(self, loss, optimizer):\\n self.loss, self.optimizer = loss, optimizer\\n self.finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"410c19aa571e63a03bd1b2ebbc7d9abe\",\n \"score\": \"0.5315652\",\n \"text\": \"def onComplete(self, task): #$NON-NLS-1$\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1295a08dfffc2d01d25f836820ae5b\",\n \"score\": \"0.53156406\",\n \"text\": \"def end_run(\\n self,\\n *,\\n state: gca_execution.Execution.State = gca_execution.Execution.State.COMPLETE,\\n ):\\n self.update_state(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e16a7f732648c81cab0445eb6ee93b03\",\n \"score\": \"0.5315085\",\n \"text\": \"def shutdown_worker(self):\\n parallel_sampler.terminate_task(scope=self.algo.scope)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6d6a40ade20a4f828ec67fd85ec522\",\n \"score\": \"0.53096217\",\n \"text\": \"def _postprocess(self):\\n if not super()._postprocess():\\n return False\\n\\n try:\\n torch.distributed.destroy_process_group()\\n except BaseException as e:\\n self._result.set_return_code(ReturnCode.DISTRIBUTED_SETTING_DESTROY_FAILURE)\\n logger.error(\\n 'Post process failed - benchmark: {}, mode: {}, message: {}.'.format(\\n self._name, self._args.mode, str(e)\\n )\\n )\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e900b0d994ae9ead66862686c7ff340e\",\n \"score\": \"0.53038293\",\n \"text\": \"def distribute_task(self, task: object):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324167904bb0fd9961af605d4ff719c6\",\n \"score\": \"0.5303111\",\n \"text\": \"def _perform_task(self):\\n self._get_zeropoint_index()\\n self._execute_compensation(self.parameters['zeropoint_index'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c9711549439bdce3d72d459b5c09943\",\n \"score\": \"0.5297899\",\n \"text\": \"def force_finalize_on_wait(self):\\n self._finalize_on_wait = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72fdcb6fa8ec3782a5e3b73fd8cac27a\",\n \"score\": \"0.52747715\",\n \"text\": \"def cleanup_resources_from_task_run(self, task_run: \\\"TaskRun\\\", server_url: str) -> None:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69a7bfbe06d86b75b6c33b6afb7651e\",\n \"score\": \"0.5271502\",\n \"text\": \"def finalize(self, ctx, shard_state):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57c62082184e724d28fe4637f12e3f4c\",\n \"score\": \"0.52652365\",\n \"text\": \"def set_pre_scale_done(self):\\n key = self._qualifyKey(NodeDecorator.PRE_SCALE_DONE)\\n self._ss_client.setRuntimeParameter(key, NodeDecorator.PRE_SCALE_DONE_SUCCESS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f59b28975b092865edcd08669ae3df\",\n \"score\": \"0.5247657\",\n \"text\": \"def release(self) -> None:\\n self.thr._task_logs.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba89a93093941599f217863e283975e\",\n \"score\": \"0.5245691\",\n \"text\": \"def clean(self):\\n\\n self.instance.send_event('Cleaning job..')\\n result = self.instance.execute_operation('croupier.interfaces.lifecycle.cleanup',\\n kwargs={\\\"name\\\": self.name})\\n\\n self.instance.send_event('.. job cleaned')\\n\\n return result.task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614831f18af27faa4291875dfbb04635\",\n \"score\": \"0.5243402\",\n \"text\": \"def signal_shutdown(self):\\n self.log.info('done')\\n self.metrics.set_ready(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a519d358eaa90f8e61aaf99e6edf2218\",\n \"score\": \"0.5241345\",\n \"text\": \"async def cleanup(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c37034d842407eba3a2b0b2b7f115e2\",\n \"score\": \"0.5238297\",\n \"text\": \"def shutdown(self):\\n\\n logger.debug('Job done!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c32973dcd51b17ae863a713ec46839a\",\n \"score\": \"0.5237835\",\n \"text\": \"def unthrottle_downlad(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c160a823daf70543e27c2a69e0ab952\",\n \"score\": \"0.52340615\",\n \"text\": \"def task(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.52276\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.52276\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d60d7c941ed7102fa6c93128493449f\",\n \"score\": \"0.52276\",\n \"text\": \"def finish(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b49537b8138bc93349fe17c35c80ce\",\n \"score\": \"0.5226661\",\n \"text\": \"def task5(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e305a9594cb2ca413d43265a9fe70d46\",\n \"score\": \"0.5226587\",\n \"text\": \"def cog_unload(self):\\n\\t\\tself.bg_task.cancel()\\n\\t\\tprint(\\\"DNfeed cog unloaded\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e5291df190d8641277d415270e513d\",\n \"score\": \"0.5218882\",\n \"text\": \"def run_post_processing(self):\\n self.update_post_processing_solutions()\\n\\n self.exports.t = self.t\\n self.exports.write(self.label_to_function, self.dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd340b26acbd6546f487de4662edef9a\",\n \"score\": \"0.5206462\",\n \"text\": \"def run(task):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b36027dcd9d76470e8cb2c3a3874f2\",\n \"score\": \"0.5203779\",\n \"text\": \"def _transition_from_CLEANING_UP(self, run_state):\\n bundle_uuid = run_state.bundle['uuid']\\n if run_state.container_id is not None:\\n while True:\\n try:\\n finished, _, _ = docker_utils.check_finished(run_state.container)\\n if finished:\\n run_state.container.remove(force=True)\\n break\\n except docker.errors.APIError:\\n logger.error(traceback.format_exc())\\n time.sleep(1)\\n\\n for dep in run_state.bundle['dependencies']:\\n self.dependency_manager.release(bundle_uuid, (dep['parent_uuid'], dep['parent_path']))\\n\\n child_path = os.path.join(run_state.bundle_path, dep['child_path'])\\n try:\\n remove_path(child_path)\\n except Exception:\\n logger.error(traceback.format_exc())\\n\\n if run_state.has_contents:\\n return run_state._replace(\\n stage=LocalRunStage.UPLOADING_RESULTS,\\n run_status='Uploading results',\\n container=None,\\n )\\n else:\\n return self.finalize_run(run_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3926745fcb5cb231035e4bde257b5088\",\n \"score\": \"0.52021843\",\n \"text\": \"def finalize(self, interrupted=False):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34f768bb2cb461e71485054bed5fa47b\",\n \"score\": \"0.5201078\",\n \"text\": \"def end(self):\\n print(\\\"AWS Task is finished.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfcdd31365a214fb0d91a0e1b45e9c32\",\n \"score\": \"0.5194666\",\n \"text\": \"def task2(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d1ab194209e043b7d54acd082a0d7f\",\n \"score\": \"0.5188646\",\n \"text\": \"def finish(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0301c6cb545a9f4a7d8fd16ea4609f2f\",\n \"score\": \"0.5180248\",\n \"text\": \"def finalize(self):\\n if self.output_stream:\\n self.output_stream.close()\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8743289f2b4ce07ce5fdac530990fdd\",\n \"score\": \"0.5179197\",\n \"text\": \"def task4(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d913aab6696403e0033cf4853d38456\",\n \"score\": \"0.51692045\",\n \"text\": \"def __finishRun(self):\\n print('finish run called')\\n os.remove(self.tempFile)\\n self.tempFile = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23690bd51b3e1892f54d195ecc51e190\",\n \"score\": \"0.5165639\",\n \"text\": \"def _on_task_run(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1046f0ac918044bb35f75ac46bfc1be\",\n \"score\": \"0.5162351\",\n \"text\": \"def localFinalizeActualSampling(self, jobObject, model, myInput):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72662e6d3fd64cc7dbc52833f5fb03d4\",\n \"score\": \"0.5157304\",\n \"text\": \"def finalize(self, kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4552d35dc0f2fe541672962e9f069f8\",\n \"score\": \"0.51567674\",\n \"text\": \"def _finalize(self, store_meta_data=True):\\n self._is_run = False\\n self.f_set_crun(None)\\n if store_meta_data:\\n self.f_store(only_init=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2cac8b1b8e4b0ee34bf6179e463d24\",\n \"score\": \"0.5147796\",\n \"text\": \"def tearDown(self):\\n del self.task\\n del self.scheduler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e9f0d6d9f088050d244043cac27296\",\n \"score\": \"0.51415724\",\n \"text\": \"def _complete_inst_progress():\\n global __INST_PROGRESS__\\n if not self._inst_uuid in __INST_PROGRESS__:\\n LOG.error(_(\\\"The progress updater has not started\\\"\\n \\\" for virtual machine %s.\\\") % self._inst_uuid)\\n raise pvcex.IBMPowerVMProgressUpdateError(uuid=self._inst_uuid)\\n\\n # before clean up, make sure we set the progress to 100\\n if __INST_PROGRESS__[self._inst_uuid] != 100:\\n conductor.API().instance_update(self._context, self._inst_uuid,\\n progress=round(100))\\n del __INST_PROGRESS__[self._inst_uuid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa491f1b220293d322be55d6a8153bb9\",\n \"score\": \"0.5140171\",\n \"text\": \"def test_downscale(runner):\\n src_path = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012.h5')\\n truth_path = os.path.join(TESTDATADIR, 'wtk/rechunk_3hr.h5')\\n var_attrs = create_var_attrs(src_path, t_chunk=(7 * 24))\\n\\n with tempfile.TemporaryDirectory() as td:\\n rechunk_path = os.path.join(td, 'rechunk.h5')\\n attrs_path = os.path.join(td, 'var_attrs.json')\\n var_attrs.to_json(attrs_path)\\n\\n result = runner.invoke(main, ['-src', src_path,\\n '-dst', rechunk_path,\\n '-vap', attrs_path,\\n '-res', '3h'])\\n msg = ('Failed with error {}'\\n .format(traceback.print_exception(*result.exc_info)))\\n assert result.exit_code == 0, msg\\n\\n check_rechunk(truth_path, rechunk_path)\\n\\n LOGGERS.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95b86610c0cf37174d265820795e625\",\n \"score\": \"0.5130314\",\n \"text\": \"def finalize(self, loss, optimizer):\\n self.loss, self.optimizer = loss, optimizer\\n self.finalized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95b86610c0cf37174d265820795e625\",\n \"score\": \"0.5130314\",\n \"text\": \"def finalize(self, loss, optimizer):\\n self.loss, self.optimizer = loss, optimizer\\n self.finalized = True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994284,"cells":{"query_id":{"kind":"string","value":"8c0e015fc5660b31466a762e418555be"},"query":{"kind":"string","value":"Updates the total amount of posts made"},"positive_passages":{"kind":"list like","value":[{"docid":"45fe69c7411c9f44665fd644334b8242","score":"0.0","text":"def updatepost(id):\n file_name = 'posts.json'\n posts = json.loads(openfile(file_name))\n posts['ids'].append(id)\n writefile(file_name, posts)","title":""}],"string":"[\n {\n \"docid\": \"45fe69c7411c9f44665fd644334b8242\",\n \"score\": \"0.0\",\n \"text\": \"def updatepost(id):\\n file_name = 'posts.json'\\n posts = json.loads(openfile(file_name))\\n posts['ids'].append(id)\\n writefile(file_name, posts)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c75a06323bbbf01792c1d8e98464103f","score":"0.6925383","text":"def total_posts():\n return Post.published.count()","title":""},{"docid":"777b5802ad52cea96062f0865321a171","score":"0.6705593","text":"def update_totals(self):\r\n result = self.votes.aggregate(\r\n score=Sum('delta'), total_votes=Count('id'))\r\n self.total_votes = result['total_votes'] or 0\r\n self.delta_votes = result['score'] or 0\r\n self.save()","title":""},{"docid":"ab8a56bbb0de96bea7cc6aa55ea53a5c","score":"0.66430277","text":"def total_posts(self):\n return self.user.post_set.count()","title":""},{"docid":"911e2128072b6c9098dbdc116b496a1d","score":"0.65555024","text":"def post_count(self, obj):\r\n return obj.post_set.count()","title":""},{"docid":"a2b73d04c56ecde527bedfd11cc7a9d8","score":"0.65479416","text":"def post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n post.views += 1\n post.save()\n return render(request,'postdetail.html',{'post': post})","title":""},{"docid":"22114b15d14a0ce0deefb90b24882fb6","score":"0.6526367","text":"def __update_total(self):\n\n self.total = sum(self.scores)","title":""},{"docid":"d93e3e30acde6dedd488e5d394482fd8","score":"0.64719266","text":"def post(self):\n update_posts()\n build()","title":""},{"docid":"4d578fb7193418522264a50847283967","score":"0.6455365","text":"def update_story_count_for_author(request):\n obj = get_key_or_none(request)\n if not obj:\n raise Http404\n logging.debug(\"Updating story count for Author %s\" % obj)\n obj.story_count = obj.get_story_count()\n obj.put()\n return HttpResponse('ok!')","title":""},{"docid":"acd956fb54bd18589073e7e815824057","score":"0.64391446","text":"def post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n post.views += 1\n post.save()\n return render(request, \"postdetail.html\", {'post':post})","title":""},{"docid":"04fe2fd00c789d49e974013fd293b98a","score":"0.63979346","text":"def update_total_clicks():\n clicks = 0\n for domain in Domain.query():\n clicks = clicks + domain.clickcount\n memcache.set('clicks_total', clicks)","title":""},{"docid":"9077418211fadf240aade6be346a4a9a","score":"0.63957876","text":"def update_posts(self):\n\n if self.ids.limit_input.text == '' or \\\n self.ids.limit_input.text == 'Limit':\n self._limit = 50\n else:\n self._limit = self.ids.limit_input.text\n\n if self.ids.uid_input.text == '' or \\\n self.ids.uid_input.text == 'User ID':\n self._uid = None\n else:\n self._uid = self.ids.uid_input.text\n\n if self.ids.tag_input.text == '' or \\\n self.ids.tag_input.text == 'Tag':\n self._tag = None\n else:\n self._tag = self.ids.tag_input.text\n try:\n self.ids.display.text = feed(self._limit, self._uid, self._tag)\n except:\n pass","title":""},{"docid":"2672e8d8d4da29a87b8211e74ed63616","score":"0.6394472","text":"def post_detail(request, pk):\n\n post = get_object_or_404(Post, pk=pk)\n post.views += 1\n post.save()\n return render(request, \"postdetail.html\", {'post': post})","title":""},{"docid":"55817e4c7cb2b867d45f1be2c8639337","score":"0.6338622","text":"def get_account_posts_counter(account: Account) -> int:\n return account.post_author_set.all().count()","title":""},{"docid":"18c8869c26284efafb3961429fa6b86f","score":"0.6323842","text":"def update_entry_category_visits_count(sender, instance, **kwargs):\n for category in instance.category_set.all():\n category.total_visits_count += 1\n category.save()","title":""},{"docid":"5f3da43108c9b90e9ac7b0a2a47163e0","score":"0.63052577","text":"def post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n post.views += 1\n post.save()\n return render(request, 'post_detail.html', {'post': post})","title":""},{"docid":"731f73ca4d82f74bcfd4203f060a827d","score":"0.6238773","text":"def increase_count(post_id):\n\n votes=None\n table = db.Solution\n\n try:\n solution = table.find_one({'_id': int(post_id)})\n votes = solution['voteup']\n\n except Exception as e:\n print('Error finding element : {}'.format(e))\n\n new_vote = int(votes)+1\n\n try:\n db.Solution.update_one(\n {'_id': int(post_id)},\n {\"$set\":{\"voteup\" : new_vote}\n }\n )\n except Exception as e:\n print('Error updating count: {}'.format(e))","title":""},{"docid":"062d0cb04e6d1d77972c74a0f25d0677","score":"0.6186989","text":"def update_tweets_total(self):\n\t\tself.statuses = 0\n\t\tfor k,v in self.followed_users.items():\n\t\t\tself.statuses += len(v.tweets)","title":""},{"docid":"6e461b468ce0177a51404964d37e9b2e","score":"0.61827165","text":"def testUpdateEntryCount(self):\n\t\tshutil.copy(TEST.fixture(u'flickr_tag_feed.xml'), self.feedurl)\n\t\tfeed = Feed.Load(self.store, self.feedurl)\n\t\toldcount = feed.entries.count()\n\t\t\n\t\tshutil.copy(TEST.fixture(u'flickr_tag_feed_updated.xml'), self.feedurl)\n\t\tfeed = Feed.FindByUrl(self.store, self.feedurl)\n\t\tfeed.update(self.store)\n\t\tself.assertEquals(feed.entries.count(), oldcount)","title":""},{"docid":"3cab530679bfbf18b09c91ac7d0ad979","score":"0.6157481","text":"def reset_upvotes():\n logging.info(\"Reset upvotes in process...\")\n Post.objects.all().update(upvotes_amount=0)\n logging.info(\"Reset upvotes successfully done!\")","title":""},{"docid":"dcfd95838da0668400fd566d1128f8dd","score":"0.6132344","text":"def posts(self, obj):\n return obj.brainbankpost_set.count()","title":""},{"docid":"ba320810388c3da00426b331520cee0c","score":"0.6128701","text":"def update_total(self, size=1):\n self.total += size","title":""},{"docid":"09a1f2a680b546e4e47111756c151a9a","score":"0.60911375","text":"def update_total(self):\n self.order_total = self.orderitems.aggregate(Sum('orderitem_total'))['orderitem_total__sum'] or 0\n self.save()","title":""},{"docid":"db2caf6584b677cf15dbb3108350fff4","score":"0.6086138","text":"async def update_posts(self, _):\n await self.parse_posts()\n return web.json_response(dict(message=\"Updated!\"))","title":""},{"docid":"93a8393287e0b93f2e70c5b903d861d3","score":"0.60603285","text":"def update_count(self):\n self.button_clicks += 1\n self.button[\"text\"] = \"total Clicks: \" + str (self.button_clicks)","title":""},{"docid":"ba74e9f8c061ffdcf458ac5f98185759","score":"0.60562676","text":"def count_posts(self):\n return self.ultra_blog_posts.all().count()","title":""},{"docid":"2c1e7474ca97b575c0c327dc2469cf0c","score":"0.6028252","text":"def ex_increase_count(post_id):\n votes=None\n table = db.Experience\n\n try:\n Experience = table.find_one({'_id': int(post_id)})\n votes = Experience['voteup']\n\n except Exception as e:\n print('Error finding element : {}'.format(e))\n\n new_vote = int(votes)+1\n\n try:\n db.Experience.update_one(\n {'_id': int(post_id)},\n {\"$set\":{\"voteup\" : new_vote}\n }\n )\n except Exception as e:\n print('Error updating count: {}'.format(e))","title":""},{"docid":"6262345df33d28f6aa66e72c91684770","score":"0.6026869","text":"def increment_total_requests(self):\n self.total_requests += 1","title":""},{"docid":"4db265ef9af7a65a73d785acace42f42","score":"0.6017058","text":"def _update_counts(self, date, job, entry):\n if job.status == 'COMPLETED':\n entry.completed_count += 1\n entry.total_count += 1\n elif job.status == 'FAILED':\n entry.failed_count += 1\n entry.total_count += 1\n elif job.status == 'CANCELED':\n entry.canceled_count += 1\n entry.total_count += 1\n\n if job.error:\n if job.error.category == 'SYSTEM':\n entry.error_system_count += 1\n elif job.error.category == 'DATA':\n entry.error_data_count += 1\n elif job.error.category == 'ALGORITHM':\n entry.error_algorithm_count += 1","title":""},{"docid":"6ad05c4b046214eb11f6227577a2e673","score":"0.59979635","text":"def count_votes(self):\n dic = Counter(self.votes.values_list(\"value\", flat=True))\n Event.objects.filter(id=self.id).update(total_votes=dic[True] - dic[False])\n self.refresh_from_db()","title":""},{"docid":"f6d11ac649e588a957e69e63596fda3b","score":"0.5995157","text":"def increase_total_hit(self):\n self.client.incr(\"total_hit\")","title":""},{"docid":"20089a2eca5ac38524a1d8627ee078ee","score":"0.5994227","text":"def add_post(self, postId, *args, **kwargs):\n if postId not in self.posts:\n self.posts.append(postId)\n self.post_count = str(len(self.posts))\n self = super().update(\n {'posts': self.posts, 'post_count': self.post_count})\n return self.save()\n return self","title":""},{"docid":"d11c6c29ed348f5f310805b78727d7e4","score":"0.59911263","text":"def sum_posts(kinesis_actors):\n total_records = 0\n for actor in kinesis_actors:\n total_records += actor.total_records\n return total_records","title":""},{"docid":"d11c6c29ed348f5f310805b78727d7e4","score":"0.59911263","text":"def sum_posts(kinesis_actors):\n total_records = 0\n for actor in kinesis_actors:\n total_records += actor.total_records\n return total_records","title":""},{"docid":"e1d58bb482dead2ee396609bade44113","score":"0.59740806","text":"def update_total(order):\n if not isinstance(order, Order):\n raise Http404(\"{} is not an instance of Order.\".format(order))\n else: \n items = order.items.all()\n order.total = 0\n for item in items:\n order.total += item.price\n order.save()","title":""},{"docid":"5886a6a9d5e00ca4a3c6ad4701282e5c","score":"0.59644246","text":"def update(self, count=1):\n self._last_update = datetime.now()\n self._count += count","title":""},{"docid":"35b45568ecf53c1c893f9873b7745990","score":"0.5955138","text":"def update_total(self, delta: int) -> None:\n with self.lock:\n self.total_count += delta\n self._update_progress()","title":""},{"docid":"292f4b5f7ca1e835000bb1725318025d","score":"0.5932628","text":"def update_count(self):\n self.bttn_clicks += 1\n self.bttn[\"text\"]=\"Total Clicks: \" + str(self.bttn_clicks)\n if self.bttn_clicks>=100:\n self.bttn[\"text\"] = \"You're such a loser... Total Clicks: \" + str(self.bttn_clicks)","title":""},{"docid":"4da32d4eede53987a80c306af3e01493","score":"0.5909062","text":"def update_on_save(sender, instance, created, **kwargs):\n instance.purchase.update_total()","title":""},{"docid":"c1cce5ff5525059dd154e84fb0b11c73","score":"0.58914053","text":"def update_total_votes(self):\n sql = \"\"\"UPDATE pollit_poll SET total_votes = v.votes\n FROM (SELECT poll_id, SUM(votes) as votes FROM pollit_pollchoice \n GROUP BY poll_id) v WHERE pollit_poll.id = v.poll_id\"\"\"\n from django.db import connection, transaction\n cursor = connection.cursor()\n cursor.execute(sql)\n transaction.commit_unless_managed()","title":""},{"docid":"39c27ea6e575dbb34fdb8192bcf8aa8a","score":"0.58754885","text":"def update(self):\n self.occurrences += 1","title":""},{"docid":"9fe9648c88e5ab72c8081f3429fc9348","score":"0.5863087","text":"def update_collections_total():\n\n data = (CollectionCount.objects.values('collection_id')\n .annotate(sum=Sum('count')))\n\n ts = [tasks.update_collections_total.subtask(args=[chunk])\n for chunk in chunked(data, 50)]\n group(ts).apply_async()","title":""},{"docid":"93a16f552da648c3b67a3e6a1edda3dd","score":"0.5837815","text":"def update_total(self):\n self.order_total = self.lineitems.aggregate(\n Sum('lineitem_total'))['lineitem_total__sum'] or 0\n self.item_quantity_count = self.lineitems.aggregate(\n Sum('quantity'))['quantity__sum']\n self.combo_quantity_count = self.lineitems.aggregate(\n Sum('combo_quantity'))['combo_quantity__sum']\n self.order_count = 0\n if self.item_quantity_count:\n self.order_count += self.item_quantity_count\n if self.combo_quantity_count:\n self.order_count += self.combo_quantity_count\n self.delivery_fee = settings.DELIVERY_FEE\n self.grand_total = float(\n self.order_total) + self.delivery_fee - float(self.discount)\n self.save()","title":""},{"docid":"13cc6b8bbe07e2caae54a388dd479c49","score":"0.58350796","text":"def update_on_save(sender, instance, created, **kwargs):\n # update_total obtained from checkout.models\n instance.order.update_total()","title":""},{"docid":"4f414d90dd41c77d938ae6dce433d7e9","score":"0.5832133","text":"def set_post_counts(self, mock_request, threads_count=1, comments_count=1):\n self._set_mock_request_data(mock_request, {\n \"threads_count\": threads_count,\n \"comments_count\": comments_count,\n })","title":""},{"docid":"5a38943061292f58712533f48e646aca","score":"0.5791238","text":"def rate_post(self, postid, token, uid):\n get_response = requests.get(base_url + '/posts')\n posts_data = json.loads(get_response.text)\n data = {}\n data['uid'] = uid\n data['token'] = token\n for item in posts_data:\n if postid == item[u'postid']:\n data['postid'] = item[u'postid']\n else:\n continue\n requests.post(base_url + '/upvotes', data)","title":""},{"docid":"f47532e83de052d6bc121655142dce2f","score":"0.5785656","text":"def testAddEntryCount(self):\n\t\tshutil.copy(TEST.fixture(u'flickr_tag_feed.xml'), self.feedurl)\n\t\tfeed = Feed.Load(self.store, self.feedurl)\n\t\toldcount = feed.entries.count()\n\t\t\n\t\tshutil.copy(TEST.fixture(u'flickr_tag_feed_added.xml'), self.feedurl)\n\t\tfeed = Feed.FindByUrl(self.store, self.feedurl)\n\t\tfeed.update(self.store)\n\t\tself.assertEquals(feed.entries.count(), oldcount + 1)","title":""},{"docid":"653f9b2e5d0a62e36ed4e93a46cea6e5","score":"0.5783948","text":"def update_count(self, delta: int) -> None:\n with self.lock:\n self.count += delta\n self._update_progress()","title":""},{"docid":"febf542a4b3656f6d5c0a1eefc3fe180","score":"0.5775445","text":"def _update(self, count=True, forced=False):","title":""},{"docid":"e1d84110cc2bf2af27e6acaf8ba055d2","score":"0.5759842","text":"def total_likes(self):\n rate=0\n likes= Like.objects.filter(post=self)\n for i in likes:\n if i.rate==True:\n rate+=1\n else:\n rate-=1\n return rate","title":""},{"docid":"2ae46f3e9802f1fc168e4d83a1ad0425","score":"0.57247436","text":"def total_ans(self):\n\n return Answer.objects.filter(post=self).count()","title":""},{"docid":"355d495bea38961430f21b96da6deb9a","score":"0.5716899","text":"def upvote_posts(self):\n list_upvotes = []\n string_upvotes = ''\n final_list = []\n token = GlobalData._user_model.get_token()\n post_getter = PostMessageInterface()\n uid = GlobalData._user_model.get_id()\n if self.ids.upvotes_input.text == '' or \\\n self.ids.upvotes_input.text == \\\n '[MAX 3 ENTRIES] Example: 1 3 20':\n list_upvotes = []\n else:\n string_upvotes = self.ids.upvotes_input.text\n list_upvotes = string_upvotes.split()\n\n for i in range(0, len(list_upvotes)):\n final_list.append(int(list_upvotes[i]))\n\n final_list.sort(key=int)\n for i in range(0, len(final_list)):\n if final_list[i] in self._current_ids:\n post_getter.rate_post(final_list[i], token, uid)\n else:\n continue\n\n self.update_posts()","title":""},{"docid":"a15bd5585973247b3cfde8901bfda19c","score":"0.5715961","text":"def get_votes(self):\n return sum([\n Upvote.query.filter_by(post_id=post.id , vote=1).count()\n for post in self.posts\n ])","title":""},{"docid":"8c81ae835106c558797b453fcafb7150","score":"0.570416","text":"def increment_views(self):\n self.views += 1\n self.save()","title":""},{"docid":"e5fa75c6e475e081ffbee53109db9a06","score":"0.56977785","text":"def updateScore(self, total_score):\n self.score = self.score + total_score","title":""},{"docid":"fdae4260a8be2d18ef02166242f76c60","score":"0.56964254","text":"def update_story_count_for_all_authors(request):\n logging.info(\"Updating story count for all Authors\")\n [taskqueue.add(\n url = '/_update_story_count_for_author/',\n params = {'key' : i.key()},\n method='GET'\n ) for i in Author.all()]\n return HttpResponse('ok!')","title":""},{"docid":"8ea92e43e191f2154b390ffde13ca1af","score":"0.56854033","text":"def decrease_count(post_id):\n votes = None\n table = db.Solution\n\n try:\n solution = table.find_one({'_id': int(post_id)})\n votes = solution['votedown']\n\n except Exception as e:\n print('Error finding element : {}'.format(e))\n\n new_vote = int(votes) + 1\n try:\n db.Solution.update_one(\n {'_id': int(post_id)},\n {\"$set\": {\"votedown\" : new_vote}\n }\n )\n except Exception as e:\n print('Error updating count: {}'.format(e))","title":""},{"docid":"781e0f517c6d04ab5cc3a230d7e6dda7","score":"0.56802917","text":"def updateTotal(self):\n self.totalPoints += self.roundPoints\n #self.roundPoints = 0","title":""},{"docid":"b46ee23cd21874928be89fafcbfdccc8","score":"0.5668422","text":"def update(self):\n sql = 'SELECT path, content FROM posts WHERE updated = 1'\n rows = self.execute(sql).fetchall()\n # posts that were not changed\n paths = [path for path, content in rows if not Post(path).diff(content)]\n self.update_records(paths=paths, mapping={'updated': 0})\n # posts that were changed\n self._delete_updated()\n posts = [Post(path) for path, content in rows if Post(path).diff(content)]\n for post in posts:\n post.update_time()\n self._load_post(post)","title":""},{"docid":"da4344666f34a3931e84131b51b7c9da","score":"0.56624925","text":"def post_count(self):\r\n return sum([item._method == 'POST' for item in self])","title":""},{"docid":"e4f1fb77c6c16ca657d199910c501c23","score":"0.5653265","text":"def upserted_count(self):\n ...","title":""},{"docid":"e2c7232a07509cf43d73712ff679168d","score":"0.5652402","text":"def get_post_count(user):\n count = Post.objects.filter(publisher=user).count()\n return count","title":""},{"docid":"8c898c8eff1c44a6f5734c2931913018","score":"0.56517","text":"def update_on_save(sender, instance, created, **kwargs):\n instance.booking.calculate_total()","title":""},{"docid":"87f261721986ffe2f1c86b40e303a8b3","score":"0.56443864","text":"def update_on_delete(sender, instance, **kwargs):\n instance.purchase.update_total()","title":""},{"docid":"b624c59ce2878c301a0f696e54092094","score":"0.56439525","text":"def update_total(self):\n\n for item in self.item_list:\n if item == \"white cake\":\n self.total += Cake.white_cake_price\n elif item == \"crepe cake\":\n self.total += Cake.crepe_cake_price\n elif item == \"birthday cake\":\n self.total += Cake.birthday_cake_price\n\n print(f\"{self.username}'s total: ${round(self.total, 2)}\")\n return round(self.total, 2)","title":""},{"docid":"02885200240c9f3b26d131ffbc350397","score":"0.5628756","text":"def postUpdate():","title":""},{"docid":"d0e728fa74d1bcc93bc27f3326e30311","score":"0.5622293","text":"def upvote(self, request, pk):\n post = get_object_or_404(Post, id=pk)\n\n post.upvotes = F(\"upvotes\") + 1 # for atomicity\n post.save(update_fields=[\"upvotes\"])\n post.refresh_from_db()\n\n return Response(PostSerializer(post).data)","title":""},{"docid":"d7b977d17b904ad88afc3e89ca123f62","score":"0.56076914","text":"def update_total_progress(self, progress):\n self.total_progress += progress","title":""},{"docid":"e4157c956c8c9d2bc1cf722b515cb9b4","score":"0.5598298","text":"def on_update(self, count):\n self._logger.info(\"Update count : %s \" % count)","title":""},{"docid":"4db84d93a86176cc62473c64c2c99c66","score":"0.5596598","text":"def update_score(self, points):\n self.score += points","title":""},{"docid":"e87761f0ed81f5599a822196e036ecc1","score":"0.5590453","text":"def tally(self, score):\n self._score += score\n self._count += 1","title":""},{"docid":"0bbb09c379f32452064c5dfa91f55d89","score":"0.5589981","text":"def update_total(self):\n\n self.order_total = self.lineitems.aggregate(\n Sum('lineitem_total'))['lineitem_total__sum'] or 0\n\n self.delivery_cost = settings.TRANSPORT_COST\n self.grand_total = self.order_total + self.delivery_cost\n self.save()","title":""},{"docid":"b088d8c6f5c3f6bed63bbf72345ef64f","score":"0.5589249","text":"def update_score(self):\r\n self.__score += 1","title":""},{"docid":"4248276be008e84c3c0597e0b35ee1d1","score":"0.5587861","text":"def customCount(num):\n\tglobal total\n\tglobal backup\n\tglobal app\n\ttry:\n\t\tvalue = int(app.getEntry(\"n=\"))\n\texcept TypeError:\n\t\treturn\n\tbackup = total\n\ttotal += value\n\tapp.setLabel(\"total\", \"Total: \" + str(total))","title":""},{"docid":"8bb711e4b13c9a9ef8da9f03d4ec2df6","score":"0.55869377","text":"def counter_view(request):\n PushTheButtonsChannels.new_push_available(\"asd\",\"asd\", \"123\")\n if request.method == 'POST':\n Counter.objects.create()\n if request.method == 'PATCH':\n counter = Counter.objects.last()\n counter.count = counter.count + 1\n counter.save()\n return get_last_counter_response()","title":""},{"docid":"222d04bfc918892c1eb54d06282b2c78","score":"0.55812633","text":"def update_total_marks(self):\n\t\tmarks = 0.0\n\t\tquestions = self.fixed_questions.all()\n\t\tfor question in questions:\n\t\t\tmarks += question.points\n\t\tfor question_set in self.random_questions.all():\n\t\t\tquestion_set.marks = question_set.questions.first().points\n\t\t\tquestion_set.save()\n\t\t\tmarks += question_set.marks * question_set.num_questions\n\t\tself.total_marks = marks\n\t\tself.save()","title":""},{"docid":"d1bc1943021730b0e3bd6bb06f632bed","score":"0.5566658","text":"def ex_decrease_count(post_id):\n votes = None\n table = db.Experience\n\n try:\n Experience = table.find_one({'_id': int(post_id)})\n votes = Experience['votedown']\n\n except Exception as e:\n print('Error finding element : {}'.format(e))\n\n new_vote = int(votes) + 1\n try:\n db.Experience.update_one(\n {'_id': int(post_id)},\n {\"$set\": {\"votedown\" : new_vote}\n }\n )\n except Exception as e:\n print('Error updating count: {}'.format(e))","title":""},{"docid":"9558738a93d6b5af00eeb0a7a27a060c","score":"0.5549557","text":"def updateGames(self):\n self.games += 1","title":""},{"docid":"1a0b89cfaa9e8a5e4039eadd68d3ec84","score":"0.55487174","text":"def test_post_stats(self):\n user_id = 0\n posts = ['Hello World!', 'I love Flask!', 'Some other very interesting post']\n for post in posts:\n add_post(user_id, post)\n\n with self.client:\n response = self.client.get(f'/posts/stats')\n data = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 200)\n today = date.today().strftime(\"%d/%-m/%Y\")\n self.assertEqual(data['data']['stats'][today], len(posts))","title":""},{"docid":"6130dcdeebab710223371db47b080784","score":"0.553675","text":"def update_api_hit(self):\n self.api_hits = self.api_hits + 1\n self.save()","title":""},{"docid":"8a34fd35b98b793987a754695e71d116","score":"0.5535996","text":"def total_count(self, total_count):\n\n self._total_count = total_count","title":""},{"docid":"08e0c9262ad8883007a2d390125aa625","score":"0.5535078","text":"def update_on_delete(sender, instance, **kwargs):\n instance.order.update_total()","title":""},{"docid":"e12b1d8d5935dd10257937f045d81cb2","score":"0.5535001","text":"def total_blogs(self, total_blogs):\n\n self._total_blogs = total_blogs","title":""},{"docid":"fe5d2cad24d42e0ba864bb761d029426","score":"0.5534335","text":"def update(self):\n\t\tself.n += 1","title":""},{"docid":"847a8d1ce32cb853b59a79219b280a83","score":"0.5526297","text":"def modified_count(self):\n ...","title":""},{"docid":"847a8d1ce32cb853b59a79219b280a83","score":"0.5526297","text":"def modified_count(self):\n ...","title":""},{"docid":"0ba79465f91353922f6ddbcdd04dfcfb","score":"0.55221945","text":"def increment(self):\r\n self.count += 1","title":""},{"docid":"28d54b694345a06562fc9d8feec686e1","score":"0.5516208","text":"def increment(self):\n self.count += 1","title":""},{"docid":"1bc0b08b736ef0dd33187b89c627bf67","score":"0.5514694","text":"def increment_hits(self, id):\n self.filter(pk=id).update(hits=F('hits') + 1, last_used=now())","title":""},{"docid":"1e8e0f2c32748f9a602583794c26c34c","score":"0.55146694","text":"async def _update_feed_update_entries(self, feed_external_ids: Set[str]) -> int:\n update_external_ids = self._managed_external_ids.intersection(feed_external_ids)\n count_updated = len(update_external_ids)\n await self._update_entities(update_external_ids)\n return count_updated","title":""},{"docid":"70eca883709f657874a4ac2ad9f98283","score":"0.55144423","text":"def add(self, count: int = 1) -> int:\n self.quantity = self.quantity + count\n return count","title":""},{"docid":"36779f08f3c995678519f1dc4801cfdd","score":"0.55022866","text":"def getTotalCount(self) -> int:\n ...","title":""},{"docid":"e8fe17ceb3c0738d9bfaa85f7f2e7054","score":"0.54985416","text":"def post_detail(request, id):\n post = get_object_or_404(Post, pk=id)\n post.views += 1 # clock up the number of post views\n post.save()\n return render(request, \"blog/postdetail.html\", {'post': post})","title":""},{"docid":"94497cf07ca978c32750b508a84f73e4","score":"0.5487382","text":"async def counter(self, ctx, member: discord.Member, points: int):\n await self.config.member(member).counter.set(points)\n await ctx.send(\n \"Done. {} now has {} total messages counted.\".format(member.mention, points)\n )","title":""},{"docid":"0b7e654c29319acfccd77629fa89554e","score":"0.5475299","text":"def increment(self, count: int):","title":""},{"docid":"4db47fbf5700ac5c9ff017f47483eb97","score":"0.5469122","text":"def total_count(self):\n return self._total_count","title":""},{"docid":"14aacbf367fa06c610d076d907d2fe8f","score":"0.5464134","text":"def update_on_delete(sender, instance, **kwargs):\n instance.booking.calculate_total()","title":""},{"docid":"98787b872693add01f03eced9ed4afc3","score":"0.54535794","text":"def update_progress(self, tray: int, count: int, total: int) -> None:\n if tray == -1:\n for i in range(6):\n self.objects[i]['progress'].setMaximum(total)\n self.objects[i]['progress'].setValue(count)\n else:\n self.objects[tray]['progress'].setMaximum(total)\n self.objects[tray]['progress'].setValue(count)","title":""},{"docid":"40797848835c3fb6e8735b704d6caa66","score":"0.5452677","text":"def upvotepost(post_id):\n\n post_id = int(post_id)\n vote_value = 1\n validateVote(post_id, current_user, vote_value)\n return redirect(redirect_url())","title":""},{"docid":"ac97307c0ca8443a1c2f5e5f5615a2ed","score":"0.5448473","text":"def process_post(self, message):\n if self.person.online == True:\n like_total = 0\n\n for topic in message.topics:\n if topic in self.interests:\n like_total += self.interests[topic]\n\n like_total = self.facets.process_post(message, like_total, self.person)\n self.model.logger.log(0, \"%r had reaction of %d to %r\" % (self.person, like_total, message))\n self.repost_decide(message)\n return like_total\n return None","title":""},{"docid":"8dd23c975f929f2953c28543f8f28138","score":"0.5446579","text":"def getTotalCount(self):\n return sum(self._tallies)","title":""},{"docid":"7833fa2b5a045589244d72e896036fbe","score":"0.5439383","text":"def update_posts_on_tick(self):\r\n for market in self.map.markets:\r\n if market.product < market.product_capacity:\r\n market.product = min(market.product + market.replenishment, market.product_capacity)\r\n for storage in self.map.storages:\r\n if storage.armor < storage.armor_capacity:\r\n storage.armor = min(storage.armor + storage.replenishment, storage.armor_capacity)","title":""}],"string":"[\n {\n \"docid\": \"c75a06323bbbf01792c1d8e98464103f\",\n \"score\": \"0.6925383\",\n \"text\": \"def total_posts():\\n return Post.published.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"777b5802ad52cea96062f0865321a171\",\n \"score\": \"0.6705593\",\n \"text\": \"def update_totals(self):\\r\\n result = self.votes.aggregate(\\r\\n score=Sum('delta'), total_votes=Count('id'))\\r\\n self.total_votes = result['total_votes'] or 0\\r\\n self.delta_votes = result['score'] or 0\\r\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8a56bbb0de96bea7cc6aa55ea53a5c\",\n \"score\": \"0.66430277\",\n \"text\": \"def total_posts(self):\\n return self.user.post_set.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911e2128072b6c9098dbdc116b496a1d\",\n \"score\": \"0.65555024\",\n \"text\": \"def post_count(self, obj):\\r\\n return obj.post_set.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b73d04c56ecde527bedfd11cc7a9d8\",\n \"score\": \"0.65479416\",\n \"text\": \"def post_detail(request, pk):\\n post = get_object_or_404(Post, pk=pk)\\n post.views += 1\\n post.save()\\n return render(request,'postdetail.html',{'post': post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22114b15d14a0ce0deefb90b24882fb6\",\n \"score\": \"0.6526367\",\n \"text\": \"def __update_total(self):\\n\\n self.total = sum(self.scores)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93e3e30acde6dedd488e5d394482fd8\",\n \"score\": \"0.64719266\",\n \"text\": \"def post(self):\\n update_posts()\\n build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d578fb7193418522264a50847283967\",\n \"score\": \"0.6455365\",\n \"text\": \"def update_story_count_for_author(request):\\n obj = get_key_or_none(request)\\n if not obj:\\n raise Http404\\n logging.debug(\\\"Updating story count for Author %s\\\" % obj)\\n obj.story_count = obj.get_story_count()\\n obj.put()\\n return HttpResponse('ok!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd956fb54bd18589073e7e815824057\",\n \"score\": \"0.64391446\",\n \"text\": \"def post_detail(request, pk):\\n post = get_object_or_404(Post, pk=pk)\\n post.views += 1\\n post.save()\\n return render(request, \\\"postdetail.html\\\", {'post':post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04fe2fd00c789d49e974013fd293b98a\",\n \"score\": \"0.63979346\",\n \"text\": \"def update_total_clicks():\\n clicks = 0\\n for domain in Domain.query():\\n clicks = clicks + domain.clickcount\\n memcache.set('clicks_total', clicks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9077418211fadf240aade6be346a4a9a\",\n \"score\": \"0.63957876\",\n \"text\": \"def update_posts(self):\\n\\n if self.ids.limit_input.text == '' or \\\\\\n self.ids.limit_input.text == 'Limit':\\n self._limit = 50\\n else:\\n self._limit = self.ids.limit_input.text\\n\\n if self.ids.uid_input.text == '' or \\\\\\n self.ids.uid_input.text == 'User ID':\\n self._uid = None\\n else:\\n self._uid = self.ids.uid_input.text\\n\\n if self.ids.tag_input.text == '' or \\\\\\n self.ids.tag_input.text == 'Tag':\\n self._tag = None\\n else:\\n self._tag = self.ids.tag_input.text\\n try:\\n self.ids.display.text = feed(self._limit, self._uid, self._tag)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2672e8d8d4da29a87b8211e74ed63616\",\n \"score\": \"0.6394472\",\n \"text\": \"def post_detail(request, pk):\\n\\n post = get_object_or_404(Post, pk=pk)\\n post.views += 1\\n post.save()\\n return render(request, \\\"postdetail.html\\\", {'post': post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55817e4c7cb2b867d45f1be2c8639337\",\n \"score\": \"0.6338622\",\n \"text\": \"def get_account_posts_counter(account: Account) -> int:\\n return account.post_author_set.all().count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c8869c26284efafb3961429fa6b86f\",\n \"score\": \"0.6323842\",\n \"text\": \"def update_entry_category_visits_count(sender, instance, **kwargs):\\n for category in instance.category_set.all():\\n category.total_visits_count += 1\\n category.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f3da43108c9b90e9ac7b0a2a47163e0\",\n \"score\": \"0.63052577\",\n \"text\": \"def post_detail(request, pk):\\n post = get_object_or_404(Post, pk=pk)\\n post.views += 1\\n post.save()\\n return render(request, 'post_detail.html', {'post': post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"731f73ca4d82f74bcfd4203f060a827d\",\n \"score\": \"0.6238773\",\n \"text\": \"def increase_count(post_id):\\n\\n votes=None\\n table = db.Solution\\n\\n try:\\n solution = table.find_one({'_id': int(post_id)})\\n votes = solution['voteup']\\n\\n except Exception as e:\\n print('Error finding element : {}'.format(e))\\n\\n new_vote = int(votes)+1\\n\\n try:\\n db.Solution.update_one(\\n {'_id': int(post_id)},\\n {\\\"$set\\\":{\\\"voteup\\\" : new_vote}\\n }\\n )\\n except Exception as e:\\n print('Error updating count: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062d0cb04e6d1d77972c74a0f25d0677\",\n \"score\": \"0.6186989\",\n \"text\": \"def update_tweets_total(self):\\n\\t\\tself.statuses = 0\\n\\t\\tfor k,v in self.followed_users.items():\\n\\t\\t\\tself.statuses += len(v.tweets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e461b468ce0177a51404964d37e9b2e\",\n \"score\": \"0.61827165\",\n \"text\": \"def testUpdateEntryCount(self):\\n\\t\\tshutil.copy(TEST.fixture(u'flickr_tag_feed.xml'), self.feedurl)\\n\\t\\tfeed = Feed.Load(self.store, self.feedurl)\\n\\t\\toldcount = feed.entries.count()\\n\\t\\t\\n\\t\\tshutil.copy(TEST.fixture(u'flickr_tag_feed_updated.xml'), self.feedurl)\\n\\t\\tfeed = Feed.FindByUrl(self.store, self.feedurl)\\n\\t\\tfeed.update(self.store)\\n\\t\\tself.assertEquals(feed.entries.count(), oldcount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cab530679bfbf18b09c91ac7d0ad979\",\n \"score\": \"0.6157481\",\n \"text\": \"def reset_upvotes():\\n logging.info(\\\"Reset upvotes in process...\\\")\\n Post.objects.all().update(upvotes_amount=0)\\n logging.info(\\\"Reset upvotes successfully done!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcfd95838da0668400fd566d1128f8dd\",\n \"score\": \"0.6132344\",\n \"text\": \"def posts(self, obj):\\n return obj.brainbankpost_set.count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba320810388c3da00426b331520cee0c\",\n \"score\": \"0.6128701\",\n \"text\": \"def update_total(self, size=1):\\n self.total += size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a1f2a680b546e4e47111756c151a9a\",\n \"score\": \"0.60911375\",\n \"text\": \"def update_total(self):\\n self.order_total = self.orderitems.aggregate(Sum('orderitem_total'))['orderitem_total__sum'] or 0\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db2caf6584b677cf15dbb3108350fff4\",\n \"score\": \"0.6086138\",\n \"text\": \"async def update_posts(self, _):\\n await self.parse_posts()\\n return web.json_response(dict(message=\\\"Updated!\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a8393287e0b93f2e70c5b903d861d3\",\n \"score\": \"0.60603285\",\n \"text\": \"def update_count(self):\\n self.button_clicks += 1\\n self.button[\\\"text\\\"] = \\\"total Clicks: \\\" + str (self.button_clicks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba74e9f8c061ffdcf458ac5f98185759\",\n \"score\": \"0.60562676\",\n \"text\": \"def count_posts(self):\\n return self.ultra_blog_posts.all().count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1e7474ca97b575c0c327dc2469cf0c\",\n \"score\": \"0.6028252\",\n \"text\": \"def ex_increase_count(post_id):\\n votes=None\\n table = db.Experience\\n\\n try:\\n Experience = table.find_one({'_id': int(post_id)})\\n votes = Experience['voteup']\\n\\n except Exception as e:\\n print('Error finding element : {}'.format(e))\\n\\n new_vote = int(votes)+1\\n\\n try:\\n db.Experience.update_one(\\n {'_id': int(post_id)},\\n {\\\"$set\\\":{\\\"voteup\\\" : new_vote}\\n }\\n )\\n except Exception as e:\\n print('Error updating count: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6262345df33d28f6aa66e72c91684770\",\n \"score\": \"0.6026869\",\n \"text\": \"def increment_total_requests(self):\\n self.total_requests += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db265ef9af7a65a73d785acace42f42\",\n \"score\": \"0.6017058\",\n \"text\": \"def _update_counts(self, date, job, entry):\\n if job.status == 'COMPLETED':\\n entry.completed_count += 1\\n entry.total_count += 1\\n elif job.status == 'FAILED':\\n entry.failed_count += 1\\n entry.total_count += 1\\n elif job.status == 'CANCELED':\\n entry.canceled_count += 1\\n entry.total_count += 1\\n\\n if job.error:\\n if job.error.category == 'SYSTEM':\\n entry.error_system_count += 1\\n elif job.error.category == 'DATA':\\n entry.error_data_count += 1\\n elif job.error.category == 'ALGORITHM':\\n entry.error_algorithm_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad05c4b046214eb11f6227577a2e673\",\n \"score\": \"0.59979635\",\n \"text\": \"def count_votes(self):\\n dic = Counter(self.votes.values_list(\\\"value\\\", flat=True))\\n Event.objects.filter(id=self.id).update(total_votes=dic[True] - dic[False])\\n self.refresh_from_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d11ac649e588a957e69e63596fda3b\",\n \"score\": \"0.5995157\",\n \"text\": \"def increase_total_hit(self):\\n self.client.incr(\\\"total_hit\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20089a2eca5ac38524a1d8627ee078ee\",\n \"score\": \"0.5994227\",\n \"text\": \"def add_post(self, postId, *args, **kwargs):\\n if postId not in self.posts:\\n self.posts.append(postId)\\n self.post_count = str(len(self.posts))\\n self = super().update(\\n {'posts': self.posts, 'post_count': self.post_count})\\n return self.save()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11c6c29ed348f5f310805b78727d7e4\",\n \"score\": \"0.59911263\",\n \"text\": \"def sum_posts(kinesis_actors):\\n total_records = 0\\n for actor in kinesis_actors:\\n total_records += actor.total_records\\n return total_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11c6c29ed348f5f310805b78727d7e4\",\n \"score\": \"0.59911263\",\n \"text\": \"def sum_posts(kinesis_actors):\\n total_records = 0\\n for actor in kinesis_actors:\\n total_records += actor.total_records\\n return total_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d58bb482dead2ee396609bade44113\",\n \"score\": \"0.59740806\",\n \"text\": \"def update_total(order):\\n if not isinstance(order, Order):\\n raise Http404(\\\"{} is not an instance of Order.\\\".format(order))\\n else: \\n items = order.items.all()\\n order.total = 0\\n for item in items:\\n order.total += item.price\\n order.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5886a6a9d5e00ca4a3c6ad4701282e5c\",\n \"score\": \"0.59644246\",\n \"text\": \"def update(self, count=1):\\n self._last_update = datetime.now()\\n self._count += count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b45568ecf53c1c893f9873b7745990\",\n \"score\": \"0.5955138\",\n \"text\": \"def update_total(self, delta: int) -> None:\\n with self.lock:\\n self.total_count += delta\\n self._update_progress()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292f4b5f7ca1e835000bb1725318025d\",\n \"score\": \"0.5932628\",\n \"text\": \"def update_count(self):\\n self.bttn_clicks += 1\\n self.bttn[\\\"text\\\"]=\\\"Total Clicks: \\\" + str(self.bttn_clicks)\\n if self.bttn_clicks>=100:\\n self.bttn[\\\"text\\\"] = \\\"You're such a loser... Total Clicks: \\\" + str(self.bttn_clicks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da32d4eede53987a80c306af3e01493\",\n \"score\": \"0.5909062\",\n \"text\": \"def update_on_save(sender, instance, created, **kwargs):\\n instance.purchase.update_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1cce5ff5525059dd154e84fb0b11c73\",\n \"score\": \"0.58914053\",\n \"text\": \"def update_total_votes(self):\\n sql = \\\"\\\"\\\"UPDATE pollit_poll SET total_votes = v.votes\\n FROM (SELECT poll_id, SUM(votes) as votes FROM pollit_pollchoice \\n GROUP BY poll_id) v WHERE pollit_poll.id = v.poll_id\\\"\\\"\\\"\\n from django.db import connection, transaction\\n cursor = connection.cursor()\\n cursor.execute(sql)\\n transaction.commit_unless_managed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c27ea6e575dbb34fdb8192bcf8aa8a\",\n \"score\": \"0.58754885\",\n \"text\": \"def update(self):\\n self.occurrences += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe9648c88e5ab72c8081f3429fc9348\",\n \"score\": \"0.5863087\",\n \"text\": \"def update_collections_total():\\n\\n data = (CollectionCount.objects.values('collection_id')\\n .annotate(sum=Sum('count')))\\n\\n ts = [tasks.update_collections_total.subtask(args=[chunk])\\n for chunk in chunked(data, 50)]\\n group(ts).apply_async()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a16f552da648c3b67a3e6a1edda3dd\",\n \"score\": \"0.5837815\",\n \"text\": \"def update_total(self):\\n self.order_total = self.lineitems.aggregate(\\n Sum('lineitem_total'))['lineitem_total__sum'] or 0\\n self.item_quantity_count = self.lineitems.aggregate(\\n Sum('quantity'))['quantity__sum']\\n self.combo_quantity_count = self.lineitems.aggregate(\\n Sum('combo_quantity'))['combo_quantity__sum']\\n self.order_count = 0\\n if self.item_quantity_count:\\n self.order_count += self.item_quantity_count\\n if self.combo_quantity_count:\\n self.order_count += self.combo_quantity_count\\n self.delivery_fee = settings.DELIVERY_FEE\\n self.grand_total = float(\\n self.order_total) + self.delivery_fee - float(self.discount)\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cc6b8bbe07e2caae54a388dd479c49\",\n \"score\": \"0.58350796\",\n \"text\": \"def update_on_save(sender, instance, created, **kwargs):\\n # update_total obtained from checkout.models\\n instance.order.update_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f414d90dd41c77d938ae6dce433d7e9\",\n \"score\": \"0.5832133\",\n \"text\": \"def set_post_counts(self, mock_request, threads_count=1, comments_count=1):\\n self._set_mock_request_data(mock_request, {\\n \\\"threads_count\\\": threads_count,\\n \\\"comments_count\\\": comments_count,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a38943061292f58712533f48e646aca\",\n \"score\": \"0.5791238\",\n \"text\": \"def rate_post(self, postid, token, uid):\\n get_response = requests.get(base_url + '/posts')\\n posts_data = json.loads(get_response.text)\\n data = {}\\n data['uid'] = uid\\n data['token'] = token\\n for item in posts_data:\\n if postid == item[u'postid']:\\n data['postid'] = item[u'postid']\\n else:\\n continue\\n requests.post(base_url + '/upvotes', data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f47532e83de052d6bc121655142dce2f\",\n \"score\": \"0.5785656\",\n \"text\": \"def testAddEntryCount(self):\\n\\t\\tshutil.copy(TEST.fixture(u'flickr_tag_feed.xml'), self.feedurl)\\n\\t\\tfeed = Feed.Load(self.store, self.feedurl)\\n\\t\\toldcount = feed.entries.count()\\n\\t\\t\\n\\t\\tshutil.copy(TEST.fixture(u'flickr_tag_feed_added.xml'), self.feedurl)\\n\\t\\tfeed = Feed.FindByUrl(self.store, self.feedurl)\\n\\t\\tfeed.update(self.store)\\n\\t\\tself.assertEquals(feed.entries.count(), oldcount + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"653f9b2e5d0a62e36ed4e93a46cea6e5\",\n \"score\": \"0.5783948\",\n \"text\": \"def update_count(self, delta: int) -> None:\\n with self.lock:\\n self.count += delta\\n self._update_progress()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"febf542a4b3656f6d5c0a1eefc3fe180\",\n \"score\": \"0.5775445\",\n \"text\": \"def _update(self, count=True, forced=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d84110cc2bf2af27e6acaf8ba055d2\",\n \"score\": \"0.5759842\",\n \"text\": \"def total_likes(self):\\n rate=0\\n likes= Like.objects.filter(post=self)\\n for i in likes:\\n if i.rate==True:\\n rate+=1\\n else:\\n rate-=1\\n return rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ae46f3e9802f1fc168e4d83a1ad0425\",\n \"score\": \"0.57247436\",\n \"text\": \"def total_ans(self):\\n\\n return Answer.objects.filter(post=self).count()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"355d495bea38961430f21b96da6deb9a\",\n \"score\": \"0.5716899\",\n \"text\": \"def upvote_posts(self):\\n list_upvotes = []\\n string_upvotes = ''\\n final_list = []\\n token = GlobalData._user_model.get_token()\\n post_getter = PostMessageInterface()\\n uid = GlobalData._user_model.get_id()\\n if self.ids.upvotes_input.text == '' or \\\\\\n self.ids.upvotes_input.text == \\\\\\n '[MAX 3 ENTRIES] Example: 1 3 20':\\n list_upvotes = []\\n else:\\n string_upvotes = self.ids.upvotes_input.text\\n list_upvotes = string_upvotes.split()\\n\\n for i in range(0, len(list_upvotes)):\\n final_list.append(int(list_upvotes[i]))\\n\\n final_list.sort(key=int)\\n for i in range(0, len(final_list)):\\n if final_list[i] in self._current_ids:\\n post_getter.rate_post(final_list[i], token, uid)\\n else:\\n continue\\n\\n self.update_posts()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15bd5585973247b3cfde8901bfda19c\",\n \"score\": \"0.5715961\",\n \"text\": \"def get_votes(self):\\n return sum([\\n Upvote.query.filter_by(post_id=post.id , vote=1).count()\\n for post in self.posts\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c81ae835106c558797b453fcafb7150\",\n \"score\": \"0.570416\",\n \"text\": \"def increment_views(self):\\n self.views += 1\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5fa75c6e475e081ffbee53109db9a06\",\n \"score\": \"0.56977785\",\n \"text\": \"def updateScore(self, total_score):\\n self.score = self.score + total_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdae4260a8be2d18ef02166242f76c60\",\n \"score\": \"0.56964254\",\n \"text\": \"def update_story_count_for_all_authors(request):\\n logging.info(\\\"Updating story count for all Authors\\\")\\n [taskqueue.add(\\n url = '/_update_story_count_for_author/',\\n params = {'key' : i.key()},\\n method='GET'\\n ) for i in Author.all()]\\n return HttpResponse('ok!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea92e43e191f2154b390ffde13ca1af\",\n \"score\": \"0.56854033\",\n \"text\": \"def decrease_count(post_id):\\n votes = None\\n table = db.Solution\\n\\n try:\\n solution = table.find_one({'_id': int(post_id)})\\n votes = solution['votedown']\\n\\n except Exception as e:\\n print('Error finding element : {}'.format(e))\\n\\n new_vote = int(votes) + 1\\n try:\\n db.Solution.update_one(\\n {'_id': int(post_id)},\\n {\\\"$set\\\": {\\\"votedown\\\" : new_vote}\\n }\\n )\\n except Exception as e:\\n print('Error updating count: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781e0f517c6d04ab5cc3a230d7e6dda7\",\n \"score\": \"0.56802917\",\n \"text\": \"def updateTotal(self):\\n self.totalPoints += self.roundPoints\\n #self.roundPoints = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46ee23cd21874928be89fafcbfdccc8\",\n \"score\": \"0.5668422\",\n \"text\": \"def update(self):\\n sql = 'SELECT path, content FROM posts WHERE updated = 1'\\n rows = self.execute(sql).fetchall()\\n # posts that were not changed\\n paths = [path for path, content in rows if not Post(path).diff(content)]\\n self.update_records(paths=paths, mapping={'updated': 0})\\n # posts that were changed\\n self._delete_updated()\\n posts = [Post(path) for path, content in rows if Post(path).diff(content)]\\n for post in posts:\\n post.update_time()\\n self._load_post(post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4344666f34a3931e84131b51b7c9da\",\n \"score\": \"0.56624925\",\n \"text\": \"def post_count(self):\\r\\n return sum([item._method == 'POST' for item in self])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f1fb77c6c16ca657d199910c501c23\",\n \"score\": \"0.5653265\",\n \"text\": \"def upserted_count(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c7232a07509cf43d73712ff679168d\",\n \"score\": \"0.5652402\",\n \"text\": \"def get_post_count(user):\\n count = Post.objects.filter(publisher=user).count()\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c898c8eff1c44a6f5734c2931913018\",\n \"score\": \"0.56517\",\n \"text\": \"def update_on_save(sender, instance, created, **kwargs):\\n instance.booking.calculate_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f261721986ffe2f1c86b40e303a8b3\",\n \"score\": \"0.56443864\",\n \"text\": \"def update_on_delete(sender, instance, **kwargs):\\n instance.purchase.update_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b624c59ce2878c301a0f696e54092094\",\n \"score\": \"0.56439525\",\n \"text\": \"def update_total(self):\\n\\n for item in self.item_list:\\n if item == \\\"white cake\\\":\\n self.total += Cake.white_cake_price\\n elif item == \\\"crepe cake\\\":\\n self.total += Cake.crepe_cake_price\\n elif item == \\\"birthday cake\\\":\\n self.total += Cake.birthday_cake_price\\n\\n print(f\\\"{self.username}'s total: ${round(self.total, 2)}\\\")\\n return round(self.total, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02885200240c9f3b26d131ffbc350397\",\n \"score\": \"0.5628756\",\n \"text\": \"def postUpdate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e728fa74d1bcc93bc27f3326e30311\",\n \"score\": \"0.5622293\",\n \"text\": \"def upvote(self, request, pk):\\n post = get_object_or_404(Post, id=pk)\\n\\n post.upvotes = F(\\\"upvotes\\\") + 1 # for atomicity\\n post.save(update_fields=[\\\"upvotes\\\"])\\n post.refresh_from_db()\\n\\n return Response(PostSerializer(post).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b977d17b904ad88afc3e89ca123f62\",\n \"score\": \"0.56076914\",\n \"text\": \"def update_total_progress(self, progress):\\n self.total_progress += progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4157c956c8c9d2bc1cf722b515cb9b4\",\n \"score\": \"0.5598298\",\n \"text\": \"def on_update(self, count):\\n self._logger.info(\\\"Update count : %s \\\" % count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db84d93a86176cc62473c64c2c99c66\",\n \"score\": \"0.5596598\",\n \"text\": \"def update_score(self, points):\\n self.score += points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e87761f0ed81f5599a822196e036ecc1\",\n \"score\": \"0.5590453\",\n \"text\": \"def tally(self, score):\\n self._score += score\\n self._count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbb09c379f32452064c5dfa91f55d89\",\n \"score\": \"0.5589981\",\n \"text\": \"def update_total(self):\\n\\n self.order_total = self.lineitems.aggregate(\\n Sum('lineitem_total'))['lineitem_total__sum'] or 0\\n\\n self.delivery_cost = settings.TRANSPORT_COST\\n self.grand_total = self.order_total + self.delivery_cost\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b088d8c6f5c3f6bed63bbf72345ef64f\",\n \"score\": \"0.5589249\",\n \"text\": \"def update_score(self):\\r\\n self.__score += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4248276be008e84c3c0597e0b35ee1d1\",\n \"score\": \"0.5587861\",\n \"text\": \"def customCount(num):\\n\\tglobal total\\n\\tglobal backup\\n\\tglobal app\\n\\ttry:\\n\\t\\tvalue = int(app.getEntry(\\\"n=\\\"))\\n\\texcept TypeError:\\n\\t\\treturn\\n\\tbackup = total\\n\\ttotal += value\\n\\tapp.setLabel(\\\"total\\\", \\\"Total: \\\" + str(total))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb711e4b13c9a9ef8da9f03d4ec2df6\",\n \"score\": \"0.55869377\",\n \"text\": \"def counter_view(request):\\n PushTheButtonsChannels.new_push_available(\\\"asd\\\",\\\"asd\\\", \\\"123\\\")\\n if request.method == 'POST':\\n Counter.objects.create()\\n if request.method == 'PATCH':\\n counter = Counter.objects.last()\\n counter.count = counter.count + 1\\n counter.save()\\n return get_last_counter_response()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"222d04bfc918892c1eb54d06282b2c78\",\n \"score\": \"0.55812633\",\n \"text\": \"def update_total_marks(self):\\n\\t\\tmarks = 0.0\\n\\t\\tquestions = self.fixed_questions.all()\\n\\t\\tfor question in questions:\\n\\t\\t\\tmarks += question.points\\n\\t\\tfor question_set in self.random_questions.all():\\n\\t\\t\\tquestion_set.marks = question_set.questions.first().points\\n\\t\\t\\tquestion_set.save()\\n\\t\\t\\tmarks += question_set.marks * question_set.num_questions\\n\\t\\tself.total_marks = marks\\n\\t\\tself.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1bc1943021730b0e3bd6bb06f632bed\",\n \"score\": \"0.5566658\",\n \"text\": \"def ex_decrease_count(post_id):\\n votes = None\\n table = db.Experience\\n\\n try:\\n Experience = table.find_one({'_id': int(post_id)})\\n votes = Experience['votedown']\\n\\n except Exception as e:\\n print('Error finding element : {}'.format(e))\\n\\n new_vote = int(votes) + 1\\n try:\\n db.Experience.update_one(\\n {'_id': int(post_id)},\\n {\\\"$set\\\": {\\\"votedown\\\" : new_vote}\\n }\\n )\\n except Exception as e:\\n print('Error updating count: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9558738a93d6b5af00eeb0a7a27a060c\",\n \"score\": \"0.5549557\",\n \"text\": \"def updateGames(self):\\n self.games += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0b89cfaa9e8a5e4039eadd68d3ec84\",\n \"score\": \"0.55487174\",\n \"text\": \"def test_post_stats(self):\\n user_id = 0\\n posts = ['Hello World!', 'I love Flask!', 'Some other very interesting post']\\n for post in posts:\\n add_post(user_id, post)\\n\\n with self.client:\\n response = self.client.get(f'/posts/stats')\\n data = json.loads(response.data.decode())\\n self.assertEqual(response.status_code, 200)\\n today = date.today().strftime(\\\"%d/%-m/%Y\\\")\\n self.assertEqual(data['data']['stats'][today], len(posts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6130dcdeebab710223371db47b080784\",\n \"score\": \"0.553675\",\n \"text\": \"def update_api_hit(self):\\n self.api_hits = self.api_hits + 1\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a34fd35b98b793987a754695e71d116\",\n \"score\": \"0.5535996\",\n \"text\": \"def total_count(self, total_count):\\n\\n self._total_count = total_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e0c9262ad8883007a2d390125aa625\",\n \"score\": \"0.5535078\",\n \"text\": \"def update_on_delete(sender, instance, **kwargs):\\n instance.order.update_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e12b1d8d5935dd10257937f045d81cb2\",\n \"score\": \"0.5535001\",\n \"text\": \"def total_blogs(self, total_blogs):\\n\\n self._total_blogs = total_blogs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5d2cad24d42e0ba864bb761d029426\",\n \"score\": \"0.5534335\",\n \"text\": \"def update(self):\\n\\t\\tself.n += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847a8d1ce32cb853b59a79219b280a83\",\n \"score\": \"0.5526297\",\n \"text\": \"def modified_count(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847a8d1ce32cb853b59a79219b280a83\",\n \"score\": \"0.5526297\",\n \"text\": \"def modified_count(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba79465f91353922f6ddbcdd04dfcfb\",\n \"score\": \"0.55221945\",\n \"text\": \"def increment(self):\\r\\n self.count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d54b694345a06562fc9d8feec686e1\",\n \"score\": \"0.5516208\",\n \"text\": \"def increment(self):\\n self.count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc0b08b736ef0dd33187b89c627bf67\",\n \"score\": \"0.5514694\",\n \"text\": \"def increment_hits(self, id):\\n self.filter(pk=id).update(hits=F('hits') + 1, last_used=now())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8e0f2c32748f9a602583794c26c34c\",\n \"score\": \"0.55146694\",\n \"text\": \"async def _update_feed_update_entries(self, feed_external_ids: Set[str]) -> int:\\n update_external_ids = self._managed_external_ids.intersection(feed_external_ids)\\n count_updated = len(update_external_ids)\\n await self._update_entities(update_external_ids)\\n return count_updated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70eca883709f657874a4ac2ad9f98283\",\n \"score\": \"0.55144423\",\n \"text\": \"def add(self, count: int = 1) -> int:\\n self.quantity = self.quantity + count\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36779f08f3c995678519f1dc4801cfdd\",\n \"score\": \"0.55022866\",\n \"text\": \"def getTotalCount(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8fe17ceb3c0738d9bfaa85f7f2e7054\",\n \"score\": \"0.54985416\",\n \"text\": \"def post_detail(request, id):\\n post = get_object_or_404(Post, pk=id)\\n post.views += 1 # clock up the number of post views\\n post.save()\\n return render(request, \\\"blog/postdetail.html\\\", {'post': post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94497cf07ca978c32750b508a84f73e4\",\n \"score\": \"0.5487382\",\n \"text\": \"async def counter(self, ctx, member: discord.Member, points: int):\\n await self.config.member(member).counter.set(points)\\n await ctx.send(\\n \\\"Done. {} now has {} total messages counted.\\\".format(member.mention, points)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7e654c29319acfccd77629fa89554e\",\n \"score\": \"0.5475299\",\n \"text\": \"def increment(self, count: int):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db47fbf5700ac5c9ff017f47483eb97\",\n \"score\": \"0.5469122\",\n \"text\": \"def total_count(self):\\n return self._total_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14aacbf367fa06c610d076d907d2fe8f\",\n \"score\": \"0.5464134\",\n \"text\": \"def update_on_delete(sender, instance, **kwargs):\\n instance.booking.calculate_total()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98787b872693add01f03eced9ed4afc3\",\n \"score\": \"0.54535794\",\n \"text\": \"def update_progress(self, tray: int, count: int, total: int) -> None:\\n if tray == -1:\\n for i in range(6):\\n self.objects[i]['progress'].setMaximum(total)\\n self.objects[i]['progress'].setValue(count)\\n else:\\n self.objects[tray]['progress'].setMaximum(total)\\n self.objects[tray]['progress'].setValue(count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40797848835c3fb6e8735b704d6caa66\",\n \"score\": \"0.5452677\",\n \"text\": \"def upvotepost(post_id):\\n\\n post_id = int(post_id)\\n vote_value = 1\\n validateVote(post_id, current_user, vote_value)\\n return redirect(redirect_url())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac97307c0ca8443a1c2f5e5f5615a2ed\",\n \"score\": \"0.5448473\",\n \"text\": \"def process_post(self, message):\\n if self.person.online == True:\\n like_total = 0\\n\\n for topic in message.topics:\\n if topic in self.interests:\\n like_total += self.interests[topic]\\n\\n like_total = self.facets.process_post(message, like_total, self.person)\\n self.model.logger.log(0, \\\"%r had reaction of %d to %r\\\" % (self.person, like_total, message))\\n self.repost_decide(message)\\n return like_total\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd23c975f929f2953c28543f8f28138\",\n \"score\": \"0.5446579\",\n \"text\": \"def getTotalCount(self):\\n return sum(self._tallies)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7833fa2b5a045589244d72e896036fbe\",\n \"score\": \"0.5439383\",\n \"text\": \"def update_posts_on_tick(self):\\r\\n for market in self.map.markets:\\r\\n if market.product < market.product_capacity:\\r\\n market.product = min(market.product + market.replenishment, market.product_capacity)\\r\\n for storage in self.map.storages:\\r\\n if storage.armor < storage.armor_capacity:\\r\\n storage.armor = min(storage.armor + storage.replenishment, storage.armor_capacity)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994285,"cells":{"query_id":{"kind":"string","value":"c513c30ba32a62d232eab81145ddc9f0"},"query":{"kind":"string","value":"Moves the aliens to the right."},"positive_passages":{"kind":"list like","value":[{"docid":"f2ac8bb5224bf272097534b659d3890c","score":"0.72090906","text":"def alien_right(self):\n for alien_col in self._aliens:\n for alien in alien_col:\n if alien != None:\n alien.x = alien.x + ALIEN_H_WALK","title":""}],"string":"[\n {\n \"docid\": \"f2ac8bb5224bf272097534b659d3890c\",\n \"score\": \"0.72090906\",\n \"text\": \"def alien_right(self):\\n for alien_col in self._aliens:\\n for alien in alien_col:\\n if alien != None:\\n alien.x = alien.x + ALIEN_H_WALK\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"323e0c73ed9e7793c55a6f67e0c32085","score":"0.80261123","text":"def move_aliens_right(self):\n self.x += ALIEN_H_WALK","title":""},{"docid":"5ea375b75597fc52cec7d2c55d54c02d","score":"0.75233734","text":"def _moveRight(self,list):\n for row in list:\n for alien in row:\n if alien != None:\n alien.right += ALIEN_H_WALK","title":""},{"docid":"e3eb2dd7b58ff345a9fd371393b81189","score":"0.7446899","text":"def move_right(self):\n self.x += 1","title":""},{"docid":"9de8b3cf988a87d2c56f4c085f4cbe86","score":"0.7301645","text":"def turn_right(self):\n self.perform_action(actions.RIGHT)","title":""},{"docid":"227afe5371c2997e2b523a503cb8ae02","score":"0.72393227","text":"def turnRight(self):\n pass","title":""},{"docid":"1a2ab0935826bd3514be1b20c7c1befe","score":"0.7196056","text":"def go_right(self):\n self.change_x = 4\n self.direction = \"R\"","title":""},{"docid":"2360cae620e7f20d2c4a4003ba0b8eb6","score":"0.71806276","text":"def go_right(self):\n self.change_x = 6\n self.direction = \"R\"","title":""},{"docid":"1b19aba17d17d42c113e8eb733163ff1","score":"0.7167389","text":"def turn_right(self):\n self.direction = self.direction.right()","title":""},{"docid":"4a07b891f9b47ebe4e70bcb28a679373","score":"0.7163237","text":"def shift_right(self):\n self.x_pos += self.rightward_shift","title":""},{"docid":"0d90d906c0cbb3afdf92bd2a9da05e5d","score":"0.7150836","text":"def go_right(self):\r\n \r\n \r\n \r\n self.change_x = 3\r\n self.direction = \"R\"\r\n \r\n self.walking = True","title":""},{"docid":"930cfbffdddca7cd122577c884c53f1c","score":"0.7147136","text":"def go_right(self):\n self.change_x = 5\n self.direction = \"R\"","title":""},{"docid":"bad9c702503382a02d29119dfa20f329","score":"0.71310544","text":"def move_right(self):\n self.steps += \"r\"","title":""},{"docid":"0618d474c53b4bdb3be08ef390554fca","score":"0.7077225","text":"def turnRight(self):","title":""},{"docid":"0618d474c53b4bdb3be08ef390554fca","score":"0.7077225","text":"def turnRight(self):","title":""},{"docid":"612c330fa2fbbde179b2f7ba0538e6d9","score":"0.7049387","text":"def turn_right(self):\n if self._direction == 'NORTH':\n self._direction = 'EAST'\n return\n if self._direction == 'SOUTH':\n self._direction = 'WEST'\n return\n if self._direction == 'EAST':\n self._direction = 'SOUTH'\n return\n if self._direction == 'WEST':\n self._direction = 'NORTH'\n return","title":""},{"docid":"46906bda119f30f91e5aaa85efd6e110","score":"0.69984835","text":"def moveRightby1(self):\n self.addTarget([(self.x + 96, self.y)])\n self.setIndex(self.index + 1)","title":""},{"docid":"b78d790ca73f44055ceeb97f27b053bd","score":"0.6969685","text":"def turn_right(self):\n\t\tif self.direction == self.north:\n\t\t\tself.direction = self.east\n\t\telif self.direction == self.east:\n\t\t\tself.direction = self.south\n\t\telif self.direction == self.south:\n\t\t\tself.direction = self.west\n\t\telif self.direction == self.west:\n\t\t\tself.direction = self.north","title":""},{"docid":"a1024a96e65dec7d7e9b5994b9b657f7","score":"0.696408","text":"def go_right(self):\n self.vel_x = 6","title":""},{"docid":"c640b59f64dbbc6daf290d291e4afdf7","score":"0.69594675","text":"def move_right(self):\n for block in self.blocks:\n block.move_right()","title":""},{"docid":"d35075befd22d44fa2f96c4d6c2a8192","score":"0.6941765","text":"def go_right(self):\n self.change_x = self.speed\n self.direction = 'r'","title":""},{"docid":"8ad8ce92c424f79671957649daafc8b7","score":"0.6869159","text":"def move_aliens_left(self):\n self.x -= ALIEN_H_WALK","title":""},{"docid":"5686caf1348d4097f372939773f23cda","score":"0.68576485","text":"def rotate_right(self):\r\n\r\n if self.direction == 'N':\r\n self.direction = 'E'\r\n elif self.direction == 'S':\r\n self.direction = 'W'\r\n elif self.direction == 'W':\r\n self.direction = 'N'\r\n else:\r\n self.direction = 'S'","title":""},{"docid":"cf92511412746a2538d37d8b31e536b6","score":"0.6848221","text":"def go_right(self):\n self.change_x = 6","title":""},{"docid":"cf92511412746a2538d37d8b31e536b6","score":"0.6848221","text":"def go_right(self):\n self.change_x = 6","title":""},{"docid":"ef9a6d5d050167c3cecd638d946de488","score":"0.67971265","text":"def move_right(self) -> None:\n if self.velocity == (-VELOCITY_NORM, 0):\n return\n self.velocity = (VELOCITY_NORM, 0)","title":""},{"docid":"f5c6602a1d12f6a801b106136f304924","score":"0.67806274","text":"def move_right(self):\n self.score += 0.02\n if self.map[self.hero.position[1]][self.hero.position[0] + 1] == Service.wall:\n return\n self.hero.right()\n self.interact()","title":""},{"docid":"71bd4c5d9c461e0cca51af2a169d67c5","score":"0.67345506","text":"def right():\n current_face_index = directions.index(robot_location[2])\n robot_location[2] = directions[current_face_index + 1] \\\n if current_face_index < 3 else directions[0]","title":""},{"docid":"4a37a07e302c03fdc0842a92ea8e61d2","score":"0.6722755","text":"def turn_right():\n turn_left()\n turn_left()\n turn_left()","title":""},{"docid":"cbc160aaff44ca34af42e3e07002bb5c","score":"0.6699034","text":"def move_right(self):\n return self.__move_right","title":""},{"docid":"fe9cce2596eb36179ac95da76520c2d3","score":"0.669735","text":"def move_right(self, ship):\n pygame.event.post(Event(pygame.KEYDOWN, key=pygame.K_RIGHT))\n gf.check_events(self.ai_settings, self.screen, self.stats,\n self.play_button, self.ship, self.aliens, self.bullets)\n self.ship.update()","title":""},{"docid":"b99ce1237605e2f1604eb72443b923d7","score":"0.6645299","text":"def _do_turn_right(self):\n self.board_state.turn_right()","title":""},{"docid":"76c8306b51fc254fc01ca2bbc1b94e3c","score":"0.6589981","text":"def move_right(self):\n self.lcd_byte(\n self.LCD_CURSORSHIFT | self.LCD_DISPLAYMOVE | self.LCD_MOVERIGHT,\n self.LCD_CMD)","title":""},{"docid":"0c88c920ee7b47ca0d5fb0297db02929","score":"0.65388656","text":"def turn_right(self):\n if self.head_direction_x == 1:\n self.head_direction_x = 0\n self.head_direction_y = 1\n elif self.head_direction_y == 1:\n self.head_direction_x = -1\n self.head_direction_y = 0\n elif self.head_direction_x == -1:\n self.head_direction_x = 0\n self.head_direction_y = -1\n else:\n self.head_direction_x = 1\n self.head_direction_y = 0","title":""},{"docid":"e19cce7580b45de3a2260f26088677f4","score":"0.65274364","text":"def right(self):\n if RIGHT in self.possible_moves():\n self.state = self.__right()\n self.__fill_random_field()","title":""},{"docid":"7ae7b5c61602c9774b1f30050e11c36c","score":"0.6526534","text":"def turn_right(self, node):\n dirs = ['N', 'E', 'S', 'W']\n return dirs[(dirs.index(node.direction)+1) % len(dirs)]","title":""},{"docid":"cf1d3e90f51611bce4a796985713b7da","score":"0.65255666","text":"def right(self):\n right()","title":""},{"docid":"60588b2825d9ae014b60d09f034f5204","score":"0.65144587","text":"def moveRight(self):\n # Check if not too close to the rightside wall, and not moving too fast\n if self.x < 497 and self.xVelocity < 8:\n self.xVelocity += 1","title":""},{"docid":"4113759d5ab6c2c5934b7313459033e5","score":"0.6506617","text":"def move_right(self):\n # global continue_up, continue_down, continue_right, continue_left\n self.continue_up = 0\n self.continue_down = 0\n self.continue_left = 0\n if self.current_line.text and self.current_line.number == self.lines.total:\n self.lines.add() # create emtpy line\n\n try: # if tab, move 4 spaces\n if self.current_line.x - 6 < self.current_line.indentation and \\\n self.current_line.text[self.current_line.x - 6:self.current_line.x - 6 + 4] == ' ' and \\\n self.current_line.y == self.current_line.end_y:\n self.current_line.x += 4\n return\n except BareException:\n pass\n\n if self.config['cursor_acceleration']:\n move_rate = min(self.config['cursor_max_horizontal_speed'], int(self.continue_right / 10.0) + 1)\n else:\n move_rate = 1\n self.continue_right += 1\n self.current_line.x += move_rate","title":""},{"docid":"100faba998257426442eb38177fcde17","score":"0.6504694","text":"def robot_right(self):\r\n\t self.x = self.x + 1\r\n\t if self.x > 9:\r\n\t\t self.x = 9","title":""},{"docid":"9813eee101b294e426a70bf04f35a7f0","score":"0.649412","text":"def right(self):\n for col in self.tiles:\n for row in reversed(col):\n if row:\n row.slide(self, (0,1))","title":""},{"docid":"e344f4b6f2514e59960bfb695221465a","score":"0.64802814","text":"def right(self):\n return self.place(self.direction, (self.facing + 0.5)%2, self.grid)","title":""},{"docid":"eeeffd80450377fa380ff319da45b9bc","score":"0.6478905","text":"def MoveRight(self):\n self.location += pygame.Vector2(5, 0)\n self.ConfirmMove()","title":""},{"docid":"9d25c0372f8b94b11008b2109d2b9240","score":"0.64629257","text":"def rightDirection(*args, **kwargs):\n \n pass","title":""},{"docid":"ccf24a7f7fcd6c0155816859aafd2514","score":"0.64615583","text":"def go_right(self):\r\n self.change_x = 6\r\n if current_level_no == 5:\r\n self.change_x -=12\r\n self.direction=\"R\"","title":""},{"docid":"9260803436090954f1bfd40b816254ff","score":"0.6449436","text":"def go_right(self): \n self.change_x = 6","title":""},{"docid":"7d7858553e417b209e086e6792172642","score":"0.64476615","text":"def go_right(self):\n if self.location.x != (Game.SIZE - 1):\n self.location.x += 1\n else:\n self.location.x = 0\n return self.location.x","title":""},{"docid":"7e0f498b98dea694caef9e24ebebc6ff","score":"0.64418066","text":"def turn_right(self) -> \"Direction\":\n index = DIRECTIONS.index(self)\n return DIRECTIONS[(index + 1) % len(DIRECTIONS)]","title":""},{"docid":"cb7639abb6583b271ee4efd9036e0288","score":"0.64379036","text":"def move_aliens_down(self):\n self.y -= ALIEN_V_WALK/ALIEN_ROWS","title":""},{"docid":"045b2a9756e75a1a4d68d6ea9aaab914","score":"0.643437","text":"def move_right(self):\r\n self.speedX = 10","title":""},{"docid":"169c0f42ef3e2aa663d1c430dbfa48a4","score":"0.64147055","text":"def step_right(self):\n if self.o == AgentOrientation.UP:\n return AgentPosition(self.x + 1, self.y, self.o)\n if self.o == AgentOrientation.RIGHT:\n return AgentPosition(self.x, self.y + 1, self.o)\n if self.o == AgentOrientation.DOWN:\n return AgentPosition(self.x - 1, self.y, self.o)\n if self.o == AgentOrientation.LEFT:\n return AgentPosition(self.x, self.y - 1, self.o)","title":""},{"docid":"4b46264681641ccbf3e5f8dd0583575b","score":"0.6411701","text":"def alien_down(self):\n for alien_col in self._aliens:\n for alien in alien_col:\n if alien != None:\n alien.y = alien.y - ALIEN_V_WALK\n if self._direction == 2:\n self._direction = -1\n elif self._direction == -2:\n self._direction = 1","title":""},{"docid":"29b721f45f8b16749cee62f63de26db5","score":"0.63975936","text":"def indent_right(self):\n self._indent_num += 2","title":""},{"docid":"c361c8fa89b710e8228edc1df27edea2","score":"0.63844","text":"def right(self):\n\n self._robot_check()\n self.hRobotEngine.rot('Right')","title":""},{"docid":"10db3fcd9109bf2987b31a10a7ad1a36","score":"0.6371906","text":"def move_right(self):\n self._time += 1\n if self._position < len(self._list) - 1:\n self._position += 1\n return True\n else:\n return False","title":""},{"docid":"6f38a970a46fa00141531fe2bd3390c9","score":"0.6366915","text":"def keyboard_right(self, _):\n self.state = g.update_keyboard(g.KeyPress.RIGHT, self.state)","title":""},{"docid":"6fe34597ab11f596e3451cadd6c5d9a5","score":"0.6365901","text":"def turn_around():\n\tturn_right()\n\tturn_right()","title":""},{"docid":"2a86b8fbe811428a2a432301541ef65d","score":"0.63468903","text":"def move_player_right(state):\n state[GameConstants.MOVE] = min(state[GameConstants.MOVE] + 1, 1)","title":""},{"docid":"78d2d53037254123a1ef6da6b28a1d95","score":"0.6317262","text":"def right(self):\n self.set_motor_dps(self.MOTOR_LEFT, self.get_speed())\n self.set_motor_dps(self.MOTOR_RIGHT, 0)","title":""},{"docid":"759e5787551ab335f93548ecd20f69d9","score":"0.6305845","text":"def push_right(self):\n excepted_turn_speed = self._turn + self._joystick_bottom_radius*self.boost\n max_turn_speed = self._joystick_bottom_radius*self._speed_limit\n self._turn = min(excepted_turn_speed, max_turn_speed)\n self._actual_joystick_position += Coordinates(self._turn, self._forward)\n self.callback(self._actual_joystick_position)","title":""},{"docid":"af49b7e2c6f8fd56f6faa8a811b23cd9","score":"0.62648904","text":"def turtle_right():\n\tx = turtle.xcor()\n\tx += 50\n\tturtle.setx(x)","title":""},{"docid":"94ea3b57c85006405fc2ef352a709f1c","score":"0.62537396","text":"def follow_right_side(self, direction):\n\n if self.look(direction+1)[0] == -1: # if can't go right\n\n if self.look(direction + 3)[0] == 0: # if can go left\n direction = direction + 3 # go left\n elif not self.look(direction)[0] == 0:\n direction = direction + 2 # go back\n else:\n direction = direction + 1 # go right\n\n return self.re_direct(direction)","title":""},{"docid":"f42d32fe3469b3519d4147113d363b2f","score":"0.6248863","text":"def set_right(self, right=True):\r\n self.right = right","title":""},{"docid":"a54cc189dadc7d8d6662a27baf3221f3","score":"0.62386817","text":"def move_right(temp):\r\n if pos[0]<475:\r\n screen.move(item, 20,0)","title":""},{"docid":"a54cc189dadc7d8d6662a27baf3221f3","score":"0.62386817","text":"def move_right(temp):\r\n if pos[0]<475:\r\n screen.move(item, 20,0)","title":""},{"docid":"424281e83bc3ab831b143142c7af7449","score":"0.62359005","text":"def move_left(self):\n self.x -= 1","title":""},{"docid":"6ff31c69586cee06ae4b3e046ff80aa2","score":"0.6217734","text":"def rotate_right(self):\n old = self.current_variant_grid.copy()\n self.current_variant_grid = []\n\n for col in range(10):\n new_row = ''\n for row in old[::-1]: # Go through rows backwards.\n new_row += row[col]\n self.current_variant_grid.append(new_row)\n\n self.current_variant += 'R'\n self.snapshot_variant()","title":""},{"docid":"82783ba063b715ce0113c3acf4a20687","score":"0.61786914","text":"def turn_left_right(self, right=True):\n if self.head_direct['x'] is 0:\n t_dir = self.head_direct['dir']\n if right:\n self.head_direct['x'] = self.speed\n self.head_direct['dir'] = 'e'\n else:\n self.head_direct['x'] = -self.speed\n self.head_direct['dir'] = 'w'\n self.head_direct['chng'] = t_dir+self.head_direct['dir'] \n self.head_direct['y'] = 0\n self.change = True","title":""},{"docid":"2bd86e818eac181de1a055ff49cbdbd8","score":"0.6168088","text":"def right(self, angle):\n self.car.turn(RoboHatEduCar.CMD_TURN_RIGHT, angle=angle)","title":""},{"docid":"96a77de9a07da9646fe8eb60dc7e7cc5","score":"0.6162483","text":"def do_right_turn(robot_name):\n global current_direction_index\n\n current_direction_index += 1\n turtleboi.right(90)\n if current_direction_index > 3:\n current_direction_index = 0\n\n return True, ' > '+robot_name+' turned right.'","title":""},{"docid":"947c6d013a6239137bd607b4daf076e1","score":"0.61555636","text":"def move_right_to_position(self, x):\n while self._center_block.x != x:\n self.move_right()","title":""},{"docid":"0bbc5211490608ce07bcf3df89593d68","score":"0.6153763","text":"def right(self):\n return Direction((self.value + 1) % 4)","title":""},{"docid":"fe7dfef740518dfe1e2acab98b91aad5","score":"0.6148186","text":"def move_player_right_stop(state):\n state[GameConstants.MOVE] -= 1","title":""},{"docid":"d6184b8779df2dd90bfcf57e83c54226","score":"0.61448944","text":"def rotateRight(self):\n\t\tnewBoard = [[row[i] for row in self._boardArray] for i in range(self._size)]\n\t\tself._boardArray = newBoard","title":""},{"docid":"938968d4b30446bb8b3b2cca91c76773","score":"0.61303926","text":"def setMovingRight(self, value):\n self.__moving_right = value","title":""},{"docid":"a21ca367a9120f7fee760ea08626fd23","score":"0.6128651","text":"def _right(self,i):\n return 2 * i + 2","title":""},{"docid":"386b1eb0ff285fa230adbcc9f3b477ea","score":"0.6119397","text":"def set_move_right(self, b):\n self.__move_right = b","title":""},{"docid":"e7e5552ceac6c36188e36c510c4a80c4","score":"0.61143905","text":"def setRight(self, r):\n self.__right = r","title":""},{"docid":"569e3ee45d769811fbbe8b2cdf436166","score":"0.61020255","text":"def right(self, i):\n return 2 * i + 2","title":""},{"docid":"06b2689c3ea26f35ea6dbe2a58956efe","score":"0.6097891","text":"def OnRightEvent(self, event):\n self.click = 'RIGHT'\n self.ProcessClick(event)","title":""},{"docid":"68f33c67435e74412d6c1e1a5101f0ac","score":"0.6093975","text":"def right(self, right):\n\n self._right = right","title":""},{"docid":"68f33c67435e74412d6c1e1a5101f0ac","score":"0.6093975","text":"def right(self, right):\n\n self._right = right","title":""},{"docid":"6c0a48360c694ff04de55549522f2bbc","score":"0.60925967","text":"def move_right(self, board, direction='d'):\n orignal_x = self.x_pos\n oendx = self.endx\n orignal_y = self.y_pos\n oendy = self.endy\n self.y_pos += 1\n self.endy += 1\n return_value_move = move_maadi(self, board, direction)\n if return_value_move == 1:\n board.bufferboard[orignal_x:oendx, orignal_y:oendy] = \"\"\n board.bufferboard[self.x_pos:self.endx, self.y_pos:self.endy] = self.struct\n return True\n elif return_value_move == 0:\n self.x_pos = orignal_x\n self.endx = oendx\n self.y_pos = orignal_y\n self.endy = oendy\n return False","title":""},{"docid":"0952dd58425ec7c0bca27fde2131a8ff","score":"0.6087421","text":"def right(self):\n return self.place(self.location, (self.facing + 0.5) % 2, self.table)","title":""},{"docid":"127a18beb9b6833ccc59e9d05e618455","score":"0.60837144","text":"def move_right(self, step_size):\n # begin homework 2 : problem 2\n # Flip the coin...\n rand = np.random.uniform(0.0, 1.0) # Get random float between 0-1\n # Determine whether to move left, right, or stay put\n if rand < self.prob_move_right_if_right:\n# print('Move right')\n step_size = step_size\n elif rand < (self.prob_move_left_if_right+self.prob_move_right_if_right): # Check if should really move left\n# print('Move left')\n step_size = -step_size\n else:\n# print('Stay put')\n step_size = 0\n # end homework 2 : problem 2\n return self._move_(step_size)","title":""},{"docid":"4e3468b6ec616593a838455c2091a47b","score":"0.60663515","text":"def right(piece, size=1):\n\t\treturn Move(piece, abs(size), 0)","title":""},{"docid":"16a4385439c0c45d7db32cd987931ed3","score":"0.6044891","text":"def move_all_right(self, start_col, how_much = 1):\n for shift_num in range(0, self.width - start_col - how_much ):\n for row_num in range(0, self.height):\n self.move_elem( [row_num, self.width - 1 - shift_num - how_much] , [row_num, self.width - 1 - shift_num])","title":""},{"docid":"d03bf9b331a09788e4e16d2597e67bcd","score":"0.60428387","text":"def set_right(self, right):\n self.right = right\n self.update()","title":""},{"docid":"d03bf9b331a09788e4e16d2597e67bcd","score":"0.60428387","text":"def set_right(self, right):\n self.right = right\n self.update()","title":""},{"docid":"58ef0b26d046f777757ed4a26f1f2920","score":"0.60296077","text":"def shift_right(self):\n self.left.append(self.head)\n if self.right:\n self.head = self.right.pop()\n else:\n self.head = self.blank_symbol","title":""},{"docid":"3ce59e538e2085bc686cdb328451cabe","score":"0.6025403","text":"def right(self):\n return self.translate(1, 0)","title":""},{"docid":"d0dcc8740211ed54050b3e27d7eae5b6","score":"0.6023065","text":"def right(self):\n self.lband[0:0] = self.rband[0:1] # insert first element of r to l\n self.rband[0:1] = [] # remove first element of r\n if self.rband == []:\n self.rband = [0]","title":""},{"docid":"2d4a2d2d52b3369a3c5cb35459fc48be","score":"0.6022119","text":"def RightActions(self):\n if self.direction == 'U':\n self.actions += ['R', 'F']\n elif self.direction == 'L':\n self.actions += ['R', 'R', 'F']\n elif self.direction == 'D':\n self.actions += ['L', 'F']\n else:\n self.actions.append('F')","title":""},{"docid":"f4554cbac68699825c74445b2730a4ea","score":"0.6017823","text":"def _right(self):\n self._band.right()","title":""},{"docid":"6a5875835d4a5481fa3f5c125163cb14","score":"0.59816206","text":"def right(self, right: List[RightElement]):\n\n self._right = right","title":""},{"docid":"3abc7b40ecd2c7b20a6c291c85a9546b","score":"0.59738564","text":"def move_right(self):\n copy = self.config[:]\n arg = copy.index(0)\n if (arg+1)%3 == 0:\n return None\n copy[arg] = copy[arg+1]\n copy[arg+1] = 0\n return PuzzleState(copy,self.n,parent = self,action=\"Right\",cost = self.cost+1)","title":""},{"docid":"c28ad6b8732a92f7d08a22e090fc7947","score":"0.5971885","text":"def turn_around():\n for i in range(2):\n turn_left()","title":""},{"docid":"32f44f9965eae252e4935966b466eced","score":"0.59695727","text":"def sh_right(self):\n if self.pointer == len(self.buf)-1:\n self.buf.append(0)\n self.pointer += 1","title":""},{"docid":"1c0057cfa247bd991827d5bce2a04c27","score":"0.5965794","text":"def set_right(self, right):\n self.right = right","title":""},{"docid":"a50a8d225af790037fecdab3adc6d262","score":"0.59386617","text":"def rotate_right(self):\n self.state = np.rot90(self.state, 3)\n return self","title":""},{"docid":"19a619d522415a4b1da396efea9f1aa6","score":"0.5929927","text":"def turnRight(self, angle=None):\n self.parent.turnRight(angle)","title":""}],"string":"[\n {\n \"docid\": \"323e0c73ed9e7793c55a6f67e0c32085\",\n \"score\": \"0.80261123\",\n \"text\": \"def move_aliens_right(self):\\n self.x += ALIEN_H_WALK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea375b75597fc52cec7d2c55d54c02d\",\n \"score\": \"0.75233734\",\n \"text\": \"def _moveRight(self,list):\\n for row in list:\\n for alien in row:\\n if alien != None:\\n alien.right += ALIEN_H_WALK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3eb2dd7b58ff345a9fd371393b81189\",\n \"score\": \"0.7446899\",\n \"text\": \"def move_right(self):\\n self.x += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de8b3cf988a87d2c56f4c085f4cbe86\",\n \"score\": \"0.7301645\",\n \"text\": \"def turn_right(self):\\n self.perform_action(actions.RIGHT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227afe5371c2997e2b523a503cb8ae02\",\n \"score\": \"0.72393227\",\n \"text\": \"def turnRight(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a2ab0935826bd3514be1b20c7c1befe\",\n \"score\": \"0.7196056\",\n \"text\": \"def go_right(self):\\n self.change_x = 4\\n self.direction = \\\"R\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2360cae620e7f20d2c4a4003ba0b8eb6\",\n \"score\": \"0.71806276\",\n \"text\": \"def go_right(self):\\n self.change_x = 6\\n self.direction = \\\"R\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b19aba17d17d42c113e8eb733163ff1\",\n \"score\": \"0.7167389\",\n \"text\": \"def turn_right(self):\\n self.direction = self.direction.right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a07b891f9b47ebe4e70bcb28a679373\",\n \"score\": \"0.7163237\",\n \"text\": \"def shift_right(self):\\n self.x_pos += self.rightward_shift\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d90d906c0cbb3afdf92bd2a9da05e5d\",\n \"score\": \"0.7150836\",\n \"text\": \"def go_right(self):\\r\\n \\r\\n \\r\\n \\r\\n self.change_x = 3\\r\\n self.direction = \\\"R\\\"\\r\\n \\r\\n self.walking = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930cfbffdddca7cd122577c884c53f1c\",\n \"score\": \"0.7147136\",\n \"text\": \"def go_right(self):\\n self.change_x = 5\\n self.direction = \\\"R\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad9c702503382a02d29119dfa20f329\",\n \"score\": \"0.71310544\",\n \"text\": \"def move_right(self):\\n self.steps += \\\"r\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0618d474c53b4bdb3be08ef390554fca\",\n \"score\": \"0.7077225\",\n \"text\": \"def turnRight(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0618d474c53b4bdb3be08ef390554fca\",\n \"score\": \"0.7077225\",\n \"text\": \"def turnRight(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612c330fa2fbbde179b2f7ba0538e6d9\",\n \"score\": \"0.7049387\",\n \"text\": \"def turn_right(self):\\n if self._direction == 'NORTH':\\n self._direction = 'EAST'\\n return\\n if self._direction == 'SOUTH':\\n self._direction = 'WEST'\\n return\\n if self._direction == 'EAST':\\n self._direction = 'SOUTH'\\n return\\n if self._direction == 'WEST':\\n self._direction = 'NORTH'\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46906bda119f30f91e5aaa85efd6e110\",\n \"score\": \"0.69984835\",\n \"text\": \"def moveRightby1(self):\\n self.addTarget([(self.x + 96, self.y)])\\n self.setIndex(self.index + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78d790ca73f44055ceeb97f27b053bd\",\n \"score\": \"0.6969685\",\n \"text\": \"def turn_right(self):\\n\\t\\tif self.direction == self.north:\\n\\t\\t\\tself.direction = self.east\\n\\t\\telif self.direction == self.east:\\n\\t\\t\\tself.direction = self.south\\n\\t\\telif self.direction == self.south:\\n\\t\\t\\tself.direction = self.west\\n\\t\\telif self.direction == self.west:\\n\\t\\t\\tself.direction = self.north\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1024a96e65dec7d7e9b5994b9b657f7\",\n \"score\": \"0.696408\",\n \"text\": \"def go_right(self):\\n self.vel_x = 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c640b59f64dbbc6daf290d291e4afdf7\",\n \"score\": \"0.69594675\",\n \"text\": \"def move_right(self):\\n for block in self.blocks:\\n block.move_right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d35075befd22d44fa2f96c4d6c2a8192\",\n \"score\": \"0.6941765\",\n \"text\": \"def go_right(self):\\n self.change_x = self.speed\\n self.direction = 'r'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad8ce92c424f79671957649daafc8b7\",\n \"score\": \"0.6869159\",\n \"text\": \"def move_aliens_left(self):\\n self.x -= ALIEN_H_WALK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5686caf1348d4097f372939773f23cda\",\n \"score\": \"0.68576485\",\n \"text\": \"def rotate_right(self):\\r\\n\\r\\n if self.direction == 'N':\\r\\n self.direction = 'E'\\r\\n elif self.direction == 'S':\\r\\n self.direction = 'W'\\r\\n elif self.direction == 'W':\\r\\n self.direction = 'N'\\r\\n else:\\r\\n self.direction = 'S'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf92511412746a2538d37d8b31e536b6\",\n \"score\": \"0.6848221\",\n \"text\": \"def go_right(self):\\n self.change_x = 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf92511412746a2538d37d8b31e536b6\",\n \"score\": \"0.6848221\",\n \"text\": \"def go_right(self):\\n self.change_x = 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9a6d5d050167c3cecd638d946de488\",\n \"score\": \"0.67971265\",\n \"text\": \"def move_right(self) -> None:\\n if self.velocity == (-VELOCITY_NORM, 0):\\n return\\n self.velocity = (VELOCITY_NORM, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c6602a1d12f6a801b106136f304924\",\n \"score\": \"0.67806274\",\n \"text\": \"def move_right(self):\\n self.score += 0.02\\n if self.map[self.hero.position[1]][self.hero.position[0] + 1] == Service.wall:\\n return\\n self.hero.right()\\n self.interact()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71bd4c5d9c461e0cca51af2a169d67c5\",\n \"score\": \"0.67345506\",\n \"text\": \"def right():\\n current_face_index = directions.index(robot_location[2])\\n robot_location[2] = directions[current_face_index + 1] \\\\\\n if current_face_index < 3 else directions[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a37a07e302c03fdc0842a92ea8e61d2\",\n \"score\": \"0.6722755\",\n \"text\": \"def turn_right():\\n turn_left()\\n turn_left()\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbc160aaff44ca34af42e3e07002bb5c\",\n \"score\": \"0.6699034\",\n \"text\": \"def move_right(self):\\n return self.__move_right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe9cce2596eb36179ac95da76520c2d3\",\n \"score\": \"0.669735\",\n \"text\": \"def move_right(self, ship):\\n pygame.event.post(Event(pygame.KEYDOWN, key=pygame.K_RIGHT))\\n gf.check_events(self.ai_settings, self.screen, self.stats,\\n self.play_button, self.ship, self.aliens, self.bullets)\\n self.ship.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b99ce1237605e2f1604eb72443b923d7\",\n \"score\": \"0.6645299\",\n \"text\": \"def _do_turn_right(self):\\n self.board_state.turn_right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c8306b51fc254fc01ca2bbc1b94e3c\",\n \"score\": \"0.6589981\",\n \"text\": \"def move_right(self):\\n self.lcd_byte(\\n self.LCD_CURSORSHIFT | self.LCD_DISPLAYMOVE | self.LCD_MOVERIGHT,\\n self.LCD_CMD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c88c920ee7b47ca0d5fb0297db02929\",\n \"score\": \"0.65388656\",\n \"text\": \"def turn_right(self):\\n if self.head_direction_x == 1:\\n self.head_direction_x = 0\\n self.head_direction_y = 1\\n elif self.head_direction_y == 1:\\n self.head_direction_x = -1\\n self.head_direction_y = 0\\n elif self.head_direction_x == -1:\\n self.head_direction_x = 0\\n self.head_direction_y = -1\\n else:\\n self.head_direction_x = 1\\n self.head_direction_y = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19cce7580b45de3a2260f26088677f4\",\n \"score\": \"0.65274364\",\n \"text\": \"def right(self):\\n if RIGHT in self.possible_moves():\\n self.state = self.__right()\\n self.__fill_random_field()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae7b5c61602c9774b1f30050e11c36c\",\n \"score\": \"0.6526534\",\n \"text\": \"def turn_right(self, node):\\n dirs = ['N', 'E', 'S', 'W']\\n return dirs[(dirs.index(node.direction)+1) % len(dirs)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1d3e90f51611bce4a796985713b7da\",\n \"score\": \"0.65255666\",\n \"text\": \"def right(self):\\n right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60588b2825d9ae014b60d09f034f5204\",\n \"score\": \"0.65144587\",\n \"text\": \"def moveRight(self):\\n # Check if not too close to the rightside wall, and not moving too fast\\n if self.x < 497 and self.xVelocity < 8:\\n self.xVelocity += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4113759d5ab6c2c5934b7313459033e5\",\n \"score\": \"0.6506617\",\n \"text\": \"def move_right(self):\\n # global continue_up, continue_down, continue_right, continue_left\\n self.continue_up = 0\\n self.continue_down = 0\\n self.continue_left = 0\\n if self.current_line.text and self.current_line.number == self.lines.total:\\n self.lines.add() # create emtpy line\\n\\n try: # if tab, move 4 spaces\\n if self.current_line.x - 6 < self.current_line.indentation and \\\\\\n self.current_line.text[self.current_line.x - 6:self.current_line.x - 6 + 4] == ' ' and \\\\\\n self.current_line.y == self.current_line.end_y:\\n self.current_line.x += 4\\n return\\n except BareException:\\n pass\\n\\n if self.config['cursor_acceleration']:\\n move_rate = min(self.config['cursor_max_horizontal_speed'], int(self.continue_right / 10.0) + 1)\\n else:\\n move_rate = 1\\n self.continue_right += 1\\n self.current_line.x += move_rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100faba998257426442eb38177fcde17\",\n \"score\": \"0.6504694\",\n \"text\": \"def robot_right(self):\\r\\n\\t self.x = self.x + 1\\r\\n\\t if self.x > 9:\\r\\n\\t\\t self.x = 9\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9813eee101b294e426a70bf04f35a7f0\",\n \"score\": \"0.649412\",\n \"text\": \"def right(self):\\n for col in self.tiles:\\n for row in reversed(col):\\n if row:\\n row.slide(self, (0,1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e344f4b6f2514e59960bfb695221465a\",\n \"score\": \"0.64802814\",\n \"text\": \"def right(self):\\n return self.place(self.direction, (self.facing + 0.5)%2, self.grid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeeffd80450377fa380ff319da45b9bc\",\n \"score\": \"0.6478905\",\n \"text\": \"def MoveRight(self):\\n self.location += pygame.Vector2(5, 0)\\n self.ConfirmMove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d25c0372f8b94b11008b2109d2b9240\",\n \"score\": \"0.64629257\",\n \"text\": \"def rightDirection(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf24a7f7fcd6c0155816859aafd2514\",\n \"score\": \"0.64615583\",\n \"text\": \"def go_right(self):\\r\\n self.change_x = 6\\r\\n if current_level_no == 5:\\r\\n self.change_x -=12\\r\\n self.direction=\\\"R\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9260803436090954f1bfd40b816254ff\",\n \"score\": \"0.6449436\",\n \"text\": \"def go_right(self): \\n self.change_x = 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7858553e417b209e086e6792172642\",\n \"score\": \"0.64476615\",\n \"text\": \"def go_right(self):\\n if self.location.x != (Game.SIZE - 1):\\n self.location.x += 1\\n else:\\n self.location.x = 0\\n return self.location.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e0f498b98dea694caef9e24ebebc6ff\",\n \"score\": \"0.64418066\",\n \"text\": \"def turn_right(self) -> \\\"Direction\\\":\\n index = DIRECTIONS.index(self)\\n return DIRECTIONS[(index + 1) % len(DIRECTIONS)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7639abb6583b271ee4efd9036e0288\",\n \"score\": \"0.64379036\",\n \"text\": \"def move_aliens_down(self):\\n self.y -= ALIEN_V_WALK/ALIEN_ROWS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045b2a9756e75a1a4d68d6ea9aaab914\",\n \"score\": \"0.643437\",\n \"text\": \"def move_right(self):\\r\\n self.speedX = 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169c0f42ef3e2aa663d1c430dbfa48a4\",\n \"score\": \"0.64147055\",\n \"text\": \"def step_right(self):\\n if self.o == AgentOrientation.UP:\\n return AgentPosition(self.x + 1, self.y, self.o)\\n if self.o == AgentOrientation.RIGHT:\\n return AgentPosition(self.x, self.y + 1, self.o)\\n if self.o == AgentOrientation.DOWN:\\n return AgentPosition(self.x - 1, self.y, self.o)\\n if self.o == AgentOrientation.LEFT:\\n return AgentPosition(self.x, self.y - 1, self.o)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b46264681641ccbf3e5f8dd0583575b\",\n \"score\": \"0.6411701\",\n \"text\": \"def alien_down(self):\\n for alien_col in self._aliens:\\n for alien in alien_col:\\n if alien != None:\\n alien.y = alien.y - ALIEN_V_WALK\\n if self._direction == 2:\\n self._direction = -1\\n elif self._direction == -2:\\n self._direction = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b721f45f8b16749cee62f63de26db5\",\n \"score\": \"0.63975936\",\n \"text\": \"def indent_right(self):\\n self._indent_num += 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c361c8fa89b710e8228edc1df27edea2\",\n \"score\": \"0.63844\",\n \"text\": \"def right(self):\\n\\n self._robot_check()\\n self.hRobotEngine.rot('Right')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10db3fcd9109bf2987b31a10a7ad1a36\",\n \"score\": \"0.6371906\",\n \"text\": \"def move_right(self):\\n self._time += 1\\n if self._position < len(self._list) - 1:\\n self._position += 1\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f38a970a46fa00141531fe2bd3390c9\",\n \"score\": \"0.6366915\",\n \"text\": \"def keyboard_right(self, _):\\n self.state = g.update_keyboard(g.KeyPress.RIGHT, self.state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe34597ab11f596e3451cadd6c5d9a5\",\n \"score\": \"0.6365901\",\n \"text\": \"def turn_around():\\n\\tturn_right()\\n\\tturn_right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a86b8fbe811428a2a432301541ef65d\",\n \"score\": \"0.63468903\",\n \"text\": \"def move_player_right(state):\\n state[GameConstants.MOVE] = min(state[GameConstants.MOVE] + 1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d2d53037254123a1ef6da6b28a1d95\",\n \"score\": \"0.6317262\",\n \"text\": \"def right(self):\\n self.set_motor_dps(self.MOTOR_LEFT, self.get_speed())\\n self.set_motor_dps(self.MOTOR_RIGHT, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"759e5787551ab335f93548ecd20f69d9\",\n \"score\": \"0.6305845\",\n \"text\": \"def push_right(self):\\n excepted_turn_speed = self._turn + self._joystick_bottom_radius*self.boost\\n max_turn_speed = self._joystick_bottom_radius*self._speed_limit\\n self._turn = min(excepted_turn_speed, max_turn_speed)\\n self._actual_joystick_position += Coordinates(self._turn, self._forward)\\n self.callback(self._actual_joystick_position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af49b7e2c6f8fd56f6faa8a811b23cd9\",\n \"score\": \"0.62648904\",\n \"text\": \"def turtle_right():\\n\\tx = turtle.xcor()\\n\\tx += 50\\n\\tturtle.setx(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94ea3b57c85006405fc2ef352a709f1c\",\n \"score\": \"0.62537396\",\n \"text\": \"def follow_right_side(self, direction):\\n\\n if self.look(direction+1)[0] == -1: # if can't go right\\n\\n if self.look(direction + 3)[0] == 0: # if can go left\\n direction = direction + 3 # go left\\n elif not self.look(direction)[0] == 0:\\n direction = direction + 2 # go back\\n else:\\n direction = direction + 1 # go right\\n\\n return self.re_direct(direction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f42d32fe3469b3519d4147113d363b2f\",\n \"score\": \"0.6248863\",\n \"text\": \"def set_right(self, right=True):\\r\\n self.right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a54cc189dadc7d8d6662a27baf3221f3\",\n \"score\": \"0.62386817\",\n \"text\": \"def move_right(temp):\\r\\n if pos[0]<475:\\r\\n screen.move(item, 20,0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a54cc189dadc7d8d6662a27baf3221f3\",\n \"score\": \"0.62386817\",\n \"text\": \"def move_right(temp):\\r\\n if pos[0]<475:\\r\\n screen.move(item, 20,0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424281e83bc3ab831b143142c7af7449\",\n \"score\": \"0.62359005\",\n \"text\": \"def move_left(self):\\n self.x -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ff31c69586cee06ae4b3e046ff80aa2\",\n \"score\": \"0.6217734\",\n \"text\": \"def rotate_right(self):\\n old = self.current_variant_grid.copy()\\n self.current_variant_grid = []\\n\\n for col in range(10):\\n new_row = ''\\n for row in old[::-1]: # Go through rows backwards.\\n new_row += row[col]\\n self.current_variant_grid.append(new_row)\\n\\n self.current_variant += 'R'\\n self.snapshot_variant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82783ba063b715ce0113c3acf4a20687\",\n \"score\": \"0.61786914\",\n \"text\": \"def turn_left_right(self, right=True):\\n if self.head_direct['x'] is 0:\\n t_dir = self.head_direct['dir']\\n if right:\\n self.head_direct['x'] = self.speed\\n self.head_direct['dir'] = 'e'\\n else:\\n self.head_direct['x'] = -self.speed\\n self.head_direct['dir'] = 'w'\\n self.head_direct['chng'] = t_dir+self.head_direct['dir'] \\n self.head_direct['y'] = 0\\n self.change = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd86e818eac181de1a055ff49cbdbd8\",\n \"score\": \"0.6168088\",\n \"text\": \"def right(self, angle):\\n self.car.turn(RoboHatEduCar.CMD_TURN_RIGHT, angle=angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96a77de9a07da9646fe8eb60dc7e7cc5\",\n \"score\": \"0.6162483\",\n \"text\": \"def do_right_turn(robot_name):\\n global current_direction_index\\n\\n current_direction_index += 1\\n turtleboi.right(90)\\n if current_direction_index > 3:\\n current_direction_index = 0\\n\\n return True, ' > '+robot_name+' turned right.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947c6d013a6239137bd607b4daf076e1\",\n \"score\": \"0.61555636\",\n \"text\": \"def move_right_to_position(self, x):\\n while self._center_block.x != x:\\n self.move_right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbc5211490608ce07bcf3df89593d68\",\n \"score\": \"0.6153763\",\n \"text\": \"def right(self):\\n return Direction((self.value + 1) % 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7dfef740518dfe1e2acab98b91aad5\",\n \"score\": \"0.6148186\",\n \"text\": \"def move_player_right_stop(state):\\n state[GameConstants.MOVE] -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6184b8779df2dd90bfcf57e83c54226\",\n \"score\": \"0.61448944\",\n \"text\": \"def rotateRight(self):\\n\\t\\tnewBoard = [[row[i] for row in self._boardArray] for i in range(self._size)]\\n\\t\\tself._boardArray = newBoard\",\n \"title\": \"\"\n },\n {\n \"docid\": \"938968d4b30446bb8b3b2cca91c76773\",\n \"score\": \"0.61303926\",\n \"text\": \"def setMovingRight(self, value):\\n self.__moving_right = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a21ca367a9120f7fee760ea08626fd23\",\n \"score\": \"0.6128651\",\n \"text\": \"def _right(self,i):\\n return 2 * i + 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386b1eb0ff285fa230adbcc9f3b477ea\",\n \"score\": \"0.6119397\",\n \"text\": \"def set_move_right(self, b):\\n self.__move_right = b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e5552ceac6c36188e36c510c4a80c4\",\n \"score\": \"0.61143905\",\n \"text\": \"def setRight(self, r):\\n self.__right = r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569e3ee45d769811fbbe8b2cdf436166\",\n \"score\": \"0.61020255\",\n \"text\": \"def right(self, i):\\n return 2 * i + 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b2689c3ea26f35ea6dbe2a58956efe\",\n \"score\": \"0.6097891\",\n \"text\": \"def OnRightEvent(self, event):\\n self.click = 'RIGHT'\\n self.ProcessClick(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f33c67435e74412d6c1e1a5101f0ac\",\n \"score\": \"0.6093975\",\n \"text\": \"def right(self, right):\\n\\n self._right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f33c67435e74412d6c1e1a5101f0ac\",\n \"score\": \"0.6093975\",\n \"text\": \"def right(self, right):\\n\\n self._right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c0a48360c694ff04de55549522f2bbc\",\n \"score\": \"0.60925967\",\n \"text\": \"def move_right(self, board, direction='d'):\\n orignal_x = self.x_pos\\n oendx = self.endx\\n orignal_y = self.y_pos\\n oendy = self.endy\\n self.y_pos += 1\\n self.endy += 1\\n return_value_move = move_maadi(self, board, direction)\\n if return_value_move == 1:\\n board.bufferboard[orignal_x:oendx, orignal_y:oendy] = \\\"\\\"\\n board.bufferboard[self.x_pos:self.endx, self.y_pos:self.endy] = self.struct\\n return True\\n elif return_value_move == 0:\\n self.x_pos = orignal_x\\n self.endx = oendx\\n self.y_pos = orignal_y\\n self.endy = oendy\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0952dd58425ec7c0bca27fde2131a8ff\",\n \"score\": \"0.6087421\",\n \"text\": \"def right(self):\\n return self.place(self.location, (self.facing + 0.5) % 2, self.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127a18beb9b6833ccc59e9d05e618455\",\n \"score\": \"0.60837144\",\n \"text\": \"def move_right(self, step_size):\\n # begin homework 2 : problem 2\\n # Flip the coin...\\n rand = np.random.uniform(0.0, 1.0) # Get random float between 0-1\\n # Determine whether to move left, right, or stay put\\n if rand < self.prob_move_right_if_right:\\n# print('Move right')\\n step_size = step_size\\n elif rand < (self.prob_move_left_if_right+self.prob_move_right_if_right): # Check if should really move left\\n# print('Move left')\\n step_size = -step_size\\n else:\\n# print('Stay put')\\n step_size = 0\\n # end homework 2 : problem 2\\n return self._move_(step_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3468b6ec616593a838455c2091a47b\",\n \"score\": \"0.60663515\",\n \"text\": \"def right(piece, size=1):\\n\\t\\treturn Move(piece, abs(size), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a4385439c0c45d7db32cd987931ed3\",\n \"score\": \"0.6044891\",\n \"text\": \"def move_all_right(self, start_col, how_much = 1):\\n for shift_num in range(0, self.width - start_col - how_much ):\\n for row_num in range(0, self.height):\\n self.move_elem( [row_num, self.width - 1 - shift_num - how_much] , [row_num, self.width - 1 - shift_num])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03bf9b331a09788e4e16d2597e67bcd\",\n \"score\": \"0.60428387\",\n \"text\": \"def set_right(self, right):\\n self.right = right\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03bf9b331a09788e4e16d2597e67bcd\",\n \"score\": \"0.60428387\",\n \"text\": \"def set_right(self, right):\\n self.right = right\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ef0b26d046f777757ed4a26f1f2920\",\n \"score\": \"0.60296077\",\n \"text\": \"def shift_right(self):\\n self.left.append(self.head)\\n if self.right:\\n self.head = self.right.pop()\\n else:\\n self.head = self.blank_symbol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce59e538e2085bc686cdb328451cabe\",\n \"score\": \"0.6025403\",\n \"text\": \"def right(self):\\n return self.translate(1, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0dcc8740211ed54050b3e27d7eae5b6\",\n \"score\": \"0.6023065\",\n \"text\": \"def right(self):\\n self.lband[0:0] = self.rband[0:1] # insert first element of r to l\\n self.rband[0:1] = [] # remove first element of r\\n if self.rband == []:\\n self.rband = [0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4a2d2d52b3369a3c5cb35459fc48be\",\n \"score\": \"0.6022119\",\n \"text\": \"def RightActions(self):\\n if self.direction == 'U':\\n self.actions += ['R', 'F']\\n elif self.direction == 'L':\\n self.actions += ['R', 'R', 'F']\\n elif self.direction == 'D':\\n self.actions += ['L', 'F']\\n else:\\n self.actions.append('F')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4554cbac68699825c74445b2730a4ea\",\n \"score\": \"0.6017823\",\n \"text\": \"def _right(self):\\n self._band.right()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5875835d4a5481fa3f5c125163cb14\",\n \"score\": \"0.59816206\",\n \"text\": \"def right(self, right: List[RightElement]):\\n\\n self._right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abc7b40ecd2c7b20a6c291c85a9546b\",\n \"score\": \"0.59738564\",\n \"text\": \"def move_right(self):\\n copy = self.config[:]\\n arg = copy.index(0)\\n if (arg+1)%3 == 0:\\n return None\\n copy[arg] = copy[arg+1]\\n copy[arg+1] = 0\\n return PuzzleState(copy,self.n,parent = self,action=\\\"Right\\\",cost = self.cost+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28ad6b8732a92f7d08a22e090fc7947\",\n \"score\": \"0.5971885\",\n \"text\": \"def turn_around():\\n for i in range(2):\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f44f9965eae252e4935966b466eced\",\n \"score\": \"0.59695727\",\n \"text\": \"def sh_right(self):\\n if self.pointer == len(self.buf)-1:\\n self.buf.append(0)\\n self.pointer += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0057cfa247bd991827d5bce2a04c27\",\n \"score\": \"0.5965794\",\n \"text\": \"def set_right(self, right):\\n self.right = right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a50a8d225af790037fecdab3adc6d262\",\n \"score\": \"0.59386617\",\n \"text\": \"def rotate_right(self):\\n self.state = np.rot90(self.state, 3)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a619d522415a4b1da396efea9f1aa6\",\n \"score\": \"0.5929927\",\n \"text\": \"def turnRight(self, angle=None):\\n self.parent.turnRight(angle)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994286,"cells":{"query_id":{"kind":"string","value":"71f970be8e50de1a97900e71ccd59979"},"query":{"kind":"string","value":"Specifies the clear text password used to encrypt traffic. This field will not be displayed."},"positive_passages":{"kind":"list like","value":[{"docid":"08db6c2f6f5e31900a83b8ab61939029","score":"0.6029469","text":"def privacy_password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"privacy_password\")","title":""}],"string":"[\n {\n \"docid\": \"08db6c2f6f5e31900a83b8ab61939029\",\n \"score\": \"0.6029469\",\n \"text\": \"def privacy_password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"privacy_password\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8175e7a43331357572d476380ddec002","score":"0.7123115","text":"def passwordInClear(self):","title":""},{"docid":"0dacc1dd60134b989efcff3f93d26faa","score":"0.69737387","text":"def password(self):\n raise AttributeError('password: write-only field')","title":""},{"docid":"9c5629f9e0fcad538c12ddfc799a02be","score":"0.6960299","text":"def password(self, value):\n self._password = value","title":""},{"docid":"4b9baffd9451afbbaabb2830e4decc1c","score":"0.6940211","text":"def get_cleartext_password(self):\n if self.umkp:\n return self.umkp.get_cleartext_password()","title":""},{"docid":"47586b03b3c7fde85048ae3d27f5a439","score":"0.68972176","text":"def password(self, value):\n self._auth_password = value","title":""},{"docid":"a4dc1c884ec6e0d2b28b6dd626368e49","score":"0.68680084","text":"def password(self):\n del self._password","title":""},{"docid":"0517788eb27ae947d53043fb5fcf3273","score":"0.6854975","text":"def password(self):\n raise AttributeError('la contrasena no es valida.')","title":""},{"docid":"d324132960eeb88f2296651ede8a86c7","score":"0.6772423","text":"def password(self):\n raise AttributeError(\"For security purposes, 'password' is not a readable attribute.\")","title":""},{"docid":"e43a352067a26710a23e139fa84efbd8","score":"0.6769146","text":"def PASSWORD(self):\n return \"PASSWORD\"","title":""},{"docid":"dc63dcc84b26a71d79a5d9a7956bae8a","score":"0.6766925","text":"def show_pwd_encrypt(self):\r\n if self.ent_encrypt_pwd[\"show\"] == \"*\":\r\n self.ent_encrypt_pwd[\"show\"] = \"\"\r\n self.ent_encrypt_confirm[\"show\"] = \"\"\r\n else:\r\n self.ent_encrypt_pwd[\"show\"] = \"*\"\r\n self.ent_encrypt_confirm[\"show\"] = \"*\"","title":""},{"docid":"dcb920963e64c088d7cef666a2eeec7c","score":"0.67080224","text":"def set_unusable_password(self):\n\t\tself._password = make_password(None)","title":""},{"docid":"e1ca76ece58a4bf305535aee50a00999","score":"0.66501784","text":"def password(self) -> str:\n return pulumi.get(self, \"password\")","title":""},{"docid":"c6cc0cdf3a5f9ebeeb93fe02c13c9c4e","score":"0.6592658","text":"def password (self): \n return self.cfg['password']","title":""},{"docid":"fc5c36efb52ed3129386db42a913d0aa","score":"0.6578044","text":"def set_Password(self, value):\n InputSet._set_input(self, 'Password', value)","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"76ed14dfcc14b7ef6ad411e4175e58bb","score":"0.65679425","text":"def password(self) -> Optional[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"445bb3c353e689747ce70a12f99381bc","score":"0.6555305","text":"def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"445bb3c353e689747ce70a12f99381bc","score":"0.6555305","text":"def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"445bb3c353e689747ce70a12f99381bc","score":"0.6555305","text":"def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"445bb3c353e689747ce70a12f99381bc","score":"0.6555305","text":"def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"445bb3c353e689747ce70a12f99381bc","score":"0.6555305","text":"def password(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"0ef4b55353ce362b779e8e5a6209c716","score":"0.655324","text":"def password(self, password):\n\n self._password = password","title":""},{"docid":"0ef4b55353ce362b779e8e5a6209c716","score":"0.655324","text":"def password(self, password):\n\n self._password = password","title":""},{"docid":"0ef4b55353ce362b779e8e5a6209c716","score":"0.655324","text":"def password(self, password):\n\n self._password = password","title":""},{"docid":"0ef4b55353ce362b779e8e5a6209c716","score":"0.655324","text":"def password(self, password):\n\n self._password = password","title":""},{"docid":"0ef4b55353ce362b779e8e5a6209c716","score":"0.655324","text":"def password(self, password):\n\n self._password = password","title":""},{"docid":"323a7fc577f10a4898e76c85c4d5ea17","score":"0.6546978","text":"def password(self) -> str:\n return STCObject(self.proto_handle)['Password']","title":""},{"docid":"bee02f2039fb7f918d55b51c30f2f8da","score":"0.65108085","text":"def password(self):\n return self.__password","title":""},{"docid":"bee02f2039fb7f918d55b51c30f2f8da","score":"0.65108085","text":"def password(self):\n return self.__password","title":""},{"docid":"d2626513d592e0c82d0334b874fcb39f","score":"0.65080214","text":"def PASSWORD(self):\n return TYPE.NEO4J + SETTING.DELIMITER + SETTING.PASSWORD","title":""},{"docid":"8ba4de5ff7d9909453b9efa9501fbeca","score":"0.65079844","text":"def set_password(self, password):\n\t\tself.password = encrypt_password(password)","title":""},{"docid":"2dec8374fa60f1aa05926b758aad27bf","score":"0.6503956","text":"def setPassword(self, p):\n self.__password = p","title":""},{"docid":"ecaa271f3f58228753c8f0a5ef293f49","score":"0.6494732","text":"def setpassword(self, pwd):\r\n self.pwd = pwd","title":""},{"docid":"6ea16c7b2358cc2cf694ad4920f60665","score":"0.6491275","text":"def password(self):\n return self._password","title":""},{"docid":"6ea16c7b2358cc2cf694ad4920f60665","score":"0.6491275","text":"def password(self):\n return self._password","title":""},{"docid":"6ea16c7b2358cc2cf694ad4920f60665","score":"0.6491275","text":"def password(self):\n return self._password","title":""},{"docid":"6ea16c7b2358cc2cf694ad4920f60665","score":"0.6491275","text":"def password(self):\n return self._password","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"1d85f61bde4a0038c134e2fe5411f1bc","score":"0.6488742","text":"def password(self):\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"7c4f232d692d4d256e3969ba56be43eb","score":"0.6462508","text":"def password(self):\n raise AttributeError(\"Password is not a readable attribute\")","title":""},{"docid":"61b605f0d2948d93a7a928521ff2b963","score":"0.64580625","text":"def password(self):\n raise AttributeError('password is not a readable attribute')","title":""},{"docid":"90ad07e2cf05c2672cc371e0f19ccae6","score":"0.64372694","text":"def set_password(self, password):\n self.password = password","title":""},{"docid":"e3f7af3f79279da5481ea2fb1e719ef0","score":"0.64292514","text":"def password(self):\n\t\traise AttributeError('password is not a readable attribute!')","title":""},{"docid":"128bbfd919ccf95b2e052c0f1981d447","score":"0.6421736","text":"def password(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"11b069bb3ddb50a205910d92f2d99d9c","score":"0.6420136","text":"def password(self):\n\n return self._password","title":""},{"docid":"81f7209463c890fa5f23dfb9a7b8aadf","score":"0.6414876","text":"def password(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"81f7209463c890fa5f23dfb9a7b8aadf","score":"0.6414876","text":"def password(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"81f7209463c890fa5f23dfb9a7b8aadf","score":"0.6414876","text":"def password(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"57d360238bddf3eafd97ea805e1a05e7","score":"0.64063025","text":"def password(self):\r\n raise AttributeError('password is not a readable attribute.')","title":""},{"docid":"e28714a55ae3032a9de3160b1fd8a99b","score":"0.63976896","text":"def show_password(ctx, new_value):\n ctx.obj.show_password = new_value\n ctx.obj.update_conf(CONF_PATH)\n ok_msg(\"Conf updated\")","title":""},{"docid":"c6fb9c90c501790fc0a2bf58a2f23226","score":"0.63892806","text":"def set_Password(self, value):\n super(GetBookmarkInputSet, self)._set_input('Password', value)","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"7927a56e129e7e30763ce8562c963c98","score":"0.636576","text":"def password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"password\")","title":""},{"docid":"1a17ab4b015edc84a8a0acda579de480","score":"0.63433045","text":"def password(self, value):\n\t\tself._password = value\n\t\tself.passwordHash = hashlib.sha224(value).hexdigest()","title":""},{"docid":"911c3786396a0cba35f1009812c5f41d","score":"0.6325003","text":"def show_pwd_decrypt(self):\r\n if self.ent_decrypt_pwd[\"show\"] == \"*\":\r\n self.ent_decrypt_pwd[\"show\"] = \"\"\r\n else:\r\n self.ent_decrypt_pwd[\"show\"] = \"*\"","title":""},{"docid":"a2012becbcd9897edafaf6b115e4c066","score":"0.6324489","text":"def password(self) -> str:\n\n return self.__decoder(self.__password)","title":""},{"docid":"e8f861cfc2c39baf8c79443100a1b71d","score":"0.6287318","text":"def setModemPass(self, string):\n string = self.comms.setModemModePassword(string, unitCode=self.device_id)\n self.modem_pass = string\n return string","title":""},{"docid":"07cb2ff1f0b506ba62a6c6c052ebeb75","score":"0.62814856","text":"def Password(self):\n if self.force_auto_sync:\n self.get('Password')\n return self._Password","title":""},{"docid":"16ab615bd09f3cbf1c92049502438e15","score":"0.62741816","text":"def remove_password(self):\n self.password.text = \"\"\n if not self.isUserAdmin():\n self.runAsAdmin()\n else:\n username = self.get_username()\n password = \"\"\n self.set_password(username, password)","title":""},{"docid":"9fdf83a6257aae4494023cf09d2b7cfd","score":"0.61588955","text":"def get_password(self):\r\n return self.password","title":""},{"docid":"f2ce94a8daf875118f366671242dbd25","score":"0.6132136","text":"def keyword_preference_window_remove_password(self):\n self.Preferences_window_clear_password()\n self.Preferences_window_clear_confirm_password()","title":""},{"docid":"15211e583e03390fb9087da0413fde75","score":"0.6121864","text":"def getPassword():","title":""},{"docid":"cb137d00eb901a20fd39d10675d88832","score":"0.6116795","text":"def the_password(text: str) -> \"Enter\":\n return Enter.the_secret(text)","title":""},{"docid":"d858021e608ad55f733b063849eda8b3","score":"0.61085296","text":"def password(self, new_password: str) -> NoReturn:\n\n self.__password = self.__encoder(new_password)","title":""},{"docid":"b0506a2a80685c0d3e1c690512aa71e4","score":"0.61043006","text":"def set_password(self, ppass):\n self.pw = crypt.crypt(ppass, crypt.mksalt())","title":""},{"docid":"966eac0b58414ebe1a96695926d031b5","score":"0.60975856","text":"def password_plain(self, password_plain):\n self.password = generate_password_hash(password_plain)","title":""},{"docid":"aaa3965330baf27e3f93406cd0b241d2","score":"0.60685927","text":"def _set_password(self, password):\n\t\tself._password = self.__encrypt_password(password)","title":""},{"docid":"680d8769794d42cd5e39ffd5ebf9beb8","score":"0.6066814","text":"def get_password(self):\n\n return str(self.password)","title":""},{"docid":"ef61a02e0b855bffe61520e89ab79bdd","score":"0.60601413","text":"def password(self, password):\n if self._configuration.client_side_validation and password is None:\n raise ValueError(\"Invalid value for `password`, must not be `None`\") # noqa: E501\n\n self._password = password","title":""},{"docid":"65b9eedc6273d545c41d86362fe22a3b","score":"0.60594463","text":"def non_autoscale_password(self):\n return self.get_raw('non_autoscale_password')","title":""},{"docid":"be4cc0296825c714d079bdcaa5cf2213","score":"0.60508627","text":"def set_haproxy_stat_password(self):\n if not self.get_state('haproxy.stat.password'):\n password = ''.join([\n random.choice(string.ascii_letters + string.digits)\n for n in range(32)])\n self.set_state('haproxy.stat.password', password)","title":""},{"docid":"c7da1288757d71d93a107440196848b4","score":"0.6035899","text":"def create_password(self):\n\n all_chars = LETTERS\n if not self._no_digits:\n all_chars += DIGITS\n if not self._no_symbols:\n all_chars += SYMBOLS\n\n self._password = ''.join(secrets.choice(all_chars)\n for i in range(self._length))\n return self._password","title":""},{"docid":"87651f8acf71d1b9d350cd0165b8a3e4","score":"0.60280085","text":"def get_args_secure(cls):\n return [\"password\"]","title":""},{"docid":"87651f8acf71d1b9d350cd0165b8a3e4","score":"0.60280085","text":"def get_args_secure(cls):\n return [\"password\"]","title":""},{"docid":"4e990a76ed27ef988f7faf47efac6839","score":"0.6025506","text":"def change_password(self):\n self.dialbox = dialogbox.Dialog(self.gui, 'change_password')","title":""},{"docid":"a24ba60cb287944380866339735d8bce","score":"0.6016152","text":"def admin_password(self):\n return self._configFile.get(Config.PASS, None)","title":""},{"docid":"fdce9373dc9d6ea616f91fe464ffdb7b","score":"0.60037684","text":"def db_pass():\n if not env.db_pass:\n env.db_pass = getpass(\"Enter the database password: \")\n return env.db_pass","title":""},{"docid":"44c585389c128c6e1dee4bdf55e38c86","score":"0.59936064","text":"def change_password(self):\n if self.password.text:\n if not self.isUserAdmin():\n self.runAsAdmin()\n else:\n username = self.get_username()\n password = self.password.text\n self.set_password(username, password)\n self.password.text = \"\"","title":""},{"docid":"335994194983329ad42f22ebd6449498","score":"0.59891045","text":"def catalog_admin_password(self) -> Optional[pulumi.Input['SecureStringArgs']]:\n return pulumi.get(self, \"catalog_admin_password\")","title":""},{"docid":"bb2a3806ae2dfda6d7a78db39874b927","score":"0.5978722","text":"def getPassword(self): \n return self.__password","title":""},{"docid":"1f9594c00c391c61e72e9e273bdee8b5","score":"0.5974075","text":"def set_password(self, raw_password):\r\n self.password = make_password(raw_password)","title":""},{"docid":"9bff18ec5d640626a704b2eed29ea740","score":"0.59662074","text":"def password(self, password):\n\t\tself.password_hash = generate_password_hash(password)","title":""},{"docid":"c31987115530ecab8bbf8ab2997015c2","score":"0.59635246","text":"def Password(request):\n schema = schemaish.Structure()\n schema.add('Password', schemaish.String())\n\n form = formish.Form(schema, 'form')\n form['Password'].widget = formish.Password()\n return form","title":""},{"docid":"ad393f94a243976f570e15be7352472f","score":"0.5963103","text":"def test_RemovesPasswordFromConnectionSettings(self):\n assert 'password' not in self.rpc.config['connection_settings']","title":""},{"docid":"0a22efd1b4b217a87f2dbada83c3a4e7","score":"0.5957486","text":"def get_password(self):\r\n\t\treturn self.__password","title":""},{"docid":"7fb7ffd2d0d5ffcb6e4ce1c850e5ebab","score":"0.5954629","text":"def set_pw(cls, pw):\n return pw","title":""},{"docid":"e0476ff3c847cc6dee1b8f1e3c0d800c","score":"0.5954385","text":"def send_password(self, password=None):\n _pw = password or self.password\n data_set = messages.DataSet(value=_pw)\n cmd = messages.CommandMessage(command=\"P\", command_type=\"1\", data_set=data_set)\n logger.info(\"Sending password to meter\")\n self.transport.send(cmd.to_bytes())","title":""}],"string":"[\n {\n \"docid\": \"8175e7a43331357572d476380ddec002\",\n \"score\": \"0.7123115\",\n \"text\": \"def passwordInClear(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dacc1dd60134b989efcff3f93d26faa\",\n \"score\": \"0.69737387\",\n \"text\": \"def password(self):\\n raise AttributeError('password: write-only field')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c5629f9e0fcad538c12ddfc799a02be\",\n \"score\": \"0.6960299\",\n \"text\": \"def password(self, value):\\n self._password = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b9baffd9451afbbaabb2830e4decc1c\",\n \"score\": \"0.6940211\",\n \"text\": \"def get_cleartext_password(self):\\n if self.umkp:\\n return self.umkp.get_cleartext_password()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47586b03b3c7fde85048ae3d27f5a439\",\n \"score\": \"0.68972176\",\n \"text\": \"def password(self, value):\\n self._auth_password = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4dc1c884ec6e0d2b28b6dd626368e49\",\n \"score\": \"0.68680084\",\n \"text\": \"def password(self):\\n del self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0517788eb27ae947d53043fb5fcf3273\",\n \"score\": \"0.6854975\",\n \"text\": \"def password(self):\\n raise AttributeError('la contrasena no es valida.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d324132960eeb88f2296651ede8a86c7\",\n \"score\": \"0.6772423\",\n \"text\": \"def password(self):\\n raise AttributeError(\\\"For security purposes, 'password' is not a readable attribute.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43a352067a26710a23e139fa84efbd8\",\n \"score\": \"0.6769146\",\n \"text\": \"def PASSWORD(self):\\n return \\\"PASSWORD\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc63dcc84b26a71d79a5d9a7956bae8a\",\n \"score\": \"0.6766925\",\n \"text\": \"def show_pwd_encrypt(self):\\r\\n if self.ent_encrypt_pwd[\\\"show\\\"] == \\\"*\\\":\\r\\n self.ent_encrypt_pwd[\\\"show\\\"] = \\\"\\\"\\r\\n self.ent_encrypt_confirm[\\\"show\\\"] = \\\"\\\"\\r\\n else:\\r\\n self.ent_encrypt_pwd[\\\"show\\\"] = \\\"*\\\"\\r\\n self.ent_encrypt_confirm[\\\"show\\\"] = \\\"*\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb920963e64c088d7cef666a2eeec7c\",\n \"score\": \"0.67080224\",\n \"text\": \"def set_unusable_password(self):\\n\\t\\tself._password = make_password(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ca76ece58a4bf305535aee50a00999\",\n \"score\": \"0.66501784\",\n \"text\": \"def password(self) -> str:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6cc0cdf3a5f9ebeeb93fe02c13c9c4e\",\n \"score\": \"0.6592658\",\n \"text\": \"def password (self): \\n return self.cfg['password']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc5c36efb52ed3129386db42a913d0aa\",\n \"score\": \"0.6578044\",\n \"text\": \"def set_Password(self, value):\\n InputSet._set_input(self, 'Password', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ed14dfcc14b7ef6ad411e4175e58bb\",\n \"score\": \"0.65679425\",\n \"text\": \"def password(self) -> Optional[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445bb3c353e689747ce70a12f99381bc\",\n \"score\": \"0.6555305\",\n \"text\": \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445bb3c353e689747ce70a12f99381bc\",\n \"score\": \"0.6555305\",\n \"text\": \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445bb3c353e689747ce70a12f99381bc\",\n \"score\": \"0.6555305\",\n \"text\": \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445bb3c353e689747ce70a12f99381bc\",\n \"score\": \"0.6555305\",\n \"text\": \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445bb3c353e689747ce70a12f99381bc\",\n \"score\": \"0.6555305\",\n \"text\": \"def password(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef4b55353ce362b779e8e5a6209c716\",\n \"score\": \"0.655324\",\n \"text\": \"def password(self, password):\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef4b55353ce362b779e8e5a6209c716\",\n \"score\": \"0.655324\",\n \"text\": \"def password(self, password):\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef4b55353ce362b779e8e5a6209c716\",\n \"score\": \"0.655324\",\n \"text\": \"def password(self, password):\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef4b55353ce362b779e8e5a6209c716\",\n \"score\": \"0.655324\",\n \"text\": \"def password(self, password):\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef4b55353ce362b779e8e5a6209c716\",\n \"score\": \"0.655324\",\n \"text\": \"def password(self, password):\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"323a7fc577f10a4898e76c85c4d5ea17\",\n \"score\": \"0.6546978\",\n \"text\": \"def password(self) -> str:\\n return STCObject(self.proto_handle)['Password']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee02f2039fb7f918d55b51c30f2f8da\",\n \"score\": \"0.65108085\",\n \"text\": \"def password(self):\\n return self.__password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee02f2039fb7f918d55b51c30f2f8da\",\n \"score\": \"0.65108085\",\n \"text\": \"def password(self):\\n return self.__password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2626513d592e0c82d0334b874fcb39f\",\n \"score\": \"0.65080214\",\n \"text\": \"def PASSWORD(self):\\n return TYPE.NEO4J + SETTING.DELIMITER + SETTING.PASSWORD\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba4de5ff7d9909453b9efa9501fbeca\",\n \"score\": \"0.65079844\",\n \"text\": \"def set_password(self, password):\\n\\t\\tself.password = encrypt_password(password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dec8374fa60f1aa05926b758aad27bf\",\n \"score\": \"0.6503956\",\n \"text\": \"def setPassword(self, p):\\n self.__password = p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecaa271f3f58228753c8f0a5ef293f49\",\n \"score\": \"0.6494732\",\n \"text\": \"def setpassword(self, pwd):\\r\\n self.pwd = pwd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea16c7b2358cc2cf694ad4920f60665\",\n \"score\": \"0.6491275\",\n \"text\": \"def password(self):\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea16c7b2358cc2cf694ad4920f60665\",\n \"score\": \"0.6491275\",\n \"text\": \"def password(self):\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea16c7b2358cc2cf694ad4920f60665\",\n \"score\": \"0.6491275\",\n \"text\": \"def password(self):\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea16c7b2358cc2cf694ad4920f60665\",\n \"score\": \"0.6491275\",\n \"text\": \"def password(self):\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d85f61bde4a0038c134e2fe5411f1bc\",\n \"score\": \"0.6488742\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4f232d692d4d256e3969ba56be43eb\",\n \"score\": \"0.6462508\",\n \"text\": \"def password(self):\\n raise AttributeError(\\\"Password is not a readable attribute\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61b605f0d2948d93a7a928521ff2b963\",\n \"score\": \"0.64580625\",\n \"text\": \"def password(self):\\n raise AttributeError('password is not a readable attribute')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ad07e2cf05c2672cc371e0f19ccae6\",\n \"score\": \"0.64372694\",\n \"text\": \"def set_password(self, password):\\n self.password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f7af3f79279da5481ea2fb1e719ef0\",\n \"score\": \"0.64292514\",\n \"text\": \"def password(self):\\n\\t\\traise AttributeError('password is not a readable attribute!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128bbfd919ccf95b2e052c0f1981d447\",\n \"score\": \"0.6421736\",\n \"text\": \"def password(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b069bb3ddb50a205910d92f2d99d9c\",\n \"score\": \"0.6420136\",\n \"text\": \"def password(self):\\n\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f7209463c890fa5f23dfb9a7b8aadf\",\n \"score\": \"0.6414876\",\n \"text\": \"def password(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f7209463c890fa5f23dfb9a7b8aadf\",\n \"score\": \"0.6414876\",\n \"text\": \"def password(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f7209463c890fa5f23dfb9a7b8aadf\",\n \"score\": \"0.6414876\",\n \"text\": \"def password(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57d360238bddf3eafd97ea805e1a05e7\",\n \"score\": \"0.64063025\",\n \"text\": \"def password(self):\\r\\n raise AttributeError('password is not a readable attribute.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e28714a55ae3032a9de3160b1fd8a99b\",\n \"score\": \"0.63976896\",\n \"text\": \"def show_password(ctx, new_value):\\n ctx.obj.show_password = new_value\\n ctx.obj.update_conf(CONF_PATH)\\n ok_msg(\\\"Conf updated\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6fb9c90c501790fc0a2bf58a2f23226\",\n \"score\": \"0.63892806\",\n \"text\": \"def set_Password(self, value):\\n super(GetBookmarkInputSet, self)._set_input('Password', value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7927a56e129e7e30763ce8562c963c98\",\n \"score\": \"0.636576\",\n \"text\": \"def password(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a17ab4b015edc84a8a0acda579de480\",\n \"score\": \"0.63433045\",\n \"text\": \"def password(self, value):\\n\\t\\tself._password = value\\n\\t\\tself.passwordHash = hashlib.sha224(value).hexdigest()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911c3786396a0cba35f1009812c5f41d\",\n \"score\": \"0.6325003\",\n \"text\": \"def show_pwd_decrypt(self):\\r\\n if self.ent_decrypt_pwd[\\\"show\\\"] == \\\"*\\\":\\r\\n self.ent_decrypt_pwd[\\\"show\\\"] = \\\"\\\"\\r\\n else:\\r\\n self.ent_decrypt_pwd[\\\"show\\\"] = \\\"*\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2012becbcd9897edafaf6b115e4c066\",\n \"score\": \"0.6324489\",\n \"text\": \"def password(self) -> str:\\n\\n return self.__decoder(self.__password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f861cfc2c39baf8c79443100a1b71d\",\n \"score\": \"0.6287318\",\n \"text\": \"def setModemPass(self, string):\\n string = self.comms.setModemModePassword(string, unitCode=self.device_id)\\n self.modem_pass = string\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07cb2ff1f0b506ba62a6c6c052ebeb75\",\n \"score\": \"0.62814856\",\n \"text\": \"def Password(self):\\n if self.force_auto_sync:\\n self.get('Password')\\n return self._Password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ab615bd09f3cbf1c92049502438e15\",\n \"score\": \"0.62741816\",\n \"text\": \"def remove_password(self):\\n self.password.text = \\\"\\\"\\n if not self.isUserAdmin():\\n self.runAsAdmin()\\n else:\\n username = self.get_username()\\n password = \\\"\\\"\\n self.set_password(username, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fdf83a6257aae4494023cf09d2b7cfd\",\n \"score\": \"0.61588955\",\n \"text\": \"def get_password(self):\\r\\n return self.password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2ce94a8daf875118f366671242dbd25\",\n \"score\": \"0.6132136\",\n \"text\": \"def keyword_preference_window_remove_password(self):\\n self.Preferences_window_clear_password()\\n self.Preferences_window_clear_confirm_password()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15211e583e03390fb9087da0413fde75\",\n \"score\": \"0.6121864\",\n \"text\": \"def getPassword():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb137d00eb901a20fd39d10675d88832\",\n \"score\": \"0.6116795\",\n \"text\": \"def the_password(text: str) -> \\\"Enter\\\":\\n return Enter.the_secret(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d858021e608ad55f733b063849eda8b3\",\n \"score\": \"0.61085296\",\n \"text\": \"def password(self, new_password: str) -> NoReturn:\\n\\n self.__password = self.__encoder(new_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0506a2a80685c0d3e1c690512aa71e4\",\n \"score\": \"0.61043006\",\n \"text\": \"def set_password(self, ppass):\\n self.pw = crypt.crypt(ppass, crypt.mksalt())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966eac0b58414ebe1a96695926d031b5\",\n \"score\": \"0.60975856\",\n \"text\": \"def password_plain(self, password_plain):\\n self.password = generate_password_hash(password_plain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa3965330baf27e3f93406cd0b241d2\",\n \"score\": \"0.60685927\",\n \"text\": \"def _set_password(self, password):\\n\\t\\tself._password = self.__encrypt_password(password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"680d8769794d42cd5e39ffd5ebf9beb8\",\n \"score\": \"0.6066814\",\n \"text\": \"def get_password(self):\\n\\n return str(self.password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef61a02e0b855bffe61520e89ab79bdd\",\n \"score\": \"0.60601413\",\n \"text\": \"def password(self, password):\\n if self._configuration.client_side_validation and password is None:\\n raise ValueError(\\\"Invalid value for `password`, must not be `None`\\\") # noqa: E501\\n\\n self._password = password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b9eedc6273d545c41d86362fe22a3b\",\n \"score\": \"0.60594463\",\n \"text\": \"def non_autoscale_password(self):\\n return self.get_raw('non_autoscale_password')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4cc0296825c714d079bdcaa5cf2213\",\n \"score\": \"0.60508627\",\n \"text\": \"def set_haproxy_stat_password(self):\\n if not self.get_state('haproxy.stat.password'):\\n password = ''.join([\\n random.choice(string.ascii_letters + string.digits)\\n for n in range(32)])\\n self.set_state('haproxy.stat.password', password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7da1288757d71d93a107440196848b4\",\n \"score\": \"0.6035899\",\n \"text\": \"def create_password(self):\\n\\n all_chars = LETTERS\\n if not self._no_digits:\\n all_chars += DIGITS\\n if not self._no_symbols:\\n all_chars += SYMBOLS\\n\\n self._password = ''.join(secrets.choice(all_chars)\\n for i in range(self._length))\\n return self._password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87651f8acf71d1b9d350cd0165b8a3e4\",\n \"score\": \"0.60280085\",\n \"text\": \"def get_args_secure(cls):\\n return [\\\"password\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87651f8acf71d1b9d350cd0165b8a3e4\",\n \"score\": \"0.60280085\",\n \"text\": \"def get_args_secure(cls):\\n return [\\\"password\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e990a76ed27ef988f7faf47efac6839\",\n \"score\": \"0.6025506\",\n \"text\": \"def change_password(self):\\n self.dialbox = dialogbox.Dialog(self.gui, 'change_password')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24ba60cb287944380866339735d8bce\",\n \"score\": \"0.6016152\",\n \"text\": \"def admin_password(self):\\n return self._configFile.get(Config.PASS, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdce9373dc9d6ea616f91fe464ffdb7b\",\n \"score\": \"0.60037684\",\n \"text\": \"def db_pass():\\n if not env.db_pass:\\n env.db_pass = getpass(\\\"Enter the database password: \\\")\\n return env.db_pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c585389c128c6e1dee4bdf55e38c86\",\n \"score\": \"0.59936064\",\n \"text\": \"def change_password(self):\\n if self.password.text:\\n if not self.isUserAdmin():\\n self.runAsAdmin()\\n else:\\n username = self.get_username()\\n password = self.password.text\\n self.set_password(username, password)\\n self.password.text = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335994194983329ad42f22ebd6449498\",\n \"score\": \"0.59891045\",\n \"text\": \"def catalog_admin_password(self) -> Optional[pulumi.Input['SecureStringArgs']]:\\n return pulumi.get(self, \\\"catalog_admin_password\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2a3806ae2dfda6d7a78db39874b927\",\n \"score\": \"0.5978722\",\n \"text\": \"def getPassword(self): \\n return self.__password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9594c00c391c61e72e9e273bdee8b5\",\n \"score\": \"0.5974075\",\n \"text\": \"def set_password(self, raw_password):\\r\\n self.password = make_password(raw_password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bff18ec5d640626a704b2eed29ea740\",\n \"score\": \"0.59662074\",\n \"text\": \"def password(self, password):\\n\\t\\tself.password_hash = generate_password_hash(password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31987115530ecab8bbf8ab2997015c2\",\n \"score\": \"0.59635246\",\n \"text\": \"def Password(request):\\n schema = schemaish.Structure()\\n schema.add('Password', schemaish.String())\\n\\n form = formish.Form(schema, 'form')\\n form['Password'].widget = formish.Password()\\n return form\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad393f94a243976f570e15be7352472f\",\n \"score\": \"0.5963103\",\n \"text\": \"def test_RemovesPasswordFromConnectionSettings(self):\\n assert 'password' not in self.rpc.config['connection_settings']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a22efd1b4b217a87f2dbada83c3a4e7\",\n \"score\": \"0.5957486\",\n \"text\": \"def get_password(self):\\r\\n\\t\\treturn self.__password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb7ffd2d0d5ffcb6e4ce1c850e5ebab\",\n \"score\": \"0.5954629\",\n \"text\": \"def set_pw(cls, pw):\\n return pw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0476ff3c847cc6dee1b8f1e3c0d800c\",\n \"score\": \"0.5954385\",\n \"text\": \"def send_password(self, password=None):\\n _pw = password or self.password\\n data_set = messages.DataSet(value=_pw)\\n cmd = messages.CommandMessage(command=\\\"P\\\", command_type=\\\"1\\\", data_set=data_set)\\n logger.info(\\\"Sending password to meter\\\")\\n self.transport.send(cmd.to_bytes())\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994287,"cells":{"query_id":{"kind":"string","value":"ffd0299e38b55062b10879e459715b0a"},"query":{"kind":"string","value":"genereates two qubit identity equal circuit with given length"},"positive_passages":{"kind":"list like","value":[{"docid":"12e2084757eca5a7228e3ce921786841","score":"0.7121084","text":"def two_qubit_circuit(length: int, qubit_one: int, qubit_two: int):\n\n p = Program()\n\n for j in range(int(length/2)):\n theta = 2 * np.pi * random.random()\n gate_list = [RZ(theta, qubit_one), RX(np.pi / 2, qubit_one), RX(- np.pi / 2, qubit_one),\n CZ(qubit_one, qubit_two),\n RZ(theta, qubit_two), RX(np.pi / 2, qubit_two), RX(- np.pi / 2, qubit_two), CZ(qubit_two, qubit_one)]\n new_gate = random.choice(gate_list)\n p.inst(new_gate)\n\n p += p.dagger()\n\n return Program('PRAGMA PRESERVE_BLOCK') + p + Program('PRAGMA END_PRESERVE_BLOCK')","title":""}],"string":"[\n {\n \"docid\": \"12e2084757eca5a7228e3ce921786841\",\n \"score\": \"0.7121084\",\n \"text\": \"def two_qubit_circuit(length: int, qubit_one: int, qubit_two: int):\\n\\n p = Program()\\n\\n for j in range(int(length/2)):\\n theta = 2 * np.pi * random.random()\\n gate_list = [RZ(theta, qubit_one), RX(np.pi / 2, qubit_one), RX(- np.pi / 2, qubit_one),\\n CZ(qubit_one, qubit_two),\\n RZ(theta, qubit_two), RX(np.pi / 2, qubit_two), RX(- np.pi / 2, qubit_two), CZ(qubit_two, qubit_one)]\\n new_gate = random.choice(gate_list)\\n p.inst(new_gate)\\n\\n p += p.dagger()\\n\\n return Program('PRAGMA PRESERVE_BLOCK') + p + Program('PRAGMA END_PRESERVE_BLOCK')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3bbdc1b872fee8bfda6527b96be9eb4e","score":"0.6130477","text":"def test_two_qubit_gates_with_symbols(gate: cirq.Gate, expected_length: int):\n q0 = cirq.GridQubit(5, 3)\n q1 = cirq.GridQubit(5, 4)\n original_circuit = cirq.Circuit(gate(q0, q1))\n converted_circuit = original_circuit.copy()\n cgoc.ConvertToSqrtIswapGates().optimize_circuit(converted_circuit)\n assert len(converted_circuit) <= expected_length\n\n # Check if unitaries are the same\n for val in np.linspace(0, 2 * np.pi, 12):\n assert _unitaries_allclose(\n cirq.resolve_parameters(original_circuit, {'t': val}),\n cirq.resolve_parameters(converted_circuit, {'t': val}))","title":""},{"docid":"323d4e692dd9d99e1d7cfeb0ac233584","score":"0.5952881","text":"def test_multi_cregs(self):\n\n # ┌───┐ ░ ┌─┐\n # qr_0: ──■────────────┤ X ├─░─┤M├─────────\n # ┌─┴─┐ ┌───┐└─┬─┘ ░ └╥┘┌─┐\n # qr_1: ┤ X ├──■──┤ H ├──■───░──╫─┤M├──────\n # └───┘┌─┴─┐└───┘ ░ ║ └╥┘┌─┐\n # qr_2: ──■──┤ X ├───────────░──╫──╫─┤M├───\n # ┌─┴─┐└───┘ ░ ║ ║ └╥┘┌─┐\n # qr_3: ┤ X ├────────────────░──╫──╫──╫─┤M├\n # └───┘ ░ ║ ║ ║ └╥┘\n # c: 2/════════════════════════╩══╬══╩══╬═\n # 0 ║ 1 ║\n # ║ ║\n # d: 2/═══════════════════════════╩═════╩═\n # 0 1\n qr = QuantumRegister(4, \"qr\")\n cr1 = ClassicalRegister(2, \"c\")\n cr2 = ClassicalRegister(2, \"d\")\n circuit = QuantumCircuit(qr, cr1, cr2)\n circuit.cx(qr[0], qr[1])\n circuit.cx(qr[2], qr[3])\n circuit.cx(qr[1], qr[2])\n circuit.h(qr[1])\n circuit.cx(qr[1], qr[0])\n circuit.barrier(qr)\n circuit.measure(qr[0], cr1[0])\n circuit.measure(qr[1], cr2[0])\n circuit.measure(qr[2], cr1[1])\n circuit.measure(qr[3], cr2[1])\n\n coupling = CouplingMap([[0, 1], [0, 2], [2, 3]]) # linear [1, 0, 2, 3]\n property_set = {}\n with self.assertWarnsRegex(DeprecationWarning, r\"^The class.*is deprecated\"):\n actual = BIPMapping(coupling, objective=\"depth\")(circuit, property_set)\n self.assertEqual(5, actual.depth())\n\n CheckMap(coupling)(actual, property_set)\n self.assertTrue(property_set[\"is_swap_mapped\"])","title":""},{"docid":"24bdea335c37bba2886a805f49781e64","score":"0.5805034","text":"def _generate_circuit_sequence(\n repeated_circuit: circuits.Circuit,\n different_circuit: circuits.Circuit,\n length: int,\n position: int,\n):\n if position >= length:\n raise ValueError(f\"Position {position} should be < {length}\")\n\n return circuits.Circuit(\n list(\n chain.from_iterable(\n [\n (\n repeated_circuit if i != position else different_circuit\n ).operations\n for i in range(length)\n ]\n )\n )\n )","title":""},{"docid":"fa7f56dbb30c6bdaf883cbce48f124de","score":"0.57560784","text":"def test_two_qubit_gates(gate: cirq.Gate, expected_length: int):\n q0 = cirq.GridQubit(5, 3)\n q1 = cirq.GridQubit(5, 4)\n original_circuit = cirq.Circuit(gate(q0, q1))\n converted_circuit = original_circuit.copy()\n cgoc.ConvertToSqrtIswapGates().optimize_circuit(converted_circuit)\n cig.SQRT_ISWAP_GATESET.serialize(converted_circuit)\n assert len(converted_circuit) <= expected_length\n assert _unitaries_allclose(original_circuit, converted_circuit)","title":""},{"docid":"2a79052d835fdfd4a228aebfa73656ce","score":"0.5681587","text":"def test_two_qubit_synthesis_to_directional_cx_multiple_registers(self):\n # TODO: should make check more explicit e.g. explicitly set gate\n # direction in test instead of using specific fake backend\n backend = FakeVigo()\n conf = backend.configuration()\n qr0 = QuantumRegister(1)\n qr1 = QuantumRegister(1)\n coupling_map = CouplingMap(conf.coupling_map)\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr0, qr1)\n qc.unitary(random_unitary(4, seed=12), [qr0[0], qr1[0]])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=None,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n\n unisynth_pass_nat = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=None,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=True,\n )\n\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\n qc_out_nat = pm_nat.run(qc)\n self.assertEqual(Operator(qc), Operator(qc_out))\n self.assertEqual(Operator(qc), Operator(qc_out_nat))","title":""},{"docid":"f6cc3e5608df757c3201d742ce1693eb","score":"0.55983335","text":"def add_singleQ_based_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\n index_1 = index % 24 # randomly sample from single qubit cliffords (24)\n # randomly sample from single qubit cliffords (24)\n index_2 = (index // 24) % 24\n add_singleQ_clifford(index_1, gate_seq_1, )\n add_singleQ_clifford(index_2, gate_seq_2)","title":""},{"docid":"4ee3f5a4b0be0a70dffcf1f4e7a33b74","score":"0.5594737","text":"def test_qubit_identity(self, rep, tol):\n\n p = 0.543\n\n dev = qml.device(\"default.tensor\", wires=1, representation=rep)\n\n @qml.qnode(dev)\n def circuit(x):\n \"\"\"Test quantum function\"\"\"\n qml.RX(x, wires=0)\n return qml.expval(qml.Identity(0))\n\n assert np.isclose(circuit(p), 1, atol=tol, rtol=0)","title":""},{"docid":"bc4b90d4734a2e6e379901635eed1604","score":"0.5589917","text":"def circuit_identity(n):\n if n <= 0:\n return CircuitZero\n if n == 1:\n return CIdentity\n return Concatenation(*((CIdentity,) * n))","title":""},{"docid":"eb1c3298d327352f75f0c189de7ffc25","score":"0.55772567","text":"def add_CNOT_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\n index_1 = index % 3 # randomly sample from S1 (3)\n index_2 = (index // 3) % 3 # randomly sample from S1 (3)\n # randomly sample from single qubit cliffords (24)\n index_3 = (index // 3 // 3) % 24\n # randomly sample from single qubit cliffords (24)\n index_4 = (index // 3 // 3 // 24) % 24\n\n generator = kwargs.get('generator', 'CZ')\n if generator == 'CZ':\n add_singleQ_S1(index_1, gate_seq_1)\n add_singleQ_S1_Y2p(index_2, gate_seq_2)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n add_singleQ_clifford(index_3, gate_seq_1)\n add_singleQ_clifford(index_4, gate_seq_2)\n\n elif generator == 'iSWAP':\n add_singleQ_S1(index_1, gate_seq_1)\n add_singleQ_S1_Z2p(index_2, gate_seq_2)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n gate_seq_1.append(Gate.X2p)\n gate_seq_2.append(Gate.I)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n add_singleQ_clifford(index_3, gate_seq_1)\n add_singleQ_clifford(index_4, gate_seq_2)","title":""},{"docid":"555890a62921b9d25189a6f87a3d6ae4","score":"0.5503414","text":"def initialize_entangled_qubits(final_measure=True):\n\n circuits = []\n qr = QuantumRegister(3)\n if final_measure:\n cr = ClassicalRegister(3)\n regs = (qr, cr)\n else:\n regs = (qr,)\n\n # Initialize |000+111> -> |000+110>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([1, 0], [0])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |010+111>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([0, 1], [1])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |000+011>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([1, 0], [2])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |000+100>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([1, 0, 0, 0], [0, 1])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |100+110>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([0, 0, 0, 1], [0, 2])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |000+100+010+110+100+101+011+111>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([0.5, 0.5, 0.5, 0.5], [1, 2])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n # Initialize |000+111> -> |100>\n circuit = QuantumCircuit(*regs)\n circuit.h(0)\n circuit.cx(0, 1)\n circuit.cx(0, 2)\n circuit.initialize([0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 2])\n if final_measure:\n circuit.barrier(qr)\n circuit.measure(qr, cr)\n circuits.append(circuit)\n\n return circuits","title":""},{"docid":"1adfaa1bdaa7fe465a968736e6f218c9","score":"0.55020934","text":"def add_SWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\n index_1 = index % 24 # randomly sample from single qubit cliffords (24)\n # randomly sample from single qubit cliffords (24)\n index_2 = (index // 24) % 24\n generator = kwargs.get('generator', 'CZ')\n if generator == 'CZ':\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.Y2p)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n gate_seq_1.append(Gate.Y2p)\n gate_seq_2.append(Gate.Y2m)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n gate_seq_1.append(Gate.Y2m)\n gate_seq_2.append(Gate.Y2p)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n add_singleQ_clifford(index_1, gate_seq_1)\n add_singleQ_clifford(index_2, gate_seq_2)\n\n elif generator == 'iSWAP':\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.X2m)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n gate_seq_1.append(Gate.X2m)\n gate_seq_2.append(Gate.I)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.X2m)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n add_singleQ_clifford(index_1, gate_seq_1)\n add_singleQ_clifford(index_2, gate_seq_2)","title":""},{"docid":"7cf0ffcfd080f5773adbdaa2f8ae599d","score":"0.55015194","text":"def q1():\n # number of different sides and length\n length = 5\n sid = 2\n\n # first set can be 2, next 2, next 2 and so on for length 5 or 2 ** 5\n tot = sid ** length\n print(\"Question 1: {}\".format(tot))","title":""},{"docid":"674c673c1fbbc7d5b3c23d58de205daa","score":"0.54571664","text":"def test_coupling_map_unequal_durations(self, opt):\n qr = QuantumRegister(2)\n circ = QuantumCircuit(qr)\n circ.append(random_unitary(4, seed=1), [1, 0])\n backend = FakeVigo()\n tqc = transpile(\n circ,\n backend=backend,\n optimization_level=opt,\n translation_method=\"synthesis\",\n layout_method=\"trivial\",\n )\n tqc_index = {qubit: index for index, qubit in enumerate(tqc.qubits)}\n self.assertTrue(\n all(\n (\n (0, 1) == (tqc_index[instr.qubits[0]], tqc_index[instr.qubits[1]])\n for instr in tqc.get_instructions(\"cx\")\n )\n )\n )","title":""},{"docid":"5bd6725709b5a3d2bdf9f8060c66da20","score":"0.5422984","text":"def test_repeat(self):\n\n features = list(range(3))\n\n expected_names = self.QUEUES[2][1] + self.QUEUES[2][1]\n expected_wires = self.QUEUES[2][2] + self.QUEUES[2][2]\n\n op = qml.IQPEmbedding(features, wires=range(3), n_repeats=2)\n tape = op.expand()\n\n for i, gate in enumerate(tape.operations):\n assert gate.name == expected_names[i]\n assert gate.wires.labels == tuple(expected_wires[i])","title":""},{"docid":"9a875a97b59aa82a53a642aa562ae753","score":"0.53944117","text":"def circuit_built(circuit):","title":""},{"docid":"8de663ad5b0ec5be3ad0a32df0e334f3","score":"0.5392747","text":"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map(self):\n # TODO: should make check more explicit e.g. explicitly set gate\n # direction in test instead of using specific fake backend\n backend = FakeVigo()\n conf = backend.configuration()\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n\n unisynth_pass_nat = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=True,\n )\n\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\n qc_out_nat = pm_nat.run(qc)\n # the decomposer defaults to the [1, 0] direction but the coupling\n # map specifies a [0, 1] direction. Check that this is respected.\n self.assertTrue(\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\"cx\")))\n )\n self.assertTrue(\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out_nat.get_instructions(\"cx\")))\n )\n self.assertEqual(Operator(qc), Operator(qc_out))\n self.assertEqual(Operator(qc), Operator(qc_out_nat))","title":""},{"docid":"8bbc7c72a7b76021aff856dc1731feec","score":"0.53244096","text":"def test_pk_gen_2():\n dev = LedgerQRL()\n\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 0)\n assert answer is not None\n assert len(answer) == 32\n leaf = binascii.hexlify(answer).upper()\n print(leaf)\n assert leaf == \"98E68D7AB40D358B5B0F4DF4C86AAE78B444BD50248C02773CF1965FAEA092AE\"\n sys.stdout.flush()\n\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 20)\n assert len(answer) == 32\n leaf = binascii.hexlify(answer).upper()\n print(leaf)\n assert leaf == \"8E66C0B26238BC9E12804A83AEF0429E9A666266001A826B5025889B45AE86A3\"\n sys.stdout.flush()\n\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 40)\n assert len(answer) == 32\n leaf = binascii.hexlify(answer).upper()\n print(leaf)\n assert leaf == \"D2BAD383B25900503A34FA126ABB19D3AAC6FC110F431929C7EB18E613E101F8\"\n sys.stdout.flush()","title":""},{"docid":"99db10fad448c8f59c7aa0e21059c839","score":"0.53199863","text":"def get2qb():\n circuit = QuantumCircuit(2, 2)\n return circuit","title":""},{"docid":"b7b2414ebc8a5f74312a9d4c95b67b43","score":"0.53060704","text":"def add_iSWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\n generator = kwargs.get('generator', 'CZ')\n index_1 = index % 3 # randomly sample from S1_Y2p (3)\n index_2 = (index // 3) % 3 # randomly sample from S1_X2p(3)\n # randomly sample from single qubit cliffords (24)\n index_3 = (index // 3 // 3) % 24\n # randomly sample from single qubit cliffords (24)\n index_4 = (index // 3 // 3 // 24) % 24\n\n if generator == 'CZ':\n add_singleQ_S1_Y2p(index_1, gate_seq_1)\n add_singleQ_S1_X2p(index_2, gate_seq_2)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n gate_seq_1.append(Gate.Y2p)\n gate_seq_2.append(Gate.X2m)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.CZ)\n add_singleQ_clifford(index_3, gate_seq_1)\n add_singleQ_clifford(index_4, gate_seq_2)\n\n elif generator == 'iSWAP':\n add_singleQ_S1(index_1, gate_seq_1)\n add_singleQ_S1(index_2, gate_seq_2)\n gate_seq_1.append(Gate.I)\n gate_seq_2.append(Gate.iSWAP)\n add_singleQ_clifford(index_3, gate_seq_1)\n add_singleQ_clifford(index_4, gate_seq_2)","title":""},{"docid":"4a2625c52202f35104d3cd6a1a88d1df","score":"0.52617085","text":"def nqubit_1pauli(pauli, i, n):\n #create identity padding\n iden1 = [qto.identity(2) for j in range(i)]\n iden2 = [qto.identity(2) for j in range(n-i-1)]\n\n #combine into total operator list that is in proper order\n oplist = iden1 + [pauli] + iden2\n\n #create final operator by using tensor product on unpacked operator list\n operator = qt.tensor(*oplist)\n\n return operator","title":""},{"docid":"73a940fb8fa0a5cf42ad2861d4b28cf2","score":"0.52558017","text":"def your_circuit(oracle):\n # phase kickback trick\n yield cirq.X(q2), cirq.H(q2)\n\n # equal superposition over input bits\n yield cirq.H(q0), cirq.H(q1)\n\n # query the function\n yield oracle\n\n # interference to get result, put last qubit into |1>\n yield cirq.H(q0), cirq.H(q1), cirq.H(q2)\n\n # a final OR gate to put result in final qubit\n yield cirq.X(q0), cirq.X(q1), cirq.CCX(q0, q1, q2)\n yield cirq.measure(q2)","title":""},{"docid":"1660f52f1e84c14cd43ef6be66e688f4","score":"0.5244218","text":"def make_bits_equal(self):\n difference = abs(len(self.num1) - len(self.num2))\n if len(self.num1) == len(self.num2):\n print(\"Equal\")\n elif len(self.num1) > len(self.num2):\n for i in range(difference):\n self.num2 = \"0\" + self.num2\n elif len(self.num2) > len(self.num1):\n for i in range(difference):\n self.num1 = \"0\" + self.num1","title":""},{"docid":"8284513df19115b1059f755f1cc8acb7","score":"0.524299","text":"def eq0405():","title":""},{"docid":"d84487e86c7583353a631f30b77e43be","score":"0.52325124","text":"def pad_with_identity(circuit, k, n):\n circuit_n = circuit.cdim\n combined_circuit = circuit + circuit_identity(n)\n permutation = (list(range(k)) + list(range(circuit_n, circuit_n + n)) +\n list(range(k, circuit_n)))\n return (CPermutation.create(invert_permutation(permutation)) <<\n combined_circuit << CPermutation.create(permutation))","title":""},{"docid":"f3380dd50ea745a606bb71d9b52b24b6","score":"0.5230486","text":"def __generate_from_4x_to_5x() -> Problem:\n a = random.choice(range(41, 50))\n b = random.choice(range(50, a + 10))\n return __extend_no_carry_borrow(a, b - a)","title":""},{"docid":"e605058e07bb0a9699116ac3d0d96c16","score":"0.5218623","text":"def nqubit_2pauli(ipauli, jpauli, i, j, n):\n #create identity padding\n iden1 = [qto.identity(2) for m in range(i)]\n iden2 = [qto.identity(2) for m in range(j-i-1)]\n iden3 = [qto.identity(2) for m in range(n-j-1)]\n\n #combine into total operator list\n oplist = iden1 + [ipauli] + iden2 + [jpauli] + iden3\n\n # apply tensor product on unpacked oplist\n operator = qt.tensor(*oplist)\n\n return operator","title":""},{"docid":"ecd7c33cc95b0801d7e5ac104f8ce4ac","score":"0.5214872","text":"def test_prep_circuit_large2(self):\n # Input vector (normalized)\n vec = np.array(list(np.ones(32)) + list(np.zeros(32)))\n\n # Make a tree from the vector\n tree = BinaryTree(vec)\n\n # Do the state preparation circuit\n qreg = QuantumRegister(6)\n circ = QuantumCircuit(qreg)\n tree.preparation_circuit(circ, qreg)\n\n # Do the swaps to get the ordering of amplitudes to match with the input vector\n for ii in range(len(qreg) // 2):\n circ.swap(qreg[ii], qreg[-ii - 1])\n\n # Check that the circuit produces the correct state\n state = np.real(self.final_state(circ))\n\n # Note: The output state has an additional ancilla needed to do the multi-controlled-Y rotations,\n # so we discard the additional (zero) amplitudes when comparing to the input vector\n self.assertTrue(np.allclose(state[:len(vec)], vec / np.linalg.norm(vec, ord=2)))","title":""},{"docid":"8966041d2e44968c492bd204b7fb99c7","score":"0.5196272","text":"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map_natural_false(self):\n # TODO: should make check more explicit e.g. explicitly set gate\n # direction in test instead of using specific fake backend\n backend = FakeVigo()\n conf = backend.configuration()\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n\n unisynth_pass_nat = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\n qc_out_nat = pm_nat.run(qc)\n # the decomposer defaults to the [1, 0] direction but the coupling\n # map specifies a [0, 1] direction. Check that this is respected.\n self.assertTrue(\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\"cx\")))\n )\n self.assertTrue(\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out_nat.get_instructions(\"cx\")))\n )\n self.assertEqual(Operator(qc), Operator(qc_out))\n self.assertEqual(Operator(qc), Operator(qc_out_nat))","title":""},{"docid":"5b8eb254dc562353375700500e67926e","score":"0.5195183","text":"def joint_card(c1,c2):\n return '{' + ('1' if c1[1] == c2[1] == '1' else '0') + ':' + ('1' if c1[3] == c2[3] == '1' else 'M') + '}'","title":""},{"docid":"357ec7d9764c28c00274f7e4e36a698e","score":"0.5186929","text":"def test_QI2():\n # Tests checked against Mathematica noteboook `costingsf.nb`\n # Arguments are otherwise random\n assert QI2(1234, 5678) == (32, 64, 5519)\n assert QI2(7120, 1340111) == (4, 32768, 204052)","title":""},{"docid":"f6ff814b48ebf1f000d51185762015d7","score":"0.5177073","text":"def test_prep_ctrl_random_twoq(self):\n for _ in range(50):\n vec = np.random.randn(4)\n tree = BinaryTree(vec)\n\n register = QuantumRegister(2)\n ctrl_reg = QuantumRegister(2)\n circ = QuantumCircuit(register, ctrl_reg)\n ctrl_key = 0\n\n tree.preparation_circuit(circ, register, control_register=ctrl_reg, control_key=ctrl_key)\n\n circ.swap(register[0], register[1])\n\n state = np.real(TestBinaryTree.final_state(circ))\n\n self.assertTrue(np.allclose(state[:len(vec)], vec / np.linalg.norm(vec, ord=2)))","title":""},{"docid":"392daa1854fbb4d394bb86c933599999","score":"0.517426","text":"def __generate_from_5x_to_4x() -> Problem:\n a = random.choice(range(50, 58))\n b = random.choice(range(a - 9, 50))\n return __extend_no_carry_borrow(a, b - a)","title":""},{"docid":"819858c64ba08ec36e573850c9e7a4b9","score":"0.51735836","text":"def create_data(self, length):\n i = np.random.randint(2 ,size=length) * 2 - 1\n q = np.random.randint(2 ,size=length) * 2 - 1\n return i, q","title":""},{"docid":"7bfd9a613df6c7abacc5fdd812ce900d","score":"0.51712984","text":"def test_prepare_negative_amplitudes_two_qubits2(self):\n # Generate all sign configurations\n one_neg = set(permutations((-1, 1, 1, 1)))\n two_neg = set(permutations((-1, -1, 1, 1)))\n three_neg = set(permutations((-1, -1, -1, 1)))\n four_neg = {(-1, -1, -1, -1)}\n\n for sign in one_neg | two_neg | three_neg | four_neg:\n # Input vector\n vec = np.array([1, 2, 3, 4], dtype=np.float64)\n vec *= np.array(sign, dtype=np.float64)\n\n # Get a BinaryTree\n tree = BinaryTree(vec)\n\n # Get a quantum register\n qreg = QuantumRegister(2)\n circ = QuantumCircuit(qreg)\n\n # Get the state preparation circuit\n tree.preparation_circuit(circ, qreg)\n\n # Swap qubits to compare with natural ordering of vector\n circ.swap(qreg[0], qreg[1])\n\n # Make sure the final state is the same as the input vector\n state = np.real(self.final_state(circ))\n self.assertTrue(np.allclose(state, vec / np.linalg.norm(vec, ord=2)))","title":""},{"docid":"ca4a04ec5ee1b1835fbd5d84b592643e","score":"0.5168186","text":"def test_selfinnerproduct(self):\n qkclass = QuantumKernel(feature_map=self.feature_map)\n qc = qkclass.construct_circuit(self.x)\n self._check_circuit(qc, check_measurements=True, check_inverse=True)","title":""},{"docid":"b2b7496d98f840eaf3a7edbd1f98224e","score":"0.51566935","text":"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map_natural_none(self):\n # TODO: should make check more explicit e.g. explicitly set gate\n # direction in test instead of using specific fake backend\n backend = FakeVigo()\n conf = backend.configuration()\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n\n unisynth_pass_nat = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=None,\n )\n\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\n qc_out_nat = pm_nat.run(qc)\n # the decomposer defaults to the [1, 0] direction but the coupling\n # map specifies a [0, 1] direction. Check that this is respected.\n self.assertTrue(\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\"cx\")))\n )\n self.assertTrue(\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out_nat.get_instructions(\"cx\")))\n )\n self.assertEqual(Operator(qc), Operator(qc_out))\n self.assertEqual(Operator(qc), Operator(qc_out_nat))","title":""},{"docid":"a8048578c69d1a5bc510dc432637ea54","score":"0.5130123","text":"def __idiv__(self, other):\n \n pass","title":""},{"docid":"104b28686f433117f1f96742c210000f","score":"0.510977","text":"def test_two_qubit_synthesis_not_pulse_optimal(self):\n backend = FakeVigo()\n conf = backend.configuration()\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=False,\n natural_direction=True,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n if isinstance(qc_out, QuantumCircuit):\n num_ops = qc_out.count_ops()\n else:\n num_ops = qc_out[0].count_ops()\n self.assertIn(\"sx\", num_ops)\n self.assertGreaterEqual(num_ops[\"sx\"], 16)","title":""},{"docid":"d3018a171de0bc3f002ec9147df2a9b1","score":"0.5099306","text":"def test_single_qubit_identity_with_target(self):\n qc = QuantumCircuit(1)\n qc.unitary([[1.0, 0.0], [0.0, 1.0]], 0)\n dag = circuit_to_dag(qc)\n unitary_synth_pass = UnitarySynthesis(target=FakeBelemV2().target)\n result_dag = unitary_synth_pass.run(dag)\n result_qc = dag_to_circuit(result_dag)\n self.assertEqual(result_qc, QuantumCircuit(1))","title":""},{"docid":"b0a6d091d722790efe91922ec25b6b58","score":"0.50964344","text":"def test_two_qubit_natural_direction_true_gate_length_raises(self):\n # this assumes iswawp pulse optimal decomposition doesn't exist\n backend = FakeVigo()\n conf = backend.configuration()\n for _, nduv in backend.properties()._gates[\"cx\"].items():\n nduv[\"gate_length\"] = (4e-7, nduv[\"gate_length\"][1])\n nduv[\"gate_error\"] = (7e-3, nduv[\"gate_error\"][1])\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 0], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=True,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n with self.assertRaises(TranspilerError):\n pm.run(qc)","title":""},{"docid":"d74dbf5029e96032b3b4865101f86628","score":"0.50770205","text":"def add_singleQ_clifford(index, gate_seq, pad_with_I=True):\n length_before = len(gate_seq)\n # Paulis\n if index == 0:\n gate_seq.append(Gate.I)\n elif index == 1:\n gate_seq.append(Gate.Xp)\n elif index == 2:\n gate_seq.append(Gate.Yp)\n elif index == 3:\n gate_seq.append(Gate.Xp)\n gate_seq.append(Gate.Yp)\n\n # 2pi/3 rotations\n elif index == 4:\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.X2p)\n elif index == 5:\n gate_seq.append(Gate.Y2m)\n gate_seq.append(Gate.X2p)\n elif index == 6:\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.X2m)\n elif index == 7:\n gate_seq.append(Gate.Y2m)\n gate_seq.append(Gate.X2m)\n elif index == 8:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Y2p)\n elif index == 9:\n gate_seq.append(Gate.X2m)\n gate_seq.append(Gate.Y2p)\n elif index == 10:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Y2m)\n elif index == 11:\n gate_seq.append(Gate.X2m)\n gate_seq.append(Gate.Y2m)\n\n # pi/2 rotations\n elif index == 12:\n gate_seq.append(Gate.X2p)\n elif index == 13:\n gate_seq.append(Gate.X2m)\n elif index == 14:\n gate_seq.append(Gate.Y2p)\n elif index == 15:\n gate_seq.append(Gate.Y2m)\n elif index == 16:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.X2m)\n elif index == 17:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Y2m)\n gate_seq.append(Gate.X2m)\n\n # Hadamard-Like\n elif index == 18:\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.Xp)\n elif index == 19:\n gate_seq.append(Gate.Y2m)\n gate_seq.append(Gate.Xp)\n elif index == 20:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Yp)\n elif index == 21:\n gate_seq.append(Gate.X2m)\n gate_seq.append(Gate.Yp)\n elif index == 22:\n gate_seq.append(Gate.X2p)\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.X2p)\n elif index == 23:\n gate_seq.append(Gate.X2m)\n gate_seq.append(Gate.Y2p)\n gate_seq.append(Gate.X2m)\n else:\n raise ValueError(\n 'index is out of range. it should be smaller than 24 and greater'\n ' or equal to 0: ', str(index))\n\n length_after = len(gate_seq)\n if pad_with_I:\n # Force the clifford to have a length of 3 gates\n for i in range(3-(length_after-length_before)):\n gate_seq.append(Gate.I)","title":""},{"docid":"2a32e5dca26c60d46d1a81730d653799","score":"0.50734425","text":"def two_qubit_AllXY(q0, q1, RO_target='all',\n sequence_type='sequential',\n replace_q1_pulses_X180=False,\n double_points=False):\n\n pulse_combinations = [['I', 'I'], ['X180', 'X180'], ['Y180', 'Y180'],\n ['X180', 'Y180'], ['Y180', 'X180'],\n ['X90', 'I'], ['Y90', 'I'], ['X90', 'Y90'],\n ['Y90', 'X90'], ['X90', 'Y180'], ['Y90', 'X180'],\n ['X180', 'Y90'], ['Y180', 'X90'], ['X90', 'X180'],\n ['X180', 'X90'], ['Y90', 'Y180'], ['Y180', 'Y90'],\n ['X180', 'I'], ['Y180', 'I'], ['X90', 'X90'],\n ['Y90', 'Y90']]\n\n pulse_combinations_tiled = pulse_combinations + pulse_combinations\n if double_points:\n pulse_combinations = [val for val in pulse_combinations\n for _ in (0, 1)]\n\n if replace_q1_pulses_X180:\n pulse_combinations_q1 = ['X180' for val in pulse_combinations]\n\n pulse_combinations_q0 = pulse_combinations\n pulse_combinations_q1 = pulse_combinations_tiled\n\n\n filename = join(base_qasm_path, 'two_qubit_AllXY.qasm')\n qasm_file = mopen(filename, mode='w')\n qasm_file.writelines('qubit {} \\nqubit {} \\n'.format(q0, q1))\n\n for pulse_comb_q0, pulse_comb_q1 in zip(pulse_combinations_q0,\n pulse_combinations_q1):\n qasm_file.writelines('\\ninit_all\\n')\n if sequence_type == 'interleaved':\n qasm_file.writelines('{} {}\\n'.format(pulse_comb_q0[0], q0) +\n '{} {}\\n'.format(pulse_comb_q1[0], q1) +\n '{} {}\\n'.format(pulse_comb_q0[1], q0) +\n '{} {}\\n'.format(pulse_comb_q1[1], q1))\n elif sequence_type == 'sandwiched':\n qasm_file.writelines('{} {}\\n'.format(pulse_comb_q1[0], q1) +\n '{} {}\\n'.format(pulse_comb_q0[0], q0) +\n '{} {}\\n'.format(pulse_comb_q0[1], q0) +\n '{} {}\\n'.format(pulse_comb_q1[1], q1))\n elif sequence_type == 'sequential':\n qasm_file.writelines('{} {}\\n'.format(pulse_comb_q0[0], q0) +\n '{} {}\\n'.format(pulse_comb_q0[1], q0) +\n '{} {}\\n'.format(pulse_comb_q1[0], q1) +\n '{} {}\\n'.format(pulse_comb_q1[1], q1))\n elif sequence_type == 'simultaneous':\n qasm_file.writelines('{} {} |'.format(pulse_comb_q0[0], q0) +\n '{} {}\\n'.format(pulse_comb_q1[0], q1) +\n '{} {} |'.format(pulse_comb_q0[1], q0) +\n '{} {}\\n'.format(pulse_comb_q1[1], q1))\n else:\n raise ValueError(\"sequence_type {} \".format(sequence_type) +\n \"['interleaved', 'simultaneous', \" +\n \"'sequential', 'sandwiched']\")\n qasm_file.writelines('RO {} \\n'.format(RO_target))\n\n qasm_file.close()\n return qasm_file","title":""},{"docid":"f76973307c3be04639b3e076ff3c812f","score":"0.5068147","text":"def test_different_number_of_virtual_and_physical_qubits(self):\n\n # q_0: ──■────■───────\n # ┌─┴─┐ │\n # q_1: ┤ X ├──┼────■──\n # └───┘ │ ┌─┴─┐\n # q_2: ──■────┼──┤ X ├\n # ┌─┴─┐┌─┴─┐└───┘\n # q_3: ┤ X ├┤ X ├─────\n # └───┘└───┘\n circuit = QuantumCircuit(4)\n circuit.cx(0, 1)\n circuit.cx(2, 3)\n circuit.cx(0, 3)\n circuit.cx(1, 2)\n\n coupling = CouplingMap.from_line(5)\n with self.assertRaises(TranspilerError):\n with self.assertWarnsRegex(DeprecationWarning, r\"^The class.*is deprecated\"):\n BIPMapping(coupling)(circuit)","title":""},{"docid":"0873a1f806dc3a21013b982b12221759","score":"0.5045172","text":"def create_circuit(self, angles):\n\n p = Program()\n ro = p.declare(\"ro\", memory_type='BIT', memory_size=4)\n\n # name the individual qubits for more clarity\n # ancilla_qubit = [0]\n # index_qubit = [1]\n # data_qubit = [2]\n # class_qubit = [3]\n\n #######################################\n #START of the state preparation routine\n\n # put the ancilla and the index qubits into uniform superposition\n p += H(0)\n p += H(1)\n\n def u3(program, theta, qubit):\n program += RZ(3*np.pi, qubit)\n program += RX(np.pi/2, qubit)\n program += RZ(theta+np.pi, qubit)\n program += RX(np.pi/2, qubit)\n program += RZ(0, qubit)\n return program\n\n # loading the test vector (which we wish to classify)\n p += CNOT(0, 2)\n p = u3(p, -angles[0], 2)\n p += CNOT(0, 2)\n p = u3(p, angles[0], 2)\n\n # flipping the ancilla qubit > this moves the input vector to the |0> state of the ancilla\n p += X(0)\n\n # loading the first training vector\n # [0,1] -> class 0\n # we can load this with a straightforward Toffoli\n\n p += CCNOT(0, 1, 2)\n\n # flip the index qubit > moves the first training vector to the |0> state of the index qubit\n p += X(1)\n\n # loading the second training vector\n # [0.78861, 0.61489] -> class 1\n\n p += CCNOT(0, 1, 2)\n\n p += CNOT(1, 2)\n p = u3(p, angles[1], 2)\n p += CNOT(1, 2)\n p = u3(p, -angles[1], 2)\n\n p += CCNOT(0, 1, 2)\n\n p += CNOT(1, 2)\n p = u3(p, -angles[1], 2)\n p += CNOT(1, 2)\n p = u3(p, angles[1], 2)\n\n # END of state preparation routine\n ####################################################\n\n # at this point we would usually swap the data and class qubit\n # however, we can be lazy and let the Qiskit compiler take care of it\n\n # flip the class label for training vector #2\n\n p += CNOT(1, 3)\n\n #############################################\n # START of the mini distance-based classifier\n\n # interfere the input vector with the training vectors\n p += H(0)\n\n # Measure all qubits and record the results in the classical registers\n p += MEASURE(0, ro[0])\n p += MEASURE(1, ro[1])\n p += MEASURE(2, ro[2])\n p += MEASURE(3, ro[3])\n\n # END of the mini distance-based classifier\n #############################################\n\n return p","title":""},{"docid":"c041f6c136be3f8619ab245e4d7e1a95","score":"0.5038822","text":"def test_no_state_modification_circuit(self) -> None:\n\n for nbqbit in range(min_nbqbit, max_nbqbit):\n prog = Program()\n qbits = prog.qalloc(nbqbit)\n\n random_angles = [rd.random() * 2 * np.pi for _ in range(3 * nbqbit)]\n\n for i in range(len(qbits)):\n prog.apply(RX(random_angles[3 * i]), qbits[i])\n prog.apply(RX(random_angles[3 * i + 1]), qbits[i])\n prog.apply(RX(random_angles[3 * i + 2]), qbits[i])\n\n prog.apply(QFT(nbqbit), qbits)\n prog.apply(QFT(nbqbit).dag(), qbits)\n\n for i in range(len(qbits)):\n prog.apply(RX(random_angles[3 * i]).dag(), qbits[i])\n prog.apply(RX(random_angles[3 * i + 1]).dag(), qbits[i])\n prog.apply(RX(random_angles[3 * i + 2]).dag(), qbits[i])\n\n circuit = prog.to_circ(inline=True)\n\n for topology in generate_custom_topologies(nbqbit):\n qpu = Sabre() | (QuameleonPlugin(topology=topology) | PyLinalg())\n result = qpu.submit(circuit.to_job())\n assert result.raw_data[0].state.int == 0","title":""},{"docid":"6de8bb1d61aef05ae792bdfc32b7f014","score":"0.50318724","text":"def test_two_qubit_no_parameters(self, device, init_state, op, mat, tol, skip_if):\n n_wires = 2\n dev = device(n_wires)\n skip_if(dev, {\"returns_probs\": False})\n if not dev.supports_operation(op(wires=range(n_wires)).name):\n pytest.skip(\"op not supported\")\n\n rnd_state = init_state(n_wires)\n\n @qml.qnode(dev)\n def circuit():\n qml.StatePrep(rnd_state, wires=range(n_wires))\n op(wires=range(n_wires))\n return qml.probs(wires=range(n_wires))\n\n res = circuit()\n\n expected = np.abs(mat @ rnd_state) ** 2\n assert np.allclose(res, expected, atol=tol(dev.shots))","title":""},{"docid":"223db33425e0a7293ab9a606bd7ba8fa","score":"0.5009359","text":"def eq02():","title":""},{"docid":"ac8059fd9fae84aac4558f9154f3333d","score":"0.49888164","text":"def __generate_plus8_eq_plus3_minus5_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([5, 6])\n return __extend_no_carry_borrow(a, 8)","title":""},{"docid":"578472535a549079a5d73875e75ed590","score":"0.49776545","text":"def test_already_executable_circuit(self) -> None:\n\n for nbqbit in range(min_nbqbit, max_nbqbit):\n prog = Program()\n qbits = prog.qalloc(nbqbit)\n\n for i in range(len(qbits) - 1):\n prog.apply(H, qbits[i])\n prog.apply(Z, qbits[i])\n prog.apply(X.ctrl(), qbits[i + 1], qbits[i])\n\n circuit = prog.to_circ(inline=True)\n\n sabre = Sabre()\n batch = Batch(jobs=[circuit.to_job()])\n hardware_specs = HardwareSpecs()\n batch_result = sabre.compile(batch, hardware_specs)\n computed_circuit = batch_result.jobs[0].circuit\n\n check_circuits_equality(circuit, computed_circuit)","title":""},{"docid":"c59f9aad0510ab9efa72ec42542a91e8","score":"0.49725848","text":"def check_circuits_equality(circuit_1: Circuit, circuit_2: Circuit) -> None:\n\n # We check the equality of circuits properties one by one excepted the qregister which can not be compared\n assert circuit_1.ops == circuit_2.ops\n assert circuit_1.gateDic == circuit_2.gateDic\n assert circuit_1.var_dic == circuit_2.var_dic","title":""},{"docid":"f0cffc616854b95819f55122d8c04236","score":"0.497025","text":"def circuit_hash(self):\n raise NotImplementedError","title":""},{"docid":"d2f1dcfc2950875b8c817bbb65814a77","score":"0.49614215","text":"def get_equal_sign_orthants(s, circuits_information_M1, circuits_information_M2):\n\n equal_sign_vectors = []\n\n #TODO: create them dynamically\n orthants = list(itertools.product([-1,0,1],repeat=s))\n\n solution_orthants = []\n #Steps 2,3,4\n for orthant in orthants:\n print(orthant)\n conformal_circuits_M1 = circuits_information_M1.get_conformal_circuits(orthant)\n\n #union of the circuits conformal to the orthant\n U_M1 = Utils.union(conformal_circuits_M1)\n\n is_zero_or_invalid = True\n if U_M1 != None:\n for elt in U_M1:\n if elt != 0:\n is_zero_or_invalid = False\n\n\n #print(U_M1)\n\n if U_M1 != None and not is_zero_or_invalid and Utils.has_equal_sign(orthant, U_M1):\n #Si el ortante tiene soporte igual a la union de los circuitos de M1, sigo con el paso 3 del algoritmo.\n conformal_circuits_M2 = circuits_information_M2.get_conformal_circuits(orthant)\n U_M2 = Utils.union(conformal_circuits_M2)\n if U_M2 != None and Utils.has_equal_sign(orthant, U_M2):\n #equal_sign_vectors.append([U_M1, U_M2])\n solution_orthants.append(orthant)\n #equal_sign_vectors.append([U_M1, U_M2])\n\n #Stop searching in this orthant\n continue\n\n print(\"Two vectors with the same sign, corresponding to the orthant %s, are %s, from M1, and %s, from M2.\" % (orthant, U_M1, U_M2))\n else:\n continue\n #Isn't useful\n return solution_orthants","title":""},{"docid":"68da894061da9ee419a8bc830a541780","score":"0.49587256","text":"def test_prep_circuit_one_qubit2(self):\n # Two element vector\n vec = [0.0, 1.0]\n\n # Make a BinaryTree\n tree = BinaryTree(vec)\n\n # Get a circuit and register to prepare the vector in\n qreg = QuantumRegister(1)\n circ = QuantumCircuit(qreg)\n\n # Get the state preparation circuit\n tree.preparation_circuit(circ, qreg)\n\n # Get the final state\n state = list(np.real(self.final_state(circ)))\n\n self.assertTrue(np.array_equal(state, vec))","title":""},{"docid":"c28fdd7894d5d61451db75696d49b6ba","score":"0.49433023","text":"def test_two_qubit_natural_direction_true_duration_fallback(self):\n # this assumes iswawp pulse optimal decomposition doesn't exist\n backend = FakeVigo()\n conf = backend.configuration()\n # conf.basis_gates = [gate if gate != \"cx\" else \"iswap\" for gate in conf.basis_gates]\n qr = QuantumRegister(2)\n coupling_map = CouplingMap([[0, 1], [1, 0], [1, 2], [1, 3], [3, 4]])\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=coupling_map,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=True,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n self.assertTrue(\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out.get_instructions(\"cx\")))\n )","title":""},{"docid":"53e42f4985e2a98d85da2c542120ecd8","score":"0.49384433","text":"def _prepare_circuits(base_circuit, observables):\n circuits = list()\n\n if isinstance(observables, ComposedOp):\n observables = SummedOp([observables])\n for obs in observables:\n circuit = base_circuit.copy()\n circuit.append(obs[1], qargs=list(range(base_circuit.num_qubits)))\n circuits.append(circuit)\n return circuits, observables","title":""},{"docid":"74e440f942ce99b7439e9ca7bd0fbab2","score":"0.493633","text":"def U_h(self, circuit, l, n_i, m, n_phiReg, w_phiReg, n_aReg, w_aReg, n_bReg, w_bReg, wReg, eReg, pReg, hReg, w_hReg,\n P_phi,\n P_a, P_b):\n for k in range(n_i + m):\n # for k in range(1):\n print(\"k: \", k)\n countsList = self.generateParticleCounts(n_i, m, k) # reduce the available number of particles\n\n for counts in countsList:\n n_phi, n_a, n_b = counts[0], counts[1], counts[2]\n # controlled R-y from |0> to |k> on all qubits with all possible angles depending on n_phi, n_a, n_b, and flavor\n # for flavor in ['phi']:\n # if n_phi == 3 and n_a == 0 and n_b ==0:\n # print(\"counts: \", counts)\n # print(\"after if : \", n_phi, n_a, n_b)\n\n for flavor in ['phi', 'a', 'b']:\n angle = self.U_hAngle(flavor, n_phi, n_a, n_b, P_phi, P_a, P_b)\n\n #phiControl, aControl, and bControl are the corresponding work registers, and since we call\n # numberControl we also add x gates on the respective number registers (aka n_phi, n_a, n_b)\n phiControl = self.numberControl(circuit, l, n_phi, n_phiReg, w_phiReg)\n # print(\"qiskit phiControl: \", phiControl)\n aControl = self.numberControl(circuit, l, n_a, n_aReg, w_aReg)\n # print(\"qiskit aControl: \", aControl)\n bControl = self.numberControl(circuit, l, n_b, n_bReg, w_bReg)\n # print(\"qiskit bControl: \", bControl)\n\n circuit.ccx(phiControl, aControl, wReg[0])\n circuit.ccx(bControl, wReg[0], wReg[1])\n\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 2,\n 4) # wReg[4] is work qubit but is reset to 0\n circuit.ccx(wReg[1], wReg[2], wReg[3])\n circuit.ccx(eReg[0], wReg[3], wReg[4])\n\n self.twoLevelControlledRy(circuit, l, angle, k + 1, wReg[4], hReg, w_hReg)\n\n circuit.ccx(eReg[0], wReg[3], wReg[4]) # next steps undo work qubits\n circuit.ccx(wReg[1], wReg[2], wReg[3])\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 2,\n 4) # wReg[4] is work qubit but is reset to 0\n circuit.ccx(bControl, wReg[0], wReg[1])\n circuit.ccx(phiControl, aControl, wReg[0])\n self.numberControlT(circuit, l, n_b, n_bReg, w_bReg)\n self.numberControlT(circuit, l, n_a, n_aReg, w_aReg)\n self.numberControlT(circuit, l, n_phi, n_phiReg, w_phiReg)\n\n # subtract from the counts register depending on which flavor particle emitted\n for flavor, countReg, workReg in zip(['phi', 'a', 'b'], [n_phiReg, n_aReg, n_bReg],\n [w_phiReg, w_aReg, w_bReg]):\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 0,\n 1) # wReg[4] is work qubit but is reset to 0\n self.minus1(circuit, l, countReg, workReg, wReg[0], wReg[1], 0)\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 0,\n 1) # wReg[4] is work qubit but is reset to 0\n\n # apply x on eReg if hReg[m] = 0, apply another x so we essentially control on not 0 instead of 0\n isZeroControl = self.numberControl(circuit, l, 0, hReg, w_hReg)\n circuit.cx(isZeroControl, eReg[0])\n circuit.x(eReg[0])\n self.numberControlT(circuit, l, 0, hReg, w_hReg)","title":""},{"docid":"58c8dfeeec411da2cdf5d9680abc0d2f","score":"0.49305674","text":"def __generate_plus2_eq_minus8_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([8, 9])\n return __extend_no_carry_borrow(a, 2)","title":""},{"docid":"665951a7772536c0aeda8f5e49492388","score":"0.49253783","text":"def two_component_identical_lj():\n T = 1\n sig = np.array([1, 1])\n eps = np.array([1, 1])\n eps_01 = geometric(eps[0], eps[1])\n sig_01 = arithmetic(sig[0], sig[1])\n rhos = np.array([0.005, 0.005]) / sig**3\n\n lj = oz.System(kT=T)\n\n r = lj.r\n lj.set_interaction(0, 0, oz.lennard_jones(r, eps[0], sig[0]))\n lj.set_interaction(1, 1, oz.lennard_jones(r, eps[1], sig[1]))\n lj.set_interaction(0, 1, oz.lennard_jones(r, eps_01, sig_01))\n\n lj.solve(rhos=rhos, closure_name='hnc')\n return lj","title":""},{"docid":"f7705933edd534e3bfc411eeea98bf01","score":"0.49243945","text":"def generateBinaryID(self):\n bid = cantorPairing(self.id1, self.id2)\n self.addNewMember('bid',bid)","title":""},{"docid":"f7705933edd534e3bfc411eeea98bf01","score":"0.49243945","text":"def generateBinaryID(self):\n bid = cantorPairing(self.id1, self.id2)\n self.addNewMember('bid',bid)","title":""},{"docid":"f7705933edd534e3bfc411eeea98bf01","score":"0.49243945","text":"def generateBinaryID(self):\n bid = cantorPairing(self.id1, self.id2)\n self.addNewMember('bid',bid)","title":""},{"docid":"f7705933edd534e3bfc411eeea98bf01","score":"0.49243945","text":"def generateBinaryID(self):\n bid = cantorPairing(self.id1, self.id2)\n self.addNewMember('bid',bid)","title":""},{"docid":"3fb6c4bce4383bc59792bac257adfb6c","score":"0.4908937","text":"def random_circuit_encoding(self, n_ops, random_state):\n return random_state.rand(3 * n_ops)","title":""},{"docid":"99b7c4e237167fac45b64bf4b3bfca06","score":"0.4902847","text":"def __generate_plus6_eq_minus4_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([4, 9])\n return __extend_no_carry_borrow(a, 6)","title":""},{"docid":"d3fb5ff6cbd7848d23116b0a021b63c0","score":"0.4900984","text":"def main():\n # Set the number of symbols (i.e. the generation size in RLNC terminology)\n # and the size of a symbol in bytes\n symbols = 8\n symbol_size = 1\n\n # Create encoder/decoder factory used to build actual encoders/decoders\n encoder_factory = kodo.PerpetualEncoderFactoryBinary(symbols, symbol_size)\n encoder = encoder_factory.build()\n\n decoder_factory = kodo.PerpetualDecoderFactoryBinary(symbols, symbol_size)\n decoder = decoder_factory.build()\n\n # The perpetual encoder supports three operation modes;\n #\n # 1) Random pivot mode (default)\n # The pivot element is drawn at random for each coding symbol\n #\n # example generated vectors (for width = 2)\n #\n # 0 0 1 X X 0\n # X X 0 0 0 1\n # X 0 0 0 1 X\n # X X 0 0 0 1\n # 0 1 X X 0 0\n # 0 1 X X 0 0\n #\n # 1 X X 0 0 0\n # 0 0 0 1 X X\n # .\n # .\n #\n # 2) Pseudo systematic\n # Pivot elements are generated with indices 0,1,2, ... , n,\n # after which the generator reverts to the default random pivot\n #\n # example generated vectors (for width = 2)\n #\n # 1 X X 0 0 0\n # 0 1 X X 0 0\n # 0 0 1 X X 0\n # 0 0 0 1 X X\n # X 0 0 0 1 X\n # X X 0 0 0 1\n #\n # (additional vectors generated using the random mode)\n #\n # 3) Pre-charging\n # For the first \"width\" symbols, the pivot index is 0. After that,\n # the pseudo-systematic mode is used. Finally, pivots are drawn at\n # random resulting in the indices 0 (width times), 1,2, ... , n\n #\n # example generated vectors (for width = 2)\n #\n # 1 X X 0 0 0\n # 1 X X 0 0 0\n # 0 1 X X 0 0\n # 0 0 1 X X 0\n # 0 0 0 1 X X\n #\n # X 0 0 0 1 X\n # X X 0 0 0 1\n #\n # (additional vectors generated using the random mode)\n #\n # The operation mode is set in the following. Note that if both\n # pre-charging and pseudo-systematic is enabled, pre-charging takes\n # precedence.\n\n # Enable the pseudo-systematic operation mode - faster\n #encoder.set_pseudo_systematic(True)\n\n # Enable the pre-charing operation mode - even faster\n encoder.set_pre_charging(True)\n\n print(\"Pseudo-systematic is {}\\nPre-charging is {}\".format(\n \"on\" if encoder.pseudo_systematic() else \"off\",\n \"on\" if encoder.pre_charging() else \"off\"))\n\n # The width of the perpetual code can be set either as a number of symbols\n # using set_width(), or as a ratio of the generation size using\n # set_width_ratio().\n #\n # The default width is set to 10% of the generation size.\n print(\"The width ratio defaults to: {} (therefore the calculated width is \"\n \"{})\".format(encoder.width_ratio(), encoder.width()))\n encoder.set_width(6)\n\n print(\"The width was set to: {} (therefore the calculated width ratio is \"\n \"{})\".format(encoder.width(), encoder.width_ratio()))\n\n encoder.set_width_ratio(0.2)\n print(\"The width ratio was set to: {} (therefore the calculated width is \"\n \"{})\".format(encoder.width_ratio(), encoder.width()))\n\n # Create some data to encode. In this case we make a buffer\n # with the same size as the encoder's block size (the max.\n # amount a single encoder can encode)\n # Just for fun - fill the input data with random data\n data_in = os.urandom(encoder.block_size())\n\n # Assign the data buffer to the encoder so that we can\n # produce encoded symbols\n encoder.set_const_symbols(data_in)\n\n while not decoder.is_complete():\n # Encode a packet into the payload buffer\n payload = encoder.write_payload()\n print(\"Payload: {}\".format(binascii.hexlify(payload)))\n if random.choice([True, False]):\n print(\"Packet dropped on channel\")\n continue\n\n # Pass that packet to the decoder\n decoder.read_payload(payload)\n\n # The decoder is complete, now copy the symbols from the decoder\n data_out = decoder.copy_from_symbols()\n\n # Check we properly decoded the data\n if data_out == data_in:\n print(\"Data decoded correctly\")\n else:\n print(\"Unexpected failure to decode please file a bug report :)\")\n sys.exit(1)","title":""},{"docid":"430ccd293f554d4fe3e0ac656887d732","score":"0.4898766","text":"def eq01():","title":""},{"docid":"18dbe39d5bbb767b290f97b0eba34331","score":"0.48945558","text":"def __generate_plus8_eq_minus2_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([2, 3, 4, 7, 8, 9])\n return __extend_no_carry_borrow(a, 8)","title":""},{"docid":"b334d51b06c52c97ede3a6c06e14d3b3","score":"0.48936254","text":"def same_quantum(self, a, b):\r\n return a.same_quantum(b)","title":""},{"docid":"a32c63c844577dfc22ff3f24ad558b82","score":"0.48922","text":"def apply_controlled_two_qubit_gate(self, mat, q_id1, q_id2, q_id3):\n # Move the qubits to the correct position\n self.swap_qubits(q_id1, self.qubits[0])\n self.swap_qubits(q_id2, self.qubits[1])\n self.swap_qubits(q_id3, self.qubits[2])\n\n first_mat = np.block([[np.eye(4), np.zeros((4, 4))], [np.zeros((4, 4)), mat]])\n total_mat = np.kron(first_mat, np.eye(2 ** (len(self.qubits) - 3)))\n\n self.qubit = np.dot(total_mat, self.qubit)","title":""},{"docid":"8aab37b94cef18d2d048bc6a65bae5d4","score":"0.48921895","text":"def test_all_to_all_topology(self) -> None:\n\n for nbqbit in range(min_nbqbit, max_nbqbit):\n circuit = generate_random_circuit(nbqbit)\n\n sabre = Sabre()\n batch = Batch(jobs=[circuit.to_job()])\n hardware_specs = HardwareSpecs()\n batch_result = sabre.compile(batch, hardware_specs)\n computed_circuit = batch_result.jobs[0].circuit\n\n check_circuits_equality(circuit, computed_circuit)","title":""},{"docid":"bccb6b5cfa6ea6e4d9d2bac633828298","score":"0.4887278","text":"def test_init_with_qnodes(self):\n dev = qml.device(\"default.qubit\", wires=1)\n\n def circuit(x):\n qml.RX(x, wires=0)\n return qml.expval(qml.PauliZ(0))\n\n qnodes = [qml.QNode(circuit, dev) for i in range(4)]\n qc = qml.QNodeCollection(qnodes)\n\n assert qc.qnodes == qnodes\n assert len(qc) == 4","title":""},{"docid":"1b9c30bf90e7dbe965016996a8f5d3b5","score":"0.48850244","text":"def build_circuit(routers):","title":""},{"docid":"68abb31619154078720ebba820638406","score":"0.48816547","text":"def test_identical_costs():\n S = np.transpose(np.array([[1.0, 1], [1, 1], [1, 1], [1, 1]]))\n M = np.zeros(np.shape(S))\n Q = np.full(np.shape(S), 1.0)\n solver = RandomizedSolver(\n [0, 0, 0, 0], [1, 1, 1, 1], [2, 2], encoder(-S, M, Q)\n )\n for _ in range(1000):\n check_test_solution(solver, T=1)","title":""},{"docid":"136298ebfc12522e165fade0ebbd16b9","score":"0.48801643","text":"def input_combos(combo_len):\n return itertools.product((False, True), repeat=combo_len)","title":""},{"docid":"c551c5474c8984a337aeb282b8801724","score":"0.48780307","text":"def generate_sequences_identical(draw, elements=[lists, integers]):\n seq_x = draw(lists(integers(min_value=1, max_value=1), min_size=3, max_size=10_000))\n seq_y = draw(\n lists(\n integers(min_value=2, max_value=2), min_size=len(seq_x), max_size=len(seq_x)\n )\n )\n\n return seq_x, seq_y","title":""},{"docid":"7f0eff5fc392a0a003d38b1d02dab236","score":"0.4875794","text":"def test_prepare_negative_amplitudes_two_qubits(self):\n # Input vector\n vec = [-0.4, 0.4, -0.8, 0.2]\n\n # Get a BinaryTree\n tree = BinaryTree(vec)\n\n # Get a Quantum Register\n regA = QuantumRegister(1)\n regB = QuantumRegister(1)\n circ = QuantumCircuit(regA, regB)\n\n # Get the state preparation circuit\n tree.preparation_circuit(circ, regA, regB)\n\n # Swap the qubits to compare to the natural ordering of the vector\n circ.swap(regA[0], regB[0])\n\n # Make sure the final state of the circuit is the same as the input vector\n state = np.real(self.final_state(circ))\n self.assertTrue(np.allclose(state, vec))","title":""},{"docid":"78ff8e29838f27b69782aaeaa09a83ba","score":"0.48712075","text":"def circuit_new(circuit):","title":""},{"docid":"c075df7b500ca16653c24dd186c60d5d","score":"0.486678","text":"def __init__(self,\n ctrl: QubitType,\n targets: Tuple[QubitType],\n params: List[float] = None,\n circuit: QuantumCircuit = None):\n\n def ccz(circuit, ctrl1, ctrl2, target):\n # Verified\n circuit.comment(\"CCZ\")\n from qiskit.extensions.standard.h import HGate\n circuit._attach(HGate(target, circuit).inverse())\n circuit.ccx(ctrl1, ctrl2, target)\n circuit._attach(HGate(target, circuit).inverse())\n\n def crzz(circuit, theta, ctrl, target):\n # Verified\n circuit.comment(\"c-RZZ\")\n circuit.cu1(theta, ctrl, target)\n circuit.cx(ctrl, target)\n circuit.cu1(theta, ctrl, target)\n circuit.cx(ctrl, target)\n\n def crzz_inv(circuit, theta, ctrl, target):\n # Verified\n circuit.comment(\"c-RZZ^{-1}\")\n circuit.cx(ctrl, target)\n circuit.cu1(-theta, ctrl, target)\n circuit.cx(ctrl, target)\n circuit.cu1(-theta, ctrl, target)\n\n def crx(circuit, theta, ctrl, target):\n # Verified\n circuit.comment(\"CRX\")\n # Apply the supposed c-RX operation.\n circuit.cu3(theta, pi/2, 3*pi/2, ctrl, target)\n # For the moment, QISKit adds a phase to the U-gate, so we\n # need to correct this phase with a controlled Rzz.\n crzz(circuit, pi, ctrl, target)\n\n def csqrtx_inv(circuit, ctrl, target):\n # Verified\n circuit.comment(\"c-sqrt(X)^{-1}\")\n circuit.h(target)\n circuit.t(target)\n circuit.cx(ctrl, target)\n circuit.t(ctrl)\n circuit.tdg(target)\n circuit.h(target)\n circuit.cz(ctrl, target)\n circuit.cx(ctrl, target)\n\n\n def csqrtx(circuit, ctrl, target):\n # Verified\n circuit.comment(\"c-sqrt(X)\")\n circuit.cx(ctrl, target)\n circuit.cz(ctrl, target)\n circuit.h(target)\n circuit.t(target)\n circuit.tdg(ctrl)\n circuit.cx(ctrl, target)\n circuit.tdg(target)\n circuit.h(target)\n\n if params is None:\n # Default parameters for a simple Hamiltonian (no powers)\n params = [0.19634953, 0.37900987, 0.9817477, 1.87900984, 0.58904862]\n\n used_qubits = [ctrl, targets[0], targets[1]]\n\n super().__init__(self.__class__.__name__, # name\n [], # parameters\n used_qubits, # qubits\n circuit) # circuit\n\n self.comment(\"[HS] Start.\")\n ccz(self, ctrl, targets[0], targets[1])\n crx(self, params[0], ctrl, targets[1])\n csqrtx_inv(self, ctrl, targets[1])\n crzz(self, params[1], ctrl, targets[1])\n crx(self, params[2], ctrl, targets[0])\n crzz(self, params[3], ctrl, targets[0])\n self.ccx(ctrl, targets[0], targets[1])\n crx(self, params[4], ctrl, targets[0])\n self.ccx(ctrl, targets[0], targets[1])\n ccz(self, ctrl, targets[0], targets[1])\n self.comment(\"[HS] End.\")","title":""},{"docid":"621d19adf53f7dbe19aae66fc3bdb4c2","score":"0.48654687","text":"def apply_controlled_gate(self, mat, q_id1, q_id2):\n first_mat = 1\n second_mat = 1\n nr1 = self.qubits.index(q_id1)\n nr2 = self.qubits.index(q_id2)\n\n min_nr = min(nr1, nr2)\n max_nr = max(nr1, nr2)\n\n total_amount = len(self.qubits)\n before = min_nr\n after = total_amount - max_nr - 1\n mid = total_amount - before - after - 2\n\n if before > 0:\n first_mat = np.eye(2 ** before)\n second_mat = np.eye(2 ** before)\n\n # Apply first part of Matrix\n if min_nr == nr1:\n first_mat = np.kron(first_mat, np.eye(2))\n second_mat = np.kron(second_mat, mat)\n else:\n first_mat = np.kron(first_mat, np.array([[1, 0], [0, 0]]))\n second_mat = np.kron(second_mat, np.array([[0, 0], [0, 1]]))\n\n if mid > 0:\n first_mat = np.kron(first_mat, np.eye(2 ** mid))\n second_mat = np.kron(second_mat, np.eye(2 ** mid))\n\n # Apply second part of Matrix\n if min_nr == nr1:\n first_mat = np.kron(first_mat, np.array([[1, 0], [0, 0]]))\n second_mat = np.kron(second_mat, np.array([[0, 0], [0, 1]]))\n else:\n first_mat = np.kron(first_mat, np.eye(2))\n second_mat = np.kron(second_mat, mat)\n\n if after > 0:\n first_mat = np.kron(first_mat, np.eye(2 ** after))\n second_mat = np.kron(second_mat, np.eye(2 ** after))\n\n apply_mat = first_mat + second_mat\n self.qubit = np.dot(apply_mat, self.qubit)","title":""},{"docid":"0227504e3c701e5ebf8a78b9699befe6","score":"0.48646224","text":"def test_providing_no_observable_and_no_wires_shot_vector(self):\n num_wires = 2\n\n shots1 = 1\n shots2 = 10\n shots3 = 1000\n dev = qml.device(\"default.qubit\", wires=num_wires, shots=[shots1, shots2, shots3])\n\n @qml.qnode(dev)\n def circuit():\n qml.Hadamard(wires=0)\n return qml.sample()\n\n res = circuit()\n\n assert isinstance(res, tuple)\n\n expected_shapes = [(num_wires,), (num_wires, shots2), (num_wires, shots3)]\n assert len(res) == len(expected_shapes)\n assert (r.shape == exp_shape for r, exp_shape in zip(res, expected_shapes))","title":""},{"docid":"1a621244a18e07d64996d9dee1d54f87","score":"0.4860904","text":"def test_append_qubit_observables(self):\n with AnnotatedQueue() as q:\n # wire repetition is deliberate, Queue contains no checks/logic\n # for circuits\n ops = [\n qml.Hadamard(wires=0),\n qml.PauliX(wires=1),\n qml.PauliY(wires=1),\n qml.Hermitian(np.ones([2, 2]), wires=7),\n ]\n assert q.queue == ops","title":""},{"docid":"bdf6bf8147f1294b0977baf912a1d03a","score":"0.48548517","text":"def cl_encode(lengths):\n dic = dict(lengths)\n items = [dic.get(i, 0) for i in range(max(dic) + 1)]\n pos = 0\n while pos < len(items):\n if items[pos] == 0:\n # count repetitions of 0\n i = pos + 1\n while i < len(items) and items[i] == 0:\n i += 1\n if i - pos < 3:\n for i in range(pos, i):\n yield items[i]\n pos = i + 1\n else:\n repeat = i - pos\n if repeat < 11:\n yield (17, repeat - 3)\n else:\n yield (18, repeat - 11)\n pos = i\n else:\n item = items[pos]\n yield item\n i = pos + 1\n while i < len(items) and items[i] == item:\n i += 1\n repeat = i - pos - 1 # number of repetitions after 1st occurrence\n if repeat < 3:\n for i in range(repeat):\n yield item\n else:\n nb = repeat - 3\n while nb > 3:\n yield (16, 3)\n nb -= 3\n yield (16, nb)\n pos += repeat + 1","title":""},{"docid":"bbe3c8a15a90dd7e35d98abdd29d83a4","score":"0.48457897","text":"def test_two_qubit_synthesis_to_directional_cx_from_gate_errors(self):\n # TODO: should make check more explicit e.g. explicitly set gate\n # direction in test instead of using specific fake backend\n backend = FakeVigo()\n conf = backend.configuration()\n qr = QuantumRegister(2)\n coupling_map = CouplingMap(conf.coupling_map)\n triv_layout_pass = TrivialLayout(coupling_map)\n qc = QuantumCircuit(qr)\n qc.unitary(random_unitary(4, seed=12), [0, 1])\n unisynth_pass = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=None,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=False,\n )\n pm = PassManager([triv_layout_pass, unisynth_pass])\n qc_out = pm.run(qc)\n\n unisynth_pass_nat = UnitarySynthesis(\n basis_gates=conf.basis_gates,\n coupling_map=None,\n backend_props=backend.properties(),\n pulse_optimize=True,\n natural_direction=True,\n )\n\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\n qc_out_nat = pm_nat.run(qc)\n self.assertEqual(Operator(qc), Operator(qc_out))\n self.assertEqual(Operator(qc), Operator(qc_out_nat))","title":""},{"docid":"e3470d7b7b341f73c8b07e1843c49491","score":"0.48433205","text":"def test_truncate_connected_qubits(self):\n backend = self.backend()\n circuit = QuantumCircuit(20, 1)\n circuit.h(5)\n circuit.cx(5, 6)\n circuit.cx(6, 2),\n circuit.cx(2, 3)\n circuit.measure(3, 0)\n result = backend.run(circuit, shots=1).result()\n metadata = result.results[0].metadata\n self.assertEqual(metadata[\"num_qubits\"], 4)\n self.assertEqual(metadata[\"active_input_qubits\"], [2, 3, 5, 6])","title":""},{"docid":"6bcac3b0aa656749da6df98bb703e0fc","score":"0.48364782","text":"def __generate_plus6_eq_plus1_minus5_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([5, 6, 7, 8])\n return __extend_no_carry_borrow(a, 6)","title":""},{"docid":"2cc7120287205b8f69dc6dde95821b44","score":"0.48315218","text":"def generate_cross_traffic(mode, rate, q, p, n):\r\n # Constants\r\n capacity = 10e9/8 # capacity in bytes;\r\n #max_variance = 8e4 # maximum variance in bytes^2, \r\n max_variance = 8e4 # maximum variance in bytes^2, \r\n mean_packet_size = 500 # mean packet size in bytes\r\n \r\n # Compute u and s^2\r\n m = mean_packet_size\r\n v = q * max_variance\r\n u = math.log(m**2/math.sqrt(v + m**2))\r\n s = math.sqrt(math.log(1 + v/m**2))\r\n\r\n # initialize packet size vector\r\n packet_size = mean_packet_size * ones(n)\r\n\r\n # generate variable packet length\r\n if mode == 1 or mode == 3:\r\n # generate n lognormal values\r\n for i in range(1,n):\r\n p_size = 0\r\n while (p_size < 72 or p_size > 1526):\r\n p_size = round(random.lognormal(u, s))\r\n packet_size[i-1] = p_size\r\n \r\n # Mean packet size after discarding packets that are too small or too big\r\n realized_mean_packet_size = mean(packet_size)\r\n \r\n # Compute average interpacket gap in bytes\r\n if mode == 2: # mode 2 is the only mode where packet length is fixed\r\n mean_ipd = round(mean_packet_size * capacity / rate)\r\n mean_ipg = mean_ipd - mean_packet_size\r\n else:\r\n mean_ipd = round(realized_mean_packet_size * capacity / rate)\r\n mean_ipg = mean_ipd - realized_mean_packet_size \r\n \r\n # initialize idle vector\r\n idle = mean_ipg * ones(n) \r\n\r\n # generate variable interpacket gap\r\n if mode == 2 or mode == 3:\r\n k = 0 # keep track of number of packets generated\r\n while k <= n:\r\n if k == 0: # first idle\r\n chain_length = 1 \r\n else:\r\n chain_length = random.geometric(p)\r\n\r\n # set zero interpacket gap for\r\n for j in range(1, chain_length):\r\n if k+j-1 < n:\r\n idle[k+j-1] = 0\r\n\r\n # set sufficient interpacket gap between the chain and the next packet to achieve the desired data rate\r\n mean_ipg_to_set = mean_ipg * chain_length \r\n\r\n if k+chain_length-1 < n:\r\n idle[k+chain_length-1] = mean_ipg_to_set # we might change this to include some randomness later\r\n\r\n k = k + chain_length\r\n\r\n \r\n # Total time in bytes\r\n stream_length = sum(idle) + sum(packet_size)\r\n\r\n # Generated data rate\r\n generated_rate = capacity * sum(packet_size)/(sum(idle) + sum(packet_size))\r\n\r\n return (idle, packet_size, stream_length, generated_rate)","title":""},{"docid":"fdaa75b597fc5eae5b261b60f64addcd","score":"0.48175004","text":"def bell(Qubit1, Qubit2):\n h=Hadamard(Q0) if Qubit1==0 else Hadamard(Q1) \n x=np.concatenate(np.outer(h, Q1 if Qubit2==1 else Q0))\n return CNOT(x)","title":""},{"docid":"716675537743fc0ce2d99d3e95895875","score":"0.48159602","text":"def cross_rp_ind(self, i1, i2):\n half = int(random.randrange(1, self.GENS - 1))\n\n gen1 = i1['gens'][:half]\n gen1.extend(i2['gens'][half:])\n\n gen2 = i2['gens'][:half]\n gen2.extend(i1['gens'][half:])\n\n return Individual(gen1), Individual(gen2)","title":""},{"docid":"52aeb83bca15e0fb25b51cad17a603b8","score":"0.48128745","text":"def test_prep_circuit_three_qubits(self):\n # Input vector\n vec = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float64)\n\n # Make a tree from the vector\n tree = BinaryTree(vec)\n\n # Get the state preparation circuit\n areg = QuantumRegister(2)\n breg = QuantumRegister(1)\n circ = QuantumCircuit(areg, breg)\n tree.preparation_circuit(circ, areg, breg)\n\n # Add a swaps to make the ordering of the qubits match the input vector\n # Note: This is because the last bit is the most significant in qiskit, not the first.\n circ.swap(areg[0], breg[0])\n\n # Check that the circuit produces the correct state\n state = list(np.real(self.final_state(circ)))\n self.assertTrue(np.allclose(state, vec / np.linalg.norm(vec, ord=2)))","title":""},{"docid":"8e349786d6fa485d05d8d545923436bb","score":"0.48094317","text":"def test_extend_multiple_interface_qnodes(self):\n qc = qml.QNodeCollection()\n\n def circuit(x):\n qml.RX(x, wires=0)\n return qml.expval(qml.PauliZ(0))\n\n dev = qml.device(\"default.qubit\", wires=1)\n qnodes = [\n qml.QNode(circuit, dev, interface=\"autograd\"),\n qml.QNode(circuit, dev, interface=None),\n ]\n\n with pytest.raises(ValueError, match=\"do not all use the same interface\"):\n qc.extend(qnodes)","title":""},{"docid":"e5b70cd6537ac800ecc721e07e7faf99","score":"0.48064592","text":"def __generate_plus4_eq_minus6_plus10() -> Problem:\n a = 10 * random.choice(__digits_no49) + random.choice([6, 7, 8, 9])\n return __extend_no_carry_borrow(a, 4)","title":""},{"docid":"472a7e7971e16a10c9cae623e2517039","score":"0.48055854","text":"def test_single_switch_offset(self):\n\n for k in range(10):\n sk = str(k)\n for i in range(9):\n si = str(i)\n for j in range(i+1, 10):\n sj = str(j)\n self.assertNotEqual(encode(sk+si+sj), encode(sk+sj+si))","title":""},{"docid":"b76edc641106a9e6b940fdfb02b5237f","score":"0.48009458","text":"def test_output(self):\n '''Every sequence has 100 bases.'''\n input_seqs = \\\n ['TTACATACCATACAGTGCGCTAGCGGGTGACAGATATAATGCAGATCCAT'\n 'ACAGACCAGATGGCAGACATGTGTTGCAGSCTGCAAGTGCAACGCGGTGA',\n 'GCAGAGTGCCGCAATGACGTGCGCCAAAGCGGTGACAGGGTGACAGTGAA'\n 'CCAAGTGACAAGTGAACAGGTGCCAGAGTGACCGAGTGACCAGTGGACCA',\n 'CAGAGTGCCGCAATGACGTGCGCCAAAGCGGACAAAGCACCATGACAAGT'\n 'ACACAGGTGACAGTGACAAGACAGAGGTGACACAGAGAAAGtGGGTGTGA',\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA']\n sample_indexes = [\n {'1' : 2}, {'1' : 2, '2' : 3}, {'1' : 4, '3' : 5},\n {'1' : 5}, {'1' : 5}, {'1' : 5}\n ]\n reversed_complement_sample_indexes = [\n {'1' : 7}, {'1' : 2, '2' : 2, '3' : 3},\n {'1' : 8, '2' : 1, '3' : 6}, {'1' : 1},\n {'1' : 1}, {'1' : 1}\n ]\n cap_sizes = []\n cap_size = 25\n while cap_size <= 50:\n cap_sizes.append(cap_size)\n cap_size = int(cap_size*2)\n if cap_size == cap_sizes[-1]:\n cap_size += 1\n if cap_size != 50:\n # Always have a start or end read of length max_readlet_size\n cap_sizes.append(50)\n with open(self.output_file, 'w') as output_stream:\n for i in xrange(6):\n print_readletized_output(\n input_seqs[i], sample_indexes[i],\n reversed_complement_sample_indexes[i],\n '0:' + str(i), cap_sizes,\n output_stream=output_stream,\n min_readlet_size=25, readlet_interval=5,\n max_readlet_size=50, no_polyA=True\n )\n collected_readlets = []\n with open(self.output_file) as processed_stream:\n for readlet in processed_stream:\n collected_readlets.append(readlet.rstrip().split('\\t')[1:])\n '''Each read from input_reads spans 100 bases, and from the\n arguments passed to go() above, noncapping readlets should span 50.\n The capping fraction should arrange for one readlet spanning 25\n bases on either end of a given read. There should be 13 readlets in\n total. Spot-check some readlets after checking for read info.'''\n read_info = [info.split('\\x1e') for _, info\n in collected_readlets]\n read_info = [info[3:4]\n + [info[-i].split('\\x1f')\n for i in xrange(4, 0, -1)]\n for info in read_info if len(info) > 3]\n sample_indexes, reversed_complement_sample_indexes \\\n = [{} for i in xrange(6)], [{} for i in xrange(6)]\n # All-A read should have been skipped completely\n self.assertEquals(len(read_info), 5)\n for i in xrange(5):\n for j in xrange(len(read_info[i][1])):\n sample_indexes[i][read_info[i][1][j]] \\\n = int(read_info[i][3][j])\n reversed_complement_sample_indexes[i][read_info[i][2][j]] \\\n = int(read_info[i][4][j])\n read_info[i] = read_info[i][:-2]\n read_info[i][1] = sample_indexes[i]\n read_info[i][2] = reversed_complement_sample_indexes[i]\n self.assertTrue([\n 'TTACATACCATACAGTGCGCTAGCGGGTGACAGATATAATGCAGATCCAT'\n 'ACAGACCAGATGGCAGACATGTGTTGCAGSCTGCAAGTGCAACGCGGTGA',\n sample_indexes[0],\n reversed_complement_sample_indexes[0]\n ] in read_info)\n self.assertTrue([\n 'GCAGAGTGCCGCAATGACGTGCGCCAAAGCGGTGACAGGGTGACAGTGAA'\n 'CCAAGTGACAAGTGAACAGGTGCCAGAGTGACCGAGTGACCAGTGGACCA',\n sample_indexes[1],\n reversed_complement_sample_indexes[1]\n ] in read_info)\n self.assertTrue([\n 'CAGAGTGCCGCAATGACGTGCGCCAAAGCGGACAAAGCACCATGACAAGT'\n 'ACACAGGTGACAGTGACAAGACAGAGGTGACACAGAGAAAGtGGGTGTGA',\n sample_indexes[2],\n reversed_complement_sample_indexes[2]\n ] in read_info)\n self.assertTrue([\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\n sample_indexes[3],\n reversed_complement_sample_indexes[3]\n ] in read_info)\n self.assertTrue([\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\n sample_indexes[3],\n reversed_complement_sample_indexes[3]\n ] in read_info)\n self.assertTrue([\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\n sample_indexes[3],\n reversed_complement_sample_indexes[3]\n ] in read_info)\n # Capping readlets\n self.assertTrue([ 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA',\n '0:3+\\x1e0\\x1e50'\n ] in collected_readlets or\n [ 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA',\n '0:3+\\x1e0\\x1e50\\x1e'\n 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGACGACAGTAGC'\n '\\x1e1\\x1e1\\x1e1\\x1e1'\n ] in collected_readlets\n )\n self.assertTrue([ 'CCAGTGCCAGATGGACGACAGTAGC',\n '0:3+\\x1e75\\x1e0'\n ] in collected_readlets or\n [ 'CCAGTGCCAGATGGACGACAGTAGC',\n '0:3+\\x1e75\\x1e0\\x1e'\n 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGACGACAGTAGC'\n '\\x1e1\\x1e1\\x1e1\\x1e1'\n ] in collected_readlets\n )\n # Noncapping readlets\n self.assertTrue([ 'GTCAG'\n 'TTATCTATTATGGGCGCAATGTCTA'\n 'TGTTATCTGTTATAGCTTAA',\n '0:3-\\x1e5\\x1e45'\n ] in collected_readlets or\n [ 'GTCAG'\n 'TTATCTATTATGGGCGCAATGTCTA'\n 'TGTTATCTGTTATAGCTTAA',\n '0:3-\\x1e5\\x1e45\\x1e'\n 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGACGACAGTAGC'\n '\\x1e1\\x1e1\\x1e1\\x1e1'\n ] in collected_readlets\n )\n self.assertTrue([ 'TAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGA',\n '0:3+\\x1e40\\x1e10'\n ] in collected_readlets or\n [ 'TAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGA',\n '0:3+\\x1e40\\x1e10\\x1e'\n 'ATCGATTAAGCTATAACAGATAACA'\n 'TAGACATTGCGCCCATAATAGATAA'\n 'CTGACACCTGACCAGTGCCAGATGA'\n 'CCAGTGCCAGATGGACGACAGTAGC'\n '\\x1e1\\x1e1\\x1e1\\x1e1'\n ] in collected_readlets\n )\n # Ensure no polyA readlets\n self.assertEquals([readlet for readlet in collected_readlets\n if set(readlet[0]) == _polyA], [])","title":""},{"docid":"281af1ea2200aae5e6bfebf99911911c","score":"0.47987187","text":"def test_associativity_exact():\n\n N = 5\n g = Grid_1D.periodic(N)\n\n a0 = g.rand(0)\n b0 = g.rand(0)\n c1 = g.rand(0).D\n\n eq1 = a0 ^ (b0 ^ c1)\n eq2 = (a0 ^ b0) ^ c1\n\n assert not eq1 == eq2","title":""},{"docid":"de8f989edf5a1d8cafe5cbb022cad5dd","score":"0.47964856","text":"def test_qv_natural(self):\n qv64 = QuantumVolume(5, seed=15)\n\n def construct_passmanager(basis_gates, coupling_map, synthesis_fidelity, pulse_optimize):\n seed = 2\n _map = [SabreLayout(coupling_map, max_iterations=2, seed=seed)]\n _unroll3q = Unroll3qOrMore()\n _swap_check = CheckMap(coupling_map)\n _swap = [\n BarrierBeforeFinalMeasurements(),\n SabreSwap(coupling_map, heuristic=\"lookahead\", seed=seed),\n ]\n _optimize = [\n Collect2qBlocks(),\n ConsolidateBlocks(basis_gates=basis_gates),\n UnitarySynthesis(\n basis_gates,\n synthesis_fidelity,\n coupling_map,\n pulse_optimize=pulse_optimize,\n natural_direction=True,\n ),\n Optimize1qGates(basis_gates),\n ]\n\n pm = PassManager()\n pm.append(_map) # map to hardware by inserting swaps\n pm.append(_unroll3q)\n pm.append(_swap_check)\n pm.append(_swap)\n pm.append(_optimize)\n return pm\n\n coupling_map = CouplingMap([[0, 1], [1, 2], [3, 2], [3, 4], [5, 4]])\n basis_gates = [\"rz\", \"sx\", \"cx\"]\n\n pm1 = construct_passmanager(\n basis_gates=basis_gates,\n coupling_map=coupling_map,\n synthesis_fidelity=0.99,\n pulse_optimize=True,\n )\n pm2 = construct_passmanager(\n basis_gates=basis_gates,\n coupling_map=coupling_map,\n synthesis_fidelity=0.99,\n pulse_optimize=False,\n )\n\n qv64_1 = pm1.run(qv64.decompose())\n qv64_2 = pm2.run(qv64.decompose())\n edges = [list(edge) for edge in coupling_map.get_edges()]\n self.assertTrue(\n all(\n [qv64_1.qubits.index(qubit) for qubit in instr.qubits] in edges\n for instr in qv64_1.get_instructions(\"cx\")\n )\n )\n self.assertEqual(Operator(qv64_1), Operator(qv64_2))","title":""},{"docid":"3f3fc7e6872534e7e7f3b584c2830f6c","score":"0.47952512","text":"def test_bc_inout():\n k = 10 # number of workers\n t = 1000\n shape = {\n \"num-phases\": 1,\n \"num-workers\": k,\n \"activity-infix\": \"Buffer\",\n \"activity-prefix\": \"Serialization\",\n \"activity-suffix\": \"Deserialization\",\n \"phase-duration\": t,\n }\n data = generate(\"bc_inout\", shape, 1.0)\n\n # the prefixes and suffixes must have the same bc\n for w in range(1, k):\n assert data[epoch(0)][('Serialization', w)] == data[epoch(0)][('Deserialization', w)]","title":""},{"docid":"e0346df113493bb8957e4faac3221ab6","score":"0.47899675","text":"def w(self, length):\n q = Qubits(length)\n l = [Integer(0)]*q.size\n for ix in xrange(length):\n number = Integer(2)**Integer(ix)\n l[number] = (Integer(1)/length)**(half)\n q.v = Matrix(l)\n return q","title":""},{"docid":"bb1a1a244003abf99a0d0582ba962bb0","score":"0.47890088","text":"def test_gate_more_two_qubits(self) -> None:\n with pytest.raises(PluginException):\n circuit = generate_qft_circuit(max_nbqbit, inline=False)\n qpu = Sabre() | (QuameleonPlugin(topology=Topology(type=TopologyType.LNN)) | PyLinalg())\n qpu.submit(circuit.to_job())","title":""},{"docid":"3b49304b52f1048b1d788902ee0b9ad3","score":"0.4785169","text":"def add_twoQ_clifford(index, gate_seq_1, gate_seq_2):\n if (index < 0):\n raise ValueError(\n 'index is out of range. it should be smaller than 11520 and '\n 'greater or equal to 0: ', str(index))\n elif (index < 576):\n add_singleQ_based_twoQ_clifford(index, gate_seq_1, gate_seq_2)\n elif (index < 5184 + 576):\n add_CNOT_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\n elif (index < 5184 + 5184 + 576):\n add_iSWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\n elif (index < 576 + 5184 + 5184 + 576):\n add_SWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\n else:\n raise ValueError(\n 'index is out of range. it should be smaller than 11520 and '\n 'greater or equal to 0: ', str(index))\n\n pass","title":""},{"docid":"90c82c2f77a2d77c7389b50a8da111f1","score":"0.47811636","text":"def generate_cuboids():\n yield (1, 1, 1)\n c = 2\n while True:\n for a in range(1, c+1):\n for b in range(a, c+1):\n yield (a, b, c)\n c += 1","title":""}],"string":"[\n {\n \"docid\": \"3bbdc1b872fee8bfda6527b96be9eb4e\",\n \"score\": \"0.6130477\",\n \"text\": \"def test_two_qubit_gates_with_symbols(gate: cirq.Gate, expected_length: int):\\n q0 = cirq.GridQubit(5, 3)\\n q1 = cirq.GridQubit(5, 4)\\n original_circuit = cirq.Circuit(gate(q0, q1))\\n converted_circuit = original_circuit.copy()\\n cgoc.ConvertToSqrtIswapGates().optimize_circuit(converted_circuit)\\n assert len(converted_circuit) <= expected_length\\n\\n # Check if unitaries are the same\\n for val in np.linspace(0, 2 * np.pi, 12):\\n assert _unitaries_allclose(\\n cirq.resolve_parameters(original_circuit, {'t': val}),\\n cirq.resolve_parameters(converted_circuit, {'t': val}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"323d4e692dd9d99e1d7cfeb0ac233584\",\n \"score\": \"0.5952881\",\n \"text\": \"def test_multi_cregs(self):\\n\\n # ┌───┐ ░ ┌─┐\\n # qr_0: ──■────────────┤ X ├─░─┤M├─────────\\n # ┌─┴─┐ ┌───┐└─┬─┘ ░ └╥┘┌─┐\\n # qr_1: ┤ X ├──■──┤ H ├──■───░──╫─┤M├──────\\n # └───┘┌─┴─┐└───┘ ░ ║ └╥┘┌─┐\\n # qr_2: ──■──┤ X ├───────────░──╫──╫─┤M├───\\n # ┌─┴─┐└───┘ ░ ║ ║ └╥┘┌─┐\\n # qr_3: ┤ X ├────────────────░──╫──╫──╫─┤M├\\n # └───┘ ░ ║ ║ ║ └╥┘\\n # c: 2/════════════════════════╩══╬══╩══╬═\\n # 0 ║ 1 ║\\n # ║ ║\\n # d: 2/═══════════════════════════╩═════╩═\\n # 0 1\\n qr = QuantumRegister(4, \\\"qr\\\")\\n cr1 = ClassicalRegister(2, \\\"c\\\")\\n cr2 = ClassicalRegister(2, \\\"d\\\")\\n circuit = QuantumCircuit(qr, cr1, cr2)\\n circuit.cx(qr[0], qr[1])\\n circuit.cx(qr[2], qr[3])\\n circuit.cx(qr[1], qr[2])\\n circuit.h(qr[1])\\n circuit.cx(qr[1], qr[0])\\n circuit.barrier(qr)\\n circuit.measure(qr[0], cr1[0])\\n circuit.measure(qr[1], cr2[0])\\n circuit.measure(qr[2], cr1[1])\\n circuit.measure(qr[3], cr2[1])\\n\\n coupling = CouplingMap([[0, 1], [0, 2], [2, 3]]) # linear [1, 0, 2, 3]\\n property_set = {}\\n with self.assertWarnsRegex(DeprecationWarning, r\\\"^The class.*is deprecated\\\"):\\n actual = BIPMapping(coupling, objective=\\\"depth\\\")(circuit, property_set)\\n self.assertEqual(5, actual.depth())\\n\\n CheckMap(coupling)(actual, property_set)\\n self.assertTrue(property_set[\\\"is_swap_mapped\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bdea335c37bba2886a805f49781e64\",\n \"score\": \"0.5805034\",\n \"text\": \"def _generate_circuit_sequence(\\n repeated_circuit: circuits.Circuit,\\n different_circuit: circuits.Circuit,\\n length: int,\\n position: int,\\n):\\n if position >= length:\\n raise ValueError(f\\\"Position {position} should be < {length}\\\")\\n\\n return circuits.Circuit(\\n list(\\n chain.from_iterable(\\n [\\n (\\n repeated_circuit if i != position else different_circuit\\n ).operations\\n for i in range(length)\\n ]\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7f56dbb30c6bdaf883cbce48f124de\",\n \"score\": \"0.57560784\",\n \"text\": \"def test_two_qubit_gates(gate: cirq.Gate, expected_length: int):\\n q0 = cirq.GridQubit(5, 3)\\n q1 = cirq.GridQubit(5, 4)\\n original_circuit = cirq.Circuit(gate(q0, q1))\\n converted_circuit = original_circuit.copy()\\n cgoc.ConvertToSqrtIswapGates().optimize_circuit(converted_circuit)\\n cig.SQRT_ISWAP_GATESET.serialize(converted_circuit)\\n assert len(converted_circuit) <= expected_length\\n assert _unitaries_allclose(original_circuit, converted_circuit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a79052d835fdfd4a228aebfa73656ce\",\n \"score\": \"0.5681587\",\n \"text\": \"def test_two_qubit_synthesis_to_directional_cx_multiple_registers(self):\\n # TODO: should make check more explicit e.g. explicitly set gate\\n # direction in test instead of using specific fake backend\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr0 = QuantumRegister(1)\\n qr1 = QuantumRegister(1)\\n coupling_map = CouplingMap(conf.coupling_map)\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr0, qr1)\\n qc.unitary(random_unitary(4, seed=12), [qr0[0], qr1[0]])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=None,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n\\n unisynth_pass_nat = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=None,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=True,\\n )\\n\\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\\n qc_out_nat = pm_nat.run(qc)\\n self.assertEqual(Operator(qc), Operator(qc_out))\\n self.assertEqual(Operator(qc), Operator(qc_out_nat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cc3e5608df757c3201d742ce1693eb\",\n \"score\": \"0.55983335\",\n \"text\": \"def add_singleQ_based_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\\n index_1 = index % 24 # randomly sample from single qubit cliffords (24)\\n # randomly sample from single qubit cliffords (24)\\n index_2 = (index // 24) % 24\\n add_singleQ_clifford(index_1, gate_seq_1, )\\n add_singleQ_clifford(index_2, gate_seq_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee3f5a4b0be0a70dffcf1f4e7a33b74\",\n \"score\": \"0.5594737\",\n \"text\": \"def test_qubit_identity(self, rep, tol):\\n\\n p = 0.543\\n\\n dev = qml.device(\\\"default.tensor\\\", wires=1, representation=rep)\\n\\n @qml.qnode(dev)\\n def circuit(x):\\n \\\"\\\"\\\"Test quantum function\\\"\\\"\\\"\\n qml.RX(x, wires=0)\\n return qml.expval(qml.Identity(0))\\n\\n assert np.isclose(circuit(p), 1, atol=tol, rtol=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc4b90d4734a2e6e379901635eed1604\",\n \"score\": \"0.5589917\",\n \"text\": \"def circuit_identity(n):\\n if n <= 0:\\n return CircuitZero\\n if n == 1:\\n return CIdentity\\n return Concatenation(*((CIdentity,) * n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1c3298d327352f75f0c189de7ffc25\",\n \"score\": \"0.55772567\",\n \"text\": \"def add_CNOT_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\\n index_1 = index % 3 # randomly sample from S1 (3)\\n index_2 = (index // 3) % 3 # randomly sample from S1 (3)\\n # randomly sample from single qubit cliffords (24)\\n index_3 = (index // 3 // 3) % 24\\n # randomly sample from single qubit cliffords (24)\\n index_4 = (index // 3 // 3 // 24) % 24\\n\\n generator = kwargs.get('generator', 'CZ')\\n if generator == 'CZ':\\n add_singleQ_S1(index_1, gate_seq_1)\\n add_singleQ_S1_Y2p(index_2, gate_seq_2)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n add_singleQ_clifford(index_3, gate_seq_1)\\n add_singleQ_clifford(index_4, gate_seq_2)\\n\\n elif generator == 'iSWAP':\\n add_singleQ_S1(index_1, gate_seq_1)\\n add_singleQ_S1_Z2p(index_2, gate_seq_2)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n gate_seq_1.append(Gate.X2p)\\n gate_seq_2.append(Gate.I)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n add_singleQ_clifford(index_3, gate_seq_1)\\n add_singleQ_clifford(index_4, gate_seq_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555890a62921b9d25189a6f87a3d6ae4\",\n \"score\": \"0.5503414\",\n \"text\": \"def initialize_entangled_qubits(final_measure=True):\\n\\n circuits = []\\n qr = QuantumRegister(3)\\n if final_measure:\\n cr = ClassicalRegister(3)\\n regs = (qr, cr)\\n else:\\n regs = (qr,)\\n\\n # Initialize |000+111> -> |000+110>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([1, 0], [0])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |010+111>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([0, 1], [1])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |000+011>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([1, 0], [2])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |000+100>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([1, 0, 0, 0], [0, 1])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |100+110>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([0, 0, 0, 1], [0, 2])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |000+100+010+110+100+101+011+111>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([0.5, 0.5, 0.5, 0.5], [1, 2])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n # Initialize |000+111> -> |100>\\n circuit = QuantumCircuit(*regs)\\n circuit.h(0)\\n circuit.cx(0, 1)\\n circuit.cx(0, 2)\\n circuit.initialize([0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 2])\\n if final_measure:\\n circuit.barrier(qr)\\n circuit.measure(qr, cr)\\n circuits.append(circuit)\\n\\n return circuits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1adfaa1bdaa7fe465a968736e6f218c9\",\n \"score\": \"0.55020934\",\n \"text\": \"def add_SWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\\n index_1 = index % 24 # randomly sample from single qubit cliffords (24)\\n # randomly sample from single qubit cliffords (24)\\n index_2 = (index // 24) % 24\\n generator = kwargs.get('generator', 'CZ')\\n if generator == 'CZ':\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.Y2p)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n gate_seq_1.append(Gate.Y2p)\\n gate_seq_2.append(Gate.Y2m)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n gate_seq_1.append(Gate.Y2m)\\n gate_seq_2.append(Gate.Y2p)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n add_singleQ_clifford(index_1, gate_seq_1)\\n add_singleQ_clifford(index_2, gate_seq_2)\\n\\n elif generator == 'iSWAP':\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.X2m)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n gate_seq_1.append(Gate.X2m)\\n gate_seq_2.append(Gate.I)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.X2m)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n add_singleQ_clifford(index_1, gate_seq_1)\\n add_singleQ_clifford(index_2, gate_seq_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf0ffcfd080f5773adbdaa2f8ae599d\",\n \"score\": \"0.55015194\",\n \"text\": \"def q1():\\n # number of different sides and length\\n length = 5\\n sid = 2\\n\\n # first set can be 2, next 2, next 2 and so on for length 5 or 2 ** 5\\n tot = sid ** length\\n print(\\\"Question 1: {}\\\".format(tot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"674c673c1fbbc7d5b3c23d58de205daa\",\n \"score\": \"0.54571664\",\n \"text\": \"def test_coupling_map_unequal_durations(self, opt):\\n qr = QuantumRegister(2)\\n circ = QuantumCircuit(qr)\\n circ.append(random_unitary(4, seed=1), [1, 0])\\n backend = FakeVigo()\\n tqc = transpile(\\n circ,\\n backend=backend,\\n optimization_level=opt,\\n translation_method=\\\"synthesis\\\",\\n layout_method=\\\"trivial\\\",\\n )\\n tqc_index = {qubit: index for index, qubit in enumerate(tqc.qubits)}\\n self.assertTrue(\\n all(\\n (\\n (0, 1) == (tqc_index[instr.qubits[0]], tqc_index[instr.qubits[1]])\\n for instr in tqc.get_instructions(\\\"cx\\\")\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bd6725709b5a3d2bdf9f8060c66da20\",\n \"score\": \"0.5422984\",\n \"text\": \"def test_repeat(self):\\n\\n features = list(range(3))\\n\\n expected_names = self.QUEUES[2][1] + self.QUEUES[2][1]\\n expected_wires = self.QUEUES[2][2] + self.QUEUES[2][2]\\n\\n op = qml.IQPEmbedding(features, wires=range(3), n_repeats=2)\\n tape = op.expand()\\n\\n for i, gate in enumerate(tape.operations):\\n assert gate.name == expected_names[i]\\n assert gate.wires.labels == tuple(expected_wires[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a875a97b59aa82a53a642aa562ae753\",\n \"score\": \"0.53944117\",\n \"text\": \"def circuit_built(circuit):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8de663ad5b0ec5be3ad0a32df0e334f3\",\n \"score\": \"0.5392747\",\n \"text\": \"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map(self):\\n # TODO: should make check more explicit e.g. explicitly set gate\\n # direction in test instead of using specific fake backend\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n\\n unisynth_pass_nat = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=True,\\n )\\n\\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\\n qc_out_nat = pm_nat.run(qc)\\n # the decomposer defaults to the [1, 0] direction but the coupling\\n # map specifies a [0, 1] direction. Check that this is respected.\\n self.assertTrue(\\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\\\"cx\\\")))\\n )\\n self.assertTrue(\\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out_nat.get_instructions(\\\"cx\\\")))\\n )\\n self.assertEqual(Operator(qc), Operator(qc_out))\\n self.assertEqual(Operator(qc), Operator(qc_out_nat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bbc7c72a7b76021aff856dc1731feec\",\n \"score\": \"0.53244096\",\n \"text\": \"def test_pk_gen_2():\\n dev = LedgerQRL()\\n\\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 0)\\n assert answer is not None\\n assert len(answer) == 32\\n leaf = binascii.hexlify(answer).upper()\\n print(leaf)\\n assert leaf == \\\"98E68D7AB40D358B5B0F4DF4C86AAE78B444BD50248C02773CF1965FAEA092AE\\\"\\n sys.stdout.flush()\\n\\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 20)\\n assert len(answer) == 32\\n leaf = binascii.hexlify(answer).upper()\\n print(leaf)\\n assert leaf == \\\"8E66C0B26238BC9E12804A83AEF0429E9A666266001A826B5025889B45AE86A3\\\"\\n sys.stdout.flush()\\n\\n answer = dev.send(INS_TEST_PK_GEN_2, 0, 40)\\n assert len(answer) == 32\\n leaf = binascii.hexlify(answer).upper()\\n print(leaf)\\n assert leaf == \\\"D2BAD383B25900503A34FA126ABB19D3AAC6FC110F431929C7EB18E613E101F8\\\"\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99db10fad448c8f59c7aa0e21059c839\",\n \"score\": \"0.53199863\",\n \"text\": \"def get2qb():\\n circuit = QuantumCircuit(2, 2)\\n return circuit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b2414ebc8a5f74312a9d4c95b67b43\",\n \"score\": \"0.53060704\",\n \"text\": \"def add_iSWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2, **kwargs):\\n generator = kwargs.get('generator', 'CZ')\\n index_1 = index % 3 # randomly sample from S1_Y2p (3)\\n index_2 = (index // 3) % 3 # randomly sample from S1_X2p(3)\\n # randomly sample from single qubit cliffords (24)\\n index_3 = (index // 3 // 3) % 24\\n # randomly sample from single qubit cliffords (24)\\n index_4 = (index // 3 // 3 // 24) % 24\\n\\n if generator == 'CZ':\\n add_singleQ_S1_Y2p(index_1, gate_seq_1)\\n add_singleQ_S1_X2p(index_2, gate_seq_2)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n gate_seq_1.append(Gate.Y2p)\\n gate_seq_2.append(Gate.X2m)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.CZ)\\n add_singleQ_clifford(index_3, gate_seq_1)\\n add_singleQ_clifford(index_4, gate_seq_2)\\n\\n elif generator == 'iSWAP':\\n add_singleQ_S1(index_1, gate_seq_1)\\n add_singleQ_S1(index_2, gate_seq_2)\\n gate_seq_1.append(Gate.I)\\n gate_seq_2.append(Gate.iSWAP)\\n add_singleQ_clifford(index_3, gate_seq_1)\\n add_singleQ_clifford(index_4, gate_seq_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2625c52202f35104d3cd6a1a88d1df\",\n \"score\": \"0.52617085\",\n \"text\": \"def nqubit_1pauli(pauli, i, n):\\n #create identity padding\\n iden1 = [qto.identity(2) for j in range(i)]\\n iden2 = [qto.identity(2) for j in range(n-i-1)]\\n\\n #combine into total operator list that is in proper order\\n oplist = iden1 + [pauli] + iden2\\n\\n #create final operator by using tensor product on unpacked operator list\\n operator = qt.tensor(*oplist)\\n\\n return operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a940fb8fa0a5cf42ad2861d4b28cf2\",\n \"score\": \"0.52558017\",\n \"text\": \"def your_circuit(oracle):\\n # phase kickback trick\\n yield cirq.X(q2), cirq.H(q2)\\n\\n # equal superposition over input bits\\n yield cirq.H(q0), cirq.H(q1)\\n\\n # query the function\\n yield oracle\\n\\n # interference to get result, put last qubit into |1>\\n yield cirq.H(q0), cirq.H(q1), cirq.H(q2)\\n\\n # a final OR gate to put result in final qubit\\n yield cirq.X(q0), cirq.X(q1), cirq.CCX(q0, q1, q2)\\n yield cirq.measure(q2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1660f52f1e84c14cd43ef6be66e688f4\",\n \"score\": \"0.5244218\",\n \"text\": \"def make_bits_equal(self):\\n difference = abs(len(self.num1) - len(self.num2))\\n if len(self.num1) == len(self.num2):\\n print(\\\"Equal\\\")\\n elif len(self.num1) > len(self.num2):\\n for i in range(difference):\\n self.num2 = \\\"0\\\" + self.num2\\n elif len(self.num2) > len(self.num1):\\n for i in range(difference):\\n self.num1 = \\\"0\\\" + self.num1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8284513df19115b1059f755f1cc8acb7\",\n \"score\": \"0.524299\",\n \"text\": \"def eq0405():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84487e86c7583353a631f30b77e43be\",\n \"score\": \"0.52325124\",\n \"text\": \"def pad_with_identity(circuit, k, n):\\n circuit_n = circuit.cdim\\n combined_circuit = circuit + circuit_identity(n)\\n permutation = (list(range(k)) + list(range(circuit_n, circuit_n + n)) +\\n list(range(k, circuit_n)))\\n return (CPermutation.create(invert_permutation(permutation)) <<\\n combined_circuit << CPermutation.create(permutation))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3380dd50ea745a606bb71d9b52b24b6\",\n \"score\": \"0.5230486\",\n \"text\": \"def __generate_from_4x_to_5x() -> Problem:\\n a = random.choice(range(41, 50))\\n b = random.choice(range(50, a + 10))\\n return __extend_no_carry_borrow(a, b - a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e605058e07bb0a9699116ac3d0d96c16\",\n \"score\": \"0.5218623\",\n \"text\": \"def nqubit_2pauli(ipauli, jpauli, i, j, n):\\n #create identity padding\\n iden1 = [qto.identity(2) for m in range(i)]\\n iden2 = [qto.identity(2) for m in range(j-i-1)]\\n iden3 = [qto.identity(2) for m in range(n-j-1)]\\n\\n #combine into total operator list\\n oplist = iden1 + [ipauli] + iden2 + [jpauli] + iden3\\n\\n # apply tensor product on unpacked oplist\\n operator = qt.tensor(*oplist)\\n\\n return operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd7c33cc95b0801d7e5ac104f8ce4ac\",\n \"score\": \"0.5214872\",\n \"text\": \"def test_prep_circuit_large2(self):\\n # Input vector (normalized)\\n vec = np.array(list(np.ones(32)) + list(np.zeros(32)))\\n\\n # Make a tree from the vector\\n tree = BinaryTree(vec)\\n\\n # Do the state preparation circuit\\n qreg = QuantumRegister(6)\\n circ = QuantumCircuit(qreg)\\n tree.preparation_circuit(circ, qreg)\\n\\n # Do the swaps to get the ordering of amplitudes to match with the input vector\\n for ii in range(len(qreg) // 2):\\n circ.swap(qreg[ii], qreg[-ii - 1])\\n\\n # Check that the circuit produces the correct state\\n state = np.real(self.final_state(circ))\\n\\n # Note: The output state has an additional ancilla needed to do the multi-controlled-Y rotations,\\n # so we discard the additional (zero) amplitudes when comparing to the input vector\\n self.assertTrue(np.allclose(state[:len(vec)], vec / np.linalg.norm(vec, ord=2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8966041d2e44968c492bd204b7fb99c7\",\n \"score\": \"0.5196272\",\n \"text\": \"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map_natural_false(self):\\n # TODO: should make check more explicit e.g. explicitly set gate\\n # direction in test instead of using specific fake backend\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n\\n unisynth_pass_nat = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n\\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\\n qc_out_nat = pm_nat.run(qc)\\n # the decomposer defaults to the [1, 0] direction but the coupling\\n # map specifies a [0, 1] direction. Check that this is respected.\\n self.assertTrue(\\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\\\"cx\\\")))\\n )\\n self.assertTrue(\\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out_nat.get_instructions(\\\"cx\\\")))\\n )\\n self.assertEqual(Operator(qc), Operator(qc_out))\\n self.assertEqual(Operator(qc), Operator(qc_out_nat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b8eb254dc562353375700500e67926e\",\n \"score\": \"0.5195183\",\n \"text\": \"def joint_card(c1,c2):\\n return '{' + ('1' if c1[1] == c2[1] == '1' else '0') + ':' + ('1' if c1[3] == c2[3] == '1' else 'M') + '}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357ec7d9764c28c00274f7e4e36a698e\",\n \"score\": \"0.5186929\",\n \"text\": \"def test_QI2():\\n # Tests checked against Mathematica noteboook `costingsf.nb`\\n # Arguments are otherwise random\\n assert QI2(1234, 5678) == (32, 64, 5519)\\n assert QI2(7120, 1340111) == (4, 32768, 204052)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ff814b48ebf1f000d51185762015d7\",\n \"score\": \"0.5177073\",\n \"text\": \"def test_prep_ctrl_random_twoq(self):\\n for _ in range(50):\\n vec = np.random.randn(4)\\n tree = BinaryTree(vec)\\n\\n register = QuantumRegister(2)\\n ctrl_reg = QuantumRegister(2)\\n circ = QuantumCircuit(register, ctrl_reg)\\n ctrl_key = 0\\n\\n tree.preparation_circuit(circ, register, control_register=ctrl_reg, control_key=ctrl_key)\\n\\n circ.swap(register[0], register[1])\\n\\n state = np.real(TestBinaryTree.final_state(circ))\\n\\n self.assertTrue(np.allclose(state[:len(vec)], vec / np.linalg.norm(vec, ord=2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392daa1854fbb4d394bb86c933599999\",\n \"score\": \"0.517426\",\n \"text\": \"def __generate_from_5x_to_4x() -> Problem:\\n a = random.choice(range(50, 58))\\n b = random.choice(range(a - 9, 50))\\n return __extend_no_carry_borrow(a, b - a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819858c64ba08ec36e573850c9e7a4b9\",\n \"score\": \"0.51735836\",\n \"text\": \"def create_data(self, length):\\n i = np.random.randint(2 ,size=length) * 2 - 1\\n q = np.random.randint(2 ,size=length) * 2 - 1\\n return i, q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bfd9a613df6c7abacc5fdd812ce900d\",\n \"score\": \"0.51712984\",\n \"text\": \"def test_prepare_negative_amplitudes_two_qubits2(self):\\n # Generate all sign configurations\\n one_neg = set(permutations((-1, 1, 1, 1)))\\n two_neg = set(permutations((-1, -1, 1, 1)))\\n three_neg = set(permutations((-1, -1, -1, 1)))\\n four_neg = {(-1, -1, -1, -1)}\\n\\n for sign in one_neg | two_neg | three_neg | four_neg:\\n # Input vector\\n vec = np.array([1, 2, 3, 4], dtype=np.float64)\\n vec *= np.array(sign, dtype=np.float64)\\n\\n # Get a BinaryTree\\n tree = BinaryTree(vec)\\n\\n # Get a quantum register\\n qreg = QuantumRegister(2)\\n circ = QuantumCircuit(qreg)\\n\\n # Get the state preparation circuit\\n tree.preparation_circuit(circ, qreg)\\n\\n # Swap qubits to compare with natural ordering of vector\\n circ.swap(qreg[0], qreg[1])\\n\\n # Make sure the final state is the same as the input vector\\n state = np.real(self.final_state(circ))\\n self.assertTrue(np.allclose(state, vec / np.linalg.norm(vec, ord=2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4a04ec5ee1b1835fbd5d84b592643e\",\n \"score\": \"0.5168186\",\n \"text\": \"def test_selfinnerproduct(self):\\n qkclass = QuantumKernel(feature_map=self.feature_map)\\n qc = qkclass.construct_circuit(self.x)\\n self._check_circuit(qc, check_measurements=True, check_inverse=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b7496d98f840eaf3a7edbd1f98224e\",\n \"score\": \"0.51566935\",\n \"text\": \"def test_two_qubit_synthesis_to_directional_cx_from_coupling_map_natural_none(self):\\n # TODO: should make check more explicit e.g. explicitly set gate\\n # direction in test instead of using specific fake backend\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n\\n unisynth_pass_nat = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=None,\\n )\\n\\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\\n qc_out_nat = pm_nat.run(qc)\\n # the decomposer defaults to the [1, 0] direction but the coupling\\n # map specifies a [0, 1] direction. Check that this is respected.\\n self.assertTrue(\\n all(((qr[1], qr[0]) == instr.qubits for instr in qc_out.get_instructions(\\\"cx\\\")))\\n )\\n self.assertTrue(\\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out_nat.get_instructions(\\\"cx\\\")))\\n )\\n self.assertEqual(Operator(qc), Operator(qc_out))\\n self.assertEqual(Operator(qc), Operator(qc_out_nat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8048578c69d1a5bc510dc432637ea54\",\n \"score\": \"0.5130123\",\n \"text\": \"def __idiv__(self, other):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104b28686f433117f1f96742c210000f\",\n \"score\": \"0.510977\",\n \"text\": \"def test_two_qubit_synthesis_not_pulse_optimal(self):\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=False,\\n natural_direction=True,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n if isinstance(qc_out, QuantumCircuit):\\n num_ops = qc_out.count_ops()\\n else:\\n num_ops = qc_out[0].count_ops()\\n self.assertIn(\\\"sx\\\", num_ops)\\n self.assertGreaterEqual(num_ops[\\\"sx\\\"], 16)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3018a171de0bc3f002ec9147df2a9b1\",\n \"score\": \"0.5099306\",\n \"text\": \"def test_single_qubit_identity_with_target(self):\\n qc = QuantumCircuit(1)\\n qc.unitary([[1.0, 0.0], [0.0, 1.0]], 0)\\n dag = circuit_to_dag(qc)\\n unitary_synth_pass = UnitarySynthesis(target=FakeBelemV2().target)\\n result_dag = unitary_synth_pass.run(dag)\\n result_qc = dag_to_circuit(result_dag)\\n self.assertEqual(result_qc, QuantumCircuit(1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a6d091d722790efe91922ec25b6b58\",\n \"score\": \"0.50964344\",\n \"text\": \"def test_two_qubit_natural_direction_true_gate_length_raises(self):\\n # this assumes iswawp pulse optimal decomposition doesn't exist\\n backend = FakeVigo()\\n conf = backend.configuration()\\n for _, nduv in backend.properties()._gates[\\\"cx\\\"].items():\\n nduv[\\\"gate_length\\\"] = (4e-7, nduv[\\\"gate_length\\\"][1])\\n nduv[\\\"gate_error\\\"] = (7e-3, nduv[\\\"gate_error\\\"][1])\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 0], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=True,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n with self.assertRaises(TranspilerError):\\n pm.run(qc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74dbf5029e96032b3b4865101f86628\",\n \"score\": \"0.50770205\",\n \"text\": \"def add_singleQ_clifford(index, gate_seq, pad_with_I=True):\\n length_before = len(gate_seq)\\n # Paulis\\n if index == 0:\\n gate_seq.append(Gate.I)\\n elif index == 1:\\n gate_seq.append(Gate.Xp)\\n elif index == 2:\\n gate_seq.append(Gate.Yp)\\n elif index == 3:\\n gate_seq.append(Gate.Xp)\\n gate_seq.append(Gate.Yp)\\n\\n # 2pi/3 rotations\\n elif index == 4:\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.X2p)\\n elif index == 5:\\n gate_seq.append(Gate.Y2m)\\n gate_seq.append(Gate.X2p)\\n elif index == 6:\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.X2m)\\n elif index == 7:\\n gate_seq.append(Gate.Y2m)\\n gate_seq.append(Gate.X2m)\\n elif index == 8:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Y2p)\\n elif index == 9:\\n gate_seq.append(Gate.X2m)\\n gate_seq.append(Gate.Y2p)\\n elif index == 10:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Y2m)\\n elif index == 11:\\n gate_seq.append(Gate.X2m)\\n gate_seq.append(Gate.Y2m)\\n\\n # pi/2 rotations\\n elif index == 12:\\n gate_seq.append(Gate.X2p)\\n elif index == 13:\\n gate_seq.append(Gate.X2m)\\n elif index == 14:\\n gate_seq.append(Gate.Y2p)\\n elif index == 15:\\n gate_seq.append(Gate.Y2m)\\n elif index == 16:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.X2m)\\n elif index == 17:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Y2m)\\n gate_seq.append(Gate.X2m)\\n\\n # Hadamard-Like\\n elif index == 18:\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.Xp)\\n elif index == 19:\\n gate_seq.append(Gate.Y2m)\\n gate_seq.append(Gate.Xp)\\n elif index == 20:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Yp)\\n elif index == 21:\\n gate_seq.append(Gate.X2m)\\n gate_seq.append(Gate.Yp)\\n elif index == 22:\\n gate_seq.append(Gate.X2p)\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.X2p)\\n elif index == 23:\\n gate_seq.append(Gate.X2m)\\n gate_seq.append(Gate.Y2p)\\n gate_seq.append(Gate.X2m)\\n else:\\n raise ValueError(\\n 'index is out of range. it should be smaller than 24 and greater'\\n ' or equal to 0: ', str(index))\\n\\n length_after = len(gate_seq)\\n if pad_with_I:\\n # Force the clifford to have a length of 3 gates\\n for i in range(3-(length_after-length_before)):\\n gate_seq.append(Gate.I)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a32e5dca26c60d46d1a81730d653799\",\n \"score\": \"0.50734425\",\n \"text\": \"def two_qubit_AllXY(q0, q1, RO_target='all',\\n sequence_type='sequential',\\n replace_q1_pulses_X180=False,\\n double_points=False):\\n\\n pulse_combinations = [['I', 'I'], ['X180', 'X180'], ['Y180', 'Y180'],\\n ['X180', 'Y180'], ['Y180', 'X180'],\\n ['X90', 'I'], ['Y90', 'I'], ['X90', 'Y90'],\\n ['Y90', 'X90'], ['X90', 'Y180'], ['Y90', 'X180'],\\n ['X180', 'Y90'], ['Y180', 'X90'], ['X90', 'X180'],\\n ['X180', 'X90'], ['Y90', 'Y180'], ['Y180', 'Y90'],\\n ['X180', 'I'], ['Y180', 'I'], ['X90', 'X90'],\\n ['Y90', 'Y90']]\\n\\n pulse_combinations_tiled = pulse_combinations + pulse_combinations\\n if double_points:\\n pulse_combinations = [val for val in pulse_combinations\\n for _ in (0, 1)]\\n\\n if replace_q1_pulses_X180:\\n pulse_combinations_q1 = ['X180' for val in pulse_combinations]\\n\\n pulse_combinations_q0 = pulse_combinations\\n pulse_combinations_q1 = pulse_combinations_tiled\\n\\n\\n filename = join(base_qasm_path, 'two_qubit_AllXY.qasm')\\n qasm_file = mopen(filename, mode='w')\\n qasm_file.writelines('qubit {} \\\\nqubit {} \\\\n'.format(q0, q1))\\n\\n for pulse_comb_q0, pulse_comb_q1 in zip(pulse_combinations_q0,\\n pulse_combinations_q1):\\n qasm_file.writelines('\\\\ninit_all\\\\n')\\n if sequence_type == 'interleaved':\\n qasm_file.writelines('{} {}\\\\n'.format(pulse_comb_q0[0], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[0], q1) +\\n '{} {}\\\\n'.format(pulse_comb_q0[1], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[1], q1))\\n elif sequence_type == 'sandwiched':\\n qasm_file.writelines('{} {}\\\\n'.format(pulse_comb_q1[0], q1) +\\n '{} {}\\\\n'.format(pulse_comb_q0[0], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q0[1], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[1], q1))\\n elif sequence_type == 'sequential':\\n qasm_file.writelines('{} {}\\\\n'.format(pulse_comb_q0[0], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q0[1], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[0], q1) +\\n '{} {}\\\\n'.format(pulse_comb_q1[1], q1))\\n elif sequence_type == 'simultaneous':\\n qasm_file.writelines('{} {} |'.format(pulse_comb_q0[0], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[0], q1) +\\n '{} {} |'.format(pulse_comb_q0[1], q0) +\\n '{} {}\\\\n'.format(pulse_comb_q1[1], q1))\\n else:\\n raise ValueError(\\\"sequence_type {} \\\".format(sequence_type) +\\n \\\"['interleaved', 'simultaneous', \\\" +\\n \\\"'sequential', 'sandwiched']\\\")\\n qasm_file.writelines('RO {} \\\\n'.format(RO_target))\\n\\n qasm_file.close()\\n return qasm_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76973307c3be04639b3e076ff3c812f\",\n \"score\": \"0.5068147\",\n \"text\": \"def test_different_number_of_virtual_and_physical_qubits(self):\\n\\n # q_0: ──■────■───────\\n # ┌─┴─┐ │\\n # q_1: ┤ X ├──┼────■──\\n # └───┘ │ ┌─┴─┐\\n # q_2: ──■────┼──┤ X ├\\n # ┌─┴─┐┌─┴─┐└───┘\\n # q_3: ┤ X ├┤ X ├─────\\n # └───┘└───┘\\n circuit = QuantumCircuit(4)\\n circuit.cx(0, 1)\\n circuit.cx(2, 3)\\n circuit.cx(0, 3)\\n circuit.cx(1, 2)\\n\\n coupling = CouplingMap.from_line(5)\\n with self.assertRaises(TranspilerError):\\n with self.assertWarnsRegex(DeprecationWarning, r\\\"^The class.*is deprecated\\\"):\\n BIPMapping(coupling)(circuit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0873a1f806dc3a21013b982b12221759\",\n \"score\": \"0.5045172\",\n \"text\": \"def create_circuit(self, angles):\\n\\n p = Program()\\n ro = p.declare(\\\"ro\\\", memory_type='BIT', memory_size=4)\\n\\n # name the individual qubits for more clarity\\n # ancilla_qubit = [0]\\n # index_qubit = [1]\\n # data_qubit = [2]\\n # class_qubit = [3]\\n\\n #######################################\\n #START of the state preparation routine\\n\\n # put the ancilla and the index qubits into uniform superposition\\n p += H(0)\\n p += H(1)\\n\\n def u3(program, theta, qubit):\\n program += RZ(3*np.pi, qubit)\\n program += RX(np.pi/2, qubit)\\n program += RZ(theta+np.pi, qubit)\\n program += RX(np.pi/2, qubit)\\n program += RZ(0, qubit)\\n return program\\n\\n # loading the test vector (which we wish to classify)\\n p += CNOT(0, 2)\\n p = u3(p, -angles[0], 2)\\n p += CNOT(0, 2)\\n p = u3(p, angles[0], 2)\\n\\n # flipping the ancilla qubit > this moves the input vector to the |0> state of the ancilla\\n p += X(0)\\n\\n # loading the first training vector\\n # [0,1] -> class 0\\n # we can load this with a straightforward Toffoli\\n\\n p += CCNOT(0, 1, 2)\\n\\n # flip the index qubit > moves the first training vector to the |0> state of the index qubit\\n p += X(1)\\n\\n # loading the second training vector\\n # [0.78861, 0.61489] -> class 1\\n\\n p += CCNOT(0, 1, 2)\\n\\n p += CNOT(1, 2)\\n p = u3(p, angles[1], 2)\\n p += CNOT(1, 2)\\n p = u3(p, -angles[1], 2)\\n\\n p += CCNOT(0, 1, 2)\\n\\n p += CNOT(1, 2)\\n p = u3(p, -angles[1], 2)\\n p += CNOT(1, 2)\\n p = u3(p, angles[1], 2)\\n\\n # END of state preparation routine\\n ####################################################\\n\\n # at this point we would usually swap the data and class qubit\\n # however, we can be lazy and let the Qiskit compiler take care of it\\n\\n # flip the class label for training vector #2\\n\\n p += CNOT(1, 3)\\n\\n #############################################\\n # START of the mini distance-based classifier\\n\\n # interfere the input vector with the training vectors\\n p += H(0)\\n\\n # Measure all qubits and record the results in the classical registers\\n p += MEASURE(0, ro[0])\\n p += MEASURE(1, ro[1])\\n p += MEASURE(2, ro[2])\\n p += MEASURE(3, ro[3])\\n\\n # END of the mini distance-based classifier\\n #############################################\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c041f6c136be3f8619ab245e4d7e1a95\",\n \"score\": \"0.5038822\",\n \"text\": \"def test_no_state_modification_circuit(self) -> None:\\n\\n for nbqbit in range(min_nbqbit, max_nbqbit):\\n prog = Program()\\n qbits = prog.qalloc(nbqbit)\\n\\n random_angles = [rd.random() * 2 * np.pi for _ in range(3 * nbqbit)]\\n\\n for i in range(len(qbits)):\\n prog.apply(RX(random_angles[3 * i]), qbits[i])\\n prog.apply(RX(random_angles[3 * i + 1]), qbits[i])\\n prog.apply(RX(random_angles[3 * i + 2]), qbits[i])\\n\\n prog.apply(QFT(nbqbit), qbits)\\n prog.apply(QFT(nbqbit).dag(), qbits)\\n\\n for i in range(len(qbits)):\\n prog.apply(RX(random_angles[3 * i]).dag(), qbits[i])\\n prog.apply(RX(random_angles[3 * i + 1]).dag(), qbits[i])\\n prog.apply(RX(random_angles[3 * i + 2]).dag(), qbits[i])\\n\\n circuit = prog.to_circ(inline=True)\\n\\n for topology in generate_custom_topologies(nbqbit):\\n qpu = Sabre() | (QuameleonPlugin(topology=topology) | PyLinalg())\\n result = qpu.submit(circuit.to_job())\\n assert result.raw_data[0].state.int == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de8bb1d61aef05ae792bdfc32b7f014\",\n \"score\": \"0.50318724\",\n \"text\": \"def test_two_qubit_no_parameters(self, device, init_state, op, mat, tol, skip_if):\\n n_wires = 2\\n dev = device(n_wires)\\n skip_if(dev, {\\\"returns_probs\\\": False})\\n if not dev.supports_operation(op(wires=range(n_wires)).name):\\n pytest.skip(\\\"op not supported\\\")\\n\\n rnd_state = init_state(n_wires)\\n\\n @qml.qnode(dev)\\n def circuit():\\n qml.StatePrep(rnd_state, wires=range(n_wires))\\n op(wires=range(n_wires))\\n return qml.probs(wires=range(n_wires))\\n\\n res = circuit()\\n\\n expected = np.abs(mat @ rnd_state) ** 2\\n assert np.allclose(res, expected, atol=tol(dev.shots))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223db33425e0a7293ab9a606bd7ba8fa\",\n \"score\": \"0.5009359\",\n \"text\": \"def eq02():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8059fd9fae84aac4558f9154f3333d\",\n \"score\": \"0.49888164\",\n \"text\": \"def __generate_plus8_eq_plus3_minus5_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([5, 6])\\n return __extend_no_carry_borrow(a, 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"578472535a549079a5d73875e75ed590\",\n \"score\": \"0.49776545\",\n \"text\": \"def test_already_executable_circuit(self) -> None:\\n\\n for nbqbit in range(min_nbqbit, max_nbqbit):\\n prog = Program()\\n qbits = prog.qalloc(nbqbit)\\n\\n for i in range(len(qbits) - 1):\\n prog.apply(H, qbits[i])\\n prog.apply(Z, qbits[i])\\n prog.apply(X.ctrl(), qbits[i + 1], qbits[i])\\n\\n circuit = prog.to_circ(inline=True)\\n\\n sabre = Sabre()\\n batch = Batch(jobs=[circuit.to_job()])\\n hardware_specs = HardwareSpecs()\\n batch_result = sabre.compile(batch, hardware_specs)\\n computed_circuit = batch_result.jobs[0].circuit\\n\\n check_circuits_equality(circuit, computed_circuit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59f9aad0510ab9efa72ec42542a91e8\",\n \"score\": \"0.49725848\",\n \"text\": \"def check_circuits_equality(circuit_1: Circuit, circuit_2: Circuit) -> None:\\n\\n # We check the equality of circuits properties one by one excepted the qregister which can not be compared\\n assert circuit_1.ops == circuit_2.ops\\n assert circuit_1.gateDic == circuit_2.gateDic\\n assert circuit_1.var_dic == circuit_2.var_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0cffc616854b95819f55122d8c04236\",\n \"score\": \"0.497025\",\n \"text\": \"def circuit_hash(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2f1dcfc2950875b8c817bbb65814a77\",\n \"score\": \"0.49614215\",\n \"text\": \"def get_equal_sign_orthants(s, circuits_information_M1, circuits_information_M2):\\n\\n equal_sign_vectors = []\\n\\n #TODO: create them dynamically\\n orthants = list(itertools.product([-1,0,1],repeat=s))\\n\\n solution_orthants = []\\n #Steps 2,3,4\\n for orthant in orthants:\\n print(orthant)\\n conformal_circuits_M1 = circuits_information_M1.get_conformal_circuits(orthant)\\n\\n #union of the circuits conformal to the orthant\\n U_M1 = Utils.union(conformal_circuits_M1)\\n\\n is_zero_or_invalid = True\\n if U_M1 != None:\\n for elt in U_M1:\\n if elt != 0:\\n is_zero_or_invalid = False\\n\\n\\n #print(U_M1)\\n\\n if U_M1 != None and not is_zero_or_invalid and Utils.has_equal_sign(orthant, U_M1):\\n #Si el ortante tiene soporte igual a la union de los circuitos de M1, sigo con el paso 3 del algoritmo.\\n conformal_circuits_M2 = circuits_information_M2.get_conformal_circuits(orthant)\\n U_M2 = Utils.union(conformal_circuits_M2)\\n if U_M2 != None and Utils.has_equal_sign(orthant, U_M2):\\n #equal_sign_vectors.append([U_M1, U_M2])\\n solution_orthants.append(orthant)\\n #equal_sign_vectors.append([U_M1, U_M2])\\n\\n #Stop searching in this orthant\\n continue\\n\\n print(\\\"Two vectors with the same sign, corresponding to the orthant %s, are %s, from M1, and %s, from M2.\\\" % (orthant, U_M1, U_M2))\\n else:\\n continue\\n #Isn't useful\\n return solution_orthants\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68da894061da9ee419a8bc830a541780\",\n \"score\": \"0.49587256\",\n \"text\": \"def test_prep_circuit_one_qubit2(self):\\n # Two element vector\\n vec = [0.0, 1.0]\\n\\n # Make a BinaryTree\\n tree = BinaryTree(vec)\\n\\n # Get a circuit and register to prepare the vector in\\n qreg = QuantumRegister(1)\\n circ = QuantumCircuit(qreg)\\n\\n # Get the state preparation circuit\\n tree.preparation_circuit(circ, qreg)\\n\\n # Get the final state\\n state = list(np.real(self.final_state(circ)))\\n\\n self.assertTrue(np.array_equal(state, vec))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28fdd7894d5d61451db75696d49b6ba\",\n \"score\": \"0.49433023\",\n \"text\": \"def test_two_qubit_natural_direction_true_duration_fallback(self):\\n # this assumes iswawp pulse optimal decomposition doesn't exist\\n backend = FakeVigo()\\n conf = backend.configuration()\\n # conf.basis_gates = [gate if gate != \\\"cx\\\" else \\\"iswap\\\" for gate in conf.basis_gates]\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap([[0, 1], [1, 0], [1, 2], [1, 3], [3, 4]])\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=coupling_map,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=True,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n self.assertTrue(\\n all(((qr[0], qr[1]) == instr.qubits for instr in qc_out.get_instructions(\\\"cx\\\")))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e42f4985e2a98d85da2c542120ecd8\",\n \"score\": \"0.49384433\",\n \"text\": \"def _prepare_circuits(base_circuit, observables):\\n circuits = list()\\n\\n if isinstance(observables, ComposedOp):\\n observables = SummedOp([observables])\\n for obs in observables:\\n circuit = base_circuit.copy()\\n circuit.append(obs[1], qargs=list(range(base_circuit.num_qubits)))\\n circuits.append(circuit)\\n return circuits, observables\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e440f942ce99b7439e9ca7bd0fbab2\",\n \"score\": \"0.493633\",\n \"text\": \"def U_h(self, circuit, l, n_i, m, n_phiReg, w_phiReg, n_aReg, w_aReg, n_bReg, w_bReg, wReg, eReg, pReg, hReg, w_hReg,\\n P_phi,\\n P_a, P_b):\\n for k in range(n_i + m):\\n # for k in range(1):\\n print(\\\"k: \\\", k)\\n countsList = self.generateParticleCounts(n_i, m, k) # reduce the available number of particles\\n\\n for counts in countsList:\\n n_phi, n_a, n_b = counts[0], counts[1], counts[2]\\n # controlled R-y from |0> to |k> on all qubits with all possible angles depending on n_phi, n_a, n_b, and flavor\\n # for flavor in ['phi']:\\n # if n_phi == 3 and n_a == 0 and n_b ==0:\\n # print(\\\"counts: \\\", counts)\\n # print(\\\"after if : \\\", n_phi, n_a, n_b)\\n\\n for flavor in ['phi', 'a', 'b']:\\n angle = self.U_hAngle(flavor, n_phi, n_a, n_b, P_phi, P_a, P_b)\\n\\n #phiControl, aControl, and bControl are the corresponding work registers, and since we call\\n # numberControl we also add x gates on the respective number registers (aka n_phi, n_a, n_b)\\n phiControl = self.numberControl(circuit, l, n_phi, n_phiReg, w_phiReg)\\n # print(\\\"qiskit phiControl: \\\", phiControl)\\n aControl = self.numberControl(circuit, l, n_a, n_aReg, w_aReg)\\n # print(\\\"qiskit aControl: \\\", aControl)\\n bControl = self.numberControl(circuit, l, n_b, n_bReg, w_bReg)\\n # print(\\\"qiskit bControl: \\\", bControl)\\n\\n circuit.ccx(phiControl, aControl, wReg[0])\\n circuit.ccx(bControl, wReg[0], wReg[1])\\n\\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 2,\\n 4) # wReg[4] is work qubit but is reset to 0\\n circuit.ccx(wReg[1], wReg[2], wReg[3])\\n circuit.ccx(eReg[0], wReg[3], wReg[4])\\n\\n self.twoLevelControlledRy(circuit, l, angle, k + 1, wReg[4], hReg, w_hReg)\\n\\n circuit.ccx(eReg[0], wReg[3], wReg[4]) # next steps undo work qubits\\n circuit.ccx(wReg[1], wReg[2], wReg[3])\\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 2,\\n 4) # wReg[4] is work qubit but is reset to 0\\n circuit.ccx(bControl, wReg[0], wReg[1])\\n circuit.ccx(phiControl, aControl, wReg[0])\\n self.numberControlT(circuit, l, n_b, n_bReg, w_bReg)\\n self.numberControlT(circuit, l, n_a, n_aReg, w_aReg)\\n self.numberControlT(circuit, l, n_phi, n_phiReg, w_phiReg)\\n\\n # subtract from the counts register depending on which flavor particle emitted\\n for flavor, countReg, workReg in zip(['phi', 'a', 'b'], [n_phiReg, n_aReg, n_bReg],\\n [w_phiReg, w_aReg, w_bReg]):\\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 0,\\n 1) # wReg[4] is work qubit but is reset to 0\\n self.minus1(circuit, l, countReg, workReg, wReg[0], wReg[1], 0)\\n self.flavorControl(circuit, flavor, pReg, wReg, wReg, (k * self._p_len), 0,\\n 1) # wReg[4] is work qubit but is reset to 0\\n\\n # apply x on eReg if hReg[m] = 0, apply another x so we essentially control on not 0 instead of 0\\n isZeroControl = self.numberControl(circuit, l, 0, hReg, w_hReg)\\n circuit.cx(isZeroControl, eReg[0])\\n circuit.x(eReg[0])\\n self.numberControlT(circuit, l, 0, hReg, w_hReg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c8dfeeec411da2cdf5d9680abc0d2f\",\n \"score\": \"0.49305674\",\n \"text\": \"def __generate_plus2_eq_minus8_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([8, 9])\\n return __extend_no_carry_borrow(a, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665951a7772536c0aeda8f5e49492388\",\n \"score\": \"0.49253783\",\n \"text\": \"def two_component_identical_lj():\\n T = 1\\n sig = np.array([1, 1])\\n eps = np.array([1, 1])\\n eps_01 = geometric(eps[0], eps[1])\\n sig_01 = arithmetic(sig[0], sig[1])\\n rhos = np.array([0.005, 0.005]) / sig**3\\n\\n lj = oz.System(kT=T)\\n\\n r = lj.r\\n lj.set_interaction(0, 0, oz.lennard_jones(r, eps[0], sig[0]))\\n lj.set_interaction(1, 1, oz.lennard_jones(r, eps[1], sig[1]))\\n lj.set_interaction(0, 1, oz.lennard_jones(r, eps_01, sig_01))\\n\\n lj.solve(rhos=rhos, closure_name='hnc')\\n return lj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7705933edd534e3bfc411eeea98bf01\",\n \"score\": \"0.49243945\",\n \"text\": \"def generateBinaryID(self):\\n bid = cantorPairing(self.id1, self.id2)\\n self.addNewMember('bid',bid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7705933edd534e3bfc411eeea98bf01\",\n \"score\": \"0.49243945\",\n \"text\": \"def generateBinaryID(self):\\n bid = cantorPairing(self.id1, self.id2)\\n self.addNewMember('bid',bid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7705933edd534e3bfc411eeea98bf01\",\n \"score\": \"0.49243945\",\n \"text\": \"def generateBinaryID(self):\\n bid = cantorPairing(self.id1, self.id2)\\n self.addNewMember('bid',bid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7705933edd534e3bfc411eeea98bf01\",\n \"score\": \"0.49243945\",\n \"text\": \"def generateBinaryID(self):\\n bid = cantorPairing(self.id1, self.id2)\\n self.addNewMember('bid',bid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fb6c4bce4383bc59792bac257adfb6c\",\n \"score\": \"0.4908937\",\n \"text\": \"def random_circuit_encoding(self, n_ops, random_state):\\n return random_state.rand(3 * n_ops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b7c4e237167fac45b64bf4b3bfca06\",\n \"score\": \"0.4902847\",\n \"text\": \"def __generate_plus6_eq_minus4_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([4, 9])\\n return __extend_no_carry_borrow(a, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3fb5ff6cbd7848d23116b0a021b63c0\",\n \"score\": \"0.4900984\",\n \"text\": \"def main():\\n # Set the number of symbols (i.e. the generation size in RLNC terminology)\\n # and the size of a symbol in bytes\\n symbols = 8\\n symbol_size = 1\\n\\n # Create encoder/decoder factory used to build actual encoders/decoders\\n encoder_factory = kodo.PerpetualEncoderFactoryBinary(symbols, symbol_size)\\n encoder = encoder_factory.build()\\n\\n decoder_factory = kodo.PerpetualDecoderFactoryBinary(symbols, symbol_size)\\n decoder = decoder_factory.build()\\n\\n # The perpetual encoder supports three operation modes;\\n #\\n # 1) Random pivot mode (default)\\n # The pivot element is drawn at random for each coding symbol\\n #\\n # example generated vectors (for width = 2)\\n #\\n # 0 0 1 X X 0\\n # X X 0 0 0 1\\n # X 0 0 0 1 X\\n # X X 0 0 0 1\\n # 0 1 X X 0 0\\n # 0 1 X X 0 0\\n #\\n # 1 X X 0 0 0\\n # 0 0 0 1 X X\\n # .\\n # .\\n #\\n # 2) Pseudo systematic\\n # Pivot elements are generated with indices 0,1,2, ... , n,\\n # after which the generator reverts to the default random pivot\\n #\\n # example generated vectors (for width = 2)\\n #\\n # 1 X X 0 0 0\\n # 0 1 X X 0 0\\n # 0 0 1 X X 0\\n # 0 0 0 1 X X\\n # X 0 0 0 1 X\\n # X X 0 0 0 1\\n #\\n # (additional vectors generated using the random mode)\\n #\\n # 3) Pre-charging\\n # For the first \\\"width\\\" symbols, the pivot index is 0. After that,\\n # the pseudo-systematic mode is used. Finally, pivots are drawn at\\n # random resulting in the indices 0 (width times), 1,2, ... , n\\n #\\n # example generated vectors (for width = 2)\\n #\\n # 1 X X 0 0 0\\n # 1 X X 0 0 0\\n # 0 1 X X 0 0\\n # 0 0 1 X X 0\\n # 0 0 0 1 X X\\n #\\n # X 0 0 0 1 X\\n # X X 0 0 0 1\\n #\\n # (additional vectors generated using the random mode)\\n #\\n # The operation mode is set in the following. Note that if both\\n # pre-charging and pseudo-systematic is enabled, pre-charging takes\\n # precedence.\\n\\n # Enable the pseudo-systematic operation mode - faster\\n #encoder.set_pseudo_systematic(True)\\n\\n # Enable the pre-charing operation mode - even faster\\n encoder.set_pre_charging(True)\\n\\n print(\\\"Pseudo-systematic is {}\\\\nPre-charging is {}\\\".format(\\n \\\"on\\\" if encoder.pseudo_systematic() else \\\"off\\\",\\n \\\"on\\\" if encoder.pre_charging() else \\\"off\\\"))\\n\\n # The width of the perpetual code can be set either as a number of symbols\\n # using set_width(), or as a ratio of the generation size using\\n # set_width_ratio().\\n #\\n # The default width is set to 10% of the generation size.\\n print(\\\"The width ratio defaults to: {} (therefore the calculated width is \\\"\\n \\\"{})\\\".format(encoder.width_ratio(), encoder.width()))\\n encoder.set_width(6)\\n\\n print(\\\"The width was set to: {} (therefore the calculated width ratio is \\\"\\n \\\"{})\\\".format(encoder.width(), encoder.width_ratio()))\\n\\n encoder.set_width_ratio(0.2)\\n print(\\\"The width ratio was set to: {} (therefore the calculated width is \\\"\\n \\\"{})\\\".format(encoder.width_ratio(), encoder.width()))\\n\\n # Create some data to encode. In this case we make a buffer\\n # with the same size as the encoder's block size (the max.\\n # amount a single encoder can encode)\\n # Just for fun - fill the input data with random data\\n data_in = os.urandom(encoder.block_size())\\n\\n # Assign the data buffer to the encoder so that we can\\n # produce encoded symbols\\n encoder.set_const_symbols(data_in)\\n\\n while not decoder.is_complete():\\n # Encode a packet into the payload buffer\\n payload = encoder.write_payload()\\n print(\\\"Payload: {}\\\".format(binascii.hexlify(payload)))\\n if random.choice([True, False]):\\n print(\\\"Packet dropped on channel\\\")\\n continue\\n\\n # Pass that packet to the decoder\\n decoder.read_payload(payload)\\n\\n # The decoder is complete, now copy the symbols from the decoder\\n data_out = decoder.copy_from_symbols()\\n\\n # Check we properly decoded the data\\n if data_out == data_in:\\n print(\\\"Data decoded correctly\\\")\\n else:\\n print(\\\"Unexpected failure to decode please file a bug report :)\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430ccd293f554d4fe3e0ac656887d732\",\n \"score\": \"0.4898766\",\n \"text\": \"def eq01():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18dbe39d5bbb767b290f97b0eba34331\",\n \"score\": \"0.48945558\",\n \"text\": \"def __generate_plus8_eq_minus2_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([2, 3, 4, 7, 8, 9])\\n return __extend_no_carry_borrow(a, 8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b334d51b06c52c97ede3a6c06e14d3b3\",\n \"score\": \"0.48936254\",\n \"text\": \"def same_quantum(self, a, b):\\r\\n return a.same_quantum(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32c63c844577dfc22ff3f24ad558b82\",\n \"score\": \"0.48922\",\n \"text\": \"def apply_controlled_two_qubit_gate(self, mat, q_id1, q_id2, q_id3):\\n # Move the qubits to the correct position\\n self.swap_qubits(q_id1, self.qubits[0])\\n self.swap_qubits(q_id2, self.qubits[1])\\n self.swap_qubits(q_id3, self.qubits[2])\\n\\n first_mat = np.block([[np.eye(4), np.zeros((4, 4))], [np.zeros((4, 4)), mat]])\\n total_mat = np.kron(first_mat, np.eye(2 ** (len(self.qubits) - 3)))\\n\\n self.qubit = np.dot(total_mat, self.qubit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aab37b94cef18d2d048bc6a65bae5d4\",\n \"score\": \"0.48921895\",\n \"text\": \"def test_all_to_all_topology(self) -> None:\\n\\n for nbqbit in range(min_nbqbit, max_nbqbit):\\n circuit = generate_random_circuit(nbqbit)\\n\\n sabre = Sabre()\\n batch = Batch(jobs=[circuit.to_job()])\\n hardware_specs = HardwareSpecs()\\n batch_result = sabre.compile(batch, hardware_specs)\\n computed_circuit = batch_result.jobs[0].circuit\\n\\n check_circuits_equality(circuit, computed_circuit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccb6b5cfa6ea6e4d9d2bac633828298\",\n \"score\": \"0.4887278\",\n \"text\": \"def test_init_with_qnodes(self):\\n dev = qml.device(\\\"default.qubit\\\", wires=1)\\n\\n def circuit(x):\\n qml.RX(x, wires=0)\\n return qml.expval(qml.PauliZ(0))\\n\\n qnodes = [qml.QNode(circuit, dev) for i in range(4)]\\n qc = qml.QNodeCollection(qnodes)\\n\\n assert qc.qnodes == qnodes\\n assert len(qc) == 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b9c30bf90e7dbe965016996a8f5d3b5\",\n \"score\": \"0.48850244\",\n \"text\": \"def build_circuit(routers):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68abb31619154078720ebba820638406\",\n \"score\": \"0.48816547\",\n \"text\": \"def test_identical_costs():\\n S = np.transpose(np.array([[1.0, 1], [1, 1], [1, 1], [1, 1]]))\\n M = np.zeros(np.shape(S))\\n Q = np.full(np.shape(S), 1.0)\\n solver = RandomizedSolver(\\n [0, 0, 0, 0], [1, 1, 1, 1], [2, 2], encoder(-S, M, Q)\\n )\\n for _ in range(1000):\\n check_test_solution(solver, T=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136298ebfc12522e165fade0ebbd16b9\",\n \"score\": \"0.48801643\",\n \"text\": \"def input_combos(combo_len):\\n return itertools.product((False, True), repeat=combo_len)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c551c5474c8984a337aeb282b8801724\",\n \"score\": \"0.48780307\",\n \"text\": \"def generate_sequences_identical(draw, elements=[lists, integers]):\\n seq_x = draw(lists(integers(min_value=1, max_value=1), min_size=3, max_size=10_000))\\n seq_y = draw(\\n lists(\\n integers(min_value=2, max_value=2), min_size=len(seq_x), max_size=len(seq_x)\\n )\\n )\\n\\n return seq_x, seq_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f0eff5fc392a0a003d38b1d02dab236\",\n \"score\": \"0.4875794\",\n \"text\": \"def test_prepare_negative_amplitudes_two_qubits(self):\\n # Input vector\\n vec = [-0.4, 0.4, -0.8, 0.2]\\n\\n # Get a BinaryTree\\n tree = BinaryTree(vec)\\n\\n # Get a Quantum Register\\n regA = QuantumRegister(1)\\n regB = QuantumRegister(1)\\n circ = QuantumCircuit(regA, regB)\\n\\n # Get the state preparation circuit\\n tree.preparation_circuit(circ, regA, regB)\\n\\n # Swap the qubits to compare to the natural ordering of the vector\\n circ.swap(regA[0], regB[0])\\n\\n # Make sure the final state of the circuit is the same as the input vector\\n state = np.real(self.final_state(circ))\\n self.assertTrue(np.allclose(state, vec))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ff8e29838f27b69782aaeaa09a83ba\",\n \"score\": \"0.48712075\",\n \"text\": \"def circuit_new(circuit):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c075df7b500ca16653c24dd186c60d5d\",\n \"score\": \"0.486678\",\n \"text\": \"def __init__(self,\\n ctrl: QubitType,\\n targets: Tuple[QubitType],\\n params: List[float] = None,\\n circuit: QuantumCircuit = None):\\n\\n def ccz(circuit, ctrl1, ctrl2, target):\\n # Verified\\n circuit.comment(\\\"CCZ\\\")\\n from qiskit.extensions.standard.h import HGate\\n circuit._attach(HGate(target, circuit).inverse())\\n circuit.ccx(ctrl1, ctrl2, target)\\n circuit._attach(HGate(target, circuit).inverse())\\n\\n def crzz(circuit, theta, ctrl, target):\\n # Verified\\n circuit.comment(\\\"c-RZZ\\\")\\n circuit.cu1(theta, ctrl, target)\\n circuit.cx(ctrl, target)\\n circuit.cu1(theta, ctrl, target)\\n circuit.cx(ctrl, target)\\n\\n def crzz_inv(circuit, theta, ctrl, target):\\n # Verified\\n circuit.comment(\\\"c-RZZ^{-1}\\\")\\n circuit.cx(ctrl, target)\\n circuit.cu1(-theta, ctrl, target)\\n circuit.cx(ctrl, target)\\n circuit.cu1(-theta, ctrl, target)\\n\\n def crx(circuit, theta, ctrl, target):\\n # Verified\\n circuit.comment(\\\"CRX\\\")\\n # Apply the supposed c-RX operation.\\n circuit.cu3(theta, pi/2, 3*pi/2, ctrl, target)\\n # For the moment, QISKit adds a phase to the U-gate, so we\\n # need to correct this phase with a controlled Rzz.\\n crzz(circuit, pi, ctrl, target)\\n\\n def csqrtx_inv(circuit, ctrl, target):\\n # Verified\\n circuit.comment(\\\"c-sqrt(X)^{-1}\\\")\\n circuit.h(target)\\n circuit.t(target)\\n circuit.cx(ctrl, target)\\n circuit.t(ctrl)\\n circuit.tdg(target)\\n circuit.h(target)\\n circuit.cz(ctrl, target)\\n circuit.cx(ctrl, target)\\n\\n\\n def csqrtx(circuit, ctrl, target):\\n # Verified\\n circuit.comment(\\\"c-sqrt(X)\\\")\\n circuit.cx(ctrl, target)\\n circuit.cz(ctrl, target)\\n circuit.h(target)\\n circuit.t(target)\\n circuit.tdg(ctrl)\\n circuit.cx(ctrl, target)\\n circuit.tdg(target)\\n circuit.h(target)\\n\\n if params is None:\\n # Default parameters for a simple Hamiltonian (no powers)\\n params = [0.19634953, 0.37900987, 0.9817477, 1.87900984, 0.58904862]\\n\\n used_qubits = [ctrl, targets[0], targets[1]]\\n\\n super().__init__(self.__class__.__name__, # name\\n [], # parameters\\n used_qubits, # qubits\\n circuit) # circuit\\n\\n self.comment(\\\"[HS] Start.\\\")\\n ccz(self, ctrl, targets[0], targets[1])\\n crx(self, params[0], ctrl, targets[1])\\n csqrtx_inv(self, ctrl, targets[1])\\n crzz(self, params[1], ctrl, targets[1])\\n crx(self, params[2], ctrl, targets[0])\\n crzz(self, params[3], ctrl, targets[0])\\n self.ccx(ctrl, targets[0], targets[1])\\n crx(self, params[4], ctrl, targets[0])\\n self.ccx(ctrl, targets[0], targets[1])\\n ccz(self, ctrl, targets[0], targets[1])\\n self.comment(\\\"[HS] End.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621d19adf53f7dbe19aae66fc3bdb4c2\",\n \"score\": \"0.48654687\",\n \"text\": \"def apply_controlled_gate(self, mat, q_id1, q_id2):\\n first_mat = 1\\n second_mat = 1\\n nr1 = self.qubits.index(q_id1)\\n nr2 = self.qubits.index(q_id2)\\n\\n min_nr = min(nr1, nr2)\\n max_nr = max(nr1, nr2)\\n\\n total_amount = len(self.qubits)\\n before = min_nr\\n after = total_amount - max_nr - 1\\n mid = total_amount - before - after - 2\\n\\n if before > 0:\\n first_mat = np.eye(2 ** before)\\n second_mat = np.eye(2 ** before)\\n\\n # Apply first part of Matrix\\n if min_nr == nr1:\\n first_mat = np.kron(first_mat, np.eye(2))\\n second_mat = np.kron(second_mat, mat)\\n else:\\n first_mat = np.kron(first_mat, np.array([[1, 0], [0, 0]]))\\n second_mat = np.kron(second_mat, np.array([[0, 0], [0, 1]]))\\n\\n if mid > 0:\\n first_mat = np.kron(first_mat, np.eye(2 ** mid))\\n second_mat = np.kron(second_mat, np.eye(2 ** mid))\\n\\n # Apply second part of Matrix\\n if min_nr == nr1:\\n first_mat = np.kron(first_mat, np.array([[1, 0], [0, 0]]))\\n second_mat = np.kron(second_mat, np.array([[0, 0], [0, 1]]))\\n else:\\n first_mat = np.kron(first_mat, np.eye(2))\\n second_mat = np.kron(second_mat, mat)\\n\\n if after > 0:\\n first_mat = np.kron(first_mat, np.eye(2 ** after))\\n second_mat = np.kron(second_mat, np.eye(2 ** after))\\n\\n apply_mat = first_mat + second_mat\\n self.qubit = np.dot(apply_mat, self.qubit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0227504e3c701e5ebf8a78b9699befe6\",\n \"score\": \"0.48646224\",\n \"text\": \"def test_providing_no_observable_and_no_wires_shot_vector(self):\\n num_wires = 2\\n\\n shots1 = 1\\n shots2 = 10\\n shots3 = 1000\\n dev = qml.device(\\\"default.qubit\\\", wires=num_wires, shots=[shots1, shots2, shots3])\\n\\n @qml.qnode(dev)\\n def circuit():\\n qml.Hadamard(wires=0)\\n return qml.sample()\\n\\n res = circuit()\\n\\n assert isinstance(res, tuple)\\n\\n expected_shapes = [(num_wires,), (num_wires, shots2), (num_wires, shots3)]\\n assert len(res) == len(expected_shapes)\\n assert (r.shape == exp_shape for r, exp_shape in zip(res, expected_shapes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a621244a18e07d64996d9dee1d54f87\",\n \"score\": \"0.4860904\",\n \"text\": \"def test_append_qubit_observables(self):\\n with AnnotatedQueue() as q:\\n # wire repetition is deliberate, Queue contains no checks/logic\\n # for circuits\\n ops = [\\n qml.Hadamard(wires=0),\\n qml.PauliX(wires=1),\\n qml.PauliY(wires=1),\\n qml.Hermitian(np.ones([2, 2]), wires=7),\\n ]\\n assert q.queue == ops\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf6bf8147f1294b0977baf912a1d03a\",\n \"score\": \"0.48548517\",\n \"text\": \"def cl_encode(lengths):\\n dic = dict(lengths)\\n items = [dic.get(i, 0) for i in range(max(dic) + 1)]\\n pos = 0\\n while pos < len(items):\\n if items[pos] == 0:\\n # count repetitions of 0\\n i = pos + 1\\n while i < len(items) and items[i] == 0:\\n i += 1\\n if i - pos < 3:\\n for i in range(pos, i):\\n yield items[i]\\n pos = i + 1\\n else:\\n repeat = i - pos\\n if repeat < 11:\\n yield (17, repeat - 3)\\n else:\\n yield (18, repeat - 11)\\n pos = i\\n else:\\n item = items[pos]\\n yield item\\n i = pos + 1\\n while i < len(items) and items[i] == item:\\n i += 1\\n repeat = i - pos - 1 # number of repetitions after 1st occurrence\\n if repeat < 3:\\n for i in range(repeat):\\n yield item\\n else:\\n nb = repeat - 3\\n while nb > 3:\\n yield (16, 3)\\n nb -= 3\\n yield (16, nb)\\n pos += repeat + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe3c8a15a90dd7e35d98abdd29d83a4\",\n \"score\": \"0.48457897\",\n \"text\": \"def test_two_qubit_synthesis_to_directional_cx_from_gate_errors(self):\\n # TODO: should make check more explicit e.g. explicitly set gate\\n # direction in test instead of using specific fake backend\\n backend = FakeVigo()\\n conf = backend.configuration()\\n qr = QuantumRegister(2)\\n coupling_map = CouplingMap(conf.coupling_map)\\n triv_layout_pass = TrivialLayout(coupling_map)\\n qc = QuantumCircuit(qr)\\n qc.unitary(random_unitary(4, seed=12), [0, 1])\\n unisynth_pass = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=None,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=False,\\n )\\n pm = PassManager([triv_layout_pass, unisynth_pass])\\n qc_out = pm.run(qc)\\n\\n unisynth_pass_nat = UnitarySynthesis(\\n basis_gates=conf.basis_gates,\\n coupling_map=None,\\n backend_props=backend.properties(),\\n pulse_optimize=True,\\n natural_direction=True,\\n )\\n\\n pm_nat = PassManager([triv_layout_pass, unisynth_pass_nat])\\n qc_out_nat = pm_nat.run(qc)\\n self.assertEqual(Operator(qc), Operator(qc_out))\\n self.assertEqual(Operator(qc), Operator(qc_out_nat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3470d7b7b341f73c8b07e1843c49491\",\n \"score\": \"0.48433205\",\n \"text\": \"def test_truncate_connected_qubits(self):\\n backend = self.backend()\\n circuit = QuantumCircuit(20, 1)\\n circuit.h(5)\\n circuit.cx(5, 6)\\n circuit.cx(6, 2),\\n circuit.cx(2, 3)\\n circuit.measure(3, 0)\\n result = backend.run(circuit, shots=1).result()\\n metadata = result.results[0].metadata\\n self.assertEqual(metadata[\\\"num_qubits\\\"], 4)\\n self.assertEqual(metadata[\\\"active_input_qubits\\\"], [2, 3, 5, 6])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcac3b0aa656749da6df98bb703e0fc\",\n \"score\": \"0.48364782\",\n \"text\": \"def __generate_plus6_eq_plus1_minus5_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([5, 6, 7, 8])\\n return __extend_no_carry_borrow(a, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc7120287205b8f69dc6dde95821b44\",\n \"score\": \"0.48315218\",\n \"text\": \"def generate_cross_traffic(mode, rate, q, p, n):\\r\\n # Constants\\r\\n capacity = 10e9/8 # capacity in bytes;\\r\\n #max_variance = 8e4 # maximum variance in bytes^2, \\r\\n max_variance = 8e4 # maximum variance in bytes^2, \\r\\n mean_packet_size = 500 # mean packet size in bytes\\r\\n \\r\\n # Compute u and s^2\\r\\n m = mean_packet_size\\r\\n v = q * max_variance\\r\\n u = math.log(m**2/math.sqrt(v + m**2))\\r\\n s = math.sqrt(math.log(1 + v/m**2))\\r\\n\\r\\n # initialize packet size vector\\r\\n packet_size = mean_packet_size * ones(n)\\r\\n\\r\\n # generate variable packet length\\r\\n if mode == 1 or mode == 3:\\r\\n # generate n lognormal values\\r\\n for i in range(1,n):\\r\\n p_size = 0\\r\\n while (p_size < 72 or p_size > 1526):\\r\\n p_size = round(random.lognormal(u, s))\\r\\n packet_size[i-1] = p_size\\r\\n \\r\\n # Mean packet size after discarding packets that are too small or too big\\r\\n realized_mean_packet_size = mean(packet_size)\\r\\n \\r\\n # Compute average interpacket gap in bytes\\r\\n if mode == 2: # mode 2 is the only mode where packet length is fixed\\r\\n mean_ipd = round(mean_packet_size * capacity / rate)\\r\\n mean_ipg = mean_ipd - mean_packet_size\\r\\n else:\\r\\n mean_ipd = round(realized_mean_packet_size * capacity / rate)\\r\\n mean_ipg = mean_ipd - realized_mean_packet_size \\r\\n \\r\\n # initialize idle vector\\r\\n idle = mean_ipg * ones(n) \\r\\n\\r\\n # generate variable interpacket gap\\r\\n if mode == 2 or mode == 3:\\r\\n k = 0 # keep track of number of packets generated\\r\\n while k <= n:\\r\\n if k == 0: # first idle\\r\\n chain_length = 1 \\r\\n else:\\r\\n chain_length = random.geometric(p)\\r\\n\\r\\n # set zero interpacket gap for\\r\\n for j in range(1, chain_length):\\r\\n if k+j-1 < n:\\r\\n idle[k+j-1] = 0\\r\\n\\r\\n # set sufficient interpacket gap between the chain and the next packet to achieve the desired data rate\\r\\n mean_ipg_to_set = mean_ipg * chain_length \\r\\n\\r\\n if k+chain_length-1 < n:\\r\\n idle[k+chain_length-1] = mean_ipg_to_set # we might change this to include some randomness later\\r\\n\\r\\n k = k + chain_length\\r\\n\\r\\n \\r\\n # Total time in bytes\\r\\n stream_length = sum(idle) + sum(packet_size)\\r\\n\\r\\n # Generated data rate\\r\\n generated_rate = capacity * sum(packet_size)/(sum(idle) + sum(packet_size))\\r\\n\\r\\n return (idle, packet_size, stream_length, generated_rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdaa75b597fc5eae5b261b60f64addcd\",\n \"score\": \"0.48175004\",\n \"text\": \"def bell(Qubit1, Qubit2):\\n h=Hadamard(Q0) if Qubit1==0 else Hadamard(Q1) \\n x=np.concatenate(np.outer(h, Q1 if Qubit2==1 else Q0))\\n return CNOT(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716675537743fc0ce2d99d3e95895875\",\n \"score\": \"0.48159602\",\n \"text\": \"def cross_rp_ind(self, i1, i2):\\n half = int(random.randrange(1, self.GENS - 1))\\n\\n gen1 = i1['gens'][:half]\\n gen1.extend(i2['gens'][half:])\\n\\n gen2 = i2['gens'][:half]\\n gen2.extend(i1['gens'][half:])\\n\\n return Individual(gen1), Individual(gen2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52aeb83bca15e0fb25b51cad17a603b8\",\n \"score\": \"0.48128745\",\n \"text\": \"def test_prep_circuit_three_qubits(self):\\n # Input vector\\n vec = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float64)\\n\\n # Make a tree from the vector\\n tree = BinaryTree(vec)\\n\\n # Get the state preparation circuit\\n areg = QuantumRegister(2)\\n breg = QuantumRegister(1)\\n circ = QuantumCircuit(areg, breg)\\n tree.preparation_circuit(circ, areg, breg)\\n\\n # Add a swaps to make the ordering of the qubits match the input vector\\n # Note: This is because the last bit is the most significant in qiskit, not the first.\\n circ.swap(areg[0], breg[0])\\n\\n # Check that the circuit produces the correct state\\n state = list(np.real(self.final_state(circ)))\\n self.assertTrue(np.allclose(state, vec / np.linalg.norm(vec, ord=2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e349786d6fa485d05d8d545923436bb\",\n \"score\": \"0.48094317\",\n \"text\": \"def test_extend_multiple_interface_qnodes(self):\\n qc = qml.QNodeCollection()\\n\\n def circuit(x):\\n qml.RX(x, wires=0)\\n return qml.expval(qml.PauliZ(0))\\n\\n dev = qml.device(\\\"default.qubit\\\", wires=1)\\n qnodes = [\\n qml.QNode(circuit, dev, interface=\\\"autograd\\\"),\\n qml.QNode(circuit, dev, interface=None),\\n ]\\n\\n with pytest.raises(ValueError, match=\\\"do not all use the same interface\\\"):\\n qc.extend(qnodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b70cd6537ac800ecc721e07e7faf99\",\n \"score\": \"0.48064592\",\n \"text\": \"def __generate_plus4_eq_minus6_plus10() -> Problem:\\n a = 10 * random.choice(__digits_no49) + random.choice([6, 7, 8, 9])\\n return __extend_no_carry_borrow(a, 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472a7e7971e16a10c9cae623e2517039\",\n \"score\": \"0.48055854\",\n \"text\": \"def test_single_switch_offset(self):\\n\\n for k in range(10):\\n sk = str(k)\\n for i in range(9):\\n si = str(i)\\n for j in range(i+1, 10):\\n sj = str(j)\\n self.assertNotEqual(encode(sk+si+sj), encode(sk+sj+si))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b76edc641106a9e6b940fdfb02b5237f\",\n \"score\": \"0.48009458\",\n \"text\": \"def test_output(self):\\n '''Every sequence has 100 bases.'''\\n input_seqs = \\\\\\n ['TTACATACCATACAGTGCGCTAGCGGGTGACAGATATAATGCAGATCCAT'\\n 'ACAGACCAGATGGCAGACATGTGTTGCAGSCTGCAAGTGCAACGCGGTGA',\\n 'GCAGAGTGCCGCAATGACGTGCGCCAAAGCGGTGACAGGGTGACAGTGAA'\\n 'CCAAGTGACAAGTGAACAGGTGCCAGAGTGACCGAGTGACCAGTGGACCA',\\n 'CAGAGTGCCGCAATGACGTGCGCCAAAGCGGACAAAGCACCATGACAAGT'\\n 'ACACAGGTGACAGTGACAAGACAGAGGTGACACAGAGAAAGtGGGTGTGA',\\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA']\\n sample_indexes = [\\n {'1' : 2}, {'1' : 2, '2' : 3}, {'1' : 4, '3' : 5},\\n {'1' : 5}, {'1' : 5}, {'1' : 5}\\n ]\\n reversed_complement_sample_indexes = [\\n {'1' : 7}, {'1' : 2, '2' : 2, '3' : 3},\\n {'1' : 8, '2' : 1, '3' : 6}, {'1' : 1},\\n {'1' : 1}, {'1' : 1}\\n ]\\n cap_sizes = []\\n cap_size = 25\\n while cap_size <= 50:\\n cap_sizes.append(cap_size)\\n cap_size = int(cap_size*2)\\n if cap_size == cap_sizes[-1]:\\n cap_size += 1\\n if cap_size != 50:\\n # Always have a start or end read of length max_readlet_size\\n cap_sizes.append(50)\\n with open(self.output_file, 'w') as output_stream:\\n for i in xrange(6):\\n print_readletized_output(\\n input_seqs[i], sample_indexes[i],\\n reversed_complement_sample_indexes[i],\\n '0:' + str(i), cap_sizes,\\n output_stream=output_stream,\\n min_readlet_size=25, readlet_interval=5,\\n max_readlet_size=50, no_polyA=True\\n )\\n collected_readlets = []\\n with open(self.output_file) as processed_stream:\\n for readlet in processed_stream:\\n collected_readlets.append(readlet.rstrip().split('\\\\t')[1:])\\n '''Each read from input_reads spans 100 bases, and from the\\n arguments passed to go() above, noncapping readlets should span 50.\\n The capping fraction should arrange for one readlet spanning 25\\n bases on either end of a given read. There should be 13 readlets in\\n total. Spot-check some readlets after checking for read info.'''\\n read_info = [info.split('\\\\x1e') for _, info\\n in collected_readlets]\\n read_info = [info[3:4]\\n + [info[-i].split('\\\\x1f')\\n for i in xrange(4, 0, -1)]\\n for info in read_info if len(info) > 3]\\n sample_indexes, reversed_complement_sample_indexes \\\\\\n = [{} for i in xrange(6)], [{} for i in xrange(6)]\\n # All-A read should have been skipped completely\\n self.assertEquals(len(read_info), 5)\\n for i in xrange(5):\\n for j in xrange(len(read_info[i][1])):\\n sample_indexes[i][read_info[i][1][j]] \\\\\\n = int(read_info[i][3][j])\\n reversed_complement_sample_indexes[i][read_info[i][2][j]] \\\\\\n = int(read_info[i][4][j])\\n read_info[i] = read_info[i][:-2]\\n read_info[i][1] = sample_indexes[i]\\n read_info[i][2] = reversed_complement_sample_indexes[i]\\n self.assertTrue([\\n 'TTACATACCATACAGTGCGCTAGCGGGTGACAGATATAATGCAGATCCAT'\\n 'ACAGACCAGATGGCAGACATGTGTTGCAGSCTGCAAGTGCAACGCGGTGA',\\n sample_indexes[0],\\n reversed_complement_sample_indexes[0]\\n ] in read_info)\\n self.assertTrue([\\n 'GCAGAGTGCCGCAATGACGTGCGCCAAAGCGGTGACAGGGTGACAGTGAA'\\n 'CCAAGTGACAAGTGAACAGGTGCCAGAGTGACCGAGTGACCAGTGGACCA',\\n sample_indexes[1],\\n reversed_complement_sample_indexes[1]\\n ] in read_info)\\n self.assertTrue([\\n 'CAGAGTGCCGCAATGACGTGCGCCAAAGCGGACAAAGCACCATGACAAGT'\\n 'ACACAGGTGACAGTGACAAGACAGAGGTGACACAGAGAAAGtGGGTGTGA',\\n sample_indexes[2],\\n reversed_complement_sample_indexes[2]\\n ] in read_info)\\n self.assertTrue([\\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\\n sample_indexes[3],\\n reversed_complement_sample_indexes[3]\\n ] in read_info)\\n self.assertTrue([\\n 'ATCGATTAAGCTATAACAGATAACATAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\\n sample_indexes[3],\\n reversed_complement_sample_indexes[3]\\n ] in read_info)\\n self.assertTrue([\\n 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'\\n 'CTGACACCTGACCAGTGCCAGATGACCAGTGCCAGATGGACGACAGTAGC',\\n sample_indexes[3],\\n reversed_complement_sample_indexes[3]\\n ] in read_info)\\n # Capping readlets\\n self.assertTrue([ 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA',\\n '0:3+\\\\x1e0\\\\x1e50'\\n ] in collected_readlets or\\n [ 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA',\\n '0:3+\\\\x1e0\\\\x1e50\\\\x1e'\\n 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGACGACAGTAGC'\\n '\\\\x1e1\\\\x1e1\\\\x1e1\\\\x1e1'\\n ] in collected_readlets\\n )\\n self.assertTrue([ 'CCAGTGCCAGATGGACGACAGTAGC',\\n '0:3+\\\\x1e75\\\\x1e0'\\n ] in collected_readlets or\\n [ 'CCAGTGCCAGATGGACGACAGTAGC',\\n '0:3+\\\\x1e75\\\\x1e0\\\\x1e'\\n 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGACGACAGTAGC'\\n '\\\\x1e1\\\\x1e1\\\\x1e1\\\\x1e1'\\n ] in collected_readlets\\n )\\n # Noncapping readlets\\n self.assertTrue([ 'GTCAG'\\n 'TTATCTATTATGGGCGCAATGTCTA'\\n 'TGTTATCTGTTATAGCTTAA',\\n '0:3-\\\\x1e5\\\\x1e45'\\n ] in collected_readlets or\\n [ 'GTCAG'\\n 'TTATCTATTATGGGCGCAATGTCTA'\\n 'TGTTATCTGTTATAGCTTAA',\\n '0:3-\\\\x1e5\\\\x1e45\\\\x1e'\\n 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGACGACAGTAGC'\\n '\\\\x1e1\\\\x1e1\\\\x1e1\\\\x1e1'\\n ] in collected_readlets\\n )\\n self.assertTrue([ 'TAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGA',\\n '0:3+\\\\x1e40\\\\x1e10'\\n ] in collected_readlets or\\n [ 'TAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGA',\\n '0:3+\\\\x1e40\\\\x1e10\\\\x1e'\\n 'ATCGATTAAGCTATAACAGATAACA'\\n 'TAGACATTGCGCCCATAATAGATAA'\\n 'CTGACACCTGACCAGTGCCAGATGA'\\n 'CCAGTGCCAGATGGACGACAGTAGC'\\n '\\\\x1e1\\\\x1e1\\\\x1e1\\\\x1e1'\\n ] in collected_readlets\\n )\\n # Ensure no polyA readlets\\n self.assertEquals([readlet for readlet in collected_readlets\\n if set(readlet[0]) == _polyA], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"281af1ea2200aae5e6bfebf99911911c\",\n \"score\": \"0.47987187\",\n \"text\": \"def test_associativity_exact():\\n\\n N = 5\\n g = Grid_1D.periodic(N)\\n\\n a0 = g.rand(0)\\n b0 = g.rand(0)\\n c1 = g.rand(0).D\\n\\n eq1 = a0 ^ (b0 ^ c1)\\n eq2 = (a0 ^ b0) ^ c1\\n\\n assert not eq1 == eq2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de8f989edf5a1d8cafe5cbb022cad5dd\",\n \"score\": \"0.47964856\",\n \"text\": \"def test_qv_natural(self):\\n qv64 = QuantumVolume(5, seed=15)\\n\\n def construct_passmanager(basis_gates, coupling_map, synthesis_fidelity, pulse_optimize):\\n seed = 2\\n _map = [SabreLayout(coupling_map, max_iterations=2, seed=seed)]\\n _unroll3q = Unroll3qOrMore()\\n _swap_check = CheckMap(coupling_map)\\n _swap = [\\n BarrierBeforeFinalMeasurements(),\\n SabreSwap(coupling_map, heuristic=\\\"lookahead\\\", seed=seed),\\n ]\\n _optimize = [\\n Collect2qBlocks(),\\n ConsolidateBlocks(basis_gates=basis_gates),\\n UnitarySynthesis(\\n basis_gates,\\n synthesis_fidelity,\\n coupling_map,\\n pulse_optimize=pulse_optimize,\\n natural_direction=True,\\n ),\\n Optimize1qGates(basis_gates),\\n ]\\n\\n pm = PassManager()\\n pm.append(_map) # map to hardware by inserting swaps\\n pm.append(_unroll3q)\\n pm.append(_swap_check)\\n pm.append(_swap)\\n pm.append(_optimize)\\n return pm\\n\\n coupling_map = CouplingMap([[0, 1], [1, 2], [3, 2], [3, 4], [5, 4]])\\n basis_gates = [\\\"rz\\\", \\\"sx\\\", \\\"cx\\\"]\\n\\n pm1 = construct_passmanager(\\n basis_gates=basis_gates,\\n coupling_map=coupling_map,\\n synthesis_fidelity=0.99,\\n pulse_optimize=True,\\n )\\n pm2 = construct_passmanager(\\n basis_gates=basis_gates,\\n coupling_map=coupling_map,\\n synthesis_fidelity=0.99,\\n pulse_optimize=False,\\n )\\n\\n qv64_1 = pm1.run(qv64.decompose())\\n qv64_2 = pm2.run(qv64.decompose())\\n edges = [list(edge) for edge in coupling_map.get_edges()]\\n self.assertTrue(\\n all(\\n [qv64_1.qubits.index(qubit) for qubit in instr.qubits] in edges\\n for instr in qv64_1.get_instructions(\\\"cx\\\")\\n )\\n )\\n self.assertEqual(Operator(qv64_1), Operator(qv64_2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3fc7e6872534e7e7f3b584c2830f6c\",\n \"score\": \"0.47952512\",\n \"text\": \"def test_bc_inout():\\n k = 10 # number of workers\\n t = 1000\\n shape = {\\n \\\"num-phases\\\": 1,\\n \\\"num-workers\\\": k,\\n \\\"activity-infix\\\": \\\"Buffer\\\",\\n \\\"activity-prefix\\\": \\\"Serialization\\\",\\n \\\"activity-suffix\\\": \\\"Deserialization\\\",\\n \\\"phase-duration\\\": t,\\n }\\n data = generate(\\\"bc_inout\\\", shape, 1.0)\\n\\n # the prefixes and suffixes must have the same bc\\n for w in range(1, k):\\n assert data[epoch(0)][('Serialization', w)] == data[epoch(0)][('Deserialization', w)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0346df113493bb8957e4faac3221ab6\",\n \"score\": \"0.47899675\",\n \"text\": \"def w(self, length):\\n q = Qubits(length)\\n l = [Integer(0)]*q.size\\n for ix in xrange(length):\\n number = Integer(2)**Integer(ix)\\n l[number] = (Integer(1)/length)**(half)\\n q.v = Matrix(l)\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1a1a244003abf99a0d0582ba962bb0\",\n \"score\": \"0.47890088\",\n \"text\": \"def test_gate_more_two_qubits(self) -> None:\\n with pytest.raises(PluginException):\\n circuit = generate_qft_circuit(max_nbqbit, inline=False)\\n qpu = Sabre() | (QuameleonPlugin(topology=Topology(type=TopologyType.LNN)) | PyLinalg())\\n qpu.submit(circuit.to_job())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b49304b52f1048b1d788902ee0b9ad3\",\n \"score\": \"0.4785169\",\n \"text\": \"def add_twoQ_clifford(index, gate_seq_1, gate_seq_2):\\n if (index < 0):\\n raise ValueError(\\n 'index is out of range. it should be smaller than 11520 and '\\n 'greater or equal to 0: ', str(index))\\n elif (index < 576):\\n add_singleQ_based_twoQ_clifford(index, gate_seq_1, gate_seq_2)\\n elif (index < 5184 + 576):\\n add_CNOT_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\\n elif (index < 5184 + 5184 + 576):\\n add_iSWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\\n elif (index < 576 + 5184 + 5184 + 576):\\n add_SWAP_like_twoQ_clifford(index, gate_seq_1, gate_seq_2)\\n else:\\n raise ValueError(\\n 'index is out of range. it should be smaller than 11520 and '\\n 'greater or equal to 0: ', str(index))\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c82c2f77a2d77c7389b50a8da111f1\",\n \"score\": \"0.47811636\",\n \"text\": \"def generate_cuboids():\\n yield (1, 1, 1)\\n c = 2\\n while True:\\n for a in range(1, c+1):\\n for b in range(a, c+1):\\n yield (a, b, c)\\n c += 1\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994288,"cells":{"query_id":{"kind":"string","value":"6f7832c72923e4e0dee8f9a49ea9635e"},"query":{"kind":"string","value":"Removes a flag from a video."},"positive_passages":{"kind":"list like","value":[{"docid":"f517e7999eef48df8f719e8d1e866d98","score":"0.63815784","text":"def allow_video(self, video_id):\r\n videos = dict([[video.video_id, video] for video in self._video_library.get_all_videos()])\r\n \r\n if video_id in videos:\r\n video = videos[video_id]\r\n if video.flagged == True:\r\n video.allow()\r\n print (f\"Successfully removed flag from video: {video.title}\")\r\n else:\r\n print(\"Cannot remove flag from video: Video is not flagged\")\r\n else:\r\n print(\"Cannot remove flag from video: Video does not exist\")","title":""}],"string":"[\n {\n \"docid\": \"f517e7999eef48df8f719e8d1e866d98\",\n \"score\": \"0.63815784\",\n \"text\": \"def allow_video(self, video_id):\\r\\n videos = dict([[video.video_id, video] for video in self._video_library.get_all_videos()])\\r\\n \\r\\n if video_id in videos:\\r\\n video = videos[video_id]\\r\\n if video.flagged == True:\\r\\n video.allow()\\r\\n print (f\\\"Successfully removed flag from video: {video.title}\\\")\\r\\n else:\\r\\n print(\\\"Cannot remove flag from video: Video is not flagged\\\")\\r\\n else:\\r\\n print(\\\"Cannot remove flag from video: Video does not exist\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c42b7812b127cfa046024ccae96f9efc","score":"0.7078656","text":"def allow_video(self, video_id):\n video_to_unflag = self._video_library.get_video(video_id)\n\n if video_to_unflag == None:\n print(\"Cannot remove flag from video: Video does not exist\")\n return\n elif video_to_unflag.video_id not in self._flagged:\n print(\"Cannot remove flag from video: Video is not flagged\")\n return\n else:\n modified_flagged = {}\n for y in self._flagged:\n if video_to_unflag.video_id == y:\n continue\n else:\n modified_flagged[y] = self._flagged[y]\n print(\"Successfully removed flag from video:\", video_to_unflag.title)\n self._flagged = modified_flagged","title":""},{"docid":"7f5e20b25a9434a8194240d211bf2f61","score":"0.70257044","text":"def allow_video(self, video_id: str):\n video_to_unflag = self._video_library.get_video(video_id)\n if video_to_unflag is None:\n print(\"Cannot remove flag from video: Video does not exist\")\n elif video_to_unflag.flag_reason == \"\":\n print(\"Cannot remove flag from video: Video is not flagged\")\n else:\n video_to_unflag.flag_reason = \"\"\n print(f\"Successfully removed flag from video: {video_to_unflag.title}\")","title":""},{"docid":"8c53200d230958ba89b3b55d903c195f","score":"0.7007885","text":"def allow_video(self, video_id):\n video = self._video_library.get_video(video_id)\n if video is None:\n print(\"Cannot remove flag from video: Video does not exist\")\n return\n\n if not video.flagged[0]:\n print(\"Cannot remove flag from video: Video is not flagged\")\n return\n\n video.flagged = [False, \"\"]\n print(\"Successfully removed flag from video: {0}\".format(video.title))","title":""},{"docid":"3b30678967af06d44161f66fae49977e","score":"0.68557155","text":"def remove_flag(self, flag):\n if self.get_flags():\n self.set_flags(''.join(set(self.get_flags()) - set(flag)))","title":""},{"docid":"7d95c7425948ff80ec686ca3a9045ea8","score":"0.6775681","text":"def remove_flag(self, flag):\n if 'Status' in self or 'X-Status' in self:\n self.set_flags(''.join(set(self.get_flags()) - set(flag)))","title":""},{"docid":"88e3dfa712edd295cb4c1b1cfc348f90","score":"0.63627285","text":"def delete_video(self):\n self.multipler_request('video/%s/delete' % self.file_id)","title":""},{"docid":"a1afceb52542302b5da36d6867e3d8dd","score":"0.6319069","text":"def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","title":""},{"docid":"a1afceb52542302b5da36d6867e3d8dd","score":"0.6319069","text":"def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","title":""},{"docid":"a1afceb52542302b5da36d6867e3d8dd","score":"0.6319069","text":"def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","title":""},{"docid":"a1afceb52542302b5da36d6867e3d8dd","score":"0.6319069","text":"def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","title":""},{"docid":"a1afceb52542302b5da36d6867e3d8dd","score":"0.6319069","text":"def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","title":""},{"docid":"7a35289ed27f1dc0c354979f70205800","score":"0.62957114","text":"def flag_video(self, video_id, flag_reason=\"\"):\r\n print(\"flag_video needs implementation\")","title":""},{"docid":"332e73ed770503daba8c2c4407673de6","score":"0.61014146","text":"def flag_video(self, video_id, flag_reason=\"\"):\n video = self._video_library.get_video(video_id)\n if video is None:\n print(\"Cannot flag video: Video does not exist\")\n return\n\n if video.flagged[0]:\n print(\"Cannot flag video: Video is already flagged\")\n return\n\n flag_reason = \"Not supplied\" if flag_reason == \"\" else flag_reason\n video.flagged = [True, flag_reason]\n if self._play_vid_tag == video_id or self._paused_vid_tag == video_id:\n # Manually make the paused video played, so that we\n # can stop it.\n self._play_vid_tag = video_id\n self.stop_video()\n print(\"Successfully flagged video: {0} (reason: {1})\".format(\n video.title, flag_reason))","title":""},{"docid":"31cdd1dc8b86a9ad34dec31fcf6b20ae","score":"0.5965828","text":"def remove():","title":""},{"docid":"4274121ee096f0a2fe3744d35b194e86","score":"0.5933706","text":"def flag_video(self, video_id: str, flag_reason=\"\"):\n video_to_flag = self._video_library.get_video(video_id)\n if video_to_flag is None:\n print(\"Cannot flag video: Video does not exist\")\n elif video_to_flag.flag_reason != \"\":\n print(\"Cannot flag video: Video is already flagged\")\n else:\n if self._currently_playing_video is not None and video_id == self._currently_playing_video.video_id:\n self.stop_video()\n video_to_flag.flag_reason = flag_reason if flag_reason != \"\" else \"Not supplied\"\n print(f\"Successfully flagged video: {video_to_flag.title} (reason: {video_to_flag.flag_reason})\")","title":""},{"docid":"835a2cc7b5d697c843c7d38cf6ea6bb9","score":"0.5906251","text":"def stop_video(self):\r\n if len(self.play) > 0:\r\n a = self.play.pop()\r\n print(\"Stopping video: \"+a)\r\n self.co = 0\r\n else:\r\n print(\"Cannot stop video: No video is currently playing\")\r\n # print(\"stop_video needs implementation\")\r","title":""},{"docid":"67d0a1b4e03505c8e5a88bb5f579bb37","score":"0.58743685","text":"def flag_video(self, video_id, flag_reason=\"\"):\n video_to_flag = self._video_library.get_video(video_id)\n if video_to_flag == None:\n print(\"Cannot flag video: Video does not exist\")\n elif video_id in self._flagged.keys():\n print(\"Cannot flag video: Video is already flagged\")\n else:\n if flag_reason == \"\":\n flag_reason = \"Not supplied\"\n if self._currently_playing == video_to_flag.video_id or self._paused == video_to_flag.video_id:\n self.stop_video()\n print(\"Successfully flagged video:\", video_to_flag.title, \"(reason:\", flag_reason + \")\")\n self._flagged[video_id] = flag_reason","title":""},{"docid":"a36c16e302c12c6e034103b502b04a6f","score":"0.5871676","text":"def delete_flag(self):\n self.__status = Flag.NotFlagged\n self.__start = None\n self.__due_date = None\n self.__completed = None\n self._track_changes()","title":""},{"docid":"701229fc7cda664c06e7d184a645c8cf","score":"0.5860052","text":"def do_delete(input_m3u, index):\n delete_video(input_m3u, index)","title":""},{"docid":"70b23b3a7d78ec0d09c41e9e8cec50e0","score":"0.5826584","text":"def flag_video(self, video_id, flag_reason=\"Not supplied\"):\r\n videos = dict([[video.video_id, video] for video in self._video_library.get_all_videos()])\r\n \r\n if video_id in videos:\r\n video = videos[video_id]\r\n if video.flagged == False:\r\n if video == self._current_video:\r\n self.stop_video()\r\n video.flag(flag_reason)\r\n print (f\"Successfully flagged video: {video.title} (reason: {video.flagged_reason})\")\r\n else:\r\n print(\"Cannot flag video: Video is already flagged\")\r\n else:\r\n print(\"Cannot flag video: Video does not exist\")","title":""},{"docid":"9ffdde65d040ff32988a9de9aacea7a6","score":"0.5796712","text":"def unset_flag(FLAG):\r\n global F\r\n F = F & ~FLAG","title":""},{"docid":"ecd1df11dec06a6cfb2eba03a1c8999b","score":"0.57002217","text":"def remove(value):","title":""},{"docid":"fd41fb2baf073adf6e37aee29907caf2","score":"0.56939673","text":"def _clear_flag(cpu: 'Cpu', flag: StatusFlag) -> None:\n if flag == StatusFlag.carry:\n cpu.status.carry = False\n elif flag == StatusFlag.decimal:\n cpu.status.decimal = False\n elif flag == StatusFlag.interrupt_disable:\n cpu.status.interrupt_disable = False\n elif flag == StatusFlag.overflow:\n cpu.status.overflow = False\n else:\n raise NotImplementedError(f'{flag} mode is not supported')","title":""},{"docid":"47f5c93d3bccb51602d7a3b87cdba8df","score":"0.5656351","text":"def stop_video(self):\n if len(self.playing) == 0:\n print(\"Cannot stop video: No video is currently playing\")\n elif len(self.playing) > 0:\n videoToStop = self.playing.pop()\n print(f\"Stopping video: {videoToStop.title}\")","title":""},{"docid":"a4f5e0cd533bcef0b91b3e1072dfea90","score":"0.5641556","text":"def video_wall_off(self):\n self.send_command(0x84, 0x0)","title":""},{"docid":"e373c51b92a1f5fe26f622c13925224f","score":"0.5591142","text":"def remove_bit(self, index: int) -> None:\r\n\t\tself.bitfield &= ~self.to_bitfield(index)","title":""},{"docid":"e123b773adf7cf210da4ae884e83ee47","score":"0.55888903","text":"def RemoveOneFlag(flag_name, flag_values=FLAGS):\n if flag_name in flag_values.FlagDict():\n flag_values.__delattr__(flag_name)","title":""},{"docid":"2ef8fd6dd4698fedbed4a7b62428f7d7","score":"0.55867434","text":"def stop_video(self):\r\n\r\n if self._current_video == None or self._video_stat == 'STP' or self._current_video.flagged == True:\r\n print(\"Cannot stop video: No video is currently playing\")\r\n\r\n else:\r\n self._stopped_video = self._current_video\r\n self._video_stat = 'STP'\r\n print(f\"Stopping video: {self._current_video.title}\")","title":""},{"docid":"ea332c9773860ccaebd3e627204a634b","score":"0.555717","text":"def remove_flags(self, messages, flags):\n return self._store('-FLAGS', messages, flags)","title":""},{"docid":"39e54f0d445d7abb41bcbcd300f240b1","score":"0.5547637","text":"def remove(self, building):\n entry = self.entries[building]\n entry[-1] = SkylineTracker.REMOVED\n pass","title":""},{"docid":"68e22021f8f53be44017f62ac41e610d","score":"0.5529644","text":"def unflag(self, msg_id):\n return objects_module.messages.unflag(self.khoros_object, msg_id)","title":""},{"docid":"b1fbc6afcfc69abbfd167cba10b61f22","score":"0.55286944","text":"def delete_flag_file(self, context, flag_file):\n try:\n os.remove(flag_file)\n except OSError:\n LOG.error(\"Failed to delete %s flag.\"\n % flag_file)\n pass","title":""},{"docid":"8f408f789e22ccc03bd59068f1e2d4fa","score":"0.5513272","text":"def delete_position(seq, frame):\n seq.keyframe_delete(data_path='[\"launch\"]', frame=frame)","title":""},{"docid":"d82587f265bc72e6fc12a6bd8078dfbd","score":"0.5442011","text":"def clear_flags(self):\n for flag in self.flags:\n self.flags[flag] = 0","title":""},{"docid":"d88f1cd16887b1df2c10ee147a5e3a54","score":"0.5391082","text":"def remove_movie (self, title, year):\n title=re.sub(r'[?|$|!|:|#]',r'',title)\n movie_meta = '%s (%d)' % (title, year)\n folder = re.sub(r'[?|$|!|:|#]',r'',self.db[self.movies_label][movie_meta]['alt_title'])\n del self.db[self.movies_label][movie_meta]\n self._update_local_db(filename=self.db_filepath, db=self.db)\n dirname = os.path.join(self.movie_path, folder)\n filename = os.path.join(self.movie_path, folder, movie_meta + '.strm')\n if xbmcvfs.exists(dirname):\n xbmcvfs.delete(filename)\n xbmcvfs.rmdir(dirname)\n return True\n return False","title":""},{"docid":"9349933f10e6bfcb34e9660c6bd31337","score":"0.5389633","text":"def stop_video(self):\n\n if self._play_vid_tag is None:\n print(\"Cannot stop video: No video is currently playing\")\n return\n\n self._paused_vid_tag = None\n print(\"Stopping video: {0}\".format(\n self._video_library.get_video(self._play_vid_tag).title))\n self._play_vid_tag = None","title":""},{"docid":"806d081aa8271322ed7c72f358972dc0","score":"0.5350334","text":"def clean_flags(container, flags):\n for flag in container:\n if flag in flags:\n flags.remove(flag)","title":""},{"docid":"b2b9ce8387ecaf46e9fe1a304f0528e9","score":"0.53394014","text":"def remove_from_playlist(self, playlist_name, video_id):\r\n print(\"remove_from_playlist needs implementation\")","title":""},{"docid":"1fa1495e795a858eca92c65192681d4d","score":"0.5337086","text":"def deleteFlag(self, flagId):\n cursor = self.__getCursor()\n cursor.execute(\"\"\"\n DELETE FROM `karma_comments` SET `Text` = %s WHERE `Id` = %s\n \"\"\", (flagId, ))\n cursor.close()\n self.__connection.commit()","title":""},{"docid":"6101a7dd3506e3cbda2389d12618b50d","score":"0.5325414","text":"def clear_flag(flag, width, flag_bits, xmin, xmax, ymin, ymax, logpath=None):\n process(['/cluster/GAMMA_SOFTWARE-20161207/ISP/bin/clear_flag', flag, width, flag_bits, xmin, xmax, ymin, ymax], logpath=logpath)","title":""},{"docid":"fe4c1cfc29f813d2b3a731fbe73dab78","score":"0.531339","text":"def video_stop(self):\n self.widgetCanvas.config(width=0, height=0)\n self.list_player.pause()\n self.video_status = 'stopped'","title":""},{"docid":"082fdba9fbbfd408c5da1219a4a49906","score":"0.5301001","text":"def stop_video(self):\n if self.playing==\"\" and self.pause==\"\":\n print(\"Cannot stop video: No video is currently playing\")\n else:\n if self.pause!=\"\":\n stop=self._video_library._videos[self.pause]._title\n print(\"Stopping video: {}\".format(stop))\n self.playing=\"\"\n self.pause=\"\"\n else:\n stop=self._video_library._videos[self.playing]._title\n print(\"Stopping video: {}\".format(stop))\n self.playing=\"\"\n self.pause=\"\"\n\n # print(\"stop_video needs implementation\")","title":""},{"docid":"67af941989ba61b541b64af70d7e2d77","score":"0.5289763","text":"def editflag(x,y,flags):\n count = flags.count(x)\n if y == 0 and count > 0:\n flags.remove(x)\n elif y == 0 and count == 0:\n print('The value', x ,'does not exist in flags.')\n elif y == 1 and count == 0:\n flags = flags.append(x)\n elif y == 1 and count > 0:\n print('The value', x ,'already exists in flags')\n else:\n print('editflag command was used incorrectly.')\n return(flags)","title":""},{"docid":"31a6622870def1a48d84ee5452868796","score":"0.52867866","text":"def remove(self, vPath):\r\n os.remove(self.ospath(vPath) )\r\n #notify(AssetRemoved(self, vpath))\r","title":""},{"docid":"4c71b981c9bd675f244b408ddf227a37","score":"0.5284856","text":"def remove(path: AnyStr, verbose: Optional[int] = 0) -> None:","title":""},{"docid":"131a4f36d74250dca9285b222e12e374","score":"0.5278465","text":"def removeTexture():\n\ttry:\n\t\tdel logic.skyTexture\n\t\tdel logic.skyTextureAnimated\n\texcept:\n\t\tpass","title":""},{"docid":"7235d2893473e0c320be1990c62b1ebc","score":"0.5274217","text":"def remove_vector(self, vec_id):\n pass","title":""},{"docid":"850cc2723fd71c567a453a74d973c5c6","score":"0.52727956","text":"def api_remove_favorite(self):\n raise NotImplementedError","title":""},{"docid":"850cc2723fd71c567a453a74d973c5c6","score":"0.52727956","text":"def api_remove_favorite(self):\n raise NotImplementedError","title":""},{"docid":"a300dbfffb8c560c3b51d88f7e3e7b87","score":"0.52612627","text":"def delete_files(self):\n\n small_video_path = self.small_video()\n try:\n os.unlink(self.videofile.path)\n if small_video_path:\n os.unlink(small_video_path)\n except:\n pass","title":""},{"docid":"daca0bb76ca2a3577020a0e661dbb172","score":"0.5259757","text":"def stop_video(self):\n if self._currently_playing_video is None:\n print(\"Cannot stop video: No video is currently playing\")\n return \n print(f\"Stopping video: {self._currently_playing_video.title}\")\n self._currently_playing_video = None\n self._current_video_status = None","title":""},{"docid":"04a77ddc9b40f3661d8c83de954bc7d8","score":"0.5249311","text":"def remove(self, obj):","title":""},{"docid":"514ede108b6fbf8783c3964f899a4c31","score":"0.5245793","text":"def remove_video_before_db(examples: List[Dict]) -> List[Dict]:\n for eg in examples:\n if \"video\" in eg:\n del eg[\"video\"]\n if \"video\" in eg:\n del eg[\"options\"]\n\n return examples","title":""},{"docid":"638994eed7c2dbcc2b77bda9421a561c","score":"0.5237131","text":"def remove(self, oid):","title":""},{"docid":"638994eed7c2dbcc2b77bda9421a561c","score":"0.5237131","text":"def remove(self, oid):","title":""},{"docid":"7bcc742fecd460a2c3dbd6faad0d9ca3","score":"0.52347565","text":"def remove_mode(self, token):\n self._switcher.remove_mode(token)","title":""},{"docid":"2e60792e5ed8e1c588ad9500d1d76d93","score":"0.5188642","text":"def remove_from_playlist(self, playlist_name, video_id):\n print(\"remove_from_playlist needs implementation\")","title":""},{"docid":"2e60792e5ed8e1c588ad9500d1d76d93","score":"0.5188642","text":"def remove_from_playlist(self, playlist_name, video_id):\n print(\"remove_from_playlist needs implementation\")","title":""},{"docid":"a71a36d0051f4f211d0dd41dcc421c29","score":"0.5184038","text":"def _remove(self, pattern):","title":""},{"docid":"2baa27afd4e8e2495a64d5886f88ab72","score":"0.51829296","text":"def remove(name, send_events=True, moving=None, loading=False):","title":""},{"docid":"69b6db305b448f9b4f0a9b5c5d9d7761","score":"0.5178258","text":"def remove_clip(self, clip, add_pending=False):\n\n self.callbacks.on_clip_stop(clip)\n if clip.name not in self.players:\n # clip was already removed/stopped ?\n return\n player = self.players[clip.name]\n player.stop()\n del self.players[clip.name]\n self.clips = [ c for c in self.clips if c.name != clip.name ]\n\n if not add_pending and len(self.clips) == 0 and self.stop_if_empty:\n self.stop()\n raise AllClipsDone()","title":""},{"docid":"90d2c89484c4acfc49340a9a76226fc1","score":"0.51759607","text":"def _remove(self, _):\n\t\tself._delete_torrent()\n\t\tself.parent.remove_widget(self)\n\t\tos.remove(self.uri)\n\t\tos.remove(self.ids.img.source)","title":""},{"docid":"4efcdf5e7ceeb4cd9ed2734b56e4ffe6","score":"0.5175291","text":"def remove_member(self, participant, recorder):\n self.set_take_for(participant, ZERO, recorder)","title":""},{"docid":"1f74d5d41ef9a0dd1043f3eacf640daf","score":"0.5173915","text":"def _delWindowStyleFlag(self, flag, advanced=False):\n\t\tmechanics = self._getWindowStyleMechanics(advanced)\n\t\tif self._constructed():\n\t\t\tmechanics[2](mechanics[1]() & (~flag))\n\t\telse:\n\t\t\tself._preInitProperties[mechanics[0]] = self._preInitProperties[mechanics[0]] & (~flag)","title":""},{"docid":"30e7c536f1d69bd3098eca4e95fcce60","score":"0.51729625","text":"async def remove(self, ctx, *, word: lower):\n await self.bot.db.update_guild_config(ctx.guild.id, {'$pull': {'detections.filters': word}})\n await ctx.send(self.bot.accept)","title":""},{"docid":"aea192d8826cacb3a7e2bddf566ae4d7","score":"0.5162678","text":"def remove(self, e):\n # write code here\n if e in self.vals:\n self.vals[e] -= 1","title":""},{"docid":"3f4873d476789e36a82cca464428dd3f","score":"0.5160594","text":"def remove(self, path):","title":""},{"docid":"8c7f134d8629a3f4956ee324e434360c","score":"0.51525944","text":"def remove(self, e):\n try:\n self.vals[e] -= 1\n except:\n self.vals[e] = 0","title":""},{"docid":"0455af9c4fed4ecb628992d19b6686cb","score":"0.5140673","text":"def remove(self, e):\r\n # write code here\r\n try:\r\n self.vals[e] -= 1\r\n except:\r\n pass","title":""},{"docid":"88862f8dc3b9a188916a072b1b190498","score":"0.5140368","text":"def remove(self, e):\r\n # write code here\r\n try:\r\n self.vals[e]-=1 \r\n except:\r\n self.vals[e]=0","title":""},{"docid":"02c82f2864189e51627989d9714fbd9f","score":"0.51352584","text":"def delete_event(widget=None, *data):\n global SILENT\n camera.release();\n fps_counter.quit();\n if not SILENT:\n sound.play(\"close\")\n time.sleep(3)\n sound.quit()\n return False","title":""},{"docid":"351cc22455b3c26d1257873739dab34b","score":"0.51309896","text":"def stop_video(self):\n if (self.currently_playing != \"none\") :\n print(f\"Stopping video: {self.currently_playing}\")\n self.currently_playing = \"none\"\n self.playing = False\n return\n print(\"Cannot stop video: No video is currently playing\")","title":""},{"docid":"bbb43f83074b2502231ab99ff3c8e315","score":"0.5114603","text":"def remove(self):\n ...","title":""},{"docid":"0a51b21ba72c75f1c9eef98baf23087f","score":"0.5103221","text":"def clearFlags(self, key: str, flags: int) -> None:\n path = self._path + key\n self._api.setEntryFlags(path, self._api.getEntryFlags(path) & ~flags)","title":""},{"docid":"c7a3e14f97a580e6d7ca3ea24d9a52e1","score":"0.51011646","text":"def remove_one(self):\n ...","title":""},{"docid":"8eaf63dd2a2ed907fed0b06412256081","score":"0.5098991","text":"def removeFrame(self, frame):\n if self._frames.__contains__(frame):\n self._frames.remove(frame)\n self._updateGrid()\n self.update()","title":""},{"docid":"1e9e35f773d49e14645c98a185e6ea9b","score":"0.50926566","text":"def stop_video(self):\n if self._currently_playing != None:\n video_playing = self._video_library.get_video(self._currently_playing)\n print(\"Stopping video:\", video_playing.title)\n self._currently_playing = None\n elif self._paused != None:\n video_paused = self._video_library.get_video(self._paused)\n print(\"Stopping video:\", video_paused.title)\n self._paused = None\n else:\n print(\"Cannot stop video: No video is currently playing\")","title":""},{"docid":"bcfdbdae0d83c3a9eddbb0c0667ecbf5","score":"0.50840265","text":"def rem(self, coisa):\r\n self.gui.rem(coisa)","title":""},{"docid":"221e1c6016726fbfdf7fdf98460c44b7","score":"0.50806963","text":"def stop_video(self, address: int = None) -> Response:\r\n address = self._get_address(address)\r\n data = {\"arg\": None}\r\n cmd_url = (\r\n \"http://\"\r\n + self.info[\"url\"]\r\n + \":8181/api/luminaire/\"\r\n + address\r\n + \"/command/PLAY_VIDEO_FILE\"\r\n )\r\n response = requests.post(\r\n cmd_url, json=data, cookies=self.info[\"cookiejar\"], verify=False\r\n )\r\n response = self._check_response_for_error(response)\r\n print(f\"Stopped video file from playing at address: {address}\")\r\n return response","title":""},{"docid":"7a68fd46b69e788df13bc28e87e20821","score":"0.50803006","text":"def unmark(self, pos):\n byte = ~(1 << self._bitpos(pos))\n byte = cast(byte, types.int8)\n self.buf[self._bytepos(pos)] &= byte","title":""},{"docid":"f8b69114c4ad8e152fb6c1b50c94bb09","score":"0.5074984","text":"def remove_state(self, state):\n state = state.format(relation_name=self.relation_name)\n value = _get_flag_value(state)\n if not value:\n return\n if self.key in value['conversations']:\n value['conversations'].remove(self.key)\n if value['conversations']:\n set_flag(state, value)\n else:\n clear_flag(state)","title":""},{"docid":"41b3cde542bc36b65913d954321ebe22","score":"0.5070644","text":"async def _remove(self, version: int) -> None:\n py_typecheck.check_type(version, (int, np.integer))\n\n path = self._get_path_for_version(version)\n if await file_utils.exists(path):\n await file_utils.rmtree(path)\n logging.info('Program state removed: %s', path)","title":""},{"docid":"44a170cb7cb08024b448005abbe9bac9","score":"0.5070521","text":"def remove(self, count = -1):\n if self.is_readonly:\n BufferBase._logger.debug('buffer is readonly')\n return\n pass","title":""},{"docid":"29d2ce7c49b9ede17bdbc97a4386507b","score":"0.5058651","text":"def remove(self):\n glDeleteBuffers(1, [self.vbo_array])\n\n if self.vdata_indices is not None:\n glDeleteBuffers(1, [self.vbo_element_array])\n\n glDeleteVertexArrays(1, [self.vao])\n self.dirty = True\n print(\"Item {}: removing myself.\".format(self.label))","title":""},{"docid":"93240a1923ff99ed298841d46eb9ccb0","score":"0.50540644","text":"def remove_option(self, parameter):\n\n if parameter in self.options.keys():\n # Find index of parameter and remove\n index = self.options[parameter].index\n self.options.pop(parameter)\n self.lines.pop(index)\n\n # Adjust indices of following in list\n for option in self.lines[index:]:\n option.index -= 1","title":""},{"docid":"5c5a2f2d4143cf7437774a621e23f47e","score":"0.50523704","text":"def release(self):\n if self._video is not None:\n del self._video","title":""},{"docid":"4eb0f22e3ef9b3f59df14a2bcd44a418","score":"0.503461","text":"def remove_face(face_id):\n vrs_client = create_vrs_client()\n collection_id = get_collection_id(vrs_client)\n vrs_client.remove_face(collection_id, face_id)\n save_dir = './results/{0}/{1}.jpg'.format(collection_id, face_id)\n if os.path.exists(save_dir):\n os.remove(save_dir)","title":""},{"docid":"56376de9f8df1b9ed2150d6e72a55314","score":"0.50332105","text":"def remove_overlay(overlay_id):\n if overlay_id != -1:\n camera.remove_overlay(overlay_id)","title":""},{"docid":"ab9c55301a732ae67ba43340668a71a2","score":"0.5031563","text":"def remove(path):\n pass","title":""},{"docid":"d99f248a1b81248f1823cbd30895b48f","score":"0.5026668","text":"def delete_frame(self, index):\n frame = self.frames[index]\n del self.frames[index]\n frame.is_attached = False\n self.renumber_from_index(index)","title":""},{"docid":"b959e758c14d0c0f50100bd922d3f85b","score":"0.5026593","text":"def remove(self, path):\n pass","title":""},{"docid":"5af4c1685255d4699e35997134b224c7","score":"0.50100666","text":"def remove(self, x, y):\n\n if self.map[y][x] == 'I':\n self.map[y][x] = self.starting_map[y][x]\n self.industrial -= 1\n\n elif self.map[y][x] == 'C':\n self.map[y][x] = self.starting_map[y][x]\n self.commercial -= 1\n\n elif self.map[y][x] == 'R':\n self.map[y][x] = self.starting_map[y][x]\n self.residential -= 1\n\n else:\n print(\"You can't remove %s\" % self.map[y][x])","title":""},{"docid":"f059b62537333ebed461aa71e0fca8d8","score":"0.500491","text":"def delExifTag(self, tag, record = None):\n self.__getExif__().removeTag(tag, record)","title":""},{"docid":"9be240cd908f276e996c1869c7acb484","score":"0.5000761","text":"def remove_activity(self, seq):\n return self.plan.remove_activity(seq)","title":""},{"docid":"5ee9b03d224d33aeb1b8f0f2bd0f9102","score":"0.49978122","text":"def remove(self):\n del self.tracker\n self.init_bounding_box = None\n # del self","title":""},{"docid":"7221093869e2f1eaa0dfe498769e2582","score":"0.49967402","text":"def apply_transform(\n self,\n video_path: str,\n output_path: str,\n metadata: Optional[List[Dict[str, Any]]] = None,\n ) -> str:\n return F.remove_audio(video_path, output_path, metadata=metadata)","title":""},{"docid":"f41f75db85e768d43dc142931e66a560","score":"0.49944264","text":"def deleteAvatar(self, av):\n if av in self.paintedAvatars:\n del self.paintedAvatars[av]\n self.scoreDirty = True","title":""},{"docid":"e7450c288fe46fc9d5334861719d6647","score":"0.4993601","text":"def remove(self, key):\n try:\n self.slots[key] = -1\n except IndexError:\n pass","title":""},{"docid":"5de8153ee7e3cf1e04372944d889bcf0","score":"0.49884316","text":"def remove(self, value):\n self.prune()\n index = self.items.index(value)\n del(self.items[index])\n del(self.times[index])","title":""},{"docid":"ccd1ac2f1e2f08fb7534168d599a43a1","score":"0.49862194","text":"def removeProjectBrowserFlags(key, prefClass=Prefs):\n prefClass.settings.remove(\"Project/BrowserFlags/\" + key)","title":""}],"string":"[\n {\n \"docid\": \"c42b7812b127cfa046024ccae96f9efc\",\n \"score\": \"0.7078656\",\n \"text\": \"def allow_video(self, video_id):\\n video_to_unflag = self._video_library.get_video(video_id)\\n\\n if video_to_unflag == None:\\n print(\\\"Cannot remove flag from video: Video does not exist\\\")\\n return\\n elif video_to_unflag.video_id not in self._flagged:\\n print(\\\"Cannot remove flag from video: Video is not flagged\\\")\\n return\\n else:\\n modified_flagged = {}\\n for y in self._flagged:\\n if video_to_unflag.video_id == y:\\n continue\\n else:\\n modified_flagged[y] = self._flagged[y]\\n print(\\\"Successfully removed flag from video:\\\", video_to_unflag.title)\\n self._flagged = modified_flagged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5e20b25a9434a8194240d211bf2f61\",\n \"score\": \"0.70257044\",\n \"text\": \"def allow_video(self, video_id: str):\\n video_to_unflag = self._video_library.get_video(video_id)\\n if video_to_unflag is None:\\n print(\\\"Cannot remove flag from video: Video does not exist\\\")\\n elif video_to_unflag.flag_reason == \\\"\\\":\\n print(\\\"Cannot remove flag from video: Video is not flagged\\\")\\n else:\\n video_to_unflag.flag_reason = \\\"\\\"\\n print(f\\\"Successfully removed flag from video: {video_to_unflag.title}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c53200d230958ba89b3b55d903c195f\",\n \"score\": \"0.7007885\",\n \"text\": \"def allow_video(self, video_id):\\n video = self._video_library.get_video(video_id)\\n if video is None:\\n print(\\\"Cannot remove flag from video: Video does not exist\\\")\\n return\\n\\n if not video.flagged[0]:\\n print(\\\"Cannot remove flag from video: Video is not flagged\\\")\\n return\\n\\n video.flagged = [False, \\\"\\\"]\\n print(\\\"Successfully removed flag from video: {0}\\\".format(video.title))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b30678967af06d44161f66fae49977e\",\n \"score\": \"0.68557155\",\n \"text\": \"def remove_flag(self, flag):\\n if self.get_flags():\\n self.set_flags(''.join(set(self.get_flags()) - set(flag)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d95c7425948ff80ec686ca3a9045ea8\",\n \"score\": \"0.6775681\",\n \"text\": \"def remove_flag(self, flag):\\n if 'Status' in self or 'X-Status' in self:\\n self.set_flags(''.join(set(self.get_flags()) - set(flag)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e3dfa712edd295cb4c1b1cfc348f90\",\n \"score\": \"0.63627285\",\n \"text\": \"def delete_video(self):\\n self.multipler_request('video/%s/delete' % self.file_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1afceb52542302b5da36d6867e3d8dd\",\n \"score\": \"0.6319069\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1afceb52542302b5da36d6867e3d8dd\",\n \"score\": \"0.6319069\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1afceb52542302b5da36d6867e3d8dd\",\n \"score\": \"0.6319069\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1afceb52542302b5da36d6867e3d8dd\",\n \"score\": \"0.6319069\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1afceb52542302b5da36d6867e3d8dd\",\n \"score\": \"0.6319069\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a35289ed27f1dc0c354979f70205800\",\n \"score\": \"0.62957114\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\r\\n print(\\\"flag_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332e73ed770503daba8c2c4407673de6\",\n \"score\": \"0.61014146\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n video = self._video_library.get_video(video_id)\\n if video is None:\\n print(\\\"Cannot flag video: Video does not exist\\\")\\n return\\n\\n if video.flagged[0]:\\n print(\\\"Cannot flag video: Video is already flagged\\\")\\n return\\n\\n flag_reason = \\\"Not supplied\\\" if flag_reason == \\\"\\\" else flag_reason\\n video.flagged = [True, flag_reason]\\n if self._play_vid_tag == video_id or self._paused_vid_tag == video_id:\\n # Manually make the paused video played, so that we\\n # can stop it.\\n self._play_vid_tag = video_id\\n self.stop_video()\\n print(\\\"Successfully flagged video: {0} (reason: {1})\\\".format(\\n video.title, flag_reason))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31cdd1dc8b86a9ad34dec31fcf6b20ae\",\n \"score\": \"0.5965828\",\n \"text\": \"def remove():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4274121ee096f0a2fe3744d35b194e86\",\n \"score\": \"0.5933706\",\n \"text\": \"def flag_video(self, video_id: str, flag_reason=\\\"\\\"):\\n video_to_flag = self._video_library.get_video(video_id)\\n if video_to_flag is None:\\n print(\\\"Cannot flag video: Video does not exist\\\")\\n elif video_to_flag.flag_reason != \\\"\\\":\\n print(\\\"Cannot flag video: Video is already flagged\\\")\\n else:\\n if self._currently_playing_video is not None and video_id == self._currently_playing_video.video_id:\\n self.stop_video()\\n video_to_flag.flag_reason = flag_reason if flag_reason != \\\"\\\" else \\\"Not supplied\\\"\\n print(f\\\"Successfully flagged video: {video_to_flag.title} (reason: {video_to_flag.flag_reason})\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"835a2cc7b5d697c843c7d38cf6ea6bb9\",\n \"score\": \"0.5906251\",\n \"text\": \"def stop_video(self):\\r\\n if len(self.play) > 0:\\r\\n a = self.play.pop()\\r\\n print(\\\"Stopping video: \\\"+a)\\r\\n self.co = 0\\r\\n else:\\r\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\r\\n # print(\\\"stop_video needs implementation\\\")\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d0a1b4e03505c8e5a88bb5f579bb37\",\n \"score\": \"0.58743685\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n video_to_flag = self._video_library.get_video(video_id)\\n if video_to_flag == None:\\n print(\\\"Cannot flag video: Video does not exist\\\")\\n elif video_id in self._flagged.keys():\\n print(\\\"Cannot flag video: Video is already flagged\\\")\\n else:\\n if flag_reason == \\\"\\\":\\n flag_reason = \\\"Not supplied\\\"\\n if self._currently_playing == video_to_flag.video_id or self._paused == video_to_flag.video_id:\\n self.stop_video()\\n print(\\\"Successfully flagged video:\\\", video_to_flag.title, \\\"(reason:\\\", flag_reason + \\\")\\\")\\n self._flagged[video_id] = flag_reason\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36c16e302c12c6e034103b502b04a6f\",\n \"score\": \"0.5871676\",\n \"text\": \"def delete_flag(self):\\n self.__status = Flag.NotFlagged\\n self.__start = None\\n self.__due_date = None\\n self.__completed = None\\n self._track_changes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"701229fc7cda664c06e7d184a645c8cf\",\n \"score\": \"0.5860052\",\n \"text\": \"def do_delete(input_m3u, index):\\n delete_video(input_m3u, index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b23b3a7d78ec0d09c41e9e8cec50e0\",\n \"score\": \"0.5826584\",\n \"text\": \"def flag_video(self, video_id, flag_reason=\\\"Not supplied\\\"):\\r\\n videos = dict([[video.video_id, video] for video in self._video_library.get_all_videos()])\\r\\n \\r\\n if video_id in videos:\\r\\n video = videos[video_id]\\r\\n if video.flagged == False:\\r\\n if video == self._current_video:\\r\\n self.stop_video()\\r\\n video.flag(flag_reason)\\r\\n print (f\\\"Successfully flagged video: {video.title} (reason: {video.flagged_reason})\\\")\\r\\n else:\\r\\n print(\\\"Cannot flag video: Video is already flagged\\\")\\r\\n else:\\r\\n print(\\\"Cannot flag video: Video does not exist\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ffdde65d040ff32988a9de9aacea7a6\",\n \"score\": \"0.5796712\",\n \"text\": \"def unset_flag(FLAG):\\r\\n global F\\r\\n F = F & ~FLAG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd1df11dec06a6cfb2eba03a1c8999b\",\n \"score\": \"0.57002217\",\n \"text\": \"def remove(value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd41fb2baf073adf6e37aee29907caf2\",\n \"score\": \"0.56939673\",\n \"text\": \"def _clear_flag(cpu: 'Cpu', flag: StatusFlag) -> None:\\n if flag == StatusFlag.carry:\\n cpu.status.carry = False\\n elif flag == StatusFlag.decimal:\\n cpu.status.decimal = False\\n elif flag == StatusFlag.interrupt_disable:\\n cpu.status.interrupt_disable = False\\n elif flag == StatusFlag.overflow:\\n cpu.status.overflow = False\\n else:\\n raise NotImplementedError(f'{flag} mode is not supported')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f5c93d3bccb51602d7a3b87cdba8df\",\n \"score\": \"0.5656351\",\n \"text\": \"def stop_video(self):\\n if len(self.playing) == 0:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\n elif len(self.playing) > 0:\\n videoToStop = self.playing.pop()\\n print(f\\\"Stopping video: {videoToStop.title}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f5e0cd533bcef0b91b3e1072dfea90\",\n \"score\": \"0.5641556\",\n \"text\": \"def video_wall_off(self):\\n self.send_command(0x84, 0x0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e373c51b92a1f5fe26f622c13925224f\",\n \"score\": \"0.5591142\",\n \"text\": \"def remove_bit(self, index: int) -> None:\\r\\n\\t\\tself.bitfield &= ~self.to_bitfield(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e123b773adf7cf210da4ae884e83ee47\",\n \"score\": \"0.55888903\",\n \"text\": \"def RemoveOneFlag(flag_name, flag_values=FLAGS):\\n if flag_name in flag_values.FlagDict():\\n flag_values.__delattr__(flag_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef8fd6dd4698fedbed4a7b62428f7d7\",\n \"score\": \"0.55867434\",\n \"text\": \"def stop_video(self):\\r\\n\\r\\n if self._current_video == None or self._video_stat == 'STP' or self._current_video.flagged == True:\\r\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\r\\n\\r\\n else:\\r\\n self._stopped_video = self._current_video\\r\\n self._video_stat = 'STP'\\r\\n print(f\\\"Stopping video: {self._current_video.title}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea332c9773860ccaebd3e627204a634b\",\n \"score\": \"0.555717\",\n \"text\": \"def remove_flags(self, messages, flags):\\n return self._store('-FLAGS', messages, flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e54f0d445d7abb41bcbcd300f240b1\",\n \"score\": \"0.5547637\",\n \"text\": \"def remove(self, building):\\n entry = self.entries[building]\\n entry[-1] = SkylineTracker.REMOVED\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e22021f8f53be44017f62ac41e610d\",\n \"score\": \"0.5529644\",\n \"text\": \"def unflag(self, msg_id):\\n return objects_module.messages.unflag(self.khoros_object, msg_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fbc6afcfc69abbfd167cba10b61f22\",\n \"score\": \"0.55286944\",\n \"text\": \"def delete_flag_file(self, context, flag_file):\\n try:\\n os.remove(flag_file)\\n except OSError:\\n LOG.error(\\\"Failed to delete %s flag.\\\"\\n % flag_file)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f408f789e22ccc03bd59068f1e2d4fa\",\n \"score\": \"0.5513272\",\n \"text\": \"def delete_position(seq, frame):\\n seq.keyframe_delete(data_path='[\\\"launch\\\"]', frame=frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82587f265bc72e6fc12a6bd8078dfbd\",\n \"score\": \"0.5442011\",\n \"text\": \"def clear_flags(self):\\n for flag in self.flags:\\n self.flags[flag] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88f1cd16887b1df2c10ee147a5e3a54\",\n \"score\": \"0.5391082\",\n \"text\": \"def remove_movie (self, title, year):\\n title=re.sub(r'[?|$|!|:|#]',r'',title)\\n movie_meta = '%s (%d)' % (title, year)\\n folder = re.sub(r'[?|$|!|:|#]',r'',self.db[self.movies_label][movie_meta]['alt_title'])\\n del self.db[self.movies_label][movie_meta]\\n self._update_local_db(filename=self.db_filepath, db=self.db)\\n dirname = os.path.join(self.movie_path, folder)\\n filename = os.path.join(self.movie_path, folder, movie_meta + '.strm')\\n if xbmcvfs.exists(dirname):\\n xbmcvfs.delete(filename)\\n xbmcvfs.rmdir(dirname)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9349933f10e6bfcb34e9660c6bd31337\",\n \"score\": \"0.5389633\",\n \"text\": \"def stop_video(self):\\n\\n if self._play_vid_tag is None:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\n return\\n\\n self._paused_vid_tag = None\\n print(\\\"Stopping video: {0}\\\".format(\\n self._video_library.get_video(self._play_vid_tag).title))\\n self._play_vid_tag = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806d081aa8271322ed7c72f358972dc0\",\n \"score\": \"0.5350334\",\n \"text\": \"def clean_flags(container, flags):\\n for flag in container:\\n if flag in flags:\\n flags.remove(flag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b9ce8387ecaf46e9fe1a304f0528e9\",\n \"score\": \"0.53394014\",\n \"text\": \"def remove_from_playlist(self, playlist_name, video_id):\\r\\n print(\\\"remove_from_playlist needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa1495e795a858eca92c65192681d4d\",\n \"score\": \"0.5337086\",\n \"text\": \"def deleteFlag(self, flagId):\\n cursor = self.__getCursor()\\n cursor.execute(\\\"\\\"\\\"\\n DELETE FROM `karma_comments` SET `Text` = %s WHERE `Id` = %s\\n \\\"\\\"\\\", (flagId, ))\\n cursor.close()\\n self.__connection.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6101a7dd3506e3cbda2389d12618b50d\",\n \"score\": \"0.5325414\",\n \"text\": \"def clear_flag(flag, width, flag_bits, xmin, xmax, ymin, ymax, logpath=None):\\n process(['/cluster/GAMMA_SOFTWARE-20161207/ISP/bin/clear_flag', flag, width, flag_bits, xmin, xmax, ymin, ymax], logpath=logpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4c1cfc29f813d2b3a731fbe73dab78\",\n \"score\": \"0.531339\",\n \"text\": \"def video_stop(self):\\n self.widgetCanvas.config(width=0, height=0)\\n self.list_player.pause()\\n self.video_status = 'stopped'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082fdba9fbbfd408c5da1219a4a49906\",\n \"score\": \"0.5301001\",\n \"text\": \"def stop_video(self):\\n if self.playing==\\\"\\\" and self.pause==\\\"\\\":\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\n else:\\n if self.pause!=\\\"\\\":\\n stop=self._video_library._videos[self.pause]._title\\n print(\\\"Stopping video: {}\\\".format(stop))\\n self.playing=\\\"\\\"\\n self.pause=\\\"\\\"\\n else:\\n stop=self._video_library._videos[self.playing]._title\\n print(\\\"Stopping video: {}\\\".format(stop))\\n self.playing=\\\"\\\"\\n self.pause=\\\"\\\"\\n\\n # print(\\\"stop_video needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67af941989ba61b541b64af70d7e2d77\",\n \"score\": \"0.5289763\",\n \"text\": \"def editflag(x,y,flags):\\n count = flags.count(x)\\n if y == 0 and count > 0:\\n flags.remove(x)\\n elif y == 0 and count == 0:\\n print('The value', x ,'does not exist in flags.')\\n elif y == 1 and count == 0:\\n flags = flags.append(x)\\n elif y == 1 and count > 0:\\n print('The value', x ,'already exists in flags')\\n else:\\n print('editflag command was used incorrectly.')\\n return(flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a6622870def1a48d84ee5452868796\",\n \"score\": \"0.52867866\",\n \"text\": \"def remove(self, vPath):\\r\\n os.remove(self.ospath(vPath) )\\r\\n #notify(AssetRemoved(self, vpath))\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c71b981c9bd675f244b408ddf227a37\",\n \"score\": \"0.5284856\",\n \"text\": \"def remove(path: AnyStr, verbose: Optional[int] = 0) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131a4f36d74250dca9285b222e12e374\",\n \"score\": \"0.5278465\",\n \"text\": \"def removeTexture():\\n\\ttry:\\n\\t\\tdel logic.skyTexture\\n\\t\\tdel logic.skyTextureAnimated\\n\\texcept:\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7235d2893473e0c320be1990c62b1ebc\",\n \"score\": \"0.5274217\",\n \"text\": \"def remove_vector(self, vec_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"850cc2723fd71c567a453a74d973c5c6\",\n \"score\": \"0.52727956\",\n \"text\": \"def api_remove_favorite(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"850cc2723fd71c567a453a74d973c5c6\",\n \"score\": \"0.52727956\",\n \"text\": \"def api_remove_favorite(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a300dbfffb8c560c3b51d88f7e3e7b87\",\n \"score\": \"0.52612627\",\n \"text\": \"def delete_files(self):\\n\\n small_video_path = self.small_video()\\n try:\\n os.unlink(self.videofile.path)\\n if small_video_path:\\n os.unlink(small_video_path)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daca0bb76ca2a3577020a0e661dbb172\",\n \"score\": \"0.5259757\",\n \"text\": \"def stop_video(self):\\n if self._currently_playing_video is None:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\n return \\n print(f\\\"Stopping video: {self._currently_playing_video.title}\\\")\\n self._currently_playing_video = None\\n self._current_video_status = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04a77ddc9b40f3661d8c83de954bc7d8\",\n \"score\": \"0.5249311\",\n \"text\": \"def remove(self, obj):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514ede108b6fbf8783c3964f899a4c31\",\n \"score\": \"0.5245793\",\n \"text\": \"def remove_video_before_db(examples: List[Dict]) -> List[Dict]:\\n for eg in examples:\\n if \\\"video\\\" in eg:\\n del eg[\\\"video\\\"]\\n if \\\"video\\\" in eg:\\n del eg[\\\"options\\\"]\\n\\n return examples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638994eed7c2dbcc2b77bda9421a561c\",\n \"score\": \"0.5237131\",\n \"text\": \"def remove(self, oid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"638994eed7c2dbcc2b77bda9421a561c\",\n \"score\": \"0.5237131\",\n \"text\": \"def remove(self, oid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bcc742fecd460a2c3dbd6faad0d9ca3\",\n \"score\": \"0.52347565\",\n \"text\": \"def remove_mode(self, token):\\n self._switcher.remove_mode(token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e60792e5ed8e1c588ad9500d1d76d93\",\n \"score\": \"0.5188642\",\n \"text\": \"def remove_from_playlist(self, playlist_name, video_id):\\n print(\\\"remove_from_playlist needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e60792e5ed8e1c588ad9500d1d76d93\",\n \"score\": \"0.5188642\",\n \"text\": \"def remove_from_playlist(self, playlist_name, video_id):\\n print(\\\"remove_from_playlist needs implementation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71a36d0051f4f211d0dd41dcc421c29\",\n \"score\": \"0.5184038\",\n \"text\": \"def _remove(self, pattern):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2baa27afd4e8e2495a64d5886f88ab72\",\n \"score\": \"0.51829296\",\n \"text\": \"def remove(name, send_events=True, moving=None, loading=False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b6db305b448f9b4f0a9b5c5d9d7761\",\n \"score\": \"0.5178258\",\n \"text\": \"def remove_clip(self, clip, add_pending=False):\\n\\n self.callbacks.on_clip_stop(clip)\\n if clip.name not in self.players:\\n # clip was already removed/stopped ?\\n return\\n player = self.players[clip.name]\\n player.stop()\\n del self.players[clip.name]\\n self.clips = [ c for c in self.clips if c.name != clip.name ]\\n\\n if not add_pending and len(self.clips) == 0 and self.stop_if_empty:\\n self.stop()\\n raise AllClipsDone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90d2c89484c4acfc49340a9a76226fc1\",\n \"score\": \"0.51759607\",\n \"text\": \"def _remove(self, _):\\n\\t\\tself._delete_torrent()\\n\\t\\tself.parent.remove_widget(self)\\n\\t\\tos.remove(self.uri)\\n\\t\\tos.remove(self.ids.img.source)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efcdf5e7ceeb4cd9ed2734b56e4ffe6\",\n \"score\": \"0.5175291\",\n \"text\": \"def remove_member(self, participant, recorder):\\n self.set_take_for(participant, ZERO, recorder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f74d5d41ef9a0dd1043f3eacf640daf\",\n \"score\": \"0.5173915\",\n \"text\": \"def _delWindowStyleFlag(self, flag, advanced=False):\\n\\t\\tmechanics = self._getWindowStyleMechanics(advanced)\\n\\t\\tif self._constructed():\\n\\t\\t\\tmechanics[2](mechanics[1]() & (~flag))\\n\\t\\telse:\\n\\t\\t\\tself._preInitProperties[mechanics[0]] = self._preInitProperties[mechanics[0]] & (~flag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e7c536f1d69bd3098eca4e95fcce60\",\n \"score\": \"0.51729625\",\n \"text\": \"async def remove(self, ctx, *, word: lower):\\n await self.bot.db.update_guild_config(ctx.guild.id, {'$pull': {'detections.filters': word}})\\n await ctx.send(self.bot.accept)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea192d8826cacb3a7e2bddf566ae4d7\",\n \"score\": \"0.5162678\",\n \"text\": \"def remove(self, e):\\n # write code here\\n if e in self.vals:\\n self.vals[e] -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4873d476789e36a82cca464428dd3f\",\n \"score\": \"0.5160594\",\n \"text\": \"def remove(self, path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7f134d8629a3f4956ee324e434360c\",\n \"score\": \"0.51525944\",\n \"text\": \"def remove(self, e):\\n try:\\n self.vals[e] -= 1\\n except:\\n self.vals[e] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0455af9c4fed4ecb628992d19b6686cb\",\n \"score\": \"0.5140673\",\n \"text\": \"def remove(self, e):\\r\\n # write code here\\r\\n try:\\r\\n self.vals[e] -= 1\\r\\n except:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88862f8dc3b9a188916a072b1b190498\",\n \"score\": \"0.5140368\",\n \"text\": \"def remove(self, e):\\r\\n # write code here\\r\\n try:\\r\\n self.vals[e]-=1 \\r\\n except:\\r\\n self.vals[e]=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c82f2864189e51627989d9714fbd9f\",\n \"score\": \"0.51352584\",\n \"text\": \"def delete_event(widget=None, *data):\\n global SILENT\\n camera.release();\\n fps_counter.quit();\\n if not SILENT:\\n sound.play(\\\"close\\\")\\n time.sleep(3)\\n sound.quit()\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"351cc22455b3c26d1257873739dab34b\",\n \"score\": \"0.51309896\",\n \"text\": \"def stop_video(self):\\n if (self.currently_playing != \\\"none\\\") :\\n print(f\\\"Stopping video: {self.currently_playing}\\\")\\n self.currently_playing = \\\"none\\\"\\n self.playing = False\\n return\\n print(\\\"Cannot stop video: No video is currently playing\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb43f83074b2502231ab99ff3c8e315\",\n \"score\": \"0.5114603\",\n \"text\": \"def remove(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a51b21ba72c75f1c9eef98baf23087f\",\n \"score\": \"0.5103221\",\n \"text\": \"def clearFlags(self, key: str, flags: int) -> None:\\n path = self._path + key\\n self._api.setEntryFlags(path, self._api.getEntryFlags(path) & ~flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7a3e14f97a580e6d7ca3ea24d9a52e1\",\n \"score\": \"0.51011646\",\n \"text\": \"def remove_one(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eaf63dd2a2ed907fed0b06412256081\",\n \"score\": \"0.5098991\",\n \"text\": \"def removeFrame(self, frame):\\n if self._frames.__contains__(frame):\\n self._frames.remove(frame)\\n self._updateGrid()\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9e35f773d49e14645c98a185e6ea9b\",\n \"score\": \"0.50926566\",\n \"text\": \"def stop_video(self):\\n if self._currently_playing != None:\\n video_playing = self._video_library.get_video(self._currently_playing)\\n print(\\\"Stopping video:\\\", video_playing.title)\\n self._currently_playing = None\\n elif self._paused != None:\\n video_paused = self._video_library.get_video(self._paused)\\n print(\\\"Stopping video:\\\", video_paused.title)\\n self._paused = None\\n else:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcfdbdae0d83c3a9eddbb0c0667ecbf5\",\n \"score\": \"0.50840265\",\n \"text\": \"def rem(self, coisa):\\r\\n self.gui.rem(coisa)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"221e1c6016726fbfdf7fdf98460c44b7\",\n \"score\": \"0.50806963\",\n \"text\": \"def stop_video(self, address: int = None) -> Response:\\r\\n address = self._get_address(address)\\r\\n data = {\\\"arg\\\": None}\\r\\n cmd_url = (\\r\\n \\\"http://\\\"\\r\\n + self.info[\\\"url\\\"]\\r\\n + \\\":8181/api/luminaire/\\\"\\r\\n + address\\r\\n + \\\"/command/PLAY_VIDEO_FILE\\\"\\r\\n )\\r\\n response = requests.post(\\r\\n cmd_url, json=data, cookies=self.info[\\\"cookiejar\\\"], verify=False\\r\\n )\\r\\n response = self._check_response_for_error(response)\\r\\n print(f\\\"Stopped video file from playing at address: {address}\\\")\\r\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a68fd46b69e788df13bc28e87e20821\",\n \"score\": \"0.50803006\",\n \"text\": \"def unmark(self, pos):\\n byte = ~(1 << self._bitpos(pos))\\n byte = cast(byte, types.int8)\\n self.buf[self._bytepos(pos)] &= byte\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b69114c4ad8e152fb6c1b50c94bb09\",\n \"score\": \"0.5074984\",\n \"text\": \"def remove_state(self, state):\\n state = state.format(relation_name=self.relation_name)\\n value = _get_flag_value(state)\\n if not value:\\n return\\n if self.key in value['conversations']:\\n value['conversations'].remove(self.key)\\n if value['conversations']:\\n set_flag(state, value)\\n else:\\n clear_flag(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b3cde542bc36b65913d954321ebe22\",\n \"score\": \"0.5070644\",\n \"text\": \"async def _remove(self, version: int) -> None:\\n py_typecheck.check_type(version, (int, np.integer))\\n\\n path = self._get_path_for_version(version)\\n if await file_utils.exists(path):\\n await file_utils.rmtree(path)\\n logging.info('Program state removed: %s', path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44a170cb7cb08024b448005abbe9bac9\",\n \"score\": \"0.5070521\",\n \"text\": \"def remove(self, count = -1):\\n if self.is_readonly:\\n BufferBase._logger.debug('buffer is readonly')\\n return\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d2ce7c49b9ede17bdbc97a4386507b\",\n \"score\": \"0.5058651\",\n \"text\": \"def remove(self):\\n glDeleteBuffers(1, [self.vbo_array])\\n\\n if self.vdata_indices is not None:\\n glDeleteBuffers(1, [self.vbo_element_array])\\n\\n glDeleteVertexArrays(1, [self.vao])\\n self.dirty = True\\n print(\\\"Item {}: removing myself.\\\".format(self.label))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93240a1923ff99ed298841d46eb9ccb0\",\n \"score\": \"0.50540644\",\n \"text\": \"def remove_option(self, parameter):\\n\\n if parameter in self.options.keys():\\n # Find index of parameter and remove\\n index = self.options[parameter].index\\n self.options.pop(parameter)\\n self.lines.pop(index)\\n\\n # Adjust indices of following in list\\n for option in self.lines[index:]:\\n option.index -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5a2f2d4143cf7437774a621e23f47e\",\n \"score\": \"0.50523704\",\n \"text\": \"def release(self):\\n if self._video is not None:\\n del self._video\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb0f22e3ef9b3f59df14a2bcd44a418\",\n \"score\": \"0.503461\",\n \"text\": \"def remove_face(face_id):\\n vrs_client = create_vrs_client()\\n collection_id = get_collection_id(vrs_client)\\n vrs_client.remove_face(collection_id, face_id)\\n save_dir = './results/{0}/{1}.jpg'.format(collection_id, face_id)\\n if os.path.exists(save_dir):\\n os.remove(save_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56376de9f8df1b9ed2150d6e72a55314\",\n \"score\": \"0.50332105\",\n \"text\": \"def remove_overlay(overlay_id):\\n if overlay_id != -1:\\n camera.remove_overlay(overlay_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9c55301a732ae67ba43340668a71a2\",\n \"score\": \"0.5031563\",\n \"text\": \"def remove(path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99f248a1b81248f1823cbd30895b48f\",\n \"score\": \"0.5026668\",\n \"text\": \"def delete_frame(self, index):\\n frame = self.frames[index]\\n del self.frames[index]\\n frame.is_attached = False\\n self.renumber_from_index(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b959e758c14d0c0f50100bd922d3f85b\",\n \"score\": \"0.5026593\",\n \"text\": \"def remove(self, path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af4c1685255d4699e35997134b224c7\",\n \"score\": \"0.50100666\",\n \"text\": \"def remove(self, x, y):\\n\\n if self.map[y][x] == 'I':\\n self.map[y][x] = self.starting_map[y][x]\\n self.industrial -= 1\\n\\n elif self.map[y][x] == 'C':\\n self.map[y][x] = self.starting_map[y][x]\\n self.commercial -= 1\\n\\n elif self.map[y][x] == 'R':\\n self.map[y][x] = self.starting_map[y][x]\\n self.residential -= 1\\n\\n else:\\n print(\\\"You can't remove %s\\\" % self.map[y][x])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f059b62537333ebed461aa71e0fca8d8\",\n \"score\": \"0.500491\",\n \"text\": \"def delExifTag(self, tag, record = None):\\n self.__getExif__().removeTag(tag, record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9be240cd908f276e996c1869c7acb484\",\n \"score\": \"0.5000761\",\n \"text\": \"def remove_activity(self, seq):\\n return self.plan.remove_activity(seq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee9b03d224d33aeb1b8f0f2bd0f9102\",\n \"score\": \"0.49978122\",\n \"text\": \"def remove(self):\\n del self.tracker\\n self.init_bounding_box = None\\n # del self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7221093869e2f1eaa0dfe498769e2582\",\n \"score\": \"0.49967402\",\n \"text\": \"def apply_transform(\\n self,\\n video_path: str,\\n output_path: str,\\n metadata: Optional[List[Dict[str, Any]]] = None,\\n ) -> str:\\n return F.remove_audio(video_path, output_path, metadata=metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41f75db85e768d43dc142931e66a560\",\n \"score\": \"0.49944264\",\n \"text\": \"def deleteAvatar(self, av):\\n if av in self.paintedAvatars:\\n del self.paintedAvatars[av]\\n self.scoreDirty = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7450c288fe46fc9d5334861719d6647\",\n \"score\": \"0.4993601\",\n \"text\": \"def remove(self, key):\\n try:\\n self.slots[key] = -1\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de8153ee7e3cf1e04372944d889bcf0\",\n \"score\": \"0.49884316\",\n \"text\": \"def remove(self, value):\\n self.prune()\\n index = self.items.index(value)\\n del(self.items[index])\\n del(self.times[index])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd1ac2f1e2f08fb7534168d599a43a1\",\n \"score\": \"0.49862194\",\n \"text\": \"def removeProjectBrowserFlags(key, prefClass=Prefs):\\n prefClass.settings.remove(\\\"Project/BrowserFlags/\\\" + key)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994289,"cells":{"query_id":{"kind":"string","value":"41be8483af4a03a53aafa33f33ad536c"},"query":{"kind":"string","value":"pc_work_time(pll_freqdet_cf_sptr self) > float"},"positive_passages":{"kind":"list like","value":[{"docid":"64d794bfd74730c9df565151b581a2c3","score":"0.7803105","text":"def pc_work_time(self):\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time(self)","title":""}],"string":"[\n {\n \"docid\": \"64d794bfd74730c9df565151b581a2c3\",\n \"score\": \"0.7803105\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time(self)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d7ad19f22cde099f35faba2f23a68439","score":"0.7412064","text":"def pc_work_time(self):\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time(self)","title":""},{"docid":"29328812c152e88a0a6f211c963781ad","score":"0.74057776","text":"def pc_work_time_var(self):\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time_var(self)","title":""},{"docid":"f9b37921b58c8140454b426c5e7777f7","score":"0.7399984","text":"def pc_work_time(self):\n return 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_analog_swig.sig_source_f_sptr_pc_work_time_avg(self)","title":""},{"docid":"ef0ecb57b4be4eb2819a1e68a183230c","score":"0.65044546","text":"def pc_work_time_total(self):\n return _analog_swig.fastnoise_source_c_sptr_pc_work_time_total(self)","title":""},{"docid":"60612a2e9e43506a06743c5c00ba8788","score":"0.6504314","text":"def pc_work_time_var(self):\n return _analog_swig.fastnoise_source_c_sptr_pc_work_time_var(self)","title":""},{"docid":"63d018c859431da2b03ccb84f83687d1","score":"0.649921","text":"def pc_work_time_var(self):\n return _analog_swig.sig_source_f_sptr_pc_work_time_var(self)","title":""},{"docid":"3f96d963e877447c823d34f72dfe238f","score":"0.6494056","text":"def pc_work_time_total(self):\n return _analog_swig.simple_squelch_cc_sptr_pc_work_time_total(self)","title":""},{"docid":"7c19e7fdc375fbc4e8328b8b00189aad","score":"0.64792967","text":"def pc_work_time_var(self):\n return _analog_swig.dpll_bb_sptr_pc_work_time_var(self)","title":""},{"docid":"131955466936a7cedc6e4fc5efa3861e","score":"0.64788073","text":"def pc_work_time_total(self):\n return _analog_swig.probe_avg_mag_sqrd_c_sptr_pc_work_time_total(self)","title":""},{"docid":"cbe999222f6220811138b1310bbf9c2c","score":"0.64755124","text":"def pc_work_time_avg(self):\n return _analog_swig.agc2_ff_sptr_pc_work_time_avg(self)","title":""},{"docid":"4a31abe3b337e4b27fe3fd1fd4660466","score":"0.6474265","text":"def pc_work_time_avg(self):\n return _analog_swig.fastnoise_source_f_sptr_pc_work_time_avg(self)","title":""},{"docid":"d94c1143f317fe2e3e2bd20b7aed30e1","score":"0.64702106","text":"def pc_work_time_var(self):\n return _analog_swig.fastnoise_source_i_sptr_pc_work_time_var(self)","title":""},{"docid":"9d2096b04f962f53bdba93eed729547a","score":"0.64698154","text":"def pc_work_time_var(self):\n return _ofdm_allocator_swig.per_measure_decimator_sptr_pc_work_time_var(self)","title":""},{"docid":"da2f47aaf8144aef672245d48524eb2a","score":"0.6468819","text":"def pc_work_time_total(self):\n return _analog_swig.fastnoise_source_i_sptr_pc_work_time_total(self)","title":""},{"docid":"1517c1927d1315d064fa7fc71bad6f58","score":"0.6467133","text":"def pc_work_time_var(self):\n return _analog_swig.fastnoise_source_s_sptr_pc_work_time_var(self)","title":""},{"docid":"e7540a28656cb1380f5d97c13d54f9ad","score":"0.64589554","text":"def pc_work_time_total(self):\n return _analog_swig.fastnoise_source_s_sptr_pc_work_time_total(self)","title":""},{"docid":"25aac2b4e2cc3e2a8db50a9a20a25df1","score":"0.6457247","text":"def pc_work_time_avg(self):\n return _analog_swig.pll_refout_cc_sptr_pc_work_time_avg(self)","title":""},{"docid":"96bf7521b39fa0fd4f0734fe798892c2","score":"0.6446931","text":"def pc_work_time_total(self):\n return _rs_cpp_swig.rs_decoder_custom_sptr_pc_work_time_total(self)","title":""}],"string":"[\n {\n \"docid\": \"d7ad19f22cde099f35faba2f23a68439\",\n \"score\": \"0.7412064\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29328812c152e88a0a6f211c963781ad\",\n \"score\": \"0.74057776\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b37921b58c8140454b426c5e7777f7\",\n \"score\": \"0.7399984\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.fmdet_cf_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e76fcdb593903055363eba18a15e0ee\",\n \"score\": \"0.73106974\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.ctcss_squelch_ff_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c829268ba67e84f20b970a5af9d9c5d\",\n \"score\": \"0.729066\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.pwr_squelch_ff_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29dc53ff637fe09e76e35f681cd9d3c1\",\n \"score\": \"0.7243308\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.rail_ff_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497714b6e48d179ef68aa7bb53bee1cb\",\n \"score\": \"0.7242102\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d5467685b180c73f227e361c257afd\",\n \"score\": \"0.7228759\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.agc2_ff_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b519a5f139ea60d2be39ffada16965\",\n \"score\": \"0.72140354\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.agc_ff_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b529ab921fcc7c95789ad6c7b7f32e48\",\n \"score\": \"0.7192511\",\n \"text\": \"def pc_work_time(self) -> \\\"float\\\":\\n return _analog_swig.vectornoise_source_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1beab8206bbe4563cb1826cea91773f\",\n \"score\": \"0.71527445\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.quadrature_demod_cf_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c87b4c6ee3ba70a11fee41d13a4388\",\n \"score\": \"0.712065\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.phase_modulator_fc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a4234288e4e87949a43332794677d1\",\n \"score\": \"0.7102779\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7242364749410f4ec10d64bb62c71064\",\n \"score\": \"0.7065058\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.probe_avg_mag_sqrd_f_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d15cab3609d7469e90807b32cdabda9\",\n \"score\": \"0.70258147\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.probe_avg_mag_sqrd_cf_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56b1e9152db8fe2f8e6de07cddc0c12c\",\n \"score\": \"0.69960356\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.fastnoise_source_f_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bad19d30eb3cbdf761887561e09a81\",\n \"score\": \"0.69819754\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f60ab09734a40f385806c54c27b7df\",\n \"score\": \"0.6972227\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.fmdet_cf_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d15d8367cef12b0b917d6c01e7425ef\",\n \"score\": \"0.6971883\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"775de39e9e33f8205f667074dbe067cc\",\n \"score\": \"0.69609547\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.pll_carriertracking_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d0427a4fa2c96a8044294e0800ce81\",\n \"score\": \"0.6958828\",\n \"text\": \"def pc_work_time_var(self) -> \\\"float\\\":\\n return _analog_swig.vectornoise_source_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf1a9f00a3a81686c5e22cd98094b27\",\n \"score\": \"0.695432\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.noise_source_f_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5be29c4e6dc5d8a1b91bf024de973e3\",\n \"score\": \"0.69370335\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.sig_source_f_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c4fb0c87ed34460775e14e7ac83456\",\n \"score\": \"0.69241405\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.pwr_squelch_ff_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4894eaa2d400316847f88d0cca1cde\",\n \"score\": \"0.692132\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.ctcss_squelch_ff_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0706ccf75c6846618b4657bb2b7ad64\",\n \"score\": \"0.69201225\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.fmdet_cf_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a6de00c5d7b4e55eb3f3fad861a5b9b\",\n \"score\": \"0.6905098\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.dpll_bb_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05fe3613fc05b88df935d5abdeb9b37\",\n \"score\": \"0.6885262\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.probe_avg_mag_sqrd_c_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c154ba1e2a9f6f2594c94d2506743c55\",\n \"score\": \"0.6867522\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.pwr_squelch_ff_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83944ba44dca3a908e56f405062612de\",\n \"score\": \"0.6850926\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.ctcss_squelch_ff_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384e51e5a41a867ee0dd7b972bafaf6f\",\n \"score\": \"0.6850233\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.pll_refout_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e6b0e50cdce5391927bb2ac9332d66\",\n \"score\": \"0.68024087\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.cpfsk_bc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3cd7711f83f24078113f6b5fe13908\",\n \"score\": \"0.6800897\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.simple_squelch_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd680a8b724bfa3179d4d9b1e106d6a\",\n \"score\": \"0.6784502\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.fmdet_cf_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f127993b6f9f20a72c21f34cc019438\",\n \"score\": \"0.6783983\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.fastnoise_source_c_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cca3c7d153aca6c6f681a9582b43d5e\",\n \"score\": \"0.6775182\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.rail_ff_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4e048b322853bcd1f0ab9f3249930f\",\n \"score\": \"0.6772286\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.probe_avg_mag_sqrd_f_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a4c9c65e3cb24cb7117e07f7075662b\",\n \"score\": \"0.6764401\",\n \"text\": \"def pc_work_time_total(self) -> \\\"float\\\":\\n return _analog_swig.vectornoise_source_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a44c21313054bfe50a77d06ade326d31\",\n \"score\": \"0.6761048\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.probe_avg_mag_sqrd_cf_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a5e7462aab557fd3f7923597932d12\",\n \"score\": \"0.6760417\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.agc2_ff_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b783309b4d42006a5cc7d50c133f0e22\",\n \"score\": \"0.67549384\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.agc3_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23034cea953da077e014699dff089aae\",\n \"score\": \"0.67503524\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.agc2_ff_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0410024186b499b536a9172007c75d5c\",\n \"score\": \"0.67487293\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.ctcss_squelch_ff_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bf79e569edbf0dd9b557e7c8a33a246\",\n \"score\": \"0.6741014\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.quadrature_demod_cf_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60bcf3c95be785f820c60141999fddd0\",\n \"score\": \"0.67409813\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.fastnoise_source_s_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e611d616af7ae1c7bc7e5ce52d484fa\",\n \"score\": \"0.6740544\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.rail_ff_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f9f4f87d1accbe042aa8dec59a2e98\",\n \"score\": \"0.67403096\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.quadrature_demod_cf_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486b0c4acf93a15bc7cb7e69b2fcf22d\",\n \"score\": \"0.67372745\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.fastnoise_source_i_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96c822c331a93841cd90bcbe3f5a333\",\n \"score\": \"0.67229986\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.feedforward_agc_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"366dff9347f24991c0b436ad944189c1\",\n \"score\": \"0.67082274\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.agc_ff_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2ccc8f3de042ba5667e920ff7936ab\",\n \"score\": \"0.6698095\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.phase_modulator_fc_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b4059d40455f07fc417276b6defa08\",\n \"score\": \"0.66918594\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.fastnoise_source_f_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7f31421ab25d09c635b72c76635f17\",\n \"score\": \"0.668798\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.pwr_squelch_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd75f870bd76343d77ad02d9cd76e27\",\n \"score\": \"0.66821015\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.noise_source_c_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e556410f58d09c28fd695921eaace9\",\n \"score\": \"0.6679886\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.agc2_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d73508157af1adb30c623c0289a723\",\n \"score\": \"0.6669301\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.agc_cc_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b899723be65a650eefc7bc6bd85c4b45\",\n \"score\": \"0.666848\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.fastnoise_source_f_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557071461d579c9de62b2a41a8352712\",\n \"score\": \"0.6664019\",\n \"text\": \"def pc_work_time(self):\\n return _ofdm_allocator_swig.per_measure_decimator_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0f3c139b2cafc886212a694b9d53d5\",\n \"score\": \"0.66571707\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.agc_ff_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708ffc5e28f7af3c30eb2fc4c594d209\",\n \"score\": \"0.66526145\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.phase_modulator_fc_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ea3aa594f1a06918328a08c55123a3\",\n \"score\": \"0.6639918\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.noise_source_i_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a069ca8dca55398572ec81cda5147ec\",\n \"score\": \"0.6639638\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.noise_source_s_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11708c7dc76e064b71884449b5c070e\",\n \"score\": \"0.6636667\",\n \"text\": \"def pc_work_time(self):\\n return _rs_cpp_swig.rs_decoder_custom_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06c33ffa3f6d3b32019f4b3caf50fdb\",\n \"score\": \"0.6635314\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.probe_avg_mag_sqrd_c_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5603adf34d37a2724e7ecb14a89d2a36\",\n \"score\": \"0.6634663\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.pwr_squelch_ff_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45c235115a1e2b12b0de26422650982a\",\n \"score\": \"0.6625815\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c76041132b1924d5ae84e60dca6af4\",\n \"score\": \"0.6625564\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.noise_source_f_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cf675f70b8879ba112517eb488e9e1\",\n \"score\": \"0.6623171\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.pll_carriertracking_cc_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e1cf475a45b338753f9fdd265d7507\",\n \"score\": \"0.6611798\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.sig_source_c_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef8b11e74afd9c1130200e6c810b7a7\",\n \"score\": \"0.66093135\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.sig_source_f_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122379aec80674a467358062cb65154b\",\n \"score\": \"0.6607422\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.quadrature_demod_cf_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5695b0a0b0a996778855439a6789310\",\n \"score\": \"0.6601821\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.sig_source_i_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a5b8e0fd6f1a007e2aeba871c8d809\",\n \"score\": \"0.6599279\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.probe_avg_mag_sqrd_f_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17a691396ac6172e7a6b3dcb7f7d3dd\",\n \"score\": \"0.6598899\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.pll_carriertracking_cc_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd541b0b9f17970550339c8796f74fb\",\n \"score\": \"0.6586623\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.probe_avg_mag_sqrd_cf_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c559163bcd09f46ea446fcf80c5c36e\",\n \"score\": \"0.6586281\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.phase_modulator_fc_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013eaef6116ec5b097abc8b4a81561f2\",\n \"score\": \"0.6581525\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.pll_carriertracking_cc_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedb7178dc05b51facc3b862166b57e3\",\n \"score\": \"0.65559775\",\n \"text\": \"def time(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fedb7178dc05b51facc3b862166b57e3\",\n \"score\": \"0.65559775\",\n \"text\": \"def time(self) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373129970d00922f898269645f95a6da\",\n \"score\": \"0.6542838\",\n \"text\": \"def pc_work_time(self):\\n return _analog_swig.sig_source_s_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b310b4a7d9add31be0af3521402e4b\",\n \"score\": \"0.65424454\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.noise_source_f_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6187f33bcc317aa983ae5d955f4071e9\",\n \"score\": \"0.6531379\",\n \"text\": \"def pc_work_time(self):\\n return _ofdm_allocator_swig.frame_equalizer_alix_sptr_pc_work_time(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78cec66ee1ee681e13be3d828d9d599a\",\n \"score\": \"0.65247273\",\n \"text\": \"def pc_work_time_avg(self) -> \\\"float\\\":\\n return _analog_swig.vectornoise_source_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6cbeaa164fc89d1d9c790117b679d0\",\n \"score\": \"0.6523604\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.rail_ff_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6894f53a5d683cece254a5a337e1d11c\",\n \"score\": \"0.6518545\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.sig_source_f_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef0ecb57b4be4eb2819a1e68a183230c\",\n \"score\": \"0.65044546\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.fastnoise_source_c_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60612a2e9e43506a06743c5c00ba8788\",\n \"score\": \"0.6504314\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.fastnoise_source_c_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d018c859431da2b03ccb84f83687d1\",\n \"score\": \"0.649921\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.sig_source_f_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f96d963e877447c823d34f72dfe238f\",\n \"score\": \"0.6494056\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.simple_squelch_cc_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c19e7fdc375fbc4e8328b8b00189aad\",\n \"score\": \"0.64792967\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.dpll_bb_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131955466936a7cedc6e4fc5efa3861e\",\n \"score\": \"0.64788073\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.probe_avg_mag_sqrd_c_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe999222f6220811138b1310bbf9c2c\",\n \"score\": \"0.64755124\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.agc2_ff_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a31abe3b337e4b27fe3fd1fd4660466\",\n \"score\": \"0.6474265\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.fastnoise_source_f_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94c1143f317fe2e3e2bd20b7aed30e1\",\n \"score\": \"0.64702106\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.fastnoise_source_i_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d2096b04f962f53bdba93eed729547a\",\n \"score\": \"0.64698154\",\n \"text\": \"def pc_work_time_var(self):\\n return _ofdm_allocator_swig.per_measure_decimator_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2f47aaf8144aef672245d48524eb2a\",\n \"score\": \"0.6468819\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.fastnoise_source_i_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1517c1927d1315d064fa7fc71bad6f58\",\n \"score\": \"0.6467133\",\n \"text\": \"def pc_work_time_var(self):\\n return _analog_swig.fastnoise_source_s_sptr_pc_work_time_var(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7540a28656cb1380f5d97c13d54f9ad\",\n \"score\": \"0.64589554\",\n \"text\": \"def pc_work_time_total(self):\\n return _analog_swig.fastnoise_source_s_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25aac2b4e2cc3e2a8db50a9a20a25df1\",\n \"score\": \"0.6457247\",\n \"text\": \"def pc_work_time_avg(self):\\n return _analog_swig.pll_refout_cc_sptr_pc_work_time_avg(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96bf7521b39fa0fd4f0734fe798892c2\",\n \"score\": \"0.6446931\",\n \"text\": \"def pc_work_time_total(self):\\n return _rs_cpp_swig.rs_decoder_custom_sptr_pc_work_time_total(self)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994290,"cells":{"query_id":{"kind":"string","value":"38a4e4a6142363076e0e5fd4ce2c8300"},"query":{"kind":"string","value":"Set the end time."},"positive_passages":{"kind":"list like","value":[{"docid":"2d79efedc2e0fe4bf7e27917603fc06a","score":"0.8860281","text":"def set_end_time(self, t):\n self.end = spss_time(t)","title":""}],"string":"[\n {\n \"docid\": \"2d79efedc2e0fe4bf7e27917603fc06a\",\n \"score\": \"0.8860281\",\n \"text\": \"def set_end_time(self, t):\\n self.end = spss_time(t)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"ea848c0b6f5e014782c1d4d2645a38c7","score":"0.89383185","text":"def end_time(self, end_time):\n\n self._end_time = end_time","title":""},{"docid":"082f56d38adb43fd76bd251087334145","score":"0.87735015","text":"def endtime(self, endtime):\n\n self._endtime = endtime","title":""},{"docid":"321461abd895ef45407ef40c4272cf65","score":"0.87264866","text":"def end_time(self, end_time: str):\n\n self._end_time = end_time","title":""},{"docid":"07c9bf091dacf7b3e112c1ca4ce7d66c","score":"0.8701888","text":"def set_end_time(self, end_time):\n self.end_time.append(end_time)","title":""},{"docid":"6faf2e3de33a910332c808bd491bd8b5","score":"0.8698118","text":"def set_end_time(self, val):\n with self._condition:\n self._end_time = val\n self._invoke_upd_callbacks()","title":""},{"docid":"3beb087196c52aac77520219f3ed34b8","score":"0.8685146","text":"def time_end(self, time_end):\n \n self._time_end = time_end","title":""},{"docid":"d8fa930514632d840c4cac5b87229b3c","score":"0.85402113","text":"def time_end(self):\n fin=tm.time()\n self.time(t=fin)\n self.end = fin","title":""},{"docid":"3722bd4b6a680acc1fbf3e97de89c117","score":"0.841898","text":"def set_end_time(self, end_time):\n self.__check_time(end_time)\n if isinstance(end_time, str):\n end_time = self.__iso_time_to_timestamp(end_time)\n self.__end_time = end_time\n return self","title":""},{"docid":"f63fe2ebdd1e926a22d8e4eb4f6f706b","score":"0.8335043","text":"def setEndTime(self, endTime):\n\n pass","title":""},{"docid":"c3b95c0f5504a13df64628d37d60d3f8","score":"0.8327098","text":"def _set_end_time(self, future):\n self._end_time = datetime.datetime.now()","title":""},{"docid":"c20fe153f9fef87ea21207cbcddd46e0","score":"0.8195801","text":"def ending_time(self, ending_time):\n\n self._ending_time = ending_time","title":""},{"docid":"b14b17c573bee04d85554f4a6b6e0df4","score":"0.81608075","text":"def setend(self, v):\n\t\tself.end = self._parse_time(v)","title":""},{"docid":"66bc6beab6da13cb37428d566546d920","score":"0.8094813","text":"def end(self, end_time: typing.Optional[int] = None) -> None:","title":""},{"docid":"c4026154e139529f2117bfcffe6b42f2","score":"0.8059397","text":"def set_end_time(self):\n\t\tself.end_time = datetime.today()\n\t\treturn self.end_time","title":""},{"docid":"9d618e2c8dca1ef12822ee0b95225bc7","score":"0.8009447","text":"def end(self):\n self.end_time = datetime.now()","title":""},{"docid":"13779c2dd47c01e56594606458280ce0","score":"0.7864838","text":"def end_time_stamp(self, end_time_stamp):\n\n self._end_time_stamp = end_time_stamp","title":""},{"docid":"49865918938e8b8e50a0bbdd96217f98","score":"0.78506166","text":"def set_end_datetime(self, end_datetime=None):\n self.end_datetime = TimePoint(end_datetime)","title":""},{"docid":"f4e9fe6ea42cfd7521d9fab398a8df39","score":"0.78215367","text":"def set_starttime(self, endtime):","title":""},{"docid":"f4e4f10462943b759ff47b51d2274587","score":"0.7795095","text":"def end_time(self, end_time, root=False):\n if root:\n root_header = self._root.name\n root_header.end_time = end_time\n else:\n header = self.current_header()\n header.end_time = end_time","title":""},{"docid":"0fb7db3c7ead74286b4e3e7422d22482","score":"0.7661106","text":"def changeEnd(self, newTime):\r\n if isinstance(newTime, datetime.datetime):\r\n self.endTime = newTime\r\n else:\r\n while not isinstance(newTime, datetime.datetime):\r\n newTime = datetime.datetime(input(\"Please enter a valid endtime \"))\r\n self.endTime = newTime","title":""},{"docid":"7b3db05e75e4bbbaa810fa5195d4f0eb","score":"0.75743765","text":"def end_timestamp(self, end_timestamp):\n\n self._end_timestamp = end_timestamp","title":""},{"docid":"7b3db05e75e4bbbaa810fa5195d4f0eb","score":"0.75743765","text":"def end_timestamp(self, end_timestamp):\n\n self._end_timestamp = end_timestamp","title":""},{"docid":"7b3db05e75e4bbbaa810fa5195d4f0eb","score":"0.75743765","text":"def end_timestamp(self, end_timestamp):\n\n self._end_timestamp = end_timestamp","title":""},{"docid":"c59256faed3fbceac277cdfd71f7434b","score":"0.75393707","text":"def end_ms(self, end_ms: int):\n\n self._end_ms = end_ms","title":""},{"docid":"718bfbf08ff73edd4b00c12dd3f1eb9e","score":"0.75385743","text":"def end(self):\n self.update()\n self.__end_time = datetime.datetime.now()","title":""},{"docid":"0ca1c9594a9bd77fbb7a9a3e1c0410df","score":"0.75230235","text":"def end_ms(self, end_ms):\n\n self._end_ms = end_ms","title":""},{"docid":"50cccd0e2a5d9552b20889121a067452","score":"0.73678607","text":"def change_time(self, start, end):\n self.start = start\n self.end = end","title":""},{"docid":"165e808e35293de335e0cccc10b35ed4","score":"0.736143","text":"def build_set_end_time(self, build_set_end_time):\n self._build_set_end_time = build_set_end_time","title":""},{"docid":"dd74f5edda3a3f5a7b33ebcbca33288d","score":"0.73559564","text":"def setfinalTime(self,finalTime):\n self.finalTime = finalTime","title":""},{"docid":"606336c0f082dc6461316bda6593e2ca","score":"0.7300512","text":"def end_time(self) -> time:\n return self._endtime","title":""},{"docid":"f4819b062b6583cb409d54bb7e356b8f","score":"0.7291166","text":"def SetEndDateTime(self, year, month, day, hour):\n self.endDateTime = datetime(year = year, month = month, day = day, hour = hour)","title":""},{"docid":"b70ad941448d12f570992b1b7e26d9a3","score":"0.72413206","text":"def end_time(self):\n return self.__end_time","title":""},{"docid":"149187edc542d07c5d734bb6345a69d1","score":"0.7227893","text":"def set_new_end_time(self, new_end_second):\n # Find index of the new end time\n if new_end_second > self.end_time:\n raise ValueError(\"New end second {} is out of bounds for \"\n \"hypnogram of length {} seconds\".format(\n new_end_second, self.end_time\n ))\n init_ind = np.where(new_end_second > self.inits)[0][-1]\n self.inits = self.inits[:init_ind+1]\n self.stages = self.stages[:init_ind+1]\n self.durations = self.durations[:init_ind+1]\n\n # Update last duration\n old_end = self.inits[-1] + self.durations[-1]\n self.durations[-1] -= old_end - new_end_second","title":""},{"docid":"66590508719b7a80ef83c6b97c9e0985","score":"0.71978176","text":"def get_end_time(self):\n return self.__end_time","title":""},{"docid":"66590508719b7a80ef83c6b97c9e0985","score":"0.71978176","text":"def get_end_time(self):\n return self.__end_time","title":""},{"docid":"93319a9da47203f1a1bdc14e26928003","score":"0.7182533","text":"def end(self, time):\n\n pass","title":""},{"docid":"139710a4b23583a61cb2dbd778e1bb81","score":"0.7172467","text":"def get_end_time(self):\n\t\treturn self.end_time","title":""},{"docid":"b8039a1b0fe97e6e6802129d36204422","score":"0.71616536","text":"def get_end_time(self):\n return self.end_time","title":""},{"docid":"9e848d64499a009c7846c64c3426d08d","score":"0.7160628","text":"def end_time(self) -> datetime:\n return self._end_time","title":""},{"docid":"9e848d64499a009c7846c64c3426d08d","score":"0.7160628","text":"def end_time(self) -> datetime:\n return self._end_time","title":""},{"docid":"97a0839c1978a72889dba5666f4f25cf","score":"0.7153701","text":"def end_hour(self, end_hour):\n\n self._end_hour = end_hour","title":""},{"docid":"5a30767ba7767d195c882fbf7d056c7d","score":"0.7148667","text":"def get_endtime(self):\n return self.endtime","title":""},{"docid":"6b39d2b64c3e448542660726ca5cdbb5","score":"0.71470857","text":"def set_run_time(self, start_time, end_time):\n self.__run_time = self.__timediff(start_time, end_time)","title":""},{"docid":"087f7537d4d0ed121203751ba8b0a47a","score":"0.71307623","text":"def get_end_time(self):\n return self.end","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"bf804e4548e7ffd434c2876478bb3440","score":"0.71289474","text":"def end_time(self):\n return self._end_time","title":""},{"docid":"c11e468f1cab6a3b7b63094825f1f919","score":"0.7127488","text":"def update_end(self, new_end):\n self.end = new_end","title":""},{"docid":"8d57d4e49c1e5d1bfe847cdb8efc66a9","score":"0.7117663","text":"def end_time(self):\n return self._end.strftime('%H:%M:%S')","title":""},{"docid":"732523a1494e4f67ae6421ab42a23f0e","score":"0.710389","text":"def game_end_date_time(self, game_end_date_time):\n\n self._game_end_date_time = game_end_date_time","title":""},{"docid":"60fbcd518dc8c4309e413ff91e7a1684","score":"0.7075628","text":"def end(self, end):\n\n self._end = end","title":""},{"docid":"60fbcd518dc8c4309e413ff91e7a1684","score":"0.7075628","text":"def end(self, end):\n\n self._end = end","title":""},{"docid":"60fbcd518dc8c4309e413ff91e7a1684","score":"0.7075628","text":"def end(self, end):\n\n self._end = end","title":""},{"docid":"69ea3c3c0d0bf8b15e7d918a788ea2f1","score":"0.7073205","text":"def endtime(self):\n return self.end","title":""},{"docid":"e8212cf2b93f60aaa6719125d9b33d13","score":"0.70285743","text":"def get_end_time(self):\n raise NotImplementedError","title":""},{"docid":"827532f4fb5a79d04bfea50e9d14594a","score":"0.70154494","text":"def end_time(self) -> str:\n return self._end_time","title":""},{"docid":"fa0655d95456fe3202154ff4be8e9006","score":"0.70113933","text":"def set_interval_end(self, end):\n start = self.interval[0]\n self.interval = [start, end]","title":""},{"docid":"c6fb3c8c9c6628faa5431253da071272","score":"0.7004442","text":"def enddate(self, enddate: datetime):\n\n self._enddate = enddate","title":""},{"docid":"3c66166c4096784f3a9697f800926d77","score":"0.69960314","text":"def get_end_time(self) -> str:\n return self.end_time","title":""},{"docid":"719aa33c19b3c7c854b70b7ca765ebc8","score":"0.69868577","text":"def end(self) -> None:\n self.ended_at = datetime.now(timezone.utc)","title":""},{"docid":"87b51530949f73e5340ac368edc31795","score":"0.69811934","text":"def end_value(self, end_value):\n\n self._end_value = end_value","title":""},{"docid":"0bee36f598daf2e625110e3fc1bbf0d4","score":"0.69797057","text":"def _update_end(self, end, tenant_id):\n self.usage_end_dt[tenant_id] = end","title":""},{"docid":"738b72e283818d780e53a22e3731b42b","score":"0.69554305","text":"def setTime(self, time):\n\n pass","title":""},{"docid":"727b7c2c5966d5e776a8563efc32a603","score":"0.69545716","text":"def end_range_duration(self, end_range_duration):\n\n self._end_range_duration = end_range_duration","title":""},{"docid":"f963c1d541ebfcda2f9f675664fb54da","score":"0.6937458","text":"def endtime(self):\n\n if hasattr(self, \"_endtime\"):\n return self._endtime\n else:\n return None","title":""},{"docid":"e5364d468dc0066b92bcb6d44eb90606","score":"0.69240916","text":"def last_end(self, ts):\n if not isinstance(ts, datetime.datetime):\n raise TypeError(\"Expected datetime.datetime, got {}\".format(ts))\n self._last_end = ts\n self.agent_mgr.status_set(\"last_end\", str(self.last_end))\n self.info(\"Last end: {}\".format(ts))","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"46718deecc92bc93459104e1a4bb3f3d","score":"0.69224596","text":"def end_time(self) -> str:\n return pulumi.get(self, \"end_time\")","title":""},{"docid":"e6cc7c8c5a1f118170f389d45fb256cc","score":"0.69179606","text":"def ended_date_time(self, ended_date_time):\n\n self._ended_date_time = ended_date_time","title":""},{"docid":"73843bfcfdd713c81d9605461bb7327c","score":"0.6907088","text":"def set_timestamp(self, start, end):\n self.__start_time = start\n self.__end_time = end","title":""},{"docid":"dcdf8776df9f5622cf078d65d470d72a","score":"0.69038534","text":"def stop_timer(self):\n self.end = time.time()\n self.date_end = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n #increment so can start and stop repeatedly\n self.time += self.end - self.start","title":""},{"docid":"cd83ea70de9ebc772837b76977dcc2e9","score":"0.6903614","text":"def get_end(self):\r\n return datetime.datetime.combine(self.end_date, self.end_time)","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""},{"docid":"428356b74a8fb87e016281fab0615eee","score":"0.6902841","text":"def end_date(self, end_date):\n\n self._end_date = end_date","title":""}],"string":"[\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea848c0b6f5e014782c1d4d2645a38c7\",\n \"score\": \"0.89383185\",\n \"text\": \"def end_time(self, end_time):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082f56d38adb43fd76bd251087334145\",\n \"score\": \"0.87735015\",\n \"text\": \"def endtime(self, endtime):\\n\\n self._endtime = endtime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321461abd895ef45407ef40c4272cf65\",\n \"score\": \"0.87264866\",\n \"text\": \"def end_time(self, end_time: str):\\n\\n self._end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c9bf091dacf7b3e112c1ca4ce7d66c\",\n \"score\": \"0.8701888\",\n \"text\": \"def set_end_time(self, end_time):\\n self.end_time.append(end_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6faf2e3de33a910332c808bd491bd8b5\",\n \"score\": \"0.8698118\",\n \"text\": \"def set_end_time(self, val):\\n with self._condition:\\n self._end_time = val\\n self._invoke_upd_callbacks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3beb087196c52aac77520219f3ed34b8\",\n \"score\": \"0.8685146\",\n \"text\": \"def time_end(self, time_end):\\n \\n self._time_end = time_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8fa930514632d840c4cac5b87229b3c\",\n \"score\": \"0.85402113\",\n \"text\": \"def time_end(self):\\n fin=tm.time()\\n self.time(t=fin)\\n self.end = fin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3722bd4b6a680acc1fbf3e97de89c117\",\n \"score\": \"0.841898\",\n \"text\": \"def set_end_time(self, end_time):\\n self.__check_time(end_time)\\n if isinstance(end_time, str):\\n end_time = self.__iso_time_to_timestamp(end_time)\\n self.__end_time = end_time\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63fe2ebdd1e926a22d8e4eb4f6f706b\",\n \"score\": \"0.8335043\",\n \"text\": \"def setEndTime(self, endTime):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b95c0f5504a13df64628d37d60d3f8\",\n \"score\": \"0.8327098\",\n \"text\": \"def _set_end_time(self, future):\\n self._end_time = datetime.datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20fe153f9fef87ea21207cbcddd46e0\",\n \"score\": \"0.8195801\",\n \"text\": \"def ending_time(self, ending_time):\\n\\n self._ending_time = ending_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14b17c573bee04d85554f4a6b6e0df4\",\n \"score\": \"0.81608075\",\n \"text\": \"def setend(self, v):\\n\\t\\tself.end = self._parse_time(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bc6beab6da13cb37428d566546d920\",\n \"score\": \"0.8094813\",\n \"text\": \"def end(self, end_time: typing.Optional[int] = None) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4026154e139529f2117bfcffe6b42f2\",\n \"score\": \"0.8059397\",\n \"text\": \"def set_end_time(self):\\n\\t\\tself.end_time = datetime.today()\\n\\t\\treturn self.end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d618e2c8dca1ef12822ee0b95225bc7\",\n \"score\": \"0.8009447\",\n \"text\": \"def end(self):\\n self.end_time = datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13779c2dd47c01e56594606458280ce0\",\n \"score\": \"0.7864838\",\n \"text\": \"def end_time_stamp(self, end_time_stamp):\\n\\n self._end_time_stamp = end_time_stamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49865918938e8b8e50a0bbdd96217f98\",\n \"score\": \"0.78506166\",\n \"text\": \"def set_end_datetime(self, end_datetime=None):\\n self.end_datetime = TimePoint(end_datetime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e9fe6ea42cfd7521d9fab398a8df39\",\n \"score\": \"0.78215367\",\n \"text\": \"def set_starttime(self, endtime):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e4f10462943b759ff47b51d2274587\",\n \"score\": \"0.7795095\",\n \"text\": \"def end_time(self, end_time, root=False):\\n if root:\\n root_header = self._root.name\\n root_header.end_time = end_time\\n else:\\n header = self.current_header()\\n header.end_time = end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb7db3c7ead74286b4e3e7422d22482\",\n \"score\": \"0.7661106\",\n \"text\": \"def changeEnd(self, newTime):\\r\\n if isinstance(newTime, datetime.datetime):\\r\\n self.endTime = newTime\\r\\n else:\\r\\n while not isinstance(newTime, datetime.datetime):\\r\\n newTime = datetime.datetime(input(\\\"Please enter a valid endtime \\\"))\\r\\n self.endTime = newTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3db05e75e4bbbaa810fa5195d4f0eb\",\n \"score\": \"0.75743765\",\n \"text\": \"def end_timestamp(self, end_timestamp):\\n\\n self._end_timestamp = end_timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3db05e75e4bbbaa810fa5195d4f0eb\",\n \"score\": \"0.75743765\",\n \"text\": \"def end_timestamp(self, end_timestamp):\\n\\n self._end_timestamp = end_timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3db05e75e4bbbaa810fa5195d4f0eb\",\n \"score\": \"0.75743765\",\n \"text\": \"def end_timestamp(self, end_timestamp):\\n\\n self._end_timestamp = end_timestamp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59256faed3fbceac277cdfd71f7434b\",\n \"score\": \"0.75393707\",\n \"text\": \"def end_ms(self, end_ms: int):\\n\\n self._end_ms = end_ms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718bfbf08ff73edd4b00c12dd3f1eb9e\",\n \"score\": \"0.75385743\",\n \"text\": \"def end(self):\\n self.update()\\n self.__end_time = datetime.datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca1c9594a9bd77fbb7a9a3e1c0410df\",\n \"score\": \"0.75230235\",\n \"text\": \"def end_ms(self, end_ms):\\n\\n self._end_ms = end_ms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50cccd0e2a5d9552b20889121a067452\",\n \"score\": \"0.73678607\",\n \"text\": \"def change_time(self, start, end):\\n self.start = start\\n self.end = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"165e808e35293de335e0cccc10b35ed4\",\n \"score\": \"0.736143\",\n \"text\": \"def build_set_end_time(self, build_set_end_time):\\n self._build_set_end_time = build_set_end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd74f5edda3a3f5a7b33ebcbca33288d\",\n \"score\": \"0.73559564\",\n \"text\": \"def setfinalTime(self,finalTime):\\n self.finalTime = finalTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606336c0f082dc6461316bda6593e2ca\",\n \"score\": \"0.7300512\",\n \"text\": \"def end_time(self) -> time:\\n return self._endtime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4819b062b6583cb409d54bb7e356b8f\",\n \"score\": \"0.7291166\",\n \"text\": \"def SetEndDateTime(self, year, month, day, hour):\\n self.endDateTime = datetime(year = year, month = month, day = day, hour = hour)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70ad941448d12f570992b1b7e26d9a3\",\n \"score\": \"0.72413206\",\n \"text\": \"def end_time(self):\\n return self.__end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149187edc542d07c5d734bb6345a69d1\",\n \"score\": \"0.7227893\",\n \"text\": \"def set_new_end_time(self, new_end_second):\\n # Find index of the new end time\\n if new_end_second > self.end_time:\\n raise ValueError(\\\"New end second {} is out of bounds for \\\"\\n \\\"hypnogram of length {} seconds\\\".format(\\n new_end_second, self.end_time\\n ))\\n init_ind = np.where(new_end_second > self.inits)[0][-1]\\n self.inits = self.inits[:init_ind+1]\\n self.stages = self.stages[:init_ind+1]\\n self.durations = self.durations[:init_ind+1]\\n\\n # Update last duration\\n old_end = self.inits[-1] + self.durations[-1]\\n self.durations[-1] -= old_end - new_end_second\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66590508719b7a80ef83c6b97c9e0985\",\n \"score\": \"0.71978176\",\n \"text\": \"def get_end_time(self):\\n return self.__end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66590508719b7a80ef83c6b97c9e0985\",\n \"score\": \"0.71978176\",\n \"text\": \"def get_end_time(self):\\n return self.__end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93319a9da47203f1a1bdc14e26928003\",\n \"score\": \"0.7182533\",\n \"text\": \"def end(self, time):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139710a4b23583a61cb2dbd778e1bb81\",\n \"score\": \"0.7172467\",\n \"text\": \"def get_end_time(self):\\n\\t\\treturn self.end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8039a1b0fe97e6e6802129d36204422\",\n \"score\": \"0.71616536\",\n \"text\": \"def get_end_time(self):\\n return self.end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e848d64499a009c7846c64c3426d08d\",\n \"score\": \"0.7160628\",\n \"text\": \"def end_time(self) -> datetime:\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e848d64499a009c7846c64c3426d08d\",\n \"score\": \"0.7160628\",\n \"text\": \"def end_time(self) -> datetime:\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97a0839c1978a72889dba5666f4f25cf\",\n \"score\": \"0.7153701\",\n \"text\": \"def end_hour(self, end_hour):\\n\\n self._end_hour = end_hour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a30767ba7767d195c882fbf7d056c7d\",\n \"score\": \"0.7148667\",\n \"text\": \"def get_endtime(self):\\n return self.endtime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b39d2b64c3e448542660726ca5cdbb5\",\n \"score\": \"0.71470857\",\n \"text\": \"def set_run_time(self, start_time, end_time):\\n self.__run_time = self.__timediff(start_time, end_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087f7537d4d0ed121203751ba8b0a47a\",\n \"score\": \"0.71307623\",\n \"text\": \"def get_end_time(self):\\n return self.end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf804e4548e7ffd434c2876478bb3440\",\n \"score\": \"0.71289474\",\n \"text\": \"def end_time(self):\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c11e468f1cab6a3b7b63094825f1f919\",\n \"score\": \"0.7127488\",\n \"text\": \"def update_end(self, new_end):\\n self.end = new_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d57d4e49c1e5d1bfe847cdb8efc66a9\",\n \"score\": \"0.7117663\",\n \"text\": \"def end_time(self):\\n return self._end.strftime('%H:%M:%S')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732523a1494e4f67ae6421ab42a23f0e\",\n \"score\": \"0.710389\",\n \"text\": \"def game_end_date_time(self, game_end_date_time):\\n\\n self._game_end_date_time = game_end_date_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60fbcd518dc8c4309e413ff91e7a1684\",\n \"score\": \"0.7075628\",\n \"text\": \"def end(self, end):\\n\\n self._end = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60fbcd518dc8c4309e413ff91e7a1684\",\n \"score\": \"0.7075628\",\n \"text\": \"def end(self, end):\\n\\n self._end = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60fbcd518dc8c4309e413ff91e7a1684\",\n \"score\": \"0.7075628\",\n \"text\": \"def end(self, end):\\n\\n self._end = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69ea3c3c0d0bf8b15e7d918a788ea2f1\",\n \"score\": \"0.7073205\",\n \"text\": \"def endtime(self):\\n return self.end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8212cf2b93f60aaa6719125d9b33d13\",\n \"score\": \"0.70285743\",\n \"text\": \"def get_end_time(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827532f4fb5a79d04bfea50e9d14594a\",\n \"score\": \"0.70154494\",\n \"text\": \"def end_time(self) -> str:\\n return self._end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0655d95456fe3202154ff4be8e9006\",\n \"score\": \"0.70113933\",\n \"text\": \"def set_interval_end(self, end):\\n start = self.interval[0]\\n self.interval = [start, end]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6fb3c8c9c6628faa5431253da071272\",\n \"score\": \"0.7004442\",\n \"text\": \"def enddate(self, enddate: datetime):\\n\\n self._enddate = enddate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c66166c4096784f3a9697f800926d77\",\n \"score\": \"0.69960314\",\n \"text\": \"def get_end_time(self) -> str:\\n return self.end_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719aa33c19b3c7c854b70b7ca765ebc8\",\n \"score\": \"0.69868577\",\n \"text\": \"def end(self) -> None:\\n self.ended_at = datetime.now(timezone.utc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b51530949f73e5340ac368edc31795\",\n \"score\": \"0.69811934\",\n \"text\": \"def end_value(self, end_value):\\n\\n self._end_value = end_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bee36f598daf2e625110e3fc1bbf0d4\",\n \"score\": \"0.69797057\",\n \"text\": \"def _update_end(self, end, tenant_id):\\n self.usage_end_dt[tenant_id] = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738b72e283818d780e53a22e3731b42b\",\n \"score\": \"0.69554305\",\n \"text\": \"def setTime(self, time):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"727b7c2c5966d5e776a8563efc32a603\",\n \"score\": \"0.69545716\",\n \"text\": \"def end_range_duration(self, end_range_duration):\\n\\n self._end_range_duration = end_range_duration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f963c1d541ebfcda2f9f675664fb54da\",\n \"score\": \"0.6937458\",\n \"text\": \"def endtime(self):\\n\\n if hasattr(self, \\\"_endtime\\\"):\\n return self._endtime\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5364d468dc0066b92bcb6d44eb90606\",\n \"score\": \"0.69240916\",\n \"text\": \"def last_end(self, ts):\\n if not isinstance(ts, datetime.datetime):\\n raise TypeError(\\\"Expected datetime.datetime, got {}\\\".format(ts))\\n self._last_end = ts\\n self.agent_mgr.status_set(\\\"last_end\\\", str(self.last_end))\\n self.info(\\\"Last end: {}\\\".format(ts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46718deecc92bc93459104e1a4bb3f3d\",\n \"score\": \"0.69224596\",\n \"text\": \"def end_time(self) -> str:\\n return pulumi.get(self, \\\"end_time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6cc7c8c5a1f118170f389d45fb256cc\",\n \"score\": \"0.69179606\",\n \"text\": \"def ended_date_time(self, ended_date_time):\\n\\n self._ended_date_time = ended_date_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73843bfcfdd713c81d9605461bb7327c\",\n \"score\": \"0.6907088\",\n \"text\": \"def set_timestamp(self, start, end):\\n self.__start_time = start\\n self.__end_time = end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcdf8776df9f5622cf078d65d470d72a\",\n \"score\": \"0.69038534\",\n \"text\": \"def stop_timer(self):\\n self.end = time.time()\\n self.date_end = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n #increment so can start and stop repeatedly\\n self.time += self.end - self.start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd83ea70de9ebc772837b76977dcc2e9\",\n \"score\": \"0.6903614\",\n \"text\": \"def get_end(self):\\r\\n return datetime.datetime.combine(self.end_date, self.end_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428356b74a8fb87e016281fab0615eee\",\n \"score\": \"0.6902841\",\n \"text\": \"def end_date(self, end_date):\\n\\n self._end_date = end_date\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994291,"cells":{"query_id":{"kind":"string","value":"db9f171c1c3e83d889bd8b0d52a853ae"},"query":{"kind":"string","value":"Main function, starts the program, setups the paths and submits the bidnumber to process"},"positive_passages":{"kind":"list like","value":[{"docid":"9f9cdb5c08ad1252520c46caea6628bc","score":"0.59576374","text":"def main(bid_number, config, logger, test=False,\n test_bids=5, timeout=30, validations=False):\n pd.options.mode.chained_assignment = None\n\n # Load paths from config\n home = os.environ[config[\"PATHS\"][\"HOME\"]]\n\n # Load paths from config\n paths_dict = createPaths(home, config)\n\n # Write initial error flag\n set_error_flag(bid_number, paths_dict['error_log_path'], str(1), logger)\n\n # to do bulk scoring: check tp_bid sample and get all unique bids\n bid_numbers = pd.DataFrame()\n if test:\n try:\n tp20_bid = pd.read_csv(home + '/data/tp_bid.csv', dtype=str)\n bid_numbers = tp20_bid['NVP_BID_NR'].unique()\n\n print \"Bid numbers found: \"\n #print bid_numbers\n except RuntimeError as e:\n print_error_message(e, \"Error 3.0: General producer error due to test run\",\n logger, False)\n sys.exit(1)\n\n master = pd.DataFrame()\n master_result = pd.DataFrame()\n sql = ''\n\n if test:\n bids_to_score = test_bids\n if bids_to_score == -1:\n bids_to_score = len(bid_numbers)\n else:\n bids_to_score = 1\n\n for i in range(0, bids_to_score):\n if test:\n bid_number = str(bid_numbers[i])\n\n if test:\n logger.info(\"Processing bid # \" + str(i + 1) + \" of \" + str(len(bid_numbers)))\n print \"Processing bid # \" + str(i + 1) + \" of \" + str(len(bid_numbers))\n\n logger.info(\"Processing bid: \" + bid_number)\n\n # Get data\n try:\n response, tp20_bid_shpr, tp20_ceiling_svc, tncvcel, tp_accessorial = \\\n get_data(home, bid_number, config, test, logger)\n except Exception, e:\n if test:\n continue\n else:\n # print_error_message(e, \"Error 2.2a: Data transformation issues: \", logger)\n sys.exit(1)\n\n try:\n # test for CWT threshold if CWT exists,\n # helps prevent CWT over threshold from going to consumer\n cwt = response[response.Product_Mode.isin(['AIR_CWT', 'GND_CWT'])]\n if not cwt.empty:\n # test CWT threshold\n cwt_filename = paths_dict['model_path'] + config[\"MODELS\"][\"CWT\"] + \".p\"\n with open(cwt_filename, \"rb\") as pickle_file:\n air_bt_threshold, air_density_threshold, air_size_threshold, air_cohort_map, \\\n air_incentive_map, gnd_bt_threshold, gnd_density_threshold, \\\n gnd_size_threshold, \\\n gnd_cohort_map, gnd_incentive_map = pickle.load(pickle_file)\n\n # air cwt check\n air_cwt = cwt[cwt.Product == 'Air_CWT']\n if not air_cwt.empty:\n air_max = air_size_threshold['MAX VALUE'].max()\n air_cwt_value = cwt[cwt.Product == 'Air_CWT']['Bid_List_Rev_Wkly'].max()\n\n if air_cwt_value > air_max:\n raise RuntimeError(\"Error 2.2a: Data transformation issues: \"\n \"CWT threshold reached\")\n\n # gnd cwt check\n gnd_cwt = cwt[cwt.Product == 'Gnd_CWT']\n if not gnd_cwt.empty:\n gnd_max = gnd_size_threshold['MAX VALUE'].max()\n gnd_cwt_value = cwt[cwt.Product == 'Gnd_CWT']['Bid_List_Rev_Wkly'].max()\n\n if gnd_cwt_value > gnd_max:\n raise RuntimeError(\"Error 2.2a: Data transformation issues: \"\n \"CWT threshold reached\")\n\n # Enqueue the data\n master = master.append(response)\n\n result, result_file, p2c_file = enqueue(response, tp20_bid_shpr, timeout, bid_number,\n paths_dict['c2p_path'],\n paths_dict['p2c_path'], tncvcel,\n paths_dict['log_path'])\n master_result = master_result.append(result)\n\n except RuntimeError as e:\n print_error_message(e, \"\", logger, False)\n\n if test:\n continue\n else:\n sys.exit(1)\n except (IOError, OSError) as e:\n print_error_message(e, \"Error 3.2a: Model cannot be loaded: \" +\n config[\"MODELS\"][\"CWT\"], logger)\n\n try:\n # store data\n if validations:\n test = True\n sql_result = put_data(home, bid_number, config, test, result,\n tp20_ceiling_svc, tp_accessorial, logger)\n if test:\n sql = sql + sql_result\n # True\n except Exception, e:\n print_error_message(e, \"\", logger, False)\n logger.warning(\"Bid \" + bid_number + \" scoring failed.\")\n else:\n cleanup(paths_dict['c2p_path'] + result_file)\n logger.info(\"Bid \" + bid_number + \" successfully scored.\")\n # Once done with everything write success flag\n set_error_flag(bid_number, paths_dict['error_log_path'], str(0), logger)\n\n # Output master dataset immediately\n #file_name = paths_dict['log_path'] + bid_number + '-' + p2c_file + '-master.csv'\n #master.to_csv(file_name)\n logger.debug('******************Exiting function main_Producer')\n if test:\n master_result.to_csv(\"results.csv\")\n sql_file = open(\"sql_results.txt\", \"w\")\n sql_file.write(sql)\n sql_file.close()","title":""}],"string":"[\n {\n \"docid\": \"9f9cdb5c08ad1252520c46caea6628bc\",\n \"score\": \"0.59576374\",\n \"text\": \"def main(bid_number, config, logger, test=False,\\n test_bids=5, timeout=30, validations=False):\\n pd.options.mode.chained_assignment = None\\n\\n # Load paths from config\\n home = os.environ[config[\\\"PATHS\\\"][\\\"HOME\\\"]]\\n\\n # Load paths from config\\n paths_dict = createPaths(home, config)\\n\\n # Write initial error flag\\n set_error_flag(bid_number, paths_dict['error_log_path'], str(1), logger)\\n\\n # to do bulk scoring: check tp_bid sample and get all unique bids\\n bid_numbers = pd.DataFrame()\\n if test:\\n try:\\n tp20_bid = pd.read_csv(home + '/data/tp_bid.csv', dtype=str)\\n bid_numbers = tp20_bid['NVP_BID_NR'].unique()\\n\\n print \\\"Bid numbers found: \\\"\\n #print bid_numbers\\n except RuntimeError as e:\\n print_error_message(e, \\\"Error 3.0: General producer error due to test run\\\",\\n logger, False)\\n sys.exit(1)\\n\\n master = pd.DataFrame()\\n master_result = pd.DataFrame()\\n sql = ''\\n\\n if test:\\n bids_to_score = test_bids\\n if bids_to_score == -1:\\n bids_to_score = len(bid_numbers)\\n else:\\n bids_to_score = 1\\n\\n for i in range(0, bids_to_score):\\n if test:\\n bid_number = str(bid_numbers[i])\\n\\n if test:\\n logger.info(\\\"Processing bid # \\\" + str(i + 1) + \\\" of \\\" + str(len(bid_numbers)))\\n print \\\"Processing bid # \\\" + str(i + 1) + \\\" of \\\" + str(len(bid_numbers))\\n\\n logger.info(\\\"Processing bid: \\\" + bid_number)\\n\\n # Get data\\n try:\\n response, tp20_bid_shpr, tp20_ceiling_svc, tncvcel, tp_accessorial = \\\\\\n get_data(home, bid_number, config, test, logger)\\n except Exception, e:\\n if test:\\n continue\\n else:\\n # print_error_message(e, \\\"Error 2.2a: Data transformation issues: \\\", logger)\\n sys.exit(1)\\n\\n try:\\n # test for CWT threshold if CWT exists,\\n # helps prevent CWT over threshold from going to consumer\\n cwt = response[response.Product_Mode.isin(['AIR_CWT', 'GND_CWT'])]\\n if not cwt.empty:\\n # test CWT threshold\\n cwt_filename = paths_dict['model_path'] + config[\\\"MODELS\\\"][\\\"CWT\\\"] + \\\".p\\\"\\n with open(cwt_filename, \\\"rb\\\") as pickle_file:\\n air_bt_threshold, air_density_threshold, air_size_threshold, air_cohort_map, \\\\\\n air_incentive_map, gnd_bt_threshold, gnd_density_threshold, \\\\\\n gnd_size_threshold, \\\\\\n gnd_cohort_map, gnd_incentive_map = pickle.load(pickle_file)\\n\\n # air cwt check\\n air_cwt = cwt[cwt.Product == 'Air_CWT']\\n if not air_cwt.empty:\\n air_max = air_size_threshold['MAX VALUE'].max()\\n air_cwt_value = cwt[cwt.Product == 'Air_CWT']['Bid_List_Rev_Wkly'].max()\\n\\n if air_cwt_value > air_max:\\n raise RuntimeError(\\\"Error 2.2a: Data transformation issues: \\\"\\n \\\"CWT threshold reached\\\")\\n\\n # gnd cwt check\\n gnd_cwt = cwt[cwt.Product == 'Gnd_CWT']\\n if not gnd_cwt.empty:\\n gnd_max = gnd_size_threshold['MAX VALUE'].max()\\n gnd_cwt_value = cwt[cwt.Product == 'Gnd_CWT']['Bid_List_Rev_Wkly'].max()\\n\\n if gnd_cwt_value > gnd_max:\\n raise RuntimeError(\\\"Error 2.2a: Data transformation issues: \\\"\\n \\\"CWT threshold reached\\\")\\n\\n # Enqueue the data\\n master = master.append(response)\\n\\n result, result_file, p2c_file = enqueue(response, tp20_bid_shpr, timeout, bid_number,\\n paths_dict['c2p_path'],\\n paths_dict['p2c_path'], tncvcel,\\n paths_dict['log_path'])\\n master_result = master_result.append(result)\\n\\n except RuntimeError as e:\\n print_error_message(e, \\\"\\\", logger, False)\\n\\n if test:\\n continue\\n else:\\n sys.exit(1)\\n except (IOError, OSError) as e:\\n print_error_message(e, \\\"Error 3.2a: Model cannot be loaded: \\\" +\\n config[\\\"MODELS\\\"][\\\"CWT\\\"], logger)\\n\\n try:\\n # store data\\n if validations:\\n test = True\\n sql_result = put_data(home, bid_number, config, test, result,\\n tp20_ceiling_svc, tp_accessorial, logger)\\n if test:\\n sql = sql + sql_result\\n # True\\n except Exception, e:\\n print_error_message(e, \\\"\\\", logger, False)\\n logger.warning(\\\"Bid \\\" + bid_number + \\\" scoring failed.\\\")\\n else:\\n cleanup(paths_dict['c2p_path'] + result_file)\\n logger.info(\\\"Bid \\\" + bid_number + \\\" successfully scored.\\\")\\n # Once done with everything write success flag\\n set_error_flag(bid_number, paths_dict['error_log_path'], str(0), logger)\\n\\n # Output master dataset immediately\\n #file_name = paths_dict['log_path'] + bid_number + '-' + p2c_file + '-master.csv'\\n #master.to_csv(file_name)\\n logger.debug('******************Exiting function main_Producer')\\n if test:\\n master_result.to_csv(\\\"results.csv\\\")\\n sql_file = open(\\\"sql_results.txt\\\", \\\"w\\\")\\n sql_file.write(sql)\\n sql_file.close()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5eb65670ae87521a2fc47c3a73258747","score":"0.67149067","text":"def main(argv):\n\n\tlogger.setLevel(logging.INFO)\n\thandleOptions(argv)\n\t# Load the config file\n\tconfigfile = 'config.json'\n\twith open(configfile, 'r') as f:\n\t\tconfig = json.load(f)\n\n\tcreateOutputFolder()\n\n\tlogger.info(\"Start the grabbing process\")\n\tgrabber = Grabber(config, linkparser)\n\tgrabber.execute()\n\n\tlogger.info(\"Start creating the web package\")\n\twebPackageCreator = WebPackageCreator(config)\n\twebPackageCreator.create()\n\n\tlogger.info(\"Start creating the dns package\")\n\tdnsPackageCreator = DNSCreater(config)\n\tdnsPackageCreator.create()","title":""},{"docid":"a66fef5e5ea4e26f9c772525accbf2cb","score":"0.6053314","text":"def main(self,argv):\n cli.setup_logging(\"/tmp/setup.out\")\n os = cli.get_os_type()\n if (os not in [\"mac\"]):\n cli.fatal(\"Untested OS: \" + os)\n cli.log.info(\"Starting Program\")\n cli.log.info(\"ARGS:\" + str(argv))\n self.cmd(\"ls / | head -5 # test ability to run shell commands\") \n self.install_grakn_in_osx() # Start Grakn \n #self.clear_grakn_db() # Erase the data (Stops services)\n #self.install_grakn_in_osx() # Start Grakn \n \n self.test_grakn_in_osx()","title":""},{"docid":"6caabe4e6793f4e23f18df52f721679e","score":"0.6003799","text":"def main():\n parser = argparse.ArgumentParser(description='boj-tool: a CLI tool for BOJ')\n parser.add_argument('-v', '--verbose', help='set log level to INFO',\n action='store_true')\n parser.add_argument('-d', '--debug', help='set log level to DEBUG',\n action='store_true')\n subparsers = parser.add_subparsers(dest='subparser')\n login_parser = subparsers.add_parser('login')\n submit_parser = subparsers.add_parser('submit')\n submit_parser.add_argument('number', type=int, help='the problem number')\n submit_parser.add_argument('filename', help='filename to submit')\n stat_parser = subparsers.add_parser('stats')\n stat_parser.add_argument('-u', '--user', type=str,\n help='the user to show stats')\n version_parser = subparsers.add_parser('version')\n args = parser.parse_args()\n\n initialize()\n if args.verbose:\n logger.setLevel(logging.INFO)\n elif args.debug:\n logger.setLevel(logging.DEBUG)\n\n if args.subparser == 'login':\n login()\n elif args.subparser == 'submit':\n submit(args.number, args.filename)\n print_result(args.number)\n elif args.subparser == 'stats':\n stats(args.user)\n elif args.subparser == 'version':\n version()","title":""},{"docid":"5c3edb446efd4212324d179ce9a483d8","score":"0.5855898","text":"def process():\n\tparser = argparse.ArgumentParser(description=description)\n\tparser.add_argument(\"config\", help=\"file name of the JSON configuration file\")\n\tparser.add_argument(\"-b\", \"--book\", action='store_true', help=\"Create an EPUB3 package\")\n\tparser.add_argument(\"-f\", \"--folder\", action='store_true', help=\"Create a folder with the book content\")\n\n\targs = parser.parse_args()\n\tfrom rp2book import start\n\t# start(args.config, package=args.book, folder=args.folder, temporary=args.tempfile,\n\t# \t logger= _create_logger(\"log\") if args.logging else None)\n\tstart(args.config, package=args.book, folder=args.folder)","title":""},{"docid":"a2d6f1ffae26dc2828c5286dcc8ee18f","score":"0.5838961","text":"def main():\n path = \"./Bag_flight/\"\n files = [f for f in os.listdir(path) if f[-4:] == '.bag']\n if not files:\n print('No bag files found!')\n return None\n for f in files:\n print(\"\\nreading bag: \" + str(f))\n with rosbag.Bag(path + f) as bag:\n bag_df_dict = get_bag_data_pandas(bag)\n data_df = processing(bag_df_dict=bag_df_dict, id=f[:-4])","title":""},{"docid":"c50c97257373e9ea439903040fb92e38","score":"0.58337957","text":"def main():\n\n # anyway output thr banner information\n banner() \n\n # set paths of project \n paths.ROOT_PATH = os.getcwd() \n setPaths()\n \n # received command >> cmdLineOptions\n cmdLineOptions.update(conf.webConfig)\n \n # loader script,target,working way(threads? gevent?),output_file from cmdLineOptions\n # and send it to conf\n initOptions(cmdLineOptions)\n\n # run!\n run()","title":""},{"docid":"37ff9a8373d4935f4d428fdc43dcf0b0","score":"0.5825765","text":"def main():\n artist = args.artist or getInput(\"Who wrote the song?\")\n album = args.album or getInput(\"What album is the song on?\")\n track = args.track or getInput(\"What is the track name?\")\n trackNum = args.num or getInput(\"What number is the song on the album?\")\n fmt = args.fmt or getInput(\"What format is the song in?\")\n itunes = args.itunes or getInput(\"Where is your iTunes library located? (default: '/Users/{}/Music/iTunes/iTunes Media/')\".format(getpass.getuser()))\n\n if itunes.strip() == \"\":\n itunes = \"/Users/{}/Music/iTunes/iTunes Media\".format(getpass.getuser())\n\n trackNum = trackNum.zfill(2)\n\n print(itunes)\n print(artist)\n print(album)\n print(track)\n print(trackNum)\n print(genPath(artist, album, track, trackNum, fmt, itunes))\n path = genPath(artist, album, track, trackNum, fmt, itunes)\n out = d.trackSeek(path, artist, album, track, trackNum, fmt)\n for song in out:\n addToiTunes(itunes, song)","title":""},{"docid":"66efb48a9e722cca9e4ed621db950062","score":"0.5799956","text":"def main():\n with logbook.NestedSetup(_get_log_handlers()).applicationbound():\n logger.info('Py-expander started!')\n try:\n # Set subliminal cache first.\n if config.SHOULD_FIND_SUBTITLES:\n logger.debug('Setting subtitles cache...')\n configure_subtitles_cache()\n # Parse input arguments.\n if len(sys.argv) == 3:\n directory = sys.argv[1]\n filename = sys.argv[2]\n if directory == config.DEFAULT_PATH:\n torrent_path = os.path.join(directory, filename)\n logger.info('Input is a file: {}'.format(torrent_path))\n else:\n torrent_path = directory\n logger.info('Input is a dir: {}'.format(torrent_path))\n expand_torrent(torrent_path)\n elif len(sys.argv) == 2:\n expand_torrent(sys.argv[1])\n else:\n expand_torrent_from_transmission()\n except:\n logger.exception('Critical exception occurred!')\n raise","title":""},{"docid":"d233dc0534600ec6d411de9283e46e50","score":"0.5783133","text":"def main():\n with open(argv[1]) as file:\n #dic = parse_file(file)\n #get_info(dic)\n path = '/Users/gustavotamasco/PycharmProjects/tcc'\n files = get_file(path)\n new_files =[]\n for f in files:\n nf = \"'{0}'\".format(f)\n new_files.append(nf)\n run_blast(new_files)","title":""},{"docid":"cf2a0ead0d7798c1f777d90c6c4f12b5","score":"0.5773897","text":"def main():\n try:\n number_accounts, ip, user_pattern, user_pwd_pattern, node_pattern, node_pwd_pattern = parse_args()\n except ValueError:\n pass\n generate_and_save(number_accounts, ip, user_pattern, user_pwd_pattern, node_pattern, node_pwd_pattern)","title":""},{"docid":"cf3352c5ac59b1128c898ab17f9110ad","score":"0.5770266","text":"def main():\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"bank_code\",\n help=\"3 letter code representing the bank that the data comes from\"\n )\n parser.add_argument(\n \"input_file\",\n help=\"PDF file containing bank transation data\"\n )\n parser.add_argument(\n \"output_file\",\n help=\"Name of CSV file that output will be saved to\"\n )\n program_args = parser.parse_args()\n bank_code = program_args.bank_code\n input_file = program_args.input_file\n output_file = program_args.output_file\n pdf2ynab(bank_code, input_file, output_file)","title":""},{"docid":"e44d640db816f6db16fb7f0cf29beb68","score":"0.57688487","text":"def main():\n # Detect BBP installation\n bbp_install = InstallCfg.getInstance()\n\n prog_base = os.path.basename(sys.argv[0])\n usage = \"usage: %s [options]\" % (prog_base)\n parser = optparse.OptionParser(usage)\n parser.add_option(\"-c\", \"--codebase\", type=\"string\", action=\"store\",\n dest=\"codebase\",\n help=\"Codebase for the simulation: %s\" %\n (CODEBASES))\n parser.add_option(\"-e\", \"--event\", type=\"string\", action=\"store\",\n dest=\"event\",\n help=\"Validation event (should be configured in BBP)\")\n parser.add_option(\"-d\", \"--dir\", type=\"string\", action=\"store\",\n dest=\"simdir\",\n help=\"Simulation directory\")\n parser.add_option(\"--skip-rupgen\", action=\"store_true\", dest=\"skiprupgen\",\n help=\"Skip the rupture generator, run only 1 simulation\")\n parser.add_option(\"--hypo-rand\", action=\"store_true\", dest=\"hyporand\",\n help=\"Enables hypocenter randomization\")\n parser.add_option(\"--no-hypo-rand\", action=\"store_false\", dest=\"hyporand\",\n help=\"Disables hypocenter randomization\")\n parser.add_option(\"-n\", \"--num-simulations\", type=\"int\", action=\"store\",\n dest=\"numsim\", help=\"Number of simulations to run\")\n parser.add_option(\"--email\", type=\"string\", action=\"store\",\n dest=\"email\", help=\"Email for job notifications\")\n (options, args) = parser.parse_args()\n\n # Validate codebase to use\n codebase = options.codebase\n if codebase is None:\n print \"Please specify a codebase!\"\n sys.exit(1)\n codebase = codebase.lower()\n if codebase not in CODEBASES:\n print \"Codebase needs to be one of: %s\" % (CODEBASES)\n\n # Check for event\n event = options.event\n if event is None:\n print \"Please provide a validation event!\"\n sys.exit(1)\n event_names = validation_cfg.VE_EVENTS.get_all_names()\n events = [v_event.lower() for v_event in event_names]\n if event.lower() not in events:\n print (\"Event %s does not appear to be properly configured on BBP\" %\n (event))\n print (\"Available options are: %s\" % (event_names))\n print \"Please provide another event or check your BBP installation.\"\n sys.exit(1)\n val_obj = validation_cfg.VE_EVENTS.get_event_by_print_name(event)\n\n # Check if we want to run the rupture generator\n skip_rupgen = options.skiprupgen\n\n # Check for hypocenter randomization\n if options.hyporand is None:\n print \"Please specify --hypo-rand or --no-hypo-rand!\"\n sys.exit(1)\n\n if options.hyporand:\n hypo_rand = True\n else:\n hypo_rand = False\n\n if not skip_rupgen:\n # Get source file\n try:\n source_file = val_obj.get_input(codebase, \"source\").strip()\n except KeyError:\n print (\"Unable to get source file for event %s, codebase %s!\" %\n (event, codebase))\n sys.exit(1)\n if not source_file:\n print (\"Source file for event %s, codebase %s not specified!\" %\n (event, codebase))\n sys.exit(1)\n else:\n # No need to get the source file, we start from the srf\n source_file = None\n try:\n srf_file = val_obj.get_input(codebase, \"srf\").strip()\n except KeyError:\n print (\"Event %s does not have a srf file for codebase %s!\" %\n (event, codebase))\n sys.exit(1)\n if not srf_file:\n print (\"Event %s does not have a srf file for codebase %s!\" %\n (event, codebase))\n sys.exit(1)\n # Force number of simulations to 1\n options.numsim = 1\n\n # Check for the simulation directory\n simdir = options.simdir\n if simdir is None:\n print \"Please provide a simulation directory!\"\n sys.exit(1)\n simdir = os.path.abspath(simdir)\n if os.path.exists(simdir):\n print \"Simulation directory exists: %s\" % (simdir)\n opt = raw_input(\"Do you want to delete its contents (y/n)? \")\n if opt.lower() != \"y\":\n print \"Please provide another simulation directory!\"\n sys.exit(1)\n opt = raw_input(\"ARE YOU SURE (y/n)? \")\n if opt.lower() != \"y\":\n print \"Please provide another simulation directory!\"\n sys.exit(1)\n # Delete existing directory (we already asked the user twice!!!)\n shutil.rmtree(simdir)\n\n # Pick up number of simulations to run\n numsim = options.numsim\n if numsim < 1 or numsim > MAX_SIMULATIONS:\n print (\"Number of simulations should be between 1 and %d\" %\n (MAX_SIMULATIONS))\n sys.exit(1)\n\n # Check for e-mail address\n email = options.email\n if email is None:\n print \"Please provide an e-mail address for job notifications\"\n sys.exit(1)\n\n # Make sure user has configured the setup_bbp_epicenter_env.sh script\n setup_bbp_env = os.path.join(bbp_install.A_INSTALL_ROOT,\n \"utils/batch/setup_bbp_epicenter_env.sh\")\n if not os.path.exists(setup_bbp_env):\n print (\"Cannot find setup_bbp_epicenter_env.sh script!\")\n print (\"Expected at: %s\" % (setup_bbp_env))\n sys.exit(1)\n # Create simulation directories\n prefix = \"%s-%s\" % (event.lower(), codebase.lower())\n # Make sure we remove spaces from prefix (e.g. for the \"Loma Prieta\" event)\n prefix = prefix.replace(\" \", '')\n os.makedirs(simdir)\n indir = os.path.join(simdir, \"Sims\", \"indata\")\n outdir = os.path.join(simdir, \"Sims\", \"outdata\")\n tmpdir = os.path.join(simdir, \"Sims\", \"tmpdata\")\n logsdir = os.path.join(simdir, \"Sims\", \"logs\")\n xmldir = os.path.join(simdir, \"Xml\")\n srcdir = os.path.join(simdir, \"Src\")\n for mdir in [indir, outdir, tmpdir, logsdir, xmldir, srcdir]:\n os.makedirs(mdir)\n # Generate source files if needed\n if source_file is not None:\n generate_src_files(numsim, source_file, srcdir, prefix, hypo_rand)\n # Generate xml files\n generate_xml(bbp_install, numsim, srcdir, xmldir,\n logsdir, event, codebase, prefix,\n skip_rupgen)\n # Write pbs file\n write_pbs(bbp_install, numsim, simdir, xmldir, email, prefix)","title":""},{"docid":"26cd6c2af325c94684ef9d3886f9c709","score":"0.5756447","text":"def launch_barrier ():\n os.chdir(\"barrier\")\n subprocess.call(\"barrier.exe\")\n os.chdir(\"..\")\n return 0","title":""},{"docid":"178409e8cf07166f24c9f38f913a6d8b","score":"0.5754545","text":"def main():\n args = get_args()\n bottlenum = args.num_bottles \n \n bottledown = bottlenum - 1\n if bottlenum < 1:\n die('N ({}) must be a positive integer'.format(bottlenum))\n while bottlenum >= 1:\n if bottlenum == 1:\n print('{} bottle of beer on the wall,'.format(bottlenum))\n print('{} bottle of beer,'.format(bottlenum)) \n else:\n print('{} bottles of beer on the wall,'.format(bottlenum))\n print('{} bottles of beer,'.format(bottlenum))\n print('Take one down, pass it around,')\n if bottledown == 1:\n print('{} bottle of beer on the wall!'.format(bottledown))\n print('') \n else:\n if bottledown == 0: \n print('{} bottles of beer on the wall!'.format(bottledown))\n else:\n print('{} bottles of beer on the wall!'.format(bottledown))\n print('') \n bottlenum -= 1\n bottledown -= 1","title":""},{"docid":"4740ffd7ba2c8e0cb2ce0717abb89438","score":"0.5730241","text":"def run(self):\n self.parse_args()\n\n if self.options.file_root:\n # check if the argument is pointing to a file on disk\n file_root = os.path.abspath(self.options.file_root)\n self.config[\"file_roots\"] = {\"base\": _expand_glob_path([file_root])}\n\n if self.options.pillar_root:\n # check if the argument is pointing to a file on disk\n pillar_root = os.path.abspath(self.options.pillar_root)\n self.config[\"pillar_roots\"] = {\"base\": _expand_glob_path([pillar_root])}\n\n if self.options.states_dir:\n # check if the argument is pointing to a file on disk\n states_dir = os.path.abspath(self.options.states_dir)\n self.config[\"states_dirs\"] = [states_dir]\n\n if self.options.local:\n self.config[\"file_client\"] = \"local\"\n if self.options.master:\n self.config[\"master\"] = self.options.master\n\n caller = salt.cli.caller.Caller.factory(self.config)\n\n if self.options.doc:\n caller.print_docs()\n self.exit(salt.defaults.exitcodes.EX_OK)\n\n if self.options.grains_run:\n caller.print_grains()\n self.exit(salt.defaults.exitcodes.EX_OK)\n\n caller.run()","title":""},{"docid":"c92c3899081029f7caaf5a4037eb5e03","score":"0.57288873","text":"def main(args):\n\n # Set up processed data directories\n # Trailing slash not expected by rest of program\n args[\"root\"] = args[\"root\"].rstrip(\"/\")\n processed_data_dir = args[\"root\"] + \"/processed_data\"\n if not os.path.exists(processed_data_dir):\n os.mkdir(processed_data_dir)\n\n # Read processor requests\n for processor in args[\"processors\"]:\n print(\"################################\\n\")\n print(\"Working on processor %s\" % processor[\"name\"])\n save_processor_info(processed_data_dir, processor)\n processor_handler = processor_handlers[processor[\"name\"]]\n processor_handler(args[\"root\"], args[\"name\"], processor[\"args\"])\n print(\"\")","title":""},{"docid":"9a1ae0b7c5547a53d55923f42bfb1ae6","score":"0.5685069","text":"def main(): \n args = get_args()\n config = get_namelist(args)\n infill(args, config)\n print '\\nFinished\\n'","title":""},{"docid":"194599c7d1746708ab091609c186477b","score":"0.5684453","text":"def main():\n\n # Parsing command line arguments -->\n cron_mode = False\n debug_mode = False\n develop_mode = False\n\n cmd_options, args = getopt.gnu_getopt(\n sys.argv[1:], \"\", [\n \"cron\",\n \"debug\",\n \"develop-mode\",\n ])\n\n for option, value in cmd_options[:]:\n if option == \"--cron\":\n cron_mode = True\n elif option == \"--debug\":\n debug_mode = True\n elif option == \"--develop-mode\":\n develop_mode = True\n else:\n raise Error(\"Logical error.\")\n # Parsing command line arguments <--\n\n pcli.log.setup(\n debug_mode = debug_mode | develop_mode,\n level = logging.ERROR if cron_mode else None)\n\n global _DEVELOP_MODE\n _DEVELOP_MODE = develop_mode\n\n pid_file = os.path.join(tempfile.gettempdir(), \"rutracker-get-new-torrents\")\n\n try:\n pid_file_fd = psys.daemon.acquire_pidfile(pid_file)\n except psys.daemon.PidFileLockedError as e:\n LOG.error(\"Exiting: the PID file is locked by another process.\")\n else:\n try:\n get_new_torrents()\n except TemporaryError as e:\n LOG.warning(e)\n finally:\n psys.daemon.release_pidfile(pid_file, pid_file_fd)","title":""},{"docid":"5b45c4529f669ea5e737629e555ae217","score":"0.56844145","text":"def main():\n input_lines = []\n if len(sys.argv) >= 1:\n input_lines = sys.argv[1:]\n if len(input_lines) == 0:\n print(\"\"\"Usage: - python bf.py \"<YOUR BRAINFUCK PROGRAM>\" to run a program\n - python --help to see a detailed help\"\"\")\n elif input_lines[0] == '--help' or input_lines[0] == '-h':\n help(main)\n return\n else:\n run(input_lines)\n return","title":""},{"docid":"62a23c40484f51bc45ca9a2ecd817b7e","score":"0.56780726","text":"def main():\n print('Importing scrapers')\n scrapers = scraper.import_scrapers()\n print('Caculating available episodes')\n avail_episode = database.fetch_available_episodes()\n if len(avail_episode) == 0:\n print('No episode is available at present')\n return\n print('These episodes are available:')\n for ep in avail_episode:\n print('{} of {}'.format(ep['ep'], ep['name']))\n print('Download starts:')\n for ep in avail_episode:\n print('Ep.{} of {} is processing'.format(ep['ep'], ep['name']))\n for __scraper in scrapers:\n try:\n url = __scraper.get_download_url(**ep)\n path = downloader.download(url=url,\n save_path=configure.TORRENT_SAVE_PATH,\n **ep)\n if configure.ENABLE_AUTO_DOWNLOAD:\n if not utorrent.is_token_initialized():\n print('Refreshing token')\n utorrent.refresh_token()\n print('Importing torrent into utorrent.')\n utorrent.add_torrent(path, ep['folder'])\n print('Import completed successfully.')\n database.set_downloaded_episode(ep['name'], ep['ep'])\n break\n except FileNotFoundError:\n print('Scraper cannot find the file')\n # If last scraper is used\n if __scraper is scrapers[-1]:\n print('File cannot be found in all scraper. Try next time.')","title":""},{"docid":"1dfafbf218933613930fae0c1b52e0da","score":"0.5674581","text":"def main():\n if not exists('config.json'):\n shutil.copy('default_config.json', 'config.json')\n print(\"Created 'config.json'. Please configure this file first.\")\n return\n\n # Load configuration of the project.\n Config.load()\n\n config = Config.get_config()\n step = config['step']\n\n if step == 'PI':\n print(\"Personality Insights analysis started..\")\n process = PersonalityInsights(config)\n elif step == 'LIWC':\n print(\"LIWC analysis started..\")\n process = LIWC(config)\n elif step == 'Preprocess':\n print(\"Preprocessing started..\")\n process = Preprocessing(config)\n elif step == 'QuickLIWC':\n print(\"Started LIWC process with pre-loaded LIWC files..\")\n process = LIWCQuick(config)\n else:\n print(\"Unknown step specified in configuration.\\nProgram terminated.\")\n return\n process.execute()","title":""},{"docid":"4615310e119d88a4053084e7ead92eaf","score":"0.5674094","text":"def main():\n logging.basicConfig(level=logging.INFO, format='%(levelname)-8s: %(message)s')\n parser = argparse.ArgumentParser(description=__doc__.strip())\n\n parser.add_argument('portfolio',\n help=('A CSV file which contains the tickers of assets and '\n 'number of units'))\n parser.add_argument('--dbdir', default=database.DEFAULT_DIR,\n help=\"Database directory to write all the downloaded files.\")\n parser.add_argument('-i', '--ignore-missing-issuer', action='store_true',\n help=\"Ignore positions where the issuer implementation is missing\")\n parser.add_argument('-o', '--ignore-options', action='store_true',\n help=(\"Ignore options positions \"\n \"(only works with Beancount export file)\"))\n\n parser.add_argument('--visible', action='store_true',\n help=\"Run with a visible browser window (not headless).\")\n parser.add_argument('-b', '--driver-exec', action='store',\n default=\"/usr/local/bin/chromedriver\",\n help=\"Path to chromedriver executable.\")\n args = parser.parse_args()\n db = database.Database(args.dbdir)\n\n # Load up the list of assets from the exported Beancount file.\n assets = beansupport.read_portfolio(args.portfolio, args.ignore_options)\n\n # Fetch baskets for each of those.\n driver = None\n for row in sorted(assets):\n if not row.issuer and args.ignore_missing_issuer:\n logging.warning(\"Ignoring missing issuer for {}\".format(row.ticker))\n continue\n try:\n driver, _ = fetch_holdings(row.ticker, row.issuer, driver, db,\n args.ignore_missing_issuer, args)\n except Exception:\n traceback.print_exc()\n continue\n if driver:\n driver.close()","title":""},{"docid":"235a326da6160ec7324f72f027fe0409","score":"0.5662144","text":"def mainProgram(self):","title":""},{"docid":"40c55bcfa6634cb4b41550de3b6516ad","score":"0.56590545","text":"def main(argv):\n # parse command line\n for p in sys.argv[1:]:\n pair = p.split('=')\n if (2 != len(pair)):\n print 'bad parameter: %s' % p\n break\n else:\n parameters[pair[0]] = pair[1]\n else:\n # set up logging\n bname = os.path.splitext( os.path.basename(__file__) )[0]\n log_config_file = os.path.join('/home/pims/dev/programs/python/pims/files', bname + '_log.conf')\n logging.config.fileConfig(log_config_file)\n log = logging.getLogger('inputs')\n if params_ok(log):\n try:\n log = logging.getLogger('process')\n backfill_ossbtmf_roadmaps(parameters['date_range'], parameters['batch_dir'], log)\n except Exception, e:\n # Log error\n log.process.error( e.message )\n return -1\n # Message with time when done\n log.debug('Done %s.\\n' % datetime.datetime.now() + '-'*99)\n return 0\n print_usage()","title":""},{"docid":"87cf60d0cd2f13d4b71927be5ba1083f","score":"0.5648502","text":"def main(self):\n try:\n self.path_runs.set_execution_directory()\n\n self.sequence = create_local_fasta_file(\n self.path_runs.get_path_execution(),\n self.opts.fromFasta,\n self.opts.inputFasta,\n self.opts.toolname,\n self.framework)\n\n copy_necessary_files(\n self.path_runs.get_path_execute(),\n self.path_runs.get_path_execution(),\n self.framework.get_framework())\n\n self.framework.set_command(\n self.path_runs.get_path_execution(),\n 'aemt-pop-up2')\n\n size = int(self.opts.sizePopulation) / 15\n\n cl = [\n self.framework.get_command(),\n str(0),\n self.opts.sizePopulation,\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(size),\n str(0),\n str(0),\n self.opts.VanderWaalsWeight,\n self.opts.ChargeWeight,\n str(0),\n str(0),\n str(0),\n str(0),\n str(0),\n self.opts.SolvWeight,\n self.opts.HbondWeight,\n os.path.join(self.path_runs.get_path_execution(), \"fasta.txt\"),\n os.path.join(self.path_runs.get_path_execution(), \"result.txt\"),\n os.path.join(self.path_runs.get_path_execution(), \"pop_meamt.txt\"),\n os.path.join(self.path_runs.get_path_execution(), \"protein.pdb\"),\n os.path.join(self.path_runs.get_path_execution(), \"saida1.txt\"),\n os.path.join(self.path_runs.get_path_execution(), \"angles.txt\"),\n str(0),\n os.path.join(self.path_runs.get_path_execution(), \"meat.txt\"),\n '&']\n\n retProcess = subprocess.Popen(cl, 0, None, None, None, False)\n retProcess.wait()\n\n path_output, file_output = os.path.split(self.opts.output)\n\n result, html = get_result_files(\n self.path_runs.get_path_execution(), self.opts.toolname)\n\n send_output_results(path_output, file_output, result)\n\n except Exception, e:\n show_error_message(str(e))","title":""},{"docid":"93c5b87b0e85d0b61b6f4155ebeb07d5","score":"0.56413895","text":"def main():\r\n print(\"Program started...\")","title":""},{"docid":"9fa8c9fbb1ee16b09824d86355e708ee","score":"0.56383514","text":"def main():\n # Check parameters\n parameters = args_check(sys.argv[1:])\n print(\"\\nExtracting PB sequences from trajectory.\\n\")\n # Extraction of PB sequences with pbxplore\n table_seq = mk_table_seq(parameters[1], parameters[2])\n print(\"\\nComputing frequences of PB (This may take a few minutes)\\n\")\n # Computation of frequencies\n table_pos_freq = mk_table_pos_freq(table_seq)\n table_dbpos_freq = mk_table_dbpos_freq(table_seq)\n print(\"Computing Mutual Information (This may take a few minutes)\\n\")\n # Computation of Mutual Information\n table_mi = mk_mi_table(table_pos_freq, table_dbpos_freq)\n mi_heatmap(table_mi, parameters[0])\n # Write results in output directory\n table_seq.to_csv(parameters[0]+\"PB_seq_table.csv\")\n table_pos_freq.to_csv(parameters[0]+\"PB_pos_freq_table.csv\")\n table_dbpos_freq.to_csv(parameters[0]+\"PB_dbpos_freq_table.csv\")\n table_mi.to_csv(parameters[0]+\"MI_table.csv\")\n print(\"Done!\\n\")\n sys.exit()","title":""},{"docid":"0387ba897ed2be3b307b469f53cb07ea","score":"0.56347436","text":"def main():\n # Parse script parameters\n parser = setup_arg_parser()\n args, _ = parser.parse_known_args()\n\n # Select a queue to add\n selected_queue = select_queue(args)\n\n # Install the selected printer\n if selected_queue:\n install_printer(selected_queue)","title":""},{"docid":"485707c004fecd46b86fd5d4f8a2ecd2","score":"0.5624613","text":"def main():\n ''' Parsing arguments from command line , there are two arguments\n a) directory : must be a directory like example c:\\\\\n b) interval : the interval must be the number of seconds\n\n example:\n python client.py --directory=\"C:\\Users\\UserName\\OpenDrop\" --interval=2\n '''\n (options, arguments) = getParse()\n\n if not validateArguments(options.directory, options.interval):\n raise ClientException(\"Invalid Arguments\")\n\n ''' Creating multiprocess elements '''\n mutex = multiprocessing.Lock()\n queue = multiprocessing.Queue()\n OpenDrop = Client(cwd=options.directory, mutex=mutex)\n interval = 1\n ''' moving all solutions to multiple processes '''\n\n message = \"\"\" ================ Welcome to OpenDrop Client ================= \\n \\\n Monitoring Folder: {0} :\"\"\".format(OpenDrop.working_directory)\n\n ''' Preparing all processes '''\n scan = multiprocessing.Process(target=OpenDrop.run, args=(queue, options.interval,))\n saver = multiprocessing.Process(target=OpenDrop.saveData, args=(queue,))\n scan.start()\n saver.start()\n \n print message\n while True:\n raw_input(\"Press enter \")\n with mutex:\n out = raw_input(\"Press q for quit : \")\n if out == 'q':\n scan.terminate()\n saver.terminate()\n return","title":""},{"docid":"997afa9348ae9bcf5a419bb07122f911","score":"0.5624492","text":"def main(self):\n\n log.divider(\"setting up\", char=\"=\")\n\n self.require_server_connection()\n\n self.finish_parameters()\n\n if self.args.certify_files:\n self.certify_local_files()\n\n if self.args.logout:\n return self.logout()\n\n self.login()\n\n if self.args.wipe_existing_files:\n self.wipe_files()\n\n self.jpoll_key = self.jpoll_open_channel()\n\n if self.args.submission_kind == \"batch\":\n submit_future = self.batch_submit_references()\n elif self.args.submission_kind == \"certify\":\n submit_future = self.certify_files()\n elif self.args.submission_kind == \"references\":\n submit_future = self.submit_references()\n elif self.args.submission_kind == \"mappings\":\n submit_future = self.submit_mappings()\n\n if self.args.monitor_processing:\n monitor_future = self.monitor()\n\n if self.args.wait_for_completion:\n self.submission_complete(submit_future)\n\n if self.args.monitor_processing:\n monitor = self.monitor_complete(monitor_future)\n if monitor.exit_status == 0:\n self._ready_url = monitor.result\n self._results_id = self._ready_url.split('/')[-1]\n\n log.standard_status()\n\n self._error_count = log.errors()\n self._warning_count = log.warnings()\n\n if self.auto_confirm is True:\n if self._error_count == 0:\n self._confirmation = self.confirm()\n else:\n self.cancel()\n\n self.get_file_map()\n\n return log.errors()","title":""},{"docid":"dda81ec024f31537cf8b4c9ded449a3e","score":"0.5622697","text":"def main():\n\n exit_status = ExitStatus.OK\n try:\n args = parser.parse_args()\n #print(args)\n if args.save:\n save_args(args.save)\n\n if not os.path.exists(args.directory):\n os.makedirs(args.directory)\n\n names = name_generator(\n args.format, type=args.payload.short, rand=args.rand_method.name,\n idx_format='{{0:0{}d}}'.format(len(str(args.number))))\n\n for i in range(args.number):\n fn = os.path.join(args.directory, next(names))\n with open(fn, 'wb+') as f:\n payload = args.payload.generate(args.rand_method, *args.chunk_size)\n for p in payload:\n f.write(p)\n\n except IOError as (errno, strerror):\n print(\"I/O error({0}): {1}\".format(errno, strerror))\n exit_status = ExitStatus.ERROR\n except OSError as (errno, strerror):\n print(\"OS error({0}): {1}\".format(errno, strerror))\n exit_status = ExitStatus.ERROR\n #except:\n # print \"Unexpected error:\", sys.exc_info()\n # exit_status = ExitStatus.ERROR\n\n return exit_status","title":""},{"docid":"0e57a5230df7191a7b8071341c7786fd","score":"0.56152314","text":"def main(self):\n parser = self.setup_parser()\n args = parser.parse_args()\n self.parse(args)\n sys.exit(self.run(args))","title":""},{"docid":"8dcadd0ddf3d2fbea99b29ceca83fa9c","score":"0.5609144","text":"def main():\n print(\"starting boids...\")\n\n parser = argparse.ArgumentParser(\n description=\"Implementing Craig Reynolds Bpid...\")\n parser.add_argument('--num_boids', dest='n', required=False)\n args = parser.parse_args()\n\n # Set the initial number of boids\n n = 100\n if args.n:\n n = int(args.n)\n\n # Create boids\n boids = Boids(n)\n\n # Set up plot\n fig = plt.figure()\n ax = plt.axes(xlim=(0, width), ylim=(0, height))\n\n # size and shape of the markers for body\n pts, = ax.plot([], [], markersize=10, c='k', marker='o',\n ls='None') # Create empty 2D Line object\n # size and shape of the markers for beak\n beak, = ax.plot([], [], markersize=4, c='r', marker='o',\n ls='None') # Create empty 2D Line object\n anim = animation.FuncAnimation(\n fig, tick, fargs=(pts, beak, boids), interval=50)\n\n # Add a 'button_press' event handler\n cid = fig.canvas.mpl_connect(\n 'button_press_event', boids.button_press) # mouse button is pressed\n\n plt.show()","title":""},{"docid":"c8f3ba8e57c28b08321b6211fdfc48a4","score":"0.5593138","text":"def main():\n\n print('Welcome to the self-checkout system of Wake-mart.')\n\n num_items, total_cost = scanPrices()\n\n total_cost = discount(num_items, total_cost)\n\n num_items, total_cost = promotion(num_items, total_cost)\n\n makePayment(total_cost)","title":""},{"docid":"b03dd98ffaa0f65c5b02397cd192b6d4","score":"0.5572765","text":"def main():\n logger.info(\"Started script.\")\n pw = PriceWatch()\n\n if pw.config[\"general\"][\"send_test_notification\"] == \"1\":\n logger.info(\"Sending test notification and exiting.\")\n notify.test_notification()\n sys.exit()\n elif (\n pw.config[\"general\"][\"wishlist_url\"]\n == \"https://www.amazon.co.uk/hz/wishlist/ls/S0M3C0D3\"\n ):\n config_file_path = Path(Path(__file__).parent, \"config.json\").resolve()\n logger.error(f\"You need to fill in the config file:\\n{config_file_path}\")\n sys.exit()\n\n page = pw.request_page()\n # In parsing the first page, pagination will be followed and requested/parsed.\n pw.parse_wishlist(page)\n new_cheaper_items = pw.compare_prices()\n if new_cheaper_items:\n notify.send_notification(wishlist_item_list=new_cheaper_items)\n\n pw.json_man.save_wishlist_json(pw.wishlist)\n logger.info(\"Finished.\")","title":""},{"docid":"b428fffb548aeea009928beeb8cc0f65","score":"0.55690086","text":"def main():\n print('Welcome to the Brain Games!')\n name = cli.welcome_user()\n print('What number is missing in the progression?')\n check = arifm.game()\n if check:\n print('Congratulations, {}!'.format(name))\n else:\n print(\"Let's try again, {}!\".format(name))","title":""},{"docid":"4ac8d3430daaa37d2cc1a3d161f21f40","score":"0.55647093","text":"def main():\n\n\tparser = OptionParser()\n\tparser.add_option(\"-p\", dest=\"pdbfile\", help=\"pdbfile\")\n\tparser.add_option(\"-P\", dest=\"pdblist\", help=\"pdblist\")\n\tparser.add_option(\"-s\", dest=\"statefile\", help=\"statefile\")\n\tparser.set_description(main.__doc__)\n\t(options, args) = parser.parse_args()\n\n\tpdbfiles = []\n\tif options.pdblist:\n\t\ttry:\n\t\t\tPDBLIST = open(options.pdblist)\n\t\texcept:\n\t\t\tprint \"unable to open pdblist\",options.pdblist\n\t\t\tsys.exit()\n\n\t\tfor line in PDBLIST.readlines():\n\t\t\tline = string.strip(line)\n\t\t\tpdbfiles.append(line)\t\n\n\telif options.pdbfile:\n\t\tpdbfiles.append(options.pdbfile)\n\telse:\n\t\tparser.print_help()\n\t\tsys.exit()\n\n\tif not options.statefile:\n\t\tparser.print_help()\n\n\n\tgridlig = grid()\n\tgridbb = grid()\n\n\ttry:\n\t\tSTATEFILE = open(options.statefile)\n\texcept:\n\t\tprint \"unable to open statefile\"\n\t\tsys.exit()\n\n\tfor line in STATEFILE.readlines():\n\t\tline = string.strip(line)\n\t\twords = string.split(line)\n\t\t\n\t\tif \"gridlig\" in line:\n\t\t\tgridlig.read(words[1])\n\n\t\tif \"gridbb\" in line:\n\t\t\tgridbb.read(words[1])\n\t\t\t\n\t\t\n\tprotein = Molecule()\n\tfor pdbfile in pdbfiles:\n\t\tprotein.clear()\n\t\tprotein.readPDB(pdbfile)\n\n\t\tfailed = False\n\t\tfor chain in protein.chain:\n\t\t\tfor residue in chain.residue:\n\t\t\t\tfor atom in residue.atom:\n\t\t\t\t\t\n\t\t\t\t\tif atom.kind == \"HETATM\":\n\t\t\t\t\t\tif atom.name[0] == \"V\":\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tx = atom.coord.x\n\t\t\t\t\t\ty = atom.coord.y\n\t\t\t\t\t\tz = atom.coord.z\n\t\t\t\t\t\tocc = atom.occupancy\n\t\t\n\t\t\t\t\t\tif occ > 0.0:\n\t\t\t\t\t\t\tif not gridlig.isInGrid(x, y, z):\n\t\t\t\t\t\t\t\tprint pdbfile,atom.name,atom.file_id,\"out of gridlig boundaries\"\n\t\t\t\t\t\t\t\tfailed = True\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tzn = gridlig.getZone(x,y,z)\t\n\t\t\t\t\t\t\tif not gridlig.zone[zn[0]][zn[1]][zn[2]]:\n\t\t\t\t\t\t\t\tprint pdbfile,atom.name,atom.file_id,\"not occupied in gridlig\"\n\t\t\t\t\t\t\t\tfailed = True\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tif not gridbb.isInGrid(x,y,z):\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tzn = gridbb.getZone(x,y,z)\n\t\t\t\t\t\tif gridbb.zone[zn[0]][zn[1]][zn[2]]:\n\t\t\t\t\t\t\tprint pdbfile,atom.name,atom.file_id,\"clashes with backbone\"\n\t\t\t\t\t\t\tfailed = True\n\t\t\t\t\t\t\tcontinue\n\n\t\tif not failed:\n\t\t\tprint pdbfile,\".... passes\"","title":""},{"docid":"42a648141e2419a95833a8034d78c490","score":"0.55433804","text":"def main() -> NoReturn:\n version = bobber_version()\n args = parse_args(version)\n execute_command(args, version)","title":""},{"docid":"f0a37186d057d3b988d1825e86e3f5e0","score":"0.5538347","text":"def main():\n try:\n logging.info('Initializing')\n\n #Get environmental values from transmission\n #Path should be $TR_TORRENT_DIR/$TR_TORRENT_NAME\n\n TORRENT_DIR, TORRENT_NAME = config.get_environmental_variables_from_transmission()\n\n extract_all(TORRENT_DIR)\n _choose_handler(TORRENT_DIR, TORRENT_NAME)\n _cleanup_temp(TORRENT_DIR)\n logging.info('Done!')\n except:\n logging.exception(\"Critical exception occurred: \")\n raise","title":""},{"docid":"79a6ce4166f71ba4a23231df630e7cd2","score":"0.5537185","text":"def main():\n application = BoaApp(0)\n application.MainLoop()","title":""},{"docid":"321fbf81842237360c4e1794541c3a6b","score":"0.5532704","text":"def main():\n _setup_logging()\n song_winners.workflow()\n spotify_songs.workflow()\n votes.workflow()","title":""},{"docid":"3ee74120d247a5df731794fa3dec5cc3","score":"0.55205685","text":"def run(self):\n my_bulb = BulbBlinker( my_id = self.id,\n bpm = self.bpm, \n host = self.host,\n adjustment = self.adjustment,\n bulb_objects_list = self.bulb_objects_list, \n above_neighbor = (self.id + 1) % 13, \n below_neighbor = (self.id - 1) % 13, \n turned_on_list = self.turned_on_list)\n my_bulb.daemon = True \n my_bulb.start()\n self.check_ordering()","title":""},{"docid":"2e2c9b9f73f8fdca22f4d25a4b07dc60","score":"0.5520292","text":"def main():\n\n\tusage = \"usage: %prog <project_id> <file>\"\n\tdescription = \"%prog imports dirb files into Lair\"\n\tparser = OptionParser(usage=usage, description=description,\n\t\t\t\t\t\t\tversion=\"%prog 0.0.1\")\n\n\t(options, args) = parser.parse_args()\n\tif len(args) < 2:\n\t\tprint parser.get_usage()\n\t\tsys.exit(1)\n\n\tproject_id, result_resource = args\n\tproject = dirb.parse(project_id, result_resource)\n\n\t# Connect to the database\n\tdb = api.db_connect()\n\tapi.save(project, db, dirb.TOOL)\n\tsys.exit(0)","title":""},{"docid":"4314c1408e37cf60dcd240257ff3a682","score":"0.5519621","text":"def main():\n args = get_args()\n setup_logs(args, args.out_prefix)\n\n\n # Parse Brenda Ligands file\n logging.info(\"Starting to parse ligands\")\n brenda_ligands_file = f\"{args.out_prefix}_brenda_ligands.json\"\n brenda_ligands = parse_ligands_file(brenda_ligands_file,\n args.brenda_ligands, args.load_prev,\n args.debug)\n logging.info(\"Done parsing ligands\")\n\n # Parse brenda ec classes and enzymes\n ec_stats_file = f\"{args.out_prefix}_brenda_ec_stats.json\"\n enzymes_data_file = f\"{args.out_prefix}_brenda_enzymes_data.json\"\n\n # Parse brenda flat file\n # Ec_stats contains enzyme class wide parameters\n # enzymes_data contains actual enzymes\n logging.info(\"Starting to parse flat file\")\n ec_stats, enzymes_data = parse_flat_file(ec_stats_file, \n enzymes_data_file, \n args.brenda_flat_file,\n args.load_prev,\n args.out_prefix,\n args.debug)\n logging.info(\"Done parsing flat file\")\n\n # Get the list of compounds from enzymes data \n logging.info(\"Starting to extract compound list\")\n compounds_list_file = f\"{args.out_prefix}_compound_list.json\"\n compound_list = get_compound_list(enzymes_data, compounds_list_file, \n args.load_prev)\n logging.info(\"Done extracting compound list\")\n\n logging.info(\"Starting to extract inchi/chebi\")\n chebi_inchi_set_file = f\"{args.out_prefix}_chebi_inchi_set.json\"\n chebi_inchi_set = extract_unique_inchi_chebi(chebi_inchi_set_file, \n brenda_ligands,\n args.load_prev)\n logging.info(\"Done extracting inchi/chebi\")\n\n logging.info(\"Starting to map chebi to smiles\")\n # Map brenda ligand chebi to smiles\n mapped_chebi_file = f\"{args.out_prefix}_chebi_to_smiles.json\"\n unmapped_chebi_file = f\"{args.out_prefix}_chebi_unmapped.json\"\n mapped_chebi, unmapped_chebi = map_chebi_to_smiles(chebi_inchi_set, \n mapped_chebi_file,\n unmapped_chebi_file, \n args.load_prev,\n args.out_prefix,\n args.use_cirpy,\n args.cirpy_log)\n logging.info(\"Done mapping chebi to smiles\")\n\n logging.info(\"Starting to map inchi to smiles\")\n # Map brenda ligand inchi to smiles\n mapped_inchi_file = f\"{args.out_prefix}_inchi_to_smiles.json\"\n unmapped_inchi_file = f\"{args.out_prefix}_inchi_unmapped.json\"\n mapped_inchi, unmapped_inchi = map_inchi_to_smiles(chebi_inchi_set,\n mapped_inchi_file,\n unmapped_inchi_file,\n args.load_prev,\n args.out_prefix,\n args.use_cirpy,\n args.cirpy_log)\n logging.info(\"Done mapping inchi to smiles\")\n\n logging.info(\"Starting to resolve all compounds to smiles\")\n # Now resolve all compounds\n mapped_comps_file = f\"{args.out_prefix}_compounds_to_smiles.json\"\n unmapped_comps_file = f\"{args.out_prefix}_compounds_unmapped.json\"\n\n # TODO: Fix this so that it doesn't return strings and lists, but only one\n # of the two types \n mapped_compounds, unmapped_compounds = map_compounds_to_smiles(compound_list, brenda_ligands, \n mapped_inchi, mapped_chebi,\n mapped_comps_file, unmapped_comps_file,\n args.load_prev, args.out_prefix,\n args.use_cirpy, args.cirpy_log, \n args.opsin_loc) \n logging.info(\"Done resolving all compounds to smiles\")\n\n\n # Standardize all smiles mappings!\n # Load from file if it exists\n if not args.no_standardize:\n logging.info(\"Starting standardizer\")\n mapped_standardized_file = f\"{args.out_prefix}_compounds_to_standardized_smiles.json\"\n mapped_compounds = standardize_smiles(mapped_standardized_file, mapped_compounds,\n args.standardizer_log, args.load_prev, \n args.multiprocess_num)\n logging.info(\"Done with standardizer\")\n\n\n logging.info(\"Beginning to export all files\")\n # Reverse the mapping for reference later \n smiles_to_names_file = f\"{args.out_prefix}_smiles_to_comp_names.json\"\n smiles_to_names = reverse_mapping(mapped_compounds, \n outfile = smiles_to_names_file)\n\n # Add gene sequences and compound smiles back to the dataframe.\n enzymes_data, rxn_set, compound_set = add_compounds(\n enzymes_data, mapped_compounds)\n rxn_set, compound_set = pd.DataFrame(rxn_set), pd.DataFrame(compound_set)\n\n # Ouput finished mapping to file\n enzymes_mapped_data_file = f\"{args.out_prefix}_enzymes_data_complete_mapped.json\"\n rxn_final_tsv = f\"{args.out_prefix}_rxn_final.tsv\"\n compounds_final_tsv = f\"{args.out_prefix}_rxn_compounds.tsv\"\n utils.dump_json(enzymes_data,\n enzymes_mapped_data_file,\n pretty_print=False)\n\n compound_set.to_csv(compounds_final_tsv, sep=\"\\t\")\n rxn_set.to_csv(rxn_final_tsv, sep=\"\\t\")\n\n # Output statistics about all the data collected\n summary_file = f\"{args.out_prefix}_stats.json\"\n stats_summary = {\n \"mapped_compound_smiles\": len(mapped_compounds),\n \"unmapped_compound_smiles\": len(unmapped_compounds),\n \"mapped_inchis\": len(mapped_inchi),\n \"unmapped_inchis\": len(unmapped_inchi),\n \"mapped_chebi\": len(mapped_chebi),\n \"unmapped_chebi\": len(unmapped_chebi),\n }\n\n stats_summary[\"Num ec classes\"] = len(ec_stats)\n\n # Update with other statistics and print this to a file\n stats_summary.update(\n parse_brenda_stats.get_rxn_compound_stats(rxn_set, compound_set)\n )\n utils.dump_json({k: str(v) for k, v in stats_summary.items()},\n summary_file)","title":""},{"docid":"161425c07e5d026e7b708d9a378b2a0a","score":"0.5518024","text":"def test_launch_bs(run_cli_process_launch_command, fixture_code, generate_remote_data):\n from aiida_kkr.cmdline.launch import launch_bs\n from aiida.engine.processes.calcjobs.tasks import PreSubmitException\n code = fixture_code('kkr.kkr').store()\n params = kkrparams(params_type='kkr')\n params.set_multiple_values(\n MIN=-10,\n MAX=5,\n NPT2=12,\n RCLUSTZ=2.3,\n TEMPR=50,\n )\n\n param_node = Dict(dict=params.get_dict()).store()\n path = os.path.abspath(os.path.join(THISFILE_PATH, '../../files/bd_dump_bs/parent_kkr'))\n remote = generate_remote_data(code.computer, path).store()\n options = ['--kkr', code.uuid, '--parameters', param_node.uuid, '--parent-folder', remote.uuid]\n run_cli_process_launch_command(launch_bs, options=options, raises=PreSubmitException)","title":""},{"docid":"9e72155a4c14785f4d6f7a5c7a836693","score":"0.5516675","text":"def main():\n args = get_cmdline_arguments()\n bord_config = configreader.ConfigReader(args.config)\n count = create_html(bord_config['content_dir'], bord_config['output_dir'])\n print ('Created', count, 'HTML files')\n if (args.server):\n print ('Serving at:', str(args.server))\n server.start_server(bord_config['output_dir'], args.server)","title":""},{"docid":"f0a8b95fc16c26f3db613753c0b17b2d","score":"0.5509966","text":"def main():\n options = Options()\n app = Trader(options)\n x = 0\n while x < 1:\n # app.call_ticker()\n test = app.balances()\n # app()\n # app.trade_history()\n # app.buy(amount=1, rate=.001, coin='BTC_ETH')\n # app.sell(amount=8, rate=.00000130, coin='BTC_DOGE')\n # order = app.buy()\n\n # if app.cancel(order):\n # print(\"Order {}.. !! canceled\".format(order))\n # time.sleep(.05)\n #\n # history = app.quote_history()\n print(test)\n x += 1\n\n if app.main():\n sys.exit('Alphagriffin.com | 2017')\n return True","title":""},{"docid":"9849beb2becdf68e2bf5a55ba6c8e058","score":"0.5497161","text":"def main():\n\n BASIC.run(PROGRAM)","title":""},{"docid":"0bd1ee4e3a3c53c0b332a70d92705c3a","score":"0.549565","text":"def run_program():\n\n if not aig.are_parameters_valid():\n raise Exception(\"You must provide the name of input file as first argument!\")\n\n graph = aig.parse_input(sys.argv[1])\n path = read_path()\n\n print(compute_path_cost(path, graph))","title":""},{"docid":"918c5dd264aa819f502eccd9910855ed","score":"0.549286","text":"def main():\n epilog = \"DT179G Assignment 3 v\" + __version__\n parser = argparse.ArgumentParser(description=__desc__, epilog=epilog, add_help=True)\n parser.add_argument('nth', metavar='nth', type=int, nargs='?', default=30,\n help=\"nth Fibonacci sequence to find.\")\n\n global LOGGER # ignore warnings raised from linters, such as PyLint!\n LOGGER = create_logger()\n\n args = parser.parse_args()\n nth_value = args.nth # nth value to sequence. Will fallback on default value!\n\n fib_details = { # store measurement information in a dictionary\n 'fib iteration': fibonacci_iterative(nth_value),\n 'fib recursion': fibonacci_recursive(nth_value),\n 'fib memory': fibonacci_memory(nth_value)\n }\n\n print_statistics(fib_details, nth_value) # print information in console\n\n write_to_file(fib_details) # write data files","title":""},{"docid":"0a84589821cca136350f458b3fcfd575","score":"0.54918534","text":"def main():\n\targs = get_args()\n\tbottles = args.num_bottles\n\n\tfor bottles in range(bottles,0,-1): # -1 decrease by 1 each time \n\t\tif bottles > 2:\n\t\t\tprint('{} bottles of beer on the wall,'.format(bottles))\n\t\t\tprint('{} bottles of beer,'.format(bottles))\n\t\t\tprint('Take one down, pass it around,')\n\t\t\tprint('{} bottles of beer on the wall!'.format(bottles-1)+'\\n')\n\t\tif bottles ==2:\n\t\t\tprint('{} bottles of beer on the wall,'.format(bottles))\n\t\t\tprint('{} bottles of beer,'.format(bottles))\n\t\t\tprint('Take one down, pass it around,')\n\t\t\tprint('{} bottle of beer on the wall!'.format(bottles-1)+'\\n') # proper grammer for 1 bottle\n\t\tif bottles == 1:\n\t\t\tprint('{} bottle of beer on the wall,'.format(bottles)) # proper grammer for 1 bottle\n\t\t\tprint('{} bottle of beer,'.format(bottles)) # proper grammer for 1 bottle\n\t\t\tprint('Take one down, pass it around,')\n\t\t\tprint('{} bottles of beer on the wall!'.format(bottles-1)) # 0 bottles is proper grammer\n\t\t\tbreak","title":""},{"docid":"483978573f788bb33960325a7b7f12f3","score":"0.5483947","text":"def main():\n args = parse_arguments()\n if args.sub == 'byname':\n request_result(punk_requests.get_beer_by_name(args.name), args.file)\n elif args.sub == 'byid':\n request_result(punk_requests.get_beer_by_id(args.id), args.file)\n elif args.sub == 'byinterval':\n request_result(punk_requests.get_beer_brewed_in(args.from_date, args.until_date), args.file)\n elif args.sub == 'byfood':\n request_result(punk_requests.get_beer_by_food(args.food_pairs), args.file)","title":""},{"docid":"ea842d0c4094cc8f0b450f6c16ebefc3","score":"0.547629","text":"def main(argv):\n keystore, signing_key, verify_key_hex, networker, blockchain_thread, gui_thread, dns = init(*parse_args(argv))\n\n # User Interaction\n while True:\n command = None\n if gui_thread.is_alive():\n if not gui_receive_queue.empty():\n command = gui_receive_queue.get(block=True)\n print('Command from gui: {}'.format(command))\n else:\n continue\n\n if not command:\n print('Action: ')\n command = get_command()\n\n if command == 'help':\n help_str = (\"\"\" Available commands:\n help: prints commands\n transaction <to> <amount> : Create transaction\n mine: mine a new block\n balance [<name>]: Print balance (name optional)\n dump: print blockchain\n peers: print peers\n key <filename> : Save current key to <filename>\n import <key> <name> : Imports a public key associated with \\\n<name> from file <file> to the keystore\n deletekey <name> : Deletes key associated with <name> from\\\nkeystore\n export <filename> : Exports one own public key to file\\\n<filename>\n save: Save blockchain to bc_file.txt\n exit: exits program\n \"\"\")\n print(help_str)\n if dns:\n dns_str = (\"\"\" DNS-only commands:\n register <domain> <ip> : Registers an available domain to an ip (costs 20 coins)\n update <domain> <ip> : Updates an existing already owned domain with a new ip (costs 20 coins)\n transfer <to> <domain> : Transfers an owned domain to another user (costs 1 coin)\n auction <domain> : Offers an owned domain for auction (costs 1 coin)\n bid <amount> <domain> : Places a bid of <amount> on the auctioned domain.\n resolve <domain> : Resolves the domain name and prints the ip (if the domain does not exist, prints '')\n \"\"\")\n print(dns_str)\n gui_send_queue.put(help_str)\n elif command == 'exit':\n receive_queue.put(('exit', '', 'local'))\n send_queue.put(None)\n keystore.save()\n blockchain_thread.join()\n networker.join()\n if gui_thread.is_alive():\n gui_thread.join()\n sys.exit()\n elif command == 'mine':\n receive_queue.put(('mine', verify_key_hex, 'local'))\n elif re.fullmatch(r'transaction \\w+ \\d+', command):\n t = command.split(' ')\n # Create new Transaction, sender = hex(public_key),\n # signature = signed hash of the transaction\n if int(t[2]) <= 0:\n print('Transactions must contain a amount greater than zero!')\n continue\n recipient = keystore.resolve_name(t[1])\n if recipient == 'Error':\n continue\n timestamp = time.time()\n # fee equals 5% of the transaction amount - at least 1\n fee = int(math.ceil(int(t[2]) * 0.05))\n transaction = create_transaction(dns, verify_key_hex, str(recipient),\n int(t[2]), fee, timestamp, DNS_Data('', '', ''), signing_key)\n\n receive_queue.put(('new_transaction',\n transaction,\n 'local'\n ))\n elif command == 'dump':\n # gui_send_queue.put(vars(my_blockchain))\n # pprint(vars(my_blockchain))\n receive_queue.put(('dump', '', 'local'))\n elif command == 'peers':\n networker_command_queue.put('print_peers')\n elif re.fullmatch(r'key \\w+', command):\n try:\n t = command.split(' ')\n save_key(signing_key, t[1])\n print('Key saved successfully')\n except Exception as e:\n print('Could not save key')\n print(e)\n elif re.fullmatch(r'import \\w+ \\w+', command):\n try:\n t = command.split(' ')\n if keystore.resolve_name(t[2]):\n keystore.update_key(t[2], t[1])\n else:\n print('importing public key')\n keystore.add_key(t[2], t[1])\n except Exception as e:\n print('Could not import key')\n print(e)\n elif re.fullmatch(r'deletekey \\w+', command):\n try:\n t = command.split(' ')\n if keystore.resolve_name(t[1]):\n keystore.update_key(t[1], '')\n else:\n print(\n f'Could not delete {t[1]} from keystore.',\n ' Was it spelt right?')\n except Exception as e:\n print('Could not delete key')\n print(e)\n elif re.fullmatch(r'export \\w+', command):\n try:\n print('Exporting public key')\n t = command.split(' ')\n save_key(verify_key_hex, t[1])\n except Exception as e:\n print('Could not export public key')\n print(e)\n elif command == 'balance':\n receive_queue.put(('print_balance',\n (verify_key_hex, time.time()),\n 'local'))\n elif re.fullmatch(r'balance \\w+', command):\n t = command.split(' ')\n account = keystore.resolve_name(t[1])\n if account != 'Error':\n receive_queue.put(('print_balance',\n (account, time.time()),\n 'local'\n ))\n elif re.fullmatch(r'resolve \\w+\\.\\w+', command):\n if not dns:\n print('Command not supported!')\n continue\n t = command.split(' ')\n receive_queue.put(('dns_lookup',\n t[1],\n 'local'\n ))\n elif re.fullmatch(r'(register|update) \\w+\\.\\w+ \\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}', command):\n if not dns:\n print('Command not supported!')\n continue\n t = command.split(' ')\n if not validate_ip(t[2]):\n print('Not a valid ip')\n continue\n recipient = '0'\n timestamp = time.time()\n # fee for domain registration equals 20\n fee = 20\n typ = t[0][0]\n data = DNS_Data(typ, t[1], t[2])\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\n fee, timestamp, data, signing_key)\n\n receive_queue.put(('new_transaction',\n transaction,\n 'local'\n ))\n elif re.fullmatch(r'transfer \\w+ \\w+\\.\\w+', command):\n if not dns:\n print('Command not supported!')\n continue\n t = command.split(' ')\n account = keystore.resolve_name(t[1])\n if account != 'Error':\n recipient = account\n timestamp = time.time()\n # fee for domain transfer equals 1\n fee = 1\n typ = 't'\n data = DNS_Data(typ, t[2], '')\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\n fee, timestamp, data, signing_key)\n receive_queue.put(('new_transaction',\n transaction,\n 'local'\n ))\n elif re.fullmatch(r'auction \\w+\\.\\w+', command):\n if not dns:\n print('Command not supported!')\n continue\n t = command.split(' ')\n recipient = '0'\n timestamp = time.time()\n # fee for auction equals 1\n fee = 1\n typ = 't'\n data = DNS_Data(typ, t[1], '')\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\n fee, timestamp, data, signing_key)\n\n receive_queue.put(('new_transaction',\n transaction,\n 'local'\n ))\n elif re.fullmatch(r'bid \\d+ \\w+\\.\\w+', command):\n if not dns:\n print('Command not supported!')\n continue\n t = command.split(' ')\n recipient = '0'\n timestamp = time.time()\n # fee for bid equals 1\n fee = 1\n typ = 'b'\n amount = int(t[1])\n data = DNS_Data(typ, t[2], '')\n transaction = create_transaction(dns, verify_key_hex, str(recipient), amount,\n fee, timestamp, data, signing_key)\n receive_queue.put(('new_transaction',\n transaction,\n 'local'\n ))\n elif command == 'save':\n receive_queue.put(('save',\n '',\n 'local'\n ))\n elif command == 'gui':\n # if dns:\n # print('gui not yet supported for DNS Chain')\n # continue\n print(\"open gui\")\n gui_thread.start()\n gui_send_queue.put(('signing_key', signing_key, 'local'))\n\n else:\n print('Command not found!')","title":""},{"docid":"102a5a04bf02a2b75f0d5da52ae8e173","score":"0.5474822","text":"def main(self, argv):","title":""},{"docid":"8fb92df960cc977514bfbcbc6c0c30fc","score":"0.5474742","text":"def main():\n engine.run_game(settings.DESCR_PRIME, prime.ask_question)","title":""},{"docid":"c8122dc64784738c878decc2c605752f","score":"0.546062","text":"def main():\n # create log file first\n create_logs()\n # log start info\n logging.info('Starting new enrollment flow now...')\n logging.info('Waiting for user space to fully load...')\n # waiting for user space\n wait_for_userspace()\n # check if the jss is available\n # commented out, only used for troubleshooting purposes, i.e. remote offices fail to enroll\n #check_jss_connection()\n # set the computer name\n logging.info('setting the computer name...')\n set_computername()\n # ensure dependencies and jamf binary is present\n logging.info('Installing initial dependencies for DEP Notify flow...')\n install_dependencies()\n # install dependency list for before we continue...\n run_jamf_policy(DEPENDENCY_LIST)\n # start DEP Notify flow\n write_to_dnlog('Command: Image: %s' % DEPSCREEN)\n # please modify this code to match the verbiage you want to display\n write_to_dnlog('Command: MainTitle: Welcome to myOrg')\n write_to_dnlog('Command: MainText: Please wait while we setup and configure your Mac at MyOrg '\n 'This should only take a few minutes and will require a restart of your Mac. '\n 'If you need assistance please contact IT at helpdesk@myorg.com')\n write_to_dnlog('Status: Preparing your system...')\n start_dep_notify()\n # install base software/config\n run_jamf_policy(POLICY_LIST)\n # apply security compliance and reboot last\n run_jamf_policy(SECURITY_LIST)\n software_updates()\n write_to_dnlog('Status: Enrollment is complete, exiting...')\n write_to_dnlog('Command: Quit')","title":""},{"docid":"54314d4beffe9402f299840136debdc4","score":"0.54560435","text":"def main():\n pickle = str(sys.argv[3])\n print \"Getting network.... \"\n net = cPickle.load(open(pickle, 'rb'))\n size = get_network_size(net)\n print \"Network size = %s\" % size\n image_path = str(sys.argv[1])\n gt = str(sys.argv[2])\n classifier = CraterClassifier(net)\n print \"Running the pyramid...\"\n pyramid = Pyramid(image_path, 10, size, gt, classifier)\n drawing_data = pyramid.runPyramid()\n cPickle.dump(pyramid.hitlist,open(DRAWING_DATA_OUT, 'wb'))","title":""},{"docid":"b13890bcbc6086e23d2b901a69ed2d1c","score":"0.5447834","text":"def main():\n print('Welcome to the Brain Games!')","title":""},{"docid":"ebcc79a5c9d535d9e0bba7b1a0f6d75f","score":"0.5444025","text":"def main():\n\n # Get the current working dir. path;\n current_dir = str(pathlib.Path().absolute())\n\n # Get current time\n now = datetime.now()\n current_time = now.strftime(\"%d-%m-%Y\") + '_' + now.strftime(\"%H_%M_%S\")\n\n # ---------------------------- Set LOG environment ----------------------------\n # set up logging to file - see previous section for more details\n logger_path = f'{current_dir}/LOGs/{current_time}.log'\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(name)-20s: %(levelname)-8s %(message)s',\n filename=logger_path,\n filemode='w')\n # define a Handler which writes INFO messages or higher to the sys.stderr\n console = logging.StreamHandler()\n console.setLevel(logging.INFO)\n # set a format which is simpler for console use\n formatter = logging.Formatter('[%(name)-20s]: [%(levelname)s] %(message)s')\n # tell the handler to use this format\n console.setFormatter(formatter)\n # add the handler to the root logger\n logging.getLogger('').addHandler(console)\n\n # Now, we can log to the root logger, or any other logger. First the root...\n logging.info(':: The Process started.')\n\n # Now, define a couple of other loggers which might represent areas in your\n # application:\n\n distance = logging.getLogger('main.distance-file')\n # ---------------------------- End LOG environment ----------------------------\n\n # reads the config file, if it exists;\n config_file_path = current_dir + \"\\\\general_config.txt\"\n # if config file exists continue, else create a new one;\n if os.path.isfile(config_file_path):\n # Get the available configuration file;\n with open(config_file_path) as file:\n config = json.load(file)\n logging.info(\":: Read configuration file complete.\\n\")\n pass\n else:\n logging.info(\":: File not found! Creating a new configuration...\")\n logging.info(\n \":: Please check the Readme file for information regarding the settings configuration standards.\\n\")\n config = config_gen(current_dir, config_file_path)\n\n # Get the configuration settings;\n (proteins_path, is_convex_relax, multi_start_phase, mdjeep_source, protein_black_list,\n global_debug_value) = config.values()\n\n # get the names and directories for all the available proteins; dict(protein_name: its_dir);\n proteins = dict(zip(os.listdir(config[\"proteins_path\"]), glob.glob(f\"{proteins_path}\\\\*\"))) # dict\n\n # ---------------------------- Now starts the tests ----------------------------\n # Create the Node test directory - Node - run date in current directory\n logging.info(':: Creating the test directory...')\n # First we will save the proteins names and paths to be read by the SDP Matlab script;\n # And also, create the test paths for each one, saving it on a dictionary\n protein_tests = {}\n\n proteins_filepath_dir = current_dir + f\"\\\\Matlab\\\\proteins.txt\"\n\n with open(proteins_filepath_dir, 'w+') as file:\n for protein, path in proteins.items():\n # We will not load the proteins on the blacklist;\n if protein in protein_black_list:\n continue\n else:\n directory = f'\\\\Tests\\\\{current_time}_{protein}'\n test_path = current_dir + directory\n # Create the directory\n os.mkdir(test_path)\n # Saves the directory\n protein_tests[f\"{protein}\"] = test_path\n logging.info(f\":: Directory '{directory}' created.\")\n\n # Writes this information to Matlab usage\n # Scheme: Node -- node_path -- test_path\n file.write(f\"{protein},{path},{test_path}\\n\")\n\n # Create the distance files for every available proteins\n for node in protein_tests.keys():\n # distance file generator -- generates the distance file, if it doesn't exists, and returns it's directory:\n try:\n # TODO: Insert the overwrite=distance_overwrite option\n gen_distance_file(node, proteins[f\"{node}\"])\n distance.info(\":: Process completed successfully, waiting for data to be read...\\n\")\n\n except OSError as err:\n distance.warning(f\":: Distance file generator found an error with node: {node} \\n\"\n f\":: {err}.\")\n distance.warning(\":: The process was interrupted!\")\n continue\n\n # ------------ Matlab (YALMIP): SDP Program\n\n # SDP launch and start phase:\n logging.info(':: Start [SDP] program phase.')\n # prepare a structure file for he matlab bash script (SDP Execution);\n launch_sdp(current_dir)\n logging.info(\"SPG environment configuration set.\\n\")\n\n # ------------ Python Refinement: SPG Program\n num_nodes = len(proteins.keys())\n nd_counter = 1 # node counter\n for node in protein_tests.keys():\n logging.info(f\":: #{nd_counter} Node: {node} of {num_nodes}\")\n # --- Open PDB File\n node_path = proteins[f'{node}']\n pdb_path = node_path + f'\\\\{node}.txt'\n dist_path = node_path + \"\\\\dist.txt\"\n test_path = protein_tests[f'{node}']\n pdb = open_pdb_file(pdb_path)\n logging.debug(':: Environmental properties successful loaded.')\n distancias, u, v, lb, ub, prop_dist = env_set(dist_path)\n\n # Now begins the refinement process of each protein\n # SPG launch and start phase\n logging.info(\":: Start [SPG] program phase.\")\n ops = (prop_dist, is_convex_relax, multi_start_phase, global_debug_value)\n launch_spg(node, pdb, test_path, distancias, lb, ub, u, v, ops)\n # -----\n nd_counter += 1\n\n # ------------ General statistics: static\n launch_statistics(f'{current_dir}/Statistics/{current_time}.txt', protein_tests)\n # End","title":""},{"docid":"0bdf51636417aa862634dcdbfb677537","score":"0.5436598","text":"def run():\n main()","title":""},{"docid":"7020080d53520238d2265b1adc42c1dd","score":"0.5423083","text":"def main():\n\n # FIXME need to verify parameters or otherwise validate input\n\n # parse command line arguments\n args = bbargparse.parse_inputs()\n\n # if args not ok, then squawk and return exit code of -1\n if not args_ok(args):\n return -1\n\n # show desired game day results\n show_results(args)\n\n # return exit code zero for success\n return 0","title":""},{"docid":"b18ab5eb6ed5ca0310ac031f5d75e6c1","score":"0.54151267","text":"def main():\n parser = argparse.ArgumentParser(description=\"Wrapper for the GROMACS grompp module.\",\n formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\n parser.add_argument('-c', '--config', required=False, help=\"This file can be a YAML file, JSON file or JSON string\")\n\n # Specific args of each building block\n required_args = parser.add_argument_group('required arguments')\n required_args.add_argument('--input_gro_path', required=True)\n required_args.add_argument('--input_top_zip_path', required=True)\n required_args.add_argument('--output_tpr_path', required=True)\n parser.add_argument('--input_cpt_path', required=False)\n parser.add_argument('--input_ndx_path', required=False)\n parser.add_argument('--input_mdp_path', required=False)\n\n args = parser.parse_args()\n config = args.config if args.config else None\n properties = settings.ConfReader(config=config).get_prop_dic()\n\n # Specific call of each building block\n grompp(input_gro_path=args.input_gro_path, input_top_zip_path=args.input_top_zip_path,\n output_tpr_path=args.output_tpr_path, input_cpt_path=args.input_cpt_path,\n input_ndx_path=args.input_ndx_path, input_mdp_path=args.input_mdp_path,\n properties=properties)","title":""},{"docid":"6b6b2570d3a95189963ebaf6039d457c","score":"0.54100776","text":"def main():\n here = os.path.abspath(os.path.dirname(__file__))\n app_file = os.path.join(here, \"app.py\")\n os.environ[\"FLASK_APP\"] = app_file\n os.environ[\"FLASK_ENV\"] = \"development\"\n command = \"python -m flask run\"\n command = command.replace(\"python\", sys.executable)\n print(\" --- Interactive Bayesian Optimisation ---\")\n print(\" * Please, open your browser at the address shown below\")\n subprocess.run(command.split(\" \"))","title":""},{"docid":"0a7914578b1dd53c90d9bf0c9f594c94","score":"0.5403009","text":"def _main():\n # connect_port support in Stem\n assert get_distribution('stem').version > '1.4.0', \\\n 'Stem module version must be greater than 1.4.0.'\n parser = ArgumentParser(description=\"Generate Tor paths.\")\n parser.add_argument(\"--port\", type=int, default=9051,\n help=\"tor control port.\")\n parser.set_defaults(network_protection=True)\n args = parser.parse_args()\n\n controller = connect_port(port=args.port)\n if not controller:\n sys.stderr.write(\"ERROR: Couldn't connect to tor.\\n\")\n sys.exit(1)\n if not controller.is_authenticated():\n controller.authenticate()\n findpath(controller)\n controller.close()","title":""},{"docid":"c18d0960bab3423d92ad4b6fbc20a6ce","score":"0.53985465","text":"def main():\n\n # Get command line arguments\n jid = sys.argv[1]\n name_temp = sys.argv[2]\n if len(sys.argv) == 4: # 'no_of_processes' is an optional command line argument that defaults to 16 if not given\n no_of_processes = int(sys.argv[3])\n sam = SamModelCaller(jid, name_temp, no_of_processes)\n elif len(sys.argv) == 5:\n no_of_processes = int(sys.argv[3])\n write_output = bool(sys.argv[4])\n sam = SamModelCaller(jid, name_temp, no_of_processes, write_output)\n else:\n sam = SamModelCaller(jid, name_temp)\n\n\n\n\n sam.sam_multiprocessing()","title":""},{"docid":"7a1d931a1d790b4b4437757cb31e0d90","score":"0.5383743","text":"def main():\n properties = read_properties_from_file('Sales.csv')\n settings = read_settings_from_file('Settings.csv')\n print(stripes)\n print(\n \"\\nWelcome to Kimberlynet - Sweden's largest residential site\\n\\nWe will help you find a property, please\"\n \" answer a few questions before we start!\\n\")\n print(stripes)\n interest = control_float('Please enter current interest (%): ')\n deposit = control_int('Please enter your deposit: ')\n deduction = control_float('Please enter current deduction (%): ')\n check = False\n while not check:\n menu(settings)\n choice = menu_choice()\n execute(choice, properties, settings, interest, deposit, deduction)\n if choice == 9:\n check = True","title":""},{"docid":"612611e765809c8a7e313ce8cc612787","score":"0.53809285","text":"def main():\n parser = bonobo.get_argument_parser()\n with bonobo.parse_args(parser) as options:\n bonobo.run(get_graph(**options), services=get_services(**options))","title":""},{"docid":"dad4e95a08cc99857681179a5b7b9f57","score":"0.53760874","text":"def main(argv):\r\n groceryfile = ''\r\n emailfile = ''\r\n try:\r\n opts, _ = getopt.getopt(argv,\"hgl:el:\",[\"glfile=\",\"elfile=\"])\r\n except getopt.GetoptError:\r\n print ('split_the_bill.py --gl <grocerylist.txt> --el <emaillist.txt>')\r\n print ('Please provide input files or -h for help')\r\n sys.exit(2)\r\n for opt, arg in opts:\r\n if opt == '-h':\r\n print ('split_the_bill.py --gl <grocerylist.txt> --el <emaillist.txt>')\r\n sys.exit()\r\n elif opt in (\"-gl\", \"--glfile\"):\r\n groceryfile = arg\r\n elif opt in (\"-el\", \"--elfile\"):\r\n emailfile = arg\r\n \r\n try:\r\n mf = open(groceryfile, \"r\", encoding='utf-8')\r\n mf.seek(0)\r\n grocery_list = mf.read()\r\n if grocery_list == '':\r\n grocery_list = \"{}\"\r\n grocery_list_json = json.loads(grocery_list)\r\n except:\r\n raise FileNotFoundError('Grocery list not found!')\r\n finally:\r\n mf.close()\r\n\r\n try:\r\n ef = open(emailfile, \"r\", encoding='utf-8')\r\n ef.seek(0)\r\n email_list = ef.read().splitlines()\r\n if email_list == []:\r\n email_list = [\"Total\"]\r\n except:\r\n raise FileNotFoundError('Email list not found!')\r\n finally:\r\n ef.close()\r\n print(json.dumps(map_price_and_email(email_list,grocery_list_json)))","title":""},{"docid":"5f433db1079924698c632e74339ed771","score":"0.5361722","text":"def main():\r\n print(\"DASHNextGen_Service_Report.py is Starting\")\r\n login_into_dash(\"./DASHLoginInfo.json\")\r\n navigate_to_reports_and_click_excel()\r\n time.sleep(5)\r\n grab_downloaded_report()\r\n csv_to_database(\"./DASHLoginInfo.json\")\r\n file_cleanup()\r\n print(\"We have uploaded to the database.\")\r\n logout_session()","title":""},{"docid":"98a51693f0501415a9e1824aabb01d83","score":"0.53591925","text":"def main():\n tidy_uploads()\n do_publications()\n do_things_grid()\n \n copy_directory('things/grid-backgrounds/')\n copy_directory('things/phd/')","title":""},{"docid":"ceda46838c16325aa24614e88a1efe47","score":"0.53558826","text":"def main():\n\n parser = argparse.ArgumentParser(description=\"Cherry picking automation.\")\n\n # Arg information\n parser.add_argument(\n \"--filename\", help=\"path to whl file we are copying\", required=True)\n parser.add_argument(\n \"--new_py_ver\", help=\"two digit py version eg. 27 or 33\", required=True)\n\n args = parser.parse_args()\n\n # Argument checking\n args.filename = os.path.abspath(args.filename)\n check_existence(args.filename)\n regex_groups = re.search(TF_NIGHTLY_REGEX, args.filename)\n directory = regex_groups.group(1)\n package = regex_groups.group(2)\n version = regex_groups.group(3)\n origin_tag = regex_groups.group(4)\n old_py_ver = re.search(r\"(cp\\d\\d)\", origin_tag).group(1)\n\n # Create new tags\n new_tag = origin_tag.replace(old_py_ver, \"cp\" + args.new_py_ver)\n\n # Copy the binary with the info we have\n copy_binary(directory, origin_tag, new_tag, version, package)","title":""},{"docid":"6fb1c589ddefa00eb547dc4546b2da58","score":"0.5355522","text":"def main():\n _main()","title":""},{"docid":"9e10366c86d668faee5952b61a636e61","score":"0.5355324","text":"def main():\n context = {}\n urls = {}\n urls[\"delivery\"] = BASE_URL + TIME_SERIES_DELIVERY\n urls[\"vaccination\"] = BASE_URL + VACCINATION_STATE\n context[\"cwd\"] = str(Path(sys.argv[0]).parent)\n\n # load last state\n sources = load_object(context)\n if sources is None:\n sources = Sources(urls[\"delivery\"], urls[\"vaccination\"])\n\n sources.download_sources()\n prepare_data(context, urls, sources.get_data())\n plot(context, sources)\n\n # store current state\n store_object(context, sources)","title":""},{"docid":"b8f4e37a763460bd6ebeae3d7c60aa40","score":"0.535298","text":"def main():\n\n\t################################\n\t# change this\n\tlog_dir = \"./beta_simulator_mac/data2/driving_log.csv\"\n\n\tmy_model = Model()\n\tmy_model.read_csv_data(log_dir)\n\tmy_model.build_and_train_model()","title":""},{"docid":"1674ebfbc119d61410be73cb43f6f664","score":"0.535035","text":"def main():\n\n # Build data\n jobs = squeue(user='lmh1', # user id - change to your own\n name=['iprPy_1', 'iprPy_4']) # job names - change to your own\n logs = parse_runner_logs()\n rundirs, runners = check_run_directories()\n\n # Merge data\n logjobs = jobs.merge(logs, how='outer', on='jobid')\n logjobs.loc[(logjobs.status=='active') & (pd.isna(logjobs.user)), 'status'] = 'crashed'\n runlogjobs = logjobs.merge(runners, how='outer', on='pid')\n\n # Loop over all run directories\n keys = ['jobid', 'pid', 'status', 'time', 'calcid', 'tmpdir']\n for i in rundirs.index:\n rundir = rundirs.loc[i]\n\n # List number of prepared calculations and number of runners\n print(rundir.run_directory, rundir.numcalcs, rundir.numrunners)\n dirjobs = runlogjobs[runlogjobs.run_directory == rundir.run_directory]\n\n # Print data for runners\n if len(dirjobs) > 0:\n print(dirjobs[keys])\n print()\n\n # List jobs with no associated bid files (usually finished)\n print('Unknown/finished jobs')\n nodir = runlogjobs[pd.isna(runlogjobs.run_directory)][keys]\n with pd.option_context('display.max_rows', None,):\n print(nodir)\n\n # Delete run logs for successfully finished jobs\n for jobid in nodir[nodir.status=='finished'].jobid.values:\n Path(f'runner_{jobid}.txt').unlink()","title":""},{"docid":"b91ecb34edfcca45b91023e878276de2","score":"0.5344869","text":"def main():\n\n # e.g. url = 'cohorts/ryan_test_2/bytes_added'\n args = parseargs()\n\n # Initialize a requester object\n logging.debug(__name__ + ' :: Attempting to create cookie jar, '\n 'logging in ..')\n umapi_req = UMAPIClient(config.UMAPI_USER,\n config.UMAPI_PASS)\n\n if args.wait < config.MIN_WAIT_REQ:\n logging.error(__name__ + ' :: Wait time to small. '\n 'Must be at least {0}'.format(\n config.MIN_WAIT_REQ))\n return\n\n # Call the request handler\n #\n # 1. URLs provided from a csv\n # 2. URL from CL\n #\n\n proc_args = [umapi_req,\n args.uri,\n args.timeout,\n args.wait,\n args.save_file,\n args.timestamp,\n args.print_res]\n\n if args.infile:\n with open(config.IN_DIR + args.infile) as f:\n urls = f.read().strip().split(args.separator)\n for url in urls:\n url = unicode(url).strip()\n logging.debug(__name__ + ' :: Processing: {0}.'.format(url))\n proc_args[1] = url\n p = Process(target=request_handler, args=proc_args)\n p.start()\n p.join()\n else:\n url = unicode(args.uri).strip()\n logging.debug(__name__ + ' :: Processing: {0}.'.format(url))\n proc_args[1] = url\n p = Process(target=request_handler, args=proc_args)\n p.start()\n p.join()\n\n logging.debug(__name__ + ' :: Shutting down umapi client.')","title":""},{"docid":"e38e160bd8991fbce437f03b70494a61","score":"0.5342661","text":"def main():\n cmd_opts = __process_args()\n database = \"/root/Desktop/vchauhan/Project/db_class/Station.db\"\n \n # create a database connection\n conn = create_connection(database)\n with conn:\n if (cmd_opts.ID):\n print(\"Querying for Trains with Station_ID: %s\" % (cmd_opts.ID))\n select_task_by_id(conn,cmd_opts.ID)\n else:\n print(\"Query all available trains in the station\")\n select_all_tasks(conn)","title":""},{"docid":"9f249ad939f81630924dfc73921e45db","score":"0.53349763","text":"def main():\n setup_server_window()\n load_queue_data_from_pickle_files()\n setup_server_socket()\n server_window.mainloop()","title":""},{"docid":"5a83a2fb0dca8d5e0ca5b87c319671e2","score":"0.53339314","text":"def run(self):\n logger=self.log()\n runhere=self.workdir\n if os.path.exists(runhere):\n logger.warning('%s: directory exists; will delete'%(runhere,))\n assert(not os.path.samefile(self.getdir('WORKhwrf'),runhere))\n shutil.rmtree(runhere)\n atime=self.__wrf.simstart()\n with NamedDir(runhere,keep=self.keeprun,logger=logger,\n keep_on_error=True) as rundir:\n try:\n logger.info('%s running in directory %s'%(\n self.taskname,realcwd()))\n self.location=runhere\n self.initial_prerun()\n self.link_fix()\n self.link_all_inputs()\n self.make_namelist()\n self.final_prerun()\n self.run_exe()\n self.postrun()\n self.deliver_products()\n except Exception as e:\n logger.critical('%s failed: %s'%(self.taskname,str(e)),\n exc_info=True)\n raise\n self.postmsg('%s: completed'%(self.taskname,))","title":""},{"docid":"560dafb446a00cb4f6b0f6812c61b19a","score":"0.53298175","text":"def main(self, *args):\n\n Launcher.main(self)\n s = SimServer(self.build_opt().copy())\n s.start()","title":""},{"docid":"1a893af2a985302a71a771cb73b96e47","score":"0.53260726","text":"def main():\n\n # Allow user to input parameters on command line.\n userInput = argparse.ArgumentParser(description=\\\n '%s version %s. Requires a .bed file with first four columns in the '\n 'format chromosome <tab> start <tab> stop <tab> sequence such as the '\n '.bed files produced by outputClean. Returns a .bed file that is '\n 'identical to the input file except that the probe sequences have been '\n 'replaced with their reverse complements.' % (scriptName, Version))\n requiredNamed = userInput.add_argument_group('required arguments')\n requiredNamed.add_argument('-f', '--file', action='store', required=True,\n help='The .bed file containing the probe '\n 'sequences to take the reverse complements '\n 'of')\n userInput.add_argument('-o', '--output', action='store', default=None,\n type=str, help='Specify the name prefix of the '\n 'output file')\n\n # Import user-specified command line values\n args = userInput.parse_args()\n inputFile = args.file\n outNameVal = args.output\n\n createRCs(inputFile, outNameVal)","title":""},{"docid":"4a1441aff7b42146a2704f22a5ad15d9","score":"0.5325459","text":"def main():\n # get options\n cli = bill_cli.CommandLine()\n cli.get_options()\n\n # init config\n config = cfg.Config(cli.option)\n\n\n # init AWS_Access instance\n aws_raw_bill = AWS_Raw_Bill(config=config, commandline=cli)\n aws_raw_bill.proc_detail_tags_bills()\n\n\n #print \"\\n\"\n aws_raw_bill.cli.msg(\"You got it ! Cheers!\")","title":""},{"docid":"5af56283008886c67ed6a41874433143","score":"0.53180116","text":"def main():\n # The block of code below attempts to open the file with the bike share\n # data. If an IOError (input/output error) occurs, it prints an error\n # message and exits the program, since the rest of the program needs\n # to be able to read the file. If you get an error, check that you\n # haven't changed the name of the file and make sure if appears in the\n # same directory as bike.py.\n try:\n file = open(\"2015MayBikeShareData.csv\")\n except IOError:\n print(\"ERROR! 2015MayBikeShareData.csv doesn't appear in the current directory.\")\n exit(1)\n \n \n # The code below provides an example of looping through the data and\n # counting the number of rides that were taken in this period.\n numRides = 0\n totalDuration = 0\n numSmithJeff = 0\n startCount = 0\n numEnter = 0\n \n for line in file:\n numRides = numRides + 1\n \n # Divide this line of the file into the above fields; we'll only care about\n # a few of them so we'll ignore the others.\n fields = line.split(\",\")\n # We haven't seen this syntax before, but you should be able to guess what it\n # does. Try experimenting with it in the interpreter to confirm your guess.\n # From here on out in the code, you can ignore the fields variable - everything\n # you need has been stored in the variables on the left hand side.\n duration, startStation, endStation = int(fields[0]), fields[2], fields[4]\n \n # You'll add additional code within this for loop to complete the assignment.\n # There is one iteration of the loop for each line of the file.\n \n #sum all the durations in each ride\n totalDuration = totalDuration + duration\n \n #finding total number of rides that start at Smithsonian / Jefferson Dr & 12th St SW\n if startStation == 'Smithsonian / Jefferson Dr & 12th St SW':\n numSmithJeff = numSmithJeff + 1\n \n #finding total number of bikes that arrive at Smithsonian / Jefferson station\n if endStation == \"Smithsonian / Jefferson Dr & 12th St SW\":\n numEnter = numEnter + 1\n \n #find average duration of each ride and convert to seconds\n averageDuration = (totalDuration / numRides) / 1000 \n \n #sum the number of bikes that have entered and left the station over the course of the data\n totalChange = numEnter - numSmithJeff\n \n # Files should always be closed when you are done with them.\n file.close()\n \n # Add additional print statements here\n print(\"There were\", numRides, \"total rides.\")\n print(\"The average duration of each ride is\", averageDuration, \"seconds\")\n \n #gives average duration of each ride in minutes and prints the result in a statement\n averageMinutes(averageDuration)\n \n print(\"The number of rides that started at Smithsonian / Jefferson Dr & 12th St SW is\", numSmithJeff)\n \n if totalChange > 0:\n print(\"The number of bikes at Smithsonian / Jefferson station over the course of the data had increased by\", totalChange, \"units\")\n elif totalChange < 0:\n print(\"The number of bikes at Smithsonian / Jefferson station over the course of the data has decreased by\", totalChange, \"units\")\n elif totalChange == 0:\n print(\"The number of bikes at Smithsonian / Jefferson station over the course of the data has not changed\")","title":""},{"docid":"5eb888e41c74d0981ab1fba610eeca06","score":"0.5317356","text":"def main():\n stats.set_time_start()\n\n if config.options.show_progress:\n stats.start_monitor()\n\n recorders = Recorder.launch(config.options.recorders)\n\n try:\n for filename in config.filenames:\n parser.parse(filename)\n\n Recorder.wait_empty()\n except KeyboardInterrupt:\n pass\n\n stats.set_time_stop()\n\n if config.options.show_progress:\n stats.stop_monitor()\n\n stats.print_summary()","title":""},{"docid":"6d20928f9bf4aa07d61d880e4df4b507","score":"0.5316588","text":"def main():\n\n args = parser.parse_args()\n start_ms_bf_server(app_id=args.ms_id,\n app_secret=args.ms_secret)","title":""},{"docid":"1e6e65f4306c215597e5b39fbd542e56","score":"0.53132766","text":"def main(args):\r\n\r\n return 0","title":""},{"docid":"d458458b43c4d46a0aec51a4f472eb0a","score":"0.5306254","text":"def main():\n print ('info: this is the seedbank library, what did you smoke today?!?')","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.5305935","text":"def main():","title":""}],"string":"[\n {\n \"docid\": \"5eb65670ae87521a2fc47c3a73258747\",\n \"score\": \"0.67149067\",\n \"text\": \"def main(argv):\\n\\n\\tlogger.setLevel(logging.INFO)\\n\\thandleOptions(argv)\\n\\t# Load the config file\\n\\tconfigfile = 'config.json'\\n\\twith open(configfile, 'r') as f:\\n\\t\\tconfig = json.load(f)\\n\\n\\tcreateOutputFolder()\\n\\n\\tlogger.info(\\\"Start the grabbing process\\\")\\n\\tgrabber = Grabber(config, linkparser)\\n\\tgrabber.execute()\\n\\n\\tlogger.info(\\\"Start creating the web package\\\")\\n\\twebPackageCreator = WebPackageCreator(config)\\n\\twebPackageCreator.create()\\n\\n\\tlogger.info(\\\"Start creating the dns package\\\")\\n\\tdnsPackageCreator = DNSCreater(config)\\n\\tdnsPackageCreator.create()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66fef5e5ea4e26f9c772525accbf2cb\",\n \"score\": \"0.6053314\",\n \"text\": \"def main(self,argv):\\n cli.setup_logging(\\\"/tmp/setup.out\\\")\\n os = cli.get_os_type()\\n if (os not in [\\\"mac\\\"]):\\n cli.fatal(\\\"Untested OS: \\\" + os)\\n cli.log.info(\\\"Starting Program\\\")\\n cli.log.info(\\\"ARGS:\\\" + str(argv))\\n self.cmd(\\\"ls / | head -5 # test ability to run shell commands\\\") \\n self.install_grakn_in_osx() # Start Grakn \\n #self.clear_grakn_db() # Erase the data (Stops services)\\n #self.install_grakn_in_osx() # Start Grakn \\n \\n self.test_grakn_in_osx()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6caabe4e6793f4e23f18df52f721679e\",\n \"score\": \"0.6003799\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(description='boj-tool: a CLI tool for BOJ')\\n parser.add_argument('-v', '--verbose', help='set log level to INFO',\\n action='store_true')\\n parser.add_argument('-d', '--debug', help='set log level to DEBUG',\\n action='store_true')\\n subparsers = parser.add_subparsers(dest='subparser')\\n login_parser = subparsers.add_parser('login')\\n submit_parser = subparsers.add_parser('submit')\\n submit_parser.add_argument('number', type=int, help='the problem number')\\n submit_parser.add_argument('filename', help='filename to submit')\\n stat_parser = subparsers.add_parser('stats')\\n stat_parser.add_argument('-u', '--user', type=str,\\n help='the user to show stats')\\n version_parser = subparsers.add_parser('version')\\n args = parser.parse_args()\\n\\n initialize()\\n if args.verbose:\\n logger.setLevel(logging.INFO)\\n elif args.debug:\\n logger.setLevel(logging.DEBUG)\\n\\n if args.subparser == 'login':\\n login()\\n elif args.subparser == 'submit':\\n submit(args.number, args.filename)\\n print_result(args.number)\\n elif args.subparser == 'stats':\\n stats(args.user)\\n elif args.subparser == 'version':\\n version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3edb446efd4212324d179ce9a483d8\",\n \"score\": \"0.5855898\",\n \"text\": \"def process():\\n\\tparser = argparse.ArgumentParser(description=description)\\n\\tparser.add_argument(\\\"config\\\", help=\\\"file name of the JSON configuration file\\\")\\n\\tparser.add_argument(\\\"-b\\\", \\\"--book\\\", action='store_true', help=\\\"Create an EPUB3 package\\\")\\n\\tparser.add_argument(\\\"-f\\\", \\\"--folder\\\", action='store_true', help=\\\"Create a folder with the book content\\\")\\n\\n\\targs = parser.parse_args()\\n\\tfrom rp2book import start\\n\\t# start(args.config, package=args.book, folder=args.folder, temporary=args.tempfile,\\n\\t# \\t logger= _create_logger(\\\"log\\\") if args.logging else None)\\n\\tstart(args.config, package=args.book, folder=args.folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d6f1ffae26dc2828c5286dcc8ee18f\",\n \"score\": \"0.5838961\",\n \"text\": \"def main():\\n path = \\\"./Bag_flight/\\\"\\n files = [f for f in os.listdir(path) if f[-4:] == '.bag']\\n if not files:\\n print('No bag files found!')\\n return None\\n for f in files:\\n print(\\\"\\\\nreading bag: \\\" + str(f))\\n with rosbag.Bag(path + f) as bag:\\n bag_df_dict = get_bag_data_pandas(bag)\\n data_df = processing(bag_df_dict=bag_df_dict, id=f[:-4])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50c97257373e9ea439903040fb92e38\",\n \"score\": \"0.58337957\",\n \"text\": \"def main():\\n\\n # anyway output thr banner information\\n banner() \\n\\n # set paths of project \\n paths.ROOT_PATH = os.getcwd() \\n setPaths()\\n \\n # received command >> cmdLineOptions\\n cmdLineOptions.update(conf.webConfig)\\n \\n # loader script,target,working way(threads? gevent?),output_file from cmdLineOptions\\n # and send it to conf\\n initOptions(cmdLineOptions)\\n\\n # run!\\n run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ff9a8373d4935f4d428fdc43dcf0b0\",\n \"score\": \"0.5825765\",\n \"text\": \"def main():\\n artist = args.artist or getInput(\\\"Who wrote the song?\\\")\\n album = args.album or getInput(\\\"What album is the song on?\\\")\\n track = args.track or getInput(\\\"What is the track name?\\\")\\n trackNum = args.num or getInput(\\\"What number is the song on the album?\\\")\\n fmt = args.fmt or getInput(\\\"What format is the song in?\\\")\\n itunes = args.itunes or getInput(\\\"Where is your iTunes library located? (default: '/Users/{}/Music/iTunes/iTunes Media/')\\\".format(getpass.getuser()))\\n\\n if itunes.strip() == \\\"\\\":\\n itunes = \\\"/Users/{}/Music/iTunes/iTunes Media\\\".format(getpass.getuser())\\n\\n trackNum = trackNum.zfill(2)\\n\\n print(itunes)\\n print(artist)\\n print(album)\\n print(track)\\n print(trackNum)\\n print(genPath(artist, album, track, trackNum, fmt, itunes))\\n path = genPath(artist, album, track, trackNum, fmt, itunes)\\n out = d.trackSeek(path, artist, album, track, trackNum, fmt)\\n for song in out:\\n addToiTunes(itunes, song)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66efb48a9e722cca9e4ed621db950062\",\n \"score\": \"0.5799956\",\n \"text\": \"def main():\\n with logbook.NestedSetup(_get_log_handlers()).applicationbound():\\n logger.info('Py-expander started!')\\n try:\\n # Set subliminal cache first.\\n if config.SHOULD_FIND_SUBTITLES:\\n logger.debug('Setting subtitles cache...')\\n configure_subtitles_cache()\\n # Parse input arguments.\\n if len(sys.argv) == 3:\\n directory = sys.argv[1]\\n filename = sys.argv[2]\\n if directory == config.DEFAULT_PATH:\\n torrent_path = os.path.join(directory, filename)\\n logger.info('Input is a file: {}'.format(torrent_path))\\n else:\\n torrent_path = directory\\n logger.info('Input is a dir: {}'.format(torrent_path))\\n expand_torrent(torrent_path)\\n elif len(sys.argv) == 2:\\n expand_torrent(sys.argv[1])\\n else:\\n expand_torrent_from_transmission()\\n except:\\n logger.exception('Critical exception occurred!')\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d233dc0534600ec6d411de9283e46e50\",\n \"score\": \"0.5783133\",\n \"text\": \"def main():\\n with open(argv[1]) as file:\\n #dic = parse_file(file)\\n #get_info(dic)\\n path = '/Users/gustavotamasco/PycharmProjects/tcc'\\n files = get_file(path)\\n new_files =[]\\n for f in files:\\n nf = \\\"'{0}'\\\".format(f)\\n new_files.append(nf)\\n run_blast(new_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2a0ead0d7798c1f777d90c6c4f12b5\",\n \"score\": \"0.5773897\",\n \"text\": \"def main():\\n try:\\n number_accounts, ip, user_pattern, user_pwd_pattern, node_pattern, node_pwd_pattern = parse_args()\\n except ValueError:\\n pass\\n generate_and_save(number_accounts, ip, user_pattern, user_pwd_pattern, node_pattern, node_pwd_pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3352c5ac59b1128c898ab17f9110ad\",\n \"score\": \"0.5770266\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"bank_code\\\",\\n help=\\\"3 letter code representing the bank that the data comes from\\\"\\n )\\n parser.add_argument(\\n \\\"input_file\\\",\\n help=\\\"PDF file containing bank transation data\\\"\\n )\\n parser.add_argument(\\n \\\"output_file\\\",\\n help=\\\"Name of CSV file that output will be saved to\\\"\\n )\\n program_args = parser.parse_args()\\n bank_code = program_args.bank_code\\n input_file = program_args.input_file\\n output_file = program_args.output_file\\n pdf2ynab(bank_code, input_file, output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44d640db816f6db16fb7f0cf29beb68\",\n \"score\": \"0.57688487\",\n \"text\": \"def main():\\n # Detect BBP installation\\n bbp_install = InstallCfg.getInstance()\\n\\n prog_base = os.path.basename(sys.argv[0])\\n usage = \\\"usage: %s [options]\\\" % (prog_base)\\n parser = optparse.OptionParser(usage)\\n parser.add_option(\\\"-c\\\", \\\"--codebase\\\", type=\\\"string\\\", action=\\\"store\\\",\\n dest=\\\"codebase\\\",\\n help=\\\"Codebase for the simulation: %s\\\" %\\n (CODEBASES))\\n parser.add_option(\\\"-e\\\", \\\"--event\\\", type=\\\"string\\\", action=\\\"store\\\",\\n dest=\\\"event\\\",\\n help=\\\"Validation event (should be configured in BBP)\\\")\\n parser.add_option(\\\"-d\\\", \\\"--dir\\\", type=\\\"string\\\", action=\\\"store\\\",\\n dest=\\\"simdir\\\",\\n help=\\\"Simulation directory\\\")\\n parser.add_option(\\\"--skip-rupgen\\\", action=\\\"store_true\\\", dest=\\\"skiprupgen\\\",\\n help=\\\"Skip the rupture generator, run only 1 simulation\\\")\\n parser.add_option(\\\"--hypo-rand\\\", action=\\\"store_true\\\", dest=\\\"hyporand\\\",\\n help=\\\"Enables hypocenter randomization\\\")\\n parser.add_option(\\\"--no-hypo-rand\\\", action=\\\"store_false\\\", dest=\\\"hyporand\\\",\\n help=\\\"Disables hypocenter randomization\\\")\\n parser.add_option(\\\"-n\\\", \\\"--num-simulations\\\", type=\\\"int\\\", action=\\\"store\\\",\\n dest=\\\"numsim\\\", help=\\\"Number of simulations to run\\\")\\n parser.add_option(\\\"--email\\\", type=\\\"string\\\", action=\\\"store\\\",\\n dest=\\\"email\\\", help=\\\"Email for job notifications\\\")\\n (options, args) = parser.parse_args()\\n\\n # Validate codebase to use\\n codebase = options.codebase\\n if codebase is None:\\n print \\\"Please specify a codebase!\\\"\\n sys.exit(1)\\n codebase = codebase.lower()\\n if codebase not in CODEBASES:\\n print \\\"Codebase needs to be one of: %s\\\" % (CODEBASES)\\n\\n # Check for event\\n event = options.event\\n if event is None:\\n print \\\"Please provide a validation event!\\\"\\n sys.exit(1)\\n event_names = validation_cfg.VE_EVENTS.get_all_names()\\n events = [v_event.lower() for v_event in event_names]\\n if event.lower() not in events:\\n print (\\\"Event %s does not appear to be properly configured on BBP\\\" %\\n (event))\\n print (\\\"Available options are: %s\\\" % (event_names))\\n print \\\"Please provide another event or check your BBP installation.\\\"\\n sys.exit(1)\\n val_obj = validation_cfg.VE_EVENTS.get_event_by_print_name(event)\\n\\n # Check if we want to run the rupture generator\\n skip_rupgen = options.skiprupgen\\n\\n # Check for hypocenter randomization\\n if options.hyporand is None:\\n print \\\"Please specify --hypo-rand or --no-hypo-rand!\\\"\\n sys.exit(1)\\n\\n if options.hyporand:\\n hypo_rand = True\\n else:\\n hypo_rand = False\\n\\n if not skip_rupgen:\\n # Get source file\\n try:\\n source_file = val_obj.get_input(codebase, \\\"source\\\").strip()\\n except KeyError:\\n print (\\\"Unable to get source file for event %s, codebase %s!\\\" %\\n (event, codebase))\\n sys.exit(1)\\n if not source_file:\\n print (\\\"Source file for event %s, codebase %s not specified!\\\" %\\n (event, codebase))\\n sys.exit(1)\\n else:\\n # No need to get the source file, we start from the srf\\n source_file = None\\n try:\\n srf_file = val_obj.get_input(codebase, \\\"srf\\\").strip()\\n except KeyError:\\n print (\\\"Event %s does not have a srf file for codebase %s!\\\" %\\n (event, codebase))\\n sys.exit(1)\\n if not srf_file:\\n print (\\\"Event %s does not have a srf file for codebase %s!\\\" %\\n (event, codebase))\\n sys.exit(1)\\n # Force number of simulations to 1\\n options.numsim = 1\\n\\n # Check for the simulation directory\\n simdir = options.simdir\\n if simdir is None:\\n print \\\"Please provide a simulation directory!\\\"\\n sys.exit(1)\\n simdir = os.path.abspath(simdir)\\n if os.path.exists(simdir):\\n print \\\"Simulation directory exists: %s\\\" % (simdir)\\n opt = raw_input(\\\"Do you want to delete its contents (y/n)? \\\")\\n if opt.lower() != \\\"y\\\":\\n print \\\"Please provide another simulation directory!\\\"\\n sys.exit(1)\\n opt = raw_input(\\\"ARE YOU SURE (y/n)? \\\")\\n if opt.lower() != \\\"y\\\":\\n print \\\"Please provide another simulation directory!\\\"\\n sys.exit(1)\\n # Delete existing directory (we already asked the user twice!!!)\\n shutil.rmtree(simdir)\\n\\n # Pick up number of simulations to run\\n numsim = options.numsim\\n if numsim < 1 or numsim > MAX_SIMULATIONS:\\n print (\\\"Number of simulations should be between 1 and %d\\\" %\\n (MAX_SIMULATIONS))\\n sys.exit(1)\\n\\n # Check for e-mail address\\n email = options.email\\n if email is None:\\n print \\\"Please provide an e-mail address for job notifications\\\"\\n sys.exit(1)\\n\\n # Make sure user has configured the setup_bbp_epicenter_env.sh script\\n setup_bbp_env = os.path.join(bbp_install.A_INSTALL_ROOT,\\n \\\"utils/batch/setup_bbp_epicenter_env.sh\\\")\\n if not os.path.exists(setup_bbp_env):\\n print (\\\"Cannot find setup_bbp_epicenter_env.sh script!\\\")\\n print (\\\"Expected at: %s\\\" % (setup_bbp_env))\\n sys.exit(1)\\n # Create simulation directories\\n prefix = \\\"%s-%s\\\" % (event.lower(), codebase.lower())\\n # Make sure we remove spaces from prefix (e.g. for the \\\"Loma Prieta\\\" event)\\n prefix = prefix.replace(\\\" \\\", '')\\n os.makedirs(simdir)\\n indir = os.path.join(simdir, \\\"Sims\\\", \\\"indata\\\")\\n outdir = os.path.join(simdir, \\\"Sims\\\", \\\"outdata\\\")\\n tmpdir = os.path.join(simdir, \\\"Sims\\\", \\\"tmpdata\\\")\\n logsdir = os.path.join(simdir, \\\"Sims\\\", \\\"logs\\\")\\n xmldir = os.path.join(simdir, \\\"Xml\\\")\\n srcdir = os.path.join(simdir, \\\"Src\\\")\\n for mdir in [indir, outdir, tmpdir, logsdir, xmldir, srcdir]:\\n os.makedirs(mdir)\\n # Generate source files if needed\\n if source_file is not None:\\n generate_src_files(numsim, source_file, srcdir, prefix, hypo_rand)\\n # Generate xml files\\n generate_xml(bbp_install, numsim, srcdir, xmldir,\\n logsdir, event, codebase, prefix,\\n skip_rupgen)\\n # Write pbs file\\n write_pbs(bbp_install, numsim, simdir, xmldir, email, prefix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26cd6c2af325c94684ef9d3886f9c709\",\n \"score\": \"0.5756447\",\n \"text\": \"def launch_barrier ():\\n os.chdir(\\\"barrier\\\")\\n subprocess.call(\\\"barrier.exe\\\")\\n os.chdir(\\\"..\\\")\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178409e8cf07166f24c9f38f913a6d8b\",\n \"score\": \"0.5754545\",\n \"text\": \"def main():\\n args = get_args()\\n bottlenum = args.num_bottles \\n \\n bottledown = bottlenum - 1\\n if bottlenum < 1:\\n die('N ({}) must be a positive integer'.format(bottlenum))\\n while bottlenum >= 1:\\n if bottlenum == 1:\\n print('{} bottle of beer on the wall,'.format(bottlenum))\\n print('{} bottle of beer,'.format(bottlenum)) \\n else:\\n print('{} bottles of beer on the wall,'.format(bottlenum))\\n print('{} bottles of beer,'.format(bottlenum))\\n print('Take one down, pass it around,')\\n if bottledown == 1:\\n print('{} bottle of beer on the wall!'.format(bottledown))\\n print('') \\n else:\\n if bottledown == 0: \\n print('{} bottles of beer on the wall!'.format(bottledown))\\n else:\\n print('{} bottles of beer on the wall!'.format(bottledown))\\n print('') \\n bottlenum -= 1\\n bottledown -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4740ffd7ba2c8e0cb2ce0717abb89438\",\n \"score\": \"0.5730241\",\n \"text\": \"def run(self):\\n self.parse_args()\\n\\n if self.options.file_root:\\n # check if the argument is pointing to a file on disk\\n file_root = os.path.abspath(self.options.file_root)\\n self.config[\\\"file_roots\\\"] = {\\\"base\\\": _expand_glob_path([file_root])}\\n\\n if self.options.pillar_root:\\n # check if the argument is pointing to a file on disk\\n pillar_root = os.path.abspath(self.options.pillar_root)\\n self.config[\\\"pillar_roots\\\"] = {\\\"base\\\": _expand_glob_path([pillar_root])}\\n\\n if self.options.states_dir:\\n # check if the argument is pointing to a file on disk\\n states_dir = os.path.abspath(self.options.states_dir)\\n self.config[\\\"states_dirs\\\"] = [states_dir]\\n\\n if self.options.local:\\n self.config[\\\"file_client\\\"] = \\\"local\\\"\\n if self.options.master:\\n self.config[\\\"master\\\"] = self.options.master\\n\\n caller = salt.cli.caller.Caller.factory(self.config)\\n\\n if self.options.doc:\\n caller.print_docs()\\n self.exit(salt.defaults.exitcodes.EX_OK)\\n\\n if self.options.grains_run:\\n caller.print_grains()\\n self.exit(salt.defaults.exitcodes.EX_OK)\\n\\n caller.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92c3899081029f7caaf5a4037eb5e03\",\n \"score\": \"0.57288873\",\n \"text\": \"def main(args):\\n\\n # Set up processed data directories\\n # Trailing slash not expected by rest of program\\n args[\\\"root\\\"] = args[\\\"root\\\"].rstrip(\\\"/\\\")\\n processed_data_dir = args[\\\"root\\\"] + \\\"/processed_data\\\"\\n if not os.path.exists(processed_data_dir):\\n os.mkdir(processed_data_dir)\\n\\n # Read processor requests\\n for processor in args[\\\"processors\\\"]:\\n print(\\\"################################\\\\n\\\")\\n print(\\\"Working on processor %s\\\" % processor[\\\"name\\\"])\\n save_processor_info(processed_data_dir, processor)\\n processor_handler = processor_handlers[processor[\\\"name\\\"]]\\n processor_handler(args[\\\"root\\\"], args[\\\"name\\\"], processor[\\\"args\\\"])\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1ae0b7c5547a53d55923f42bfb1ae6\",\n \"score\": \"0.5685069\",\n \"text\": \"def main(): \\n args = get_args()\\n config = get_namelist(args)\\n infill(args, config)\\n print '\\\\nFinished\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"194599c7d1746708ab091609c186477b\",\n \"score\": \"0.5684453\",\n \"text\": \"def main():\\n\\n # Parsing command line arguments -->\\n cron_mode = False\\n debug_mode = False\\n develop_mode = False\\n\\n cmd_options, args = getopt.gnu_getopt(\\n sys.argv[1:], \\\"\\\", [\\n \\\"cron\\\",\\n \\\"debug\\\",\\n \\\"develop-mode\\\",\\n ])\\n\\n for option, value in cmd_options[:]:\\n if option == \\\"--cron\\\":\\n cron_mode = True\\n elif option == \\\"--debug\\\":\\n debug_mode = True\\n elif option == \\\"--develop-mode\\\":\\n develop_mode = True\\n else:\\n raise Error(\\\"Logical error.\\\")\\n # Parsing command line arguments <--\\n\\n pcli.log.setup(\\n debug_mode = debug_mode | develop_mode,\\n level = logging.ERROR if cron_mode else None)\\n\\n global _DEVELOP_MODE\\n _DEVELOP_MODE = develop_mode\\n\\n pid_file = os.path.join(tempfile.gettempdir(), \\\"rutracker-get-new-torrents\\\")\\n\\n try:\\n pid_file_fd = psys.daemon.acquire_pidfile(pid_file)\\n except psys.daemon.PidFileLockedError as e:\\n LOG.error(\\\"Exiting: the PID file is locked by another process.\\\")\\n else:\\n try:\\n get_new_torrents()\\n except TemporaryError as e:\\n LOG.warning(e)\\n finally:\\n psys.daemon.release_pidfile(pid_file, pid_file_fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b45c4529f669ea5e737629e555ae217\",\n \"score\": \"0.56844145\",\n \"text\": \"def main():\\n input_lines = []\\n if len(sys.argv) >= 1:\\n input_lines = sys.argv[1:]\\n if len(input_lines) == 0:\\n print(\\\"\\\"\\\"Usage: - python bf.py \\\"<YOUR BRAINFUCK PROGRAM>\\\" to run a program\\n - python --help to see a detailed help\\\"\\\"\\\")\\n elif input_lines[0] == '--help' or input_lines[0] == '-h':\\n help(main)\\n return\\n else:\\n run(input_lines)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a23c40484f51bc45ca9a2ecd817b7e\",\n \"score\": \"0.56780726\",\n \"text\": \"def main():\\n print('Importing scrapers')\\n scrapers = scraper.import_scrapers()\\n print('Caculating available episodes')\\n avail_episode = database.fetch_available_episodes()\\n if len(avail_episode) == 0:\\n print('No episode is available at present')\\n return\\n print('These episodes are available:')\\n for ep in avail_episode:\\n print('{} of {}'.format(ep['ep'], ep['name']))\\n print('Download starts:')\\n for ep in avail_episode:\\n print('Ep.{} of {} is processing'.format(ep['ep'], ep['name']))\\n for __scraper in scrapers:\\n try:\\n url = __scraper.get_download_url(**ep)\\n path = downloader.download(url=url,\\n save_path=configure.TORRENT_SAVE_PATH,\\n **ep)\\n if configure.ENABLE_AUTO_DOWNLOAD:\\n if not utorrent.is_token_initialized():\\n print('Refreshing token')\\n utorrent.refresh_token()\\n print('Importing torrent into utorrent.')\\n utorrent.add_torrent(path, ep['folder'])\\n print('Import completed successfully.')\\n database.set_downloaded_episode(ep['name'], ep['ep'])\\n break\\n except FileNotFoundError:\\n print('Scraper cannot find the file')\\n # If last scraper is used\\n if __scraper is scrapers[-1]:\\n print('File cannot be found in all scraper. Try next time.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dfafbf218933613930fae0c1b52e0da\",\n \"score\": \"0.5674581\",\n \"text\": \"def main():\\n if not exists('config.json'):\\n shutil.copy('default_config.json', 'config.json')\\n print(\\\"Created 'config.json'. Please configure this file first.\\\")\\n return\\n\\n # Load configuration of the project.\\n Config.load()\\n\\n config = Config.get_config()\\n step = config['step']\\n\\n if step == 'PI':\\n print(\\\"Personality Insights analysis started..\\\")\\n process = PersonalityInsights(config)\\n elif step == 'LIWC':\\n print(\\\"LIWC analysis started..\\\")\\n process = LIWC(config)\\n elif step == 'Preprocess':\\n print(\\\"Preprocessing started..\\\")\\n process = Preprocessing(config)\\n elif step == 'QuickLIWC':\\n print(\\\"Started LIWC process with pre-loaded LIWC files..\\\")\\n process = LIWCQuick(config)\\n else:\\n print(\\\"Unknown step specified in configuration.\\\\nProgram terminated.\\\")\\n return\\n process.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4615310e119d88a4053084e7ead92eaf\",\n \"score\": \"0.5674094\",\n \"text\": \"def main():\\n logging.basicConfig(level=logging.INFO, format='%(levelname)-8s: %(message)s')\\n parser = argparse.ArgumentParser(description=__doc__.strip())\\n\\n parser.add_argument('portfolio',\\n help=('A CSV file which contains the tickers of assets and '\\n 'number of units'))\\n parser.add_argument('--dbdir', default=database.DEFAULT_DIR,\\n help=\\\"Database directory to write all the downloaded files.\\\")\\n parser.add_argument('-i', '--ignore-missing-issuer', action='store_true',\\n help=\\\"Ignore positions where the issuer implementation is missing\\\")\\n parser.add_argument('-o', '--ignore-options', action='store_true',\\n help=(\\\"Ignore options positions \\\"\\n \\\"(only works with Beancount export file)\\\"))\\n\\n parser.add_argument('--visible', action='store_true',\\n help=\\\"Run with a visible browser window (not headless).\\\")\\n parser.add_argument('-b', '--driver-exec', action='store',\\n default=\\\"/usr/local/bin/chromedriver\\\",\\n help=\\\"Path to chromedriver executable.\\\")\\n args = parser.parse_args()\\n db = database.Database(args.dbdir)\\n\\n # Load up the list of assets from the exported Beancount file.\\n assets = beansupport.read_portfolio(args.portfolio, args.ignore_options)\\n\\n # Fetch baskets for each of those.\\n driver = None\\n for row in sorted(assets):\\n if not row.issuer and args.ignore_missing_issuer:\\n logging.warning(\\\"Ignoring missing issuer for {}\\\".format(row.ticker))\\n continue\\n try:\\n driver, _ = fetch_holdings(row.ticker, row.issuer, driver, db,\\n args.ignore_missing_issuer, args)\\n except Exception:\\n traceback.print_exc()\\n continue\\n if driver:\\n driver.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235a326da6160ec7324f72f027fe0409\",\n \"score\": \"0.5662144\",\n \"text\": \"def mainProgram(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c55bcfa6634cb4b41550de3b6516ad\",\n \"score\": \"0.56590545\",\n \"text\": \"def main(argv):\\n # parse command line\\n for p in sys.argv[1:]:\\n pair = p.split('=')\\n if (2 != len(pair)):\\n print 'bad parameter: %s' % p\\n break\\n else:\\n parameters[pair[0]] = pair[1]\\n else:\\n # set up logging\\n bname = os.path.splitext( os.path.basename(__file__) )[0]\\n log_config_file = os.path.join('/home/pims/dev/programs/python/pims/files', bname + '_log.conf')\\n logging.config.fileConfig(log_config_file)\\n log = logging.getLogger('inputs')\\n if params_ok(log):\\n try:\\n log = logging.getLogger('process')\\n backfill_ossbtmf_roadmaps(parameters['date_range'], parameters['batch_dir'], log)\\n except Exception, e:\\n # Log error\\n log.process.error( e.message )\\n return -1\\n # Message with time when done\\n log.debug('Done %s.\\\\n' % datetime.datetime.now() + '-'*99)\\n return 0\\n print_usage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87cf60d0cd2f13d4b71927be5ba1083f\",\n \"score\": \"0.5648502\",\n \"text\": \"def main(self):\\n try:\\n self.path_runs.set_execution_directory()\\n\\n self.sequence = create_local_fasta_file(\\n self.path_runs.get_path_execution(),\\n self.opts.fromFasta,\\n self.opts.inputFasta,\\n self.opts.toolname,\\n self.framework)\\n\\n copy_necessary_files(\\n self.path_runs.get_path_execute(),\\n self.path_runs.get_path_execution(),\\n self.framework.get_framework())\\n\\n self.framework.set_command(\\n self.path_runs.get_path_execution(),\\n 'aemt-pop-up2')\\n\\n size = int(self.opts.sizePopulation) / 15\\n\\n cl = [\\n self.framework.get_command(),\\n str(0),\\n self.opts.sizePopulation,\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(size),\\n str(0),\\n str(0),\\n self.opts.VanderWaalsWeight,\\n self.opts.ChargeWeight,\\n str(0),\\n str(0),\\n str(0),\\n str(0),\\n str(0),\\n self.opts.SolvWeight,\\n self.opts.HbondWeight,\\n os.path.join(self.path_runs.get_path_execution(), \\\"fasta.txt\\\"),\\n os.path.join(self.path_runs.get_path_execution(), \\\"result.txt\\\"),\\n os.path.join(self.path_runs.get_path_execution(), \\\"pop_meamt.txt\\\"),\\n os.path.join(self.path_runs.get_path_execution(), \\\"protein.pdb\\\"),\\n os.path.join(self.path_runs.get_path_execution(), \\\"saida1.txt\\\"),\\n os.path.join(self.path_runs.get_path_execution(), \\\"angles.txt\\\"),\\n str(0),\\n os.path.join(self.path_runs.get_path_execution(), \\\"meat.txt\\\"),\\n '&']\\n\\n retProcess = subprocess.Popen(cl, 0, None, None, None, False)\\n retProcess.wait()\\n\\n path_output, file_output = os.path.split(self.opts.output)\\n\\n result, html = get_result_files(\\n self.path_runs.get_path_execution(), self.opts.toolname)\\n\\n send_output_results(path_output, file_output, result)\\n\\n except Exception, e:\\n show_error_message(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93c5b87b0e85d0b61b6f4155ebeb07d5\",\n \"score\": \"0.56413895\",\n \"text\": \"def main():\\r\\n print(\\\"Program started...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa8c9fbb1ee16b09824d86355e708ee\",\n \"score\": \"0.56383514\",\n \"text\": \"def main():\\n # Check parameters\\n parameters = args_check(sys.argv[1:])\\n print(\\\"\\\\nExtracting PB sequences from trajectory.\\\\n\\\")\\n # Extraction of PB sequences with pbxplore\\n table_seq = mk_table_seq(parameters[1], parameters[2])\\n print(\\\"\\\\nComputing frequences of PB (This may take a few minutes)\\\\n\\\")\\n # Computation of frequencies\\n table_pos_freq = mk_table_pos_freq(table_seq)\\n table_dbpos_freq = mk_table_dbpos_freq(table_seq)\\n print(\\\"Computing Mutual Information (This may take a few minutes)\\\\n\\\")\\n # Computation of Mutual Information\\n table_mi = mk_mi_table(table_pos_freq, table_dbpos_freq)\\n mi_heatmap(table_mi, parameters[0])\\n # Write results in output directory\\n table_seq.to_csv(parameters[0]+\\\"PB_seq_table.csv\\\")\\n table_pos_freq.to_csv(parameters[0]+\\\"PB_pos_freq_table.csv\\\")\\n table_dbpos_freq.to_csv(parameters[0]+\\\"PB_dbpos_freq_table.csv\\\")\\n table_mi.to_csv(parameters[0]+\\\"MI_table.csv\\\")\\n print(\\\"Done!\\\\n\\\")\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0387ba897ed2be3b307b469f53cb07ea\",\n \"score\": \"0.56347436\",\n \"text\": \"def main():\\n # Parse script parameters\\n parser = setup_arg_parser()\\n args, _ = parser.parse_known_args()\\n\\n # Select a queue to add\\n selected_queue = select_queue(args)\\n\\n # Install the selected printer\\n if selected_queue:\\n install_printer(selected_queue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485707c004fecd46b86fd5d4f8a2ecd2\",\n \"score\": \"0.5624613\",\n \"text\": \"def main():\\n ''' Parsing arguments from command line , there are two arguments\\n a) directory : must be a directory like example c:\\\\\\\\\\n b) interval : the interval must be the number of seconds\\n\\n example:\\n python client.py --directory=\\\"C:\\\\Users\\\\UserName\\\\OpenDrop\\\" --interval=2\\n '''\\n (options, arguments) = getParse()\\n\\n if not validateArguments(options.directory, options.interval):\\n raise ClientException(\\\"Invalid Arguments\\\")\\n\\n ''' Creating multiprocess elements '''\\n mutex = multiprocessing.Lock()\\n queue = multiprocessing.Queue()\\n OpenDrop = Client(cwd=options.directory, mutex=mutex)\\n interval = 1\\n ''' moving all solutions to multiple processes '''\\n\\n message = \\\"\\\"\\\" ================ Welcome to OpenDrop Client ================= \\\\n \\\\\\n Monitoring Folder: {0} :\\\"\\\"\\\".format(OpenDrop.working_directory)\\n\\n ''' Preparing all processes '''\\n scan = multiprocessing.Process(target=OpenDrop.run, args=(queue, options.interval,))\\n saver = multiprocessing.Process(target=OpenDrop.saveData, args=(queue,))\\n scan.start()\\n saver.start()\\n \\n print message\\n while True:\\n raw_input(\\\"Press enter \\\")\\n with mutex:\\n out = raw_input(\\\"Press q for quit : \\\")\\n if out == 'q':\\n scan.terminate()\\n saver.terminate()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"997afa9348ae9bcf5a419bb07122f911\",\n \"score\": \"0.5624492\",\n \"text\": \"def main(self):\\n\\n log.divider(\\\"setting up\\\", char=\\\"=\\\")\\n\\n self.require_server_connection()\\n\\n self.finish_parameters()\\n\\n if self.args.certify_files:\\n self.certify_local_files()\\n\\n if self.args.logout:\\n return self.logout()\\n\\n self.login()\\n\\n if self.args.wipe_existing_files:\\n self.wipe_files()\\n\\n self.jpoll_key = self.jpoll_open_channel()\\n\\n if self.args.submission_kind == \\\"batch\\\":\\n submit_future = self.batch_submit_references()\\n elif self.args.submission_kind == \\\"certify\\\":\\n submit_future = self.certify_files()\\n elif self.args.submission_kind == \\\"references\\\":\\n submit_future = self.submit_references()\\n elif self.args.submission_kind == \\\"mappings\\\":\\n submit_future = self.submit_mappings()\\n\\n if self.args.monitor_processing:\\n monitor_future = self.monitor()\\n\\n if self.args.wait_for_completion:\\n self.submission_complete(submit_future)\\n\\n if self.args.monitor_processing:\\n monitor = self.monitor_complete(monitor_future)\\n if monitor.exit_status == 0:\\n self._ready_url = monitor.result\\n self._results_id = self._ready_url.split('/')[-1]\\n\\n log.standard_status()\\n\\n self._error_count = log.errors()\\n self._warning_count = log.warnings()\\n\\n if self.auto_confirm is True:\\n if self._error_count == 0:\\n self._confirmation = self.confirm()\\n else:\\n self.cancel()\\n\\n self.get_file_map()\\n\\n return log.errors()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda81ec024f31537cf8b4c9ded449a3e\",\n \"score\": \"0.5622697\",\n \"text\": \"def main():\\n\\n exit_status = ExitStatus.OK\\n try:\\n args = parser.parse_args()\\n #print(args)\\n if args.save:\\n save_args(args.save)\\n\\n if not os.path.exists(args.directory):\\n os.makedirs(args.directory)\\n\\n names = name_generator(\\n args.format, type=args.payload.short, rand=args.rand_method.name,\\n idx_format='{{0:0{}d}}'.format(len(str(args.number))))\\n\\n for i in range(args.number):\\n fn = os.path.join(args.directory, next(names))\\n with open(fn, 'wb+') as f:\\n payload = args.payload.generate(args.rand_method, *args.chunk_size)\\n for p in payload:\\n f.write(p)\\n\\n except IOError as (errno, strerror):\\n print(\\\"I/O error({0}): {1}\\\".format(errno, strerror))\\n exit_status = ExitStatus.ERROR\\n except OSError as (errno, strerror):\\n print(\\\"OS error({0}): {1}\\\".format(errno, strerror))\\n exit_status = ExitStatus.ERROR\\n #except:\\n # print \\\"Unexpected error:\\\", sys.exc_info()\\n # exit_status = ExitStatus.ERROR\\n\\n return exit_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e57a5230df7191a7b8071341c7786fd\",\n \"score\": \"0.56152314\",\n \"text\": \"def main(self):\\n parser = self.setup_parser()\\n args = parser.parse_args()\\n self.parse(args)\\n sys.exit(self.run(args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcadd0ddf3d2fbea99b29ceca83fa9c\",\n \"score\": \"0.5609144\",\n \"text\": \"def main():\\n print(\\\"starting boids...\\\")\\n\\n parser = argparse.ArgumentParser(\\n description=\\\"Implementing Craig Reynolds Bpid...\\\")\\n parser.add_argument('--num_boids', dest='n', required=False)\\n args = parser.parse_args()\\n\\n # Set the initial number of boids\\n n = 100\\n if args.n:\\n n = int(args.n)\\n\\n # Create boids\\n boids = Boids(n)\\n\\n # Set up plot\\n fig = plt.figure()\\n ax = plt.axes(xlim=(0, width), ylim=(0, height))\\n\\n # size and shape of the markers for body\\n pts, = ax.plot([], [], markersize=10, c='k', marker='o',\\n ls='None') # Create empty 2D Line object\\n # size and shape of the markers for beak\\n beak, = ax.plot([], [], markersize=4, c='r', marker='o',\\n ls='None') # Create empty 2D Line object\\n anim = animation.FuncAnimation(\\n fig, tick, fargs=(pts, beak, boids), interval=50)\\n\\n # Add a 'button_press' event handler\\n cid = fig.canvas.mpl_connect(\\n 'button_press_event', boids.button_press) # mouse button is pressed\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8f3ba8e57c28b08321b6211fdfc48a4\",\n \"score\": \"0.5593138\",\n \"text\": \"def main():\\n\\n print('Welcome to the self-checkout system of Wake-mart.')\\n\\n num_items, total_cost = scanPrices()\\n\\n total_cost = discount(num_items, total_cost)\\n\\n num_items, total_cost = promotion(num_items, total_cost)\\n\\n makePayment(total_cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b03dd98ffaa0f65c5b02397cd192b6d4\",\n \"score\": \"0.5572765\",\n \"text\": \"def main():\\n logger.info(\\\"Started script.\\\")\\n pw = PriceWatch()\\n\\n if pw.config[\\\"general\\\"][\\\"send_test_notification\\\"] == \\\"1\\\":\\n logger.info(\\\"Sending test notification and exiting.\\\")\\n notify.test_notification()\\n sys.exit()\\n elif (\\n pw.config[\\\"general\\\"][\\\"wishlist_url\\\"]\\n == \\\"https://www.amazon.co.uk/hz/wishlist/ls/S0M3C0D3\\\"\\n ):\\n config_file_path = Path(Path(__file__).parent, \\\"config.json\\\").resolve()\\n logger.error(f\\\"You need to fill in the config file:\\\\n{config_file_path}\\\")\\n sys.exit()\\n\\n page = pw.request_page()\\n # In parsing the first page, pagination will be followed and requested/parsed.\\n pw.parse_wishlist(page)\\n new_cheaper_items = pw.compare_prices()\\n if new_cheaper_items:\\n notify.send_notification(wishlist_item_list=new_cheaper_items)\\n\\n pw.json_man.save_wishlist_json(pw.wishlist)\\n logger.info(\\\"Finished.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b428fffb548aeea009928beeb8cc0f65\",\n \"score\": \"0.55690086\",\n \"text\": \"def main():\\n print('Welcome to the Brain Games!')\\n name = cli.welcome_user()\\n print('What number is missing in the progression?')\\n check = arifm.game()\\n if check:\\n print('Congratulations, {}!'.format(name))\\n else:\\n print(\\\"Let's try again, {}!\\\".format(name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac8d3430daaa37d2cc1a3d161f21f40\",\n \"score\": \"0.55647093\",\n \"text\": \"def main():\\n\\n\\tparser = OptionParser()\\n\\tparser.add_option(\\\"-p\\\", dest=\\\"pdbfile\\\", help=\\\"pdbfile\\\")\\n\\tparser.add_option(\\\"-P\\\", dest=\\\"pdblist\\\", help=\\\"pdblist\\\")\\n\\tparser.add_option(\\\"-s\\\", dest=\\\"statefile\\\", help=\\\"statefile\\\")\\n\\tparser.set_description(main.__doc__)\\n\\t(options, args) = parser.parse_args()\\n\\n\\tpdbfiles = []\\n\\tif options.pdblist:\\n\\t\\ttry:\\n\\t\\t\\tPDBLIST = open(options.pdblist)\\n\\t\\texcept:\\n\\t\\t\\tprint \\\"unable to open pdblist\\\",options.pdblist\\n\\t\\t\\tsys.exit()\\n\\n\\t\\tfor line in PDBLIST.readlines():\\n\\t\\t\\tline = string.strip(line)\\n\\t\\t\\tpdbfiles.append(line)\\t\\n\\n\\telif options.pdbfile:\\n\\t\\tpdbfiles.append(options.pdbfile)\\n\\telse:\\n\\t\\tparser.print_help()\\n\\t\\tsys.exit()\\n\\n\\tif not options.statefile:\\n\\t\\tparser.print_help()\\n\\n\\n\\tgridlig = grid()\\n\\tgridbb = grid()\\n\\n\\ttry:\\n\\t\\tSTATEFILE = open(options.statefile)\\n\\texcept:\\n\\t\\tprint \\\"unable to open statefile\\\"\\n\\t\\tsys.exit()\\n\\n\\tfor line in STATEFILE.readlines():\\n\\t\\tline = string.strip(line)\\n\\t\\twords = string.split(line)\\n\\t\\t\\n\\t\\tif \\\"gridlig\\\" in line:\\n\\t\\t\\tgridlig.read(words[1])\\n\\n\\t\\tif \\\"gridbb\\\" in line:\\n\\t\\t\\tgridbb.read(words[1])\\n\\t\\t\\t\\n\\t\\t\\n\\tprotein = Molecule()\\n\\tfor pdbfile in pdbfiles:\\n\\t\\tprotein.clear()\\n\\t\\tprotein.readPDB(pdbfile)\\n\\n\\t\\tfailed = False\\n\\t\\tfor chain in protein.chain:\\n\\t\\t\\tfor residue in chain.residue:\\n\\t\\t\\t\\tfor atom in residue.atom:\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tif atom.kind == \\\"HETATM\\\":\\n\\t\\t\\t\\t\\t\\tif atom.name[0] == \\\"V\\\":\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\tx = atom.coord.x\\n\\t\\t\\t\\t\\t\\ty = atom.coord.y\\n\\t\\t\\t\\t\\t\\tz = atom.coord.z\\n\\t\\t\\t\\t\\t\\tocc = atom.occupancy\\n\\t\\t\\n\\t\\t\\t\\t\\t\\tif occ > 0.0:\\n\\t\\t\\t\\t\\t\\t\\tif not gridlig.isInGrid(x, y, z):\\n\\t\\t\\t\\t\\t\\t\\t\\tprint pdbfile,atom.name,atom.file_id,\\\"out of gridlig boundaries\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\tfailed = True\\n\\t\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\t\\tzn = gridlig.getZone(x,y,z)\\t\\n\\t\\t\\t\\t\\t\\t\\tif not gridlig.zone[zn[0]][zn[1]][zn[2]]:\\n\\t\\t\\t\\t\\t\\t\\t\\tprint pdbfile,atom.name,atom.file_id,\\\"not occupied in gridlig\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\tfailed = True\\n\\t\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\tif not gridbb.isInGrid(x,y,z):\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t\\tzn = gridbb.getZone(x,y,z)\\n\\t\\t\\t\\t\\t\\tif gridbb.zone[zn[0]][zn[1]][zn[2]]:\\n\\t\\t\\t\\t\\t\\t\\tprint pdbfile,atom.name,atom.file_id,\\\"clashes with backbone\\\"\\n\\t\\t\\t\\t\\t\\t\\tfailed = True\\n\\t\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\tif not failed:\\n\\t\\t\\tprint pdbfile,\\\".... passes\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a648141e2419a95833a8034d78c490\",\n \"score\": \"0.55433804\",\n \"text\": \"def main() -> NoReturn:\\n version = bobber_version()\\n args = parse_args(version)\\n execute_command(args, version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a37186d057d3b988d1825e86e3f5e0\",\n \"score\": \"0.5538347\",\n \"text\": \"def main():\\n try:\\n logging.info('Initializing')\\n\\n #Get environmental values from transmission\\n #Path should be $TR_TORRENT_DIR/$TR_TORRENT_NAME\\n\\n TORRENT_DIR, TORRENT_NAME = config.get_environmental_variables_from_transmission()\\n\\n extract_all(TORRENT_DIR)\\n _choose_handler(TORRENT_DIR, TORRENT_NAME)\\n _cleanup_temp(TORRENT_DIR)\\n logging.info('Done!')\\n except:\\n logging.exception(\\\"Critical exception occurred: \\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a6ce4166f71ba4a23231df630e7cd2\",\n \"score\": \"0.5537185\",\n \"text\": \"def main():\\n application = BoaApp(0)\\n application.MainLoop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321fbf81842237360c4e1794541c3a6b\",\n \"score\": \"0.5532704\",\n \"text\": \"def main():\\n _setup_logging()\\n song_winners.workflow()\\n spotify_songs.workflow()\\n votes.workflow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee74120d247a5df731794fa3dec5cc3\",\n \"score\": \"0.55205685\",\n \"text\": \"def run(self):\\n my_bulb = BulbBlinker( my_id = self.id,\\n bpm = self.bpm, \\n host = self.host,\\n adjustment = self.adjustment,\\n bulb_objects_list = self.bulb_objects_list, \\n above_neighbor = (self.id + 1) % 13, \\n below_neighbor = (self.id - 1) % 13, \\n turned_on_list = self.turned_on_list)\\n my_bulb.daemon = True \\n my_bulb.start()\\n self.check_ordering()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2c9b9f73f8fdca22f4d25a4b07dc60\",\n \"score\": \"0.5520292\",\n \"text\": \"def main():\\n\\n\\tusage = \\\"usage: %prog <project_id> <file>\\\"\\n\\tdescription = \\\"%prog imports dirb files into Lair\\\"\\n\\tparser = OptionParser(usage=usage, description=description,\\n\\t\\t\\t\\t\\t\\t\\tversion=\\\"%prog 0.0.1\\\")\\n\\n\\t(options, args) = parser.parse_args()\\n\\tif len(args) < 2:\\n\\t\\tprint parser.get_usage()\\n\\t\\tsys.exit(1)\\n\\n\\tproject_id, result_resource = args\\n\\tproject = dirb.parse(project_id, result_resource)\\n\\n\\t# Connect to the database\\n\\tdb = api.db_connect()\\n\\tapi.save(project, db, dirb.TOOL)\\n\\tsys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4314c1408e37cf60dcd240257ff3a682\",\n \"score\": \"0.5519621\",\n \"text\": \"def main():\\n args = get_args()\\n setup_logs(args, args.out_prefix)\\n\\n\\n # Parse Brenda Ligands file\\n logging.info(\\\"Starting to parse ligands\\\")\\n brenda_ligands_file = f\\\"{args.out_prefix}_brenda_ligands.json\\\"\\n brenda_ligands = parse_ligands_file(brenda_ligands_file,\\n args.brenda_ligands, args.load_prev,\\n args.debug)\\n logging.info(\\\"Done parsing ligands\\\")\\n\\n # Parse brenda ec classes and enzymes\\n ec_stats_file = f\\\"{args.out_prefix}_brenda_ec_stats.json\\\"\\n enzymes_data_file = f\\\"{args.out_prefix}_brenda_enzymes_data.json\\\"\\n\\n # Parse brenda flat file\\n # Ec_stats contains enzyme class wide parameters\\n # enzymes_data contains actual enzymes\\n logging.info(\\\"Starting to parse flat file\\\")\\n ec_stats, enzymes_data = parse_flat_file(ec_stats_file, \\n enzymes_data_file, \\n args.brenda_flat_file,\\n args.load_prev,\\n args.out_prefix,\\n args.debug)\\n logging.info(\\\"Done parsing flat file\\\")\\n\\n # Get the list of compounds from enzymes data \\n logging.info(\\\"Starting to extract compound list\\\")\\n compounds_list_file = f\\\"{args.out_prefix}_compound_list.json\\\"\\n compound_list = get_compound_list(enzymes_data, compounds_list_file, \\n args.load_prev)\\n logging.info(\\\"Done extracting compound list\\\")\\n\\n logging.info(\\\"Starting to extract inchi/chebi\\\")\\n chebi_inchi_set_file = f\\\"{args.out_prefix}_chebi_inchi_set.json\\\"\\n chebi_inchi_set = extract_unique_inchi_chebi(chebi_inchi_set_file, \\n brenda_ligands,\\n args.load_prev)\\n logging.info(\\\"Done extracting inchi/chebi\\\")\\n\\n logging.info(\\\"Starting to map chebi to smiles\\\")\\n # Map brenda ligand chebi to smiles\\n mapped_chebi_file = f\\\"{args.out_prefix}_chebi_to_smiles.json\\\"\\n unmapped_chebi_file = f\\\"{args.out_prefix}_chebi_unmapped.json\\\"\\n mapped_chebi, unmapped_chebi = map_chebi_to_smiles(chebi_inchi_set, \\n mapped_chebi_file,\\n unmapped_chebi_file, \\n args.load_prev,\\n args.out_prefix,\\n args.use_cirpy,\\n args.cirpy_log)\\n logging.info(\\\"Done mapping chebi to smiles\\\")\\n\\n logging.info(\\\"Starting to map inchi to smiles\\\")\\n # Map brenda ligand inchi to smiles\\n mapped_inchi_file = f\\\"{args.out_prefix}_inchi_to_smiles.json\\\"\\n unmapped_inchi_file = f\\\"{args.out_prefix}_inchi_unmapped.json\\\"\\n mapped_inchi, unmapped_inchi = map_inchi_to_smiles(chebi_inchi_set,\\n mapped_inchi_file,\\n unmapped_inchi_file,\\n args.load_prev,\\n args.out_prefix,\\n args.use_cirpy,\\n args.cirpy_log)\\n logging.info(\\\"Done mapping inchi to smiles\\\")\\n\\n logging.info(\\\"Starting to resolve all compounds to smiles\\\")\\n # Now resolve all compounds\\n mapped_comps_file = f\\\"{args.out_prefix}_compounds_to_smiles.json\\\"\\n unmapped_comps_file = f\\\"{args.out_prefix}_compounds_unmapped.json\\\"\\n\\n # TODO: Fix this so that it doesn't return strings and lists, but only one\\n # of the two types \\n mapped_compounds, unmapped_compounds = map_compounds_to_smiles(compound_list, brenda_ligands, \\n mapped_inchi, mapped_chebi,\\n mapped_comps_file, unmapped_comps_file,\\n args.load_prev, args.out_prefix,\\n args.use_cirpy, args.cirpy_log, \\n args.opsin_loc) \\n logging.info(\\\"Done resolving all compounds to smiles\\\")\\n\\n\\n # Standardize all smiles mappings!\\n # Load from file if it exists\\n if not args.no_standardize:\\n logging.info(\\\"Starting standardizer\\\")\\n mapped_standardized_file = f\\\"{args.out_prefix}_compounds_to_standardized_smiles.json\\\"\\n mapped_compounds = standardize_smiles(mapped_standardized_file, mapped_compounds,\\n args.standardizer_log, args.load_prev, \\n args.multiprocess_num)\\n logging.info(\\\"Done with standardizer\\\")\\n\\n\\n logging.info(\\\"Beginning to export all files\\\")\\n # Reverse the mapping for reference later \\n smiles_to_names_file = f\\\"{args.out_prefix}_smiles_to_comp_names.json\\\"\\n smiles_to_names = reverse_mapping(mapped_compounds, \\n outfile = smiles_to_names_file)\\n\\n # Add gene sequences and compound smiles back to the dataframe.\\n enzymes_data, rxn_set, compound_set = add_compounds(\\n enzymes_data, mapped_compounds)\\n rxn_set, compound_set = pd.DataFrame(rxn_set), pd.DataFrame(compound_set)\\n\\n # Ouput finished mapping to file\\n enzymes_mapped_data_file = f\\\"{args.out_prefix}_enzymes_data_complete_mapped.json\\\"\\n rxn_final_tsv = f\\\"{args.out_prefix}_rxn_final.tsv\\\"\\n compounds_final_tsv = f\\\"{args.out_prefix}_rxn_compounds.tsv\\\"\\n utils.dump_json(enzymes_data,\\n enzymes_mapped_data_file,\\n pretty_print=False)\\n\\n compound_set.to_csv(compounds_final_tsv, sep=\\\"\\\\t\\\")\\n rxn_set.to_csv(rxn_final_tsv, sep=\\\"\\\\t\\\")\\n\\n # Output statistics about all the data collected\\n summary_file = f\\\"{args.out_prefix}_stats.json\\\"\\n stats_summary = {\\n \\\"mapped_compound_smiles\\\": len(mapped_compounds),\\n \\\"unmapped_compound_smiles\\\": len(unmapped_compounds),\\n \\\"mapped_inchis\\\": len(mapped_inchi),\\n \\\"unmapped_inchis\\\": len(unmapped_inchi),\\n \\\"mapped_chebi\\\": len(mapped_chebi),\\n \\\"unmapped_chebi\\\": len(unmapped_chebi),\\n }\\n\\n stats_summary[\\\"Num ec classes\\\"] = len(ec_stats)\\n\\n # Update with other statistics and print this to a file\\n stats_summary.update(\\n parse_brenda_stats.get_rxn_compound_stats(rxn_set, compound_set)\\n )\\n utils.dump_json({k: str(v) for k, v in stats_summary.items()},\\n summary_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"161425c07e5d026e7b708d9a378b2a0a\",\n \"score\": \"0.5518024\",\n \"text\": \"def test_launch_bs(run_cli_process_launch_command, fixture_code, generate_remote_data):\\n from aiida_kkr.cmdline.launch import launch_bs\\n from aiida.engine.processes.calcjobs.tasks import PreSubmitException\\n code = fixture_code('kkr.kkr').store()\\n params = kkrparams(params_type='kkr')\\n params.set_multiple_values(\\n MIN=-10,\\n MAX=5,\\n NPT2=12,\\n RCLUSTZ=2.3,\\n TEMPR=50,\\n )\\n\\n param_node = Dict(dict=params.get_dict()).store()\\n path = os.path.abspath(os.path.join(THISFILE_PATH, '../../files/bd_dump_bs/parent_kkr'))\\n remote = generate_remote_data(code.computer, path).store()\\n options = ['--kkr', code.uuid, '--parameters', param_node.uuid, '--parent-folder', remote.uuid]\\n run_cli_process_launch_command(launch_bs, options=options, raises=PreSubmitException)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e72155a4c14785f4d6f7a5c7a836693\",\n \"score\": \"0.5516675\",\n \"text\": \"def main():\\n args = get_cmdline_arguments()\\n bord_config = configreader.ConfigReader(args.config)\\n count = create_html(bord_config['content_dir'], bord_config['output_dir'])\\n print ('Created', count, 'HTML files')\\n if (args.server):\\n print ('Serving at:', str(args.server))\\n server.start_server(bord_config['output_dir'], args.server)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a8b95fc16c26f3db613753c0b17b2d\",\n \"score\": \"0.5509966\",\n \"text\": \"def main():\\n options = Options()\\n app = Trader(options)\\n x = 0\\n while x < 1:\\n # app.call_ticker()\\n test = app.balances()\\n # app()\\n # app.trade_history()\\n # app.buy(amount=1, rate=.001, coin='BTC_ETH')\\n # app.sell(amount=8, rate=.00000130, coin='BTC_DOGE')\\n # order = app.buy()\\n\\n # if app.cancel(order):\\n # print(\\\"Order {}.. !! canceled\\\".format(order))\\n # time.sleep(.05)\\n #\\n # history = app.quote_history()\\n print(test)\\n x += 1\\n\\n if app.main():\\n sys.exit('Alphagriffin.com | 2017')\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9849beb2becdf68e2bf5a55ba6c8e058\",\n \"score\": \"0.5497161\",\n \"text\": \"def main():\\n\\n BASIC.run(PROGRAM)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd1ee4e3a3c53c0b332a70d92705c3a\",\n \"score\": \"0.549565\",\n \"text\": \"def run_program():\\n\\n if not aig.are_parameters_valid():\\n raise Exception(\\\"You must provide the name of input file as first argument!\\\")\\n\\n graph = aig.parse_input(sys.argv[1])\\n path = read_path()\\n\\n print(compute_path_cost(path, graph))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"918c5dd264aa819f502eccd9910855ed\",\n \"score\": \"0.549286\",\n \"text\": \"def main():\\n epilog = \\\"DT179G Assignment 3 v\\\" + __version__\\n parser = argparse.ArgumentParser(description=__desc__, epilog=epilog, add_help=True)\\n parser.add_argument('nth', metavar='nth', type=int, nargs='?', default=30,\\n help=\\\"nth Fibonacci sequence to find.\\\")\\n\\n global LOGGER # ignore warnings raised from linters, such as PyLint!\\n LOGGER = create_logger()\\n\\n args = parser.parse_args()\\n nth_value = args.nth # nth value to sequence. Will fallback on default value!\\n\\n fib_details = { # store measurement information in a dictionary\\n 'fib iteration': fibonacci_iterative(nth_value),\\n 'fib recursion': fibonacci_recursive(nth_value),\\n 'fib memory': fibonacci_memory(nth_value)\\n }\\n\\n print_statistics(fib_details, nth_value) # print information in console\\n\\n write_to_file(fib_details) # write data files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a84589821cca136350f458b3fcfd575\",\n \"score\": \"0.54918534\",\n \"text\": \"def main():\\n\\targs = get_args()\\n\\tbottles = args.num_bottles\\n\\n\\tfor bottles in range(bottles,0,-1): # -1 decrease by 1 each time \\n\\t\\tif bottles > 2:\\n\\t\\t\\tprint('{} bottles of beer on the wall,'.format(bottles))\\n\\t\\t\\tprint('{} bottles of beer,'.format(bottles))\\n\\t\\t\\tprint('Take one down, pass it around,')\\n\\t\\t\\tprint('{} bottles of beer on the wall!'.format(bottles-1)+'\\\\n')\\n\\t\\tif bottles ==2:\\n\\t\\t\\tprint('{} bottles of beer on the wall,'.format(bottles))\\n\\t\\t\\tprint('{} bottles of beer,'.format(bottles))\\n\\t\\t\\tprint('Take one down, pass it around,')\\n\\t\\t\\tprint('{} bottle of beer on the wall!'.format(bottles-1)+'\\\\n') # proper grammer for 1 bottle\\n\\t\\tif bottles == 1:\\n\\t\\t\\tprint('{} bottle of beer on the wall,'.format(bottles)) # proper grammer for 1 bottle\\n\\t\\t\\tprint('{} bottle of beer,'.format(bottles)) # proper grammer for 1 bottle\\n\\t\\t\\tprint('Take one down, pass it around,')\\n\\t\\t\\tprint('{} bottles of beer on the wall!'.format(bottles-1)) # 0 bottles is proper grammer\\n\\t\\t\\tbreak\",\n \"title\": \"\"\n },\n {\n \"docid\": \"483978573f788bb33960325a7b7f12f3\",\n \"score\": \"0.5483947\",\n \"text\": \"def main():\\n args = parse_arguments()\\n if args.sub == 'byname':\\n request_result(punk_requests.get_beer_by_name(args.name), args.file)\\n elif args.sub == 'byid':\\n request_result(punk_requests.get_beer_by_id(args.id), args.file)\\n elif args.sub == 'byinterval':\\n request_result(punk_requests.get_beer_brewed_in(args.from_date, args.until_date), args.file)\\n elif args.sub == 'byfood':\\n request_result(punk_requests.get_beer_by_food(args.food_pairs), args.file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea842d0c4094cc8f0b450f6c16ebefc3\",\n \"score\": \"0.547629\",\n \"text\": \"def main(argv):\\n keystore, signing_key, verify_key_hex, networker, blockchain_thread, gui_thread, dns = init(*parse_args(argv))\\n\\n # User Interaction\\n while True:\\n command = None\\n if gui_thread.is_alive():\\n if not gui_receive_queue.empty():\\n command = gui_receive_queue.get(block=True)\\n print('Command from gui: {}'.format(command))\\n else:\\n continue\\n\\n if not command:\\n print('Action: ')\\n command = get_command()\\n\\n if command == 'help':\\n help_str = (\\\"\\\"\\\" Available commands:\\n help: prints commands\\n transaction <to> <amount> : Create transaction\\n mine: mine a new block\\n balance [<name>]: Print balance (name optional)\\n dump: print blockchain\\n peers: print peers\\n key <filename> : Save current key to <filename>\\n import <key> <name> : Imports a public key associated with \\\\\\n<name> from file <file> to the keystore\\n deletekey <name> : Deletes key associated with <name> from\\\\\\nkeystore\\n export <filename> : Exports one own public key to file\\\\\\n<filename>\\n save: Save blockchain to bc_file.txt\\n exit: exits program\\n \\\"\\\"\\\")\\n print(help_str)\\n if dns:\\n dns_str = (\\\"\\\"\\\" DNS-only commands:\\n register <domain> <ip> : Registers an available domain to an ip (costs 20 coins)\\n update <domain> <ip> : Updates an existing already owned domain with a new ip (costs 20 coins)\\n transfer <to> <domain> : Transfers an owned domain to another user (costs 1 coin)\\n auction <domain> : Offers an owned domain for auction (costs 1 coin)\\n bid <amount> <domain> : Places a bid of <amount> on the auctioned domain.\\n resolve <domain> : Resolves the domain name and prints the ip (if the domain does not exist, prints '')\\n \\\"\\\"\\\")\\n print(dns_str)\\n gui_send_queue.put(help_str)\\n elif command == 'exit':\\n receive_queue.put(('exit', '', 'local'))\\n send_queue.put(None)\\n keystore.save()\\n blockchain_thread.join()\\n networker.join()\\n if gui_thread.is_alive():\\n gui_thread.join()\\n sys.exit()\\n elif command == 'mine':\\n receive_queue.put(('mine', verify_key_hex, 'local'))\\n elif re.fullmatch(r'transaction \\\\w+ \\\\d+', command):\\n t = command.split(' ')\\n # Create new Transaction, sender = hex(public_key),\\n # signature = signed hash of the transaction\\n if int(t[2]) <= 0:\\n print('Transactions must contain a amount greater than zero!')\\n continue\\n recipient = keystore.resolve_name(t[1])\\n if recipient == 'Error':\\n continue\\n timestamp = time.time()\\n # fee equals 5% of the transaction amount - at least 1\\n fee = int(math.ceil(int(t[2]) * 0.05))\\n transaction = create_transaction(dns, verify_key_hex, str(recipient),\\n int(t[2]), fee, timestamp, DNS_Data('', '', ''), signing_key)\\n\\n receive_queue.put(('new_transaction',\\n transaction,\\n 'local'\\n ))\\n elif command == 'dump':\\n # gui_send_queue.put(vars(my_blockchain))\\n # pprint(vars(my_blockchain))\\n receive_queue.put(('dump', '', 'local'))\\n elif command == 'peers':\\n networker_command_queue.put('print_peers')\\n elif re.fullmatch(r'key \\\\w+', command):\\n try:\\n t = command.split(' ')\\n save_key(signing_key, t[1])\\n print('Key saved successfully')\\n except Exception as e:\\n print('Could not save key')\\n print(e)\\n elif re.fullmatch(r'import \\\\w+ \\\\w+', command):\\n try:\\n t = command.split(' ')\\n if keystore.resolve_name(t[2]):\\n keystore.update_key(t[2], t[1])\\n else:\\n print('importing public key')\\n keystore.add_key(t[2], t[1])\\n except Exception as e:\\n print('Could not import key')\\n print(e)\\n elif re.fullmatch(r'deletekey \\\\w+', command):\\n try:\\n t = command.split(' ')\\n if keystore.resolve_name(t[1]):\\n keystore.update_key(t[1], '')\\n else:\\n print(\\n f'Could not delete {t[1]} from keystore.',\\n ' Was it spelt right?')\\n except Exception as e:\\n print('Could not delete key')\\n print(e)\\n elif re.fullmatch(r'export \\\\w+', command):\\n try:\\n print('Exporting public key')\\n t = command.split(' ')\\n save_key(verify_key_hex, t[1])\\n except Exception as e:\\n print('Could not export public key')\\n print(e)\\n elif command == 'balance':\\n receive_queue.put(('print_balance',\\n (verify_key_hex, time.time()),\\n 'local'))\\n elif re.fullmatch(r'balance \\\\w+', command):\\n t = command.split(' ')\\n account = keystore.resolve_name(t[1])\\n if account != 'Error':\\n receive_queue.put(('print_balance',\\n (account, time.time()),\\n 'local'\\n ))\\n elif re.fullmatch(r'resolve \\\\w+\\\\.\\\\w+', command):\\n if not dns:\\n print('Command not supported!')\\n continue\\n t = command.split(' ')\\n receive_queue.put(('dns_lookup',\\n t[1],\\n 'local'\\n ))\\n elif re.fullmatch(r'(register|update) \\\\w+\\\\.\\\\w+ \\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}', command):\\n if not dns:\\n print('Command not supported!')\\n continue\\n t = command.split(' ')\\n if not validate_ip(t[2]):\\n print('Not a valid ip')\\n continue\\n recipient = '0'\\n timestamp = time.time()\\n # fee for domain registration equals 20\\n fee = 20\\n typ = t[0][0]\\n data = DNS_Data(typ, t[1], t[2])\\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\\n fee, timestamp, data, signing_key)\\n\\n receive_queue.put(('new_transaction',\\n transaction,\\n 'local'\\n ))\\n elif re.fullmatch(r'transfer \\\\w+ \\\\w+\\\\.\\\\w+', command):\\n if not dns:\\n print('Command not supported!')\\n continue\\n t = command.split(' ')\\n account = keystore.resolve_name(t[1])\\n if account != 'Error':\\n recipient = account\\n timestamp = time.time()\\n # fee for domain transfer equals 1\\n fee = 1\\n typ = 't'\\n data = DNS_Data(typ, t[2], '')\\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\\n fee, timestamp, data, signing_key)\\n receive_queue.put(('new_transaction',\\n transaction,\\n 'local'\\n ))\\n elif re.fullmatch(r'auction \\\\w+\\\\.\\\\w+', command):\\n if not dns:\\n print('Command not supported!')\\n continue\\n t = command.split(' ')\\n recipient = '0'\\n timestamp = time.time()\\n # fee for auction equals 1\\n fee = 1\\n typ = 't'\\n data = DNS_Data(typ, t[1], '')\\n transaction = create_transaction(dns, verify_key_hex, str(recipient), 0,\\n fee, timestamp, data, signing_key)\\n\\n receive_queue.put(('new_transaction',\\n transaction,\\n 'local'\\n ))\\n elif re.fullmatch(r'bid \\\\d+ \\\\w+\\\\.\\\\w+', command):\\n if not dns:\\n print('Command not supported!')\\n continue\\n t = command.split(' ')\\n recipient = '0'\\n timestamp = time.time()\\n # fee for bid equals 1\\n fee = 1\\n typ = 'b'\\n amount = int(t[1])\\n data = DNS_Data(typ, t[2], '')\\n transaction = create_transaction(dns, verify_key_hex, str(recipient), amount,\\n fee, timestamp, data, signing_key)\\n receive_queue.put(('new_transaction',\\n transaction,\\n 'local'\\n ))\\n elif command == 'save':\\n receive_queue.put(('save',\\n '',\\n 'local'\\n ))\\n elif command == 'gui':\\n # if dns:\\n # print('gui not yet supported for DNS Chain')\\n # continue\\n print(\\\"open gui\\\")\\n gui_thread.start()\\n gui_send_queue.put(('signing_key', signing_key, 'local'))\\n\\n else:\\n print('Command not found!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102a5a04bf02a2b75f0d5da52ae8e173\",\n \"score\": \"0.5474822\",\n \"text\": \"def main(self, argv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb92df960cc977514bfbcbc6c0c30fc\",\n \"score\": \"0.5474742\",\n \"text\": \"def main():\\n engine.run_game(settings.DESCR_PRIME, prime.ask_question)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8122dc64784738c878decc2c605752f\",\n \"score\": \"0.546062\",\n \"text\": \"def main():\\n # create log file first\\n create_logs()\\n # log start info\\n logging.info('Starting new enrollment flow now...')\\n logging.info('Waiting for user space to fully load...')\\n # waiting for user space\\n wait_for_userspace()\\n # check if the jss is available\\n # commented out, only used for troubleshooting purposes, i.e. remote offices fail to enroll\\n #check_jss_connection()\\n # set the computer name\\n logging.info('setting the computer name...')\\n set_computername()\\n # ensure dependencies and jamf binary is present\\n logging.info('Installing initial dependencies for DEP Notify flow...')\\n install_dependencies()\\n # install dependency list for before we continue...\\n run_jamf_policy(DEPENDENCY_LIST)\\n # start DEP Notify flow\\n write_to_dnlog('Command: Image: %s' % DEPSCREEN)\\n # please modify this code to match the verbiage you want to display\\n write_to_dnlog('Command: MainTitle: Welcome to myOrg')\\n write_to_dnlog('Command: MainText: Please wait while we setup and configure your Mac at MyOrg '\\n 'This should only take a few minutes and will require a restart of your Mac. '\\n 'If you need assistance please contact IT at helpdesk@myorg.com')\\n write_to_dnlog('Status: Preparing your system...')\\n start_dep_notify()\\n # install base software/config\\n run_jamf_policy(POLICY_LIST)\\n # apply security compliance and reboot last\\n run_jamf_policy(SECURITY_LIST)\\n software_updates()\\n write_to_dnlog('Status: Enrollment is complete, exiting...')\\n write_to_dnlog('Command: Quit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54314d4beffe9402f299840136debdc4\",\n \"score\": \"0.54560435\",\n \"text\": \"def main():\\n pickle = str(sys.argv[3])\\n print \\\"Getting network.... \\\"\\n net = cPickle.load(open(pickle, 'rb'))\\n size = get_network_size(net)\\n print \\\"Network size = %s\\\" % size\\n image_path = str(sys.argv[1])\\n gt = str(sys.argv[2])\\n classifier = CraterClassifier(net)\\n print \\\"Running the pyramid...\\\"\\n pyramid = Pyramid(image_path, 10, size, gt, classifier)\\n drawing_data = pyramid.runPyramid()\\n cPickle.dump(pyramid.hitlist,open(DRAWING_DATA_OUT, 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b13890bcbc6086e23d2b901a69ed2d1c\",\n \"score\": \"0.5447834\",\n \"text\": \"def main():\\n print('Welcome to the Brain Games!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcc79a5c9d535d9e0bba7b1a0f6d75f\",\n \"score\": \"0.5444025\",\n \"text\": \"def main():\\n\\n # Get the current working dir. path;\\n current_dir = str(pathlib.Path().absolute())\\n\\n # Get current time\\n now = datetime.now()\\n current_time = now.strftime(\\\"%d-%m-%Y\\\") + '_' + now.strftime(\\\"%H_%M_%S\\\")\\n\\n # ---------------------------- Set LOG environment ----------------------------\\n # set up logging to file - see previous section for more details\\n logger_path = f'{current_dir}/LOGs/{current_time}.log'\\n logging.basicConfig(level=logging.DEBUG,\\n format='%(asctime)s %(name)-20s: %(levelname)-8s %(message)s',\\n filename=logger_path,\\n filemode='w')\\n # define a Handler which writes INFO messages or higher to the sys.stderr\\n console = logging.StreamHandler()\\n console.setLevel(logging.INFO)\\n # set a format which is simpler for console use\\n formatter = logging.Formatter('[%(name)-20s]: [%(levelname)s] %(message)s')\\n # tell the handler to use this format\\n console.setFormatter(formatter)\\n # add the handler to the root logger\\n logging.getLogger('').addHandler(console)\\n\\n # Now, we can log to the root logger, or any other logger. First the root...\\n logging.info(':: The Process started.')\\n\\n # Now, define a couple of other loggers which might represent areas in your\\n # application:\\n\\n distance = logging.getLogger('main.distance-file')\\n # ---------------------------- End LOG environment ----------------------------\\n\\n # reads the config file, if it exists;\\n config_file_path = current_dir + \\\"\\\\\\\\general_config.txt\\\"\\n # if config file exists continue, else create a new one;\\n if os.path.isfile(config_file_path):\\n # Get the available configuration file;\\n with open(config_file_path) as file:\\n config = json.load(file)\\n logging.info(\\\":: Read configuration file complete.\\\\n\\\")\\n pass\\n else:\\n logging.info(\\\":: File not found! Creating a new configuration...\\\")\\n logging.info(\\n \\\":: Please check the Readme file for information regarding the settings configuration standards.\\\\n\\\")\\n config = config_gen(current_dir, config_file_path)\\n\\n # Get the configuration settings;\\n (proteins_path, is_convex_relax, multi_start_phase, mdjeep_source, protein_black_list,\\n global_debug_value) = config.values()\\n\\n # get the names and directories for all the available proteins; dict(protein_name: its_dir);\\n proteins = dict(zip(os.listdir(config[\\\"proteins_path\\\"]), glob.glob(f\\\"{proteins_path}\\\\\\\\*\\\"))) # dict\\n\\n # ---------------------------- Now starts the tests ----------------------------\\n # Create the Node test directory - Node - run date in current directory\\n logging.info(':: Creating the test directory...')\\n # First we will save the proteins names and paths to be read by the SDP Matlab script;\\n # And also, create the test paths for each one, saving it on a dictionary\\n protein_tests = {}\\n\\n proteins_filepath_dir = current_dir + f\\\"\\\\\\\\Matlab\\\\\\\\proteins.txt\\\"\\n\\n with open(proteins_filepath_dir, 'w+') as file:\\n for protein, path in proteins.items():\\n # We will not load the proteins on the blacklist;\\n if protein in protein_black_list:\\n continue\\n else:\\n directory = f'\\\\\\\\Tests\\\\\\\\{current_time}_{protein}'\\n test_path = current_dir + directory\\n # Create the directory\\n os.mkdir(test_path)\\n # Saves the directory\\n protein_tests[f\\\"{protein}\\\"] = test_path\\n logging.info(f\\\":: Directory '{directory}' created.\\\")\\n\\n # Writes this information to Matlab usage\\n # Scheme: Node -- node_path -- test_path\\n file.write(f\\\"{protein},{path},{test_path}\\\\n\\\")\\n\\n # Create the distance files for every available proteins\\n for node in protein_tests.keys():\\n # distance file generator -- generates the distance file, if it doesn't exists, and returns it's directory:\\n try:\\n # TODO: Insert the overwrite=distance_overwrite option\\n gen_distance_file(node, proteins[f\\\"{node}\\\"])\\n distance.info(\\\":: Process completed successfully, waiting for data to be read...\\\\n\\\")\\n\\n except OSError as err:\\n distance.warning(f\\\":: Distance file generator found an error with node: {node} \\\\n\\\"\\n f\\\":: {err}.\\\")\\n distance.warning(\\\":: The process was interrupted!\\\")\\n continue\\n\\n # ------------ Matlab (YALMIP): SDP Program\\n\\n # SDP launch and start phase:\\n logging.info(':: Start [SDP] program phase.')\\n # prepare a structure file for he matlab bash script (SDP Execution);\\n launch_sdp(current_dir)\\n logging.info(\\\"SPG environment configuration set.\\\\n\\\")\\n\\n # ------------ Python Refinement: SPG Program\\n num_nodes = len(proteins.keys())\\n nd_counter = 1 # node counter\\n for node in protein_tests.keys():\\n logging.info(f\\\":: #{nd_counter} Node: {node} of {num_nodes}\\\")\\n # --- Open PDB File\\n node_path = proteins[f'{node}']\\n pdb_path = node_path + f'\\\\\\\\{node}.txt'\\n dist_path = node_path + \\\"\\\\\\\\dist.txt\\\"\\n test_path = protein_tests[f'{node}']\\n pdb = open_pdb_file(pdb_path)\\n logging.debug(':: Environmental properties successful loaded.')\\n distancias, u, v, lb, ub, prop_dist = env_set(dist_path)\\n\\n # Now begins the refinement process of each protein\\n # SPG launch and start phase\\n logging.info(\\\":: Start [SPG] program phase.\\\")\\n ops = (prop_dist, is_convex_relax, multi_start_phase, global_debug_value)\\n launch_spg(node, pdb, test_path, distancias, lb, ub, u, v, ops)\\n # -----\\n nd_counter += 1\\n\\n # ------------ General statistics: static\\n launch_statistics(f'{current_dir}/Statistics/{current_time}.txt', protein_tests)\\n # End\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdf51636417aa862634dcdbfb677537\",\n \"score\": \"0.5436598\",\n \"text\": \"def run():\\n main()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7020080d53520238d2265b1adc42c1dd\",\n \"score\": \"0.5423083\",\n \"text\": \"def main():\\n\\n # FIXME need to verify parameters or otherwise validate input\\n\\n # parse command line arguments\\n args = bbargparse.parse_inputs()\\n\\n # if args not ok, then squawk and return exit code of -1\\n if not args_ok(args):\\n return -1\\n\\n # show desired game day results\\n show_results(args)\\n\\n # return exit code zero for success\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18ab5eb6ed5ca0310ac031f5d75e6c1\",\n \"score\": \"0.54151267\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Wrapper for the GROMACS grompp module.\\\",\\n formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\\n parser.add_argument('-c', '--config', required=False, help=\\\"This file can be a YAML file, JSON file or JSON string\\\")\\n\\n # Specific args of each building block\\n required_args = parser.add_argument_group('required arguments')\\n required_args.add_argument('--input_gro_path', required=True)\\n required_args.add_argument('--input_top_zip_path', required=True)\\n required_args.add_argument('--output_tpr_path', required=True)\\n parser.add_argument('--input_cpt_path', required=False)\\n parser.add_argument('--input_ndx_path', required=False)\\n parser.add_argument('--input_mdp_path', required=False)\\n\\n args = parser.parse_args()\\n config = args.config if args.config else None\\n properties = settings.ConfReader(config=config).get_prop_dic()\\n\\n # Specific call of each building block\\n grompp(input_gro_path=args.input_gro_path, input_top_zip_path=args.input_top_zip_path,\\n output_tpr_path=args.output_tpr_path, input_cpt_path=args.input_cpt_path,\\n input_ndx_path=args.input_ndx_path, input_mdp_path=args.input_mdp_path,\\n properties=properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6b2570d3a95189963ebaf6039d457c\",\n \"score\": \"0.54100776\",\n \"text\": \"def main():\\n here = os.path.abspath(os.path.dirname(__file__))\\n app_file = os.path.join(here, \\\"app.py\\\")\\n os.environ[\\\"FLASK_APP\\\"] = app_file\\n os.environ[\\\"FLASK_ENV\\\"] = \\\"development\\\"\\n command = \\\"python -m flask run\\\"\\n command = command.replace(\\\"python\\\", sys.executable)\\n print(\\\" --- Interactive Bayesian Optimisation ---\\\")\\n print(\\\" * Please, open your browser at the address shown below\\\")\\n subprocess.run(command.split(\\\" \\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7914578b1dd53c90d9bf0c9f594c94\",\n \"score\": \"0.5403009\",\n \"text\": \"def _main():\\n # connect_port support in Stem\\n assert get_distribution('stem').version > '1.4.0', \\\\\\n 'Stem module version must be greater than 1.4.0.'\\n parser = ArgumentParser(description=\\\"Generate Tor paths.\\\")\\n parser.add_argument(\\\"--port\\\", type=int, default=9051,\\n help=\\\"tor control port.\\\")\\n parser.set_defaults(network_protection=True)\\n args = parser.parse_args()\\n\\n controller = connect_port(port=args.port)\\n if not controller:\\n sys.stderr.write(\\\"ERROR: Couldn't connect to tor.\\\\n\\\")\\n sys.exit(1)\\n if not controller.is_authenticated():\\n controller.authenticate()\\n findpath(controller)\\n controller.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c18d0960bab3423d92ad4b6fbc20a6ce\",\n \"score\": \"0.53985465\",\n \"text\": \"def main():\\n\\n # Get command line arguments\\n jid = sys.argv[1]\\n name_temp = sys.argv[2]\\n if len(sys.argv) == 4: # 'no_of_processes' is an optional command line argument that defaults to 16 if not given\\n no_of_processes = int(sys.argv[3])\\n sam = SamModelCaller(jid, name_temp, no_of_processes)\\n elif len(sys.argv) == 5:\\n no_of_processes = int(sys.argv[3])\\n write_output = bool(sys.argv[4])\\n sam = SamModelCaller(jid, name_temp, no_of_processes, write_output)\\n else:\\n sam = SamModelCaller(jid, name_temp)\\n\\n\\n\\n\\n sam.sam_multiprocessing()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1d931a1d790b4b4437757cb31e0d90\",\n \"score\": \"0.5383743\",\n \"text\": \"def main():\\n properties = read_properties_from_file('Sales.csv')\\n settings = read_settings_from_file('Settings.csv')\\n print(stripes)\\n print(\\n \\\"\\\\nWelcome to Kimberlynet - Sweden's largest residential site\\\\n\\\\nWe will help you find a property, please\\\"\\n \\\" answer a few questions before we start!\\\\n\\\")\\n print(stripes)\\n interest = control_float('Please enter current interest (%): ')\\n deposit = control_int('Please enter your deposit: ')\\n deduction = control_float('Please enter current deduction (%): ')\\n check = False\\n while not check:\\n menu(settings)\\n choice = menu_choice()\\n execute(choice, properties, settings, interest, deposit, deduction)\\n if choice == 9:\\n check = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612611e765809c8a7e313ce8cc612787\",\n \"score\": \"0.53809285\",\n \"text\": \"def main():\\n parser = bonobo.get_argument_parser()\\n with bonobo.parse_args(parser) as options:\\n bonobo.run(get_graph(**options), services=get_services(**options))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad4e95a08cc99857681179a5b7b9f57\",\n \"score\": \"0.53760874\",\n \"text\": \"def main(argv):\\r\\n groceryfile = ''\\r\\n emailfile = ''\\r\\n try:\\r\\n opts, _ = getopt.getopt(argv,\\\"hgl:el:\\\",[\\\"glfile=\\\",\\\"elfile=\\\"])\\r\\n except getopt.GetoptError:\\r\\n print ('split_the_bill.py --gl <grocerylist.txt> --el <emaillist.txt>')\\r\\n print ('Please provide input files or -h for help')\\r\\n sys.exit(2)\\r\\n for opt, arg in opts:\\r\\n if opt == '-h':\\r\\n print ('split_the_bill.py --gl <grocerylist.txt> --el <emaillist.txt>')\\r\\n sys.exit()\\r\\n elif opt in (\\\"-gl\\\", \\\"--glfile\\\"):\\r\\n groceryfile = arg\\r\\n elif opt in (\\\"-el\\\", \\\"--elfile\\\"):\\r\\n emailfile = arg\\r\\n \\r\\n try:\\r\\n mf = open(groceryfile, \\\"r\\\", encoding='utf-8')\\r\\n mf.seek(0)\\r\\n grocery_list = mf.read()\\r\\n if grocery_list == '':\\r\\n grocery_list = \\\"{}\\\"\\r\\n grocery_list_json = json.loads(grocery_list)\\r\\n except:\\r\\n raise FileNotFoundError('Grocery list not found!')\\r\\n finally:\\r\\n mf.close()\\r\\n\\r\\n try:\\r\\n ef = open(emailfile, \\\"r\\\", encoding='utf-8')\\r\\n ef.seek(0)\\r\\n email_list = ef.read().splitlines()\\r\\n if email_list == []:\\r\\n email_list = [\\\"Total\\\"]\\r\\n except:\\r\\n raise FileNotFoundError('Email list not found!')\\r\\n finally:\\r\\n ef.close()\\r\\n print(json.dumps(map_price_and_email(email_list,grocery_list_json)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f433db1079924698c632e74339ed771\",\n \"score\": \"0.5361722\",\n \"text\": \"def main():\\r\\n print(\\\"DASHNextGen_Service_Report.py is Starting\\\")\\r\\n login_into_dash(\\\"./DASHLoginInfo.json\\\")\\r\\n navigate_to_reports_and_click_excel()\\r\\n time.sleep(5)\\r\\n grab_downloaded_report()\\r\\n csv_to_database(\\\"./DASHLoginInfo.json\\\")\\r\\n file_cleanup()\\r\\n print(\\\"We have uploaded to the database.\\\")\\r\\n logout_session()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a51693f0501415a9e1824aabb01d83\",\n \"score\": \"0.53591925\",\n \"text\": \"def main():\\n tidy_uploads()\\n do_publications()\\n do_things_grid()\\n \\n copy_directory('things/grid-backgrounds/')\\n copy_directory('things/phd/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceda46838c16325aa24614e88a1efe47\",\n \"score\": \"0.53558826\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser(description=\\\"Cherry picking automation.\\\")\\n\\n # Arg information\\n parser.add_argument(\\n \\\"--filename\\\", help=\\\"path to whl file we are copying\\\", required=True)\\n parser.add_argument(\\n \\\"--new_py_ver\\\", help=\\\"two digit py version eg. 27 or 33\\\", required=True)\\n\\n args = parser.parse_args()\\n\\n # Argument checking\\n args.filename = os.path.abspath(args.filename)\\n check_existence(args.filename)\\n regex_groups = re.search(TF_NIGHTLY_REGEX, args.filename)\\n directory = regex_groups.group(1)\\n package = regex_groups.group(2)\\n version = regex_groups.group(3)\\n origin_tag = regex_groups.group(4)\\n old_py_ver = re.search(r\\\"(cp\\\\d\\\\d)\\\", origin_tag).group(1)\\n\\n # Create new tags\\n new_tag = origin_tag.replace(old_py_ver, \\\"cp\\\" + args.new_py_ver)\\n\\n # Copy the binary with the info we have\\n copy_binary(directory, origin_tag, new_tag, version, package)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb1c589ddefa00eb547dc4546b2da58\",\n \"score\": \"0.5355522\",\n \"text\": \"def main():\\n _main()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e10366c86d668faee5952b61a636e61\",\n \"score\": \"0.5355324\",\n \"text\": \"def main():\\n context = {}\\n urls = {}\\n urls[\\\"delivery\\\"] = BASE_URL + TIME_SERIES_DELIVERY\\n urls[\\\"vaccination\\\"] = BASE_URL + VACCINATION_STATE\\n context[\\\"cwd\\\"] = str(Path(sys.argv[0]).parent)\\n\\n # load last state\\n sources = load_object(context)\\n if sources is None:\\n sources = Sources(urls[\\\"delivery\\\"], urls[\\\"vaccination\\\"])\\n\\n sources.download_sources()\\n prepare_data(context, urls, sources.get_data())\\n plot(context, sources)\\n\\n # store current state\\n store_object(context, sources)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f4e37a763460bd6ebeae3d7c60aa40\",\n \"score\": \"0.535298\",\n \"text\": \"def main():\\n\\n\\t################################\\n\\t# change this\\n\\tlog_dir = \\\"./beta_simulator_mac/data2/driving_log.csv\\\"\\n\\n\\tmy_model = Model()\\n\\tmy_model.read_csv_data(log_dir)\\n\\tmy_model.build_and_train_model()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1674ebfbc119d61410be73cb43f6f664\",\n \"score\": \"0.535035\",\n \"text\": \"def main():\\n\\n # Build data\\n jobs = squeue(user='lmh1', # user id - change to your own\\n name=['iprPy_1', 'iprPy_4']) # job names - change to your own\\n logs = parse_runner_logs()\\n rundirs, runners = check_run_directories()\\n\\n # Merge data\\n logjobs = jobs.merge(logs, how='outer', on='jobid')\\n logjobs.loc[(logjobs.status=='active') & (pd.isna(logjobs.user)), 'status'] = 'crashed'\\n runlogjobs = logjobs.merge(runners, how='outer', on='pid')\\n\\n # Loop over all run directories\\n keys = ['jobid', 'pid', 'status', 'time', 'calcid', 'tmpdir']\\n for i in rundirs.index:\\n rundir = rundirs.loc[i]\\n\\n # List number of prepared calculations and number of runners\\n print(rundir.run_directory, rundir.numcalcs, rundir.numrunners)\\n dirjobs = runlogjobs[runlogjobs.run_directory == rundir.run_directory]\\n\\n # Print data for runners\\n if len(dirjobs) > 0:\\n print(dirjobs[keys])\\n print()\\n\\n # List jobs with no associated bid files (usually finished)\\n print('Unknown/finished jobs')\\n nodir = runlogjobs[pd.isna(runlogjobs.run_directory)][keys]\\n with pd.option_context('display.max_rows', None,):\\n print(nodir)\\n\\n # Delete run logs for successfully finished jobs\\n for jobid in nodir[nodir.status=='finished'].jobid.values:\\n Path(f'runner_{jobid}.txt').unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91ecb34edfcca45b91023e878276de2\",\n \"score\": \"0.5344869\",\n \"text\": \"def main():\\n\\n # e.g. url = 'cohorts/ryan_test_2/bytes_added'\\n args = parseargs()\\n\\n # Initialize a requester object\\n logging.debug(__name__ + ' :: Attempting to create cookie jar, '\\n 'logging in ..')\\n umapi_req = UMAPIClient(config.UMAPI_USER,\\n config.UMAPI_PASS)\\n\\n if args.wait < config.MIN_WAIT_REQ:\\n logging.error(__name__ + ' :: Wait time to small. '\\n 'Must be at least {0}'.format(\\n config.MIN_WAIT_REQ))\\n return\\n\\n # Call the request handler\\n #\\n # 1. URLs provided from a csv\\n # 2. URL from CL\\n #\\n\\n proc_args = [umapi_req,\\n args.uri,\\n args.timeout,\\n args.wait,\\n args.save_file,\\n args.timestamp,\\n args.print_res]\\n\\n if args.infile:\\n with open(config.IN_DIR + args.infile) as f:\\n urls = f.read().strip().split(args.separator)\\n for url in urls:\\n url = unicode(url).strip()\\n logging.debug(__name__ + ' :: Processing: {0}.'.format(url))\\n proc_args[1] = url\\n p = Process(target=request_handler, args=proc_args)\\n p.start()\\n p.join()\\n else:\\n url = unicode(args.uri).strip()\\n logging.debug(__name__ + ' :: Processing: {0}.'.format(url))\\n proc_args[1] = url\\n p = Process(target=request_handler, args=proc_args)\\n p.start()\\n p.join()\\n\\n logging.debug(__name__ + ' :: Shutting down umapi client.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38e160bd8991fbce437f03b70494a61\",\n \"score\": \"0.5342661\",\n \"text\": \"def main():\\n cmd_opts = __process_args()\\n database = \\\"/root/Desktop/vchauhan/Project/db_class/Station.db\\\"\\n \\n # create a database connection\\n conn = create_connection(database)\\n with conn:\\n if (cmd_opts.ID):\\n print(\\\"Querying for Trains with Station_ID: %s\\\" % (cmd_opts.ID))\\n select_task_by_id(conn,cmd_opts.ID)\\n else:\\n print(\\\"Query all available trains in the station\\\")\\n select_all_tasks(conn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f249ad939f81630924dfc73921e45db\",\n \"score\": \"0.53349763\",\n \"text\": \"def main():\\n setup_server_window()\\n load_queue_data_from_pickle_files()\\n setup_server_socket()\\n server_window.mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a83a2fb0dca8d5e0ca5b87c319671e2\",\n \"score\": \"0.53339314\",\n \"text\": \"def run(self):\\n logger=self.log()\\n runhere=self.workdir\\n if os.path.exists(runhere):\\n logger.warning('%s: directory exists; will delete'%(runhere,))\\n assert(not os.path.samefile(self.getdir('WORKhwrf'),runhere))\\n shutil.rmtree(runhere)\\n atime=self.__wrf.simstart()\\n with NamedDir(runhere,keep=self.keeprun,logger=logger,\\n keep_on_error=True) as rundir:\\n try:\\n logger.info('%s running in directory %s'%(\\n self.taskname,realcwd()))\\n self.location=runhere\\n self.initial_prerun()\\n self.link_fix()\\n self.link_all_inputs()\\n self.make_namelist()\\n self.final_prerun()\\n self.run_exe()\\n self.postrun()\\n self.deliver_products()\\n except Exception as e:\\n logger.critical('%s failed: %s'%(self.taskname,str(e)),\\n exc_info=True)\\n raise\\n self.postmsg('%s: completed'%(self.taskname,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560dafb446a00cb4f6b0f6812c61b19a\",\n \"score\": \"0.53298175\",\n \"text\": \"def main(self, *args):\\n\\n Launcher.main(self)\\n s = SimServer(self.build_opt().copy())\\n s.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a893af2a985302a71a771cb73b96e47\",\n \"score\": \"0.53260726\",\n \"text\": \"def main():\\n\\n # Allow user to input parameters on command line.\\n userInput = argparse.ArgumentParser(description=\\\\\\n '%s version %s. Requires a .bed file with first four columns in the '\\n 'format chromosome <tab> start <tab> stop <tab> sequence such as the '\\n '.bed files produced by outputClean. Returns a .bed file that is '\\n 'identical to the input file except that the probe sequences have been '\\n 'replaced with their reverse complements.' % (scriptName, Version))\\n requiredNamed = userInput.add_argument_group('required arguments')\\n requiredNamed.add_argument('-f', '--file', action='store', required=True,\\n help='The .bed file containing the probe '\\n 'sequences to take the reverse complements '\\n 'of')\\n userInput.add_argument('-o', '--output', action='store', default=None,\\n type=str, help='Specify the name prefix of the '\\n 'output file')\\n\\n # Import user-specified command line values\\n args = userInput.parse_args()\\n inputFile = args.file\\n outNameVal = args.output\\n\\n createRCs(inputFile, outNameVal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1441aff7b42146a2704f22a5ad15d9\",\n \"score\": \"0.5325459\",\n \"text\": \"def main():\\n # get options\\n cli = bill_cli.CommandLine()\\n cli.get_options()\\n\\n # init config\\n config = cfg.Config(cli.option)\\n\\n\\n # init AWS_Access instance\\n aws_raw_bill = AWS_Raw_Bill(config=config, commandline=cli)\\n aws_raw_bill.proc_detail_tags_bills()\\n\\n\\n #print \\\"\\\\n\\\"\\n aws_raw_bill.cli.msg(\\\"You got it ! Cheers!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af56283008886c67ed6a41874433143\",\n \"score\": \"0.53180116\",\n \"text\": \"def main():\\n # The block of code below attempts to open the file with the bike share\\n # data. If an IOError (input/output error) occurs, it prints an error\\n # message and exits the program, since the rest of the program needs\\n # to be able to read the file. If you get an error, check that you\\n # haven't changed the name of the file and make sure if appears in the\\n # same directory as bike.py.\\n try:\\n file = open(\\\"2015MayBikeShareData.csv\\\")\\n except IOError:\\n print(\\\"ERROR! 2015MayBikeShareData.csv doesn't appear in the current directory.\\\")\\n exit(1)\\n \\n \\n # The code below provides an example of looping through the data and\\n # counting the number of rides that were taken in this period.\\n numRides = 0\\n totalDuration = 0\\n numSmithJeff = 0\\n startCount = 0\\n numEnter = 0\\n \\n for line in file:\\n numRides = numRides + 1\\n \\n # Divide this line of the file into the above fields; we'll only care about\\n # a few of them so we'll ignore the others.\\n fields = line.split(\\\",\\\")\\n # We haven't seen this syntax before, but you should be able to guess what it\\n # does. Try experimenting with it in the interpreter to confirm your guess.\\n # From here on out in the code, you can ignore the fields variable - everything\\n # you need has been stored in the variables on the left hand side.\\n duration, startStation, endStation = int(fields[0]), fields[2], fields[4]\\n \\n # You'll add additional code within this for loop to complete the assignment.\\n # There is one iteration of the loop for each line of the file.\\n \\n #sum all the durations in each ride\\n totalDuration = totalDuration + duration\\n \\n #finding total number of rides that start at Smithsonian / Jefferson Dr & 12th St SW\\n if startStation == 'Smithsonian / Jefferson Dr & 12th St SW':\\n numSmithJeff = numSmithJeff + 1\\n \\n #finding total number of bikes that arrive at Smithsonian / Jefferson station\\n if endStation == \\\"Smithsonian / Jefferson Dr & 12th St SW\\\":\\n numEnter = numEnter + 1\\n \\n #find average duration of each ride and convert to seconds\\n averageDuration = (totalDuration / numRides) / 1000 \\n \\n #sum the number of bikes that have entered and left the station over the course of the data\\n totalChange = numEnter - numSmithJeff\\n \\n # Files should always be closed when you are done with them.\\n file.close()\\n \\n # Add additional print statements here\\n print(\\\"There were\\\", numRides, \\\"total rides.\\\")\\n print(\\\"The average duration of each ride is\\\", averageDuration, \\\"seconds\\\")\\n \\n #gives average duration of each ride in minutes and prints the result in a statement\\n averageMinutes(averageDuration)\\n \\n print(\\\"The number of rides that started at Smithsonian / Jefferson Dr & 12th St SW is\\\", numSmithJeff)\\n \\n if totalChange > 0:\\n print(\\\"The number of bikes at Smithsonian / Jefferson station over the course of the data had increased by\\\", totalChange, \\\"units\\\")\\n elif totalChange < 0:\\n print(\\\"The number of bikes at Smithsonian / Jefferson station over the course of the data has decreased by\\\", totalChange, \\\"units\\\")\\n elif totalChange == 0:\\n print(\\\"The number of bikes at Smithsonian / Jefferson station over the course of the data has not changed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb888e41c74d0981ab1fba610eeca06\",\n \"score\": \"0.5317356\",\n \"text\": \"def main():\\n stats.set_time_start()\\n\\n if config.options.show_progress:\\n stats.start_monitor()\\n\\n recorders = Recorder.launch(config.options.recorders)\\n\\n try:\\n for filename in config.filenames:\\n parser.parse(filename)\\n\\n Recorder.wait_empty()\\n except KeyboardInterrupt:\\n pass\\n\\n stats.set_time_stop()\\n\\n if config.options.show_progress:\\n stats.stop_monitor()\\n\\n stats.print_summary()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d20928f9bf4aa07d61d880e4df4b507\",\n \"score\": \"0.5316588\",\n \"text\": \"def main():\\n\\n args = parser.parse_args()\\n start_ms_bf_server(app_id=args.ms_id,\\n app_secret=args.ms_secret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6e65f4306c215597e5b39fbd542e56\",\n \"score\": \"0.53132766\",\n \"text\": \"def main(args):\\r\\n\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d458458b43c4d46a0aec51a4f472eb0a\",\n \"score\": \"0.5306254\",\n \"text\": \"def main():\\n print ('info: this is the seedbank library, what did you smoke today?!?')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.5305935\",\n \"text\": \"def main():\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994292,"cells":{"query_id":{"kind":"string","value":"22d35a6550c7797bc852d7e1e578c7a7"},"query":{"kind":"string","value":"Test we get the expected triggers from a button."},"positive_passages":{"kind":"list like","value":[{"docid":"f8100c7694ef9f6d37756857cd0f71c9","score":"0.62324995","text":"async def test_get_triggers(\n hass: HomeAssistant,\n device_reg: device_registry.DeviceRegistry,\n entity_reg: EntityRegistry,\n) -> None:\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_entry.add_to_hass(hass)\n device_entry = device_reg.async_get_or_create(\n config_entry_id=config_entry.entry_id,\n connections={(device_registry.CONNECTION_NETWORK_MAC, \"12:34:56:AB:CD:EF\")},\n )\n entity_reg.async_get_or_create(DOMAIN, \"test\", \"5678\", device_id=device_entry.id)\n expected_triggers = [\n {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": \"pressed\",\n \"device_id\": device_entry.id,\n \"entity_id\": f\"{DOMAIN}.test_5678\",\n }\n ]\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device_entry.id\n )\n assert_lists_same(triggers, expected_triggers)","title":""}],"string":"[\n {\n \"docid\": \"f8100c7694ef9f6d37756857cd0f71c9\",\n \"score\": \"0.62324995\",\n \"text\": \"async def test_get_triggers(\\n hass: HomeAssistant,\\n device_reg: device_registry.DeviceRegistry,\\n entity_reg: EntityRegistry,\\n) -> None:\\n config_entry = MockConfigEntry(domain=\\\"test\\\", data={})\\n config_entry.add_to_hass(hass)\\n device_entry = device_reg.async_get_or_create(\\n config_entry_id=config_entry.entry_id,\\n connections={(device_registry.CONNECTION_NETWORK_MAC, \\\"12:34:56:AB:CD:EF\\\")},\\n )\\n entity_reg.async_get_or_create(DOMAIN, \\\"test\\\", \\\"5678\\\", device_id=device_entry.id)\\n expected_triggers = [\\n {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": \\\"pressed\\\",\\n \\\"device_id\\\": device_entry.id,\\n \\\"entity_id\\\": f\\\"{DOMAIN}.test_5678\\\",\\n }\\n ]\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device_entry.id\\n )\\n assert_lists_same(triggers, expected_triggers)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6923c51cf107f5f2eea29735f0a6cc08","score":"0.70568377","text":"def test_get_triggers(self):\n pass","title":""},{"docid":"74a38034de88a9c3953c027771e7e558","score":"0.7040851","text":"def getTriggerPressed(self) -> bool:\n ...","title":""},{"docid":"e2c7ec4699a61a6dd5472c083ba9442e","score":"0.68977433","text":"def test_get_trigger(self):\n pass","title":""},{"docid":"ad5dc9b6c69e828ccb99d4174418852c","score":"0.66726124","text":"def test_button_without_name(self):","title":""},{"docid":"2ecece6161de64c8951cb67cce23bf1e","score":"0.6605904","text":"def test_repeated_button():","title":""},{"docid":"eeab186daac25950932d509e865e5885","score":"0.6526919","text":"def test_add_trigger(self):\n pass","title":""},{"docid":"120a477afbf6e23a229561e9ac692172","score":"0.65125096","text":"def test_gui_Button() -> None:\n pos = 200, 200\n size = 100, 100\n cpos_x = pos[0], pos[0] + int(size[0] / 2), pos[0] + size[0]\n cpos_y = pos[1], pos[1] + int(size[1] / 2), pos[1] + size[1]\n wsize = (ut.BSIZE[0] * ut.TILE, ut.BSIZE[1] * ut.TILE)\n\n img = images.BUTT_TMP_IMG\n img2 = images.SPLASH_IMG\n img3 = images.BACK_IMG\n\n tname = 'test_trigger'\n text = ('test', ut.GAME_FONT)\n triggers1 = [('test_trigger', 0, 2)]\n triggers2 = [('test_trig', 0, 4), ('test_trigger', 4, 3), ('t', 3, 4)]\n\n # Test 0: incorrect initial values\n test = gui.Button((-1, -1), (-1, -1), tname, img)\n assert 0 <= test.pos[0] < wsize[0]\n assert 0 <= test.pos[1] < wsize[1]\n assert test.size[0] > 0\n assert test.size[1] > 0\n assert test.text is None\n\n test = gui.Button(wsize, (0, 0), tname, img, None, text)\n assert 0 <= test.pos[0] < wsize[0]\n assert 0 <= test.pos[1] < wsize[1]\n assert test.size[0] > 0\n assert test.size[1] > 0\n assert test.text is not None\n\n test = gui.Button(pos, size, tname, img2)\n assert test.image.get_size()[0] == size[0]\n assert test.image.get_size()[1] == size[1]\n\n test = gui.Button(pos, size, tname, img3)\n assert test.image.get_size()[0] == size[0]\n assert test.image.get_size()[1] == size[1]\n\n # Test 1: proper work of init_pdown() method\n\n for ix in range(3):\n for iy in range(3):\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[ix], cpos_y[iy]))\n assert test.is_pressed is True\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[ix], cpos_y[iy]))\n assert test.is_pressed is True\n assert test.text.is_pressed is True\n\n for ix in range(3):\n for iy in range(3):\n if ix == iy and iy == 1:\n continue\n\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[ix]+ix-1, cpos_y[iy]+iy-1))\n assert test.is_pressed is False\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[ix]+ix-1, cpos_y[iy]+iy-1))\n assert test.is_pressed is False\n assert test.text.is_pressed is False\n\n # Test 2: proper work of init_pup() method\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]))\n assert test.is_pressed is False\n\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), [])\n\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), triggers1)\n assert triggers1[0][1] == 1\n\n test = gui.Button(pos, size, tname, img)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), triggers2)\n assert triggers2[1][1] == 2\n\n triggers1 = [('test_trigger', 0, 2)]\n triggers2 = [('test_trig', 0, 4), ('test_trigger', 4, 3), ('t', 3, 4)]\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]))\n assert test.is_pressed is False\n assert test.text.is_pressed is False\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), [])\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), triggers1)\n assert triggers1[0][1] == 1\n\n test = gui.Button(pos, size, tname, img, None, text)\n test.init_pdown((cpos_x[1], cpos_y[1]))\n test.init_pup((cpos_x[1], cpos_y[1]), triggers2)\n assert triggers2[1][1] == 2","title":""},{"docid":"f4efef40153a1f11e2ee16af6042fcdb","score":"0.63937","text":"def verify_button(self, locator, expected):\n button = self.state._get_typed_item_by_locator(Button, locator)\n self.state._verify_value(expected, button.Text)","title":""},{"docid":"eb072dfb4e43f75a6f52a23a2926dc0a","score":"0.61948365","text":"def _findBtnElem(browser, triggers):\n elemTypes = [\"button\", \"input\", \"a\"]\n for t in elemTypes:\n log.debug(\"Looking for element by type {}.\", t)\n elems = browser.find_by_tag(t)\n for elem in elems:\n elemText = elem.value or elem.text\n if not elemText:\n continue\n # Make lowercase, remove spaces,tabs,newlines,non alphab chars.\n elemText = \"\".join(char for char in elemText if char.isalpha())\n elemText = \"\".join(elemText.lower().split())\n # Compare against list\n for trig in triggers:\n if trig in elemText:\n return elem\n # Did not find any element.\n log.debug(\"Did not find any element.\")\n return None","title":""},{"docid":"f61c292e762c5deb3a01dd4afbe6fb5f","score":"0.6126174","text":"async def test_if_fires_on_state_change(hass, calls):\n hass.states.async_set(\"button.entity\", \"unknown\")\n\n assert await async_setup_component(\n hass,\n automation.DOMAIN,\n {\n automation.DOMAIN: [\n {\n \"trigger\": {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"device_id\": \"\",\n \"entity_id\": \"button.entity\",\n \"type\": \"pressed\",\n },\n \"action\": {\n \"service\": \"test.automation\",\n \"data\": {\n \"some\": (\n \"to - {{ trigger.platform}} - \"\n \"{{ trigger.entity_id}} - {{ trigger.from_state.state}} - \"\n \"{{ trigger.to_state.state}} - {{ trigger.for }} - \"\n \"{{ trigger.id}}\"\n )\n },\n },\n }\n ]\n },\n )\n\n # Test triggering device trigger with a to state\n hass.states.async_set(\"button.entity\", \"2021-01-01T23:59:59+00:00\")\n await hass.async_block_till_done()\n assert len(calls) == 1\n assert calls[0].data[\n \"some\"\n ] == \"to - device - {} - unknown - 2021-01-01T23:59:59+00:00 - None - 0\".format(\n \"button.entity\"\n )","title":""},{"docid":"8d61cdf9a1942842c830c17e0f61af39","score":"0.6063574","text":"def test_submit_button():","title":""},{"docid":"80edad7322659bd77437bea57210db23","score":"0.60603464","text":"def getButton(self, button: ButtonType) -> bool:\n ...","title":""},{"docid":"80ba6d9b405b8bff2e022f72b081e548","score":"0.6039221","text":"def test_selected_buttons() -> None:\n term = Terminal()\n tui = TextUI(term)\n buttons = [Button(tui) for _ in range(3)]\n menu = Menu(tui, buttons=buttons)\n assert menu.selected is buttons[0]","title":""},{"docid":"3ebaa7fcdb2e9faa2d7215777775c2ad","score":"0.6024014","text":"async def test_get_triggers(\n hass: HomeAssistant,\n device_registry: dr.DeviceRegistry,\n entity_registry: er.EntityRegistry,\n set_state,\n features_reg,\n features_state,\n expected_trigger_types,\n) -> None:\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_entry.add_to_hass(hass)\n device_entry = device_registry.async_get_or_create(\n config_entry_id=config_entry.entry_id,\n connections={(dr.CONNECTION_NETWORK_MAC, \"12:34:56:AB:CD:EF\")},\n )\n entity_entry = entity_registry.async_get_or_create(\n DOMAIN,\n \"test\",\n \"5678\",\n device_id=device_entry.id,\n supported_features=features_reg,\n )\n if set_state:\n hass.states.async_set(\n entity_entry.entity_id,\n \"attributes\",\n {\"supported_features\": features_state},\n )\n\n expected_triggers = []\n\n expected_triggers += [\n {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": trigger,\n \"device_id\": device_entry.id,\n \"entity_id\": entity_entry.id,\n \"metadata\": {\"secondary\": False},\n }\n for trigger in expected_trigger_types\n ]\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device_entry.id\n )\n assert triggers == unordered(expected_triggers)","title":""},{"docid":"b8bad0492bb969fe0f6c22fc57e3a788","score":"0.6008939","text":"def verify_action_button_status(self, **kwargs):\n for button,expected_value in kwargs.items():\n locator = eda_lex_locators[\"eda_settings\"][\"action_button\"].format(button)\n self.selenium.page_should_contain_element(locator)\n self.selenium.wait_until_element_is_visible(locator,\n error= f\"Element '{button}' button is not displayed for the user\")\n time.sleep(1)\n actual_value = self.selenium.get_webelement(locator).get_attribute(\"disabled\")\n expected_value = bool(expected_value == \"disabled\")\n if not str(expected_value).lower() == str(actual_value).lower() :\n raise Exception (f\"Element {button} button status is {actual_value} instead of {expected_value}\")","title":""},{"docid":"11f6931ac33aab0f5601a9b0dd3f812e","score":"0.5992467","text":"def getTrigger(self) -> bool:\n ...","title":""},{"docid":"6d8adc23251ea993fae0f5759fb596e7","score":"0.5991863","text":"async def test_button(hass: HomeAssistant) -> None:\n button = ButtonEntity()\n assert button.state is None\n\n button.hass = hass\n\n with pytest.raises(NotImplementedError):\n await button.async_press()\n\n button.press = MagicMock()\n await button.async_press()\n\n assert button.press.called","title":""},{"docid":"bb128b720979d5de8ae31492ded29c45","score":"0.59495145","text":"def test_choose_submit_buttons(expected_post):\n text = \"\"\"\n <form method=\"post\" action=\"mock://form.com/post\">\n <button type=\"butTon\" name=\"sub1\" value=\"val1\">Val1</button>\n <button type=\"suBmit\" name=\"sub2\" value=\"val2\">Val2</button>\n <button type=\"reset\" name=\"sub3\" value=\"val3\">Val3</button>\n <button name=\"sub4\" value=\"val4\">Val4</button>\n <input type=\"subMit\" name=\"sub5\" value=\"val5\">\n </form>\n \"\"\"\n browser, url = setup_mock_browser(expected_post=expected_post, text=text)\n browser.open(url)\n browser.select_form()\n res = browser.submit_selected(btnName=expected_post[0][0])\n assert res.status_code == 200 and res.text == 'Success!'","title":""},{"docid":"53343de55ba0e8a9212d60be0343e9ee","score":"0.5919839","text":"def test_get_trigger_setting(self):\n pass","title":""},{"docid":"44deacb5017eca4c48630dacbe788cc5","score":"0.5918713","text":"def test_triggered(self, h5reader):\n h5reader.open()\n for i, event in enumerate(h5reader):\n assert event.triggered == bool(i)\n h5reader.close()","title":""},{"docid":"5228843791b9f0f3f3a92a92b1599682","score":"0.5917064","text":"def buttons(self):\n\t\traise HaventGottenToThatYetError, \"I ain't done writing this code yet.\"","title":""},{"docid":"81a84e335ca25498d084f8fefa9c9ced","score":"0.5897216","text":"def _verify_wheel_actions(self, expected_actions):\n self.assertEqual(self._wheel_actions, expected_actions)","title":""},{"docid":"d16851c2c0592200b2d75de755ebd5d3","score":"0.58840644","text":"async def test_button_simple(hass: HomeAssistant, knx: KNXTestKit) -> None:\n events = async_capture_events(hass, \"state_changed\")\n await knx.setup_integration(\n {\n ButtonSchema.PLATFORM: {\n CONF_NAME: \"test\",\n KNX_ADDRESS: \"1/2/3\",\n }\n }\n )\n assert len(hass.states.async_all()) == 1\n assert len(events) == 1\n events.pop()\n\n # press button\n await hass.services.async_call(\n \"button\", \"press\", {\"entity_id\": \"button.test\"}, blocking=True\n )\n await knx.assert_write(\"1/2/3\", True)\n assert len(events) == 1\n events.pop()\n\n # received telegrams on button GA are ignored by the entity\n old_state = hass.states.get(\"button.test\")\n async_fire_time_changed(hass, dt.utcnow() + timedelta(seconds=3))\n await knx.receive_write(\"1/2/3\", False)\n await knx.receive_write(\"1/2/3\", True)\n new_state = hass.states.get(\"button.test\")\n assert old_state == new_state\n assert len(events) == 0\n\n # button does not respond to read\n await knx.receive_read(\"1/2/3\")\n await knx.assert_telegram_count(0)","title":""},{"docid":"4ce5d376cd2a764f1425cbb0bc6406be","score":"0.5875481","text":"def test_file_buttons(self):\n dialog = self.dialog\n button_sources = [(self.dialog.readme, 'README.txt', 'readme'),\n (self.dialog.idle_news, 'NEWS.txt', 'news'),\n (self.dialog.idle_credits, 'CREDITS.txt', 'credits')]\n\n for button, filename, name in button_sources:\n with self.subTest(name=name):\n button.invoke()\n fn = findfile(filename, subdir='idlelib')\n get = dialog._current_textview.viewframe.textframe.text.get\n with open(fn, encoding='utf-8') as f:\n self.assertEqual(f.readline().strip(), get('1.0', '1.end'))\n f.readline()\n self.assertEqual(f.readline().strip(), get('3.0', '3.end'))\n dialog._current_textview.destroy()","title":""},{"docid":"f5fc6c730c70640147baf9d21fccbd19","score":"0.5857787","text":"def test_generate_button_press(self):\n params = {'state': 1, 'counter': 62552}\n result = self.node_obj.generate_button_press(params)\n expected = b'\\t\\x00\\x01\\x00\\x01X\\xf4\\x00\\x00'\n self.assertEqual(result, expected)","title":""},{"docid":"a130514a94065085b1e078a92164424d","score":"0.582386","text":"def testGetActions(self):\r\n iq = self.Iq()\r\n iq['command']['node'] = 'foo'\r\n iq['command']['actions'] = ['prev', 'next']\r\n\r\n results = iq['command']['actions']\r\n expected = set(['prev', 'next'])\r\n self.assertEqual(results, expected,\r\n \"Incorrect next actions: %s\" % results)","title":""},{"docid":"7f1886ae65de7d864c086a30feb71392","score":"0.5813623","text":"async def test_button_type(hass: HomeAssistant, knx: KNXTestKit) -> None:\n await knx.setup_integration(\n {\n ButtonSchema.PLATFORM: {\n CONF_NAME: \"test\",\n KNX_ADDRESS: \"1/2/3\",\n ButtonSchema.CONF_VALUE: 21.5,\n CONF_TYPE: \"2byte_float\",\n }\n }\n )\n # press button\n await hass.services.async_call(\n \"button\", \"press\", {\"entity_id\": \"button.test\"}, blocking=True\n )\n await knx.assert_write(\"1/2/3\", (0x0C, 0x33))","title":""},{"docid":"4976e01c22d2f8a2ed0e9adb400cba1e","score":"0.5800542","text":"def check_actions(self, expected, elements):\r\n for idx, item in enumerate(expected):\r\n text, form_value = item\r\n e = elements.eq(idx)\r\n eq_(e.parent().text(), text)\r\n eq_(e.attr('name'), 'action')\r\n eq_(e.val(), form_value)","title":""},{"docid":"7e94b9e010814b6063f549215074f2f8","score":"0.5797616","text":"def test_create_button(self):\n self.root, s, array = self.populate_gui()\n # Mocking \"Create button\" click. Idea - http://code.activestate.com/recipes/578978-using-tkinters-invoke-method-for-testing/\n s.frame_bottom5.children['but_create'].invoke()\n # Mocked data as an array\n\n # Compare fresh Tuna with mocked data\n z = Data.tunas[0].getTunaFeatures\n for x, y in zip (z, array):\n # Must be equal. If not - error\n self.assertEqual(x, y)\n if self.root:\n self.root.destroy()","title":""},{"docid":"9b4e128bb7f65592d7eacc41e217ad69","score":"0.57582724","text":"def test_change_trigger_setting(self):\n pass","title":""},{"docid":"ae8e20d8b26f0bd241217a403be66d45","score":"0.5746524","text":"def test_replace_triggers(self):\n pass","title":""},{"docid":"7ecb71ad90f441a936a274233a1fd47b","score":"0.5740988","text":"def test_call_button(qtbot):\n\n @magicgui(call_button=\"my_button\", auto_call=True)\n def func(a: int, b: int = 3, c=7.1) -> None:\n assert a == 7\n\n magic_widget = func.Gui()\n\n assert hasattr(magic_widget, \"call_button\")\n assert isinstance(magic_widget.call_button, QtW.QPushButton)\n magic_widget.a = 7\n\n qtbot.mouseClick(magic_widget.call_button, Qt.LeftButton)","title":""},{"docid":"7275ca9d46acff246dc95da9656d8599","score":"0.5714357","text":"def test_object_buttons(self):\n r = self.renderer(\n context=self.portal[\"news\"],\n assignment=actions.Assignment(\n ptitle=\"actions\", category=\"object_buttons\", show_icons=False\n ),\n )\n r.update()\n output = r.actionLinks()\n\n # Have our expected tabs ?\n expected = {\"Cut\", \"Copy\", \"Rename\", \"Delete\"}\n got = {str(link[\"title\"]) for link in output}\n self.assertTrue(expected.issubset(got))","title":""},{"docid":"3c85d3bf3a40f27ce6c0834b4a67ee57","score":"0.5689448","text":"def test_action(\n widget,\n action_name,\n input_text,\n expected_result,\n selected_text=None,\n cursor_placement=None\n):\n app = QtWidgets.QApplication.instance()\n window = app.activeWindow()\n\n for action in widget.actions():\n if action.text() == action_name:\n break\n\n assert action_name == action.text()\n\n try:\n setup_test(\n widget,\n input_text,\n selected_text=selected_text,\n cursor_placement=cursor_placement\n )\n\n # widget.last_key_pressed may be necessary here for some actions... but should those actions really fall under this domain? or be placed on the editor?\n action.trigger()\n\n text = widget.toPlainText()\n assert text == expected_result\n except AssertionError:\n raise\n finally:\n window.activateWindow()","title":""},{"docid":"2619c809c051daab8d80c51138eae921","score":"0.56763","text":"def test_get_behaviors(self):\n pass","title":""},{"docid":"c9e11074eba811a72adc9368867b0034","score":"0.56531715","text":"def test_printer_buttons(self):\n dialog = self.dialog\n button_sources = [(dialog.py_license, license, 'license'),\n (dialog.py_copyright, copyright, 'copyright'),\n (dialog.py_credits, credits, 'credits')]\n\n for button, printer, name in button_sources:\n with self.subTest(name=name):\n printer._Printer__setup()\n button.invoke()\n get = dialog._current_textview.viewframe.textframe.text.get\n lines = printer._Printer__lines\n self.assertEqual(lines[0], get('1.0', '1.end'))\n self.assertEqual(lines[1], get('2.0', '2.end'))\n dialog._current_textview.destroy()","title":""},{"docid":"3e73da42a24d87c5e69f4f34200766a8","score":"0.56365985","text":"def test_main_button(self):\n self.assertTrue(self.browser.is_text_present('SELECCIONAR IMAGEN'))\n self.assertTrue(self.browser.is_text_present('CLASIFICAR'))","title":""},{"docid":"ef3a43fbd947df52e772a280c08df58e","score":"0.56074643","text":"def test_checkBookButton(self):\n\n homePage = HomePage(self.driver)\n self.explicit_wait(HomePage.FromBooking)\n homePage.bookButton().click()\n self.explicit_wait(HomePage.ContinueButton)\n assert homePage.continueButton().is_displayed(), \"Book Button didn't work correctly\"","title":""},{"docid":"18f647709dee22339c7187aabc5d1091","score":"0.5595025","text":"def Button(self) -> _n_11_t_4:","title":""},{"docid":"eba6ce0e9113a5145a53162d59a770a2","score":"0.5585998","text":"def test_button_switch_up() -> None:\n term = Terminal()\n tui = TextUI(term)\n buttons = [Button(tui) for _ in range(3)]\n menu = Menu(tui, buttons=buttons)\n assert menu.selected is buttons[0]\n menu.selection_up()\n assert menu.selected is buttons[2]\n menu.selection_up()\n assert menu.selected is buttons[1]\n menu.selection_up()\n assert menu.selected is buttons[0]","title":""},{"docid":"856a534f6e16ea198909c707a8679242","score":"0.55750066","text":"def test_name_in_actions(self, trigger_actions_mock):\n name = ACTIONS.GET\n\n ds_config = CHAOS_CONFIG.datastore\n\n trigger_action(name, ds_config)\n\n trigger_actions_mock.assert_called_once_with(name, ds_config)","title":""},{"docid":"498ca2b80db53489d47259a10bee0fb3","score":"0.555756","text":"def test_submit_button(self):\n button = get_button(self.get_form())\n self.assertEqual(button.get('type'), 'submit')","title":""},{"docid":"ec9a150d48c80843e2b30313522b1b68","score":"0.5556827","text":"async def test_get_triggers(opp, device_reg, entity_reg):\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_entry.add_to_opp(opp)\n device_entry = device_reg.async_get_or_create(\n config_entry_id=config_entry.entry_id,\n connections={(device_registry.CONNECTION_NETWORK_MAC, \"12:34:56:AB:CD:EF\")},\n )\n entity_reg.async_get_or_create(DOMAIN, \"test\", \"5678\", device_id=device_entry.id)\n expected_triggers = [\n {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": \"turned_off\",\n \"device_id\": device_entry.id,\n \"entity_id\": f\"{DOMAIN}.test_5678\",\n },\n {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": \"turned_on\",\n \"device_id\": device_entry.id,\n \"entity_id\": f\"{DOMAIN}.test_5678\",\n },\n ]\n triggers = await async_get_device_automations(opp, \"trigger\", device_entry.id)\n assert_lists_same(triggers, expected_triggers)","title":""},{"docid":"d19b31d39c617f88801539774d382d7b","score":"0.5552215","text":"def _verify_back_arm_actions(self, expected_actions):\n self.assertEqual(self._back_arm_actions, expected_actions)","title":""},{"docid":"d479da65719d58980a14bd2d167d27a3","score":"0.5544555","text":"def test_actions_available(admin_client, article):\n url = reverse('admin:blog_article_changelist')\n changeview = admin_client.get(url)\n path = (\n './/table[@id=\"result_list\"]' '//thead//th//*[starts-with(text(), \"Actions\")]'\n )\n assert len(changeview.lxml.xpath(path)) == 1","title":""},{"docid":"bb89546863d2b6cd5aad54ff3fb808b9","score":"0.55428463","text":"def test_actions(self):\n serialized = self._runner.serialized_model\n actions = serialized['Objects']['?'].get('_actions')\n self.assertIsInstance(actions, dict)\n action_names = [action['name'] for action in actions.values()]\n self.assertIn('testAction', action_names)\n self.assertNotIn('notAction', action_names)\n self.assertIn('testRootMethod', action_names)\n action_meta = None\n for action in actions.values():\n self.assertIsInstance(action.get('enabled'), bool)\n self.assertIsInstance(action.get('name'), str)\n self.assertThat(\n action['name'],\n matchers.StartsWith('test'))\n if action['name'] == 'testActionMeta':\n action_meta = action\n else:\n self.assertEqual(action['title'], action['name'])\n self.assertIsNotNone(action_meta)\n self.assertEqual(action_meta['title'], \"Title of the method\")\n self.assertEqual(action_meta['description'],\n \"Description of the method\")\n self.assertEqual(action_meta['helpText'], \"HelpText of the method\")","title":""},{"docid":"f3ad0ff2f900dee6313aab175e3c6c26","score":"0.55339754","text":"def test_submit_button(self):\n button = get_button(self.form())\n self.assertEqual(button.get('type'), 'submit')","title":""},{"docid":"f3ad0ff2f900dee6313aab175e3c6c26","score":"0.55339754","text":"def test_submit_button(self):\n button = get_button(self.form())\n self.assertEqual(button.get('type'), 'submit')","title":""},{"docid":"f3ad0ff2f900dee6313aab175e3c6c26","score":"0.55339754","text":"def test_submit_button(self):\n button = get_button(self.form())\n self.assertEqual(button.get('type'), 'submit')","title":""},{"docid":"25a7a5afa50302f5166053d2efb8f007","score":"0.5505979","text":"def events(self):\n # Button pressed (originally none)\n button = \"\"\n # Check event queue\n for event in pygame.event.get():\n # If exit button clicked, exit game\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n # If a click, see if it is over a virtual button\n elif event.type == pygame.MOUSEBUTTONUP:\n button = self.get_button(event.pos)\n # Return the virtual button clicked\n return button","title":""},{"docid":"a936a25c82a9396e9d559b4a8ed7e3e0","score":"0.5503027","text":"def detect_button_collide(self):\n for button in self.clickables:\n clicked_button = button.check_collide()\n if clicked_button:\n print('clicked: ' + button.name)\n return button","title":""},{"docid":"01915a994d2afe27718838c74b97ac22","score":"0.54878205","text":"async def test_button_raw(hass: HomeAssistant, knx: KNXTestKit) -> None:\n await knx.setup_integration(\n {\n ButtonSchema.PLATFORM: {\n CONF_NAME: \"test\",\n KNX_ADDRESS: \"1/2/3\",\n CONF_PAYLOAD: False,\n CONF_PAYLOAD_LENGTH: 0,\n }\n }\n )\n # press button\n await hass.services.async_call(\n \"button\", \"press\", {\"entity_id\": \"button.test\"}, blocking=True\n )\n await knx.assert_write(\"1/2/3\", False)","title":""},{"docid":"102eec5e6e9c3d68a4aa2982d3a4088f","score":"0.54876107","text":"def _verify_front_drum_actions(self, expected_actions):\n self.assertEqual(self._front_drum_actions, expected_actions)","title":""},{"docid":"f4fc6bb9d9f3134a834fa1dfb539e975","score":"0.54778445","text":"def test_set_actions_should_set_navigate(self):\n self.navigate_mock.side_effect = lambda x: self.assertTrue(self.driver_mock is x)\n self.form_action.set_actions(self.navigate_mock, self.action_mock, self.data_mock)\n self.form_action.navigate()","title":""},{"docid":"f37adb87a612d886fc32172fad77e1bc","score":"0.5472817","text":"def _verify_back_drum_actions(self, expected_actions):\n self.assertEqual(self._back_drum_actions, expected_actions)","title":""},{"docid":"fe8206e18839c5889e90fac72d1bdddf","score":"0.546266","text":"def test_simple(self):\n control = self.window\n mock = self.app._the_mock\n control.on_prev = mock.on_prev\n control.on_next = mock.on_next\n event = wx.CommandEvent()\n\n LOG.info(\"Dimensions changed\")\n control.on_dimensions(event)\n expect = [\n call.on_dimensions(event),\n call.App.new_dimensions(),\n call.App.refresh_contents(),\n call.CommandEvent.Skip(),\n ]\n\n LOG.info(\"Variant changed\")\n control.on_variant(event)\n expect += [\n call.on_variant(event),\n call.App.refresh_contents(),\n call.CommandEvent.Skip(),\n ]\n\n LOG.info(\"Option changed\")\n control.on_option(event)\n expect += [\n call.on_option(event),\n call.App.grab_contents(),\n call.CommandEvent.Skip(),\n ]\n\n LOG.info(\"Reload and prev\")\n control.on_reload_and_prev(event)\n expect += [\n call.on_reload_and_prev(event),\n call.App.book.reload(),\n call.on_prev(event),\n ]\n\n LOG.info(\"Changes applied\")\n control.on_apply_and_next(event)\n expect += [call.on_apply_and_next(event), call.on_next(event), call.App.apply()]\n\n mock.assert_has_calls(expect)","title":""},{"docid":"9eeec4a183812051645bda0f0fe535ef","score":"0.5457157","text":"def is_button_pressed(button):\r\n return ui.is_pressed(button)","title":""},{"docid":"c8433fa84b1c4dedde774ec7d78554bb","score":"0.5456086","text":"def test_button_text_subscribe(self):\n button = get_button(self.get_form())\n self.assertEqual(button.string, 'Subscribe')","title":""},{"docid":"1d8a9f7f88cbd4fbcbbd7a761c98fe6b","score":"0.5448595","text":"def test_delete_trigger(self):\n pass","title":""},{"docid":"424e43ebe5d05571d8994bd1b7dc79e2","score":"0.54470354","text":"def test_parse_button_press(self):\n result = self.node_obj.parse_button_press(b'\\t\\x00\\x00\\x00\\x02\\xbf\\xc3\\x00\\x00')\n expected = {'counter': 50111, 'button_state': 0}\n self.assertEqual(result, expected, \"State OFF, Counter 50111\")\n\n result = self.node_obj.parse_button_press(b'\\t\\x00\\x01\\x00\\x01\\x12\\xca\\x00\\x00')\n expected = {'counter': 51730, 'button_state': 1}\n self.assertEqual(result, expected, \"State ON, Counter 51730\")","title":""},{"docid":"bab4fc1a0399dd4e271598d2d0db2d4b","score":"0.5445742","text":"def test_button_switch_down() -> None:\n term = Terminal()\n tui = TextUI(term)\n buttons = [Button(tui) for _ in range(3)]\n menu = Menu(tui, buttons=buttons)\n assert menu.selected is buttons[0]\n menu.selection_down()\n assert menu.selected is buttons[1]\n menu.selection_down()\n assert menu.selected is buttons[2]\n menu.selection_down()\n assert menu.selected is buttons[0]","title":""},{"docid":"b8dcb63fb8ed3b1ddf842ba3dfeec589","score":"0.5445198","text":"def test_alert_triggering(self):\n\t\tself.called = 0\n\t\talert = self.alertservice.create(None, (self.fake_method, tuple()), (model.LoadBalancer.RESP_TIME, model.Alert.GE, 2))\n\t\tself.assertEqual(False, alert.triggered)\n\t\tself.alertservice.validate(alert, 1)\n\t\tself.assertEqual(0, self.called)\n\t\tself.alertservice.validate(alert, 2)\n\t\tself.assertEqual(True, alert.triggered)\n\t\tself.assertEqual(1, self.called)","title":""},{"docid":"83623382d652a64f807b2a67d4e8e168","score":"0.54440373","text":"def _verify_routine_actions(self, expected_actions):\n self.assertEqual(self._routine_actions, expected_actions)","title":""},{"docid":"842fa44fd78195a63ee437b44f24f471","score":"0.54278517","text":"def test_on_btn_norm(self, caw, multishell_edit, inputs, locks, expected):\n if multishell_edit:\n caw.rbs_multi_apply.index = 0\n for i, item in enumerate(caw.input_items):\n item.value = inputs[i]\n caw.checks[i].value = locks[i]\n\n caw.on_btn_norm(None)\n\n for i, item in enumerate(caw.input_items):\n assert item.value == expected[i]\n\n start_no = caw.irs_shell_range.value[0]\n end_no = caw.irs_shell_range.value[1]\n\n for i, v in enumerate(expected):\n line = caw.fig.data[2 + i].y[start_no - 1 : end_no]\n unique_v = set(line)\n assert len(unique_v) == 1\n unique_v = float(\"{:.3g}\".format(list(unique_v)[0]))\n assert unique_v == v\n else:\n for i, item in enumerate(caw.input_items):\n item.value = inputs[i]\n caw.checks[i].value = locks[i]\n\n caw.on_btn_norm(None)\n\n for i, item in enumerate(caw.input_items):\n assert item.value == expected[i]","title":""},{"docid":"f5553e90dfdb6fa8d90ee71120a3c056","score":"0.5427106","text":"def tell_buttonbox(self, data):\n self.factory.tell_buttonbox(data)","title":""},{"docid":"d3bf4f0212cd482aa9d6f5396b91babf","score":"0.54119587","text":"def detect_events(self):\n for button in self.clickables:\n button.check_collide()\n for event in pygame.event.get():\n self.detect_quit(event)\n mouse_xy = self.get_click_xy(event)\n if mouse_xy:\n button = self.detect_button_collide()\n return button","title":""},{"docid":"9459ec8e6d259d6768d4bb83493c4dfa","score":"0.5403557","text":"def correct_button(self, e):\n if e.button == 2:\n return 3\n elif e.button == 3:\n return 2\n else:\n return e.button","title":""},{"docid":"fcfdb876762d81dc8052f794029979c2","score":"0.53995323","text":"def test_set_button_label(button):\n set_button_property(button, \"red\", \"test_button\")\n assert button.get_label() == \"test_button\"","title":""},{"docid":"cd6ea4c517bacc65ae462cf56b1878d0","score":"0.5398053","text":"def test_subcontrols_can_be_selected_by_value():","title":""},{"docid":"8cac19b51c33b839efa978616c5cdf64","score":"0.53964406","text":"async def test_get_basic_value_notification_triggers(\n hass: HomeAssistant, client, ge_in_wall_dimmer_switch, integration\n) -> None:\n dev_reg = async_get_dev_reg(hass)\n device = dev_reg.async_get_device(\n identifiers={get_device_id(client.driver, ge_in_wall_dimmer_switch)}\n )\n assert device\n expected_trigger = {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": \"event.value_notification.basic\",\n \"device_id\": device.id,\n \"command_class\": CommandClass.BASIC,\n \"property\": \"event\",\n \"property_key\": None,\n \"endpoint\": 0,\n \"subtype\": \"Endpoint 0\",\n \"metadata\": {},\n }\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device.id\n )\n assert expected_trigger in triggers","title":""},{"docid":"914b57812637da3a4f2df5c779fac567","score":"0.53924584","text":"def test_set_actions_should_set_action(self):\n self.action_mock = lambda x: self.assertTrue(self.driver_mock is x)\n self.form_action.set_actions(self.navigate_mock, self.action_mock, self.action_mock)\n self.form_action.action()","title":""},{"docid":"ede56193f87dab34009ce122f21edeb1","score":"0.53825533","text":"def test_actions_rendered(admin_client, article, action):\n url = reverse('admin:blog_article_changelist')\n changelist = admin_client.get(url)\n\n input_name = '_action__articleadmin__admin__{}__blog__article__{}'.format(\n action, article.pk\n )\n assert input_name in dict(changelist.form.fields)","title":""},{"docid":"3c0bfd7aafa57c2a45342c87edcca13b","score":"0.5360911","text":"async def test_event(\n hass: HomeAssistant, mock_bridge_v2, v2_resources_test_data\n) -> None:\n await mock_bridge_v2.api.load_test_data(v2_resources_test_data)\n await setup_platform(hass, mock_bridge_v2, \"event\")\n # 7 entities should be created from test data\n assert len(hass.states.async_all()) == 7\n\n # pick one of the remote buttons\n state = hass.states.get(\"event.hue_dimmer_switch_with_4_controls_button_1\")\n assert state\n assert state.state == \"unknown\"\n assert state.name == \"Hue Dimmer switch with 4 controls Button 1\"\n # check event_types\n assert state.attributes[ATTR_EVENT_TYPES] == [\n \"initial_press\",\n \"repeat\",\n \"short_release\",\n \"long_press\",\n \"long_release\",\n ]\n # trigger firing 'initial_press' event from the device\n btn_event = {\n \"button\": {\"last_event\": \"initial_press\"},\n \"id\": \"f92aa267-1387-4f02-9950-210fb7ca1f5a\",\n \"metadata\": {\"control_id\": 1},\n \"type\": \"button\",\n }\n mock_bridge_v2.api.emit_event(\"update\", btn_event)\n await hass.async_block_till_done()\n state = hass.states.get(\"event.hue_dimmer_switch_with_4_controls_button_1\")\n assert state.attributes[ATTR_EVENT_TYPE] == \"initial_press\"\n # trigger firing 'long_release' event from the device\n btn_event = {\n \"button\": {\"last_event\": \"long_release\"},\n \"id\": \"f92aa267-1387-4f02-9950-210fb7ca1f5a\",\n \"metadata\": {\"control_id\": 1},\n \"type\": \"button\",\n }\n mock_bridge_v2.api.emit_event(\"update\", btn_event)\n await hass.async_block_till_done()\n state = hass.states.get(\"event.hue_dimmer_switch_with_4_controls_button_1\")\n assert state.attributes[ATTR_EVENT_TYPE] == \"long_release\"","title":""},{"docid":"4f157ce76166496fe3fc5d229d1383d2","score":"0.53346133","text":"async def test_get_trigger_capabilities(\n hass: HomeAssistant,\n device_registry: dr.DeviceRegistry,\n entity_registry: er.EntityRegistry,\n enable_custom_integrations: None,\n) -> None:\n platform = getattr(hass.components, f\"test.{DOMAIN}\")\n platform.init()\n ent = platform.ENTITIES[0]\n assert await async_setup_component(hass, DOMAIN, {DOMAIN: {CONF_PLATFORM: \"test\"}})\n await hass.async_block_till_done()\n\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_entry.add_to_hass(hass)\n device_entry = device_registry.async_get_or_create(\n config_entry_id=config_entry.entry_id,\n connections={(dr.CONNECTION_NETWORK_MAC, \"12:34:56:AB:CD:EF\")},\n )\n entity_registry.async_get_or_create(\n DOMAIN, \"test\", ent.unique_id, device_id=device_entry.id\n )\n\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device_entry.id\n )\n assert len(triggers) == 4\n for trigger in triggers:\n capabilities = await async_get_device_automation_capabilities(\n hass, DeviceAutomationType.TRIGGER, trigger\n )\n assert capabilities == {\n \"extra_fields\": [\n {\"name\": \"for\", \"optional\": True, \"type\": \"positive_time_period_dict\"}\n ]\n }","title":""},{"docid":"4019e90fb79757be24d590e65569b020","score":"0.53253406","text":"def test_trig():\n pass","title":""},{"docid":"d12199d1ef2a4ca526f7009c3665cd6a","score":"0.5322279","text":"def btn_pressed():\n for k in pyxel.__dict__.keys():\n if (\n k.startswith('KEY_')\n or k.startswith('GAMEPAD_')\n or k.startswith('MOUSE_')\n ) and pyxel.btn(getattr(pyxel, k)):\n return k","title":""},{"docid":"93809d120757a332551d2cf4b8f41c9f","score":"0.53117657","text":"def _verify_front_arm_actions(self, expected_actions):\n self.assertEqual(self._front_arm_actions, expected_actions)","title":""},{"docid":"1a7a5195b6914fef302c55c500ff131a","score":"0.5308277","text":"async def test_get_triggers_hidden_auxiliary(\n hass: HomeAssistant,\n device_registry: dr.DeviceRegistry,\n entity_registry: er.EntityRegistry,\n hidden_by,\n entity_category,\n) -> None:\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_entry.add_to_hass(hass)\n device_entry = device_registry.async_get_or_create(\n config_entry_id=config_entry.entry_id,\n connections={(dr.CONNECTION_NETWORK_MAC, \"12:34:56:AB:CD:EF\")},\n )\n entity_entry = entity_registry.async_get_or_create(\n DOMAIN,\n \"test\",\n \"5678\",\n device_id=device_entry.id,\n entity_category=entity_category,\n hidden_by=hidden_by,\n supported_features=CoverEntityFeature.OPEN,\n )\n expected_triggers = [\n {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": trigger,\n \"device_id\": device_entry.id,\n \"entity_id\": entity_entry.id,\n \"metadata\": {\"secondary\": True},\n }\n for trigger in [\"opened\", \"closed\", \"opening\", \"closing\"]\n ]\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device_entry.id\n )\n assert triggers == unordered(expected_triggers)","title":""},{"docid":"ef48bbaf22b7c3acdbded5c2f656b156","score":"0.52773756","text":"def click_target_buy_overrides_button(self):\n is_clicked = None\n try:\n self.logger.info('Start: click target buy overrides button')\n self._buy_page.click_target_buy_overrides_button()\n is_clicked = True\n except WebDriverException as exp:\n is_clicked = False\n self.logger.error(exp.msg)\n raise\n finally:\n self.logger.info('End: click target buy overrides button')\n return is_clicked","title":""},{"docid":"d8770d1db5bd698c6a53cea172cff84e","score":"0.5271562","text":"def __init__(self, *triggers):\n\n\t\tif len(triggers) == 0:\n\t\t\traise ValueError(\"No Triggers have been passed in!\")\n\n\t\tfor trigger in triggers:\n\t\t\tif not callable(trigger):\n\t\t\t\traise ValueError(\"A trigger passed into this custom button is not a function!\")\n\n\t\tself.triggers = triggers\n\t\t#super.__init__()","title":""},{"docid":"d0d36af2e939d8f222f070278ad0e04d","score":"0.5271143","text":"async def test_if_fires_on_event(hass: HomeAssistant, calls, setup_comp) -> None:\n assert await async_setup_component(\n hass,\n \"automation\",\n {\n \"automation\": {\n \"trigger\": {\n \"platform\": \"conversation\",\n \"command\": [\n \"Hey yo\",\n \"Ha ha ha\",\n ],\n },\n \"action\": {\n \"service\": \"test.automation\",\n \"data_template\": {\"data\": \"{{ trigger }}\"},\n },\n }\n },\n )\n\n await hass.services.async_call(\n \"conversation\",\n \"process\",\n {\n \"text\": \"Ha ha ha\",\n },\n blocking=True,\n )\n\n await hass.async_block_till_done()\n assert len(calls) == 1\n assert calls[0].data[\"data\"] == {\n \"alias\": None,\n \"id\": \"0\",\n \"idx\": \"0\",\n \"platform\": \"conversation\",\n \"sentence\": \"Ha ha ha\",\n \"slots\": {},\n \"details\": {},\n }","title":""},{"docid":"4a4e5dc33e7e82339bb1edbe1ff0aa49","score":"0.526724","text":"def test_gui_MenuMode() -> None:\n img = images.SPLASH_IMG\n pos = (0, 0)\n wsize = (ut.BSIZE[0] * ut.TILE, ut.BSIZE[1] * ut.TILE)\n\n b_pos1 = (pos[0], pos[1])\n b_pos2 = (pos[0]+50, pos[1]+50)\n b_size = (100, 100)\n b_img = images.BUTT_TMP_IMG\n\n test_gui = [gui.Button(b_pos1, b_size, 'trigger_1', b_img),\n gui.Button(b_pos2, b_size, 'trigger_2', b_img)]\n trigs = [('trigger1', 0, 2), ('trigger2', 0, 2)]\n\n # Test 0: incorrect initial values\n test = gui.MenuMode(img, (-1, -1))\n assert 0 <= test.menu_pos[0] < wsize[0]\n assert 0 <= test.menu_pos[1] < wsize[1]\n assert test.focused is None\n assert test.gui is None\n assert test.triggers is None\n\n test = gui.MenuMode(img, wsize)\n assert 0 <= test.menu_pos[0] < wsize[0]\n assert 0 <= test.menu_pos[1] < wsize[1]\n assert test.focused is None\n assert test.gui is None\n assert test.triggers is None\n\n # Test 1: proper work of init() method\n # pygame.display.init()\n # test = gui.MenuMode(img, pos, test_gui, trigs)\n # test.init(dummy_screen)\n # assert pygame.mouse.get_visible() is False # only from pygame 2.0.0\n # pygame.mouse.set_visible(True)\n\n # Test 2: proper work of update_focus() method\n test = gui.MenuMode(img, pos, test_gui, trigs)\n test.update_focus((25, 25))\n assert test.focused == 0\n test.update_focus((50, 50))\n assert test.focused == 0\n test.update_focus((75, 75))\n assert test.focused == 0\n test.update_focus((125, 125))\n assert test.focused == 1\n test.update_focus((100, 100))\n assert test.focused == 1\n test.update_focus((75, 75))\n assert test.focused == 1\n test.update_focus((0, 150))\n assert test.focused is None\n test.update_focus((75, 75))\n assert test.focused == 0\n\n # Test 3: proper work of events() method\n\n # Test 4: proper work of leave() method\n # test = gui.MenuMode()\n # test.init()\n # test.leave()\n # assert pygame.mouse.get_visible() is True","title":""},{"docid":"a96d14799612e8719d42f871b8ff660c","score":"0.52647483","text":"def test_controls(self):\n user_input = Input()\n print(\"Gimme anyol input:\")\n event = InputEvent.NONE\n while event == InputEvent.NONE:\n event = user_input.poll()\n\n print(\"Event:\", event)\n print(\"Got event\", event.description)\n self.assertIsNotNone(event)","title":""},{"docid":"c9e80fd5434c7b63c499c64670d1aba7","score":"0.5263074","text":"def test_delete_button(self):\n self.root, s, array = self.populate_gui()\n # Mocking \"Create button\" click. Idea - http://code.activestate.com/recipes/578978-using-tkinters-invoke-method-for-testing/\n s.frame_bottom5.children['but_create'].invoke()\n # Mocked data as an array\n\n # Compare fresh Tuna with mocked data\n z = Data.tunas[0].getTunaFeatures\n for x, y in zip (z, array):\n # Must be equal. If not - error\n self.assertEqual(x, y)\n # Setting focus\n s.tree.focus(0)\n # \"Press\" Delete entry button\n s.frame_bottom5.children['but_delete'].invoke()\n # After deletion, tunas length should be 0\n self.assertEqual(len(Data.tunas), 0)\n if self.root:\n self.root.destroy()","title":""},{"docid":"0b53113818c2a2282fec5ac20c13d51e","score":"0.5257707","text":"def _TriggerSetUp(self):\n raise NotImplementedError","title":""},{"docid":"dd51c27620ec254720a0769b4086453a","score":"0.52566755","text":"def test_set_button_color(button):\n set_button_property(button, \"red\", \"\")\n assert button.get_color() == \"red\"","title":""},{"docid":"1571a7dba78c1b2262d76b6b1cc00a6b","score":"0.52473766","text":"def test_search_button_enabled(self):\n # Get Search Button\n search_button = self.driver.find_element_by_class_name(\"btn-prim\")\n # Check Search Button is enabled\n self.assertTrue(search_button.is_enabled())","title":""},{"docid":"d73831ad1c90cb2926a4b571ee0546b0","score":"0.52439314","text":"def test_get_event_matches_keys(self):\n pass","title":""},{"docid":"96055e161e7ee0ec8cd1e4047d77d8b4","score":"0.5232518","text":"def test_radio_buttons_cannot_be_unselected():","title":""},{"docid":"df79075394e4caf544b56f6fc0b4efee","score":"0.5229247","text":"def test_controls_without_value(self):","title":""},{"docid":"b16835cd6167dd515c1b7e41a46ebb9f","score":"0.52196145","text":"def defineClickActions(self):\n pass","title":""},{"docid":"860d3afc0bd46811d16dc131bfc70c71","score":"0.5218555","text":"async def test_get_scene_activation_value_notification_triggers(\n hass: HomeAssistant, client, hank_binary_switch, integration\n) -> None:\n dev_reg = async_get_dev_reg(hass)\n device = dev_reg.async_get_device(\n identifiers={get_device_id(client.driver, hank_binary_switch)}\n )\n assert device\n expected_trigger = {\n \"platform\": \"device\",\n \"domain\": DOMAIN,\n \"type\": \"event.value_notification.scene_activation\",\n \"device_id\": device.id,\n \"command_class\": CommandClass.SCENE_ACTIVATION.value,\n \"property\": \"sceneId\",\n \"property_key\": None,\n \"endpoint\": 0,\n \"subtype\": \"Endpoint 0\",\n \"metadata\": {},\n }\n triggers = await async_get_device_automations(\n hass, DeviceAutomationType.TRIGGER, device.id\n )\n assert expected_trigger in triggers","title":""},{"docid":"1af0c659c644779ac876d4ad778d50a6","score":"0.52004796","text":"def assert_action_performed(_widget, _action):\n try:\n EventLog.performed_actions(_widget, _action)\n except AttributeError as e:\n pytest.fail(str(e))","title":""},{"docid":"e2c2daea300e8acc5dfdd2f782bf35d1","score":"0.5199976","text":"def button_pressed(self, button: Button) -> bool:\n return button == self.input","title":""},{"docid":"025672332dcf05ee9dab0b1e8ebb87e6","score":"0.51872325","text":"def test_controls_with_slightly_invalid_ids(self):","title":""},{"docid":"538f18accff0d5a575fd5461ae52c1b1","score":"0.51822996","text":"def get_triggers():\n # TODO: filter out nonetypes?\n for trigger in triggers:\n for i in trigger:\n xbmc.log(msg='The value of %s is %s and a %s' % (i, trigger[i], type(trigger[i])),\n level=xbmc.LOGNOTICE)\n return triggers","title":""},{"docid":"a5dcc04f5c620c30dd6304a57c0064f9","score":"0.51805586","text":"def on_click(self, button):\n pass","title":""},{"docid":"97b8c5a9a5d459de423a216eb9294a1f","score":"0.5180535","text":"def execute_buttons(self):\n arms = 0\n button_states = self.get_buttons()\n for button_name in BUTTON_PORTS:\n port = BUTTON_PORTS[button_name]\n angle = -1\n if isinstance(port, list):\n angle = port[1]\n port = port[0]\n if port in button_states and button_states[port] is True:\n # button was just pressed\n if button_name in self.button_toggles:\n # needs toggled\n if button_name == \"GRAB\":\n pass\n # print(button_name, port == POV,\n # self.get_raw_buttons()[POV], angle)\n if not (port == POV and not self.get_raw_buttons()[\n POV] == angle):\n new_state = not self.button_toggles[button_name]\n self.button_toggles[button_name] = new_state\n self.set_rumble(new_state)\n # print(button_name, new_state)\n elif self.get_raw_buttons()[POV] == angle:\n # Button angle correct, check button name\n if button_name == \"INC SPEED\":\n self.max_enhancer += SPEED_ADJUST\n self.set_rumble(True, 1)\n elif button_name == \"DEC SPEED\":\n self.max_enhancer -= SPEED_ADJUST\n self.set_rumble(False, 1)\n elif button_name == \"RES SPEED\":\n self.max_enhancer = 0\n self.set_rumble(True, length=200)\n elif port in button_states:\n # BUTTON BEING PRESSED\n if button_name == \"ARM IN\":\n arms = max(-1, min(-(ARM_SPEED_IN), 1))\n self.button_toggles[\"GRAB\"] = False\n elif button_name == \"ARM OUT\":\n arms = max(-1, min(ARM_SPEED_OUT, 1))\n self.button_toggles[\"GRAB\"] = False\n # if arms == 0 and self.button_toggles[\"GRAB\"]:\n # self.robot_lift_2.set(-ARM_SPEED_IN)\n # else:\n # self.robot_lift_2.set(arms)\n\n self.check_rumble()","title":""},{"docid":"f57bded989269779c0f1a519e5b2bac7","score":"0.5177102","text":"def inputButton(self, button):\n pass","title":""},{"docid":"06c1235719c588f61b8b89ea5c86301e","score":"0.51698107","text":"def _get_triggers(\n self, link: 'Link', src_spec: 'SourceModelSpec'\n ) -> Tuple[List[str], List[str]]:\n return [], []","title":""}],"string":"[\n {\n \"docid\": \"6923c51cf107f5f2eea29735f0a6cc08\",\n \"score\": \"0.70568377\",\n \"text\": \"def test_get_triggers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a38034de88a9c3953c027771e7e558\",\n \"score\": \"0.7040851\",\n \"text\": \"def getTriggerPressed(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c7ec4699a61a6dd5472c083ba9442e\",\n \"score\": \"0.68977433\",\n \"text\": \"def test_get_trigger(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad5dc9b6c69e828ccb99d4174418852c\",\n \"score\": \"0.66726124\",\n \"text\": \"def test_button_without_name(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ecece6161de64c8951cb67cce23bf1e\",\n \"score\": \"0.6605904\",\n \"text\": \"def test_repeated_button():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeab186daac25950932d509e865e5885\",\n \"score\": \"0.6526919\",\n \"text\": \"def test_add_trigger(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"120a477afbf6e23a229561e9ac692172\",\n \"score\": \"0.65125096\",\n \"text\": \"def test_gui_Button() -> None:\\n pos = 200, 200\\n size = 100, 100\\n cpos_x = pos[0], pos[0] + int(size[0] / 2), pos[0] + size[0]\\n cpos_y = pos[1], pos[1] + int(size[1] / 2), pos[1] + size[1]\\n wsize = (ut.BSIZE[0] * ut.TILE, ut.BSIZE[1] * ut.TILE)\\n\\n img = images.BUTT_TMP_IMG\\n img2 = images.SPLASH_IMG\\n img3 = images.BACK_IMG\\n\\n tname = 'test_trigger'\\n text = ('test', ut.GAME_FONT)\\n triggers1 = [('test_trigger', 0, 2)]\\n triggers2 = [('test_trig', 0, 4), ('test_trigger', 4, 3), ('t', 3, 4)]\\n\\n # Test 0: incorrect initial values\\n test = gui.Button((-1, -1), (-1, -1), tname, img)\\n assert 0 <= test.pos[0] < wsize[0]\\n assert 0 <= test.pos[1] < wsize[1]\\n assert test.size[0] > 0\\n assert test.size[1] > 0\\n assert test.text is None\\n\\n test = gui.Button(wsize, (0, 0), tname, img, None, text)\\n assert 0 <= test.pos[0] < wsize[0]\\n assert 0 <= test.pos[1] < wsize[1]\\n assert test.size[0] > 0\\n assert test.size[1] > 0\\n assert test.text is not None\\n\\n test = gui.Button(pos, size, tname, img2)\\n assert test.image.get_size()[0] == size[0]\\n assert test.image.get_size()[1] == size[1]\\n\\n test = gui.Button(pos, size, tname, img3)\\n assert test.image.get_size()[0] == size[0]\\n assert test.image.get_size()[1] == size[1]\\n\\n # Test 1: proper work of init_pdown() method\\n\\n for ix in range(3):\\n for iy in range(3):\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[ix], cpos_y[iy]))\\n assert test.is_pressed is True\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[ix], cpos_y[iy]))\\n assert test.is_pressed is True\\n assert test.text.is_pressed is True\\n\\n for ix in range(3):\\n for iy in range(3):\\n if ix == iy and iy == 1:\\n continue\\n\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[ix]+ix-1, cpos_y[iy]+iy-1))\\n assert test.is_pressed is False\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[ix]+ix-1, cpos_y[iy]+iy-1))\\n assert test.is_pressed is False\\n assert test.text.is_pressed is False\\n\\n # Test 2: proper work of init_pup() method\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]))\\n assert test.is_pressed is False\\n\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), [])\\n\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), triggers1)\\n assert triggers1[0][1] == 1\\n\\n test = gui.Button(pos, size, tname, img)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), triggers2)\\n assert triggers2[1][1] == 2\\n\\n triggers1 = [('test_trigger', 0, 2)]\\n triggers2 = [('test_trig', 0, 4), ('test_trigger', 4, 3), ('t', 3, 4)]\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]))\\n assert test.is_pressed is False\\n assert test.text.is_pressed is False\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), [])\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), triggers1)\\n assert triggers1[0][1] == 1\\n\\n test = gui.Button(pos, size, tname, img, None, text)\\n test.init_pdown((cpos_x[1], cpos_y[1]))\\n test.init_pup((cpos_x[1], cpos_y[1]), triggers2)\\n assert triggers2[1][1] == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4efef40153a1f11e2ee16af6042fcdb\",\n \"score\": \"0.63937\",\n \"text\": \"def verify_button(self, locator, expected):\\n button = self.state._get_typed_item_by_locator(Button, locator)\\n self.state._verify_value(expected, button.Text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb072dfb4e43f75a6f52a23a2926dc0a\",\n \"score\": \"0.61948365\",\n \"text\": \"def _findBtnElem(browser, triggers):\\n elemTypes = [\\\"button\\\", \\\"input\\\", \\\"a\\\"]\\n for t in elemTypes:\\n log.debug(\\\"Looking for element by type {}.\\\", t)\\n elems = browser.find_by_tag(t)\\n for elem in elems:\\n elemText = elem.value or elem.text\\n if not elemText:\\n continue\\n # Make lowercase, remove spaces,tabs,newlines,non alphab chars.\\n elemText = \\\"\\\".join(char for char in elemText if char.isalpha())\\n elemText = \\\"\\\".join(elemText.lower().split())\\n # Compare against list\\n for trig in triggers:\\n if trig in elemText:\\n return elem\\n # Did not find any element.\\n log.debug(\\\"Did not find any element.\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61c292e762c5deb3a01dd4afbe6fb5f\",\n \"score\": \"0.6126174\",\n \"text\": \"async def test_if_fires_on_state_change(hass, calls):\\n hass.states.async_set(\\\"button.entity\\\", \\\"unknown\\\")\\n\\n assert await async_setup_component(\\n hass,\\n automation.DOMAIN,\\n {\\n automation.DOMAIN: [\\n {\\n \\\"trigger\\\": {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"device_id\\\": \\\"\\\",\\n \\\"entity_id\\\": \\\"button.entity\\\",\\n \\\"type\\\": \\\"pressed\\\",\\n },\\n \\\"action\\\": {\\n \\\"service\\\": \\\"test.automation\\\",\\n \\\"data\\\": {\\n \\\"some\\\": (\\n \\\"to - {{ trigger.platform}} - \\\"\\n \\\"{{ trigger.entity_id}} - {{ trigger.from_state.state}} - \\\"\\n \\\"{{ trigger.to_state.state}} - {{ trigger.for }} - \\\"\\n \\\"{{ trigger.id}}\\\"\\n )\\n },\\n },\\n }\\n ]\\n },\\n )\\n\\n # Test triggering device trigger with a to state\\n hass.states.async_set(\\\"button.entity\\\", \\\"2021-01-01T23:59:59+00:00\\\")\\n await hass.async_block_till_done()\\n assert len(calls) == 1\\n assert calls[0].data[\\n \\\"some\\\"\\n ] == \\\"to - device - {} - unknown - 2021-01-01T23:59:59+00:00 - None - 0\\\".format(\\n \\\"button.entity\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d61cdf9a1942842c830c17e0f61af39\",\n \"score\": \"0.6063574\",\n \"text\": \"def test_submit_button():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80edad7322659bd77437bea57210db23\",\n \"score\": \"0.60603464\",\n \"text\": \"def getButton(self, button: ButtonType) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ba6d9b405b8bff2e022f72b081e548\",\n \"score\": \"0.6039221\",\n \"text\": \"def test_selected_buttons() -> None:\\n term = Terminal()\\n tui = TextUI(term)\\n buttons = [Button(tui) for _ in range(3)]\\n menu = Menu(tui, buttons=buttons)\\n assert menu.selected is buttons[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ebaa7fcdb2e9faa2d7215777775c2ad\",\n \"score\": \"0.6024014\",\n \"text\": \"async def test_get_triggers(\\n hass: HomeAssistant,\\n device_registry: dr.DeviceRegistry,\\n entity_registry: er.EntityRegistry,\\n set_state,\\n features_reg,\\n features_state,\\n expected_trigger_types,\\n) -> None:\\n config_entry = MockConfigEntry(domain=\\\"test\\\", data={})\\n config_entry.add_to_hass(hass)\\n device_entry = device_registry.async_get_or_create(\\n config_entry_id=config_entry.entry_id,\\n connections={(dr.CONNECTION_NETWORK_MAC, \\\"12:34:56:AB:CD:EF\\\")},\\n )\\n entity_entry = entity_registry.async_get_or_create(\\n DOMAIN,\\n \\\"test\\\",\\n \\\"5678\\\",\\n device_id=device_entry.id,\\n supported_features=features_reg,\\n )\\n if set_state:\\n hass.states.async_set(\\n entity_entry.entity_id,\\n \\\"attributes\\\",\\n {\\\"supported_features\\\": features_state},\\n )\\n\\n expected_triggers = []\\n\\n expected_triggers += [\\n {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": trigger,\\n \\\"device_id\\\": device_entry.id,\\n \\\"entity_id\\\": entity_entry.id,\\n \\\"metadata\\\": {\\\"secondary\\\": False},\\n }\\n for trigger in expected_trigger_types\\n ]\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device_entry.id\\n )\\n assert triggers == unordered(expected_triggers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8bad0492bb969fe0f6c22fc57e3a788\",\n \"score\": \"0.6008939\",\n \"text\": \"def verify_action_button_status(self, **kwargs):\\n for button,expected_value in kwargs.items():\\n locator = eda_lex_locators[\\\"eda_settings\\\"][\\\"action_button\\\"].format(button)\\n self.selenium.page_should_contain_element(locator)\\n self.selenium.wait_until_element_is_visible(locator,\\n error= f\\\"Element '{button}' button is not displayed for the user\\\")\\n time.sleep(1)\\n actual_value = self.selenium.get_webelement(locator).get_attribute(\\\"disabled\\\")\\n expected_value = bool(expected_value == \\\"disabled\\\")\\n if not str(expected_value).lower() == str(actual_value).lower() :\\n raise Exception (f\\\"Element {button} button status is {actual_value} instead of {expected_value}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f6931ac33aab0f5601a9b0dd3f812e\",\n \"score\": \"0.5992467\",\n \"text\": \"def getTrigger(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8adc23251ea993fae0f5759fb596e7\",\n \"score\": \"0.5991863\",\n \"text\": \"async def test_button(hass: HomeAssistant) -> None:\\n button = ButtonEntity()\\n assert button.state is None\\n\\n button.hass = hass\\n\\n with pytest.raises(NotImplementedError):\\n await button.async_press()\\n\\n button.press = MagicMock()\\n await button.async_press()\\n\\n assert button.press.called\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb128b720979d5de8ae31492ded29c45\",\n \"score\": \"0.59495145\",\n \"text\": \"def test_choose_submit_buttons(expected_post):\\n text = \\\"\\\"\\\"\\n <form method=\\\"post\\\" action=\\\"mock://form.com/post\\\">\\n <button type=\\\"butTon\\\" name=\\\"sub1\\\" value=\\\"val1\\\">Val1</button>\\n <button type=\\\"suBmit\\\" name=\\\"sub2\\\" value=\\\"val2\\\">Val2</button>\\n <button type=\\\"reset\\\" name=\\\"sub3\\\" value=\\\"val3\\\">Val3</button>\\n <button name=\\\"sub4\\\" value=\\\"val4\\\">Val4</button>\\n <input type=\\\"subMit\\\" name=\\\"sub5\\\" value=\\\"val5\\\">\\n </form>\\n \\\"\\\"\\\"\\n browser, url = setup_mock_browser(expected_post=expected_post, text=text)\\n browser.open(url)\\n browser.select_form()\\n res = browser.submit_selected(btnName=expected_post[0][0])\\n assert res.status_code == 200 and res.text == 'Success!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53343de55ba0e8a9212d60be0343e9ee\",\n \"score\": \"0.5919839\",\n \"text\": \"def test_get_trigger_setting(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44deacb5017eca4c48630dacbe788cc5\",\n \"score\": \"0.5918713\",\n \"text\": \"def test_triggered(self, h5reader):\\n h5reader.open()\\n for i, event in enumerate(h5reader):\\n assert event.triggered == bool(i)\\n h5reader.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5228843791b9f0f3f3a92a92b1599682\",\n \"score\": \"0.5917064\",\n \"text\": \"def buttons(self):\\n\\t\\traise HaventGottenToThatYetError, \\\"I ain't done writing this code yet.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a84e335ca25498d084f8fefa9c9ced\",\n \"score\": \"0.5897216\",\n \"text\": \"def _verify_wheel_actions(self, expected_actions):\\n self.assertEqual(self._wheel_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d16851c2c0592200b2d75de755ebd5d3\",\n \"score\": \"0.58840644\",\n \"text\": \"async def test_button_simple(hass: HomeAssistant, knx: KNXTestKit) -> None:\\n events = async_capture_events(hass, \\\"state_changed\\\")\\n await knx.setup_integration(\\n {\\n ButtonSchema.PLATFORM: {\\n CONF_NAME: \\\"test\\\",\\n KNX_ADDRESS: \\\"1/2/3\\\",\\n }\\n }\\n )\\n assert len(hass.states.async_all()) == 1\\n assert len(events) == 1\\n events.pop()\\n\\n # press button\\n await hass.services.async_call(\\n \\\"button\\\", \\\"press\\\", {\\\"entity_id\\\": \\\"button.test\\\"}, blocking=True\\n )\\n await knx.assert_write(\\\"1/2/3\\\", True)\\n assert len(events) == 1\\n events.pop()\\n\\n # received telegrams on button GA are ignored by the entity\\n old_state = hass.states.get(\\\"button.test\\\")\\n async_fire_time_changed(hass, dt.utcnow() + timedelta(seconds=3))\\n await knx.receive_write(\\\"1/2/3\\\", False)\\n await knx.receive_write(\\\"1/2/3\\\", True)\\n new_state = hass.states.get(\\\"button.test\\\")\\n assert old_state == new_state\\n assert len(events) == 0\\n\\n # button does not respond to read\\n await knx.receive_read(\\\"1/2/3\\\")\\n await knx.assert_telegram_count(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce5d376cd2a764f1425cbb0bc6406be\",\n \"score\": \"0.5875481\",\n \"text\": \"def test_file_buttons(self):\\n dialog = self.dialog\\n button_sources = [(self.dialog.readme, 'README.txt', 'readme'),\\n (self.dialog.idle_news, 'NEWS.txt', 'news'),\\n (self.dialog.idle_credits, 'CREDITS.txt', 'credits')]\\n\\n for button, filename, name in button_sources:\\n with self.subTest(name=name):\\n button.invoke()\\n fn = findfile(filename, subdir='idlelib')\\n get = dialog._current_textview.viewframe.textframe.text.get\\n with open(fn, encoding='utf-8') as f:\\n self.assertEqual(f.readline().strip(), get('1.0', '1.end'))\\n f.readline()\\n self.assertEqual(f.readline().strip(), get('3.0', '3.end'))\\n dialog._current_textview.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5fc6c730c70640147baf9d21fccbd19\",\n \"score\": \"0.5857787\",\n \"text\": \"def test_generate_button_press(self):\\n params = {'state': 1, 'counter': 62552}\\n result = self.node_obj.generate_button_press(params)\\n expected = b'\\\\t\\\\x00\\\\x01\\\\x00\\\\x01X\\\\xf4\\\\x00\\\\x00'\\n self.assertEqual(result, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a130514a94065085b1e078a92164424d\",\n \"score\": \"0.582386\",\n \"text\": \"def testGetActions(self):\\r\\n iq = self.Iq()\\r\\n iq['command']['node'] = 'foo'\\r\\n iq['command']['actions'] = ['prev', 'next']\\r\\n\\r\\n results = iq['command']['actions']\\r\\n expected = set(['prev', 'next'])\\r\\n self.assertEqual(results, expected,\\r\\n \\\"Incorrect next actions: %s\\\" % results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1886ae65de7d864c086a30feb71392\",\n \"score\": \"0.5813623\",\n \"text\": \"async def test_button_type(hass: HomeAssistant, knx: KNXTestKit) -> None:\\n await knx.setup_integration(\\n {\\n ButtonSchema.PLATFORM: {\\n CONF_NAME: \\\"test\\\",\\n KNX_ADDRESS: \\\"1/2/3\\\",\\n ButtonSchema.CONF_VALUE: 21.5,\\n CONF_TYPE: \\\"2byte_float\\\",\\n }\\n }\\n )\\n # press button\\n await hass.services.async_call(\\n \\\"button\\\", \\\"press\\\", {\\\"entity_id\\\": \\\"button.test\\\"}, blocking=True\\n )\\n await knx.assert_write(\\\"1/2/3\\\", (0x0C, 0x33))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4976e01c22d2f8a2ed0e9adb400cba1e\",\n \"score\": \"0.5800542\",\n \"text\": \"def check_actions(self, expected, elements):\\r\\n for idx, item in enumerate(expected):\\r\\n text, form_value = item\\r\\n e = elements.eq(idx)\\r\\n eq_(e.parent().text(), text)\\r\\n eq_(e.attr('name'), 'action')\\r\\n eq_(e.val(), form_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e94b9e010814b6063f549215074f2f8\",\n \"score\": \"0.5797616\",\n \"text\": \"def test_create_button(self):\\n self.root, s, array = self.populate_gui()\\n # Mocking \\\"Create button\\\" click. Idea - http://code.activestate.com/recipes/578978-using-tkinters-invoke-method-for-testing/\\n s.frame_bottom5.children['but_create'].invoke()\\n # Mocked data as an array\\n\\n # Compare fresh Tuna with mocked data\\n z = Data.tunas[0].getTunaFeatures\\n for x, y in zip (z, array):\\n # Must be equal. If not - error\\n self.assertEqual(x, y)\\n if self.root:\\n self.root.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4e128bb7f65592d7eacc41e217ad69\",\n \"score\": \"0.57582724\",\n \"text\": \"def test_change_trigger_setting(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8e20d8b26f0bd241217a403be66d45\",\n \"score\": \"0.5746524\",\n \"text\": \"def test_replace_triggers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ecb71ad90f441a936a274233a1fd47b\",\n \"score\": \"0.5740988\",\n \"text\": \"def test_call_button(qtbot):\\n\\n @magicgui(call_button=\\\"my_button\\\", auto_call=True)\\n def func(a: int, b: int = 3, c=7.1) -> None:\\n assert a == 7\\n\\n magic_widget = func.Gui()\\n\\n assert hasattr(magic_widget, \\\"call_button\\\")\\n assert isinstance(magic_widget.call_button, QtW.QPushButton)\\n magic_widget.a = 7\\n\\n qtbot.mouseClick(magic_widget.call_button, Qt.LeftButton)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7275ca9d46acff246dc95da9656d8599\",\n \"score\": \"0.5714357\",\n \"text\": \"def test_object_buttons(self):\\n r = self.renderer(\\n context=self.portal[\\\"news\\\"],\\n assignment=actions.Assignment(\\n ptitle=\\\"actions\\\", category=\\\"object_buttons\\\", show_icons=False\\n ),\\n )\\n r.update()\\n output = r.actionLinks()\\n\\n # Have our expected tabs ?\\n expected = {\\\"Cut\\\", \\\"Copy\\\", \\\"Rename\\\", \\\"Delete\\\"}\\n got = {str(link[\\\"title\\\"]) for link in output}\\n self.assertTrue(expected.issubset(got))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c85d3bf3a40f27ce6c0834b4a67ee57\",\n \"score\": \"0.5689448\",\n \"text\": \"def test_action(\\n widget,\\n action_name,\\n input_text,\\n expected_result,\\n selected_text=None,\\n cursor_placement=None\\n):\\n app = QtWidgets.QApplication.instance()\\n window = app.activeWindow()\\n\\n for action in widget.actions():\\n if action.text() == action_name:\\n break\\n\\n assert action_name == action.text()\\n\\n try:\\n setup_test(\\n widget,\\n input_text,\\n selected_text=selected_text,\\n cursor_placement=cursor_placement\\n )\\n\\n # widget.last_key_pressed may be necessary here for some actions... but should those actions really fall under this domain? or be placed on the editor?\\n action.trigger()\\n\\n text = widget.toPlainText()\\n assert text == expected_result\\n except AssertionError:\\n raise\\n finally:\\n window.activateWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2619c809c051daab8d80c51138eae921\",\n \"score\": \"0.56763\",\n \"text\": \"def test_get_behaviors(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e11074eba811a72adc9368867b0034\",\n \"score\": \"0.56531715\",\n \"text\": \"def test_printer_buttons(self):\\n dialog = self.dialog\\n button_sources = [(dialog.py_license, license, 'license'),\\n (dialog.py_copyright, copyright, 'copyright'),\\n (dialog.py_credits, credits, 'credits')]\\n\\n for button, printer, name in button_sources:\\n with self.subTest(name=name):\\n printer._Printer__setup()\\n button.invoke()\\n get = dialog._current_textview.viewframe.textframe.text.get\\n lines = printer._Printer__lines\\n self.assertEqual(lines[0], get('1.0', '1.end'))\\n self.assertEqual(lines[1], get('2.0', '2.end'))\\n dialog._current_textview.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e73da42a24d87c5e69f4f34200766a8\",\n \"score\": \"0.56365985\",\n \"text\": \"def test_main_button(self):\\n self.assertTrue(self.browser.is_text_present('SELECCIONAR IMAGEN'))\\n self.assertTrue(self.browser.is_text_present('CLASIFICAR'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3a43fbd947df52e772a280c08df58e\",\n \"score\": \"0.56074643\",\n \"text\": \"def test_checkBookButton(self):\\n\\n homePage = HomePage(self.driver)\\n self.explicit_wait(HomePage.FromBooking)\\n homePage.bookButton().click()\\n self.explicit_wait(HomePage.ContinueButton)\\n assert homePage.continueButton().is_displayed(), \\\"Book Button didn't work correctly\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f647709dee22339c7187aabc5d1091\",\n \"score\": \"0.5595025\",\n \"text\": \"def Button(self) -> _n_11_t_4:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba6ce0e9113a5145a53162d59a770a2\",\n \"score\": \"0.5585998\",\n \"text\": \"def test_button_switch_up() -> None:\\n term = Terminal()\\n tui = TextUI(term)\\n buttons = [Button(tui) for _ in range(3)]\\n menu = Menu(tui, buttons=buttons)\\n assert menu.selected is buttons[0]\\n menu.selection_up()\\n assert menu.selected is buttons[2]\\n menu.selection_up()\\n assert menu.selected is buttons[1]\\n menu.selection_up()\\n assert menu.selected is buttons[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856a534f6e16ea198909c707a8679242\",\n \"score\": \"0.55750066\",\n \"text\": \"def test_name_in_actions(self, trigger_actions_mock):\\n name = ACTIONS.GET\\n\\n ds_config = CHAOS_CONFIG.datastore\\n\\n trigger_action(name, ds_config)\\n\\n trigger_actions_mock.assert_called_once_with(name, ds_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498ca2b80db53489d47259a10bee0fb3\",\n \"score\": \"0.555756\",\n \"text\": \"def test_submit_button(self):\\n button = get_button(self.get_form())\\n self.assertEqual(button.get('type'), 'submit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec9a150d48c80843e2b30313522b1b68\",\n \"score\": \"0.5556827\",\n \"text\": \"async def test_get_triggers(opp, device_reg, entity_reg):\\n config_entry = MockConfigEntry(domain=\\\"test\\\", data={})\\n config_entry.add_to_opp(opp)\\n device_entry = device_reg.async_get_or_create(\\n config_entry_id=config_entry.entry_id,\\n connections={(device_registry.CONNECTION_NETWORK_MAC, \\\"12:34:56:AB:CD:EF\\\")},\\n )\\n entity_reg.async_get_or_create(DOMAIN, \\\"test\\\", \\\"5678\\\", device_id=device_entry.id)\\n expected_triggers = [\\n {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": \\\"turned_off\\\",\\n \\\"device_id\\\": device_entry.id,\\n \\\"entity_id\\\": f\\\"{DOMAIN}.test_5678\\\",\\n },\\n {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": \\\"turned_on\\\",\\n \\\"device_id\\\": device_entry.id,\\n \\\"entity_id\\\": f\\\"{DOMAIN}.test_5678\\\",\\n },\\n ]\\n triggers = await async_get_device_automations(opp, \\\"trigger\\\", device_entry.id)\\n assert_lists_same(triggers, expected_triggers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d19b31d39c617f88801539774d382d7b\",\n \"score\": \"0.5552215\",\n \"text\": \"def _verify_back_arm_actions(self, expected_actions):\\n self.assertEqual(self._back_arm_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d479da65719d58980a14bd2d167d27a3\",\n \"score\": \"0.5544555\",\n \"text\": \"def test_actions_available(admin_client, article):\\n url = reverse('admin:blog_article_changelist')\\n changeview = admin_client.get(url)\\n path = (\\n './/table[@id=\\\"result_list\\\"]' '//thead//th//*[starts-with(text(), \\\"Actions\\\")]'\\n )\\n assert len(changeview.lxml.xpath(path)) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb89546863d2b6cd5aad54ff3fb808b9\",\n \"score\": \"0.55428463\",\n \"text\": \"def test_actions(self):\\n serialized = self._runner.serialized_model\\n actions = serialized['Objects']['?'].get('_actions')\\n self.assertIsInstance(actions, dict)\\n action_names = [action['name'] for action in actions.values()]\\n self.assertIn('testAction', action_names)\\n self.assertNotIn('notAction', action_names)\\n self.assertIn('testRootMethod', action_names)\\n action_meta = None\\n for action in actions.values():\\n self.assertIsInstance(action.get('enabled'), bool)\\n self.assertIsInstance(action.get('name'), str)\\n self.assertThat(\\n action['name'],\\n matchers.StartsWith('test'))\\n if action['name'] == 'testActionMeta':\\n action_meta = action\\n else:\\n self.assertEqual(action['title'], action['name'])\\n self.assertIsNotNone(action_meta)\\n self.assertEqual(action_meta['title'], \\\"Title of the method\\\")\\n self.assertEqual(action_meta['description'],\\n \\\"Description of the method\\\")\\n self.assertEqual(action_meta['helpText'], \\\"HelpText of the method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ad0ff2f900dee6313aab175e3c6c26\",\n \"score\": \"0.55339754\",\n \"text\": \"def test_submit_button(self):\\n button = get_button(self.form())\\n self.assertEqual(button.get('type'), 'submit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ad0ff2f900dee6313aab175e3c6c26\",\n \"score\": \"0.55339754\",\n \"text\": \"def test_submit_button(self):\\n button = get_button(self.form())\\n self.assertEqual(button.get('type'), 'submit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ad0ff2f900dee6313aab175e3c6c26\",\n \"score\": \"0.55339754\",\n \"text\": \"def test_submit_button(self):\\n button = get_button(self.form())\\n self.assertEqual(button.get('type'), 'submit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a7a5afa50302f5166053d2efb8f007\",\n \"score\": \"0.5505979\",\n \"text\": \"def events(self):\\n # Button pressed (originally none)\\n button = \\\"\\\"\\n # Check event queue\\n for event in pygame.event.get():\\n # If exit button clicked, exit game\\n if event.type == pygame.QUIT:\\n pygame.quit()\\n sys.exit()\\n # If a click, see if it is over a virtual button\\n elif event.type == pygame.MOUSEBUTTONUP:\\n button = self.get_button(event.pos)\\n # Return the virtual button clicked\\n return button\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a936a25c82a9396e9d559b4a8ed7e3e0\",\n \"score\": \"0.5503027\",\n \"text\": \"def detect_button_collide(self):\\n for button in self.clickables:\\n clicked_button = button.check_collide()\\n if clicked_button:\\n print('clicked: ' + button.name)\\n return button\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01915a994d2afe27718838c74b97ac22\",\n \"score\": \"0.54878205\",\n \"text\": \"async def test_button_raw(hass: HomeAssistant, knx: KNXTestKit) -> None:\\n await knx.setup_integration(\\n {\\n ButtonSchema.PLATFORM: {\\n CONF_NAME: \\\"test\\\",\\n KNX_ADDRESS: \\\"1/2/3\\\",\\n CONF_PAYLOAD: False,\\n CONF_PAYLOAD_LENGTH: 0,\\n }\\n }\\n )\\n # press button\\n await hass.services.async_call(\\n \\\"button\\\", \\\"press\\\", {\\\"entity_id\\\": \\\"button.test\\\"}, blocking=True\\n )\\n await knx.assert_write(\\\"1/2/3\\\", False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102eec5e6e9c3d68a4aa2982d3a4088f\",\n \"score\": \"0.54876107\",\n \"text\": \"def _verify_front_drum_actions(self, expected_actions):\\n self.assertEqual(self._front_drum_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fc6bb9d9f3134a834fa1dfb539e975\",\n \"score\": \"0.54778445\",\n \"text\": \"def test_set_actions_should_set_navigate(self):\\n self.navigate_mock.side_effect = lambda x: self.assertTrue(self.driver_mock is x)\\n self.form_action.set_actions(self.navigate_mock, self.action_mock, self.data_mock)\\n self.form_action.navigate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37adb87a612d886fc32172fad77e1bc\",\n \"score\": \"0.5472817\",\n \"text\": \"def _verify_back_drum_actions(self, expected_actions):\\n self.assertEqual(self._back_drum_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8206e18839c5889e90fac72d1bdddf\",\n \"score\": \"0.546266\",\n \"text\": \"def test_simple(self):\\n control = self.window\\n mock = self.app._the_mock\\n control.on_prev = mock.on_prev\\n control.on_next = mock.on_next\\n event = wx.CommandEvent()\\n\\n LOG.info(\\\"Dimensions changed\\\")\\n control.on_dimensions(event)\\n expect = [\\n call.on_dimensions(event),\\n call.App.new_dimensions(),\\n call.App.refresh_contents(),\\n call.CommandEvent.Skip(),\\n ]\\n\\n LOG.info(\\\"Variant changed\\\")\\n control.on_variant(event)\\n expect += [\\n call.on_variant(event),\\n call.App.refresh_contents(),\\n call.CommandEvent.Skip(),\\n ]\\n\\n LOG.info(\\\"Option changed\\\")\\n control.on_option(event)\\n expect += [\\n call.on_option(event),\\n call.App.grab_contents(),\\n call.CommandEvent.Skip(),\\n ]\\n\\n LOG.info(\\\"Reload and prev\\\")\\n control.on_reload_and_prev(event)\\n expect += [\\n call.on_reload_and_prev(event),\\n call.App.book.reload(),\\n call.on_prev(event),\\n ]\\n\\n LOG.info(\\\"Changes applied\\\")\\n control.on_apply_and_next(event)\\n expect += [call.on_apply_and_next(event), call.on_next(event), call.App.apply()]\\n\\n mock.assert_has_calls(expect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eeec4a183812051645bda0f0fe535ef\",\n \"score\": \"0.5457157\",\n \"text\": \"def is_button_pressed(button):\\r\\n return ui.is_pressed(button)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8433fa84b1c4dedde774ec7d78554bb\",\n \"score\": \"0.5456086\",\n \"text\": \"def test_button_text_subscribe(self):\\n button = get_button(self.get_form())\\n self.assertEqual(button.string, 'Subscribe')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d8a9f7f88cbd4fbcbbd7a761c98fe6b\",\n \"score\": \"0.5448595\",\n \"text\": \"def test_delete_trigger(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424e43ebe5d05571d8994bd1b7dc79e2\",\n \"score\": \"0.54470354\",\n \"text\": \"def test_parse_button_press(self):\\n result = self.node_obj.parse_button_press(b'\\\\t\\\\x00\\\\x00\\\\x00\\\\x02\\\\xbf\\\\xc3\\\\x00\\\\x00')\\n expected = {'counter': 50111, 'button_state': 0}\\n self.assertEqual(result, expected, \\\"State OFF, Counter 50111\\\")\\n\\n result = self.node_obj.parse_button_press(b'\\\\t\\\\x00\\\\x01\\\\x00\\\\x01\\\\x12\\\\xca\\\\x00\\\\x00')\\n expected = {'counter': 51730, 'button_state': 1}\\n self.assertEqual(result, expected, \\\"State ON, Counter 51730\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab4fc1a0399dd4e271598d2d0db2d4b\",\n \"score\": \"0.5445742\",\n \"text\": \"def test_button_switch_down() -> None:\\n term = Terminal()\\n tui = TextUI(term)\\n buttons = [Button(tui) for _ in range(3)]\\n menu = Menu(tui, buttons=buttons)\\n assert menu.selected is buttons[0]\\n menu.selection_down()\\n assert menu.selected is buttons[1]\\n menu.selection_down()\\n assert menu.selected is buttons[2]\\n menu.selection_down()\\n assert menu.selected is buttons[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8dcb63fb8ed3b1ddf842ba3dfeec589\",\n \"score\": \"0.5445198\",\n \"text\": \"def test_alert_triggering(self):\\n\\t\\tself.called = 0\\n\\t\\talert = self.alertservice.create(None, (self.fake_method, tuple()), (model.LoadBalancer.RESP_TIME, model.Alert.GE, 2))\\n\\t\\tself.assertEqual(False, alert.triggered)\\n\\t\\tself.alertservice.validate(alert, 1)\\n\\t\\tself.assertEqual(0, self.called)\\n\\t\\tself.alertservice.validate(alert, 2)\\n\\t\\tself.assertEqual(True, alert.triggered)\\n\\t\\tself.assertEqual(1, self.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83623382d652a64f807b2a67d4e8e168\",\n \"score\": \"0.54440373\",\n \"text\": \"def _verify_routine_actions(self, expected_actions):\\n self.assertEqual(self._routine_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842fa44fd78195a63ee437b44f24f471\",\n \"score\": \"0.54278517\",\n \"text\": \"def test_on_btn_norm(self, caw, multishell_edit, inputs, locks, expected):\\n if multishell_edit:\\n caw.rbs_multi_apply.index = 0\\n for i, item in enumerate(caw.input_items):\\n item.value = inputs[i]\\n caw.checks[i].value = locks[i]\\n\\n caw.on_btn_norm(None)\\n\\n for i, item in enumerate(caw.input_items):\\n assert item.value == expected[i]\\n\\n start_no = caw.irs_shell_range.value[0]\\n end_no = caw.irs_shell_range.value[1]\\n\\n for i, v in enumerate(expected):\\n line = caw.fig.data[2 + i].y[start_no - 1 : end_no]\\n unique_v = set(line)\\n assert len(unique_v) == 1\\n unique_v = float(\\\"{:.3g}\\\".format(list(unique_v)[0]))\\n assert unique_v == v\\n else:\\n for i, item in enumerate(caw.input_items):\\n item.value = inputs[i]\\n caw.checks[i].value = locks[i]\\n\\n caw.on_btn_norm(None)\\n\\n for i, item in enumerate(caw.input_items):\\n assert item.value == expected[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5553e90dfdb6fa8d90ee71120a3c056\",\n \"score\": \"0.5427106\",\n \"text\": \"def tell_buttonbox(self, data):\\n self.factory.tell_buttonbox(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bf4f0212cd482aa9d6f5396b91babf\",\n \"score\": \"0.54119587\",\n \"text\": \"def detect_events(self):\\n for button in self.clickables:\\n button.check_collide()\\n for event in pygame.event.get():\\n self.detect_quit(event)\\n mouse_xy = self.get_click_xy(event)\\n if mouse_xy:\\n button = self.detect_button_collide()\\n return button\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9459ec8e6d259d6768d4bb83493c4dfa\",\n \"score\": \"0.5403557\",\n \"text\": \"def correct_button(self, e):\\n if e.button == 2:\\n return 3\\n elif e.button == 3:\\n return 2\\n else:\\n return e.button\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfdb876762d81dc8052f794029979c2\",\n \"score\": \"0.53995323\",\n \"text\": \"def test_set_button_label(button):\\n set_button_property(button, \\\"red\\\", \\\"test_button\\\")\\n assert button.get_label() == \\\"test_button\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd6ea4c517bacc65ae462cf56b1878d0\",\n \"score\": \"0.5398053\",\n \"text\": \"def test_subcontrols_can_be_selected_by_value():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cac19b51c33b839efa978616c5cdf64\",\n \"score\": \"0.53964406\",\n \"text\": \"async def test_get_basic_value_notification_triggers(\\n hass: HomeAssistant, client, ge_in_wall_dimmer_switch, integration\\n) -> None:\\n dev_reg = async_get_dev_reg(hass)\\n device = dev_reg.async_get_device(\\n identifiers={get_device_id(client.driver, ge_in_wall_dimmer_switch)}\\n )\\n assert device\\n expected_trigger = {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": \\\"event.value_notification.basic\\\",\\n \\\"device_id\\\": device.id,\\n \\\"command_class\\\": CommandClass.BASIC,\\n \\\"property\\\": \\\"event\\\",\\n \\\"property_key\\\": None,\\n \\\"endpoint\\\": 0,\\n \\\"subtype\\\": \\\"Endpoint 0\\\",\\n \\\"metadata\\\": {},\\n }\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device.id\\n )\\n assert expected_trigger in triggers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914b57812637da3a4f2df5c779fac567\",\n \"score\": \"0.53924584\",\n \"text\": \"def test_set_actions_should_set_action(self):\\n self.action_mock = lambda x: self.assertTrue(self.driver_mock is x)\\n self.form_action.set_actions(self.navigate_mock, self.action_mock, self.action_mock)\\n self.form_action.action()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede56193f87dab34009ce122f21edeb1\",\n \"score\": \"0.53825533\",\n \"text\": \"def test_actions_rendered(admin_client, article, action):\\n url = reverse('admin:blog_article_changelist')\\n changelist = admin_client.get(url)\\n\\n input_name = '_action__articleadmin__admin__{}__blog__article__{}'.format(\\n action, article.pk\\n )\\n assert input_name in dict(changelist.form.fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c0bfd7aafa57c2a45342c87edcca13b\",\n \"score\": \"0.5360911\",\n \"text\": \"async def test_event(\\n hass: HomeAssistant, mock_bridge_v2, v2_resources_test_data\\n) -> None:\\n await mock_bridge_v2.api.load_test_data(v2_resources_test_data)\\n await setup_platform(hass, mock_bridge_v2, \\\"event\\\")\\n # 7 entities should be created from test data\\n assert len(hass.states.async_all()) == 7\\n\\n # pick one of the remote buttons\\n state = hass.states.get(\\\"event.hue_dimmer_switch_with_4_controls_button_1\\\")\\n assert state\\n assert state.state == \\\"unknown\\\"\\n assert state.name == \\\"Hue Dimmer switch with 4 controls Button 1\\\"\\n # check event_types\\n assert state.attributes[ATTR_EVENT_TYPES] == [\\n \\\"initial_press\\\",\\n \\\"repeat\\\",\\n \\\"short_release\\\",\\n \\\"long_press\\\",\\n \\\"long_release\\\",\\n ]\\n # trigger firing 'initial_press' event from the device\\n btn_event = {\\n \\\"button\\\": {\\\"last_event\\\": \\\"initial_press\\\"},\\n \\\"id\\\": \\\"f92aa267-1387-4f02-9950-210fb7ca1f5a\\\",\\n \\\"metadata\\\": {\\\"control_id\\\": 1},\\n \\\"type\\\": \\\"button\\\",\\n }\\n mock_bridge_v2.api.emit_event(\\\"update\\\", btn_event)\\n await hass.async_block_till_done()\\n state = hass.states.get(\\\"event.hue_dimmer_switch_with_4_controls_button_1\\\")\\n assert state.attributes[ATTR_EVENT_TYPE] == \\\"initial_press\\\"\\n # trigger firing 'long_release' event from the device\\n btn_event = {\\n \\\"button\\\": {\\\"last_event\\\": \\\"long_release\\\"},\\n \\\"id\\\": \\\"f92aa267-1387-4f02-9950-210fb7ca1f5a\\\",\\n \\\"metadata\\\": {\\\"control_id\\\": 1},\\n \\\"type\\\": \\\"button\\\",\\n }\\n mock_bridge_v2.api.emit_event(\\\"update\\\", btn_event)\\n await hass.async_block_till_done()\\n state = hass.states.get(\\\"event.hue_dimmer_switch_with_4_controls_button_1\\\")\\n assert state.attributes[ATTR_EVENT_TYPE] == \\\"long_release\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f157ce76166496fe3fc5d229d1383d2\",\n \"score\": \"0.53346133\",\n \"text\": \"async def test_get_trigger_capabilities(\\n hass: HomeAssistant,\\n device_registry: dr.DeviceRegistry,\\n entity_registry: er.EntityRegistry,\\n enable_custom_integrations: None,\\n) -> None:\\n platform = getattr(hass.components, f\\\"test.{DOMAIN}\\\")\\n platform.init()\\n ent = platform.ENTITIES[0]\\n assert await async_setup_component(hass, DOMAIN, {DOMAIN: {CONF_PLATFORM: \\\"test\\\"}})\\n await hass.async_block_till_done()\\n\\n config_entry = MockConfigEntry(domain=\\\"test\\\", data={})\\n config_entry.add_to_hass(hass)\\n device_entry = device_registry.async_get_or_create(\\n config_entry_id=config_entry.entry_id,\\n connections={(dr.CONNECTION_NETWORK_MAC, \\\"12:34:56:AB:CD:EF\\\")},\\n )\\n entity_registry.async_get_or_create(\\n DOMAIN, \\\"test\\\", ent.unique_id, device_id=device_entry.id\\n )\\n\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device_entry.id\\n )\\n assert len(triggers) == 4\\n for trigger in triggers:\\n capabilities = await async_get_device_automation_capabilities(\\n hass, DeviceAutomationType.TRIGGER, trigger\\n )\\n assert capabilities == {\\n \\\"extra_fields\\\": [\\n {\\\"name\\\": \\\"for\\\", \\\"optional\\\": True, \\\"type\\\": \\\"positive_time_period_dict\\\"}\\n ]\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4019e90fb79757be24d590e65569b020\",\n \"score\": \"0.53253406\",\n \"text\": \"def test_trig():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12199d1ef2a4ca526f7009c3665cd6a\",\n \"score\": \"0.5322279\",\n \"text\": \"def btn_pressed():\\n for k in pyxel.__dict__.keys():\\n if (\\n k.startswith('KEY_')\\n or k.startswith('GAMEPAD_')\\n or k.startswith('MOUSE_')\\n ) and pyxel.btn(getattr(pyxel, k)):\\n return k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93809d120757a332551d2cf4b8f41c9f\",\n \"score\": \"0.53117657\",\n \"text\": \"def _verify_front_arm_actions(self, expected_actions):\\n self.assertEqual(self._front_arm_actions, expected_actions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7a5195b6914fef302c55c500ff131a\",\n \"score\": \"0.5308277\",\n \"text\": \"async def test_get_triggers_hidden_auxiliary(\\n hass: HomeAssistant,\\n device_registry: dr.DeviceRegistry,\\n entity_registry: er.EntityRegistry,\\n hidden_by,\\n entity_category,\\n) -> None:\\n config_entry = MockConfigEntry(domain=\\\"test\\\", data={})\\n config_entry.add_to_hass(hass)\\n device_entry = device_registry.async_get_or_create(\\n config_entry_id=config_entry.entry_id,\\n connections={(dr.CONNECTION_NETWORK_MAC, \\\"12:34:56:AB:CD:EF\\\")},\\n )\\n entity_entry = entity_registry.async_get_or_create(\\n DOMAIN,\\n \\\"test\\\",\\n \\\"5678\\\",\\n device_id=device_entry.id,\\n entity_category=entity_category,\\n hidden_by=hidden_by,\\n supported_features=CoverEntityFeature.OPEN,\\n )\\n expected_triggers = [\\n {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": trigger,\\n \\\"device_id\\\": device_entry.id,\\n \\\"entity_id\\\": entity_entry.id,\\n \\\"metadata\\\": {\\\"secondary\\\": True},\\n }\\n for trigger in [\\\"opened\\\", \\\"closed\\\", \\\"opening\\\", \\\"closing\\\"]\\n ]\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device_entry.id\\n )\\n assert triggers == unordered(expected_triggers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef48bbaf22b7c3acdbded5c2f656b156\",\n \"score\": \"0.52773756\",\n \"text\": \"def click_target_buy_overrides_button(self):\\n is_clicked = None\\n try:\\n self.logger.info('Start: click target buy overrides button')\\n self._buy_page.click_target_buy_overrides_button()\\n is_clicked = True\\n except WebDriverException as exp:\\n is_clicked = False\\n self.logger.error(exp.msg)\\n raise\\n finally:\\n self.logger.info('End: click target buy overrides button')\\n return is_clicked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8770d1db5bd698c6a53cea172cff84e\",\n \"score\": \"0.5271562\",\n \"text\": \"def __init__(self, *triggers):\\n\\n\\t\\tif len(triggers) == 0:\\n\\t\\t\\traise ValueError(\\\"No Triggers have been passed in!\\\")\\n\\n\\t\\tfor trigger in triggers:\\n\\t\\t\\tif not callable(trigger):\\n\\t\\t\\t\\traise ValueError(\\\"A trigger passed into this custom button is not a function!\\\")\\n\\n\\t\\tself.triggers = triggers\\n\\t\\t#super.__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d36af2e939d8f222f070278ad0e04d\",\n \"score\": \"0.5271143\",\n \"text\": \"async def test_if_fires_on_event(hass: HomeAssistant, calls, setup_comp) -> None:\\n assert await async_setup_component(\\n hass,\\n \\\"automation\\\",\\n {\\n \\\"automation\\\": {\\n \\\"trigger\\\": {\\n \\\"platform\\\": \\\"conversation\\\",\\n \\\"command\\\": [\\n \\\"Hey yo\\\",\\n \\\"Ha ha ha\\\",\\n ],\\n },\\n \\\"action\\\": {\\n \\\"service\\\": \\\"test.automation\\\",\\n \\\"data_template\\\": {\\\"data\\\": \\\"{{ trigger }}\\\"},\\n },\\n }\\n },\\n )\\n\\n await hass.services.async_call(\\n \\\"conversation\\\",\\n \\\"process\\\",\\n {\\n \\\"text\\\": \\\"Ha ha ha\\\",\\n },\\n blocking=True,\\n )\\n\\n await hass.async_block_till_done()\\n assert len(calls) == 1\\n assert calls[0].data[\\\"data\\\"] == {\\n \\\"alias\\\": None,\\n \\\"id\\\": \\\"0\\\",\\n \\\"idx\\\": \\\"0\\\",\\n \\\"platform\\\": \\\"conversation\\\",\\n \\\"sentence\\\": \\\"Ha ha ha\\\",\\n \\\"slots\\\": {},\\n \\\"details\\\": {},\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4e5dc33e7e82339bb1edbe1ff0aa49\",\n \"score\": \"0.526724\",\n \"text\": \"def test_gui_MenuMode() -> None:\\n img = images.SPLASH_IMG\\n pos = (0, 0)\\n wsize = (ut.BSIZE[0] * ut.TILE, ut.BSIZE[1] * ut.TILE)\\n\\n b_pos1 = (pos[0], pos[1])\\n b_pos2 = (pos[0]+50, pos[1]+50)\\n b_size = (100, 100)\\n b_img = images.BUTT_TMP_IMG\\n\\n test_gui = [gui.Button(b_pos1, b_size, 'trigger_1', b_img),\\n gui.Button(b_pos2, b_size, 'trigger_2', b_img)]\\n trigs = [('trigger1', 0, 2), ('trigger2', 0, 2)]\\n\\n # Test 0: incorrect initial values\\n test = gui.MenuMode(img, (-1, -1))\\n assert 0 <= test.menu_pos[0] < wsize[0]\\n assert 0 <= test.menu_pos[1] < wsize[1]\\n assert test.focused is None\\n assert test.gui is None\\n assert test.triggers is None\\n\\n test = gui.MenuMode(img, wsize)\\n assert 0 <= test.menu_pos[0] < wsize[0]\\n assert 0 <= test.menu_pos[1] < wsize[1]\\n assert test.focused is None\\n assert test.gui is None\\n assert test.triggers is None\\n\\n # Test 1: proper work of init() method\\n # pygame.display.init()\\n # test = gui.MenuMode(img, pos, test_gui, trigs)\\n # test.init(dummy_screen)\\n # assert pygame.mouse.get_visible() is False # only from pygame 2.0.0\\n # pygame.mouse.set_visible(True)\\n\\n # Test 2: proper work of update_focus() method\\n test = gui.MenuMode(img, pos, test_gui, trigs)\\n test.update_focus((25, 25))\\n assert test.focused == 0\\n test.update_focus((50, 50))\\n assert test.focused == 0\\n test.update_focus((75, 75))\\n assert test.focused == 0\\n test.update_focus((125, 125))\\n assert test.focused == 1\\n test.update_focus((100, 100))\\n assert test.focused == 1\\n test.update_focus((75, 75))\\n assert test.focused == 1\\n test.update_focus((0, 150))\\n assert test.focused is None\\n test.update_focus((75, 75))\\n assert test.focused == 0\\n\\n # Test 3: proper work of events() method\\n\\n # Test 4: proper work of leave() method\\n # test = gui.MenuMode()\\n # test.init()\\n # test.leave()\\n # assert pygame.mouse.get_visible() is True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96d14799612e8719d42f871b8ff660c\",\n \"score\": \"0.52647483\",\n \"text\": \"def test_controls(self):\\n user_input = Input()\\n print(\\\"Gimme anyol input:\\\")\\n event = InputEvent.NONE\\n while event == InputEvent.NONE:\\n event = user_input.poll()\\n\\n print(\\\"Event:\\\", event)\\n print(\\\"Got event\\\", event.description)\\n self.assertIsNotNone(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9e80fd5434c7b63c499c64670d1aba7\",\n \"score\": \"0.5263074\",\n \"text\": \"def test_delete_button(self):\\n self.root, s, array = self.populate_gui()\\n # Mocking \\\"Create button\\\" click. Idea - http://code.activestate.com/recipes/578978-using-tkinters-invoke-method-for-testing/\\n s.frame_bottom5.children['but_create'].invoke()\\n # Mocked data as an array\\n\\n # Compare fresh Tuna with mocked data\\n z = Data.tunas[0].getTunaFeatures\\n for x, y in zip (z, array):\\n # Must be equal. If not - error\\n self.assertEqual(x, y)\\n # Setting focus\\n s.tree.focus(0)\\n # \\\"Press\\\" Delete entry button\\n s.frame_bottom5.children['but_delete'].invoke()\\n # After deletion, tunas length should be 0\\n self.assertEqual(len(Data.tunas), 0)\\n if self.root:\\n self.root.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b53113818c2a2282fec5ac20c13d51e\",\n \"score\": \"0.5257707\",\n \"text\": \"def _TriggerSetUp(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd51c27620ec254720a0769b4086453a\",\n \"score\": \"0.52566755\",\n \"text\": \"def test_set_button_color(button):\\n set_button_property(button, \\\"red\\\", \\\"\\\")\\n assert button.get_color() == \\\"red\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1571a7dba78c1b2262d76b6b1cc00a6b\",\n \"score\": \"0.52473766\",\n \"text\": \"def test_search_button_enabled(self):\\n # Get Search Button\\n search_button = self.driver.find_element_by_class_name(\\\"btn-prim\\\")\\n # Check Search Button is enabled\\n self.assertTrue(search_button.is_enabled())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73831ad1c90cb2926a4b571ee0546b0\",\n \"score\": \"0.52439314\",\n \"text\": \"def test_get_event_matches_keys(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96055e161e7ee0ec8cd1e4047d77d8b4\",\n \"score\": \"0.5232518\",\n \"text\": \"def test_radio_buttons_cannot_be_unselected():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df79075394e4caf544b56f6fc0b4efee\",\n \"score\": \"0.5229247\",\n \"text\": \"def test_controls_without_value(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16835cd6167dd515c1b7e41a46ebb9f\",\n \"score\": \"0.52196145\",\n \"text\": \"def defineClickActions(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860d3afc0bd46811d16dc131bfc70c71\",\n \"score\": \"0.5218555\",\n \"text\": \"async def test_get_scene_activation_value_notification_triggers(\\n hass: HomeAssistant, client, hank_binary_switch, integration\\n) -> None:\\n dev_reg = async_get_dev_reg(hass)\\n device = dev_reg.async_get_device(\\n identifiers={get_device_id(client.driver, hank_binary_switch)}\\n )\\n assert device\\n expected_trigger = {\\n \\\"platform\\\": \\\"device\\\",\\n \\\"domain\\\": DOMAIN,\\n \\\"type\\\": \\\"event.value_notification.scene_activation\\\",\\n \\\"device_id\\\": device.id,\\n \\\"command_class\\\": CommandClass.SCENE_ACTIVATION.value,\\n \\\"property\\\": \\\"sceneId\\\",\\n \\\"property_key\\\": None,\\n \\\"endpoint\\\": 0,\\n \\\"subtype\\\": \\\"Endpoint 0\\\",\\n \\\"metadata\\\": {},\\n }\\n triggers = await async_get_device_automations(\\n hass, DeviceAutomationType.TRIGGER, device.id\\n )\\n assert expected_trigger in triggers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af0c659c644779ac876d4ad778d50a6\",\n \"score\": \"0.52004796\",\n \"text\": \"def assert_action_performed(_widget, _action):\\n try:\\n EventLog.performed_actions(_widget, _action)\\n except AttributeError as e:\\n pytest.fail(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c2daea300e8acc5dfdd2f782bf35d1\",\n \"score\": \"0.5199976\",\n \"text\": \"def button_pressed(self, button: Button) -> bool:\\n return button == self.input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025672332dcf05ee9dab0b1e8ebb87e6\",\n \"score\": \"0.51872325\",\n \"text\": \"def test_controls_with_slightly_invalid_ids(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538f18accff0d5a575fd5461ae52c1b1\",\n \"score\": \"0.51822996\",\n \"text\": \"def get_triggers():\\n # TODO: filter out nonetypes?\\n for trigger in triggers:\\n for i in trigger:\\n xbmc.log(msg='The value of %s is %s and a %s' % (i, trigger[i], type(trigger[i])),\\n level=xbmc.LOGNOTICE)\\n return triggers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5dcc04f5c620c30dd6304a57c0064f9\",\n \"score\": \"0.51805586\",\n \"text\": \"def on_click(self, button):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b8c5a9a5d459de423a216eb9294a1f\",\n \"score\": \"0.5180535\",\n \"text\": \"def execute_buttons(self):\\n arms = 0\\n button_states = self.get_buttons()\\n for button_name in BUTTON_PORTS:\\n port = BUTTON_PORTS[button_name]\\n angle = -1\\n if isinstance(port, list):\\n angle = port[1]\\n port = port[0]\\n if port in button_states and button_states[port] is True:\\n # button was just pressed\\n if button_name in self.button_toggles:\\n # needs toggled\\n if button_name == \\\"GRAB\\\":\\n pass\\n # print(button_name, port == POV,\\n # self.get_raw_buttons()[POV], angle)\\n if not (port == POV and not self.get_raw_buttons()[\\n POV] == angle):\\n new_state = not self.button_toggles[button_name]\\n self.button_toggles[button_name] = new_state\\n self.set_rumble(new_state)\\n # print(button_name, new_state)\\n elif self.get_raw_buttons()[POV] == angle:\\n # Button angle correct, check button name\\n if button_name == \\\"INC SPEED\\\":\\n self.max_enhancer += SPEED_ADJUST\\n self.set_rumble(True, 1)\\n elif button_name == \\\"DEC SPEED\\\":\\n self.max_enhancer -= SPEED_ADJUST\\n self.set_rumble(False, 1)\\n elif button_name == \\\"RES SPEED\\\":\\n self.max_enhancer = 0\\n self.set_rumble(True, length=200)\\n elif port in button_states:\\n # BUTTON BEING PRESSED\\n if button_name == \\\"ARM IN\\\":\\n arms = max(-1, min(-(ARM_SPEED_IN), 1))\\n self.button_toggles[\\\"GRAB\\\"] = False\\n elif button_name == \\\"ARM OUT\\\":\\n arms = max(-1, min(ARM_SPEED_OUT, 1))\\n self.button_toggles[\\\"GRAB\\\"] = False\\n # if arms == 0 and self.button_toggles[\\\"GRAB\\\"]:\\n # self.robot_lift_2.set(-ARM_SPEED_IN)\\n # else:\\n # self.robot_lift_2.set(arms)\\n\\n self.check_rumble()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f57bded989269779c0f1a519e5b2bac7\",\n \"score\": \"0.5177102\",\n \"text\": \"def inputButton(self, button):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c1235719c588f61b8b89ea5c86301e\",\n \"score\": \"0.51698107\",\n \"text\": \"def _get_triggers(\\n self, link: 'Link', src_spec: 'SourceModelSpec'\\n ) -> Tuple[List[str], List[str]]:\\n return [], []\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994293,"cells":{"query_id":{"kind":"string","value":"9535fe6c46cb906bf64698332e3cd3aa"},"query":{"kind":"string","value":"Blueprint for a simple model assigning stellar mass and quiescent/active designation to a subhalo catalog."},"positive_passages":{"kind":"list like","value":[{"docid":"7013fc7bb776bc7cea36b4c0a616cfd0","score":"0.0","text":"def SmHmBinarySFR_blueprint(\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \n smhm_model=smhm_components.Moster13SmHm, \n scatter_level = 0.2, \n redshift = sim_defaults.default_redshift, \n sfr_abcissa = [12, 15], sfr_ordinates = [0.25, 0.75], logparam=True, \n **kwargs):\n\n sfr_model = sfr_components.BinaryGalpropInterpolModel(\n galprop_key='quiescent', prim_haloprop_key=prim_haloprop_key, \n abcissa=sfr_abcissa, ordinates=sfr_ordinates, logparam=logparam)\n\n sm_model = smhm_components.Moster13SmHm(\n prim_haloprop_key=prim_haloprop_key, redshift=redshift, \n scatter_abcissa = [12], scatter_ordinates = [scatter_level])\n\n blueprint = {sm_model.galprop_key: sm_model, sfr_model.galprop_key: sfr_model, 'mock_factory': SubhaloMockFactory}\n\n return blueprint","title":""}],"string":"[\n {\n \"docid\": \"7013fc7bb776bc7cea36b4c0a616cfd0\",\n \"score\": \"0.0\",\n \"text\": \"def SmHmBinarySFR_blueprint(\\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \\n smhm_model=smhm_components.Moster13SmHm, \\n scatter_level = 0.2, \\n redshift = sim_defaults.default_redshift, \\n sfr_abcissa = [12, 15], sfr_ordinates = [0.25, 0.75], logparam=True, \\n **kwargs):\\n\\n sfr_model = sfr_components.BinaryGalpropInterpolModel(\\n galprop_key='quiescent', prim_haloprop_key=prim_haloprop_key, \\n abcissa=sfr_abcissa, ordinates=sfr_ordinates, logparam=logparam)\\n\\n sm_model = smhm_components.Moster13SmHm(\\n prim_haloprop_key=prim_haloprop_key, redshift=redshift, \\n scatter_abcissa = [12], scatter_ordinates = [scatter_level])\\n\\n blueprint = {sm_model.galprop_key: sm_model, sfr_model.galprop_key: sfr_model, 'mock_factory': SubhaloMockFactory}\\n\\n return blueprint\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3193e2ab42a83b12b66740baeb711288","score":"0.6471101","text":"def Campbell15_blueprint(\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \n sec_galprop_key = 'ssfr', sec_haloprop_key = 'vpeak', \n smhm_model=smhm_components.Moster13SmHm, \n scatter_level = 0.2, \n redshift = sim_defaults.default_redshift, **kwargs):\n\n stellar_mass_model = smhm_model(\n prim_haloprop_key=prim_haloprop_key, redshift=redshift, \n scatter_abcissa = [12], scatter_ordinates = [scatter_level])\n\n fake_mock = FakeMock(approximate_ngals = 1e5)\n input_galaxy_table = fake_mock.galaxy_table\n prim_galprop_bins = np.logspace(8, 12, num=15) \n\n ssfr_model = ConditionalAbunMatch(input_galaxy_table=input_galaxy_table, \n prim_galprop_key=stellar_mass_model.galprop_key, \n galprop_key=sec_galprop_key, \n sec_haloprop_key=sec_haloprop_key, \n prim_galprop_bins=prim_galprop_bins, \n **kwargs)\n blueprint = ({\n stellar_mass_model.galprop_key: stellar_mass_model, \n ssfr_model.galprop_key: ssfr_model, \n 'mock_factory': SubhaloMockFactory})\n\n return blueprint","title":""},{"docid":"412e98b8820f7be92caa064dc48f9eab","score":"0.61549777","text":"def create_model():\n ###########################################################################\n # Flowsheet and Property Package #\n ###########################################################################\n m = pyo.ConcreteModel(name=\"Steam Cycle Model\")\n m.fs = FlowsheetBlock(default={\"dynamic\": False})\n m.fs.prop_water = iapws95.Iapws95ParameterBlock(\n default={\"phase_presentation\": iapws95.PhaseType.LG}\n )\n\n m.fs.prop_water2 = iapws95.Iapws95ParameterBlock()\n m.fs.therminol66 = ThermalOilParameterBlock()\n\n m.fs.charge_hx = HeatExchanger(\n default={\"delta_temperature_callback\": delta_temperature_underwood_callback,\n \"shell\": {\"property_package\": m.fs.prop_water2},\n \"tube\": {\"property_package\": m.fs.therminol66},\n \"flow_pattern\": HeatExchangerFlowPattern.countercurrent})\n\n m.fs.hp_splitter = HelmSplitter(default={\"dynamic\": False,\n \"property_package\": m.fs.prop_water2})\n m.fs.ip_splitter = HelmSplitter(default={\"dynamic\": False,\n \"property_package\": m.fs.prop_water2})\n\n m.fs.storage_cooler = Heater(default={\"dynamic\": False,\n \"property_package\": m.fs.prop_water2,\n \"has_pressure_change\": True})\n \n m.fs.hx_pump = WaterPump(default={\"property_package\": m.fs.prop_water2})\n\n # The enthalpy at the outlet of the cooler is required to be subcooled, that is,\n # below the ehntalpy of saturation. This condition was selected instead of using\n # temperatures, which cause certain difficulty in converging the model.\n # return (m.fs.storage_cooler.control_volume.properties_out[0].temperature <= \n # m.fs.storage_cooler.control_volume.properties_out[0].temperature_sat - 5)\n @m.fs.storage_cooler.Constraint(m.fs.time)\n def constraint_cooler_enth(b, t):\n return (m.fs.storage_cooler.control_volume.properties_out[0].enth_mol <= \n m.fs.storage_cooler.control_volume.properties_out[0].enth_mol_sat_phase['Liq'])\n \n ###########################################################################\n # Turbine declarations #\n ###########################################################################\n\n for i in range(9):\n\n turbine = HelmTurbineStage(\n default={\n \"property_package\": m.fs.prop_water2\n }\n )\n setattr(m.fs, \"turbine_\" + str(i+1), turbine)\n\n ###########################################################################\n # Boiler section declarations: #\n ###########################################################################\n # Boiler section is set up using two heater blocks, as following:\n # 1) For the main steam the heater block is named 'boiler'\n # 2) For the reheated steam the heater block is named 'reheater'\n m.fs.boiler = Heater(\n default={\n \"dynamic\": False,\n \"property_package\": m.fs.prop_water,\n \"has_pressure_change\": True\n }\n )\n m.fs.reheater = Heater(\n default={\n \"dynamic\": False,\n \"property_package\": m.fs.prop_water,\n \"has_pressure_change\": True\n }\n )\n\n # Outlet temperature of boiler is set to 866.15 K\n @m.fs.boiler.Constraint(m.fs.time)\n def boiler_temperature_constraint(b, t):\n return b.control_volume.properties_out[t].temperature == 866.15 # K\n\n # Outlet temperature of reheater is set to 866.15 K\n @m.fs.reheater.Constraint(m.fs.time)\n def reheater_temperature_constraint(b, t):\n return b.control_volume.properties_out[t].temperature == 866.15 # K\n\n ###########################################################################\n # Add Condenser Mixer, Condenser, and Condensate pump #\n ###########################################################################\n # condenser mix\n m.fs.condenser_mix = Mixer(\n default={\n \"momentum_mixing_type\": MomentumMixingType.none,\n \"inlet_list\": [\"main\", \"bfpt\", \"drain\", \"makeup\"],\n \"property_package\": m.fs.prop_water,\n }\n )\n\n # The inlet 'main' refers to the main steam coming from the turbine train\n # Inlet 'bfpt' refers to the steam coming from the bolier feed pump turbine\n # Inlet 'drain' refers to the condensed steam from the feed water heater 1\n # Inlet 'makeup' refers to the make up water\n # The outlet pressure of condenser mixer is equal to the minimum pressure\n # Since the turbine (#9) outlet (or, mixer inlet 'main') pressure\n # has the minimum pressure, the following constraint sets the outlet\n # pressure of the condenser mixer to the pressure of the inlet 'main'\n @m.fs.condenser_mix.Constraint(m.fs.time)\n def mixer_pressure_constraint(b, t):\n return b.main_state[t].pressure == b.mixed_state[t].pressure\n\n m.fs.condenser = CondenserHelm(default={\"shell\":{\"has_pressure_change\": False,\n \"property_package\": m.fs.prop_water2},\n \"tube\": {\"has_pressure_change\": False,\n \"property_package\": m.fs.prop_water2}})\n \n iscale.set_scaling_factor(m.fs.condenser.side_1.heat, 1e-9)\n iscale.set_scaling_factor(m.fs.condenser.side_2.heat, 1e-9)\n\n # condensate pump\n m.fs.cond_pump = WaterPump(\n default={\n \"property_package\": m.fs.prop_water2,\n }\n )\n ###########################################################################\n # Feedwater heater declaration #\n ###########################################################################\n # Feed water heaters (FWHs) are declared as 0D heat exchangers\n # Tube side is for feed water & Shell side is for steam condensing\n # Pressure drop on both sides are accounted for by setting the respective\n # outlet pressure based on the following assumptions:\n # (1) Feed water side: A constant 4% pressure drop is assumed\n # on the feedwater side for all FWHs. For this,\n # the outlet pressure is set to 0.96 times the inlet pressure,\n # on the feed water side for all FWHs\n # (2) Steam condensing side: Going from high pressure to\n # low pressure FWHs, the outlet pressure of\n # the condensed steam in assumed to be 10% more than that\n # of the pressure of steam extracted for the immediately\n # next lower pressure feedwater heater.\n # e.g. the outlet condensate pressure of FWH 'n'\n # = 1.1 * pressure of steam extracted for FWH 'n-1'\n # In case of FWH1 the FWH 'n-1' is used for Condenser,\n # and in case of FWH6, FWH 'n-1' is for Deaerator. Here,\n # the steam pressure for FWH 'n-1' is known because the\n # pressure ratios for turbines are fixed.\n # The condensing steam is assumed to leave the FWH as saturated liquid\n # Thus, each FWH is accompanied by 3 constraints, 2 for pressure drop\n # and 1 for the enthalpy.\n\n # Scaling factors for area and overall heat transfer coefficients for\n # FWHs have all been set appropriately (user may change these values,\n # if needed) if not set, the scaling factors = 1 (IDAES default)\n\n ###########################################################################\n # DEFINITION OF FEED WATER HEATERS MIXERS\n ###########################################################################\n FWH_Mixers_list = ['fwh1_mix', 'fwh2_mix', 'fwh3_mix', 'fwh6_mix']\n\n for i in FWH_Mixers_list:\n FWH_Mixer = Mixer(\n default={\n \"momentum_mixing_type\": MomentumMixingType.none,\n \"inlet_list\": [\"steam\", \"drain\"],\n \"property_package\": m.fs.prop_water,\n }\n )\n setattr(m.fs, i, FWH_Mixer)\n\n m.fs.fwh7_mix = Mixer(\n default={\n \"momentum_mixing_type\": MomentumMixingType.none,\n \"inlet_list\": [\"steam\", \"drain\", \"from_hx_pump\"],\n \"property_package\": m.fs.prop_water,\n }\n )\n \n m.fs.bfp_mix = Mixer(\n default={\n \"momentum_mixing_type\": MomentumMixingType.none,\n \"inlet_list\": [\"from_bfp\", \"from_hx_pump\"],\n \"property_package\": m.fs.prop_water,\n }\n )\n # @m.fs.hx_pump.Constraint(m.fs.time)\n # def hx_pump_pressure_out(b, t):\n # return (m.fs.hx_pump.control_volume.properties_out[0.0].pressure == \n # m.fs.fwh7_mix.steam_state[0].pressure*1.15)\n ###########################################################################\n # DEFINITION OF OUTLET PRESSURE OF FEED WATER HEATERS MIXERS\n ###########################################################################\n\n # The outlet pressure of an FWH mixer is equal to the minimum pressure\n # Since the pressure of mixer inlet 'steam' has the minimum pressure,\n # the following constraints set the outlet pressure of FWH mixers to be same\n # as the pressure of the inlet 'steam'\n\n def fwhmixer_pressure_constraint(b, t):\n return b.steam_state[t].pressure == b.mixed_state[t].pressure\n\n for i in FWH_Mixers_list:\n setattr(getattr(m.fs, i), \"mixer_pressure_constraint\", pyo.Constraint(m.fs.config.time, rule=fwhmixer_pressure_constraint))\n\n @m.fs.fwh7_mix.Constraint(m.fs.time)\n def fwh7mixer_pressure_constraint(b, t):\n return b.steam_state[t].pressure == b.mixed_state[t].pressure\n \n @m.fs.bfp_mix.Constraint(m.fs.time)\n def bfp_mix_pressure_constraint(b, t):\n return b.from_bfp_state[t].pressure == b.mixed_state[t].pressure\n ###########################################################################\n # DEFINITION OF FEED WATER HEATERS\n ###########################################################################\n FWH_list = ['fwh1', 'fwh2', 'fwh3', 'fwh4', 'fwh6', 'fwh7', 'fwh8']\n\n for i in FWH_list:\n FWH = HeatExchanger(\n default={\n \"delta_temperature_callback\": delta_temperature_underwood_callback,\n \"shell\": {\n \"property_package\": m.fs.prop_water,\n \"material_balance_type\": MaterialBalanceType.componentTotal,\n \"has_pressure_change\": True,\n },\n \"tube\": {\n \"property_package\": m.fs.prop_water,\n \"material_balance_type\": MaterialBalanceType.componentTotal,\n \"has_pressure_change\": True,\n },\n }\n )\n setattr(m.fs, i, FWH)\n\n ###########################################################################\n # SETTING SCALING FACTORS FOR AREA AND HEAT TRANSFER COEFFICIENT\n ###########################################################################\n\n for i in FWH_list:\n c = getattr(m.fs, i)\n iscale.set_scaling_factor(getattr(c, \"area\"), 1e-2)\n iscale.set_scaling_factor(getattr(c, \"overall_heat_transfer_coefficient\"), 1e-3)\n\n ###########################################################################\n # Setting the outlet enthalpy of condensate in an FWH to be same as saturated liquid\n ###########################################################################\n def fwh_vaporfrac_constraint(b, t):\n return (\n b.side_1.properties_out[t].enth_mol\n == b.side_1.properties_out[t].enth_mol_sat_phase['Liq'])\n\n for i in FWH_list:\n setattr(getattr(m.fs, i), i + \"_vaporfrac_constraint\", pyo.Constraint(m.fs.time, rule=fwh_vaporfrac_constraint))\n\n ###########################################################################\n # Setting a 4% pressure drop on the feedwater side (P_out = 0.96 * P_in)\n ###########################################################################\n\n def fwh_s2pdrop_constraint(b, t):\n return (\n b.side_2.properties_out[t].pressure\n == 0.96 * b.side_2.properties_in[t].pressure)\n\n for i in FWH_list:\n setattr(getattr(m.fs, i), i + \"_s2pdrop_constraint\", pyo.Constraint(m.fs.time, rule=fwh_s2pdrop_constraint))\n\n ###########################################################################\n # Setting the outlet pressure of condensate to be 10% more than that of\n # steam routed to condenser, as described in FWH description\n ###########################################################################\n # FWH1: 0.5 is the pressure ratio for turbine #9 (see set_inputs)\n # FWH2: 0.64^2 is the pressure ratio for turbine #8 (see set_inputs)\n # FWH3: 0.64^2 is the pressure ratio for turbine #7 (see set_inputs)\n # FWH4: 0.64^2 is the pressure ratio for turbine #6 (see set_inputs)\n # FWH6: 0.79^6 is the pressure ratio for turbine #4 (see set_inputs)\n # FWH7: 0.79^4 is the pressure ratio for turbine #3 (see set_inputs)\n # FWH8: 0.8^2 is the pressure ratio for turbine #2 (see set_inputs)\n \n pressure_ratio_list = { 'fwh1': 0.5,\n 'fwh2': 0.64**2,\n 'fwh3': 0.64**2,\n 'fwh4': 0.64**2,\n 'fwh6': 0.79**6,\n 'fwh7': 0.79**4,\n 'fwh8': 0.8**2}\n \n def fwh_s1pdrop_constraint(b, t):\n return (\n b.side_1.properties_out[t].pressure\n == 1.1 * b.turbine_pressure_ratio * b.side_1.properties_in[t].pressure)\n\n for i in FWH_list:\n b = getattr(m.fs, i)\n b.turbine_pressure_ratio = pyo.Param(initialize = pressure_ratio_list[i])\n setattr(b, i+\"_s1pdrop_constraint\", pyo.Constraint(m.fs.config.time, rule=fwh_s1pdrop_constraint))\n\n ###########################################################################\n # Add deaerator and boiler feed pump (BFP) #\n ###########################################################################\n m.fs.fwh5_da = Mixer(\n default={\n \"momentum_mixing_type\": MomentumMixingType.none,\n \"inlet_list\": [\"steam\", \"drain\", \"feedwater\"],\n \"property_package\": m.fs.prop_water,\n }\n )\n\n # The outlet pressure of deaerator is equal to the minimum pressure\n # Since the pressure of deaerator inlet 'feedwater' has\n # the minimum pressure, the following constraint sets the outlet pressure\n # of deaerator to be same as the pressure of the inlet 'feedwater'\n @m.fs.fwh5_da.Constraint(m.fs.time)\n def fwh5mixer_pressure_constraint(b, t):\n return b.feedwater_state[t].pressure == b.mixed_state[t].pressure\n\n m.fs.bfp = WaterPump(\n default={\n \"property_package\": m.fs.prop_water2,\n }\n )\n m.fs.bfpt = HelmTurbineStage(\n default={\n \"property_package\": m.fs.prop_water2,\n }\n )\n\n # The following constraint sets the outlet pressure of steam extracted\n # for boiler feed water turbine to be same as that of condenser\n @m.fs.Constraint(m.fs.time)\n def constraint_out_pressure(b, t):\n return (\n b.bfpt.control_volume.properties_out[t].pressure\n == b.condenser_mix.mixed_state[t].pressure\n )\n\n # The following constraint demands that the work done by the\n # boiler feed water pump is same as that of boiler feed water turbine\n # Essentially, this says that boiler feed water turbine produces just\n # enough power to meet the demand of boiler feed water pump\n @m.fs.Constraint(m.fs.time)\n def constraint_bfp_power(b, t):\n return (\n b.bfp.control_volume.work[t] + b.bfpt.control_volume.work[t]\n == 0\n )\n\n ###########################################################################\n # Turbine outlet splitter constraints #\n ###########################################################################\n # Equality constraints have been written as following to define\n # the split fractions within the turbine train\n\n splitter_list = ['t1_splitter', 't2_splitter', 't3_splitter', 't5_splitter', 't6_splitter', 't7_splitter', 't8_splitter']\n\n for i in splitter_list:\n\n Splitter = HelmSplitter(default={\"dynamic\": False,\n \"property_package\": m.fs.prop_water})\n setattr(m.fs, i, Splitter)\n \n m.fs.t4_splitter = HelmSplitter(default={\"dynamic\": False,\n \"property_package\": m.fs.prop_water,\n \"num_outlets\": 3})\n\n # The power plant with storage for a charge scenario is now ready\n # Declaraing a plant power out variable for easy analysis of various\n # design and operating scenarios\n m.fs.plant_power_out = pyo.Var(\n m.fs.time,\n domain=pyo.Reals,\n initialize=620,\n doc=\"Net Power MWe out from the power plant\"\n )\n\n # Constraint on Plant Power Output\n # Plant Power Out = Turbine Power - Power required for HX Pump\n @m.fs.Constraint(m.fs.time)\n def production_cons(b, t):\n return (\n (-1*(m.fs.turbine_1.work_mechanical[t]\n + m.fs.turbine_2.work_mechanical[t]\n + m.fs.turbine_3.work_mechanical[t]\n + m.fs.turbine_4.work_mechanical[t]\n + m.fs.turbine_5.work_mechanical[t]\n + m.fs.turbine_6.work_mechanical[t]\n + m.fs.turbine_7.work_mechanical[t]\n + m.fs.turbine_8.work_mechanical[t]\n + m.fs.turbine_9.work_mechanical[t])\n ) * 1e-6\n == m.fs.plant_power_out[t]\n )\n\n ###########################################################################\n # Create the stream Arcs and return the model #\n ###########################################################################\n _create_arcs(m)\n pyo.TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n return m","title":""},{"docid":"22c8c809f4bcbb7a6755fae6d9c80968","score":"0.61260045","text":"def main():\n stage = Usd.Stage.CreateInMemory()\n stage.GetRootLayer().documentation = (\n \"This is an example of setting a Model Prim kind\"\n )\n\n sphere1 = UsdGeom.Sphere.Define(stage, \"/SomeSphere\")\n Usd.ModelAPI(sphere1).SetKind(Kind.Tokens.component)\n sphere2 = UsdGeom.Sphere.Define(stage, \"/SomeSphere/SphereChild\")\n Usd.ModelAPI(sphere2).SetKind(Kind.Tokens.subcomponent)\n sphere3 = UsdGeom.Sphere.Define(stage, \"/SomeSphere/Foo\")\n Usd.ModelAPI(sphere3).SetKind(\"does_not_exist\")\n sphere3.GetPrim().SetMetadata(\n \"comment\",\n \"XXX: This kind is made up. But it could be real if we added to the KindRegistry\\n\"\n \"https://graphics.pixar.com/usd/docs/api/class_kind_registry.html\",\n )\n\n print(stage.GetRootLayer().ExportToString())","title":""},{"docid":"f54114e099db8df2eeefbae3e2bfbb70","score":"0.58137923","text":"def __init__(self, catalog_version, catalog_path=None):\n if catalog_path is None:\n catalog_path = (f'/home/mike/research/'\n 'ac6_microburst_scale_sizes'\n '/data/coincident_microbursts_catalogues/'\n 'AC6_coincident_microbursts_sorted_'\n f'Brady_v{catalog_version}.txt')\n # Load catalog.\n self.microburst_catalog = pd.read_csv(catalog_path)\n self.model = IRBEM.MagFields(kext='OPQ77')\n # print(f'Number of microbursts {self.microburst_catalog.shape[0]}')\n return","title":""},{"docid":"e8341d1942ab1d33ea5faba68460cb35","score":"0.5692982","text":"def __init__(self,description=\"My Model\\n Is here.\",\n geometry={\"wiresegs\":[] # A straight list of the wiresegments\n },\n excitation=None,\n freq=LinearFrequencyRange(),\n radiationPattern=None,\n computeCharges=True):\n attributesFromDict(locals())","title":""},{"docid":"70d3b922eb2e50f0f3f650974e8bb455","score":"0.5619619","text":"def company_skeleton():\n\n E, P, B, D, F = EPBDF()\n entities = ['Paul', 'Roger', 'Quinn', 'Sally', 'Thomas',\n 'Case', 'Adapter', 'Laptop', 'Tablet', 'Smartphone',\n 'Accessories', 'Devices']\n\n entity_types = {'Paul': E, 'Roger': E, 'Quinn': E, 'Sally': E, 'Thomas': E,\n 'Case': P, 'Adapter': P, 'Laptop': P, 'Tablet': P, 'Smartphone': P,\n 'Accessories': B, 'Devices': B}\n skeleton = RelationalSkeleton(company_schema(), True)\n p, r, q, s, t, c, a, l, ta, sm, ac, d = ents = tuple([SkItem(e, entity_types[e]) for e in entities])\n skeleton.add_entities(*ents)\n for emp, prods in ((p, {c}), (q, {c, a, l}), (s, {l, ta}), (t, {sm, ta}), (r, {l})):\n for prod in prods:\n skeleton.add_relationship(SkItem(emp.name + '-' + prod.name, D), {emp, prod})\n for biz, prods in ((ac, {c, a}), (d, {l, ta, sm})):\n for prod in prods:\n skeleton.add_relationship(SkItem(biz.name + '-' + prod.name, F), {biz, prod})\n\n return skeleton","title":""},{"docid":"628dae5ba9c89d6e4a015de048634080","score":"0.5599956","text":"def __init__(self, design):\n super(ols_model, self).__init__()\n self.initialize(design)","title":""},{"docid":"6478bc8f50257f1ec8feb0d35d0f34f7","score":"0.5526171","text":"def __init__(self, model, colour, serial):\n self.model = model\n self.colour = colour\n self.serial = serial","title":""},{"docid":"7f0c8476fbe7de141e200de50dd4ee7b","score":"0.5524039","text":"def setupModelInstance(self, geom, dssatexe):\n return super(Model, self).setupModelInstance(geom, \"DSSAT_Ex.exe\")","title":""},{"docid":"45d6ebfdfcbac8c858c3c34cd87e0339","score":"0.5518273","text":"def apcupsd_model(self):\n self.writeCommand('apcupsd_model')\n return self","title":""},{"docid":"c2ad82bc902d315973108e63404d969c","score":"0.5479451","text":"def pwr_core():\n model = openmc.model.Model()\n\n # Define materials.\n fuel = openmc.Material(1, name='UOX fuel')\n fuel.set_density('g/cm3', 10.062)\n fuel.add_nuclide('U234', 4.9476e-6)\n fuel.add_nuclide('U235', 4.8218e-4)\n fuel.add_nuclide('U238', 2.1504e-2)\n fuel.add_nuclide('Xe135', 1.0801e-8)\n fuel.add_nuclide('O16', 4.5737e-2)\n\n clad = openmc.Material(2, name='Zircaloy')\n clad.set_density('g/cm3', 5.77)\n clad.add_nuclide('Zr90', 0.5145)\n clad.add_nuclide('Zr91', 0.1122)\n clad.add_nuclide('Zr92', 0.1715)\n clad.add_nuclide('Zr94', 0.1738)\n clad.add_nuclide('Zr96', 0.0280)\n\n cold_water = openmc.Material(3, name='Cold borated water')\n cold_water.set_density('atom/b-cm', 0.07416)\n cold_water.add_nuclide('H1', 2.0)\n cold_water.add_nuclide('O16', 1.0)\n cold_water.add_nuclide('B10', 6.490e-4)\n cold_water.add_nuclide('B11', 2.689e-3)\n cold_water.add_s_alpha_beta('c_H_in_H2O')\n\n hot_water = openmc.Material(4, name='Hot borated water')\n hot_water.set_density('atom/b-cm', 0.06614)\n hot_water.add_nuclide('H1', 2.0)\n hot_water.add_nuclide('O16', 1.0)\n hot_water.add_nuclide('B10', 6.490e-4)\n hot_water.add_nuclide('B11', 2.689e-3)\n hot_water.add_s_alpha_beta('c_H_in_H2O')\n\n rpv_steel = openmc.Material(5, name='Reactor pressure vessel steel')\n rpv_steel.set_density('g/cm3', 7.9)\n rpv_steel.add_nuclide('Fe54', 0.05437098, 'wo')\n rpv_steel.add_nuclide('Fe56', 0.88500663, 'wo')\n rpv_steel.add_nuclide('Fe57', 0.0208008, 'wo')\n rpv_steel.add_nuclide('Fe58', 0.00282159, 'wo')\n rpv_steel.add_nuclide('Ni58', 0.0067198, 'wo')\n rpv_steel.add_nuclide('Ni60', 0.0026776, 'wo')\n rpv_steel.add_nuclide('Mn55', 0.01, 'wo')\n rpv_steel.add_nuclide('Cr52', 0.002092475, 'wo')\n rpv_steel.add_nuclide('C0', 0.0025, 'wo')\n rpv_steel.add_nuclide('Cu63', 0.0013696, 'wo')\n\n lower_rad_ref = openmc.Material(6, name='Lower radial reflector')\n lower_rad_ref.set_density('g/cm3', 4.32)\n lower_rad_ref.add_nuclide('H1', 0.0095661, 'wo')\n lower_rad_ref.add_nuclide('O16', 0.0759107, 'wo')\n lower_rad_ref.add_nuclide('B10', 3.08409e-5, 'wo')\n lower_rad_ref.add_nuclide('B11', 1.40499e-4, 'wo')\n lower_rad_ref.add_nuclide('Fe54', 0.035620772088, 'wo')\n lower_rad_ref.add_nuclide('Fe56', 0.579805982228, 'wo')\n lower_rad_ref.add_nuclide('Fe57', 0.01362750048, 'wo')\n lower_rad_ref.add_nuclide('Fe58', 0.001848545204, 'wo')\n lower_rad_ref.add_nuclide('Ni58', 0.055298376566, 'wo')\n lower_rad_ref.add_nuclide('Mn55', 0.0182870, 'wo')\n lower_rad_ref.add_nuclide('Cr52', 0.145407678031, 'wo')\n lower_rad_ref.add_s_alpha_beta('c_H_in_H2O')\n\n upper_rad_ref = openmc.Material(7, name='Upper radial reflector / Top plate region')\n upper_rad_ref.set_density('g/cm3', 4.28)\n upper_rad_ref.add_nuclide('H1', 0.0086117, 'wo')\n upper_rad_ref.add_nuclide('O16', 0.0683369, 'wo')\n upper_rad_ref.add_nuclide('B10', 2.77638e-5, 'wo')\n upper_rad_ref.add_nuclide('B11', 1.26481e-4, 'wo')\n upper_rad_ref.add_nuclide('Fe54', 0.035953677186, 'wo')\n upper_rad_ref.add_nuclide('Fe56', 0.585224740891, 'wo')\n upper_rad_ref.add_nuclide('Fe57', 0.01375486056, 'wo')\n upper_rad_ref.add_nuclide('Fe58', 0.001865821363, 'wo')\n upper_rad_ref.add_nuclide('Ni58', 0.055815129186, 'wo')\n upper_rad_ref.add_nuclide('Mn55', 0.0184579, 'wo')\n upper_rad_ref.add_nuclide('Cr52', 0.146766614995, 'wo')\n upper_rad_ref.add_s_alpha_beta('c_H_in_H2O')\n\n bot_plate = openmc.Material(8, name='Bottom plate region')\n bot_plate.set_density('g/cm3', 7.184)\n bot_plate.add_nuclide('H1', 0.0011505, 'wo')\n bot_plate.add_nuclide('O16', 0.0091296, 'wo')\n bot_plate.add_nuclide('B10', 3.70915e-6, 'wo')\n bot_plate.add_nuclide('B11', 1.68974e-5, 'wo')\n bot_plate.add_nuclide('Fe54', 0.03855611055, 'wo')\n bot_plate.add_nuclide('Fe56', 0.627585036425, 'wo')\n bot_plate.add_nuclide('Fe57', 0.014750478, 'wo')\n bot_plate.add_nuclide('Fe58', 0.002000875025, 'wo')\n bot_plate.add_nuclide('Ni58', 0.059855207342, 'wo')\n bot_plate.add_nuclide('Mn55', 0.0197940, 'wo')\n bot_plate.add_nuclide('Cr52', 0.157390026871, 'wo')\n bot_plate.add_s_alpha_beta('c_H_in_H2O')\n\n bot_nozzle = openmc.Material(9, name='Bottom nozzle region')\n bot_nozzle.set_density('g/cm3', 2.53)\n bot_nozzle.add_nuclide('H1', 0.0245014, 'wo')\n bot_nozzle.add_nuclide('O16', 0.1944274, 'wo')\n bot_nozzle.add_nuclide('B10', 7.89917e-5, 'wo')\n bot_nozzle.add_nuclide('B11', 3.59854e-4, 'wo')\n bot_nozzle.add_nuclide('Fe54', 0.030411411144, 'wo')\n bot_nozzle.add_nuclide('Fe56', 0.495012237964, 'wo')\n bot_nozzle.add_nuclide('Fe57', 0.01163454624, 'wo')\n bot_nozzle.add_nuclide('Fe58', 0.001578204652, 'wo')\n bot_nozzle.add_nuclide('Ni58', 0.047211231662, 'wo')\n bot_nozzle.add_nuclide('Mn55', 0.0156126, 'wo')\n bot_nozzle.add_nuclide('Cr52', 0.124142524198, 'wo')\n bot_nozzle.add_s_alpha_beta('c_H_in_H2O')\n\n top_nozzle = openmc.Material(10, name='Top nozzle region')\n top_nozzle.set_density('g/cm3', 1.746)\n top_nozzle.add_nuclide('H1', 0.0358870, 'wo')\n top_nozzle.add_nuclide('O16', 0.2847761, 'wo')\n top_nozzle.add_nuclide('B10', 1.15699e-4, 'wo')\n top_nozzle.add_nuclide('B11', 5.27075e-4, 'wo')\n top_nozzle.add_nuclide('Fe54', 0.02644016154, 'wo')\n top_nozzle.add_nuclide('Fe56', 0.43037146399, 'wo')\n top_nozzle.add_nuclide('Fe57', 0.0101152584, 'wo')\n top_nozzle.add_nuclide('Fe58', 0.00137211607, 'wo')\n top_nozzle.add_nuclide('Ni58', 0.04104621835, 'wo')\n top_nozzle.add_nuclide('Mn55', 0.0135739, 'wo')\n top_nozzle.add_nuclide('Cr52', 0.107931450781, 'wo')\n top_nozzle.add_s_alpha_beta('c_H_in_H2O')\n\n top_fa = openmc.Material(11, name='Top of fuel assemblies')\n top_fa.set_density('g/cm3', 3.044)\n top_fa.add_nuclide('H1', 0.0162913, 'wo')\n top_fa.add_nuclide('O16', 0.1292776, 'wo')\n top_fa.add_nuclide('B10', 5.25228e-5, 'wo')\n top_fa.add_nuclide('B11', 2.39272e-4, 'wo')\n top_fa.add_nuclide('Zr90', 0.43313403903, 'wo')\n top_fa.add_nuclide('Zr91', 0.09549277374, 'wo')\n top_fa.add_nuclide('Zr92', 0.14759527104, 'wo')\n top_fa.add_nuclide('Zr94', 0.15280552077, 'wo')\n top_fa.add_nuclide('Zr96', 0.02511169542, 'wo')\n top_fa.add_s_alpha_beta('c_H_in_H2O')\n\n bot_fa = openmc.Material(12, name='Bottom of fuel assemblies')\n bot_fa.set_density('g/cm3', 1.762)\n bot_fa.add_nuclide('H1', 0.0292856, 'wo')\n bot_fa.add_nuclide('O16', 0.2323919, 'wo')\n bot_fa.add_nuclide('B10', 9.44159e-5, 'wo')\n bot_fa.add_nuclide('B11', 4.30120e-4, 'wo')\n bot_fa.add_nuclide('Zr90', 0.3741373658, 'wo')\n bot_fa.add_nuclide('Zr91', 0.0824858164, 'wo')\n bot_fa.add_nuclide('Zr92', 0.1274914944, 'wo')\n bot_fa.add_nuclide('Zr94', 0.1319920622, 'wo')\n bot_fa.add_nuclide('Zr96', 0.0216912612, 'wo')\n bot_fa.add_s_alpha_beta('c_H_in_H2O')\n\n # Define the materials file.\n model.materials = (fuel, clad, cold_water, hot_water, rpv_steel,\n lower_rad_ref, upper_rad_ref, bot_plate,\n bot_nozzle, top_nozzle, top_fa, bot_fa)\n\n # Define surfaces.\n s1 = openmc.ZCylinder(r=0.41, surface_id=1)\n s2 = openmc.ZCylinder(r=0.475, surface_id=2)\n s3 = openmc.ZCylinder(r=0.56, surface_id=3)\n s4 = openmc.ZCylinder(r=0.62, surface_id=4)\n s5 = openmc.ZCylinder(r=187.6, surface_id=5)\n s6 = openmc.ZCylinder(r=209.0, surface_id=6)\n s7 = openmc.ZCylinder(r=229.0, surface_id=7)\n s8 = openmc.ZCylinder(r=249.0, surface_id=8, boundary_type='vacuum')\n\n s31 = openmc.ZPlane(z0=-229.0, surface_id=31, boundary_type='vacuum')\n s32 = openmc.ZPlane(z0=-199.0, surface_id=32)\n s33 = openmc.ZPlane(z0=-193.0, surface_id=33)\n s34 = openmc.ZPlane(z0=-183.0, surface_id=34)\n s35 = openmc.ZPlane(z0=0.0, surface_id=35)\n s36 = openmc.ZPlane(z0=183.0, surface_id=36)\n s37 = openmc.ZPlane(z0=203.0, surface_id=37)\n s38 = openmc.ZPlane(z0=215.0, surface_id=38)\n s39 = openmc.ZPlane(z0=223.0, surface_id=39, boundary_type='vacuum')\n\n # Define pin cells.\n fuel_cold = openmc.Universe(name='Fuel pin, cladding, cold water',\n universe_id=1)\n c21 = openmc.Cell(cell_id=21, fill=fuel, region=-s1)\n c22 = openmc.Cell(cell_id=22, fill=clad, region=+s1 & -s2)\n c23 = openmc.Cell(cell_id=23, fill=cold_water, region=+s2)\n fuel_cold.add_cells((c21, c22, c23))\n\n tube_cold = openmc.Universe(name='Instrumentation guide tube, '\n 'cold water', universe_id=2)\n c24 = openmc.Cell(cell_id=24, fill=cold_water, region=-s3)\n c25 = openmc.Cell(cell_id=25, fill=clad, region=+s3 & -s4)\n c26 = openmc.Cell(cell_id=26, fill=cold_water, region=+s4)\n tube_cold.add_cells((c24, c25, c26))\n\n fuel_hot = openmc.Universe(name='Fuel pin, cladding, hot water',\n universe_id=3)\n c27 = openmc.Cell(cell_id=27, fill=fuel, region=-s1)\n c28 = openmc.Cell(cell_id=28, fill=clad, region=+s1 & -s2)\n c29 = openmc.Cell(cell_id=29, fill=hot_water, region=+s2)\n fuel_hot.add_cells((c27, c28, c29))\n\n tube_hot = openmc.Universe(name='Instrumentation guide tube, hot water',\n universe_id=4)\n c30 = openmc.Cell(cell_id=30, fill=hot_water, region=-s3)\n c31 = openmc.Cell(cell_id=31, fill=clad, region=+s3 & -s4)\n c32 = openmc.Cell(cell_id=32, fill=hot_water, region=+s4)\n tube_hot.add_cells((c30, c31, c32))\n\n # Set positions occupied by guide tubes\n tube_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8, 11, 14,\n 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n tube_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8,\n 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n\n # Define fuel lattices.\n l100 = openmc.RectLattice(name='Fuel assembly (lower half)', lattice_id=100)\n l100.lower_left = (-10.71, -10.71)\n l100.pitch = (1.26, 1.26)\n l100.universes = np.tile(fuel_cold, (17, 17))\n l100.universes[tube_x, tube_y] = tube_cold\n\n l101 = openmc.RectLattice(name='Fuel assembly (upper half)', lattice_id=101)\n l101.lower_left = (-10.71, -10.71)\n l101.pitch = (1.26, 1.26)\n l101.universes = np.tile(fuel_hot, (17, 17))\n l101.universes[tube_x, tube_y] = tube_hot\n\n # Define assemblies.\n fa_cw = openmc.Universe(name='Water assembly (cold)', universe_id=5)\n c50 = openmc.Cell(cell_id=50, fill=cold_water, region=+s34 & -s35)\n fa_cw.add_cell(c50)\n\n fa_hw = openmc.Universe(name='Water assembly (hot)', universe_id=7)\n c70 = openmc.Cell(cell_id=70, fill=hot_water, region=+s35 & -s36)\n fa_hw.add_cell(c70)\n\n fa_cold = openmc.Universe(name='Fuel assembly (cold)', universe_id=6)\n c60 = openmc.Cell(cell_id=60, fill=l100, region=+s34 & -s35)\n fa_cold.add_cell(c60)\n\n fa_hot = openmc.Universe(name='Fuel assembly (hot)', universe_id=8)\n c80 = openmc.Cell(cell_id=80, fill=l101, region=+s35 & -s36)\n fa_hot.add_cell(c80)\n\n # Define core lattices\n l200 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=200)\n l200.lower_left = (-224.91, -224.91)\n l200.pitch = (21.42, 21.42)\n l200.universes = [\n [fa_cw]*21,\n [fa_cw]*21,\n [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7,\n [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5,\n [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4,\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\n [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4,\n [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5,\n [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7,\n [fa_cw]*21,\n [fa_cw]*21]\n\n l201 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=201)\n l201.lower_left = (-224.91, -224.91)\n l201.pitch = (21.42, 21.42)\n l201.universes = [\n [fa_hw]*21,\n [fa_hw]*21,\n [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7,\n [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5,\n [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4,\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\n [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4,\n [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5,\n [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7,\n [fa_hw]*21,\n [fa_hw]*21]\n\n # Define root universe.\n root = openmc.Universe(universe_id=0, name='root universe')\n c1 = openmc.Cell(cell_id=1, fill=l200, region=-s6 & +s34 & -s35)\n c2 = openmc.Cell(cell_id=2, fill=l201, region=-s6 & +s35 & -s36)\n c3 = openmc.Cell(cell_id=3, fill=bot_plate, region=-s7 & +s31 & -s32)\n c4 = openmc.Cell(cell_id=4, fill=bot_nozzle, region=-s5 & +s32 & -s33)\n c5 = openmc.Cell(cell_id=5, fill=bot_fa, region=-s5 & +s33 & -s34)\n c6 = openmc.Cell(cell_id=6, fill=top_fa, region=-s5 & +s36 & -s37)\n c7 = openmc.Cell(cell_id=7, fill=top_nozzle, region=-s5 & +s37 & -s38)\n c8 = openmc.Cell(cell_id=8, fill=upper_rad_ref, region=-s7 & +s38 & -s39)\n c9 = openmc.Cell(cell_id=9, fill=bot_nozzle, region=+s6 & -s7 & +s32 & -s38)\n c10 = openmc.Cell(cell_id=10, fill=rpv_steel, region=+s7 & -s8 & +s31 & -s39)\n c11 = openmc.Cell(cell_id=11, fill=lower_rad_ref, region=+s5 & -s6 & +s32 & -s34)\n c12 = openmc.Cell(cell_id=12, fill=upper_rad_ref, region=+s5 & -s6 & +s36 & -s38)\n root.add_cells((c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12))\n\n # Assign root universe to geometry\n model.geometry.root_universe = root\n\n model.settings.batches = 10\n model.settings.inactive = 5\n model.settings.particles = 100\n model.settings.source = openmc.Source(space=openmc.stats.Box(\n [-160, -160, -183], [160, 160, 183]))\n\n plot = openmc.Plot()\n plot.origin = (125, 125, 0)\n plot.width = (250, 250)\n plot.pixels = (3000, 3000)\n plot.color_by = 'material'\n model.plots.append(plot)\n\n return model","title":""},{"docid":"e38c691b3c7b5d3db4ec3e58594b72d7","score":"0.544043","text":"def create_some_object(ii,y_co,mass):\n name = ''\n diff = (rand.uniform(0,1),rand.uniform(0,1),rand.uniform(0,1))\n spec = (rand.uniform(0,1),rand.uniform(0,1),rand.uniform(0,1))\n alp = rand.uniform(0,1)\n if ii % 5 == 0:\n x_co = 8.1\n name = \"obj_torus\"+str(ii)\n bpy.ops.mesh.primitive_torus_add(location = (x_co,y_co,2))\n selobj = bpy.context.active_object\n selobj.name = name\n selobj.game.mass = mass\n selobj.scale = (1.5,1.5,1.5)\n if ii % 5 == 1:\n x_co = 3.5\n name = \"obj_cube\"+str(ii)\n bpy.ops.mesh.primitive_cube_add(location = (x_co,y_co,2))\n selobj = bpy.context.active_object\n selobj.name = name\n selobj.game.mass = mass\n selobj.scale = (2,2,2)\n if ii % 5 == 2:\n x_co =-1\n name = \"obj_cone\"+str(ii)\n bpy.ops.mesh.primitive_cone_add(location = (x_co,y_co,2))\n selobj = bpy.context.active_object\n selobj.name = name\n selobj.game.mass = mass\n selobj.scale = (2,2,2)\n if ii % 5 == 3:\n x_co = -5.4\n name = \"obj_sphere\"+str(ii)\n bpy.ops.mesh.primitive_uv_sphere_add(location = (x_co,y_co,2))\n selobj = bpy.context.active_object\n selobj.name = name\n selobj.game.mass = mass\n selobj.scale = (2,2,2)\n if ii % 5 == 4:\n x_co = -9.0\n name = \"obj_monkey\"+str(ii)\n bpy.ops.mesh.primitive_monkey_add(location = (x_co,y_co,2))\n selobj = bpy.context.active_object\n selobj.name = name\n selobj.game.mass = mass\n selobj.scale = (1.2,1.2,1.2)\n tempmat = makeMaterial(name, diff, spec, alp)\n setMaterial(bpy.context.active_object,tempmat)","title":""},{"docid":"cd1e72f4d86cc8e24bc38b1df1620380","score":"0.5433962","text":"def __init__(self, make, model, year):\n super().__init__(make, model, year)\n self.battery_size = 70","title":""},{"docid":"96454f002910b607afedac5f1dc9f3dd","score":"0.54304534","text":"def forward_model(f_grid, atm_fields_compact, verbosity=0):\n ws = pyarts.workspace.Workspace(verbosity=0)\n ws.water_p_eq_agendaSet()\n ws.PlanetSet(option=\"Earth\")\n ws.verbositySetScreen(ws.verbosity, verbosity)\n\n # standard emission agenda\n ws.iy_main_agendaSet(option=\"Emission\")\n\n # cosmic background radiation\n ws.iy_space_agendaSet(option=\"CosmicBackground\")\n\n # standard surface agenda (i.e., make use of surface_rtprop_agenda)\n ws.iy_surface_agendaSet(option=\"UseSurfaceRtprop\")\n\n # sensor-only path\n ws.ppath_agendaSet(option=\"FollowSensorLosPath\")\n\n # no refraction\n ws.ppath_step_agendaSet(option=\"GeometricPath\")\n\n # Non reflecting surface\n ws.surface_rtprop_agendaSet(\n option=\"Specular_NoPol_ReflFix_SurfTFromt_surface\")\n\n # Number of Stokes components to be computed\n ws.IndexSet(ws.stokes_dim, 1)\n\n #########################################################################\n\n # Definition of absorption species\n ws.abs_speciesSet(species=[\n \"H2O, H2O-SelfContCKDMT400, H2O-ForeignContCKDMT400\",\n \"O2-TRE05\",\n \"N2, N2-CIAfunCKDMT252, N2-CIArotCKDMT252\",\n ])\n\n ws.abs_lines_per_speciesReadSpeciesSplitCatalog(basename=\"lines/\")\n\n # Load CKDMT400 model data\n ws.ReadXML(ws.predefined_model_data, \"model/mt_ckd_4.0/H2O.xml\")\n\n # ws.abs_lines_per_speciesLineShapeType(option=lineshape)\n ws.abs_lines_per_speciesCutoff(option=\"ByLine\", value=750e9)\n # ws.abs_lines_per_speciesNormalization(option=normalization)\n\n ws.VectorSetConstant(ws.surface_scalar_reflectivity, 1, 0.4)\n\n # Set the frequency grid\n ws.f_grid = f_grid\n\n # Throw away lines outside f_grid\n ws.abs_lines_per_speciesCompact()\n\n # No sensor properties\n ws.sensorOff()\n\n # We select here to use Planck brightness temperatures\n ws.StringSet(ws.iy_unit, \"PlanckBT\")\n\n #########################################################################\n\n # Atmosphere and surface\n ws.AtmosphereSet1D()\n ws.atm_fields_compact = atm_fields_compact\n ws.atm_fields_compactAddConstant(ws.atm_fields_compact, \"abs_species-N2\",\n 0.78, 0, [\"abs_species-H2O\"])\n ws.atm_fields_compactAddConstant(ws.atm_fields_compact, \"abs_species-O2\",\n 0.21, 0, [\"abs_species-H2O\"])\n ws.AtmFieldsAndParticleBulkPropFieldFromCompact()\n\n ws.Extract(ws.z_surface, ws.z_field, 0)\n ws.Extract(ws.t_surface, ws.t_field, 0)\n\n # Definition of sensor position and line of sight (LOS)\n ws.MatrixSet(ws.sensor_pos, np.array([[10e3]]))\n ws.MatrixSet(ws.sensor_los, np.array([[0]]))\n ws.sensorOff()\n\n # Jacobian calculation\n ws.jacobianInit()\n ws.jacobianAddAbsSpecies(\n g1=ws.p_grid,\n g2=ws.lat_grid,\n g3=ws.lon_grid,\n species=\"H2O, H2O-SelfContCKDMT400, H2O-ForeignContCKDMT400\",\n unit=\"vmr\",\n )\n ws.jacobianClose()\n\n # Clearsky = No scattering\n ws.cloudboxOff()\n\n # on-the-fly absorption\n ws.propmat_clearsky_agendaAuto()\n\n # Perform RT calculations\n ws.lbl_checkedCalc()\n ws.atmfields_checkedCalc()\n ws.atmgeom_checkedCalc()\n ws.cloudbox_checkedCalc()\n ws.sensor_checkedCalc()\n\n ws.yCalc()\n\n return ws.y.value[:].copy(), ws.jacobian.value[:].copy()","title":""},{"docid":"cd2fa7e69f447c18eb36afe998fdb08d","score":"0.5421357","text":"def main():\n stage = Usd.Stage.CreateInMemory()\n stage.SetStartTimeCode(0)\n stage.SetEndTimeCode(2)\n\n prim = stage.DefinePrim(\"/Set\")\n non_template_set_name = \"non_template_clips\"\n model = Usd.ClipsAPI(prim)\n model.SetClipActive([(0.0, 0)], non_template_set_name)\n model.SetClipAssetPaths(\n [Sdf.AssetPath(\"./non_template_clip.usda\")], non_template_set_name\n )\n model.SetClipPrimPath(\"/NonTemplate\", non_template_set_name)\n\n template_set_name = \"template_clips\"\n model.SetClipTemplateAssetPath(\"./template_clip.##.usda\", template_set_name)\n model.SetClipTemplateEndTime(2, template_set_name)\n model.SetClipTemplateStartTime(0, template_set_name)\n model.SetClipTemplateStride(1, template_set_name)\n model.SetClipPrimPath(\"/Template\", template_set_name)\n\n prim.GetReferences().AddReference(assetPath=\"./set.usda\", primPath=\"/Set\")\n\n print(stage.GetRootLayer().ExportToString())","title":""},{"docid":"afd40b6d51940a11430eaf5f579c6d95","score":"0.5414305","text":"def __new__(cls,*arg,**kwarg):\n \n cls.FREEPARAMETERS_STD = [\"a%d\"%(i+1) for i in range(len(cls.STANDARDIZATION))]\n cls.FREEPARAMETERS = [\"M0\"]+cls.FREEPARAMETERS_STD\n return super(ModelStandardization,cls).__new__(cls)","title":""},{"docid":"b518f9f879242aeb4a2e49ce0dda73cf","score":"0.54063696","text":"def __init__(self, species, qty):\n super().__init__(species, qty, \"domestic\", 0.08)","title":""},{"docid":"c829db1d0eb021c2af1a2733b81b68b4","score":"0.5396692","text":"def create_model( fullMoleculeList, moleculePartition, parameterMap, includeSoluteDielectricAsParameter ):\n\n # Define deterministic functions for hydration free energies.\n\n model = parameterMap['model']\n parameters = parameterMap['stochastic']\n radiusParameterMap = parameterMap['radiusParameterMap']\n gammaParameterMap = parameterMap['gammaParameterMap']\n\n for (molecule_index, molecule) in enumerate(fullMoleculeList):\n\n if( moleculePartition[molecule_index] == 1 ):\n \n molecule_name = molecule.GetTitle()\n variable_name = \"dg_gbvi_%08d\" % molecule_index\n \n # Determine which parameters are involved in this molecule to limit number of parents for caching.\n \n parents = dict()\n for atom in molecule.GetAtoms():\n atomtype = atom.GetStringData(\"gbvi_type\") # GBVI atomtype\n for parameter_name in ['gamma', 'radius']:\n stochastic_name = '%s_%s' % (atomtype,parameter_name)\n if( stochastic_name in parameters ):\n parents[stochastic_name] = parameters[stochastic_name]\n else:\n print \"create_model Warning: parameter=%s missing for %40s\" % (stochastic_name, molecule_name )\n \n if( includeSoluteDielectricAsParameter ):\n parents['soluteDielectric'] = parameters['soluteDielectric']\n \n print \"create_model %40s: %s\" % (molecule_name, parents.keys() )\n \n # Create deterministic variable for computed hydration free energy.\n \n #if( energyCalculations == 'Swig' ):\n function = hydration_energy_factory_swig(molecule_index, radiusParameterMap, gammaParameterMap)\n \n # if( energyCalculations == 'OpenMM' ):\n # function = hydration_energy_factory_OpenMM(molecule)\n #\n # if( energyCalculations == 'Test' ):\n # function = hydration_energy_factory_test( molecule, molecule_index, radiusParameterMap, gammaParameterMap)\n \n model[variable_name] = pymc.Deterministic(eval=function,\n name=variable_name,\n parents=parents,\n doc=molecule_name,\n trace=True,\n verbose=1,\n dtype=float,\n plot=False,\n cache_depth=2)\n \n # Define error model\n log_sigma_min = math.log(0.01) # kcal/mol\n log_sigma_max = math.log(10.0) # kcal/mol\n log_sigma_guess = math.log(1.0) # kcal/mol\n model['log_sigma'] = pymc.Uniform('log_sigma', lower=log_sigma_min, upper=log_sigma_max, value=log_sigma_guess)\n model['sigma'] = pymc.Lambda('sigma', lambda log_sigma=model['log_sigma'] : math.exp(log_sigma) ) \n model['tau'] = pymc.Lambda('tau', lambda sigma=model['sigma'] : sigma**(-2) )\n\n for (molecule_index, molecule) in enumerate(fullMoleculeList):\n\n if( moleculePartition[molecule_index] == 1 ):\n molecule_name = molecule.GetTitle()\n variable_name = \"dg_exp_%08d\" % molecule_index\n dg_exp = float(OEGetSDData(molecule, 'dG(exp)')) # observed hydration free energy in kcal/mol\n print \"Mol=%4d dG=%15.7e %s \" % (molecule_index, dg_exp, molecule_name )\n sys.stdout.flush()\n model[variable_name] = pymc.Normal(mu=model['dg_gbvi_%08d' % molecule_index], tau=model['tau'], value=dg_exp, observed=True) \n\n return","title":""},{"docid":"7565df4cc08ccad217eeaec3123665dc","score":"0.53920263","text":"def create_snowmodel(self, hspfmodel, verbose = True, vverbose = False):\n\n filename = hspfmodel.filename + '_snow'\n\n submodel = HSPFModel()\n\n submodel.build_from_existing(hspfmodel, filename, directory = \n hspfmodel.filepath[:-1],\n verbose = vverbose)\n\n # simplify the landtypes to one developed which contains one implnd and \n # one perlnd (since they are all the same)\n\n for subbasin in submodel.subbasins:\n year = min(submodel.subbasins[subbasin].landuse.keys())\n submodel.subbasins[subbasin].landuse = {year: {'Developed': 100}}\n\n submodel.build()\n\n # get rid of the reaches\n\n submodel.rchreses = []\n\n # add in the modules\n\n submodel.add_temp()\n \n densities = [o.RDENPF \n for o in hspfmodel.perlnds + hspfmodel.implnds]\n depths = [o.packsnow / o.RDENPF \n for o in hspfmodel.perlnds + hspfmodel.implnds]\n\n depth = sum(depths) / len(depths)\n density = sum(densities) / len(densities)\n\n submodel.add_snow(depth = depth, density = density) \n \n # overwrite the time series dictionaries for the model\n\n for subbasin in hspfmodel.subbasins:\n if subbasin in submodel.subbasins:\n start, tstep, data = hspfmodel.precipitations['%d' % subbasin]\n submodel.add_timeseries('precipitation', '%d' % subbasin, \n start, data)\n submodel.assign_subbasin_timeseries('precipitation', subbasin, \n '%d' % subbasin)\n\n start, tstep, data = hspfmodel.temperatures[hspfmodel.description]\n\n submodel.add_timeseries('temperature', submodel.description, \n start, data)\n\n start, tstep, data = hspfmodel.dewpoints[hspfmodel.description]\n\n submodel.add_timeseries('dewpoint', submodel.description, start, data)\n\n submodel.assign_watershed_timeseries('temperature', \n submodel.description)\n submodel.assign_watershed_timeseries('dewpoint',\n submodel.description)\n\n self.snowmodel = submodel.filepath + submodel.filename\n\n with open(self.snowmodel, 'wb') as f: pickle.dump(submodel, f)","title":""},{"docid":"4ea41ca331df38389e0231e0e4df605e","score":"0.53883773","text":"def __init__(self, name=None):\n\n # Always call parent class' constructor FIRST:\n CoupledDEVS.__init__(self, name)\n\n # Declare the coupled model's output ports:\n # Autonomous, so no output ports\n #self.OUT = self.addOutPort(name=\"OUT\")\n\n # Declare the coupled model's sub-models:\n\n # The Policeman generating interrupts \n self.policeman = self.addSubModel(Policeman(name=\"policeman\"))\n\n # The TrafficLight \n self.trafficLight = self.addSubModel(TrafficLight(name=\"trafficLight\"))\n\n # Only connect ...\n self.connectPorts(self.policeman.OUT, self.trafficLight.INTERRUPT)\n #self.connectPorts(self.trafficLight.OBSERVED, self.OUT)","title":""},{"docid":"2a6e185ce95912ec70c2c39b0cfe58ff","score":"0.5372091","text":"def calibrate_submodel(self):\n \n model = self.model \n cell = self.knowledge_base.cell\n nucleus = model.compartments.get_one(id='n')\n mitochondrion = model.compartments.get_one(id='m')\n cytoplasm = model.compartments.get_one(id='c')\n\n beta = self.options.get('beta')\n\n Avogadro = self.model.parameters.get_or_create(\n id='Avogadro',\n type=None,\n value=scipy.constants.Avogadro,\n units=unit_registry.parse_units('molecule mol^-1')) \n\n rnas_kb = cell.species_types.get(__type=wc_kb.eukaryote.TranscriptSpeciesType)\n undetermined_model_kcat = []\n determined_kcat = []\n for rna_kb, reaction in zip(rnas_kb, self.submodel.reactions):\n\n init_species_counts = {}\n \n modifier_species = self._degradation_modifier[reaction.name] \n init_species_counts[modifier_species.gen_id()] = modifier_species.distribution_init_concentration.mean\n \n rna_kb_compartment_id = rna_kb.species[0].compartment.id\n if rna_kb_compartment_id == 'c':\n rna_compartment = cytoplasm\n degradation_compartment = cytoplasm\n else:\n rna_compartment = mitochondrion\n degradation_compartment = mitochondrion \n\n rna_reactant = model.species_types.get_one(id=rna_kb.id).species.get_one(compartment=rna_compartment)\n\n half_life = rna_kb.properties.get_one(property='half-life').get_value()\n mean_concentration = rna_reactant.distribution_init_concentration.mean\n\n average_rate = utils.calc_avg_deg_rate(mean_concentration, half_life)\n \n for species in reaction.get_reactants():\n\n init_species_counts[species.gen_id()] = species.distribution_init_concentration.mean\n\n if model.parameters.get(id='K_m_{}_{}'.format(reaction.id, species.species_type.id)):\n model_Km = model.parameters.get_one(\n id='K_m_{}_{}'.format(reaction.id, species.species_type.id))\n if species.distribution_init_concentration.mean:\n model_Km.value = beta * species.distribution_init_concentration.mean \\\n / Avogadro.value / species.compartment.init_volume.mean\n model_Km.comments = 'The value was assumed to be {} times the concentration of {} in {}'.format(\n beta, species.species_type.id, species.compartment.name)\n else:\n model_Km.value = 1e-05\n model_Km.comments = 'The value was assigned to 1e-05 because the concentration of ' +\\\n '{} in {} was zero'.format(species.species_type.id, species.compartment.name)\n\n model_kcat = model.parameters.get_one(id='k_cat_{}'.format(reaction.id))\n\n if average_rate: \n model_kcat.value = 1.\n eval_rate_law = reaction.rate_laws[0].expression._parsed_expression.eval({\n wc_lang.Species: init_species_counts,\n wc_lang.Compartment: {\n rna_compartment.id: rna_compartment.init_volume.mean * \\\n rna_compartment.init_density.value,\n degradation_compartment.id: degradation_compartment.init_volume.mean * \\\n degradation_compartment.init_density.value}\n })\n if eval_rate_law:\n model_kcat.value = average_rate / eval_rate_law\n determined_kcat.append(model_kcat.value)\n else:\n undetermined_model_kcat.append(model_kcat) \n else: \n undetermined_model_kcat.append(model_kcat)\n \n median_kcat = numpy.median(determined_kcat)\n for model_kcat in undetermined_model_kcat:\n model_kcat.value = median_kcat\n model_kcat.comments = 'Set to the median value because it could not be determined from data' \n\n print('RNA degradation submodel has been generated')","title":""},{"docid":"6bbfcf5951a375728bdff97674fbc037","score":"0.5360838","text":"def pole_bending_modeling(model1,leftsupportx,rightsupportx,supporty,leftplatecenterx,rightplatecenterx,plateheighty,lengthx,heighty,stiffness):\r\n \r\n \r\n # add loading plate\r\n model1 = pole_contact_plate(model1,'rightloadplate',(rightplatecenterx,heighty,0),lengthx,1000,plateheighty,stiffness)\r\n model1 = pole_contact_plate(model1,'leftloadplate',(leftplatecenterx,heighty,0),lengthx,1000,plateheighty,stiffness)\r\n \r\n \r\n # add support\r\n model1 = add_support(model1,'leftsupport',leftsupportx,supporty,0,lengthx * 2,10000,default=[1,1,1,0,0,0]) # due to fact that only one side of plate will be selected\r\n model1 = add_support(model1,'rightsupport',rightsupportx,supporty,0,lengthx * 2,10000,default=[0,1,1,0,0,0]) # big width number to seelct all nodes\r\n \r\n \r\n model1 = add_material(model1)\r\n model1.table('loadtable',1,['time'],[[0,0],[1,1]])\r\n model1.load('leftrightload',{'xyz':[0,1,0,0,0,0],'DOF':6,'scalar':-1,'setnamelist':['leftloadplate_plate','rightloadplate_plate'],'tabletag':'loadtable'})\r\n \r\n\r\n model1.section('sec_1','shell_section',{'thickness':0.1875})\r\n\r\n \r\n model1.property('prop1','quad4',{'type':75,'thinkness':0.01})\r\n model1.property('prop_dent','quad4',{'type':75})\r\n \r\n \r\n model1.elemset_sub_setname('surface_elements','dentelems')\r\n \r\n model1.link_prop_conn('prop1',setnamelist=['surface_elements-dentelems'])\r\n model1.link_prop_conn('prop_dent',setnamelist=['dentelems','extension_elements_surface_leftend','extension_elements_surface_rightend'])\r\n \r\n model1.link_sec_prop('sec_1','prop1')\r\n model1.link_sec_prop('sec_1','prop_dent')\r\n \r\n # associate the material\r\n model1.link_mat_prop('pole_alum','prop1')\r\n model1.link_mat_prop('pole_alum_dent','prop_dent')\r\n \r\n model1.loadcase('loadcase1','static_arclength',{'boundarylist':['leftsupport','rightsupport','leftrightload'],'para':{'nstep':50}})\r\n \r\n #model1.job('job1','static_job',{'loadcaselist':['loadcase0','loadcase1'],'submit':True,'reqresultslist':['stress','total_strain','plastic_strain']})\r\n model1.job('job1','static_job',{'loadcaselist':['loadcase0','loadcase1'],'submit':False,'reqresultslist':['stress','total_strain','plastic_strain']})\r\n \r\n return model1","title":""},{"docid":"931a784adbb0e80ab049882aa7d0a382","score":"0.53536534","text":"def scaffold():\n pass","title":""},{"docid":"772924833d87c7975ec747b5eb019b0b","score":"0.5339107","text":"def model():\n return DBC14(dist_jb=10, mag=6, v_s30=600, depth_hyp=10, mechanism='SS')","title":""},{"docid":"f5bb6a8b9a604f242b3c2505439154c4","score":"0.53274566","text":"def __init__(self):\n \n super(Sellar, self).__init__()\n \n #add the discipline components to the assembly\n self.add('dis1', SellarDiscipline1())\n self.add('dis2', SellarDiscipline2())\n \n #START OF MDAO Problem Definition\n #Global Des Vars\n self.add_parameter((\"dis1.z1\",\"dis2.z1\"),low=-10,high=10)\n self.add_parameter((\"dis1.z2\",\"dis2.z2\"),low=0,high=10)\n \n #Local Des Vars \n self.add_parameter(\"dis1.x1\",low=0,high=10)\n \n #Coupling Vars\n self.add_coupling_var(\"dis2.y1\",\"dis1.y1\")\n self.add_coupling_var(\"dis1.y2\",\"dis2.y2\")\n \n self.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')\n self.add_constraint('3.16 < dis1.y1')\n self.add_constraint('dis2.y2 < 24.0')\n \n #END OF MDAO Problem Definition\n \n self.dis1.z1 = self.dis2.z1 = 5.0\n self.dis1.z2 = self.dis2.z2 = 2.0\n self.dis1.x1 = 1.0\n self.dis1.y2 = 3.0","title":""},{"docid":"46f38cf86d36765820ac82e2cc6bce10","score":"0.53215253","text":"def assembly_model():\n\n model = openmc.model.Model()\n\n # Create fuel assembly Lattice\n pitch = 21.42\n assembly = openmc.RectLattice(name='Fuel Assembly')\n assembly.pitch = (pitch/17, pitch/17)\n assembly.lower_left = (-pitch/2, -pitch/2)\n\n # Create array indices for guide tube locations in lattice\n gt_pos = np.array([\n [2, 5], [2, 8], [2, 11],\n [3, 3], [3, 13],\n [5, 2], [5, 5], [5, 8], [5, 11], [5, 14],\n [8, 2], [8, 5], [8, 8], [8, 11], [8, 14],\n [11, 2], [11, 5], [11, 8], [11, 11], [11, 14],\n [13, 3], [13, 13],\n [14, 5], [14, 8], [14, 11]\n ])\n\n # Create 17x17 array of universes. First we create a 17x17 array all filled\n # with the fuel pin universe. Then, we replace the guide tube positions with\n # the guide tube pin universe (note the use of numpy fancy indexing to\n # achieve this).\n assembly.universes = np.full((17, 17), fuel_pin())\n assembly.universes[gt_pos[:, 0], gt_pos[:, 1]] = guide_tube_pin()\n\n # Create outer boundary of the geometry to surround the lattice\n outer_boundary = openmc.model.rectangular_prism(\n pitch, pitch, boundary_type='reflective')\n\n # Create a cell filled with the lattice\n main_cell = openmc.Cell(fill=assembly, region=outer_boundary)\n\n # Finally, create geometry by providing a list of cells that fill the root\n # universe\n model.geometry = openmc.Geometry([main_cell])\n\n model.settings.batches = 150\n model.settings.inactive = 50\n model.settings.particles = 1000\n model.settings.source = openmc.Source(space=openmc.stats.Box(\n (-pitch/2, -pitch/2, -1),\n (pitch/2, pitch/2, 1),\n only_fissionable=True\n ))\n\n # NOTE: We never actually created a Materials object. When you export/run\n # using the Model object, if no materials were assigned it will look through\n # the Geometry object and automatically export any materials that are\n # necessary to build the model.\n return model","title":""},{"docid":"50e38c22bacf8f6aa559c93e282dc4a5","score":"0.53204226","text":"def _configure(self):\n SpatialDBObj._configure(self)\n self.label(\"SCEC CVM-H\")\n self.dataDir(self.inventory.dataDir)\n self.minVs(self.inventory.minVs.value)\n self.squash(self.inventory.squash, self.inventory.squashLimit.value)\n return","title":""},{"docid":"ed02d4bfd211d2473c9fc1c40d0d8dfa","score":"0.52838945","text":"def __init__(self, make, model, year, battery=70):\n super().__init__(make, model, year)\n self.battery_size = battery","title":""},{"docid":"82f144d6691eadf5238b16e0bebcbfe2","score":"0.527416","text":"def __init__(self, make, model, year):\n\n super().__init__(make, model, year) \n self.battery_size = 75 # Here, We add an extra attribute to our child class. ","title":""},{"docid":"82f144d6691eadf5238b16e0bebcbfe2","score":"0.527416","text":"def __init__(self, make, model, year):\n\n super().__init__(make, model, year) \n self.battery_size = 75 # Here, We add an extra attribute to our child class. ","title":""},{"docid":"a14b8f584528aa2d8692bd34af5d91c0","score":"0.5255351","text":"def sol553(design_parameters):\r\n prt('Design governing by this mantras (in oder of importance): \\n size, cost, stall margin, off.design', 'blue')\r\n pass","title":""},{"docid":"29a00f4e7e1dfca61dafd9bc95836c98","score":"0.52360165","text":"def construct_model(self):\r\n\r\n # Initialise model object\r\n m = ConcreteModel()\r\n\r\n # Add component allowing dual variables to be imported\r\n m.dual = Suffix(direction=Suffix.IMPORT)\r\n\r\n # Define sets\r\n m = self.components.define_sets(m)\r\n\r\n # Define parameters common to unit commitment sub-problems and investment plan\r\n m = self.components.define_parameters(m)\r\n\r\n # Define parameters specific to unit commitment sub-problem\r\n m = self.define_parameters(m)\r\n\r\n # Define variables\r\n m = self.define_variables(m)\r\n\r\n # Define expressions\r\n m = self.define_expressions(m)\r\n\r\n # Define constraints\r\n m = self.define_constraints(m)\r\n\r\n # Define objective\r\n m = self.define_objective(m)\r\n\r\n return m","title":""},{"docid":"799f4ce017f5053655d21a467eea6d34","score":"0.5233242","text":"def Schemata():","title":""},{"docid":"6bca46ea6ca5bae506893a67f20e0a74","score":"0.52153826","text":"def main():\n\n # We get the user model choice.\n selected_model = model_selection()\n\n # We print equations to show the different variables to fill.\n print(selected_model[\"class\"].eq_to_string())\n\n # We create an object from the model chosen\n model_obj = selected_model[\"class\"]()\n\n # We write the output to the file\n model_obj.write_file()","title":""},{"docid":"0d0b8b9a11a3a040e38d0968d79837dd","score":"0.52152205","text":"def model_defense():\n pass","title":""},{"docid":"58f692f2614373b660e1a4cf0caea7f5","score":"0.51889247","text":"def __init__(self, size_float=0.0):\n Organ.__init__(self,'Heart',size_float)","title":""},{"docid":"e4eb460f7f049c91bbb1badff8242642","score":"0.51871455","text":"def test_create_superModel(warnings = True, stdout_msgs = True):\n # Model path\n model_path = home_dir + 'work/models/Escherichia_coli/iJO1366/'\n\n model_organism = organism(id = 'Ecoli', name = 'Escherichia coli',domain = 'Bacteria', genus = 'Escherichia', species = 'coli', strain = 'MG1655')\n\n flux_bounds_dict = {'EX_glc(e)':[-100,1000], 'EX_o2(e)':[-200,1000]}\n flux_bounds_filename = model_path + 'iJO1366_minimal_glucose_aerobic.py'\n\n # Orignal iJo1266 model\n model = create_model(model_organism = model_organism, model_info = {'id':'iJO1366', 'file_format':'sbml', 'model_filename':model_path + 'iJO1366_updated.xml', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': flux_bounds_dict}, validate = True, stdout_msgs = True, warnings = True) \n\n model.organism.ModelSEED_type = 'bacteria_GramNegative'\n\n \"\"\"\n #rxn_id = 'GAPD'\n #rxn_id = 'ZN2abcpp'\n rxn_id = 'VALtex'\n get_cpds_ModelSEED_id(cpds_list = model.reactions_by_id[rxn_id].compounds)\n for cpd in model.reactions_by_id[rxn_id].compounds:\n print cpd.id,': ',cpd.ModelSEED_id,' stoic = ',model.reactions_by_id[rxn_id].stoichiometry[cpd]\n rxn_ModelSEED_id, ModelSEED_id_found_by = match_rxn_eqn(rxn = model.reactions_by_id[rxn_id]) \n print 'rxn_ModelSEED_id = {} , ModelSEED_id_found_by = {}\\n'.format(rxn_ModelSEED_id, ModelSEED_id_found_by)\n \"\"\"\n\n print '\\n----- Getting ModelSEED ids ----'\n get_cpds_ModelSEED_id(cpds_list = model.compounds)\n get_rxns_ModelSEED_id(rxns_list = model.reactions)\n sys.stdout.flush()\n\n \"\"\"\n #--- Text exporiting to pydict and importing back ----\n # Save the model into a python dictionary\n model.export(output_format = 'pydict', output_filename = 'iJO1366.py')\n\n # Re-import the pydict model\n model = create_model(model_info = {'id':'iJO1366', 'file_format':'pydict', 'model_filename':'iJO1366.py', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': flux_bounds_dict}, validate = True, stdout_msgs = True, warnings = True) \n \"\"\"\n\n print '\\n----- Creating super_model ----'\n sys.stdout.flush()\n\n super_model = create_superModel_from_ModelSEED(original_model = model, standard_to_model_compartID_map = {'c':'c','e':'e','p':'p'}, validate = True)\n print 'super_model statistics: # of compounds (total/original/external) = {}/{}/{} , # of reactions (total/original/external) = {}/{}/{}'.format(len(super_model.compounds), len([c for c in super_model.compounds if not c.external]), len([c for c in super_model.compounds if c.external]), len(super_model.reactions), len([r for r in super_model.reactions if not r.external]), len([r for r in super_model.reactions if r.external])) \n\n print '\\n----- fba with super_model ----'\n for rxn in [r for r in super_model.reactions if r.external]:\n rxn.flux_bounds = [0,0]\n for rxn in super_model.reactions:\n rxn.objective_coefficient = 0\n super_model.reactions_by_id['Ec_biomass_iJO1366_core_53p95M'].objective_coefficient = 1\n super_model.fba()\n\n print '\\n----- Exporting super_model to pydict ----'\n # Export to a pydict model\n super_model.export(output_format = 'pydict', output_filename = 'super_model_iJO1366.py')\n\n print '\\n----- Re-importing super_model from pydict ----'\n # Re-import the pydict model\n super_modelmodel = create_model(model_info = {'id':'super_model_iJO1366', 'file_format':'pydict', 'model_filename':'super_model_iJO1366.py', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': dict(flux_bounds_dict.items() + [(r.id,[0,0]) for r in super_model.reactions if r.external])}, validate = True, stdout_msgs = True, warnings = True)","title":""},{"docid":"bb20c49d693b341d5afb650a0d189b3c","score":"0.5186923","text":"def __init__(self, number_of_cheeses, number_of_stools):\n self.number_of_cheeses = number_of_cheeses\n self.number_of_stools = number_of_stools\n self.model = TOAHModel(self.number_of_stools)\n self.model.fill_first_stool(self.number_of_cheeses)","title":""},{"docid":"0027ae7e48bb44a362a776ed4977c263","score":"0.51864123","text":"def createSynthModel():\n # Create the synthetic model\n world = mt.createCircle(boundaryMarker=-1, nSegments=64)\n tri = mt.createPolygon([[-0.8, -0], [-0.5, -0.7], [0.7, 0.5]],\n isClosed=True, area=0.0015)\n c1 = mt.createCircle(radius=0.2, pos=[-0.2, 0.5], nSegments=32,\n area=0.0025, marker=3)\n c2 = mt.createCircle(radius=0.2, pos=[0.32, -0.3], nSegments=32,\n area=0.0025, marker=3)\n\n poly = mt.mergePLC([world, tri, c1, c2])\n\n poly.addRegionMarker([0.0, 0, 0], 1, area=0.0015)\n poly.addRegionMarker([-0.9, 0, 0], 2, area=0.0015)\n\n c = mt.createCircle(radius=0.99, nSegments=16, start=np.pi, end=np.pi*3)\n [poly.createNode(p.pos(), -99) for p in c.nodes()]\n mesh = pg.meshtools.createMesh(poly, q=34.4, smooth=[1, 10])\n mesh.scale(1.0/5.0)\n mesh.rotate([0., 0., 3.1415/3])\n mesh.rotate([0., 0., 3.1415])\n\n petro = pg.solver.parseArgToArray([[1, 0.9], [2, 0.6], [3, 0.3]],\n mesh.cellCount(), mesh)\n\n # Create the parametric mesh that only reflect the domain geometry\n world = mt.createCircle(boundaryMarker=-1, nSegments=32, area=0.0051)\n paraMesh = pg.meshtools.createMesh(world, q=34.0, smooth=[1, 10])\n paraMesh.scale(1.0/5.0)\n\n return mesh, paraMesh, petro","title":""},{"docid":"f69f2c3de08a8fea2106af210c45100c","score":"0.51764035","text":"def __init__(self, model, shore_coef):\r\n\r\n self.model = model\r\n self._shore_coef = shore_coef\r\n self.gtab = model.gtab\r\n self.radial_order = model.radial_order\r\n self.zeta = model.zeta","title":""},{"docid":"6a9ee048ebf306caf8af1fef06c56ad3","score":"0.5174483","text":"def __init__(self, model):\n\n self.model = model\n self.fix_none_bounds(10, 813)\n self.info = {\n \"obj\": \"{}_{}\".format([reac for reac in self.model.get_objective()][0], self.model.id),\n \"fobj\": FBA(self.model).fobj\n }","title":""},{"docid":"9ed8dcdc305216bc0634bee71a575d05","score":"0.5164404","text":"def product_main(self):\n return","title":""},{"docid":"931cadce3dc70d56bfa59dd4e8cc866b","score":"0.5154811","text":"def __init__(self, species, qty):\n\n #these set the initial attributes for species, quantity and shipped status\n self.species = species\n self.qty = qty\n self.shipped = False\n self.tax = 0","title":""},{"docid":"b7f4264d43a26687940e9e7a49bb9da0","score":"0.51524115","text":"def __init__(self, name, fuel, fanciness):\n super().__init__(name, fuel)\n self.fanciness = fanciness\n self.price_per_km *= fanciness","title":""},{"docid":"f599d45d4a45e7888ab38854d88fe0a7","score":"0.51432717","text":"def __init__(self, name, fuel, fanciness):\n super().__init__(name, fuel)\n self.fanciness = fanciness\n self.price_per_km *= self.fanciness\n self.flagfall = 4.50","title":""},{"docid":"cf436a669586d189c271cb9e8b38316f","score":"0.5142965","text":"def __init__(self, make, model, year):\n # super() allows you to call methods from parent class\n # superclass is parent, subclass is child\n super().__init__(make, model, year)\n\n # can also define additional attributes\n self.battery_size = 75","title":""},{"docid":"1542ec9e58a9ab3a9bdbf240d0a86296","score":"0.5134287","text":"def model(self):","title":""},{"docid":"61e7feb2fb5a65a88b23ed6514ea3b98","score":"0.5133308","text":"def __init__(self, z, p, dz_soil, ctr, loc):\n #print(p)\n self.StomaModel = 'MEDLYN_FARQUHAR' # stomatal model\n \n self.Switch_pheno = ctr['pheno_cycle'] # include phenology\n self.Switch_lai = ctr['seasonal_LAI'] # seasonal LAI\n self.Switch_WaterStress = ctr['WaterStress'] # water stress affects stomata\n\n self.name = p['name']\n\n # phenology model\n if self.Switch_pheno:\n self.Pheno_Model = Photo_cycle(p['phenop']) # phenology model instance\n self.pheno_state = self.Pheno_Model.f # phenology state [0...1]\n else:\n self.pheno_state = 1.0\n\n # dynamic LAI model\n if self.Switch_lai:\n # seasonality of leaf area\n self.LAI_Model = LAI_cycle(p['laip'], loc) # LAI model instance\n self.relative_LAI = self.LAI_Model.f # LAI relative to annual maximum [0...1]\n else:\n self.relative_LAI = 1.0\n\n # physical structure\n self.LAImax = p['LAImax'] # maximum annual 1-sided LAI [m2m-2]\n self.LAI = self.LAImax * self.relative_LAI # current LAI\n self.lad_normed = p['lad'] # normalized leaf-area density [m-1]\n self.lad = self.LAI * self.lad_normed # current leaf-area density [m2m-3]\n\n # root properties\n self.Roots = RootUptake(p['rootp'], dz_soil, self.LAImax)\n\n self.dz = z[1] - z[0]\n\n # leaf gas-exchange parameters\n self.photop0 = p['photop'] # A-gs parameters at pheno_state = 1.0 (dict)\n self.photop = self.photop0.copy() # current A-gs parameters (dict)\n # leaf properties\n self.leafp = p['leafp'] # leaf properties (dict)","title":""},{"docid":"46350117caed49e308e33e79f13ed55e","score":"0.51327664","text":"def _profile(self):\n flexible_data_set = cx.FlexibleDataSetType(\n Code=\"QBIC_SOMATIC_SV_V{:.1f}\".format(SOMATIC_SV_VERSION),\n Systemwide=\"false\",\n FlexibleDataSetType=\"MEASUREMENT\",\n Category=\"LABOR\")\n multi_lang_de = cx.MultilingualEntryType(Lang='de', Value='QBIC Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION))\n multi_lang_en = cx.MultilingualEntryType(Lang='en', Value='QBIC Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION))\n flexible_data_set.NameMultilingualEntries = [multi_lang_de, multi_lang_en] \n \n flexible_data_set.FlexibleValueComplexRefs = [\n cx.FlexibleValueRefType(FlexibleValueRef=field_type, Required=\"false\") for field_type in SV_FIELD_TYPES\n ]\n \n self._catalogue_data.append(flexible_data_set)\n\n cfr_template = cx.CrfTemplateType(Name='Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION),\n FlexibleDataSetRef='QBIC_SOMATIC_SV_V{:.1f}'.format(SOMATIC_SV_VERSION),\n TemplateType='LABORMETHOD', Version='{:.0f}'.format(SOMATIC_SV_VERSION-1), EntityStatus='ACTIVE', Global='false', MultipleUse='false', Active='false')\n\n cfr_template_section = cx.CrfTemplateSectionType(Name='Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION),\n Height=len(SV_FIELD_TYPES), Width='1', Position='-1')\n \n cfr_template_section.CrfTemplateField = [\n cx.CrfTemplateFieldType(LaborValue=value,\n LowerRow=ind,\n LowerColumn='0',\n UpperRow=ind,\n UpperColumn='0',\n Mandatory='false',\n VisibleCaption='true',\n FieldType='LABORVALUE') for ind, value in enumerate(SV_FIELD_TYPES)]\n\n cfr_template.CrfTemplateSection = [cfr_template_section]\n\n self._catalogue_data.append(cfr_template)","title":""},{"docid":"cb82bc65e036a57c35f5f5156e807680","score":"0.51289624","text":"def __init__(self: 'ConsoleController',\n number_of_cheeses: int, number_of_stools: int):\n #get cheeses and stools\n self.number_of_cheeses = number_of_cheeses\n self.num_of_stools = number_of_stools\n\n #create a model using cheeses and stools\n self.model = TOAHModel(number_of_stools)\n self.model.fill_first_stool(number_of_cheeses)","title":""},{"docid":"133482de99420624ce5fecf560610a41","score":"0.5127023","text":"def create_top_level_model(sbml_file):\n sbmlns = SBMLNamespaces(3, 1, \"comp\", 1)\n doc = SBMLDocument(sbmlns)\n doc.setPackageRequired(\"comp\", True)\n doc.setPackageRequired(\"fbc\", False)\n\n mdoc = doc.getPlugin(\"comp\")\n\n # create listOfExternalModelDefinitions\n emd_bounds = comp.create_ExternalModelDefinition(mdoc, \"toy_ode_bounds\", sbml_file=ode_bounds_file)\n emd_fba = comp.create_ExternalModelDefinition(mdoc, \"toy_fba\", sbml_file=fba_file)\n emd_update = comp.create_ExternalModelDefinition(mdoc, \"toy_ode_update\", sbml_file=ode_update_file)\n emd_model = comp.create_ExternalModelDefinition(mdoc, \"toy_ode_model\", sbml_file=ode_model_file)\n\n # create models and submodels\n model = doc.createModel()\n model.setId(\"toy_top_level\")\n model.setName(\"Top level model\")\n model_factory.add_generic_info(model)\n mplugin = model.getPlugin(\"comp\")\n model.setSBOTerm(comp.SBO_CONTINOUS_FRAMEWORK)\n\n # add submodel which references the external model definition\n comp.add_submodel_from_emd(mplugin, submodel_sid=\"bounds\", emd=emd_bounds)\n comp.add_submodel_from_emd(mplugin, submodel_sid=\"fba\", emd=emd_fba)\n comp.add_submodel_from_emd(mplugin, submodel_sid=\"update\", emd=emd_update)\n comp.add_submodel_from_emd(mplugin, submodel_sid=\"model\", emd=emd_model)\n\n # --- compartments ---\n create_compartments(model, [\n {A_ID: \"extern\", A_NAME: \"external compartment\", A_VALUE: 1.0, A_SPATIAL_DIMENSION: 3, A_UNIT: model_factory.UNIT_VOLUME},\n {A_ID: 'cell', A_NAME: 'cell', A_VALUE: 1.0, A_SPATIAL_DIMENSION: 3, A_UNIT: model_factory.UNIT_VOLUME}\n ])\n\n # --- species ---\n # replaced species\n # (fba species are not replaced, because they need their boundaryConditions for the FBA,\n # and do not depend on the actual concentrations)\n create_species(model, [\n {A_ID: 'C', A_NAME: \"C\", A_VALUE: 0, A_UNIT: model_factory.UNIT_AMOUNT, A_HAS_ONLY_SUBSTANCE_UNITS: True,\n A_COMPARTMENT: \"extern\"},\n ])\n\n # --- parameters ---\n create_parameters(model, [\n # bounds\n {A_ID: 'ub_R1', A_VALUE: 1.0, A_UNIT: model_factory.UNIT_FLUX, A_NAME: 'ub_R1', A_CONSTANT: False},\n {A_ID: \"vR3\", A_NAME: \"vR3 (FBA flux)\", A_VALUE: 0.1, A_UNIT: model_factory.UNIT_FLUX, A_CONSTANT: False}\n ])\n\n # --- reactions ---\n # dummy reaction in top model\n r1 = create_reaction(model, rid=\"R3\", name=\"R3 dummy\", fast=False, reversible=True,\n reactants={}, products={\"C\": 1}, compartment=\"extern\")\n # assignment rule\n create_assignment_rules(model, [{A_ID: \"vR3\", A_VALUE: \"R3\"}])\n\n # --- replacements ---\n # replace compartments\n comp.replace_elements(model, 'extern', ref_type=comp.SBASE_REF_TYPE_PORT,\n replaced_elements={'fba': ['extern_port'], 'update': ['extern_port'], 'model': ['extern_port']})\n\n comp.replace_elements(model, 'cell', ref_type=comp.SBASE_REF_TYPE_PORT,\n replaced_elements={'fba': ['cell_port']})\n\n # replace parameters\n comp.replace_elements(model, 'ub_R1', ref_type=comp.SBASE_REF_TYPE_PORT,\n replaced_elements={'bounds': ['ub_R1_port'], 'fba': ['ub_R1_port']})\n comp.replace_elements(model, 'vR3', ref_type=comp.SBASE_REF_TYPE_PORT,\n replaced_elements={'update': ['vR3_port']})\n\n # replace species\n comp.replace_elements(model, 'C', ref_type=comp.SBASE_REF_TYPE_PORT,\n replaced_elements={'fba': ['C_port'], 'update': ['C_port'],\n 'model': ['C_port']})\n\n # replace reaction by fba reaction\n comp.replaced_by(model, 'R3', ref_type=comp.SBASE_REF_TYPE_PORT,\n submodel='fba', replaced_by=\"R3_port\")\n\n # replace units\n for uid in ['s', 'kg', 'm3', 'm2', 'mM', 'item_per_m3', 'm', 'per_s', 'item_per_s']:\n comp.replace_element_in_submodels(model, uid, ref_type=comp.SBASE_REF_TYPE_UNIT,\n submodels=['bounds', 'fba', 'update', 'model'])\n\n # write SBML file\n sbml_io.write_and_check(doc, sbml_file)","title":""},{"docid":"8a61b3ec62b3cdb766201f4d60c90ea9","score":"0.51260775","text":"def __init__(self, modelfits='/global/project/projectdirs/desi/science/td/timedomain-github/snmodels/vincenzi_2019_models.fits'):\n # Set up core-collapse models.\n modtab = Table.read(modelfits)\n \n # Blacklist models with odd-looking spectra for some epochs (Eddie Sepeku).\n blacklist = [b'SN2013by', b'SN2013fs', b'SN2009bw', b'SN2012aw', b'SN2009kr', b'ASASSN14jb', b'SN2013am', b'SN2008ax', b'SN2008fq', b'SN2009ip', b'iPTF13bvn', b'SN2008D', b'SN1994I', b'SN2007gr', b'SN2009bb', b'SN2007ru']\n select = ~np.in1d(modtab['Name'].value, blacklist)\n modtab = modtab[select]\n\n sntypes = np.unique(modtab['Type'])\n self.models = {}\n for sntype in sntypes:\n self.models[sntype] = []\n for nm in modtab['Name'][modtab['Type'] == sntype]:\n # Get the corresponding sncosmo model with host dust correction applied.\n if nm.startswith('SN'):\n model_name = 'v19-{}-corr'.format(nm[2:].lower())\n else:\n model_name = 'v19-{}-corr'.format(nm.lower())\n self.models[sntype].append(model_name)\n\n # Add Ia models.\n self.models['Ia'] = ['hsiao']\n \n # Add kilonova models.\n self.models['kN'] = []\n knfiles = sorted(glob('/global/u2/l/lehsani/kilonova/models_0.2_0.8/*knova*.csv'))\n for knfile in knfiles:\n kn = KNSource(knfile)\n sncosmo.register(kn, kn.name, force=True)\n self.models['kN'].append(kn.name)","title":""},{"docid":"9ad07bd3f5ada935f0138b4b2f61e43d","score":"0.51237166","text":"def __init__(self, community_id, models, extracellular_compartment_id = \"e\", empty_flag = False):\n self.community_id = community_id\n self.models = models\n self.extracellular_compartment_id = extracellular_compartment_id\n self.empty_flag = empty_flag\n self.model_dic = self.__model_dic()\n self.cmodel = self.__create_cmodel()\n self.objectives = self.cmodel.get_objective()\n self.pex_cons = self.__get_pex_constraints()\n self.biomass_reactions = self.__biomass_reactions()","title":""},{"docid":"75ba048ca022f541a1ee1e10167a7962","score":"0.5115951","text":"def __init__(self):\n self.label = \"Apply model\"\n self.description = 'Applies a model to a series of data rasters obtain a response raster'\n self.canRunInBackground = False\n self.category = \"Modelling\"","title":""},{"docid":"5eea97c6320b8410c80c39d70ab96c84","score":"0.51052773","text":"def __init__(self, *args):\r\n self.__stencils = {}\r\n self.__shapes = {}\r\n #self.add_stencil(\"Basic Shapes.vss\")\r\n for stencil in args:\r\n self.add_stencil(stencil)","title":""},{"docid":"9c650c7e58dccfdea82af9a8d8eccb70","score":"0.5098148","text":"def main():\n # sde = SDE()\n # sde.import_and_export()\n market = Market()\n market.update_marketData()","title":""},{"docid":"f3408a0e1bd4491b367d3a628b5fbcd4","score":"0.5096738","text":"def __init__(self,\n sbml_file,\n extra_cellular_id,\n periplasm_id,\n cytoplasm_id,\n model_dir='..',\n json_model=None,\n groups_size=150,\n kon=0.8,\n koff=0.8,\n rates='0:0:0:1',\n enzymes=1000,\n metabolites=600000):\n\n self.groups_size = groups_size\n self.parameter_writer = XMLParametersWriter(model_dir=model_dir)\n self.coder = ModelCodeWriter(model_dir=model_dir)\n self.parser = SBMLParser(sbml_file,\n extra_cellular_id,\n periplasm_id,\n cytoplasm_id,\n json_model=json_model,\n default_konSTP=kon,\n default_konPTS=kon,\n default_koffSTP=koff,\n default_koffPTS=koff,\n default_metabolite_amount=metabolites,\n default_enzyme_amount=enzymes,\n default_rate=rates,\n default_reject_rate=rates,\n default_interval_time=rates)\n\n special_compartment_ids = [extra_cellular_id, periplasm_id, cytoplasm_id]\n\n c_external_enzyme_sets = {cid: [MEMBRANE]\n for cid in self.parser.get_compartments()\n if cid not in special_compartment_ids}\n c_external_enzyme_sets[periplasm_id] = [INNER, TRANS]\n\n e_external_enzyme_sets = {periplasm_id: [OUTER, TRANS]}\n\n # Compartment model structures\n self.periplasm = ModelStructure(periplasm_id,\n self.parser,\n membranes=[OUTER, INNER, TRANS])\n\n self.extra_cellular = ModelStructure(extra_cellular_id,\n self.parser,\n external_enzyme_sets=e_external_enzyme_sets)\n\n self.cytoplasm = ModelStructure(cytoplasm_id,\n self.parser,\n external_enzyme_sets=c_external_enzyme_sets)\n\n self.organelles = {comp_id: ModelStructure(comp_id, self.parser, [MEMBRANE])\n for comp_id in self.parser.get_compartments()\n if comp_id not in special_compartment_ids}","title":""},{"docid":"294fb43d0e5c8ae0155134b298fd02f7","score":"0.5083324","text":"def __init__(self, model_file_path='../model/model_final', target_var='SalePrice'): \n self.model_file_path = model_file_path\n self.load_model()\n self.target_var = target_var","title":""},{"docid":"597249eb3dedd47cc6937d9357251e99","score":"0.5083237","text":"def construct(self, geom):\n\n main_shape = geom.shapes.Tubs(self.name, rmin = self.halfDimension[\"rmin\"],\n rmax = self.halfDimension[\"rmax\"], dz = self.halfDimension[\"dz\"])\n main_lv = geom.structure.Volume(self.name+\"_lv\", material=self.Material, shape=main_shape)\n if isinstance(self.Sensitive,str):\n main_lv.params.append((\"SensDet\",self.Sensitive))\n self.add_volume(main_lv)\n\n straw_shape = geom.shapes.Tubs(self.name+\"_straw\", rmin = self.halfSTDimension[\"rmin\"],\n rmax = self.halfSTDimension[\"rmax\"], dz = self.halfSTDimension[\"dz\"])\n straw_lv = geom.structure.Volume(self.name+\"_straw_lv\", material=self.STMaterial, shape=straw_shape)\n\n wire_shape = geom.shapes.Tubs(self.name+\"_wire\", self.halfWireDimension[\"rmin\"],\n rmax = self.halfWireDimension[\"rmax\"], dz = self.halfWireDimension[\"dz\"])\n wire_lv = geom.structure.Volume(self.name+\"_wire_lv\", material=self.WireMaterial, shape=wire_shape)\n\n straw_pla = geom.structure.Placement( self.name+\"_straw_pla\", volume = straw_lv )\n wire_pla = geom.structure.Placement( self.name+\"_wire_pla\", volume = wire_lv )\n main_lv.placements.append( straw_pla.name )\n main_lv.placements.append( wire_pla.name )","title":""},{"docid":"46b3b31f7f083d4ab5495942c3a43018","score":"0.5074051","text":"def __init__(self, make, model, year):\n super().__init__(make, model, year)\n #new 'Battery' instance above defined as an attribute within 'ElectricCar' instance\n self.battery = Battery()","title":""},{"docid":"e02764166a3d23a0857f6965f383a596","score":"0.50672626","text":"def stadardization_model(corr):\n class ModelStandardization_mystand( ModelStandardization ):\n STANDARDIZATION = corr\n\n return ModelStandardization_mystand()","title":""},{"docid":"a7963a1de6ca2f53960b2a73a0bcab7a","score":"0.5061793","text":"def build_car(self, model):","title":""},{"docid":"1634cd3a5d6f896a8f6d2f44ecf2a1fb","score":"0.5059821","text":"def __init__(self):\r\n\r\n super(Model, self).__init__()\r\n\r\n # Define private dictionary attributes.\r\n\r\n # Define private list attributes.\r\n\r\n # Define private scalar attributes.\r\n\r\n # Define public dictionary attributes.\r\n\r\n # Define public list attributes.\r\n self.lst_derate_criteria = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]\r\n\r\n # Define public scalar attributes.\r\n self.application = 0 # Application index.\r\n self.base_hr = 0.0 # Base hazard rate.\r\n self.piE = 0.0 # Environment pi factor.\r\n self.reason = \"\" # Overstress reason.\r","title":""},{"docid":"cfcf82f9b272d87c28d4128972cdbdc4","score":"0.5057847","text":"def pwr_assembly():\n\n model = openmc.model.Model()\n\n # Define materials.\n fuel = openmc.Material(name='Fuel')\n fuel.set_density('g/cm3', 10.29769)\n fuel.add_nuclide('U234', 4.4843e-6)\n fuel.add_nuclide('U235', 5.5815e-4)\n fuel.add_nuclide('U238', 2.2408e-2)\n fuel.add_nuclide('O16', 4.5829e-2)\n\n clad = openmc.Material(name='Cladding')\n clad.set_density('g/cm3', 6.55)\n clad.add_nuclide('Zr90', 2.1827e-2)\n clad.add_nuclide('Zr91', 4.7600e-3)\n clad.add_nuclide('Zr92', 7.2758e-3)\n clad.add_nuclide('Zr94', 7.3734e-3)\n clad.add_nuclide('Zr96', 1.1879e-3)\n\n hot_water = openmc.Material(name='Hot borated water')\n hot_water.set_density('g/cm3', 0.740582)\n hot_water.add_nuclide('H1', 4.9457e-2)\n hot_water.add_nuclide('O16', 2.4672e-2)\n hot_water.add_nuclide('B10', 8.0042e-6)\n hot_water.add_nuclide('B11', 3.2218e-5)\n hot_water.add_s_alpha_beta('c_H_in_H2O')\n\n # Define the materials file.\n model.materials = (fuel, clad, hot_water)\n\n # Instantiate ZCylinder surfaces\n fuel_or = openmc.ZCylinder(x0=0, y0=0, r=0.39218, name='Fuel OR')\n clad_or = openmc.ZCylinder(x0=0, y0=0, r=0.45720, name='Clad OR')\n\n # Create boundary planes to surround the geometry\n pitch = 21.42\n min_x = openmc.XPlane(x0=-pitch/2, boundary_type='reflective')\n max_x = openmc.XPlane(x0=+pitch/2, boundary_type='reflective')\n min_y = openmc.YPlane(y0=-pitch/2, boundary_type='reflective')\n max_y = openmc.YPlane(y0=+pitch/2, boundary_type='reflective')\n\n # Create a fuel pin universe\n fuel_pin_universe = openmc.Universe(name='Fuel Pin')\n fuel_cell = openmc.Cell(name='fuel', fill=fuel, region=-fuel_or)\n clad_cell = openmc.Cell(name='clad', fill=clad, region=+fuel_or & -clad_or)\n hot_water_cell = openmc.Cell(name='hot water', fill=hot_water, region=+clad_or)\n fuel_pin_universe.add_cells([fuel_cell, clad_cell, hot_water_cell])\n\n\n # Create a control rod guide tube universe\n guide_tube_universe = openmc.Universe(name='Guide Tube')\n gt_inner_cell = openmc.Cell(name='guide tube inner water', fill=hot_water,\n region=-fuel_or)\n gt_clad_cell = openmc.Cell(name='guide tube clad', fill=clad,\n region=+fuel_or & -clad_or)\n gt_outer_cell = openmc.Cell(name='guide tube outer water', fill=hot_water,\n region=+clad_or)\n guide_tube_universe.add_cells([gt_inner_cell, gt_clad_cell, gt_outer_cell])\n\n # Create fuel assembly Lattice\n assembly = openmc.RectLattice(name='Fuel Assembly')\n assembly.pitch = (pitch/17, pitch/17)\n assembly.lower_left = (-pitch/2, -pitch/2)\n\n # Create array indices for guide tube locations in lattice\n template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n\n # Create 17x17 array of universes\n assembly.universes = np.tile(fuel_pin_universe, (17, 17))\n assembly.universes[template_x, template_y] = guide_tube_universe\n\n # Create root Cell\n root_cell = openmc.Cell(name='root cell', fill=assembly)\n root_cell.region = +min_x & -max_x & +min_y & -max_y\n\n # Create root Universe\n model.geometry.root_universe = openmc.Universe(name='root universe')\n model.geometry.root_universe.add_cell(root_cell)\n\n model.settings.batches = 10\n model.settings.inactive = 5\n model.settings.particles = 100\n model.settings.source = openmc.Source(space=openmc.stats.Box(\n [-pitch/2, -pitch/2, -1], [pitch/2, pitch/2, 1], only_fissionable=True))\n\n plot = openmc.Plot()\n plot.origin = (0.0, 0.0, 0)\n plot.width = (21.42, 21.42)\n plot.pixels = (300, 300)\n plot.color_by = 'material'\n model.plots.append(plot)\n\n return model","title":""},{"docid":"7b2a7740fe2af300e9f4af151c64dc95","score":"0.5050282","text":"def create_submodel(self, verbose = True, vverbose = False):\n\n with open(self.basemodel, 'rb') as f: hspfmodel = pickle.load(f)\n\n comid = self.gagecomid\n upcomids = self.upcomids\n filename = hspfmodel.filename + comid\n picklefile = hspfmodel.filepath + filename\n\n submodel = HSPFModel()\n submodel.build_from_existing(hspfmodel, filename, directory = \n hspfmodel.filepath[:-1],\n verbose = vverbose)\n\n # find the subbasins between the outlet and the upstream comids and\n # store in an updown dictionary\n\n updown = {comid: 0}\n\n current = 0\n\n while current != len(updown):\n\n # see if the current length changes to check if done\n\n current = len(updown)\n\n # iterate throught the subbasins and see if any need to be added\n\n for up, down in hspfmodel.updown.items():\n\n if (up not in updown and # not already there\n up not in upcomids and # between the boundaries\n down in updown): # downstream is there\n \n updown[up] = down\n \n # overwrite the old updown dictionary\n\n submodel.updown = updown\n\n # overwrite the inlets and outlets\n\n submodel.inlets = [hspfmodel.updown[c] for c in upcomids]\n submodel.outlets = [comid]\n\n # overwrite the old subbasin dictionary\n\n submodel.subbasins = {comid: subbasin for comid, subbasin in \n submodel.subbasins.items() if comid in updown}\n\n # build with the updated model subbasin info\n\n submodel.build()\n\n # add in the modules\n\n if self.temp: submodel.add_temp()\n\n if self.snow: \n \n densities = [o.RDENPF \n for o in hspfmodel.perlnds + hspfmodel.implnds]\n depths = [o.packsnow / o.RDENPF \n for o in hspfmodel.perlnds + hspfmodel.implnds]\n\n depth = sum(depths) / len(depths)\n density = sum(densities) / len(densities)\n\n submodel.add_snow(depth = depth, density = density) \n\n if self.hydrology: submodel.add_hydrology()\n \n # add the flowgage data to the model\n\n for identifier in hspfmodel.flowgages:\n if identifier == self.gageid:\n start_date, tstep, data = hspfmodel.flowgages[identifier]\n submodel.add_timeseries('flowgage', identifier, start_date, \n data, tstep = tstep)\n\n # add the watershed time series dictionaries for the model\n\n timeseries = {'inflow': hspfmodel.inflows,\n 'temperature': hspfmodel.temperatures,\n 'dewpoint': hspfmodel.dewpoints,\n 'wind': hspfmodel.windspeeds,\n 'solar': hspfmodel.solars,\n 'snowfall': hspfmodel.snowfalls,\n 'snowdepth': hspfmodel.snowdepths,\n }\n\n for tstype, d in timeseries.items():\n for identifier in d: \n start_date, tstep, data = d[identifier]\n submodel.add_timeseries(tstype, identifier, start_date, data, \n tstep = tstep)\n\n # add the subbasin timeseries as needed\n\n for identifier in hspfmodel.precipitations:\n if identifier in submodel.subbasins.keys():\n start_date, tstep, data = hspfmodel.precipitations[identifier]\n submodel.add_timeseries('precipitation', identifier, start_date,\n data, tstep = tstep)\n\n # add the landuse timeseries as needed\n\n landuse_keys = {'Corn': 'cereals',\n 'Soybeans': 'legumes',\n 'Pasture/grass': 'pasture',\n 'Other grain': 'cereals',\n 'Hay/alfalfa': 'alfalfa',\n 'Water/wetland': 'wetlands',\n 'Fallow land': 'fallow',\n 'Forest': 'others',\n 'Developed': 'others',\n 'Impervious': 'others',\n 'Other': 'others',\n }\n\n ltypes = [landuse_keys[i] for i in hspfmodel.landuse]\n\n for identifier in hspfmodel.evaporations:\n if identifier in ltypes:\n start_date, tstep, data = hspfmodel.evaporations[identifier]\n submodel.add_timeseries('evaporation', identifier, start_date,\n data, tstep = tstep)\n\n # add the influent flows as needed\n \n for upcomid in upcomids:\n\n # find the upstream gage number\n\n \n upgage = [v for k, v in \n hspfmodel.subbasin_timeseries['flowgage'].items() \n if k == upcomid][0]\n incomid = hspfmodel.updown[upcomid]\n\n # find the outlet flows from the previous upstream calibration\n\n t = (self.directory, self.HUC8, upgage)\n flowfile = '{}/{}/calibrations/{}/outletflows'.format(*t)\n \n # get the time series and add it to the model\n\n if not os.path.isfile(flowfile): \n raise RuntimeError('warning: upstream calibration of gage ' +\n '{} does not exist\\n'.format(upgage))\n with open(flowfile, 'rb') as f: times, data = pickle.load(f)\n\n tstep = math.ceil((times[1] - times[0]).total_seconds() / 60)\n\n submodel.add_timeseries('inflow', '{}'.format(incomid), times[0], \n data, tstep = tstep)\n\n # assign the inflows from upstream to any subbasins\n\n otype = 'Reach'\n\n submodel.assign_operation_timeseries('inflow', incomid, 'Reach', \n '{}'.format(incomid))\n \n # assign as needed\n\n for tstype, identifier in hspfmodel.watershed_timeseries.items():\n \n submodel.assign_watershed_timeseries(tstype, identifier)\n\n for tstype, d in hspfmodel.subbasin_timeseries.items():\n\n for subbasin, identifier in d.items():\n \n if subbasin in submodel.subbasins:\n\n submodel.assign_subbasin_timeseries(tstype, subbasin,\n identifier)\n\n for tstype, d in hspfmodel.landuse_timeseries.items():\n\n for landtype, identifier in d.items():\n \n if landtype in submodel.landuse:\n\n submodel.assign_landuse_timeseries(tstype, landtype,\n identifier)\n\n for tstype, d1 in hspfmodel.operation_timeseries.items():\n\n for subbasin, d2 in d1.items():\n\n for otype, identifier in d2.items():\n\n if subbasin in submodel.subbasins:\n\n submodel.assign_operation_timeseries(tstype, subbasin,\n otype, identifier)\n\n with open(picklefile, 'wb') as f: pickle.dump(submodel, f)\n\n self.basemodel = picklefile","title":""},{"docid":"2bf96a316ed8b50e59e35c107ca30910","score":"0.5044699","text":"def __init__(self, Model, settings):\n KratosMultiphysics.Process.__init__(self)\n\n # The value can be a double or a string (function)\n default_settings = KratosMultiphysics.Parameters(\n \"\"\"\n {\n \"help\" : \"This sets the initial conditions in terms of imposed strain, stress or deformation gradient\",\n \"mesh_id\" : 0,\n \"model_part_name\" : \"please_specify_model_part_name\",\n \"dimension\" : 3,\n \"imposed_strain\" : [\"0*t\",\"0*t\",\"0*t\",0,0,0],\n \"imposed_stress\" : [0,0,0,0,0,0],\n \"imposed_deformation_gradient\" : [[1,0,0],[0,1,0],[0,0,1]],\n \"interval\" : [0.0, 1e30]\n }\n \"\"\"\n )\n\n # assign this here since it will change the \"interval\" prior to validation\n self.interval = KratosMultiphysics.IntervalUtility(settings)\n\n settings.ValidateAndAssignDefaults(default_settings)\n self.model_part = Model[settings[\"model_part_name\"].GetString()]\n self.dimension = settings[\"dimension\"].GetInt()\n\n # init strain\n self.strain_functions = components_to_functions(settings[\"imposed_strain\"])\n nr_comps = len(self.strain_functions)\n self.imposed_strain = KratosMultiphysics.Vector(nr_comps)\n\n # init stress\n self.stress_functions = components_to_functions(settings[\"imposed_stress\"])\n nr_comps = len(self.stress_functions)\n self.imposed_stress = KratosMultiphysics.Vector(nr_comps)\n\n # init deformation gradient\n aux_matrix = settings[\"imposed_deformation_gradient\"]\n self.deformation_functions = []\n for row in aux_matrix.values():\n aux_vector = components_to_functions(row)\n self.deformation_functions.append(aux_vector)\n nrows = aux_matrix.size()\n ncols = aux_matrix[0].size()\n self.imposed_deformation_gradient = KratosMultiphysics.Matrix(nrows, ncols)","title":""},{"docid":"60d10862cdba968122eaf36625483b31","score":"0.50441337","text":"def setShadeModel(*args):\n return _coin.SoLazyElement_setShadeModel(*args)","title":""},{"docid":"d116762b520f239d193616f2b238969b","score":"0.50420004","text":"def __init__(self, Mv):\n \n # get the gravitational constant (the value is 4.498502151575286e-06)\n self.G = const.G.to(u.kpc**3/u.Msun/u.Gyr**2).value\n \n # initialize the virial mass global variable \n self.Mvir = Mv\n \n ## Cosmology Same as Patel 2020\n self.h = 0.7 # Hubble constant at z=0 / 100 \n self.omegaM = 0.27\n self.DelVir = 359 # default z=0 overdensity for this cosmology","title":""},{"docid":"1129bdd69386cff2ceb1c8cb6f8afae2","score":"0.50415975","text":"def setup_prod():\n setup_general()","title":""},{"docid":"bc28a96787b33b44f751dbe4c93bd9f9","score":"0.5040365","text":"def generate_catalogue(cube_side, mass_params, z, h):\n\n print(\"Generating catalogue for a volume of ({:.2f} Mpc/h)^3\\n\".format(cube_side))\n\n catalogue_volume = cube_side**3\n\n # Get the bin width and generate the bins.\n bin_width = mass_params[2]\n mass_range = 10 ** np.arange(mass_params[0], mass_params[1], mass_params[2]) #log[Msun]\n\n # Generate the mass function itself - this is from the colossus toolbox\n local_mass_function = mass_function.massFunction(mass_range, z, mdef='200m', model='tinker08', q_out='dndlnM') \\\n * np.log(10) / h # dn/dlog10M\n\n # We determine the Cumulative HMF starting from the high mass end, multiplied by the bin width.\n # This effectively gives the cumulative probability of a halo existing.\n cumulative_mass_function = np.flip(np.cumsum(np.flip(local_mass_function, 0)), 0) * bin_width\n\n ########################################################################\n # Interpolation Tests\n # Interpolator for the testing - we will update this with the volume in a second.\n # This is essentially for a volume of size unity.\n interpolator = sp.interpolate.interp1d(cumulative_mass_function, mass_range)\n\n sample_index = int(np.floor(len(cumulative_mass_function) / 2)) # index of the half way point\n num_test = cumulative_mass_function[sample_index] # The value of the cum function at this index\n mass_test = interpolator(num_test) # Interpolate to get the mass that this predicts\n # Check that these values are equivalent.\n assert mass_range[sample_index] == mass_test, \\\n \"Interpolation method incorrect: Back interpolation at midpoint failed\"\n # Check first element is equivalent to the total to 10 SF accuracy\n assert np.round(cumulative_mass_function[0], 10) ==\\\n np.round(np.sum(local_mass_function) * bin_width, 10), \"Final cum sum element != total sum\"\n ########################################################################\n\n # Multiply by volume\n cumulative_mass_function = cumulative_mass_function * catalogue_volume\n\n # Get the maximum cumulative number.\n max_number = np.floor(cumulative_mass_function[0])\n range_numbers = np.arange(max_number)\n\n # Update interpolator\n interpolator = sp.interpolate.interp1d(cumulative_mass_function, mass_range)\n mass_catalog = interpolator(range_numbers[range_numbers >= np.amin(cumulative_mass_function)])\n\n print(\"Number of halos generated: {:d}\\n\".format(len(mass_catalog)))\n\n mass_catalog = np.log10(mass_catalog)\n return mass_catalog","title":""},{"docid":"3967922f2cab6aa214c6a5c1206ec360","score":"0.5034182","text":"def __init__(self, x=0, Np=10, error_factor=1.0, dist='Gaussian', Energy=None, relement='Au', NrDep='False', H=1.0, HvvgtR=False,\n Rsig=0.0, norm=1.0, norm_err=0.01, sbkg=0.0, cbkg=0.0, abkg=0.0, D=1.0, phi=0.1, U=-1.0, SF='None',Nalf=200,term='Total',\n mpar={'Layers': {'Material': ['Au', 'H2O'], 'Density': [19.32, 1.0], 'SolDensity': [1.0, 1.0],\n 'Rmoles': [1.0, 1.0], 'R': [1.0, 0.0]}}):\n if type(x)==list:\n self.x=np.array(x)\n else:\n self.x=x\n self.norm=norm\n self.norm_err = norm_err\n self.sbkg=sbkg\n self.cbkg=cbkg\n self.abkg=abkg\n self.dist=dist\n self.Rsig=Rsig\n self.Np=Np\n self.H=H\n self.HvvgtR=HvvgtR\n self.Nalf=Nalf\n self.Energy=Energy\n self.relement=relement\n self.NrDep=NrDep\n #self.rhosol=rhosol\n self.error_factor=error_factor\n self.D=D\n self.phi=phi\n self.U=U\n self.term=term\n self.__mpar__=mpar #If there is any multivalued parameter\n self.SF=SF\n self.choices={'HvvgtR':['True','False'],\n 'dist':['Gaussian','LogNormal'],'NrDep':['True','False'],\n 'SF':['None','Hard-Sphere', 'Sticky-Sphere'],\n 'term': ['SAXS-term', 'Cross-term', 'Resonant-term',\n 'Total']\n } #If there are choices available for any fixed parameters\n self.__cf__=Chemical_Formula()\n self.__fit__=False\n self.output_params={'scaler_parameters':{}}\n self.__mkeys__=list(self.__mpar__.keys())\n self.init_params()","title":""},{"docid":"27c8b444ddf946a871eb505614b83a58","score":"0.50339866","text":"def __init__(self):\n from desisim.io import read_basis_templates\n \n balflux, balwave, balmeta = read_basis_templates(objtype='BAL')\n self.balflux = balflux\n self.balwave = balwave\n self.balmeta = balmeta","title":""},{"docid":"0c5b7ebda2ef1b74500cdd4c5f229e27","score":"0.5030364","text":"def __init__(self):\n \n self.fdn = csmFoundation()\n self.transportation = 0.0\n self.roadsCivil = 0.0\n self.portStaging = 0.0\n self.installation = 0.0\n self.electrical = 0.0\n self.engPermits = 0.0\n self.pai = 0.0\n self.scour = 0.0","title":""},{"docid":"6c59dc482900e1b624eb3025101a1959","score":"0.5028235","text":"def __init__(self, env, sub_model):\n super(ResidualModel, self).__init__()\n self.env = env\n self.model = hydra.utils.instantiate(sub_model)","title":""},{"docid":"f4c539b7e75e8572870810e761acafa8","score":"0.5015839","text":"def cg():\n\n L = 250 # bar length\n m = 4 # number of requests\n w = [187, 119, 74, 90] # size of each item\n b = [1, 2, 2, 1] # demand for each item\n\n # creating models and auxiliary lists\n master = Model(SOLVER)\n lambdas = []\n constraints = []\n\n # creating an initial pattern (which cut one item per bar)\n # to provide the restricted master problem with a feasible solution\n for i in range(m):\n lambdas.append(master.add_var(obj=1, name='lambda_%d' % (len(lambdas) + 1)))\n\n # creating constraints\n for i in range(m):\n constraints.append(master.add_constr(lambdas[i] >= b[i], name='i_%d' % (i + 1)))\n\n # creating the pricing problem\n pricing = Model(SOLVER)\n\n # creating pricing variables\n a = []\n for i in range(m):\n a.append(pricing.add_var(obj=0, var_type=INTEGER, name='a_%d' % (i + 1)))\n\n # creating pricing constraint\n pricing += xsum(w[i] * a[i] for i in range(m)) <= L, 'bar_length'\n\n pricing.write('pricing.lp')\n\n new_vars = True\n while (new_vars):\n\n ##########\n # STEP 1: solving restricted master problem\n ##########\n\n master.optimize()\n master.write('master.lp')\n\n # printing dual values\n print_solution(master)\n print('pi = ', end='')\n print([constraints[i].pi for i in range(m)])\n print('')\n\n ##########\n # STEP 2: updating pricing objective with dual values from master\n ##########\n\n pricing.objective = 1\n for i in range(m):\n a[i].obj = -constraints[i].pi\n\n # solving pricing problem\n pricing.optimize()\n\n # printing pricing solution\n z_val = pricing.objective_value()\n print('Pricing:')\n print(' z = {z_val}'.format(**locals()))\n print(' a = ', end='')\n print([v.x for v in pricing.vars])\n print('')\n\n ##########\n # STEP 3: adding the new columns\n ##########\n\n # checking if columns with negative reduced cost were produced and\n # adding them into the restricted master problem\n if pricing.objective_value() < - EPS:\n coeffs = [a[i].x for i in range(m)]\n column = Column(constraints, coeffs)\n lambdas.append(master.add_var(obj=1, column=column, name='lambda_%d' % (len(lambdas) + 1)))\n\n print('new pattern = {coeffs}'.format(**locals()))\n\n # if no column with negative reduced cost was produced, then linear\n # relaxation of the restricted master problem is solved\n else:\n new_vars = False\n\n pricing.write('pricing.lp')\n # pdb.set_trace()\n\n print_solution(master)","title":""},{"docid":"51cb817be985002f692d35e089526f32","score":"0.5011754","text":"def __init__(self, ngrps=2, nints=2, teff=5700.0, logg=4.0, feh=0.0, alpha=0.0, jmag=9.0, stellar_model='phoenix', filter='CLEAR', subarray='SUBSTRIP256', run=True, add_planet=False, scale=1., **kwargs):\n # Retrieve stellar model\n if stellar_model.lower() == 'phoenix':\n wav, flx = ma.get_phoenix_model(feh, alpha, teff, logg)\n elif stellar_model.lower() == 'atlas':\n wav, flx = ma.get_atlas_model(feh, teff, logg)\n\n # Scale model spectrum to user-input J-band\n flx = ma.scale_spectrum(wav, flx, jmag)\n\n # Initialize base class\n super().__init__(ngrps=ngrps, nints=nints, star=[wav, flx], subarray=subarray, filter=filter, **kwargs)\n\n # Add planet\n if add_planet:\n self.planet = hu.PLANET_DATA\n self.tmodel = hu.transit_params(self.time.jd)\n self.tmodel.t0 = np.mean(self.time.jd)\n self.tmodel.teff = teff\n self.tmodel.logg = logg\n self.tmodel.feh = feh\n self.tmodel.alpha = alpha\n\n # Run the simulation\n if run:\n self.create()","title":""},{"docid":"6a05009c7e19f6c93fff49a0de3225fb","score":"0.50089544","text":"def __init__(self, coreRadius=0.0, rAC50=0.0, coreStrandCount=0, radius=0.0, material=\"aluminum\", rDC20=0.0, sizeDescription='', rAC75=0.0, gmr=0.0, ratedCurrent=0.0, strandCount=0, rAC25=0.0, WireArrangements=None, ConcentricNeutralCableInfos=None, *args, **kw_args):\n #: (if there is a different core material) Radius of the central core.\n self.coreRadius = coreRadius\n\n #: AC resistance per unit length of the conductor at 50 oC.\n self.rAC50 = rAC50\n\n #: (if used) Number of strands in the steel core.\n self.coreStrandCount = coreStrandCount\n\n #: Outside radius of the wire.\n self.radius = radius\n\n #: Wire material. Values are: \"aluminum\", \"copper\", \"other\", \"steel\", \"acsr\"\n self.material = material\n\n #: DC resistance per unit length of the conductor at 20 oC.\n self.rDC20 = rDC20\n\n #: Describes the wire guage or cross section (e.g., 4/0, #2, 336.5).\n self.sizeDescription = sizeDescription\n\n #: AC resistance per unit length of the conductor at 75 oC.\n self.rAC75 = rAC75\n\n #: Geometric mean radius. If we replace the conductor by a thin walled tube of radius GMR, then its reactance is identical to the reactance of the actual conductor.\n self.gmr = gmr\n\n #: Current carrying capacity of the wire under stated thermal conditions.\n self.ratedCurrent = ratedCurrent\n\n #: Number of strands in the wire.\n self.strandCount = strandCount\n\n #: AC resistance per unit length of the conductor at 25 oC.\n self.rAC25 = rAC25\n\n self._WireArrangements = []\n self.WireArrangements = [] if WireArrangements is None else WireArrangements\n\n self._ConcentricNeutralCableInfos = []\n self.ConcentricNeutralCableInfos = [] if ConcentricNeutralCableInfos is None else ConcentricNeutralCableInfos\n\n super(WireType, self).__init__(*args, **kw_args)","title":""},{"docid":"a547e87cba133b63b251f82a2dbf2774","score":"0.49989748","text":"def canada_model_cli():\n pass","title":""},{"docid":"9ee6a2dbf18053a2ef3b8991df176cb5","score":"0.4996126","text":"def for_model(self):","title":""},{"docid":"ed169bf514baefb9d0ea3e8fe334cd8b","score":"0.49942312","text":"def __init__(self, \n Omega_c, Omega_b, Omega_l, h, n_s, A_s=None, sigma_8=None,\n Omega_g=0.0, Omega_n_mass=0.0, Omega_n_rel=0.0, \n w0=-1., wa=0., N_nu_mass=0, N_nu_rel = 3.046, mnu=0.0):\n # Set parameter values\n self.Omega_c = Omega_c\n self.Omega_b = Omega_b\n self.Omega_l = Omega_l\n self.h = h\n self.n_s = n_s\n self.A_s = A_s\n self.sigma_8 = sigma_8\n self.Omega_g = Omega_g\n self.Omega_n_mass = Omega_n_mass\n self.Omega_n_rel = Omega_n_rel\n self.w0 = w0\n self.wa = wa\n self.N_nu_mass = N_nu_mass\n self.N_nu_rel = N_nu_rel\n self.mnu = mnu\n self.sigma_8 = sigma_8\n \n # Set density parameters according to consistency relations\n self.Omega_m = self.Omega_c + self.Omega_b + self.Omega_n_mass\n self.Omega_k = 1. - ( self.Omega_m + self.Omega_l \n + self.Omega_g + self.Omega_n_rel )","title":""},{"docid":"bc4743d680f30dab73d7a5e7a95ac6a3","score":"0.4993503","text":"def run_model(self, kwargs):\r\n \r\n loc = kwargs.pop(\"lookup shortage loc\")\r\n speed = kwargs.pop(\"lookup shortage speed\")\r\n kwargs['shortage price effect lookup'] = [self.f(x/10, speed, loc) for x in range(0,100)]\r\n \r\n speed = kwargs.pop(\"lookup price substitute speed\")\r\n begin = kwargs.pop(\"lookup price substitute begin\")\r\n end = kwargs.pop(\"lookup price substitute end\")\r\n kwargs['relative price substitute lookup'] = [self.priceSubstite(x, speed, begin, end) for x in range(0,100, 10)]\r\n \r\n scale = kwargs.pop(\"lookup returns to scale speed\")\r\n speed = kwargs.pop(\"lookup returns to scale scale\")\r\n kwargs['returns to scale lookup'] = [self.returnsToScale(x, speed, scale) for x in range(0, 101, 10)]\r\n \r\n scale = kwargs.pop(\"lookup approximated learning speed\")\r\n speed = kwargs.pop(\"lookup approximated learning scale\")\r\n start = kwargs.pop(\"lookup approximated learning start\")\r\n kwargs['approximated learning effect lookup'] = [self.approxLearning(x, speed, scale, start) for x in range(0, 101, 10)] \r\n \r\n super(ScarcityModel, self).run_model(kwargs)","title":""},{"docid":"6af6ffd1cf00f5021f8de2ef98d8f58e","score":"0.49905622","text":"def main(args):\n print('Reading galaxy catalog')\n halocat_galcat_merged = pd.read_hdf('../'+args.catalog_to_use,\\\n key='halocat_galcat_merged')\n print('Plotting SMHM')\n plot_SMHM(halocat_galcat_merged,args.host_halo_mass_to_plot,\\\n args.populate_mock_key)","title":""},{"docid":"75315723e31accaeefa82eab6f1de578","score":"0.498815","text":"def _configure(self):\n Component._configure(self)\n pylith.perf.Material.Material.__init__(self, self.inventory.label, self.inventory.size)\n return","title":""},{"docid":"c67776bc83722ed0d7c8fcef236986c7","score":"0.49846137","text":"def model():\n pass","title":""},{"docid":"b1240b372e0e828f142843a9972882e1","score":"0.49844307","text":"def __init__(self, name, parent=None):\n\t\t\n\t\tself.name = name\n\t\tconfig_data = courses_config[self.name]\n\t\t\n\t\tself.orgEntity = config_data['orgEntity']\n\t\tself.orgKey = config_data['orgKey']\n\t\t\n\t\tTopicRecord.dowrites = 1\n\t\tChapterRecord.dowrites = 1\n\t\t\n\t\tBackPackModelObject.__init__ (self, parent)\n\t\t\t\n\t\t# curriculum children are Unit instances\n\t\tself.curriculum = Curriculum (self.name)","title":""},{"docid":"8a56536094ca1303e2f6d5ae779e4ddc","score":"0.4982808","text":"def set_model_logic(start_serial, stop_serial, df):\n\n time_step_in_days = 2.0\n commodity = \"Rice\"\n\n model = Model(\n starttime=start_serial,\n stoptime=stop_serial,\n dt=1.0 * time_step_in_days,\n )\n\n # INITIALISE VARIABLES\n\n # DISPLACEMENT\n hp_pop = model.constant(\"Host Population\")\n idp_pop, idp_pop_iv = create_model_stock(model, \"IDP Population\")\n aap_pop, aap_pop_iv = create_model_stock(model, \"Affected Area Population\")\n disp_rate = model.flow(\"Displacement Rate\")\n ret_rate = model.flow(\"Return Rate\")\n\n t_disp = model.constant(\"Displacement Time\")\n t_ret = model.constant(\"Return Time\")\n danger = model.converter(\"Perceived Danger\")\n\n\n # FARMER\n # physical\n f_stock, f_stock_iv = create_model_stock(model, \"Farmer Stock\")\n f_prod = model.flow(\"Farmer Production\")\n f2t_vol = model.flow(\"Farmer to Trader Volume\")\n f_supply = model.converter(\"Farmer Supply\")\n f2t_demand = model.converter(\"Farmer to Trader Demand\")\n f2t_leadtime = model.constant(\"Farmer to Trader Leadtime\")\n f_loss = model.flow(\"Farmer Loss Rate\")\n t_f_loss = model.constant(\"Farmer Loss Time\")\n # price\n baseline_price = model.constant(\"Baseline Price\")\n f_ds_ratio = model.converter(\"Farmer Demand-to-Supply Ratio\")\n f_price = model.converter(\"Farmer Price\")\n # cash\n t2f_cashflow = model.flow(\"Trader to Farmer Cashflow\")\n\n # TRADER\n # physical\n t_stock, t_stock_iv = create_model_stock(model, \"Trader Stock\")\n t2w_vol = model.flow(\"Trader to Wholesaler Volume\")\n t_supply = model.converter(\"Trader Supply\")\n t2w_leadtime = model.constant(\"Trader to Wholesaler Leadtime\")\n # price\n t_ds_ratio = model.converter(\"Trader Demand-to-Supply Ratio\")\n t_price = model.converter(\"Trader Price\")\n # cash\n t_cash, t_cash_iv = create_model_stock(model, \"Trader Cash\")\n w2t_cashflow = model.flow(\"Wholesaler to Trader Cashflow\")\n\n wholesaler = SupplyChainActor(\n model,\n \"Wholesaler\",\n upstream_actor=\"Trader\",\n downstream_actor=\"Retailer\"\n )\n\n retailer = SupplyChainActor(\n model,\n \"Retailer\",\n upstream_actor=\"Wholesaler\",\n downstream_actor=\"Consumer\"\n )\n\n retailer.connect_to_upstream(wholesaler)\n\n # CONSUMER\n hp_income_baseline = model.constant(\"Host Population Income Baseline\")\n idp_income_baseline = model.constant(\"IDP Income Baseline\")\n c_needs = model.constant(f\"{commodity} Needs Per Capita\")\n c_max_income_frac = model.constant(f\"Maximum Fraction of Income Spent on {commodity}\")\n hp_income = model.converter(\"Host Population Income\")\n idp_income = model.converter(\"IDP Income\")\n hp_pc_demand = model.converter(\"HP Per Capita Demand\")\n idp_pc_demand = model.converter(\"IDP Per Capita Demand\")\n hp_demand = model.converter(\"HP Demand\")\n idp_demand = model.converter(\"IDP Demand\")\n c_demand = model.converter(\"Retailer to Consumer Demand\")\n\n # DATA\n data_prod_usda = create_model_data_variable(model, df, \"Production (USDA)\")\n data_deaths_ucdp = create_model_data_variable(model, df, \"Deaths (UCDP)\")\n data_ip_iom = create_model_data_variable(model, df, \"IDP Population (IOM)\")\n data_ruralinflation_nbs = create_model_data_variable(model, df, \"Rural Inflation\")\n\n # CONNECT STOCKS AND FLOWS\n\n # displacement\n idp_pop.equation = disp_rate - ret_rate\n aap_pop.equation = ret_rate - disp_rate\n\n # physical\n f_stock.equation = f_prod - f2t_vol - f_loss\n t_stock.equation = f2t_vol - t2w_vol\n\n # cash\n t_cash.equation = w2t_cashflow - t2f_cashflow\n\n # DEFINE THE REST\n\n # displacement\n danger.equation = smooth_model_variable(model, data_deaths_ucdp, 30.0, 0.0) / 90.0\n disp_rate.equation = aap_pop * danger / t_disp\n ret_rate.equation = idp_pop * (1.0 - danger) / t_ret\n\n # farmer\n f_prod.equation = data_prod_usda\n f2t_vol.equation = sd.min(f2t_demand, f_supply)\n f_ds_ratio.equation = f2t_demand / f_supply\n f_price.equation = baseline_price\n f2t_demand.equation = wholesaler.demand()\n f_supply.equation = f_stock / f2t_leadtime\n f_loss.equation = f_stock / t_f_loss\n\n # trader\n t2w_vol.equation = sd.min(wholesaler.demand(), t_supply)\n t_ds_ratio.equation = wholesaler.demand() / t_supply\n t_price.equation = f_price\n t_supply.equation = t_stock / t2w_leadtime\n t2f_cashflow.equation = f_price * f2t_vol\n w2t_cashflow.equation = t_price * t2w_vol\n\n\n # consumer\n hp_income.equation = hp_income_baseline\n idp_income.equation = idp_income_baseline\n hp_pc_demand.equation = sd.min(c_needs, hp_income * c_max_income_frac / wholesaler.price())\n hp_demand.equation = hp_pc_demand * hp_pop\n idp_pc_demand.equation = sd.min(c_needs, idp_income * c_max_income_frac / wholesaler.price())\n idp_demand.equation = idp_pc_demand * idp_pop\n c_demand.equation = hp_demand + idp_demand\n\n retailer.demand().equation = c_demand\n\n # CONSTANTS\n\n # initial values\n idp_pop_iv.equation = 1500000.0\n aap_pop_iv.equation = 1900000.0\n f_stock_iv.equation = 100000.0\n t_stock_iv.equation = 100000.0\n t_cash_iv.equation = 0.0\n\n wholesaler.stock.initial_value = 100000.0\n wholesaler.cash.initial_value = 0.0\n retailer.stock.initial_value = 100000.0\n retailer.cash.initial_value = 0.0\n\n # time constants\n t_disp.equation = 30.0\n t_ret.equation = 6.0 * 30.0\n t_f_loss.equation = 7.0\n\n # leadtimes\n f2t_leadtime.equation = 1.0\n t2w_leadtime.equation = 1.0\n wholesaler.leadtime.equation = 1.0\n retailer.leadtime.equation = 1.0\n\n # others\n baseline_price.equation = 200.0\n\n # consumer\n hp_pop.equation = 1000000.0\n c_max_income_frac.equation = 0.5\n c_needs.equation = 10.0 / 30.0\n hp_income_baseline.equation = 4000.0 / 30.0\n idp_income_baseline.equation = 2000.0 / 30.0\n\n retailer.connect_eggs(wholesaler)\n\n return model","title":""},{"docid":"c06bb52057148e139bbda0742193149d","score":"0.4982488","text":"def __init__(self, species, qty):\n super(DomesticMelonOrder, self).__init__(species, qty)\n self.tax = 0.08","title":""},{"docid":"04e0bbf703ec278076d432ada05de292","score":"0.49807692","text":"def __init__(self, mu, sigma, corr, rf, risk_aversion=None, short_sell=True):\n\n # Asseert data indexes and organize\n self._assert_indexes(mu, sigma, corr)\n\n # Save inputs as attributes\n self.mu = mu\n self.sigma = sigma\n self.corr = corr\n self.rf = rf\n self.risk_aversion = risk_aversion\n self.short_selling = short_sell\n\n # Compute atributes\n self.n_assets = self._n_assets()\n self.cov = self._get_cov_matrix()\n\n # Get the optimal risky porfolio\n self.mu_p, self.sigma_p, self.risky_weights, self.sharpe_p = self._get_optimal_risky_portfolio()\n\n # get the minimal variance portfolio\n self.mu_mv, self.sigma_mv, self.mv_weights, self.sharpe_mv = self._get_minimal_variance_portfolio()\n\n # Get the investor's portfolio and build the complete set of weights\n self.weight_p, self.complete_weights, self.mu_c, self.sigma_c, self.certain_equivalent \\\n = self._investor_allocation()","title":""},{"docid":"51f23f93c80d32888dadf1994a0b0f31","score":"0.49710152","text":"def __init__(self,\n size: Size,\n stuffing: Stuffing = Stuffing.POLYESTER_Fiberfill,\n fabric: Fabric = Fabric.ACRYLIC,\n has_glow: bool = True,\n name: str = \"Dancing Skeleton\",\n description: str = \"Actually this skeleton is not terrible, it is cute as coco.\",\n product_id: str = \"\"\n ) -> None:\n self._check_input(size, stuffing, fabric, has_glow, name, description, product_id)\n if len(product_id) == 0:\n product_id = \"S%04dD\" % DancingSkeleton._generate_id\n self._increment_id()\n self._has_glow = has_glow\n super().__init__(name, description, product_id, size, stuffing, fabric)","title":""},{"docid":"d7691b30843657f3a37af9a89b211ecb","score":"0.49695474","text":"def main():\n env = bootstrap('../development.ini')\n settings = env['registry'].settings\n location_file_dir = settings.location_file_dir\n pearl_harbor_dir = os.path.join(location_file_dir, 'pearl_harbor')\n pearl_harbor_data = os.path.join(pearl_harbor_dir, 'data')\n location_file = os.path.join(pearl_harbor_dir, 'location.json')\n\n if os.path.exists(location_file):\n message = 'File already exists:\\n %s\\nRemove? (y/n) ' % location_file\n if raw_input(message).lower() == 'y':\n os.unlink(location_file)\n else:\n print 'Cancelled.'\n return\n\n model = settings.Model.create()\n\n # Map file path is relative to package root\n map_file = os.path.join('location_files', 'pearl_harbor', 'data',\n 'pearl_harbor.bna')\n model.add_bna_map(map_file, {\n 'refloat_halflife': 1 * 3600, # seconds\n })\n\n start_time = datetime(2013, 1, 1, 1, 0)\n model.time_step = 900\n model.start_time = start_time\n model.duration = timedelta(days=1)\n model.uncertain = False\n\n # adding a wind mover\n\n series = np.zeros((3,), dtype=gnome.basic_types.datetime_value_2d)\n series[0] = (start_time, ( 4, 180) )\n series[1] = (start_time+timedelta(hours=12), ( 2, 270) )\n series[2] = (start_time+timedelta(hours=24), ( 4, 180) )\n \n wind = WebWind(timeseries=series, units='knots')\n model.environment += wind\n w_mover = WebWindMover(wind)\n model.movers += w_mover\n\n # adding a random mover\n random_mover = WebRandomMover(diffusion_coef=10000)\n model.movers += random_mover\n\n # adding a grid current mover:\n\n curr_file=os.path.join( pearl_harbor_data, \"ch3d2013.nc\")\n topology_file=os.path.join( pearl_harbor_data, \"PearlHarborTop.dat\")\n model.movers += gnome.movers.GridCurrentMover(curr_file,topology_file)\n\n\n # adding a spill\n\n spill = WebPointSourceRelease(num_elements=1000,\n start_position=(\n -157.97064, 21.331524, 0.0),\n release_time=start_time)\n\n model.spills += spill\n\n serialized_model = ModelSchema().bind().serialize(model.to_dict())\n model_json = json.dumps(serialized_model, default=util.json_encoder,\n indent=4)\n\n with open(location_file, 'wb') as f:\n f.write(model_json)\n\n env['closer']()","title":""},{"docid":"745e24caf916ae6f0eb397731c90d1d7","score":"0.4967229","text":"def initCatalog():\n # catalog es utilizado para interactuar con el modelo\n catalog = model.newCatalog()\n return catalog","title":""},{"docid":"745e24caf916ae6f0eb397731c90d1d7","score":"0.4967229","text":"def initCatalog():\n # catalog es utilizado para interactuar con el modelo\n catalog = model.newCatalog()\n return catalog","title":""},{"docid":"52f7e9357ca7a239a92e68d1390c5df2","score":"0.49666688","text":"def doParametersOfInterest(self):\n\t\t# --- POI and other parameters ----\n\t\t\n\t\tmaxmix = {\n\t\t\t\"a_tilde\" : 1.0,\n\t\t\t\"b_tilde\" : 1.0,\n\t\t}\n\t\t\n\t\tself.modelBuilder.doVar(\"muF[1.0,0.0,5.0]\")\n\t\tself.modelBuilder.doVar(\"muV[1.0,0.0,5.0]\")\n\t\tself.modelBuilder.doVar(\"cpmixing[0.0,0.0,1.0]\") # CP mixing angle in units of pi/2\n\t\t\n\t\tself.modelBuilder.factory_('expr::cosalpha(\"cos(@0*{pi}/2)\", cpmixing)'.format(pi=math.pi))\n\t\tself.modelBuilder.factory_('expr::sinalpha(\"sin(@0*{pi}/2)\", cpmixing)'.format(pi=math.pi))\n\t\t\n\t\tself.modelBuilder.factory_('expr::a(\"@0\", cosalpha)')\n\t\tself.modelBuilder.factory_('expr::b(\"@0\", sinalpha)')\n\t\t\n\t\tself.modelBuilder.factory_('expr::sm_scaling(\"@0*@0-@0*@1*{a_tilde}/{b_tilde}\", a, b)'.format(**maxmix))\n\t\tself.modelBuilder.factory_('expr::ps_scaling(\"@1*@1-@0*@1*{b_tilde}/{a_tilde}\", a, b)'.format(**maxmix))\n\t\tself.modelBuilder.factory_('expr::mm_scaling(\"@0*@1/({a_tilde}*{b_tilde})\", a, b)'.format(**maxmix))\n\t\t\n\t\tfor production in [\"muF\", \"muV\"]:\n\t\t\tfor decay in [\"muF\"]:\n\t\t\t\tself.modelBuilder.factory_('expr::{production}_{decay}(\"@0*@1\", {production}, {decay})'.format(\n\t\t\t\t\t\tproduction=production, decay=decay)\n\t\t\t\t)\n\t\t\t\tfor cp in [\"sm_scaling\", \"ps_scaling\", \"mm_scaling\"]:\n\t\t\t\t\tself.modelBuilder.factory_('expr::{production}_{decay}_{cp}(\"@0*@1*@2\", {production}, {decay}, {cp})'.format(\n\t\t\t\t\t\t\tproduction=production, decay=decay, cp=cp)\n\t\t\t\t\t)\n\t\t\n\t\tself.modelBuilder.doSet(\"POI\", \"muF,muV,cpmixing\")","title":""},{"docid":"1c431bf8b97b271aab0bd250a8981500","score":"0.49630284","text":"def __init__(self,submodels):\n self.submodels = submodels\n self.obstypes = [obstype for submodel in submodels\n for obstype in submodel.obstypes]\n gauges = list()\n for submodel in submodels:\n gauges.extend(\n gauge for gauge in submodel.gauges if gauge not in gauges\n )\n super().__init__(gauges)","title":""},{"docid":"7321a20d59302f62a6127f10cc6c27af","score":"0.4953475","text":"def SBML_setSpecies(self):\n if self._debug:\n print('SBML_setSpecies')\n reagList = self.model_obj.__species__ + self.model_obj.__fixed_species__\n for reagent in range(len(reagList)):\n s = self.sbml_model.createSpecies()\n s.setId(reagList[reagent])\n s.setName(reagList[reagent])\n s.setCompartment(self.model_compartment_name)\n if reagList[reagent] in self.model_obj.__fixed_species__:\n s.setBoundaryCondition(True)\n s.setConstant(True)\n else:\n s.setBoundaryCondition(False)\n\n if reagent < len(self.model_obj.__species__):\n reagName = reagList[reagent] + '_init'\n else:\n reagName = reagList[reagent]\n\n if self.sbml_level == 1:\n s.setInitialAmount(getattr(self.model_obj, reagName))\n else:\n s.setInitialConcentration(getattr(self.model_obj, reagName))","title":""},{"docid":"413cc3068285fa798ccedccfa13621eb","score":"0.49522227","text":"def simple_model(neuron_instance):\n\n h, rxd, data, save_path = neuron_instance\n dend = h.Section(name='dend')\n dend.diam = 2\n dend.nseg = 5 \n dend.L = 5\n ecs = rxd.Extracellular(-10, -10, -10, 10, 10, 10, dx=3)\n cyt = rxd.Region(dend, name='cyt', nrn_region='i')\n k = rxd.Species([cyt, ecs], name='k', d=1, charge=1,\n initial=lambda nd: 140 if nd.region == cyt else 3)\n decay = rxd.Rate(k, -0.1*k)\n model = (dend, cyt, ecs, k, decay)\n yield (neuron_instance, model)","title":""},{"docid":"824acd1b90426fb7a92c88542e1c33e0","score":"0.49509418","text":"def generate_instance(self):\n config = self._generate_config()\n manager_stop = self._generate_manager()\n\n output_file = open(self.output_file,'w')\n text = \"VEHICLE\"\n output_file.write(text + \"\\n\")\n text = \"NUMBER CAPACTIY\"\n output_file.write(text + \"\\n\")\n output_file.write(\"150 \"+ str(config.capacity_cst) + \"\\n\")\n\n\n # config.dump_to_file_capacity(output_file)\n manager_stop.dump_to_file(output_file)","title":""},{"docid":"bf87f8a6bc4c0c1ba05b2913fe859fc2","score":"0.49496803","text":"def _construct_model(self, model):","title":""},{"docid":"f3a4b69a019519f5a7e93ad5996a7d49","score":"0.49493036","text":"def __init__(self, config):\n self.config = config # md control\n # self.model = model # dynamic variable & ff parameters\n return","title":""},{"docid":"7e11a39ffce7792f58a49a943eefbf13","score":"0.4947336","text":"def SBML_buildBasicModel(\n self,\n mod,\n filename,\n slvl=2,\n dir=None,\n substance=(1, 0),\n volume=(1, 0),\n time=(1, 0),\n arules=None,\n notes=None,\n ):\n self.SBML_createModel(mod, filename, slvl, dir)\n self.SBML_setCompartment()\n self.SBML_setNotes(txt=notes)\n self.SBML_setUnits(substance=substance, volume=volume, time=time)\n if arules != None:\n self.SBML_setAssignmentRules(arules)\n self.SBML_setSpecies()\n self.SBML_setReactions()\n self.SBML_setModel()","title":""},{"docid":"8fd4bb2e043b5c55db8595d6334db530","score":"0.49463186","text":"def gen_ssmodel(self):\n\t\tprint \"generating full neural model\"\n\n\t\t#Generate field meshgrid\n simulation_field_space_x,simulation_field_space_y=pb.meshgrid(self.simulation_space_x_y,self.simulation_space_x_y)\n\n\n K=0\n for i in range(len(self.kernel.Psi)):\n K+=self.kernel.weights[i]*self.kernel.Psi[i](simulation_field_space_x,simulation_field_space_y)\n \n self.K=K\n\n\n\t\t#calculate field disturbance covariance matrix and its Cholesky decomposition\n self.Sigma_e_c=sp.linalg.cholesky(self.Sigma_e,lower=1) \n\n #calculate Cholesky decomposition of observation noise covariance matrix\n Sigma_varepsilon_c=sp.linalg.cholesky(self.Sigma_varepsilon,lower=1)\n self.Sigma_varepsilon_c=Sigma_varepsilon_c\n\n #Calculate sensors at each spatial locations, it's not the same as C in the IDE model\t\n t0=time.time()\n gamma_space=pb.array(zip(simulation_field_space_x.flatten(),simulation_field_space_y.flatten()))\n N1,D1 = gamma_space.shape\n sensor_space=self.obs_locns\n N2,D2 = sensor_space.shape\n diff = sensor_space.reshape(N2,1,D2) - gamma_space.reshape(1,N1,D1)\n C=np.exp(-np.sum(np.square(diff),-1)*(1./self.sensor_kernel.width))\n self.C=C","title":""}],"string":"[\n {\n \"docid\": \"3193e2ab42a83b12b66740baeb711288\",\n \"score\": \"0.6471101\",\n \"text\": \"def Campbell15_blueprint(\\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \\n sec_galprop_key = 'ssfr', sec_haloprop_key = 'vpeak', \\n smhm_model=smhm_components.Moster13SmHm, \\n scatter_level = 0.2, \\n redshift = sim_defaults.default_redshift, **kwargs):\\n\\n stellar_mass_model = smhm_model(\\n prim_haloprop_key=prim_haloprop_key, redshift=redshift, \\n scatter_abcissa = [12], scatter_ordinates = [scatter_level])\\n\\n fake_mock = FakeMock(approximate_ngals = 1e5)\\n input_galaxy_table = fake_mock.galaxy_table\\n prim_galprop_bins = np.logspace(8, 12, num=15) \\n\\n ssfr_model = ConditionalAbunMatch(input_galaxy_table=input_galaxy_table, \\n prim_galprop_key=stellar_mass_model.galprop_key, \\n galprop_key=sec_galprop_key, \\n sec_haloprop_key=sec_haloprop_key, \\n prim_galprop_bins=prim_galprop_bins, \\n **kwargs)\\n blueprint = ({\\n stellar_mass_model.galprop_key: stellar_mass_model, \\n ssfr_model.galprop_key: ssfr_model, \\n 'mock_factory': SubhaloMockFactory})\\n\\n return blueprint\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412e98b8820f7be92caa064dc48f9eab\",\n \"score\": \"0.61549777\",\n \"text\": \"def create_model():\\n ###########################################################################\\n # Flowsheet and Property Package #\\n ###########################################################################\\n m = pyo.ConcreteModel(name=\\\"Steam Cycle Model\\\")\\n m.fs = FlowsheetBlock(default={\\\"dynamic\\\": False})\\n m.fs.prop_water = iapws95.Iapws95ParameterBlock(\\n default={\\\"phase_presentation\\\": iapws95.PhaseType.LG}\\n )\\n\\n m.fs.prop_water2 = iapws95.Iapws95ParameterBlock()\\n m.fs.therminol66 = ThermalOilParameterBlock()\\n\\n m.fs.charge_hx = HeatExchanger(\\n default={\\\"delta_temperature_callback\\\": delta_temperature_underwood_callback,\\n \\\"shell\\\": {\\\"property_package\\\": m.fs.prop_water2},\\n \\\"tube\\\": {\\\"property_package\\\": m.fs.therminol66},\\n \\\"flow_pattern\\\": HeatExchangerFlowPattern.countercurrent})\\n\\n m.fs.hp_splitter = HelmSplitter(default={\\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water2})\\n m.fs.ip_splitter = HelmSplitter(default={\\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water2})\\n\\n m.fs.storage_cooler = Heater(default={\\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water2,\\n \\\"has_pressure_change\\\": True})\\n \\n m.fs.hx_pump = WaterPump(default={\\\"property_package\\\": m.fs.prop_water2})\\n\\n # The enthalpy at the outlet of the cooler is required to be subcooled, that is,\\n # below the ehntalpy of saturation. This condition was selected instead of using\\n # temperatures, which cause certain difficulty in converging the model.\\n # return (m.fs.storage_cooler.control_volume.properties_out[0].temperature <= \\n # m.fs.storage_cooler.control_volume.properties_out[0].temperature_sat - 5)\\n @m.fs.storage_cooler.Constraint(m.fs.time)\\n def constraint_cooler_enth(b, t):\\n return (m.fs.storage_cooler.control_volume.properties_out[0].enth_mol <= \\n m.fs.storage_cooler.control_volume.properties_out[0].enth_mol_sat_phase['Liq'])\\n \\n ###########################################################################\\n # Turbine declarations #\\n ###########################################################################\\n\\n for i in range(9):\\n\\n turbine = HelmTurbineStage(\\n default={\\n \\\"property_package\\\": m.fs.prop_water2\\n }\\n )\\n setattr(m.fs, \\\"turbine_\\\" + str(i+1), turbine)\\n\\n ###########################################################################\\n # Boiler section declarations: #\\n ###########################################################################\\n # Boiler section is set up using two heater blocks, as following:\\n # 1) For the main steam the heater block is named 'boiler'\\n # 2) For the reheated steam the heater block is named 'reheater'\\n m.fs.boiler = Heater(\\n default={\\n \\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water,\\n \\\"has_pressure_change\\\": True\\n }\\n )\\n m.fs.reheater = Heater(\\n default={\\n \\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water,\\n \\\"has_pressure_change\\\": True\\n }\\n )\\n\\n # Outlet temperature of boiler is set to 866.15 K\\n @m.fs.boiler.Constraint(m.fs.time)\\n def boiler_temperature_constraint(b, t):\\n return b.control_volume.properties_out[t].temperature == 866.15 # K\\n\\n # Outlet temperature of reheater is set to 866.15 K\\n @m.fs.reheater.Constraint(m.fs.time)\\n def reheater_temperature_constraint(b, t):\\n return b.control_volume.properties_out[t].temperature == 866.15 # K\\n\\n ###########################################################################\\n # Add Condenser Mixer, Condenser, and Condensate pump #\\n ###########################################################################\\n # condenser mix\\n m.fs.condenser_mix = Mixer(\\n default={\\n \\\"momentum_mixing_type\\\": MomentumMixingType.none,\\n \\\"inlet_list\\\": [\\\"main\\\", \\\"bfpt\\\", \\\"drain\\\", \\\"makeup\\\"],\\n \\\"property_package\\\": m.fs.prop_water,\\n }\\n )\\n\\n # The inlet 'main' refers to the main steam coming from the turbine train\\n # Inlet 'bfpt' refers to the steam coming from the bolier feed pump turbine\\n # Inlet 'drain' refers to the condensed steam from the feed water heater 1\\n # Inlet 'makeup' refers to the make up water\\n # The outlet pressure of condenser mixer is equal to the minimum pressure\\n # Since the turbine (#9) outlet (or, mixer inlet 'main') pressure\\n # has the minimum pressure, the following constraint sets the outlet\\n # pressure of the condenser mixer to the pressure of the inlet 'main'\\n @m.fs.condenser_mix.Constraint(m.fs.time)\\n def mixer_pressure_constraint(b, t):\\n return b.main_state[t].pressure == b.mixed_state[t].pressure\\n\\n m.fs.condenser = CondenserHelm(default={\\\"shell\\\":{\\\"has_pressure_change\\\": False,\\n \\\"property_package\\\": m.fs.prop_water2},\\n \\\"tube\\\": {\\\"has_pressure_change\\\": False,\\n \\\"property_package\\\": m.fs.prop_water2}})\\n \\n iscale.set_scaling_factor(m.fs.condenser.side_1.heat, 1e-9)\\n iscale.set_scaling_factor(m.fs.condenser.side_2.heat, 1e-9)\\n\\n # condensate pump\\n m.fs.cond_pump = WaterPump(\\n default={\\n \\\"property_package\\\": m.fs.prop_water2,\\n }\\n )\\n ###########################################################################\\n # Feedwater heater declaration #\\n ###########################################################################\\n # Feed water heaters (FWHs) are declared as 0D heat exchangers\\n # Tube side is for feed water & Shell side is for steam condensing\\n # Pressure drop on both sides are accounted for by setting the respective\\n # outlet pressure based on the following assumptions:\\n # (1) Feed water side: A constant 4% pressure drop is assumed\\n # on the feedwater side for all FWHs. For this,\\n # the outlet pressure is set to 0.96 times the inlet pressure,\\n # on the feed water side for all FWHs\\n # (2) Steam condensing side: Going from high pressure to\\n # low pressure FWHs, the outlet pressure of\\n # the condensed steam in assumed to be 10% more than that\\n # of the pressure of steam extracted for the immediately\\n # next lower pressure feedwater heater.\\n # e.g. the outlet condensate pressure of FWH 'n'\\n # = 1.1 * pressure of steam extracted for FWH 'n-1'\\n # In case of FWH1 the FWH 'n-1' is used for Condenser,\\n # and in case of FWH6, FWH 'n-1' is for Deaerator. Here,\\n # the steam pressure for FWH 'n-1' is known because the\\n # pressure ratios for turbines are fixed.\\n # The condensing steam is assumed to leave the FWH as saturated liquid\\n # Thus, each FWH is accompanied by 3 constraints, 2 for pressure drop\\n # and 1 for the enthalpy.\\n\\n # Scaling factors for area and overall heat transfer coefficients for\\n # FWHs have all been set appropriately (user may change these values,\\n # if needed) if not set, the scaling factors = 1 (IDAES default)\\n\\n ###########################################################################\\n # DEFINITION OF FEED WATER HEATERS MIXERS\\n ###########################################################################\\n FWH_Mixers_list = ['fwh1_mix', 'fwh2_mix', 'fwh3_mix', 'fwh6_mix']\\n\\n for i in FWH_Mixers_list:\\n FWH_Mixer = Mixer(\\n default={\\n \\\"momentum_mixing_type\\\": MomentumMixingType.none,\\n \\\"inlet_list\\\": [\\\"steam\\\", \\\"drain\\\"],\\n \\\"property_package\\\": m.fs.prop_water,\\n }\\n )\\n setattr(m.fs, i, FWH_Mixer)\\n\\n m.fs.fwh7_mix = Mixer(\\n default={\\n \\\"momentum_mixing_type\\\": MomentumMixingType.none,\\n \\\"inlet_list\\\": [\\\"steam\\\", \\\"drain\\\", \\\"from_hx_pump\\\"],\\n \\\"property_package\\\": m.fs.prop_water,\\n }\\n )\\n \\n m.fs.bfp_mix = Mixer(\\n default={\\n \\\"momentum_mixing_type\\\": MomentumMixingType.none,\\n \\\"inlet_list\\\": [\\\"from_bfp\\\", \\\"from_hx_pump\\\"],\\n \\\"property_package\\\": m.fs.prop_water,\\n }\\n )\\n # @m.fs.hx_pump.Constraint(m.fs.time)\\n # def hx_pump_pressure_out(b, t):\\n # return (m.fs.hx_pump.control_volume.properties_out[0.0].pressure == \\n # m.fs.fwh7_mix.steam_state[0].pressure*1.15)\\n ###########################################################################\\n # DEFINITION OF OUTLET PRESSURE OF FEED WATER HEATERS MIXERS\\n ###########################################################################\\n\\n # The outlet pressure of an FWH mixer is equal to the minimum pressure\\n # Since the pressure of mixer inlet 'steam' has the minimum pressure,\\n # the following constraints set the outlet pressure of FWH mixers to be same\\n # as the pressure of the inlet 'steam'\\n\\n def fwhmixer_pressure_constraint(b, t):\\n return b.steam_state[t].pressure == b.mixed_state[t].pressure\\n\\n for i in FWH_Mixers_list:\\n setattr(getattr(m.fs, i), \\\"mixer_pressure_constraint\\\", pyo.Constraint(m.fs.config.time, rule=fwhmixer_pressure_constraint))\\n\\n @m.fs.fwh7_mix.Constraint(m.fs.time)\\n def fwh7mixer_pressure_constraint(b, t):\\n return b.steam_state[t].pressure == b.mixed_state[t].pressure\\n \\n @m.fs.bfp_mix.Constraint(m.fs.time)\\n def bfp_mix_pressure_constraint(b, t):\\n return b.from_bfp_state[t].pressure == b.mixed_state[t].pressure\\n ###########################################################################\\n # DEFINITION OF FEED WATER HEATERS\\n ###########################################################################\\n FWH_list = ['fwh1', 'fwh2', 'fwh3', 'fwh4', 'fwh6', 'fwh7', 'fwh8']\\n\\n for i in FWH_list:\\n FWH = HeatExchanger(\\n default={\\n \\\"delta_temperature_callback\\\": delta_temperature_underwood_callback,\\n \\\"shell\\\": {\\n \\\"property_package\\\": m.fs.prop_water,\\n \\\"material_balance_type\\\": MaterialBalanceType.componentTotal,\\n \\\"has_pressure_change\\\": True,\\n },\\n \\\"tube\\\": {\\n \\\"property_package\\\": m.fs.prop_water,\\n \\\"material_balance_type\\\": MaterialBalanceType.componentTotal,\\n \\\"has_pressure_change\\\": True,\\n },\\n }\\n )\\n setattr(m.fs, i, FWH)\\n\\n ###########################################################################\\n # SETTING SCALING FACTORS FOR AREA AND HEAT TRANSFER COEFFICIENT\\n ###########################################################################\\n\\n for i in FWH_list:\\n c = getattr(m.fs, i)\\n iscale.set_scaling_factor(getattr(c, \\\"area\\\"), 1e-2)\\n iscale.set_scaling_factor(getattr(c, \\\"overall_heat_transfer_coefficient\\\"), 1e-3)\\n\\n ###########################################################################\\n # Setting the outlet enthalpy of condensate in an FWH to be same as saturated liquid\\n ###########################################################################\\n def fwh_vaporfrac_constraint(b, t):\\n return (\\n b.side_1.properties_out[t].enth_mol\\n == b.side_1.properties_out[t].enth_mol_sat_phase['Liq'])\\n\\n for i in FWH_list:\\n setattr(getattr(m.fs, i), i + \\\"_vaporfrac_constraint\\\", pyo.Constraint(m.fs.time, rule=fwh_vaporfrac_constraint))\\n\\n ###########################################################################\\n # Setting a 4% pressure drop on the feedwater side (P_out = 0.96 * P_in)\\n ###########################################################################\\n\\n def fwh_s2pdrop_constraint(b, t):\\n return (\\n b.side_2.properties_out[t].pressure\\n == 0.96 * b.side_2.properties_in[t].pressure)\\n\\n for i in FWH_list:\\n setattr(getattr(m.fs, i), i + \\\"_s2pdrop_constraint\\\", pyo.Constraint(m.fs.time, rule=fwh_s2pdrop_constraint))\\n\\n ###########################################################################\\n # Setting the outlet pressure of condensate to be 10% more than that of\\n # steam routed to condenser, as described in FWH description\\n ###########################################################################\\n # FWH1: 0.5 is the pressure ratio for turbine #9 (see set_inputs)\\n # FWH2: 0.64^2 is the pressure ratio for turbine #8 (see set_inputs)\\n # FWH3: 0.64^2 is the pressure ratio for turbine #7 (see set_inputs)\\n # FWH4: 0.64^2 is the pressure ratio for turbine #6 (see set_inputs)\\n # FWH6: 0.79^6 is the pressure ratio for turbine #4 (see set_inputs)\\n # FWH7: 0.79^4 is the pressure ratio for turbine #3 (see set_inputs)\\n # FWH8: 0.8^2 is the pressure ratio for turbine #2 (see set_inputs)\\n \\n pressure_ratio_list = { 'fwh1': 0.5,\\n 'fwh2': 0.64**2,\\n 'fwh3': 0.64**2,\\n 'fwh4': 0.64**2,\\n 'fwh6': 0.79**6,\\n 'fwh7': 0.79**4,\\n 'fwh8': 0.8**2}\\n \\n def fwh_s1pdrop_constraint(b, t):\\n return (\\n b.side_1.properties_out[t].pressure\\n == 1.1 * b.turbine_pressure_ratio * b.side_1.properties_in[t].pressure)\\n\\n for i in FWH_list:\\n b = getattr(m.fs, i)\\n b.turbine_pressure_ratio = pyo.Param(initialize = pressure_ratio_list[i])\\n setattr(b, i+\\\"_s1pdrop_constraint\\\", pyo.Constraint(m.fs.config.time, rule=fwh_s1pdrop_constraint))\\n\\n ###########################################################################\\n # Add deaerator and boiler feed pump (BFP) #\\n ###########################################################################\\n m.fs.fwh5_da = Mixer(\\n default={\\n \\\"momentum_mixing_type\\\": MomentumMixingType.none,\\n \\\"inlet_list\\\": [\\\"steam\\\", \\\"drain\\\", \\\"feedwater\\\"],\\n \\\"property_package\\\": m.fs.prop_water,\\n }\\n )\\n\\n # The outlet pressure of deaerator is equal to the minimum pressure\\n # Since the pressure of deaerator inlet 'feedwater' has\\n # the minimum pressure, the following constraint sets the outlet pressure\\n # of deaerator to be same as the pressure of the inlet 'feedwater'\\n @m.fs.fwh5_da.Constraint(m.fs.time)\\n def fwh5mixer_pressure_constraint(b, t):\\n return b.feedwater_state[t].pressure == b.mixed_state[t].pressure\\n\\n m.fs.bfp = WaterPump(\\n default={\\n \\\"property_package\\\": m.fs.prop_water2,\\n }\\n )\\n m.fs.bfpt = HelmTurbineStage(\\n default={\\n \\\"property_package\\\": m.fs.prop_water2,\\n }\\n )\\n\\n # The following constraint sets the outlet pressure of steam extracted\\n # for boiler feed water turbine to be same as that of condenser\\n @m.fs.Constraint(m.fs.time)\\n def constraint_out_pressure(b, t):\\n return (\\n b.bfpt.control_volume.properties_out[t].pressure\\n == b.condenser_mix.mixed_state[t].pressure\\n )\\n\\n # The following constraint demands that the work done by the\\n # boiler feed water pump is same as that of boiler feed water turbine\\n # Essentially, this says that boiler feed water turbine produces just\\n # enough power to meet the demand of boiler feed water pump\\n @m.fs.Constraint(m.fs.time)\\n def constraint_bfp_power(b, t):\\n return (\\n b.bfp.control_volume.work[t] + b.bfpt.control_volume.work[t]\\n == 0\\n )\\n\\n ###########################################################################\\n # Turbine outlet splitter constraints #\\n ###########################################################################\\n # Equality constraints have been written as following to define\\n # the split fractions within the turbine train\\n\\n splitter_list = ['t1_splitter', 't2_splitter', 't3_splitter', 't5_splitter', 't6_splitter', 't7_splitter', 't8_splitter']\\n\\n for i in splitter_list:\\n\\n Splitter = HelmSplitter(default={\\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water})\\n setattr(m.fs, i, Splitter)\\n \\n m.fs.t4_splitter = HelmSplitter(default={\\\"dynamic\\\": False,\\n \\\"property_package\\\": m.fs.prop_water,\\n \\\"num_outlets\\\": 3})\\n\\n # The power plant with storage for a charge scenario is now ready\\n # Declaraing a plant power out variable for easy analysis of various\\n # design and operating scenarios\\n m.fs.plant_power_out = pyo.Var(\\n m.fs.time,\\n domain=pyo.Reals,\\n initialize=620,\\n doc=\\\"Net Power MWe out from the power plant\\\"\\n )\\n\\n # Constraint on Plant Power Output\\n # Plant Power Out = Turbine Power - Power required for HX Pump\\n @m.fs.Constraint(m.fs.time)\\n def production_cons(b, t):\\n return (\\n (-1*(m.fs.turbine_1.work_mechanical[t]\\n + m.fs.turbine_2.work_mechanical[t]\\n + m.fs.turbine_3.work_mechanical[t]\\n + m.fs.turbine_4.work_mechanical[t]\\n + m.fs.turbine_5.work_mechanical[t]\\n + m.fs.turbine_6.work_mechanical[t]\\n + m.fs.turbine_7.work_mechanical[t]\\n + m.fs.turbine_8.work_mechanical[t]\\n + m.fs.turbine_9.work_mechanical[t])\\n ) * 1e-6\\n == m.fs.plant_power_out[t]\\n )\\n\\n ###########################################################################\\n # Create the stream Arcs and return the model #\\n ###########################################################################\\n _create_arcs(m)\\n pyo.TransformationFactory(\\\"network.expand_arcs\\\").apply_to(m.fs)\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22c8c809f4bcbb7a6755fae6d9c80968\",\n \"score\": \"0.61260045\",\n \"text\": \"def main():\\n stage = Usd.Stage.CreateInMemory()\\n stage.GetRootLayer().documentation = (\\n \\\"This is an example of setting a Model Prim kind\\\"\\n )\\n\\n sphere1 = UsdGeom.Sphere.Define(stage, \\\"/SomeSphere\\\")\\n Usd.ModelAPI(sphere1).SetKind(Kind.Tokens.component)\\n sphere2 = UsdGeom.Sphere.Define(stage, \\\"/SomeSphere/SphereChild\\\")\\n Usd.ModelAPI(sphere2).SetKind(Kind.Tokens.subcomponent)\\n sphere3 = UsdGeom.Sphere.Define(stage, \\\"/SomeSphere/Foo\\\")\\n Usd.ModelAPI(sphere3).SetKind(\\\"does_not_exist\\\")\\n sphere3.GetPrim().SetMetadata(\\n \\\"comment\\\",\\n \\\"XXX: This kind is made up. But it could be real if we added to the KindRegistry\\\\n\\\"\\n \\\"https://graphics.pixar.com/usd/docs/api/class_kind_registry.html\\\",\\n )\\n\\n print(stage.GetRootLayer().ExportToString())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f54114e099db8df2eeefbae3e2bfbb70\",\n \"score\": \"0.58137923\",\n \"text\": \"def __init__(self, catalog_version, catalog_path=None):\\n if catalog_path is None:\\n catalog_path = (f'/home/mike/research/'\\n 'ac6_microburst_scale_sizes'\\n '/data/coincident_microbursts_catalogues/'\\n 'AC6_coincident_microbursts_sorted_'\\n f'Brady_v{catalog_version}.txt')\\n # Load catalog.\\n self.microburst_catalog = pd.read_csv(catalog_path)\\n self.model = IRBEM.MagFields(kext='OPQ77')\\n # print(f'Number of microbursts {self.microburst_catalog.shape[0]}')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8341d1942ab1d33ea5faba68460cb35\",\n \"score\": \"0.5692982\",\n \"text\": \"def __init__(self,description=\\\"My Model\\\\n Is here.\\\",\\n geometry={\\\"wiresegs\\\":[] # A straight list of the wiresegments\\n },\\n excitation=None,\\n freq=LinearFrequencyRange(),\\n radiationPattern=None,\\n computeCharges=True):\\n attributesFromDict(locals())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d3b922eb2e50f0f3f650974e8bb455\",\n \"score\": \"0.5619619\",\n \"text\": \"def company_skeleton():\\n\\n E, P, B, D, F = EPBDF()\\n entities = ['Paul', 'Roger', 'Quinn', 'Sally', 'Thomas',\\n 'Case', 'Adapter', 'Laptop', 'Tablet', 'Smartphone',\\n 'Accessories', 'Devices']\\n\\n entity_types = {'Paul': E, 'Roger': E, 'Quinn': E, 'Sally': E, 'Thomas': E,\\n 'Case': P, 'Adapter': P, 'Laptop': P, 'Tablet': P, 'Smartphone': P,\\n 'Accessories': B, 'Devices': B}\\n skeleton = RelationalSkeleton(company_schema(), True)\\n p, r, q, s, t, c, a, l, ta, sm, ac, d = ents = tuple([SkItem(e, entity_types[e]) for e in entities])\\n skeleton.add_entities(*ents)\\n for emp, prods in ((p, {c}), (q, {c, a, l}), (s, {l, ta}), (t, {sm, ta}), (r, {l})):\\n for prod in prods:\\n skeleton.add_relationship(SkItem(emp.name + '-' + prod.name, D), {emp, prod})\\n for biz, prods in ((ac, {c, a}), (d, {l, ta, sm})):\\n for prod in prods:\\n skeleton.add_relationship(SkItem(biz.name + '-' + prod.name, F), {biz, prod})\\n\\n return skeleton\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628dae5ba9c89d6e4a015de048634080\",\n \"score\": \"0.5599956\",\n \"text\": \"def __init__(self, design):\\n super(ols_model, self).__init__()\\n self.initialize(design)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6478bc8f50257f1ec8feb0d35d0f34f7\",\n \"score\": \"0.5526171\",\n \"text\": \"def __init__(self, model, colour, serial):\\n self.model = model\\n self.colour = colour\\n self.serial = serial\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f0c8476fbe7de141e200de50dd4ee7b\",\n \"score\": \"0.5524039\",\n \"text\": \"def setupModelInstance(self, geom, dssatexe):\\n return super(Model, self).setupModelInstance(geom, \\\"DSSAT_Ex.exe\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d6ebfdfcbac8c858c3c34cd87e0339\",\n \"score\": \"0.5518273\",\n \"text\": \"def apcupsd_model(self):\\n self.writeCommand('apcupsd_model')\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ad82bc902d315973108e63404d969c\",\n \"score\": \"0.5479451\",\n \"text\": \"def pwr_core():\\n model = openmc.model.Model()\\n\\n # Define materials.\\n fuel = openmc.Material(1, name='UOX fuel')\\n fuel.set_density('g/cm3', 10.062)\\n fuel.add_nuclide('U234', 4.9476e-6)\\n fuel.add_nuclide('U235', 4.8218e-4)\\n fuel.add_nuclide('U238', 2.1504e-2)\\n fuel.add_nuclide('Xe135', 1.0801e-8)\\n fuel.add_nuclide('O16', 4.5737e-2)\\n\\n clad = openmc.Material(2, name='Zircaloy')\\n clad.set_density('g/cm3', 5.77)\\n clad.add_nuclide('Zr90', 0.5145)\\n clad.add_nuclide('Zr91', 0.1122)\\n clad.add_nuclide('Zr92', 0.1715)\\n clad.add_nuclide('Zr94', 0.1738)\\n clad.add_nuclide('Zr96', 0.0280)\\n\\n cold_water = openmc.Material(3, name='Cold borated water')\\n cold_water.set_density('atom/b-cm', 0.07416)\\n cold_water.add_nuclide('H1', 2.0)\\n cold_water.add_nuclide('O16', 1.0)\\n cold_water.add_nuclide('B10', 6.490e-4)\\n cold_water.add_nuclide('B11', 2.689e-3)\\n cold_water.add_s_alpha_beta('c_H_in_H2O')\\n\\n hot_water = openmc.Material(4, name='Hot borated water')\\n hot_water.set_density('atom/b-cm', 0.06614)\\n hot_water.add_nuclide('H1', 2.0)\\n hot_water.add_nuclide('O16', 1.0)\\n hot_water.add_nuclide('B10', 6.490e-4)\\n hot_water.add_nuclide('B11', 2.689e-3)\\n hot_water.add_s_alpha_beta('c_H_in_H2O')\\n\\n rpv_steel = openmc.Material(5, name='Reactor pressure vessel steel')\\n rpv_steel.set_density('g/cm3', 7.9)\\n rpv_steel.add_nuclide('Fe54', 0.05437098, 'wo')\\n rpv_steel.add_nuclide('Fe56', 0.88500663, 'wo')\\n rpv_steel.add_nuclide('Fe57', 0.0208008, 'wo')\\n rpv_steel.add_nuclide('Fe58', 0.00282159, 'wo')\\n rpv_steel.add_nuclide('Ni58', 0.0067198, 'wo')\\n rpv_steel.add_nuclide('Ni60', 0.0026776, 'wo')\\n rpv_steel.add_nuclide('Mn55', 0.01, 'wo')\\n rpv_steel.add_nuclide('Cr52', 0.002092475, 'wo')\\n rpv_steel.add_nuclide('C0', 0.0025, 'wo')\\n rpv_steel.add_nuclide('Cu63', 0.0013696, 'wo')\\n\\n lower_rad_ref = openmc.Material(6, name='Lower radial reflector')\\n lower_rad_ref.set_density('g/cm3', 4.32)\\n lower_rad_ref.add_nuclide('H1', 0.0095661, 'wo')\\n lower_rad_ref.add_nuclide('O16', 0.0759107, 'wo')\\n lower_rad_ref.add_nuclide('B10', 3.08409e-5, 'wo')\\n lower_rad_ref.add_nuclide('B11', 1.40499e-4, 'wo')\\n lower_rad_ref.add_nuclide('Fe54', 0.035620772088, 'wo')\\n lower_rad_ref.add_nuclide('Fe56', 0.579805982228, 'wo')\\n lower_rad_ref.add_nuclide('Fe57', 0.01362750048, 'wo')\\n lower_rad_ref.add_nuclide('Fe58', 0.001848545204, 'wo')\\n lower_rad_ref.add_nuclide('Ni58', 0.055298376566, 'wo')\\n lower_rad_ref.add_nuclide('Mn55', 0.0182870, 'wo')\\n lower_rad_ref.add_nuclide('Cr52', 0.145407678031, 'wo')\\n lower_rad_ref.add_s_alpha_beta('c_H_in_H2O')\\n\\n upper_rad_ref = openmc.Material(7, name='Upper radial reflector / Top plate region')\\n upper_rad_ref.set_density('g/cm3', 4.28)\\n upper_rad_ref.add_nuclide('H1', 0.0086117, 'wo')\\n upper_rad_ref.add_nuclide('O16', 0.0683369, 'wo')\\n upper_rad_ref.add_nuclide('B10', 2.77638e-5, 'wo')\\n upper_rad_ref.add_nuclide('B11', 1.26481e-4, 'wo')\\n upper_rad_ref.add_nuclide('Fe54', 0.035953677186, 'wo')\\n upper_rad_ref.add_nuclide('Fe56', 0.585224740891, 'wo')\\n upper_rad_ref.add_nuclide('Fe57', 0.01375486056, 'wo')\\n upper_rad_ref.add_nuclide('Fe58', 0.001865821363, 'wo')\\n upper_rad_ref.add_nuclide('Ni58', 0.055815129186, 'wo')\\n upper_rad_ref.add_nuclide('Mn55', 0.0184579, 'wo')\\n upper_rad_ref.add_nuclide('Cr52', 0.146766614995, 'wo')\\n upper_rad_ref.add_s_alpha_beta('c_H_in_H2O')\\n\\n bot_plate = openmc.Material(8, name='Bottom plate region')\\n bot_plate.set_density('g/cm3', 7.184)\\n bot_plate.add_nuclide('H1', 0.0011505, 'wo')\\n bot_plate.add_nuclide('O16', 0.0091296, 'wo')\\n bot_plate.add_nuclide('B10', 3.70915e-6, 'wo')\\n bot_plate.add_nuclide('B11', 1.68974e-5, 'wo')\\n bot_plate.add_nuclide('Fe54', 0.03855611055, 'wo')\\n bot_plate.add_nuclide('Fe56', 0.627585036425, 'wo')\\n bot_plate.add_nuclide('Fe57', 0.014750478, 'wo')\\n bot_plate.add_nuclide('Fe58', 0.002000875025, 'wo')\\n bot_plate.add_nuclide('Ni58', 0.059855207342, 'wo')\\n bot_plate.add_nuclide('Mn55', 0.0197940, 'wo')\\n bot_plate.add_nuclide('Cr52', 0.157390026871, 'wo')\\n bot_plate.add_s_alpha_beta('c_H_in_H2O')\\n\\n bot_nozzle = openmc.Material(9, name='Bottom nozzle region')\\n bot_nozzle.set_density('g/cm3', 2.53)\\n bot_nozzle.add_nuclide('H1', 0.0245014, 'wo')\\n bot_nozzle.add_nuclide('O16', 0.1944274, 'wo')\\n bot_nozzle.add_nuclide('B10', 7.89917e-5, 'wo')\\n bot_nozzle.add_nuclide('B11', 3.59854e-4, 'wo')\\n bot_nozzle.add_nuclide('Fe54', 0.030411411144, 'wo')\\n bot_nozzle.add_nuclide('Fe56', 0.495012237964, 'wo')\\n bot_nozzle.add_nuclide('Fe57', 0.01163454624, 'wo')\\n bot_nozzle.add_nuclide('Fe58', 0.001578204652, 'wo')\\n bot_nozzle.add_nuclide('Ni58', 0.047211231662, 'wo')\\n bot_nozzle.add_nuclide('Mn55', 0.0156126, 'wo')\\n bot_nozzle.add_nuclide('Cr52', 0.124142524198, 'wo')\\n bot_nozzle.add_s_alpha_beta('c_H_in_H2O')\\n\\n top_nozzle = openmc.Material(10, name='Top nozzle region')\\n top_nozzle.set_density('g/cm3', 1.746)\\n top_nozzle.add_nuclide('H1', 0.0358870, 'wo')\\n top_nozzle.add_nuclide('O16', 0.2847761, 'wo')\\n top_nozzle.add_nuclide('B10', 1.15699e-4, 'wo')\\n top_nozzle.add_nuclide('B11', 5.27075e-4, 'wo')\\n top_nozzle.add_nuclide('Fe54', 0.02644016154, 'wo')\\n top_nozzle.add_nuclide('Fe56', 0.43037146399, 'wo')\\n top_nozzle.add_nuclide('Fe57', 0.0101152584, 'wo')\\n top_nozzle.add_nuclide('Fe58', 0.00137211607, 'wo')\\n top_nozzle.add_nuclide('Ni58', 0.04104621835, 'wo')\\n top_nozzle.add_nuclide('Mn55', 0.0135739, 'wo')\\n top_nozzle.add_nuclide('Cr52', 0.107931450781, 'wo')\\n top_nozzle.add_s_alpha_beta('c_H_in_H2O')\\n\\n top_fa = openmc.Material(11, name='Top of fuel assemblies')\\n top_fa.set_density('g/cm3', 3.044)\\n top_fa.add_nuclide('H1', 0.0162913, 'wo')\\n top_fa.add_nuclide('O16', 0.1292776, 'wo')\\n top_fa.add_nuclide('B10', 5.25228e-5, 'wo')\\n top_fa.add_nuclide('B11', 2.39272e-4, 'wo')\\n top_fa.add_nuclide('Zr90', 0.43313403903, 'wo')\\n top_fa.add_nuclide('Zr91', 0.09549277374, 'wo')\\n top_fa.add_nuclide('Zr92', 0.14759527104, 'wo')\\n top_fa.add_nuclide('Zr94', 0.15280552077, 'wo')\\n top_fa.add_nuclide('Zr96', 0.02511169542, 'wo')\\n top_fa.add_s_alpha_beta('c_H_in_H2O')\\n\\n bot_fa = openmc.Material(12, name='Bottom of fuel assemblies')\\n bot_fa.set_density('g/cm3', 1.762)\\n bot_fa.add_nuclide('H1', 0.0292856, 'wo')\\n bot_fa.add_nuclide('O16', 0.2323919, 'wo')\\n bot_fa.add_nuclide('B10', 9.44159e-5, 'wo')\\n bot_fa.add_nuclide('B11', 4.30120e-4, 'wo')\\n bot_fa.add_nuclide('Zr90', 0.3741373658, 'wo')\\n bot_fa.add_nuclide('Zr91', 0.0824858164, 'wo')\\n bot_fa.add_nuclide('Zr92', 0.1274914944, 'wo')\\n bot_fa.add_nuclide('Zr94', 0.1319920622, 'wo')\\n bot_fa.add_nuclide('Zr96', 0.0216912612, 'wo')\\n bot_fa.add_s_alpha_beta('c_H_in_H2O')\\n\\n # Define the materials file.\\n model.materials = (fuel, clad, cold_water, hot_water, rpv_steel,\\n lower_rad_ref, upper_rad_ref, bot_plate,\\n bot_nozzle, top_nozzle, top_fa, bot_fa)\\n\\n # Define surfaces.\\n s1 = openmc.ZCylinder(r=0.41, surface_id=1)\\n s2 = openmc.ZCylinder(r=0.475, surface_id=2)\\n s3 = openmc.ZCylinder(r=0.56, surface_id=3)\\n s4 = openmc.ZCylinder(r=0.62, surface_id=4)\\n s5 = openmc.ZCylinder(r=187.6, surface_id=5)\\n s6 = openmc.ZCylinder(r=209.0, surface_id=6)\\n s7 = openmc.ZCylinder(r=229.0, surface_id=7)\\n s8 = openmc.ZCylinder(r=249.0, surface_id=8, boundary_type='vacuum')\\n\\n s31 = openmc.ZPlane(z0=-229.0, surface_id=31, boundary_type='vacuum')\\n s32 = openmc.ZPlane(z0=-199.0, surface_id=32)\\n s33 = openmc.ZPlane(z0=-193.0, surface_id=33)\\n s34 = openmc.ZPlane(z0=-183.0, surface_id=34)\\n s35 = openmc.ZPlane(z0=0.0, surface_id=35)\\n s36 = openmc.ZPlane(z0=183.0, surface_id=36)\\n s37 = openmc.ZPlane(z0=203.0, surface_id=37)\\n s38 = openmc.ZPlane(z0=215.0, surface_id=38)\\n s39 = openmc.ZPlane(z0=223.0, surface_id=39, boundary_type='vacuum')\\n\\n # Define pin cells.\\n fuel_cold = openmc.Universe(name='Fuel pin, cladding, cold water',\\n universe_id=1)\\n c21 = openmc.Cell(cell_id=21, fill=fuel, region=-s1)\\n c22 = openmc.Cell(cell_id=22, fill=clad, region=+s1 & -s2)\\n c23 = openmc.Cell(cell_id=23, fill=cold_water, region=+s2)\\n fuel_cold.add_cells((c21, c22, c23))\\n\\n tube_cold = openmc.Universe(name='Instrumentation guide tube, '\\n 'cold water', universe_id=2)\\n c24 = openmc.Cell(cell_id=24, fill=cold_water, region=-s3)\\n c25 = openmc.Cell(cell_id=25, fill=clad, region=+s3 & -s4)\\n c26 = openmc.Cell(cell_id=26, fill=cold_water, region=+s4)\\n tube_cold.add_cells((c24, c25, c26))\\n\\n fuel_hot = openmc.Universe(name='Fuel pin, cladding, hot water',\\n universe_id=3)\\n c27 = openmc.Cell(cell_id=27, fill=fuel, region=-s1)\\n c28 = openmc.Cell(cell_id=28, fill=clad, region=+s1 & -s2)\\n c29 = openmc.Cell(cell_id=29, fill=hot_water, region=+s2)\\n fuel_hot.add_cells((c27, c28, c29))\\n\\n tube_hot = openmc.Universe(name='Instrumentation guide tube, hot water',\\n universe_id=4)\\n c30 = openmc.Cell(cell_id=30, fill=hot_water, region=-s3)\\n c31 = openmc.Cell(cell_id=31, fill=clad, region=+s3 & -s4)\\n c32 = openmc.Cell(cell_id=32, fill=hot_water, region=+s4)\\n tube_hot.add_cells((c30, c31, c32))\\n\\n # Set positions occupied by guide tubes\\n tube_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8, 11, 14,\\n 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\\n tube_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8,\\n 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\\n\\n # Define fuel lattices.\\n l100 = openmc.RectLattice(name='Fuel assembly (lower half)', lattice_id=100)\\n l100.lower_left = (-10.71, -10.71)\\n l100.pitch = (1.26, 1.26)\\n l100.universes = np.tile(fuel_cold, (17, 17))\\n l100.universes[tube_x, tube_y] = tube_cold\\n\\n l101 = openmc.RectLattice(name='Fuel assembly (upper half)', lattice_id=101)\\n l101.lower_left = (-10.71, -10.71)\\n l101.pitch = (1.26, 1.26)\\n l101.universes = np.tile(fuel_hot, (17, 17))\\n l101.universes[tube_x, tube_y] = tube_hot\\n\\n # Define assemblies.\\n fa_cw = openmc.Universe(name='Water assembly (cold)', universe_id=5)\\n c50 = openmc.Cell(cell_id=50, fill=cold_water, region=+s34 & -s35)\\n fa_cw.add_cell(c50)\\n\\n fa_hw = openmc.Universe(name='Water assembly (hot)', universe_id=7)\\n c70 = openmc.Cell(cell_id=70, fill=hot_water, region=+s35 & -s36)\\n fa_hw.add_cell(c70)\\n\\n fa_cold = openmc.Universe(name='Fuel assembly (cold)', universe_id=6)\\n c60 = openmc.Cell(cell_id=60, fill=l100, region=+s34 & -s35)\\n fa_cold.add_cell(c60)\\n\\n fa_hot = openmc.Universe(name='Fuel assembly (hot)', universe_id=8)\\n c80 = openmc.Cell(cell_id=80, fill=l101, region=+s35 & -s36)\\n fa_hot.add_cell(c80)\\n\\n # Define core lattices\\n l200 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=200)\\n l200.lower_left = (-224.91, -224.91)\\n l200.pitch = (21.42, 21.42)\\n l200.universes = [\\n [fa_cw]*21,\\n [fa_cw]*21,\\n [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7,\\n [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5,\\n [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4,\\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*2 + [fa_cold]*17 + [fa_cw]*2,\\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\\n [fa_cw]*3 + [fa_cold]*15 + [fa_cw]*3,\\n [fa_cw]*4 + [fa_cold]*13 + [fa_cw]*4,\\n [fa_cw]*5 + [fa_cold]*11 + [fa_cw]*5,\\n [fa_cw]*7 + [fa_cold]*7 + [fa_cw]*7,\\n [fa_cw]*21,\\n [fa_cw]*21]\\n\\n l201 = openmc.RectLattice(name='Core lattice (lower half)', lattice_id=201)\\n l201.lower_left = (-224.91, -224.91)\\n l201.pitch = (21.42, 21.42)\\n l201.universes = [\\n [fa_hw]*21,\\n [fa_hw]*21,\\n [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7,\\n [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5,\\n [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4,\\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*2 + [fa_hot]*17 + [fa_hw]*2,\\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\\n [fa_hw]*3 + [fa_hot]*15 + [fa_hw]*3,\\n [fa_hw]*4 + [fa_hot]*13 + [fa_hw]*4,\\n [fa_hw]*5 + [fa_hot]*11 + [fa_hw]*5,\\n [fa_hw]*7 + [fa_hot]*7 + [fa_hw]*7,\\n [fa_hw]*21,\\n [fa_hw]*21]\\n\\n # Define root universe.\\n root = openmc.Universe(universe_id=0, name='root universe')\\n c1 = openmc.Cell(cell_id=1, fill=l200, region=-s6 & +s34 & -s35)\\n c2 = openmc.Cell(cell_id=2, fill=l201, region=-s6 & +s35 & -s36)\\n c3 = openmc.Cell(cell_id=3, fill=bot_plate, region=-s7 & +s31 & -s32)\\n c4 = openmc.Cell(cell_id=4, fill=bot_nozzle, region=-s5 & +s32 & -s33)\\n c5 = openmc.Cell(cell_id=5, fill=bot_fa, region=-s5 & +s33 & -s34)\\n c6 = openmc.Cell(cell_id=6, fill=top_fa, region=-s5 & +s36 & -s37)\\n c7 = openmc.Cell(cell_id=7, fill=top_nozzle, region=-s5 & +s37 & -s38)\\n c8 = openmc.Cell(cell_id=8, fill=upper_rad_ref, region=-s7 & +s38 & -s39)\\n c9 = openmc.Cell(cell_id=9, fill=bot_nozzle, region=+s6 & -s7 & +s32 & -s38)\\n c10 = openmc.Cell(cell_id=10, fill=rpv_steel, region=+s7 & -s8 & +s31 & -s39)\\n c11 = openmc.Cell(cell_id=11, fill=lower_rad_ref, region=+s5 & -s6 & +s32 & -s34)\\n c12 = openmc.Cell(cell_id=12, fill=upper_rad_ref, region=+s5 & -s6 & +s36 & -s38)\\n root.add_cells((c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12))\\n\\n # Assign root universe to geometry\\n model.geometry.root_universe = root\\n\\n model.settings.batches = 10\\n model.settings.inactive = 5\\n model.settings.particles = 100\\n model.settings.source = openmc.Source(space=openmc.stats.Box(\\n [-160, -160, -183], [160, 160, 183]))\\n\\n plot = openmc.Plot()\\n plot.origin = (125, 125, 0)\\n plot.width = (250, 250)\\n plot.pixels = (3000, 3000)\\n plot.color_by = 'material'\\n model.plots.append(plot)\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38c691b3c7b5d3db4ec3e58594b72d7\",\n \"score\": \"0.544043\",\n \"text\": \"def create_some_object(ii,y_co,mass):\\n name = ''\\n diff = (rand.uniform(0,1),rand.uniform(0,1),rand.uniform(0,1))\\n spec = (rand.uniform(0,1),rand.uniform(0,1),rand.uniform(0,1))\\n alp = rand.uniform(0,1)\\n if ii % 5 == 0:\\n x_co = 8.1\\n name = \\\"obj_torus\\\"+str(ii)\\n bpy.ops.mesh.primitive_torus_add(location = (x_co,y_co,2))\\n selobj = bpy.context.active_object\\n selobj.name = name\\n selobj.game.mass = mass\\n selobj.scale = (1.5,1.5,1.5)\\n if ii % 5 == 1:\\n x_co = 3.5\\n name = \\\"obj_cube\\\"+str(ii)\\n bpy.ops.mesh.primitive_cube_add(location = (x_co,y_co,2))\\n selobj = bpy.context.active_object\\n selobj.name = name\\n selobj.game.mass = mass\\n selobj.scale = (2,2,2)\\n if ii % 5 == 2:\\n x_co =-1\\n name = \\\"obj_cone\\\"+str(ii)\\n bpy.ops.mesh.primitive_cone_add(location = (x_co,y_co,2))\\n selobj = bpy.context.active_object\\n selobj.name = name\\n selobj.game.mass = mass\\n selobj.scale = (2,2,2)\\n if ii % 5 == 3:\\n x_co = -5.4\\n name = \\\"obj_sphere\\\"+str(ii)\\n bpy.ops.mesh.primitive_uv_sphere_add(location = (x_co,y_co,2))\\n selobj = bpy.context.active_object\\n selobj.name = name\\n selobj.game.mass = mass\\n selobj.scale = (2,2,2)\\n if ii % 5 == 4:\\n x_co = -9.0\\n name = \\\"obj_monkey\\\"+str(ii)\\n bpy.ops.mesh.primitive_monkey_add(location = (x_co,y_co,2))\\n selobj = bpy.context.active_object\\n selobj.name = name\\n selobj.game.mass = mass\\n selobj.scale = (1.2,1.2,1.2)\\n tempmat = makeMaterial(name, diff, spec, alp)\\n setMaterial(bpy.context.active_object,tempmat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1e72f4d86cc8e24bc38b1df1620380\",\n \"score\": \"0.5433962\",\n \"text\": \"def __init__(self, make, model, year):\\n super().__init__(make, model, year)\\n self.battery_size = 70\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96454f002910b607afedac5f1dc9f3dd\",\n \"score\": \"0.54304534\",\n \"text\": \"def forward_model(f_grid, atm_fields_compact, verbosity=0):\\n ws = pyarts.workspace.Workspace(verbosity=0)\\n ws.water_p_eq_agendaSet()\\n ws.PlanetSet(option=\\\"Earth\\\")\\n ws.verbositySetScreen(ws.verbosity, verbosity)\\n\\n # standard emission agenda\\n ws.iy_main_agendaSet(option=\\\"Emission\\\")\\n\\n # cosmic background radiation\\n ws.iy_space_agendaSet(option=\\\"CosmicBackground\\\")\\n\\n # standard surface agenda (i.e., make use of surface_rtprop_agenda)\\n ws.iy_surface_agendaSet(option=\\\"UseSurfaceRtprop\\\")\\n\\n # sensor-only path\\n ws.ppath_agendaSet(option=\\\"FollowSensorLosPath\\\")\\n\\n # no refraction\\n ws.ppath_step_agendaSet(option=\\\"GeometricPath\\\")\\n\\n # Non reflecting surface\\n ws.surface_rtprop_agendaSet(\\n option=\\\"Specular_NoPol_ReflFix_SurfTFromt_surface\\\")\\n\\n # Number of Stokes components to be computed\\n ws.IndexSet(ws.stokes_dim, 1)\\n\\n #########################################################################\\n\\n # Definition of absorption species\\n ws.abs_speciesSet(species=[\\n \\\"H2O, H2O-SelfContCKDMT400, H2O-ForeignContCKDMT400\\\",\\n \\\"O2-TRE05\\\",\\n \\\"N2, N2-CIAfunCKDMT252, N2-CIArotCKDMT252\\\",\\n ])\\n\\n ws.abs_lines_per_speciesReadSpeciesSplitCatalog(basename=\\\"lines/\\\")\\n\\n # Load CKDMT400 model data\\n ws.ReadXML(ws.predefined_model_data, \\\"model/mt_ckd_4.0/H2O.xml\\\")\\n\\n # ws.abs_lines_per_speciesLineShapeType(option=lineshape)\\n ws.abs_lines_per_speciesCutoff(option=\\\"ByLine\\\", value=750e9)\\n # ws.abs_lines_per_speciesNormalization(option=normalization)\\n\\n ws.VectorSetConstant(ws.surface_scalar_reflectivity, 1, 0.4)\\n\\n # Set the frequency grid\\n ws.f_grid = f_grid\\n\\n # Throw away lines outside f_grid\\n ws.abs_lines_per_speciesCompact()\\n\\n # No sensor properties\\n ws.sensorOff()\\n\\n # We select here to use Planck brightness temperatures\\n ws.StringSet(ws.iy_unit, \\\"PlanckBT\\\")\\n\\n #########################################################################\\n\\n # Atmosphere and surface\\n ws.AtmosphereSet1D()\\n ws.atm_fields_compact = atm_fields_compact\\n ws.atm_fields_compactAddConstant(ws.atm_fields_compact, \\\"abs_species-N2\\\",\\n 0.78, 0, [\\\"abs_species-H2O\\\"])\\n ws.atm_fields_compactAddConstant(ws.atm_fields_compact, \\\"abs_species-O2\\\",\\n 0.21, 0, [\\\"abs_species-H2O\\\"])\\n ws.AtmFieldsAndParticleBulkPropFieldFromCompact()\\n\\n ws.Extract(ws.z_surface, ws.z_field, 0)\\n ws.Extract(ws.t_surface, ws.t_field, 0)\\n\\n # Definition of sensor position and line of sight (LOS)\\n ws.MatrixSet(ws.sensor_pos, np.array([[10e3]]))\\n ws.MatrixSet(ws.sensor_los, np.array([[0]]))\\n ws.sensorOff()\\n\\n # Jacobian calculation\\n ws.jacobianInit()\\n ws.jacobianAddAbsSpecies(\\n g1=ws.p_grid,\\n g2=ws.lat_grid,\\n g3=ws.lon_grid,\\n species=\\\"H2O, H2O-SelfContCKDMT400, H2O-ForeignContCKDMT400\\\",\\n unit=\\\"vmr\\\",\\n )\\n ws.jacobianClose()\\n\\n # Clearsky = No scattering\\n ws.cloudboxOff()\\n\\n # on-the-fly absorption\\n ws.propmat_clearsky_agendaAuto()\\n\\n # Perform RT calculations\\n ws.lbl_checkedCalc()\\n ws.atmfields_checkedCalc()\\n ws.atmgeom_checkedCalc()\\n ws.cloudbox_checkedCalc()\\n ws.sensor_checkedCalc()\\n\\n ws.yCalc()\\n\\n return ws.y.value[:].copy(), ws.jacobian.value[:].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2fa7e69f447c18eb36afe998fdb08d\",\n \"score\": \"0.5421357\",\n \"text\": \"def main():\\n stage = Usd.Stage.CreateInMemory()\\n stage.SetStartTimeCode(0)\\n stage.SetEndTimeCode(2)\\n\\n prim = stage.DefinePrim(\\\"/Set\\\")\\n non_template_set_name = \\\"non_template_clips\\\"\\n model = Usd.ClipsAPI(prim)\\n model.SetClipActive([(0.0, 0)], non_template_set_name)\\n model.SetClipAssetPaths(\\n [Sdf.AssetPath(\\\"./non_template_clip.usda\\\")], non_template_set_name\\n )\\n model.SetClipPrimPath(\\\"/NonTemplate\\\", non_template_set_name)\\n\\n template_set_name = \\\"template_clips\\\"\\n model.SetClipTemplateAssetPath(\\\"./template_clip.##.usda\\\", template_set_name)\\n model.SetClipTemplateEndTime(2, template_set_name)\\n model.SetClipTemplateStartTime(0, template_set_name)\\n model.SetClipTemplateStride(1, template_set_name)\\n model.SetClipPrimPath(\\\"/Template\\\", template_set_name)\\n\\n prim.GetReferences().AddReference(assetPath=\\\"./set.usda\\\", primPath=\\\"/Set\\\")\\n\\n print(stage.GetRootLayer().ExportToString())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd40b6d51940a11430eaf5f579c6d95\",\n \"score\": \"0.5414305\",\n \"text\": \"def __new__(cls,*arg,**kwarg):\\n \\n cls.FREEPARAMETERS_STD = [\\\"a%d\\\"%(i+1) for i in range(len(cls.STANDARDIZATION))]\\n cls.FREEPARAMETERS = [\\\"M0\\\"]+cls.FREEPARAMETERS_STD\\n return super(ModelStandardization,cls).__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b518f9f879242aeb4a2e49ce0dda73cf\",\n \"score\": \"0.54063696\",\n \"text\": \"def __init__(self, species, qty):\\n super().__init__(species, qty, \\\"domestic\\\", 0.08)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c829db1d0eb021c2af1a2733b81b68b4\",\n \"score\": \"0.5396692\",\n \"text\": \"def create_model( fullMoleculeList, moleculePartition, parameterMap, includeSoluteDielectricAsParameter ):\\n\\n # Define deterministic functions for hydration free energies.\\n\\n model = parameterMap['model']\\n parameters = parameterMap['stochastic']\\n radiusParameterMap = parameterMap['radiusParameterMap']\\n gammaParameterMap = parameterMap['gammaParameterMap']\\n\\n for (molecule_index, molecule) in enumerate(fullMoleculeList):\\n\\n if( moleculePartition[molecule_index] == 1 ):\\n \\n molecule_name = molecule.GetTitle()\\n variable_name = \\\"dg_gbvi_%08d\\\" % molecule_index\\n \\n # Determine which parameters are involved in this molecule to limit number of parents for caching.\\n \\n parents = dict()\\n for atom in molecule.GetAtoms():\\n atomtype = atom.GetStringData(\\\"gbvi_type\\\") # GBVI atomtype\\n for parameter_name in ['gamma', 'radius']:\\n stochastic_name = '%s_%s' % (atomtype,parameter_name)\\n if( stochastic_name in parameters ):\\n parents[stochastic_name] = parameters[stochastic_name]\\n else:\\n print \\\"create_model Warning: parameter=%s missing for %40s\\\" % (stochastic_name, molecule_name )\\n \\n if( includeSoluteDielectricAsParameter ):\\n parents['soluteDielectric'] = parameters['soluteDielectric']\\n \\n print \\\"create_model %40s: %s\\\" % (molecule_name, parents.keys() )\\n \\n # Create deterministic variable for computed hydration free energy.\\n \\n #if( energyCalculations == 'Swig' ):\\n function = hydration_energy_factory_swig(molecule_index, radiusParameterMap, gammaParameterMap)\\n \\n # if( energyCalculations == 'OpenMM' ):\\n # function = hydration_energy_factory_OpenMM(molecule)\\n #\\n # if( energyCalculations == 'Test' ):\\n # function = hydration_energy_factory_test( molecule, molecule_index, radiusParameterMap, gammaParameterMap)\\n \\n model[variable_name] = pymc.Deterministic(eval=function,\\n name=variable_name,\\n parents=parents,\\n doc=molecule_name,\\n trace=True,\\n verbose=1,\\n dtype=float,\\n plot=False,\\n cache_depth=2)\\n \\n # Define error model\\n log_sigma_min = math.log(0.01) # kcal/mol\\n log_sigma_max = math.log(10.0) # kcal/mol\\n log_sigma_guess = math.log(1.0) # kcal/mol\\n model['log_sigma'] = pymc.Uniform('log_sigma', lower=log_sigma_min, upper=log_sigma_max, value=log_sigma_guess)\\n model['sigma'] = pymc.Lambda('sigma', lambda log_sigma=model['log_sigma'] : math.exp(log_sigma) ) \\n model['tau'] = pymc.Lambda('tau', lambda sigma=model['sigma'] : sigma**(-2) )\\n\\n for (molecule_index, molecule) in enumerate(fullMoleculeList):\\n\\n if( moleculePartition[molecule_index] == 1 ):\\n molecule_name = molecule.GetTitle()\\n variable_name = \\\"dg_exp_%08d\\\" % molecule_index\\n dg_exp = float(OEGetSDData(molecule, 'dG(exp)')) # observed hydration free energy in kcal/mol\\n print \\\"Mol=%4d dG=%15.7e %s \\\" % (molecule_index, dg_exp, molecule_name )\\n sys.stdout.flush()\\n model[variable_name] = pymc.Normal(mu=model['dg_gbvi_%08d' % molecule_index], tau=model['tau'], value=dg_exp, observed=True) \\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7565df4cc08ccad217eeaec3123665dc\",\n \"score\": \"0.53920263\",\n \"text\": \"def create_snowmodel(self, hspfmodel, verbose = True, vverbose = False):\\n\\n filename = hspfmodel.filename + '_snow'\\n\\n submodel = HSPFModel()\\n\\n submodel.build_from_existing(hspfmodel, filename, directory = \\n hspfmodel.filepath[:-1],\\n verbose = vverbose)\\n\\n # simplify the landtypes to one developed which contains one implnd and \\n # one perlnd (since they are all the same)\\n\\n for subbasin in submodel.subbasins:\\n year = min(submodel.subbasins[subbasin].landuse.keys())\\n submodel.subbasins[subbasin].landuse = {year: {'Developed': 100}}\\n\\n submodel.build()\\n\\n # get rid of the reaches\\n\\n submodel.rchreses = []\\n\\n # add in the modules\\n\\n submodel.add_temp()\\n \\n densities = [o.RDENPF \\n for o in hspfmodel.perlnds + hspfmodel.implnds]\\n depths = [o.packsnow / o.RDENPF \\n for o in hspfmodel.perlnds + hspfmodel.implnds]\\n\\n depth = sum(depths) / len(depths)\\n density = sum(densities) / len(densities)\\n\\n submodel.add_snow(depth = depth, density = density) \\n \\n # overwrite the time series dictionaries for the model\\n\\n for subbasin in hspfmodel.subbasins:\\n if subbasin in submodel.subbasins:\\n start, tstep, data = hspfmodel.precipitations['%d' % subbasin]\\n submodel.add_timeseries('precipitation', '%d' % subbasin, \\n start, data)\\n submodel.assign_subbasin_timeseries('precipitation', subbasin, \\n '%d' % subbasin)\\n\\n start, tstep, data = hspfmodel.temperatures[hspfmodel.description]\\n\\n submodel.add_timeseries('temperature', submodel.description, \\n start, data)\\n\\n start, tstep, data = hspfmodel.dewpoints[hspfmodel.description]\\n\\n submodel.add_timeseries('dewpoint', submodel.description, start, data)\\n\\n submodel.assign_watershed_timeseries('temperature', \\n submodel.description)\\n submodel.assign_watershed_timeseries('dewpoint',\\n submodel.description)\\n\\n self.snowmodel = submodel.filepath + submodel.filename\\n\\n with open(self.snowmodel, 'wb') as f: pickle.dump(submodel, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea41ca331df38389e0231e0e4df605e\",\n \"score\": \"0.53883773\",\n \"text\": \"def __init__(self, name=None):\\n\\n # Always call parent class' constructor FIRST:\\n CoupledDEVS.__init__(self, name)\\n\\n # Declare the coupled model's output ports:\\n # Autonomous, so no output ports\\n #self.OUT = self.addOutPort(name=\\\"OUT\\\")\\n\\n # Declare the coupled model's sub-models:\\n\\n # The Policeman generating interrupts \\n self.policeman = self.addSubModel(Policeman(name=\\\"policeman\\\"))\\n\\n # The TrafficLight \\n self.trafficLight = self.addSubModel(TrafficLight(name=\\\"trafficLight\\\"))\\n\\n # Only connect ...\\n self.connectPorts(self.policeman.OUT, self.trafficLight.INTERRUPT)\\n #self.connectPorts(self.trafficLight.OBSERVED, self.OUT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6e185ce95912ec70c2c39b0cfe58ff\",\n \"score\": \"0.5372091\",\n \"text\": \"def calibrate_submodel(self):\\n \\n model = self.model \\n cell = self.knowledge_base.cell\\n nucleus = model.compartments.get_one(id='n')\\n mitochondrion = model.compartments.get_one(id='m')\\n cytoplasm = model.compartments.get_one(id='c')\\n\\n beta = self.options.get('beta')\\n\\n Avogadro = self.model.parameters.get_or_create(\\n id='Avogadro',\\n type=None,\\n value=scipy.constants.Avogadro,\\n units=unit_registry.parse_units('molecule mol^-1')) \\n\\n rnas_kb = cell.species_types.get(__type=wc_kb.eukaryote.TranscriptSpeciesType)\\n undetermined_model_kcat = []\\n determined_kcat = []\\n for rna_kb, reaction in zip(rnas_kb, self.submodel.reactions):\\n\\n init_species_counts = {}\\n \\n modifier_species = self._degradation_modifier[reaction.name] \\n init_species_counts[modifier_species.gen_id()] = modifier_species.distribution_init_concentration.mean\\n \\n rna_kb_compartment_id = rna_kb.species[0].compartment.id\\n if rna_kb_compartment_id == 'c':\\n rna_compartment = cytoplasm\\n degradation_compartment = cytoplasm\\n else:\\n rna_compartment = mitochondrion\\n degradation_compartment = mitochondrion \\n\\n rna_reactant = model.species_types.get_one(id=rna_kb.id).species.get_one(compartment=rna_compartment)\\n\\n half_life = rna_kb.properties.get_one(property='half-life').get_value()\\n mean_concentration = rna_reactant.distribution_init_concentration.mean\\n\\n average_rate = utils.calc_avg_deg_rate(mean_concentration, half_life)\\n \\n for species in reaction.get_reactants():\\n\\n init_species_counts[species.gen_id()] = species.distribution_init_concentration.mean\\n\\n if model.parameters.get(id='K_m_{}_{}'.format(reaction.id, species.species_type.id)):\\n model_Km = model.parameters.get_one(\\n id='K_m_{}_{}'.format(reaction.id, species.species_type.id))\\n if species.distribution_init_concentration.mean:\\n model_Km.value = beta * species.distribution_init_concentration.mean \\\\\\n / Avogadro.value / species.compartment.init_volume.mean\\n model_Km.comments = 'The value was assumed to be {} times the concentration of {} in {}'.format(\\n beta, species.species_type.id, species.compartment.name)\\n else:\\n model_Km.value = 1e-05\\n model_Km.comments = 'The value was assigned to 1e-05 because the concentration of ' +\\\\\\n '{} in {} was zero'.format(species.species_type.id, species.compartment.name)\\n\\n model_kcat = model.parameters.get_one(id='k_cat_{}'.format(reaction.id))\\n\\n if average_rate: \\n model_kcat.value = 1.\\n eval_rate_law = reaction.rate_laws[0].expression._parsed_expression.eval({\\n wc_lang.Species: init_species_counts,\\n wc_lang.Compartment: {\\n rna_compartment.id: rna_compartment.init_volume.mean * \\\\\\n rna_compartment.init_density.value,\\n degradation_compartment.id: degradation_compartment.init_volume.mean * \\\\\\n degradation_compartment.init_density.value}\\n })\\n if eval_rate_law:\\n model_kcat.value = average_rate / eval_rate_law\\n determined_kcat.append(model_kcat.value)\\n else:\\n undetermined_model_kcat.append(model_kcat) \\n else: \\n undetermined_model_kcat.append(model_kcat)\\n \\n median_kcat = numpy.median(determined_kcat)\\n for model_kcat in undetermined_model_kcat:\\n model_kcat.value = median_kcat\\n model_kcat.comments = 'Set to the median value because it could not be determined from data' \\n\\n print('RNA degradation submodel has been generated')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bbfcf5951a375728bdff97674fbc037\",\n \"score\": \"0.5360838\",\n \"text\": \"def pole_bending_modeling(model1,leftsupportx,rightsupportx,supporty,leftplatecenterx,rightplatecenterx,plateheighty,lengthx,heighty,stiffness):\\r\\n \\r\\n \\r\\n # add loading plate\\r\\n model1 = pole_contact_plate(model1,'rightloadplate',(rightplatecenterx,heighty,0),lengthx,1000,plateheighty,stiffness)\\r\\n model1 = pole_contact_plate(model1,'leftloadplate',(leftplatecenterx,heighty,0),lengthx,1000,plateheighty,stiffness)\\r\\n \\r\\n \\r\\n # add support\\r\\n model1 = add_support(model1,'leftsupport',leftsupportx,supporty,0,lengthx * 2,10000,default=[1,1,1,0,0,0]) # due to fact that only one side of plate will be selected\\r\\n model1 = add_support(model1,'rightsupport',rightsupportx,supporty,0,lengthx * 2,10000,default=[0,1,1,0,0,0]) # big width number to seelct all nodes\\r\\n \\r\\n \\r\\n model1 = add_material(model1)\\r\\n model1.table('loadtable',1,['time'],[[0,0],[1,1]])\\r\\n model1.load('leftrightload',{'xyz':[0,1,0,0,0,0],'DOF':6,'scalar':-1,'setnamelist':['leftloadplate_plate','rightloadplate_plate'],'tabletag':'loadtable'})\\r\\n \\r\\n\\r\\n model1.section('sec_1','shell_section',{'thickness':0.1875})\\r\\n\\r\\n \\r\\n model1.property('prop1','quad4',{'type':75,'thinkness':0.01})\\r\\n model1.property('prop_dent','quad4',{'type':75})\\r\\n \\r\\n \\r\\n model1.elemset_sub_setname('surface_elements','dentelems')\\r\\n \\r\\n model1.link_prop_conn('prop1',setnamelist=['surface_elements-dentelems'])\\r\\n model1.link_prop_conn('prop_dent',setnamelist=['dentelems','extension_elements_surface_leftend','extension_elements_surface_rightend'])\\r\\n \\r\\n model1.link_sec_prop('sec_1','prop1')\\r\\n model1.link_sec_prop('sec_1','prop_dent')\\r\\n \\r\\n # associate the material\\r\\n model1.link_mat_prop('pole_alum','prop1')\\r\\n model1.link_mat_prop('pole_alum_dent','prop_dent')\\r\\n \\r\\n model1.loadcase('loadcase1','static_arclength',{'boundarylist':['leftsupport','rightsupport','leftrightload'],'para':{'nstep':50}})\\r\\n \\r\\n #model1.job('job1','static_job',{'loadcaselist':['loadcase0','loadcase1'],'submit':True,'reqresultslist':['stress','total_strain','plastic_strain']})\\r\\n model1.job('job1','static_job',{'loadcaselist':['loadcase0','loadcase1'],'submit':False,'reqresultslist':['stress','total_strain','plastic_strain']})\\r\\n \\r\\n return model1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"931a784adbb0e80ab049882aa7d0a382\",\n \"score\": \"0.53536534\",\n \"text\": \"def scaffold():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772924833d87c7975ec747b5eb019b0b\",\n \"score\": \"0.5339107\",\n \"text\": \"def model():\\n return DBC14(dist_jb=10, mag=6, v_s30=600, depth_hyp=10, mechanism='SS')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5bb6a8b9a604f242b3c2505439154c4\",\n \"score\": \"0.53274566\",\n \"text\": \"def __init__(self):\\n \\n super(Sellar, self).__init__()\\n \\n #add the discipline components to the assembly\\n self.add('dis1', SellarDiscipline1())\\n self.add('dis2', SellarDiscipline2())\\n \\n #START OF MDAO Problem Definition\\n #Global Des Vars\\n self.add_parameter((\\\"dis1.z1\\\",\\\"dis2.z1\\\"),low=-10,high=10)\\n self.add_parameter((\\\"dis1.z2\\\",\\\"dis2.z2\\\"),low=0,high=10)\\n \\n #Local Des Vars \\n self.add_parameter(\\\"dis1.x1\\\",low=0,high=10)\\n \\n #Coupling Vars\\n self.add_coupling_var(\\\"dis2.y1\\\",\\\"dis1.y1\\\")\\n self.add_coupling_var(\\\"dis1.y2\\\",\\\"dis2.y2\\\")\\n \\n self.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')\\n self.add_constraint('3.16 < dis1.y1')\\n self.add_constraint('dis2.y2 < 24.0')\\n \\n #END OF MDAO Problem Definition\\n \\n self.dis1.z1 = self.dis2.z1 = 5.0\\n self.dis1.z2 = self.dis2.z2 = 2.0\\n self.dis1.x1 = 1.0\\n self.dis1.y2 = 3.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f38cf86d36765820ac82e2cc6bce10\",\n \"score\": \"0.53215253\",\n \"text\": \"def assembly_model():\\n\\n model = openmc.model.Model()\\n\\n # Create fuel assembly Lattice\\n pitch = 21.42\\n assembly = openmc.RectLattice(name='Fuel Assembly')\\n assembly.pitch = (pitch/17, pitch/17)\\n assembly.lower_left = (-pitch/2, -pitch/2)\\n\\n # Create array indices for guide tube locations in lattice\\n gt_pos = np.array([\\n [2, 5], [2, 8], [2, 11],\\n [3, 3], [3, 13],\\n [5, 2], [5, 5], [5, 8], [5, 11], [5, 14],\\n [8, 2], [8, 5], [8, 8], [8, 11], [8, 14],\\n [11, 2], [11, 5], [11, 8], [11, 11], [11, 14],\\n [13, 3], [13, 13],\\n [14, 5], [14, 8], [14, 11]\\n ])\\n\\n # Create 17x17 array of universes. First we create a 17x17 array all filled\\n # with the fuel pin universe. Then, we replace the guide tube positions with\\n # the guide tube pin universe (note the use of numpy fancy indexing to\\n # achieve this).\\n assembly.universes = np.full((17, 17), fuel_pin())\\n assembly.universes[gt_pos[:, 0], gt_pos[:, 1]] = guide_tube_pin()\\n\\n # Create outer boundary of the geometry to surround the lattice\\n outer_boundary = openmc.model.rectangular_prism(\\n pitch, pitch, boundary_type='reflective')\\n\\n # Create a cell filled with the lattice\\n main_cell = openmc.Cell(fill=assembly, region=outer_boundary)\\n\\n # Finally, create geometry by providing a list of cells that fill the root\\n # universe\\n model.geometry = openmc.Geometry([main_cell])\\n\\n model.settings.batches = 150\\n model.settings.inactive = 50\\n model.settings.particles = 1000\\n model.settings.source = openmc.Source(space=openmc.stats.Box(\\n (-pitch/2, -pitch/2, -1),\\n (pitch/2, pitch/2, 1),\\n only_fissionable=True\\n ))\\n\\n # NOTE: We never actually created a Materials object. When you export/run\\n # using the Model object, if no materials were assigned it will look through\\n # the Geometry object and automatically export any materials that are\\n # necessary to build the model.\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e38c22bacf8f6aa559c93e282dc4a5\",\n \"score\": \"0.53204226\",\n \"text\": \"def _configure(self):\\n SpatialDBObj._configure(self)\\n self.label(\\\"SCEC CVM-H\\\")\\n self.dataDir(self.inventory.dataDir)\\n self.minVs(self.inventory.minVs.value)\\n self.squash(self.inventory.squash, self.inventory.squashLimit.value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed02d4bfd211d2473c9fc1c40d0d8dfa\",\n \"score\": \"0.52838945\",\n \"text\": \"def __init__(self, make, model, year, battery=70):\\n super().__init__(make, model, year)\\n self.battery_size = battery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f144d6691eadf5238b16e0bebcbfe2\",\n \"score\": \"0.527416\",\n \"text\": \"def __init__(self, make, model, year):\\n\\n super().__init__(make, model, year) \\n self.battery_size = 75 # Here, We add an extra attribute to our child class. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f144d6691eadf5238b16e0bebcbfe2\",\n \"score\": \"0.527416\",\n \"text\": \"def __init__(self, make, model, year):\\n\\n super().__init__(make, model, year) \\n self.battery_size = 75 # Here, We add an extra attribute to our child class. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14b8f584528aa2d8692bd34af5d91c0\",\n \"score\": \"0.5255351\",\n \"text\": \"def sol553(design_parameters):\\r\\n prt('Design governing by this mantras (in oder of importance): \\\\n size, cost, stall margin, off.design', 'blue')\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a00f4e7e1dfca61dafd9bc95836c98\",\n \"score\": \"0.52360165\",\n \"text\": \"def construct_model(self):\\r\\n\\r\\n # Initialise model object\\r\\n m = ConcreteModel()\\r\\n\\r\\n # Add component allowing dual variables to be imported\\r\\n m.dual = Suffix(direction=Suffix.IMPORT)\\r\\n\\r\\n # Define sets\\r\\n m = self.components.define_sets(m)\\r\\n\\r\\n # Define parameters common to unit commitment sub-problems and investment plan\\r\\n m = self.components.define_parameters(m)\\r\\n\\r\\n # Define parameters specific to unit commitment sub-problem\\r\\n m = self.define_parameters(m)\\r\\n\\r\\n # Define variables\\r\\n m = self.define_variables(m)\\r\\n\\r\\n # Define expressions\\r\\n m = self.define_expressions(m)\\r\\n\\r\\n # Define constraints\\r\\n m = self.define_constraints(m)\\r\\n\\r\\n # Define objective\\r\\n m = self.define_objective(m)\\r\\n\\r\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799f4ce017f5053655d21a467eea6d34\",\n \"score\": \"0.5233242\",\n \"text\": \"def Schemata():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bca46ea6ca5bae506893a67f20e0a74\",\n \"score\": \"0.52153826\",\n \"text\": \"def main():\\n\\n # We get the user model choice.\\n selected_model = model_selection()\\n\\n # We print equations to show the different variables to fill.\\n print(selected_model[\\\"class\\\"].eq_to_string())\\n\\n # We create an object from the model chosen\\n model_obj = selected_model[\\\"class\\\"]()\\n\\n # We write the output to the file\\n model_obj.write_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d0b8b9a11a3a040e38d0968d79837dd\",\n \"score\": \"0.52152205\",\n \"text\": \"def model_defense():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f692f2614373b660e1a4cf0caea7f5\",\n \"score\": \"0.51889247\",\n \"text\": \"def __init__(self, size_float=0.0):\\n Organ.__init__(self,'Heart',size_float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4eb460f7f049c91bbb1badff8242642\",\n \"score\": \"0.51871455\",\n \"text\": \"def test_create_superModel(warnings = True, stdout_msgs = True):\\n # Model path\\n model_path = home_dir + 'work/models/Escherichia_coli/iJO1366/'\\n\\n model_organism = organism(id = 'Ecoli', name = 'Escherichia coli',domain = 'Bacteria', genus = 'Escherichia', species = 'coli', strain = 'MG1655')\\n\\n flux_bounds_dict = {'EX_glc(e)':[-100,1000], 'EX_o2(e)':[-200,1000]}\\n flux_bounds_filename = model_path + 'iJO1366_minimal_glucose_aerobic.py'\\n\\n # Orignal iJo1266 model\\n model = create_model(model_organism = model_organism, model_info = {'id':'iJO1366', 'file_format':'sbml', 'model_filename':model_path + 'iJO1366_updated.xml', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': flux_bounds_dict}, validate = True, stdout_msgs = True, warnings = True) \\n\\n model.organism.ModelSEED_type = 'bacteria_GramNegative'\\n\\n \\\"\\\"\\\"\\n #rxn_id = 'GAPD'\\n #rxn_id = 'ZN2abcpp'\\n rxn_id = 'VALtex'\\n get_cpds_ModelSEED_id(cpds_list = model.reactions_by_id[rxn_id].compounds)\\n for cpd in model.reactions_by_id[rxn_id].compounds:\\n print cpd.id,': ',cpd.ModelSEED_id,' stoic = ',model.reactions_by_id[rxn_id].stoichiometry[cpd]\\n rxn_ModelSEED_id, ModelSEED_id_found_by = match_rxn_eqn(rxn = model.reactions_by_id[rxn_id]) \\n print 'rxn_ModelSEED_id = {} , ModelSEED_id_found_by = {}\\\\n'.format(rxn_ModelSEED_id, ModelSEED_id_found_by)\\n \\\"\\\"\\\"\\n\\n print '\\\\n----- Getting ModelSEED ids ----'\\n get_cpds_ModelSEED_id(cpds_list = model.compounds)\\n get_rxns_ModelSEED_id(rxns_list = model.reactions)\\n sys.stdout.flush()\\n\\n \\\"\\\"\\\"\\n #--- Text exporiting to pydict and importing back ----\\n # Save the model into a python dictionary\\n model.export(output_format = 'pydict', output_filename = 'iJO1366.py')\\n\\n # Re-import the pydict model\\n model = create_model(model_info = {'id':'iJO1366', 'file_format':'pydict', 'model_filename':'iJO1366.py', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': flux_bounds_dict}, validate = True, stdout_msgs = True, warnings = True) \\n \\\"\\\"\\\"\\n\\n print '\\\\n----- Creating super_model ----'\\n sys.stdout.flush()\\n\\n super_model = create_superModel_from_ModelSEED(original_model = model, standard_to_model_compartID_map = {'c':'c','e':'e','p':'p'}, validate = True)\\n print 'super_model statistics: # of compounds (total/original/external) = {}/{}/{} , # of reactions (total/original/external) = {}/{}/{}'.format(len(super_model.compounds), len([c for c in super_model.compounds if not c.external]), len([c for c in super_model.compounds if c.external]), len(super_model.reactions), len([r for r in super_model.reactions if not r.external]), len([r for r in super_model.reactions if r.external])) \\n\\n print '\\\\n----- fba with super_model ----'\\n for rxn in [r for r in super_model.reactions if r.external]:\\n rxn.flux_bounds = [0,0]\\n for rxn in super_model.reactions:\\n rxn.objective_coefficient = 0\\n super_model.reactions_by_id['Ec_biomass_iJO1366_core_53p95M'].objective_coefficient = 1\\n super_model.fba()\\n\\n print '\\\\n----- Exporting super_model to pydict ----'\\n # Export to a pydict model\\n super_model.export(output_format = 'pydict', output_filename = 'super_model_iJO1366.py')\\n\\n print '\\\\n----- Re-importing super_model from pydict ----'\\n # Re-import the pydict model\\n super_modelmodel = create_model(model_info = {'id':'super_model_iJO1366', 'file_format':'pydict', 'model_filename':'super_model_iJO1366.py', 'biomassrxn_id':'Ec_biomass_iJO1366_core_53p95M'}, growthMedium_flux_bounds = {'flux_bounds_filename':flux_bounds_filename, 'flux_bounds_dict': dict(flux_bounds_dict.items() + [(r.id,[0,0]) for r in super_model.reactions if r.external])}, validate = True, stdout_msgs = True, warnings = True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb20c49d693b341d5afb650a0d189b3c\",\n \"score\": \"0.5186923\",\n \"text\": \"def __init__(self, number_of_cheeses, number_of_stools):\\n self.number_of_cheeses = number_of_cheeses\\n self.number_of_stools = number_of_stools\\n self.model = TOAHModel(self.number_of_stools)\\n self.model.fill_first_stool(self.number_of_cheeses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0027ae7e48bb44a362a776ed4977c263\",\n \"score\": \"0.51864123\",\n \"text\": \"def createSynthModel():\\n # Create the synthetic model\\n world = mt.createCircle(boundaryMarker=-1, nSegments=64)\\n tri = mt.createPolygon([[-0.8, -0], [-0.5, -0.7], [0.7, 0.5]],\\n isClosed=True, area=0.0015)\\n c1 = mt.createCircle(radius=0.2, pos=[-0.2, 0.5], nSegments=32,\\n area=0.0025, marker=3)\\n c2 = mt.createCircle(radius=0.2, pos=[0.32, -0.3], nSegments=32,\\n area=0.0025, marker=3)\\n\\n poly = mt.mergePLC([world, tri, c1, c2])\\n\\n poly.addRegionMarker([0.0, 0, 0], 1, area=0.0015)\\n poly.addRegionMarker([-0.9, 0, 0], 2, area=0.0015)\\n\\n c = mt.createCircle(radius=0.99, nSegments=16, start=np.pi, end=np.pi*3)\\n [poly.createNode(p.pos(), -99) for p in c.nodes()]\\n mesh = pg.meshtools.createMesh(poly, q=34.4, smooth=[1, 10])\\n mesh.scale(1.0/5.0)\\n mesh.rotate([0., 0., 3.1415/3])\\n mesh.rotate([0., 0., 3.1415])\\n\\n petro = pg.solver.parseArgToArray([[1, 0.9], [2, 0.6], [3, 0.3]],\\n mesh.cellCount(), mesh)\\n\\n # Create the parametric mesh that only reflect the domain geometry\\n world = mt.createCircle(boundaryMarker=-1, nSegments=32, area=0.0051)\\n paraMesh = pg.meshtools.createMesh(world, q=34.0, smooth=[1, 10])\\n paraMesh.scale(1.0/5.0)\\n\\n return mesh, paraMesh, petro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69f2c3de08a8fea2106af210c45100c\",\n \"score\": \"0.51764035\",\n \"text\": \"def __init__(self, model, shore_coef):\\r\\n\\r\\n self.model = model\\r\\n self._shore_coef = shore_coef\\r\\n self.gtab = model.gtab\\r\\n self.radial_order = model.radial_order\\r\\n self.zeta = model.zeta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9ee048ebf306caf8af1fef06c56ad3\",\n \"score\": \"0.5174483\",\n \"text\": \"def __init__(self, model):\\n\\n self.model = model\\n self.fix_none_bounds(10, 813)\\n self.info = {\\n \\\"obj\\\": \\\"{}_{}\\\".format([reac for reac in self.model.get_objective()][0], self.model.id),\\n \\\"fobj\\\": FBA(self.model).fobj\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed8dcdc305216bc0634bee71a575d05\",\n \"score\": \"0.5164404\",\n \"text\": \"def product_main(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"931cadce3dc70d56bfa59dd4e8cc866b\",\n \"score\": \"0.5154811\",\n \"text\": \"def __init__(self, species, qty):\\n\\n #these set the initial attributes for species, quantity and shipped status\\n self.species = species\\n self.qty = qty\\n self.shipped = False\\n self.tax = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f4264d43a26687940e9e7a49bb9da0\",\n \"score\": \"0.51524115\",\n \"text\": \"def __init__(self, name, fuel, fanciness):\\n super().__init__(name, fuel)\\n self.fanciness = fanciness\\n self.price_per_km *= fanciness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f599d45d4a45e7888ab38854d88fe0a7\",\n \"score\": \"0.51432717\",\n \"text\": \"def __init__(self, name, fuel, fanciness):\\n super().__init__(name, fuel)\\n self.fanciness = fanciness\\n self.price_per_km *= self.fanciness\\n self.flagfall = 4.50\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf436a669586d189c271cb9e8b38316f\",\n \"score\": \"0.5142965\",\n \"text\": \"def __init__(self, make, model, year):\\n # super() allows you to call methods from parent class\\n # superclass is parent, subclass is child\\n super().__init__(make, model, year)\\n\\n # can also define additional attributes\\n self.battery_size = 75\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1542ec9e58a9ab3a9bdbf240d0a86296\",\n \"score\": \"0.5134287\",\n \"text\": \"def model(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e7feb2fb5a65a88b23ed6514ea3b98\",\n \"score\": \"0.5133308\",\n \"text\": \"def __init__(self, z, p, dz_soil, ctr, loc):\\n #print(p)\\n self.StomaModel = 'MEDLYN_FARQUHAR' # stomatal model\\n \\n self.Switch_pheno = ctr['pheno_cycle'] # include phenology\\n self.Switch_lai = ctr['seasonal_LAI'] # seasonal LAI\\n self.Switch_WaterStress = ctr['WaterStress'] # water stress affects stomata\\n\\n self.name = p['name']\\n\\n # phenology model\\n if self.Switch_pheno:\\n self.Pheno_Model = Photo_cycle(p['phenop']) # phenology model instance\\n self.pheno_state = self.Pheno_Model.f # phenology state [0...1]\\n else:\\n self.pheno_state = 1.0\\n\\n # dynamic LAI model\\n if self.Switch_lai:\\n # seasonality of leaf area\\n self.LAI_Model = LAI_cycle(p['laip'], loc) # LAI model instance\\n self.relative_LAI = self.LAI_Model.f # LAI relative to annual maximum [0...1]\\n else:\\n self.relative_LAI = 1.0\\n\\n # physical structure\\n self.LAImax = p['LAImax'] # maximum annual 1-sided LAI [m2m-2]\\n self.LAI = self.LAImax * self.relative_LAI # current LAI\\n self.lad_normed = p['lad'] # normalized leaf-area density [m-1]\\n self.lad = self.LAI * self.lad_normed # current leaf-area density [m2m-3]\\n\\n # root properties\\n self.Roots = RootUptake(p['rootp'], dz_soil, self.LAImax)\\n\\n self.dz = z[1] - z[0]\\n\\n # leaf gas-exchange parameters\\n self.photop0 = p['photop'] # A-gs parameters at pheno_state = 1.0 (dict)\\n self.photop = self.photop0.copy() # current A-gs parameters (dict)\\n # leaf properties\\n self.leafp = p['leafp'] # leaf properties (dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46350117caed49e308e33e79f13ed55e\",\n \"score\": \"0.51327664\",\n \"text\": \"def _profile(self):\\n flexible_data_set = cx.FlexibleDataSetType(\\n Code=\\\"QBIC_SOMATIC_SV_V{:.1f}\\\".format(SOMATIC_SV_VERSION),\\n Systemwide=\\\"false\\\",\\n FlexibleDataSetType=\\\"MEASUREMENT\\\",\\n Category=\\\"LABOR\\\")\\n multi_lang_de = cx.MultilingualEntryType(Lang='de', Value='QBIC Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION))\\n multi_lang_en = cx.MultilingualEntryType(Lang='en', Value='QBIC Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION))\\n flexible_data_set.NameMultilingualEntries = [multi_lang_de, multi_lang_en] \\n \\n flexible_data_set.FlexibleValueComplexRefs = [\\n cx.FlexibleValueRefType(FlexibleValueRef=field_type, Required=\\\"false\\\") for field_type in SV_FIELD_TYPES\\n ]\\n \\n self._catalogue_data.append(flexible_data_set)\\n\\n cfr_template = cx.CrfTemplateType(Name='Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION),\\n FlexibleDataSetRef='QBIC_SOMATIC_SV_V{:.1f}'.format(SOMATIC_SV_VERSION),\\n TemplateType='LABORMETHOD', Version='{:.0f}'.format(SOMATIC_SV_VERSION-1), EntityStatus='ACTIVE', Global='false', MultipleUse='false', Active='false')\\n\\n cfr_template_section = cx.CrfTemplateSectionType(Name='Somatic SV V{:.1f}'.format(SOMATIC_SV_VERSION),\\n Height=len(SV_FIELD_TYPES), Width='1', Position='-1')\\n \\n cfr_template_section.CrfTemplateField = [\\n cx.CrfTemplateFieldType(LaborValue=value,\\n LowerRow=ind,\\n LowerColumn='0',\\n UpperRow=ind,\\n UpperColumn='0',\\n Mandatory='false',\\n VisibleCaption='true',\\n FieldType='LABORVALUE') for ind, value in enumerate(SV_FIELD_TYPES)]\\n\\n cfr_template.CrfTemplateSection = [cfr_template_section]\\n\\n self._catalogue_data.append(cfr_template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb82bc65e036a57c35f5f5156e807680\",\n \"score\": \"0.51289624\",\n \"text\": \"def __init__(self: 'ConsoleController',\\n number_of_cheeses: int, number_of_stools: int):\\n #get cheeses and stools\\n self.number_of_cheeses = number_of_cheeses\\n self.num_of_stools = number_of_stools\\n\\n #create a model using cheeses and stools\\n self.model = TOAHModel(number_of_stools)\\n self.model.fill_first_stool(number_of_cheeses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"133482de99420624ce5fecf560610a41\",\n \"score\": \"0.5127023\",\n \"text\": \"def create_top_level_model(sbml_file):\\n sbmlns = SBMLNamespaces(3, 1, \\\"comp\\\", 1)\\n doc = SBMLDocument(sbmlns)\\n doc.setPackageRequired(\\\"comp\\\", True)\\n doc.setPackageRequired(\\\"fbc\\\", False)\\n\\n mdoc = doc.getPlugin(\\\"comp\\\")\\n\\n # create listOfExternalModelDefinitions\\n emd_bounds = comp.create_ExternalModelDefinition(mdoc, \\\"toy_ode_bounds\\\", sbml_file=ode_bounds_file)\\n emd_fba = comp.create_ExternalModelDefinition(mdoc, \\\"toy_fba\\\", sbml_file=fba_file)\\n emd_update = comp.create_ExternalModelDefinition(mdoc, \\\"toy_ode_update\\\", sbml_file=ode_update_file)\\n emd_model = comp.create_ExternalModelDefinition(mdoc, \\\"toy_ode_model\\\", sbml_file=ode_model_file)\\n\\n # create models and submodels\\n model = doc.createModel()\\n model.setId(\\\"toy_top_level\\\")\\n model.setName(\\\"Top level model\\\")\\n model_factory.add_generic_info(model)\\n mplugin = model.getPlugin(\\\"comp\\\")\\n model.setSBOTerm(comp.SBO_CONTINOUS_FRAMEWORK)\\n\\n # add submodel which references the external model definition\\n comp.add_submodel_from_emd(mplugin, submodel_sid=\\\"bounds\\\", emd=emd_bounds)\\n comp.add_submodel_from_emd(mplugin, submodel_sid=\\\"fba\\\", emd=emd_fba)\\n comp.add_submodel_from_emd(mplugin, submodel_sid=\\\"update\\\", emd=emd_update)\\n comp.add_submodel_from_emd(mplugin, submodel_sid=\\\"model\\\", emd=emd_model)\\n\\n # --- compartments ---\\n create_compartments(model, [\\n {A_ID: \\\"extern\\\", A_NAME: \\\"external compartment\\\", A_VALUE: 1.0, A_SPATIAL_DIMENSION: 3, A_UNIT: model_factory.UNIT_VOLUME},\\n {A_ID: 'cell', A_NAME: 'cell', A_VALUE: 1.0, A_SPATIAL_DIMENSION: 3, A_UNIT: model_factory.UNIT_VOLUME}\\n ])\\n\\n # --- species ---\\n # replaced species\\n # (fba species are not replaced, because they need their boundaryConditions for the FBA,\\n # and do not depend on the actual concentrations)\\n create_species(model, [\\n {A_ID: 'C', A_NAME: \\\"C\\\", A_VALUE: 0, A_UNIT: model_factory.UNIT_AMOUNT, A_HAS_ONLY_SUBSTANCE_UNITS: True,\\n A_COMPARTMENT: \\\"extern\\\"},\\n ])\\n\\n # --- parameters ---\\n create_parameters(model, [\\n # bounds\\n {A_ID: 'ub_R1', A_VALUE: 1.0, A_UNIT: model_factory.UNIT_FLUX, A_NAME: 'ub_R1', A_CONSTANT: False},\\n {A_ID: \\\"vR3\\\", A_NAME: \\\"vR3 (FBA flux)\\\", A_VALUE: 0.1, A_UNIT: model_factory.UNIT_FLUX, A_CONSTANT: False}\\n ])\\n\\n # --- reactions ---\\n # dummy reaction in top model\\n r1 = create_reaction(model, rid=\\\"R3\\\", name=\\\"R3 dummy\\\", fast=False, reversible=True,\\n reactants={}, products={\\\"C\\\": 1}, compartment=\\\"extern\\\")\\n # assignment rule\\n create_assignment_rules(model, [{A_ID: \\\"vR3\\\", A_VALUE: \\\"R3\\\"}])\\n\\n # --- replacements ---\\n # replace compartments\\n comp.replace_elements(model, 'extern', ref_type=comp.SBASE_REF_TYPE_PORT,\\n replaced_elements={'fba': ['extern_port'], 'update': ['extern_port'], 'model': ['extern_port']})\\n\\n comp.replace_elements(model, 'cell', ref_type=comp.SBASE_REF_TYPE_PORT,\\n replaced_elements={'fba': ['cell_port']})\\n\\n # replace parameters\\n comp.replace_elements(model, 'ub_R1', ref_type=comp.SBASE_REF_TYPE_PORT,\\n replaced_elements={'bounds': ['ub_R1_port'], 'fba': ['ub_R1_port']})\\n comp.replace_elements(model, 'vR3', ref_type=comp.SBASE_REF_TYPE_PORT,\\n replaced_elements={'update': ['vR3_port']})\\n\\n # replace species\\n comp.replace_elements(model, 'C', ref_type=comp.SBASE_REF_TYPE_PORT,\\n replaced_elements={'fba': ['C_port'], 'update': ['C_port'],\\n 'model': ['C_port']})\\n\\n # replace reaction by fba reaction\\n comp.replaced_by(model, 'R3', ref_type=comp.SBASE_REF_TYPE_PORT,\\n submodel='fba', replaced_by=\\\"R3_port\\\")\\n\\n # replace units\\n for uid in ['s', 'kg', 'm3', 'm2', 'mM', 'item_per_m3', 'm', 'per_s', 'item_per_s']:\\n comp.replace_element_in_submodels(model, uid, ref_type=comp.SBASE_REF_TYPE_UNIT,\\n submodels=['bounds', 'fba', 'update', 'model'])\\n\\n # write SBML file\\n sbml_io.write_and_check(doc, sbml_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a61b3ec62b3cdb766201f4d60c90ea9\",\n \"score\": \"0.51260775\",\n \"text\": \"def __init__(self, modelfits='/global/project/projectdirs/desi/science/td/timedomain-github/snmodels/vincenzi_2019_models.fits'):\\n # Set up core-collapse models.\\n modtab = Table.read(modelfits)\\n \\n # Blacklist models with odd-looking spectra for some epochs (Eddie Sepeku).\\n blacklist = [b'SN2013by', b'SN2013fs', b'SN2009bw', b'SN2012aw', b'SN2009kr', b'ASASSN14jb', b'SN2013am', b'SN2008ax', b'SN2008fq', b'SN2009ip', b'iPTF13bvn', b'SN2008D', b'SN1994I', b'SN2007gr', b'SN2009bb', b'SN2007ru']\\n select = ~np.in1d(modtab['Name'].value, blacklist)\\n modtab = modtab[select]\\n\\n sntypes = np.unique(modtab['Type'])\\n self.models = {}\\n for sntype in sntypes:\\n self.models[sntype] = []\\n for nm in modtab['Name'][modtab['Type'] == sntype]:\\n # Get the corresponding sncosmo model with host dust correction applied.\\n if nm.startswith('SN'):\\n model_name = 'v19-{}-corr'.format(nm[2:].lower())\\n else:\\n model_name = 'v19-{}-corr'.format(nm.lower())\\n self.models[sntype].append(model_name)\\n\\n # Add Ia models.\\n self.models['Ia'] = ['hsiao']\\n \\n # Add kilonova models.\\n self.models['kN'] = []\\n knfiles = sorted(glob('/global/u2/l/lehsani/kilonova/models_0.2_0.8/*knova*.csv'))\\n for knfile in knfiles:\\n kn = KNSource(knfile)\\n sncosmo.register(kn, kn.name, force=True)\\n self.models['kN'].append(kn.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad07bd3f5ada935f0138b4b2f61e43d\",\n \"score\": \"0.51237166\",\n \"text\": \"def __init__(self, community_id, models, extracellular_compartment_id = \\\"e\\\", empty_flag = False):\\n self.community_id = community_id\\n self.models = models\\n self.extracellular_compartment_id = extracellular_compartment_id\\n self.empty_flag = empty_flag\\n self.model_dic = self.__model_dic()\\n self.cmodel = self.__create_cmodel()\\n self.objectives = self.cmodel.get_objective()\\n self.pex_cons = self.__get_pex_constraints()\\n self.biomass_reactions = self.__biomass_reactions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ba048ca022f541a1ee1e10167a7962\",\n \"score\": \"0.5115951\",\n \"text\": \"def __init__(self):\\n self.label = \\\"Apply model\\\"\\n self.description = 'Applies a model to a series of data rasters obtain a response raster'\\n self.canRunInBackground = False\\n self.category = \\\"Modelling\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eea97c6320b8410c80c39d70ab96c84\",\n \"score\": \"0.51052773\",\n \"text\": \"def __init__(self, *args):\\r\\n self.__stencils = {}\\r\\n self.__shapes = {}\\r\\n #self.add_stencil(\\\"Basic Shapes.vss\\\")\\r\\n for stencil in args:\\r\\n self.add_stencil(stencil)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c650c7e58dccfdea82af9a8d8eccb70\",\n \"score\": \"0.5098148\",\n \"text\": \"def main():\\n # sde = SDE()\\n # sde.import_and_export()\\n market = Market()\\n market.update_marketData()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3408a0e1bd4491b367d3a628b5fbcd4\",\n \"score\": \"0.5096738\",\n \"text\": \"def __init__(self,\\n sbml_file,\\n extra_cellular_id,\\n periplasm_id,\\n cytoplasm_id,\\n model_dir='..',\\n json_model=None,\\n groups_size=150,\\n kon=0.8,\\n koff=0.8,\\n rates='0:0:0:1',\\n enzymes=1000,\\n metabolites=600000):\\n\\n self.groups_size = groups_size\\n self.parameter_writer = XMLParametersWriter(model_dir=model_dir)\\n self.coder = ModelCodeWriter(model_dir=model_dir)\\n self.parser = SBMLParser(sbml_file,\\n extra_cellular_id,\\n periplasm_id,\\n cytoplasm_id,\\n json_model=json_model,\\n default_konSTP=kon,\\n default_konPTS=kon,\\n default_koffSTP=koff,\\n default_koffPTS=koff,\\n default_metabolite_amount=metabolites,\\n default_enzyme_amount=enzymes,\\n default_rate=rates,\\n default_reject_rate=rates,\\n default_interval_time=rates)\\n\\n special_compartment_ids = [extra_cellular_id, periplasm_id, cytoplasm_id]\\n\\n c_external_enzyme_sets = {cid: [MEMBRANE]\\n for cid in self.parser.get_compartments()\\n if cid not in special_compartment_ids}\\n c_external_enzyme_sets[periplasm_id] = [INNER, TRANS]\\n\\n e_external_enzyme_sets = {periplasm_id: [OUTER, TRANS]}\\n\\n # Compartment model structures\\n self.periplasm = ModelStructure(periplasm_id,\\n self.parser,\\n membranes=[OUTER, INNER, TRANS])\\n\\n self.extra_cellular = ModelStructure(extra_cellular_id,\\n self.parser,\\n external_enzyme_sets=e_external_enzyme_sets)\\n\\n self.cytoplasm = ModelStructure(cytoplasm_id,\\n self.parser,\\n external_enzyme_sets=c_external_enzyme_sets)\\n\\n self.organelles = {comp_id: ModelStructure(comp_id, self.parser, [MEMBRANE])\\n for comp_id in self.parser.get_compartments()\\n if comp_id not in special_compartment_ids}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294fb43d0e5c8ae0155134b298fd02f7\",\n \"score\": \"0.5083324\",\n \"text\": \"def __init__(self, model_file_path='../model/model_final', target_var='SalePrice'): \\n self.model_file_path = model_file_path\\n self.load_model()\\n self.target_var = target_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597249eb3dedd47cc6937d9357251e99\",\n \"score\": \"0.5083237\",\n \"text\": \"def construct(self, geom):\\n\\n main_shape = geom.shapes.Tubs(self.name, rmin = self.halfDimension[\\\"rmin\\\"],\\n rmax = self.halfDimension[\\\"rmax\\\"], dz = self.halfDimension[\\\"dz\\\"])\\n main_lv = geom.structure.Volume(self.name+\\\"_lv\\\", material=self.Material, shape=main_shape)\\n if isinstance(self.Sensitive,str):\\n main_lv.params.append((\\\"SensDet\\\",self.Sensitive))\\n self.add_volume(main_lv)\\n\\n straw_shape = geom.shapes.Tubs(self.name+\\\"_straw\\\", rmin = self.halfSTDimension[\\\"rmin\\\"],\\n rmax = self.halfSTDimension[\\\"rmax\\\"], dz = self.halfSTDimension[\\\"dz\\\"])\\n straw_lv = geom.structure.Volume(self.name+\\\"_straw_lv\\\", material=self.STMaterial, shape=straw_shape)\\n\\n wire_shape = geom.shapes.Tubs(self.name+\\\"_wire\\\", self.halfWireDimension[\\\"rmin\\\"],\\n rmax = self.halfWireDimension[\\\"rmax\\\"], dz = self.halfWireDimension[\\\"dz\\\"])\\n wire_lv = geom.structure.Volume(self.name+\\\"_wire_lv\\\", material=self.WireMaterial, shape=wire_shape)\\n\\n straw_pla = geom.structure.Placement( self.name+\\\"_straw_pla\\\", volume = straw_lv )\\n wire_pla = geom.structure.Placement( self.name+\\\"_wire_pla\\\", volume = wire_lv )\\n main_lv.placements.append( straw_pla.name )\\n main_lv.placements.append( wire_pla.name )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b3b31f7f083d4ab5495942c3a43018\",\n \"score\": \"0.5074051\",\n \"text\": \"def __init__(self, make, model, year):\\n super().__init__(make, model, year)\\n #new 'Battery' instance above defined as an attribute within 'ElectricCar' instance\\n self.battery = Battery()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e02764166a3d23a0857f6965f383a596\",\n \"score\": \"0.50672626\",\n \"text\": \"def stadardization_model(corr):\\n class ModelStandardization_mystand( ModelStandardization ):\\n STANDARDIZATION = corr\\n\\n return ModelStandardization_mystand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7963a1de6ca2f53960b2a73a0bcab7a\",\n \"score\": \"0.5061793\",\n \"text\": \"def build_car(self, model):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1634cd3a5d6f896a8f6d2f44ecf2a1fb\",\n \"score\": \"0.5059821\",\n \"text\": \"def __init__(self):\\r\\n\\r\\n super(Model, self).__init__()\\r\\n\\r\\n # Define private dictionary attributes.\\r\\n\\r\\n # Define private list attributes.\\r\\n\\r\\n # Define private scalar attributes.\\r\\n\\r\\n # Define public dictionary attributes.\\r\\n\\r\\n # Define public list attributes.\\r\\n self.lst_derate_criteria = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]\\r\\n\\r\\n # Define public scalar attributes.\\r\\n self.application = 0 # Application index.\\r\\n self.base_hr = 0.0 # Base hazard rate.\\r\\n self.piE = 0.0 # Environment pi factor.\\r\\n self.reason = \\\"\\\" # Overstress reason.\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfcf82f9b272d87c28d4128972cdbdc4\",\n \"score\": \"0.5057847\",\n \"text\": \"def pwr_assembly():\\n\\n model = openmc.model.Model()\\n\\n # Define materials.\\n fuel = openmc.Material(name='Fuel')\\n fuel.set_density('g/cm3', 10.29769)\\n fuel.add_nuclide('U234', 4.4843e-6)\\n fuel.add_nuclide('U235', 5.5815e-4)\\n fuel.add_nuclide('U238', 2.2408e-2)\\n fuel.add_nuclide('O16', 4.5829e-2)\\n\\n clad = openmc.Material(name='Cladding')\\n clad.set_density('g/cm3', 6.55)\\n clad.add_nuclide('Zr90', 2.1827e-2)\\n clad.add_nuclide('Zr91', 4.7600e-3)\\n clad.add_nuclide('Zr92', 7.2758e-3)\\n clad.add_nuclide('Zr94', 7.3734e-3)\\n clad.add_nuclide('Zr96', 1.1879e-3)\\n\\n hot_water = openmc.Material(name='Hot borated water')\\n hot_water.set_density('g/cm3', 0.740582)\\n hot_water.add_nuclide('H1', 4.9457e-2)\\n hot_water.add_nuclide('O16', 2.4672e-2)\\n hot_water.add_nuclide('B10', 8.0042e-6)\\n hot_water.add_nuclide('B11', 3.2218e-5)\\n hot_water.add_s_alpha_beta('c_H_in_H2O')\\n\\n # Define the materials file.\\n model.materials = (fuel, clad, hot_water)\\n\\n # Instantiate ZCylinder surfaces\\n fuel_or = openmc.ZCylinder(x0=0, y0=0, r=0.39218, name='Fuel OR')\\n clad_or = openmc.ZCylinder(x0=0, y0=0, r=0.45720, name='Clad OR')\\n\\n # Create boundary planes to surround the geometry\\n pitch = 21.42\\n min_x = openmc.XPlane(x0=-pitch/2, boundary_type='reflective')\\n max_x = openmc.XPlane(x0=+pitch/2, boundary_type='reflective')\\n min_y = openmc.YPlane(y0=-pitch/2, boundary_type='reflective')\\n max_y = openmc.YPlane(y0=+pitch/2, boundary_type='reflective')\\n\\n # Create a fuel pin universe\\n fuel_pin_universe = openmc.Universe(name='Fuel Pin')\\n fuel_cell = openmc.Cell(name='fuel', fill=fuel, region=-fuel_or)\\n clad_cell = openmc.Cell(name='clad', fill=clad, region=+fuel_or & -clad_or)\\n hot_water_cell = openmc.Cell(name='hot water', fill=hot_water, region=+clad_or)\\n fuel_pin_universe.add_cells([fuel_cell, clad_cell, hot_water_cell])\\n\\n\\n # Create a control rod guide tube universe\\n guide_tube_universe = openmc.Universe(name='Guide Tube')\\n gt_inner_cell = openmc.Cell(name='guide tube inner water', fill=hot_water,\\n region=-fuel_or)\\n gt_clad_cell = openmc.Cell(name='guide tube clad', fill=clad,\\n region=+fuel_or & -clad_or)\\n gt_outer_cell = openmc.Cell(name='guide tube outer water', fill=hot_water,\\n region=+clad_or)\\n guide_tube_universe.add_cells([gt_inner_cell, gt_clad_cell, gt_outer_cell])\\n\\n # Create fuel assembly Lattice\\n assembly = openmc.RectLattice(name='Fuel Assembly')\\n assembly.pitch = (pitch/17, pitch/17)\\n assembly.lower_left = (-pitch/2, -pitch/2)\\n\\n # Create array indices for guide tube locations in lattice\\n template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\\n 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\\n template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\\n 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\\n\\n # Create 17x17 array of universes\\n assembly.universes = np.tile(fuel_pin_universe, (17, 17))\\n assembly.universes[template_x, template_y] = guide_tube_universe\\n\\n # Create root Cell\\n root_cell = openmc.Cell(name='root cell', fill=assembly)\\n root_cell.region = +min_x & -max_x & +min_y & -max_y\\n\\n # Create root Universe\\n model.geometry.root_universe = openmc.Universe(name='root universe')\\n model.geometry.root_universe.add_cell(root_cell)\\n\\n model.settings.batches = 10\\n model.settings.inactive = 5\\n model.settings.particles = 100\\n model.settings.source = openmc.Source(space=openmc.stats.Box(\\n [-pitch/2, -pitch/2, -1], [pitch/2, pitch/2, 1], only_fissionable=True))\\n\\n plot = openmc.Plot()\\n plot.origin = (0.0, 0.0, 0)\\n plot.width = (21.42, 21.42)\\n plot.pixels = (300, 300)\\n plot.color_by = 'material'\\n model.plots.append(plot)\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b2a7740fe2af300e9f4af151c64dc95\",\n \"score\": \"0.5050282\",\n \"text\": \"def create_submodel(self, verbose = True, vverbose = False):\\n\\n with open(self.basemodel, 'rb') as f: hspfmodel = pickle.load(f)\\n\\n comid = self.gagecomid\\n upcomids = self.upcomids\\n filename = hspfmodel.filename + comid\\n picklefile = hspfmodel.filepath + filename\\n\\n submodel = HSPFModel()\\n submodel.build_from_existing(hspfmodel, filename, directory = \\n hspfmodel.filepath[:-1],\\n verbose = vverbose)\\n\\n # find the subbasins between the outlet and the upstream comids and\\n # store in an updown dictionary\\n\\n updown = {comid: 0}\\n\\n current = 0\\n\\n while current != len(updown):\\n\\n # see if the current length changes to check if done\\n\\n current = len(updown)\\n\\n # iterate throught the subbasins and see if any need to be added\\n\\n for up, down in hspfmodel.updown.items():\\n\\n if (up not in updown and # not already there\\n up not in upcomids and # between the boundaries\\n down in updown): # downstream is there\\n \\n updown[up] = down\\n \\n # overwrite the old updown dictionary\\n\\n submodel.updown = updown\\n\\n # overwrite the inlets and outlets\\n\\n submodel.inlets = [hspfmodel.updown[c] for c in upcomids]\\n submodel.outlets = [comid]\\n\\n # overwrite the old subbasin dictionary\\n\\n submodel.subbasins = {comid: subbasin for comid, subbasin in \\n submodel.subbasins.items() if comid in updown}\\n\\n # build with the updated model subbasin info\\n\\n submodel.build()\\n\\n # add in the modules\\n\\n if self.temp: submodel.add_temp()\\n\\n if self.snow: \\n \\n densities = [o.RDENPF \\n for o in hspfmodel.perlnds + hspfmodel.implnds]\\n depths = [o.packsnow / o.RDENPF \\n for o in hspfmodel.perlnds + hspfmodel.implnds]\\n\\n depth = sum(depths) / len(depths)\\n density = sum(densities) / len(densities)\\n\\n submodel.add_snow(depth = depth, density = density) \\n\\n if self.hydrology: submodel.add_hydrology()\\n \\n # add the flowgage data to the model\\n\\n for identifier in hspfmodel.flowgages:\\n if identifier == self.gageid:\\n start_date, tstep, data = hspfmodel.flowgages[identifier]\\n submodel.add_timeseries('flowgage', identifier, start_date, \\n data, tstep = tstep)\\n\\n # add the watershed time series dictionaries for the model\\n\\n timeseries = {'inflow': hspfmodel.inflows,\\n 'temperature': hspfmodel.temperatures,\\n 'dewpoint': hspfmodel.dewpoints,\\n 'wind': hspfmodel.windspeeds,\\n 'solar': hspfmodel.solars,\\n 'snowfall': hspfmodel.snowfalls,\\n 'snowdepth': hspfmodel.snowdepths,\\n }\\n\\n for tstype, d in timeseries.items():\\n for identifier in d: \\n start_date, tstep, data = d[identifier]\\n submodel.add_timeseries(tstype, identifier, start_date, data, \\n tstep = tstep)\\n\\n # add the subbasin timeseries as needed\\n\\n for identifier in hspfmodel.precipitations:\\n if identifier in submodel.subbasins.keys():\\n start_date, tstep, data = hspfmodel.precipitations[identifier]\\n submodel.add_timeseries('precipitation', identifier, start_date,\\n data, tstep = tstep)\\n\\n # add the landuse timeseries as needed\\n\\n landuse_keys = {'Corn': 'cereals',\\n 'Soybeans': 'legumes',\\n 'Pasture/grass': 'pasture',\\n 'Other grain': 'cereals',\\n 'Hay/alfalfa': 'alfalfa',\\n 'Water/wetland': 'wetlands',\\n 'Fallow land': 'fallow',\\n 'Forest': 'others',\\n 'Developed': 'others',\\n 'Impervious': 'others',\\n 'Other': 'others',\\n }\\n\\n ltypes = [landuse_keys[i] for i in hspfmodel.landuse]\\n\\n for identifier in hspfmodel.evaporations:\\n if identifier in ltypes:\\n start_date, tstep, data = hspfmodel.evaporations[identifier]\\n submodel.add_timeseries('evaporation', identifier, start_date,\\n data, tstep = tstep)\\n\\n # add the influent flows as needed\\n \\n for upcomid in upcomids:\\n\\n # find the upstream gage number\\n\\n \\n upgage = [v for k, v in \\n hspfmodel.subbasin_timeseries['flowgage'].items() \\n if k == upcomid][0]\\n incomid = hspfmodel.updown[upcomid]\\n\\n # find the outlet flows from the previous upstream calibration\\n\\n t = (self.directory, self.HUC8, upgage)\\n flowfile = '{}/{}/calibrations/{}/outletflows'.format(*t)\\n \\n # get the time series and add it to the model\\n\\n if not os.path.isfile(flowfile): \\n raise RuntimeError('warning: upstream calibration of gage ' +\\n '{} does not exist\\\\n'.format(upgage))\\n with open(flowfile, 'rb') as f: times, data = pickle.load(f)\\n\\n tstep = math.ceil((times[1] - times[0]).total_seconds() / 60)\\n\\n submodel.add_timeseries('inflow', '{}'.format(incomid), times[0], \\n data, tstep = tstep)\\n\\n # assign the inflows from upstream to any subbasins\\n\\n otype = 'Reach'\\n\\n submodel.assign_operation_timeseries('inflow', incomid, 'Reach', \\n '{}'.format(incomid))\\n \\n # assign as needed\\n\\n for tstype, identifier in hspfmodel.watershed_timeseries.items():\\n \\n submodel.assign_watershed_timeseries(tstype, identifier)\\n\\n for tstype, d in hspfmodel.subbasin_timeseries.items():\\n\\n for subbasin, identifier in d.items():\\n \\n if subbasin in submodel.subbasins:\\n\\n submodel.assign_subbasin_timeseries(tstype, subbasin,\\n identifier)\\n\\n for tstype, d in hspfmodel.landuse_timeseries.items():\\n\\n for landtype, identifier in d.items():\\n \\n if landtype in submodel.landuse:\\n\\n submodel.assign_landuse_timeseries(tstype, landtype,\\n identifier)\\n\\n for tstype, d1 in hspfmodel.operation_timeseries.items():\\n\\n for subbasin, d2 in d1.items():\\n\\n for otype, identifier in d2.items():\\n\\n if subbasin in submodel.subbasins:\\n\\n submodel.assign_operation_timeseries(tstype, subbasin,\\n otype, identifier)\\n\\n with open(picklefile, 'wb') as f: pickle.dump(submodel, f)\\n\\n self.basemodel = picklefile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf96a316ed8b50e59e35c107ca30910\",\n \"score\": \"0.5044699\",\n \"text\": \"def __init__(self, Model, settings):\\n KratosMultiphysics.Process.__init__(self)\\n\\n # The value can be a double or a string (function)\\n default_settings = KratosMultiphysics.Parameters(\\n \\\"\\\"\\\"\\n {\\n \\\"help\\\" : \\\"This sets the initial conditions in terms of imposed strain, stress or deformation gradient\\\",\\n \\\"mesh_id\\\" : 0,\\n \\\"model_part_name\\\" : \\\"please_specify_model_part_name\\\",\\n \\\"dimension\\\" : 3,\\n \\\"imposed_strain\\\" : [\\\"0*t\\\",\\\"0*t\\\",\\\"0*t\\\",0,0,0],\\n \\\"imposed_stress\\\" : [0,0,0,0,0,0],\\n \\\"imposed_deformation_gradient\\\" : [[1,0,0],[0,1,0],[0,0,1]],\\n \\\"interval\\\" : [0.0, 1e30]\\n }\\n \\\"\\\"\\\"\\n )\\n\\n # assign this here since it will change the \\\"interval\\\" prior to validation\\n self.interval = KratosMultiphysics.IntervalUtility(settings)\\n\\n settings.ValidateAndAssignDefaults(default_settings)\\n self.model_part = Model[settings[\\\"model_part_name\\\"].GetString()]\\n self.dimension = settings[\\\"dimension\\\"].GetInt()\\n\\n # init strain\\n self.strain_functions = components_to_functions(settings[\\\"imposed_strain\\\"])\\n nr_comps = len(self.strain_functions)\\n self.imposed_strain = KratosMultiphysics.Vector(nr_comps)\\n\\n # init stress\\n self.stress_functions = components_to_functions(settings[\\\"imposed_stress\\\"])\\n nr_comps = len(self.stress_functions)\\n self.imposed_stress = KratosMultiphysics.Vector(nr_comps)\\n\\n # init deformation gradient\\n aux_matrix = settings[\\\"imposed_deformation_gradient\\\"]\\n self.deformation_functions = []\\n for row in aux_matrix.values():\\n aux_vector = components_to_functions(row)\\n self.deformation_functions.append(aux_vector)\\n nrows = aux_matrix.size()\\n ncols = aux_matrix[0].size()\\n self.imposed_deformation_gradient = KratosMultiphysics.Matrix(nrows, ncols)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d10862cdba968122eaf36625483b31\",\n \"score\": \"0.50441337\",\n \"text\": \"def setShadeModel(*args):\\n return _coin.SoLazyElement_setShadeModel(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d116762b520f239d193616f2b238969b\",\n \"score\": \"0.50420004\",\n \"text\": \"def __init__(self, Mv):\\n \\n # get the gravitational constant (the value is 4.498502151575286e-06)\\n self.G = const.G.to(u.kpc**3/u.Msun/u.Gyr**2).value\\n \\n # initialize the virial mass global variable \\n self.Mvir = Mv\\n \\n ## Cosmology Same as Patel 2020\\n self.h = 0.7 # Hubble constant at z=0 / 100 \\n self.omegaM = 0.27\\n self.DelVir = 359 # default z=0 overdensity for this cosmology\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1129bdd69386cff2ceb1c8cb6f8afae2\",\n \"score\": \"0.50415975\",\n \"text\": \"def setup_prod():\\n setup_general()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc28a96787b33b44f751dbe4c93bd9f9\",\n \"score\": \"0.5040365\",\n \"text\": \"def generate_catalogue(cube_side, mass_params, z, h):\\n\\n print(\\\"Generating catalogue for a volume of ({:.2f} Mpc/h)^3\\\\n\\\".format(cube_side))\\n\\n catalogue_volume = cube_side**3\\n\\n # Get the bin width and generate the bins.\\n bin_width = mass_params[2]\\n mass_range = 10 ** np.arange(mass_params[0], mass_params[1], mass_params[2]) #log[Msun]\\n\\n # Generate the mass function itself - this is from the colossus toolbox\\n local_mass_function = mass_function.massFunction(mass_range, z, mdef='200m', model='tinker08', q_out='dndlnM') \\\\\\n * np.log(10) / h # dn/dlog10M\\n\\n # We determine the Cumulative HMF starting from the high mass end, multiplied by the bin width.\\n # This effectively gives the cumulative probability of a halo existing.\\n cumulative_mass_function = np.flip(np.cumsum(np.flip(local_mass_function, 0)), 0) * bin_width\\n\\n ########################################################################\\n # Interpolation Tests\\n # Interpolator for the testing - we will update this with the volume in a second.\\n # This is essentially for a volume of size unity.\\n interpolator = sp.interpolate.interp1d(cumulative_mass_function, mass_range)\\n\\n sample_index = int(np.floor(len(cumulative_mass_function) / 2)) # index of the half way point\\n num_test = cumulative_mass_function[sample_index] # The value of the cum function at this index\\n mass_test = interpolator(num_test) # Interpolate to get the mass that this predicts\\n # Check that these values are equivalent.\\n assert mass_range[sample_index] == mass_test, \\\\\\n \\\"Interpolation method incorrect: Back interpolation at midpoint failed\\\"\\n # Check first element is equivalent to the total to 10 SF accuracy\\n assert np.round(cumulative_mass_function[0], 10) ==\\\\\\n np.round(np.sum(local_mass_function) * bin_width, 10), \\\"Final cum sum element != total sum\\\"\\n ########################################################################\\n\\n # Multiply by volume\\n cumulative_mass_function = cumulative_mass_function * catalogue_volume\\n\\n # Get the maximum cumulative number.\\n max_number = np.floor(cumulative_mass_function[0])\\n range_numbers = np.arange(max_number)\\n\\n # Update interpolator\\n interpolator = sp.interpolate.interp1d(cumulative_mass_function, mass_range)\\n mass_catalog = interpolator(range_numbers[range_numbers >= np.amin(cumulative_mass_function)])\\n\\n print(\\\"Number of halos generated: {:d}\\\\n\\\".format(len(mass_catalog)))\\n\\n mass_catalog = np.log10(mass_catalog)\\n return mass_catalog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3967922f2cab6aa214c6a5c1206ec360\",\n \"score\": \"0.5034182\",\n \"text\": \"def __init__(self, x=0, Np=10, error_factor=1.0, dist='Gaussian', Energy=None, relement='Au', NrDep='False', H=1.0, HvvgtR=False,\\n Rsig=0.0, norm=1.0, norm_err=0.01, sbkg=0.0, cbkg=0.0, abkg=0.0, D=1.0, phi=0.1, U=-1.0, SF='None',Nalf=200,term='Total',\\n mpar={'Layers': {'Material': ['Au', 'H2O'], 'Density': [19.32, 1.0], 'SolDensity': [1.0, 1.0],\\n 'Rmoles': [1.0, 1.0], 'R': [1.0, 0.0]}}):\\n if type(x)==list:\\n self.x=np.array(x)\\n else:\\n self.x=x\\n self.norm=norm\\n self.norm_err = norm_err\\n self.sbkg=sbkg\\n self.cbkg=cbkg\\n self.abkg=abkg\\n self.dist=dist\\n self.Rsig=Rsig\\n self.Np=Np\\n self.H=H\\n self.HvvgtR=HvvgtR\\n self.Nalf=Nalf\\n self.Energy=Energy\\n self.relement=relement\\n self.NrDep=NrDep\\n #self.rhosol=rhosol\\n self.error_factor=error_factor\\n self.D=D\\n self.phi=phi\\n self.U=U\\n self.term=term\\n self.__mpar__=mpar #If there is any multivalued parameter\\n self.SF=SF\\n self.choices={'HvvgtR':['True','False'],\\n 'dist':['Gaussian','LogNormal'],'NrDep':['True','False'],\\n 'SF':['None','Hard-Sphere', 'Sticky-Sphere'],\\n 'term': ['SAXS-term', 'Cross-term', 'Resonant-term',\\n 'Total']\\n } #If there are choices available for any fixed parameters\\n self.__cf__=Chemical_Formula()\\n self.__fit__=False\\n self.output_params={'scaler_parameters':{}}\\n self.__mkeys__=list(self.__mpar__.keys())\\n self.init_params()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c8b444ddf946a871eb505614b83a58\",\n \"score\": \"0.50339866\",\n \"text\": \"def __init__(self):\\n from desisim.io import read_basis_templates\\n \\n balflux, balwave, balmeta = read_basis_templates(objtype='BAL')\\n self.balflux = balflux\\n self.balwave = balwave\\n self.balmeta = balmeta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c5b7ebda2ef1b74500cdd4c5f229e27\",\n \"score\": \"0.5030364\",\n \"text\": \"def __init__(self):\\n \\n self.fdn = csmFoundation()\\n self.transportation = 0.0\\n self.roadsCivil = 0.0\\n self.portStaging = 0.0\\n self.installation = 0.0\\n self.electrical = 0.0\\n self.engPermits = 0.0\\n self.pai = 0.0\\n self.scour = 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c59dc482900e1b624eb3025101a1959\",\n \"score\": \"0.5028235\",\n \"text\": \"def __init__(self, env, sub_model):\\n super(ResidualModel, self).__init__()\\n self.env = env\\n self.model = hydra.utils.instantiate(sub_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c539b7e75e8572870810e761acafa8\",\n \"score\": \"0.5015839\",\n \"text\": \"def cg():\\n\\n L = 250 # bar length\\n m = 4 # number of requests\\n w = [187, 119, 74, 90] # size of each item\\n b = [1, 2, 2, 1] # demand for each item\\n\\n # creating models and auxiliary lists\\n master = Model(SOLVER)\\n lambdas = []\\n constraints = []\\n\\n # creating an initial pattern (which cut one item per bar)\\n # to provide the restricted master problem with a feasible solution\\n for i in range(m):\\n lambdas.append(master.add_var(obj=1, name='lambda_%d' % (len(lambdas) + 1)))\\n\\n # creating constraints\\n for i in range(m):\\n constraints.append(master.add_constr(lambdas[i] >= b[i], name='i_%d' % (i + 1)))\\n\\n # creating the pricing problem\\n pricing = Model(SOLVER)\\n\\n # creating pricing variables\\n a = []\\n for i in range(m):\\n a.append(pricing.add_var(obj=0, var_type=INTEGER, name='a_%d' % (i + 1)))\\n\\n # creating pricing constraint\\n pricing += xsum(w[i] * a[i] for i in range(m)) <= L, 'bar_length'\\n\\n pricing.write('pricing.lp')\\n\\n new_vars = True\\n while (new_vars):\\n\\n ##########\\n # STEP 1: solving restricted master problem\\n ##########\\n\\n master.optimize()\\n master.write('master.lp')\\n\\n # printing dual values\\n print_solution(master)\\n print('pi = ', end='')\\n print([constraints[i].pi for i in range(m)])\\n print('')\\n\\n ##########\\n # STEP 2: updating pricing objective with dual values from master\\n ##########\\n\\n pricing.objective = 1\\n for i in range(m):\\n a[i].obj = -constraints[i].pi\\n\\n # solving pricing problem\\n pricing.optimize()\\n\\n # printing pricing solution\\n z_val = pricing.objective_value()\\n print('Pricing:')\\n print(' z = {z_val}'.format(**locals()))\\n print(' a = ', end='')\\n print([v.x for v in pricing.vars])\\n print('')\\n\\n ##########\\n # STEP 3: adding the new columns\\n ##########\\n\\n # checking if columns with negative reduced cost were produced and\\n # adding them into the restricted master problem\\n if pricing.objective_value() < - EPS:\\n coeffs = [a[i].x for i in range(m)]\\n column = Column(constraints, coeffs)\\n lambdas.append(master.add_var(obj=1, column=column, name='lambda_%d' % (len(lambdas) + 1)))\\n\\n print('new pattern = {coeffs}'.format(**locals()))\\n\\n # if no column with negative reduced cost was produced, then linear\\n # relaxation of the restricted master problem is solved\\n else:\\n new_vars = False\\n\\n pricing.write('pricing.lp')\\n # pdb.set_trace()\\n\\n print_solution(master)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51cb817be985002f692d35e089526f32\",\n \"score\": \"0.5011754\",\n \"text\": \"def __init__(self, ngrps=2, nints=2, teff=5700.0, logg=4.0, feh=0.0, alpha=0.0, jmag=9.0, stellar_model='phoenix', filter='CLEAR', subarray='SUBSTRIP256', run=True, add_planet=False, scale=1., **kwargs):\\n # Retrieve stellar model\\n if stellar_model.lower() == 'phoenix':\\n wav, flx = ma.get_phoenix_model(feh, alpha, teff, logg)\\n elif stellar_model.lower() == 'atlas':\\n wav, flx = ma.get_atlas_model(feh, teff, logg)\\n\\n # Scale model spectrum to user-input J-band\\n flx = ma.scale_spectrum(wav, flx, jmag)\\n\\n # Initialize base class\\n super().__init__(ngrps=ngrps, nints=nints, star=[wav, flx], subarray=subarray, filter=filter, **kwargs)\\n\\n # Add planet\\n if add_planet:\\n self.planet = hu.PLANET_DATA\\n self.tmodel = hu.transit_params(self.time.jd)\\n self.tmodel.t0 = np.mean(self.time.jd)\\n self.tmodel.teff = teff\\n self.tmodel.logg = logg\\n self.tmodel.feh = feh\\n self.tmodel.alpha = alpha\\n\\n # Run the simulation\\n if run:\\n self.create()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a05009c7e19f6c93fff49a0de3225fb\",\n \"score\": \"0.50089544\",\n \"text\": \"def __init__(self, coreRadius=0.0, rAC50=0.0, coreStrandCount=0, radius=0.0, material=\\\"aluminum\\\", rDC20=0.0, sizeDescription='', rAC75=0.0, gmr=0.0, ratedCurrent=0.0, strandCount=0, rAC25=0.0, WireArrangements=None, ConcentricNeutralCableInfos=None, *args, **kw_args):\\n #: (if there is a different core material) Radius of the central core.\\n self.coreRadius = coreRadius\\n\\n #: AC resistance per unit length of the conductor at 50 oC.\\n self.rAC50 = rAC50\\n\\n #: (if used) Number of strands in the steel core.\\n self.coreStrandCount = coreStrandCount\\n\\n #: Outside radius of the wire.\\n self.radius = radius\\n\\n #: Wire material. Values are: \\\"aluminum\\\", \\\"copper\\\", \\\"other\\\", \\\"steel\\\", \\\"acsr\\\"\\n self.material = material\\n\\n #: DC resistance per unit length of the conductor at 20 oC.\\n self.rDC20 = rDC20\\n\\n #: Describes the wire guage or cross section (e.g., 4/0, #2, 336.5).\\n self.sizeDescription = sizeDescription\\n\\n #: AC resistance per unit length of the conductor at 75 oC.\\n self.rAC75 = rAC75\\n\\n #: Geometric mean radius. If we replace the conductor by a thin walled tube of radius GMR, then its reactance is identical to the reactance of the actual conductor.\\n self.gmr = gmr\\n\\n #: Current carrying capacity of the wire under stated thermal conditions.\\n self.ratedCurrent = ratedCurrent\\n\\n #: Number of strands in the wire.\\n self.strandCount = strandCount\\n\\n #: AC resistance per unit length of the conductor at 25 oC.\\n self.rAC25 = rAC25\\n\\n self._WireArrangements = []\\n self.WireArrangements = [] if WireArrangements is None else WireArrangements\\n\\n self._ConcentricNeutralCableInfos = []\\n self.ConcentricNeutralCableInfos = [] if ConcentricNeutralCableInfos is None else ConcentricNeutralCableInfos\\n\\n super(WireType, self).__init__(*args, **kw_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a547e87cba133b63b251f82a2dbf2774\",\n \"score\": \"0.49989748\",\n \"text\": \"def canada_model_cli():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee6a2dbf18053a2ef3b8991df176cb5\",\n \"score\": \"0.4996126\",\n \"text\": \"def for_model(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed169bf514baefb9d0ea3e8fe334cd8b\",\n \"score\": \"0.49942312\",\n \"text\": \"def __init__(self, \\n Omega_c, Omega_b, Omega_l, h, n_s, A_s=None, sigma_8=None,\\n Omega_g=0.0, Omega_n_mass=0.0, Omega_n_rel=0.0, \\n w0=-1., wa=0., N_nu_mass=0, N_nu_rel = 3.046, mnu=0.0):\\n # Set parameter values\\n self.Omega_c = Omega_c\\n self.Omega_b = Omega_b\\n self.Omega_l = Omega_l\\n self.h = h\\n self.n_s = n_s\\n self.A_s = A_s\\n self.sigma_8 = sigma_8\\n self.Omega_g = Omega_g\\n self.Omega_n_mass = Omega_n_mass\\n self.Omega_n_rel = Omega_n_rel\\n self.w0 = w0\\n self.wa = wa\\n self.N_nu_mass = N_nu_mass\\n self.N_nu_rel = N_nu_rel\\n self.mnu = mnu\\n self.sigma_8 = sigma_8\\n \\n # Set density parameters according to consistency relations\\n self.Omega_m = self.Omega_c + self.Omega_b + self.Omega_n_mass\\n self.Omega_k = 1. - ( self.Omega_m + self.Omega_l \\n + self.Omega_g + self.Omega_n_rel )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc4743d680f30dab73d7a5e7a95ac6a3\",\n \"score\": \"0.4993503\",\n \"text\": \"def run_model(self, kwargs):\\r\\n \\r\\n loc = kwargs.pop(\\\"lookup shortage loc\\\")\\r\\n speed = kwargs.pop(\\\"lookup shortage speed\\\")\\r\\n kwargs['shortage price effect lookup'] = [self.f(x/10, speed, loc) for x in range(0,100)]\\r\\n \\r\\n speed = kwargs.pop(\\\"lookup price substitute speed\\\")\\r\\n begin = kwargs.pop(\\\"lookup price substitute begin\\\")\\r\\n end = kwargs.pop(\\\"lookup price substitute end\\\")\\r\\n kwargs['relative price substitute lookup'] = [self.priceSubstite(x, speed, begin, end) for x in range(0,100, 10)]\\r\\n \\r\\n scale = kwargs.pop(\\\"lookup returns to scale speed\\\")\\r\\n speed = kwargs.pop(\\\"lookup returns to scale scale\\\")\\r\\n kwargs['returns to scale lookup'] = [self.returnsToScale(x, speed, scale) for x in range(0, 101, 10)]\\r\\n \\r\\n scale = kwargs.pop(\\\"lookup approximated learning speed\\\")\\r\\n speed = kwargs.pop(\\\"lookup approximated learning scale\\\")\\r\\n start = kwargs.pop(\\\"lookup approximated learning start\\\")\\r\\n kwargs['approximated learning effect lookup'] = [self.approxLearning(x, speed, scale, start) for x in range(0, 101, 10)] \\r\\n \\r\\n super(ScarcityModel, self).run_model(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af6ffd1cf00f5021f8de2ef98d8f58e\",\n \"score\": \"0.49905622\",\n \"text\": \"def main(args):\\n print('Reading galaxy catalog')\\n halocat_galcat_merged = pd.read_hdf('../'+args.catalog_to_use,\\\\\\n key='halocat_galcat_merged')\\n print('Plotting SMHM')\\n plot_SMHM(halocat_galcat_merged,args.host_halo_mass_to_plot,\\\\\\n args.populate_mock_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75315723e31accaeefa82eab6f1de578\",\n \"score\": \"0.498815\",\n \"text\": \"def _configure(self):\\n Component._configure(self)\\n pylith.perf.Material.Material.__init__(self, self.inventory.label, self.inventory.size)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67776bc83722ed0d7c8fcef236986c7\",\n \"score\": \"0.49846137\",\n \"text\": \"def model():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1240b372e0e828f142843a9972882e1\",\n \"score\": \"0.49844307\",\n \"text\": \"def __init__(self, name, parent=None):\\n\\t\\t\\n\\t\\tself.name = name\\n\\t\\tconfig_data = courses_config[self.name]\\n\\t\\t\\n\\t\\tself.orgEntity = config_data['orgEntity']\\n\\t\\tself.orgKey = config_data['orgKey']\\n\\t\\t\\n\\t\\tTopicRecord.dowrites = 1\\n\\t\\tChapterRecord.dowrites = 1\\n\\t\\t\\n\\t\\tBackPackModelObject.__init__ (self, parent)\\n\\t\\t\\t\\n\\t\\t# curriculum children are Unit instances\\n\\t\\tself.curriculum = Curriculum (self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a56536094ca1303e2f6d5ae779e4ddc\",\n \"score\": \"0.4982808\",\n \"text\": \"def set_model_logic(start_serial, stop_serial, df):\\n\\n time_step_in_days = 2.0\\n commodity = \\\"Rice\\\"\\n\\n model = Model(\\n starttime=start_serial,\\n stoptime=stop_serial,\\n dt=1.0 * time_step_in_days,\\n )\\n\\n # INITIALISE VARIABLES\\n\\n # DISPLACEMENT\\n hp_pop = model.constant(\\\"Host Population\\\")\\n idp_pop, idp_pop_iv = create_model_stock(model, \\\"IDP Population\\\")\\n aap_pop, aap_pop_iv = create_model_stock(model, \\\"Affected Area Population\\\")\\n disp_rate = model.flow(\\\"Displacement Rate\\\")\\n ret_rate = model.flow(\\\"Return Rate\\\")\\n\\n t_disp = model.constant(\\\"Displacement Time\\\")\\n t_ret = model.constant(\\\"Return Time\\\")\\n danger = model.converter(\\\"Perceived Danger\\\")\\n\\n\\n # FARMER\\n # physical\\n f_stock, f_stock_iv = create_model_stock(model, \\\"Farmer Stock\\\")\\n f_prod = model.flow(\\\"Farmer Production\\\")\\n f2t_vol = model.flow(\\\"Farmer to Trader Volume\\\")\\n f_supply = model.converter(\\\"Farmer Supply\\\")\\n f2t_demand = model.converter(\\\"Farmer to Trader Demand\\\")\\n f2t_leadtime = model.constant(\\\"Farmer to Trader Leadtime\\\")\\n f_loss = model.flow(\\\"Farmer Loss Rate\\\")\\n t_f_loss = model.constant(\\\"Farmer Loss Time\\\")\\n # price\\n baseline_price = model.constant(\\\"Baseline Price\\\")\\n f_ds_ratio = model.converter(\\\"Farmer Demand-to-Supply Ratio\\\")\\n f_price = model.converter(\\\"Farmer Price\\\")\\n # cash\\n t2f_cashflow = model.flow(\\\"Trader to Farmer Cashflow\\\")\\n\\n # TRADER\\n # physical\\n t_stock, t_stock_iv = create_model_stock(model, \\\"Trader Stock\\\")\\n t2w_vol = model.flow(\\\"Trader to Wholesaler Volume\\\")\\n t_supply = model.converter(\\\"Trader Supply\\\")\\n t2w_leadtime = model.constant(\\\"Trader to Wholesaler Leadtime\\\")\\n # price\\n t_ds_ratio = model.converter(\\\"Trader Demand-to-Supply Ratio\\\")\\n t_price = model.converter(\\\"Trader Price\\\")\\n # cash\\n t_cash, t_cash_iv = create_model_stock(model, \\\"Trader Cash\\\")\\n w2t_cashflow = model.flow(\\\"Wholesaler to Trader Cashflow\\\")\\n\\n wholesaler = SupplyChainActor(\\n model,\\n \\\"Wholesaler\\\",\\n upstream_actor=\\\"Trader\\\",\\n downstream_actor=\\\"Retailer\\\"\\n )\\n\\n retailer = SupplyChainActor(\\n model,\\n \\\"Retailer\\\",\\n upstream_actor=\\\"Wholesaler\\\",\\n downstream_actor=\\\"Consumer\\\"\\n )\\n\\n retailer.connect_to_upstream(wholesaler)\\n\\n # CONSUMER\\n hp_income_baseline = model.constant(\\\"Host Population Income Baseline\\\")\\n idp_income_baseline = model.constant(\\\"IDP Income Baseline\\\")\\n c_needs = model.constant(f\\\"{commodity} Needs Per Capita\\\")\\n c_max_income_frac = model.constant(f\\\"Maximum Fraction of Income Spent on {commodity}\\\")\\n hp_income = model.converter(\\\"Host Population Income\\\")\\n idp_income = model.converter(\\\"IDP Income\\\")\\n hp_pc_demand = model.converter(\\\"HP Per Capita Demand\\\")\\n idp_pc_demand = model.converter(\\\"IDP Per Capita Demand\\\")\\n hp_demand = model.converter(\\\"HP Demand\\\")\\n idp_demand = model.converter(\\\"IDP Demand\\\")\\n c_demand = model.converter(\\\"Retailer to Consumer Demand\\\")\\n\\n # DATA\\n data_prod_usda = create_model_data_variable(model, df, \\\"Production (USDA)\\\")\\n data_deaths_ucdp = create_model_data_variable(model, df, \\\"Deaths (UCDP)\\\")\\n data_ip_iom = create_model_data_variable(model, df, \\\"IDP Population (IOM)\\\")\\n data_ruralinflation_nbs = create_model_data_variable(model, df, \\\"Rural Inflation\\\")\\n\\n # CONNECT STOCKS AND FLOWS\\n\\n # displacement\\n idp_pop.equation = disp_rate - ret_rate\\n aap_pop.equation = ret_rate - disp_rate\\n\\n # physical\\n f_stock.equation = f_prod - f2t_vol - f_loss\\n t_stock.equation = f2t_vol - t2w_vol\\n\\n # cash\\n t_cash.equation = w2t_cashflow - t2f_cashflow\\n\\n # DEFINE THE REST\\n\\n # displacement\\n danger.equation = smooth_model_variable(model, data_deaths_ucdp, 30.0, 0.0) / 90.0\\n disp_rate.equation = aap_pop * danger / t_disp\\n ret_rate.equation = idp_pop * (1.0 - danger) / t_ret\\n\\n # farmer\\n f_prod.equation = data_prod_usda\\n f2t_vol.equation = sd.min(f2t_demand, f_supply)\\n f_ds_ratio.equation = f2t_demand / f_supply\\n f_price.equation = baseline_price\\n f2t_demand.equation = wholesaler.demand()\\n f_supply.equation = f_stock / f2t_leadtime\\n f_loss.equation = f_stock / t_f_loss\\n\\n # trader\\n t2w_vol.equation = sd.min(wholesaler.demand(), t_supply)\\n t_ds_ratio.equation = wholesaler.demand() / t_supply\\n t_price.equation = f_price\\n t_supply.equation = t_stock / t2w_leadtime\\n t2f_cashflow.equation = f_price * f2t_vol\\n w2t_cashflow.equation = t_price * t2w_vol\\n\\n\\n # consumer\\n hp_income.equation = hp_income_baseline\\n idp_income.equation = idp_income_baseline\\n hp_pc_demand.equation = sd.min(c_needs, hp_income * c_max_income_frac / wholesaler.price())\\n hp_demand.equation = hp_pc_demand * hp_pop\\n idp_pc_demand.equation = sd.min(c_needs, idp_income * c_max_income_frac / wholesaler.price())\\n idp_demand.equation = idp_pc_demand * idp_pop\\n c_demand.equation = hp_demand + idp_demand\\n\\n retailer.demand().equation = c_demand\\n\\n # CONSTANTS\\n\\n # initial values\\n idp_pop_iv.equation = 1500000.0\\n aap_pop_iv.equation = 1900000.0\\n f_stock_iv.equation = 100000.0\\n t_stock_iv.equation = 100000.0\\n t_cash_iv.equation = 0.0\\n\\n wholesaler.stock.initial_value = 100000.0\\n wholesaler.cash.initial_value = 0.0\\n retailer.stock.initial_value = 100000.0\\n retailer.cash.initial_value = 0.0\\n\\n # time constants\\n t_disp.equation = 30.0\\n t_ret.equation = 6.0 * 30.0\\n t_f_loss.equation = 7.0\\n\\n # leadtimes\\n f2t_leadtime.equation = 1.0\\n t2w_leadtime.equation = 1.0\\n wholesaler.leadtime.equation = 1.0\\n retailer.leadtime.equation = 1.0\\n\\n # others\\n baseline_price.equation = 200.0\\n\\n # consumer\\n hp_pop.equation = 1000000.0\\n c_max_income_frac.equation = 0.5\\n c_needs.equation = 10.0 / 30.0\\n hp_income_baseline.equation = 4000.0 / 30.0\\n idp_income_baseline.equation = 2000.0 / 30.0\\n\\n retailer.connect_eggs(wholesaler)\\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06bb52057148e139bbda0742193149d\",\n \"score\": \"0.4982488\",\n \"text\": \"def __init__(self, species, qty):\\n super(DomesticMelonOrder, self).__init__(species, qty)\\n self.tax = 0.08\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04e0bbf703ec278076d432ada05de292\",\n \"score\": \"0.49807692\",\n \"text\": \"def __init__(self, mu, sigma, corr, rf, risk_aversion=None, short_sell=True):\\n\\n # Asseert data indexes and organize\\n self._assert_indexes(mu, sigma, corr)\\n\\n # Save inputs as attributes\\n self.mu = mu\\n self.sigma = sigma\\n self.corr = corr\\n self.rf = rf\\n self.risk_aversion = risk_aversion\\n self.short_selling = short_sell\\n\\n # Compute atributes\\n self.n_assets = self._n_assets()\\n self.cov = self._get_cov_matrix()\\n\\n # Get the optimal risky porfolio\\n self.mu_p, self.sigma_p, self.risky_weights, self.sharpe_p = self._get_optimal_risky_portfolio()\\n\\n # get the minimal variance portfolio\\n self.mu_mv, self.sigma_mv, self.mv_weights, self.sharpe_mv = self._get_minimal_variance_portfolio()\\n\\n # Get the investor's portfolio and build the complete set of weights\\n self.weight_p, self.complete_weights, self.mu_c, self.sigma_c, self.certain_equivalent \\\\\\n = self._investor_allocation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f23f93c80d32888dadf1994a0b0f31\",\n \"score\": \"0.49710152\",\n \"text\": \"def __init__(self,\\n size: Size,\\n stuffing: Stuffing = Stuffing.POLYESTER_Fiberfill,\\n fabric: Fabric = Fabric.ACRYLIC,\\n has_glow: bool = True,\\n name: str = \\\"Dancing Skeleton\\\",\\n description: str = \\\"Actually this skeleton is not terrible, it is cute as coco.\\\",\\n product_id: str = \\\"\\\"\\n ) -> None:\\n self._check_input(size, stuffing, fabric, has_glow, name, description, product_id)\\n if len(product_id) == 0:\\n product_id = \\\"S%04dD\\\" % DancingSkeleton._generate_id\\n self._increment_id()\\n self._has_glow = has_glow\\n super().__init__(name, description, product_id, size, stuffing, fabric)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7691b30843657f3a37af9a89b211ecb\",\n \"score\": \"0.49695474\",\n \"text\": \"def main():\\n env = bootstrap('../development.ini')\\n settings = env['registry'].settings\\n location_file_dir = settings.location_file_dir\\n pearl_harbor_dir = os.path.join(location_file_dir, 'pearl_harbor')\\n pearl_harbor_data = os.path.join(pearl_harbor_dir, 'data')\\n location_file = os.path.join(pearl_harbor_dir, 'location.json')\\n\\n if os.path.exists(location_file):\\n message = 'File already exists:\\\\n %s\\\\nRemove? (y/n) ' % location_file\\n if raw_input(message).lower() == 'y':\\n os.unlink(location_file)\\n else:\\n print 'Cancelled.'\\n return\\n\\n model = settings.Model.create()\\n\\n # Map file path is relative to package root\\n map_file = os.path.join('location_files', 'pearl_harbor', 'data',\\n 'pearl_harbor.bna')\\n model.add_bna_map(map_file, {\\n 'refloat_halflife': 1 * 3600, # seconds\\n })\\n\\n start_time = datetime(2013, 1, 1, 1, 0)\\n model.time_step = 900\\n model.start_time = start_time\\n model.duration = timedelta(days=1)\\n model.uncertain = False\\n\\n # adding a wind mover\\n\\n series = np.zeros((3,), dtype=gnome.basic_types.datetime_value_2d)\\n series[0] = (start_time, ( 4, 180) )\\n series[1] = (start_time+timedelta(hours=12), ( 2, 270) )\\n series[2] = (start_time+timedelta(hours=24), ( 4, 180) )\\n \\n wind = WebWind(timeseries=series, units='knots')\\n model.environment += wind\\n w_mover = WebWindMover(wind)\\n model.movers += w_mover\\n\\n # adding a random mover\\n random_mover = WebRandomMover(diffusion_coef=10000)\\n model.movers += random_mover\\n\\n # adding a grid current mover:\\n\\n curr_file=os.path.join( pearl_harbor_data, \\\"ch3d2013.nc\\\")\\n topology_file=os.path.join( pearl_harbor_data, \\\"PearlHarborTop.dat\\\")\\n model.movers += gnome.movers.GridCurrentMover(curr_file,topology_file)\\n\\n\\n # adding a spill\\n\\n spill = WebPointSourceRelease(num_elements=1000,\\n start_position=(\\n -157.97064, 21.331524, 0.0),\\n release_time=start_time)\\n\\n model.spills += spill\\n\\n serialized_model = ModelSchema().bind().serialize(model.to_dict())\\n model_json = json.dumps(serialized_model, default=util.json_encoder,\\n indent=4)\\n\\n with open(location_file, 'wb') as f:\\n f.write(model_json)\\n\\n env['closer']()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745e24caf916ae6f0eb397731c90d1d7\",\n \"score\": \"0.4967229\",\n \"text\": \"def initCatalog():\\n # catalog es utilizado para interactuar con el modelo\\n catalog = model.newCatalog()\\n return catalog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745e24caf916ae6f0eb397731c90d1d7\",\n \"score\": \"0.4967229\",\n \"text\": \"def initCatalog():\\n # catalog es utilizado para interactuar con el modelo\\n catalog = model.newCatalog()\\n return catalog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f7e9357ca7a239a92e68d1390c5df2\",\n \"score\": \"0.49666688\",\n \"text\": \"def doParametersOfInterest(self):\\n\\t\\t# --- POI and other parameters ----\\n\\t\\t\\n\\t\\tmaxmix = {\\n\\t\\t\\t\\\"a_tilde\\\" : 1.0,\\n\\t\\t\\t\\\"b_tilde\\\" : 1.0,\\n\\t\\t}\\n\\t\\t\\n\\t\\tself.modelBuilder.doVar(\\\"muF[1.0,0.0,5.0]\\\")\\n\\t\\tself.modelBuilder.doVar(\\\"muV[1.0,0.0,5.0]\\\")\\n\\t\\tself.modelBuilder.doVar(\\\"cpmixing[0.0,0.0,1.0]\\\") # CP mixing angle in units of pi/2\\n\\t\\t\\n\\t\\tself.modelBuilder.factory_('expr::cosalpha(\\\"cos(@0*{pi}/2)\\\", cpmixing)'.format(pi=math.pi))\\n\\t\\tself.modelBuilder.factory_('expr::sinalpha(\\\"sin(@0*{pi}/2)\\\", cpmixing)'.format(pi=math.pi))\\n\\t\\t\\n\\t\\tself.modelBuilder.factory_('expr::a(\\\"@0\\\", cosalpha)')\\n\\t\\tself.modelBuilder.factory_('expr::b(\\\"@0\\\", sinalpha)')\\n\\t\\t\\n\\t\\tself.modelBuilder.factory_('expr::sm_scaling(\\\"@0*@0-@0*@1*{a_tilde}/{b_tilde}\\\", a, b)'.format(**maxmix))\\n\\t\\tself.modelBuilder.factory_('expr::ps_scaling(\\\"@1*@1-@0*@1*{b_tilde}/{a_tilde}\\\", a, b)'.format(**maxmix))\\n\\t\\tself.modelBuilder.factory_('expr::mm_scaling(\\\"@0*@1/({a_tilde}*{b_tilde})\\\", a, b)'.format(**maxmix))\\n\\t\\t\\n\\t\\tfor production in [\\\"muF\\\", \\\"muV\\\"]:\\n\\t\\t\\tfor decay in [\\\"muF\\\"]:\\n\\t\\t\\t\\tself.modelBuilder.factory_('expr::{production}_{decay}(\\\"@0*@1\\\", {production}, {decay})'.format(\\n\\t\\t\\t\\t\\t\\tproduction=production, decay=decay)\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\tfor cp in [\\\"sm_scaling\\\", \\\"ps_scaling\\\", \\\"mm_scaling\\\"]:\\n\\t\\t\\t\\t\\tself.modelBuilder.factory_('expr::{production}_{decay}_{cp}(\\\"@0*@1*@2\\\", {production}, {decay}, {cp})'.format(\\n\\t\\t\\t\\t\\t\\t\\tproduction=production, decay=decay, cp=cp)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\n\\t\\tself.modelBuilder.doSet(\\\"POI\\\", \\\"muF,muV,cpmixing\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c431bf8b97b271aab0bd250a8981500\",\n \"score\": \"0.49630284\",\n \"text\": \"def __init__(self,submodels):\\n self.submodels = submodels\\n self.obstypes = [obstype for submodel in submodels\\n for obstype in submodel.obstypes]\\n gauges = list()\\n for submodel in submodels:\\n gauges.extend(\\n gauge for gauge in submodel.gauges if gauge not in gauges\\n )\\n super().__init__(gauges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7321a20d59302f62a6127f10cc6c27af\",\n \"score\": \"0.4953475\",\n \"text\": \"def SBML_setSpecies(self):\\n if self._debug:\\n print('SBML_setSpecies')\\n reagList = self.model_obj.__species__ + self.model_obj.__fixed_species__\\n for reagent in range(len(reagList)):\\n s = self.sbml_model.createSpecies()\\n s.setId(reagList[reagent])\\n s.setName(reagList[reagent])\\n s.setCompartment(self.model_compartment_name)\\n if reagList[reagent] in self.model_obj.__fixed_species__:\\n s.setBoundaryCondition(True)\\n s.setConstant(True)\\n else:\\n s.setBoundaryCondition(False)\\n\\n if reagent < len(self.model_obj.__species__):\\n reagName = reagList[reagent] + '_init'\\n else:\\n reagName = reagList[reagent]\\n\\n if self.sbml_level == 1:\\n s.setInitialAmount(getattr(self.model_obj, reagName))\\n else:\\n s.setInitialConcentration(getattr(self.model_obj, reagName))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413cc3068285fa798ccedccfa13621eb\",\n \"score\": \"0.49522227\",\n \"text\": \"def simple_model(neuron_instance):\\n\\n h, rxd, data, save_path = neuron_instance\\n dend = h.Section(name='dend')\\n dend.diam = 2\\n dend.nseg = 5 \\n dend.L = 5\\n ecs = rxd.Extracellular(-10, -10, -10, 10, 10, 10, dx=3)\\n cyt = rxd.Region(dend, name='cyt', nrn_region='i')\\n k = rxd.Species([cyt, ecs], name='k', d=1, charge=1,\\n initial=lambda nd: 140 if nd.region == cyt else 3)\\n decay = rxd.Rate(k, -0.1*k)\\n model = (dend, cyt, ecs, k, decay)\\n yield (neuron_instance, model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"824acd1b90426fb7a92c88542e1c33e0\",\n \"score\": \"0.49509418\",\n \"text\": \"def generate_instance(self):\\n config = self._generate_config()\\n manager_stop = self._generate_manager()\\n\\n output_file = open(self.output_file,'w')\\n text = \\\"VEHICLE\\\"\\n output_file.write(text + \\\"\\\\n\\\")\\n text = \\\"NUMBER CAPACTIY\\\"\\n output_file.write(text + \\\"\\\\n\\\")\\n output_file.write(\\\"150 \\\"+ str(config.capacity_cst) + \\\"\\\\n\\\")\\n\\n\\n # config.dump_to_file_capacity(output_file)\\n manager_stop.dump_to_file(output_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf87f8a6bc4c0c1ba05b2913fe859fc2\",\n \"score\": \"0.49496803\",\n \"text\": \"def _construct_model(self, model):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a4b69a019519f5a7e93ad5996a7d49\",\n \"score\": \"0.49493036\",\n \"text\": \"def __init__(self, config):\\n self.config = config # md control\\n # self.model = model # dynamic variable & ff parameters\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e11a39ffce7792f58a49a943eefbf13\",\n \"score\": \"0.4947336\",\n \"text\": \"def SBML_buildBasicModel(\\n self,\\n mod,\\n filename,\\n slvl=2,\\n dir=None,\\n substance=(1, 0),\\n volume=(1, 0),\\n time=(1, 0),\\n arules=None,\\n notes=None,\\n ):\\n self.SBML_createModel(mod, filename, slvl, dir)\\n self.SBML_setCompartment()\\n self.SBML_setNotes(txt=notes)\\n self.SBML_setUnits(substance=substance, volume=volume, time=time)\\n if arules != None:\\n self.SBML_setAssignmentRules(arules)\\n self.SBML_setSpecies()\\n self.SBML_setReactions()\\n self.SBML_setModel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd4bb2e043b5c55db8595d6334db530\",\n \"score\": \"0.49463186\",\n \"text\": \"def gen_ssmodel(self):\\n\\t\\tprint \\\"generating full neural model\\\"\\n\\n\\t\\t#Generate field meshgrid\\n simulation_field_space_x,simulation_field_space_y=pb.meshgrid(self.simulation_space_x_y,self.simulation_space_x_y)\\n\\n\\n K=0\\n for i in range(len(self.kernel.Psi)):\\n K+=self.kernel.weights[i]*self.kernel.Psi[i](simulation_field_space_x,simulation_field_space_y)\\n \\n self.K=K\\n\\n\\n\\t\\t#calculate field disturbance covariance matrix and its Cholesky decomposition\\n self.Sigma_e_c=sp.linalg.cholesky(self.Sigma_e,lower=1) \\n\\n #calculate Cholesky decomposition of observation noise covariance matrix\\n Sigma_varepsilon_c=sp.linalg.cholesky(self.Sigma_varepsilon,lower=1)\\n self.Sigma_varepsilon_c=Sigma_varepsilon_c\\n\\n #Calculate sensors at each spatial locations, it's not the same as C in the IDE model\\t\\n t0=time.time()\\n gamma_space=pb.array(zip(simulation_field_space_x.flatten(),simulation_field_space_y.flatten()))\\n N1,D1 = gamma_space.shape\\n sensor_space=self.obs_locns\\n N2,D2 = sensor_space.shape\\n diff = sensor_space.reshape(N2,1,D2) - gamma_space.reshape(1,N1,D1)\\n C=np.exp(-np.sum(np.square(diff),-1)*(1./self.sensor_kernel.width))\\n self.C=C\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994294,"cells":{"query_id":{"kind":"string","value":"e3c73bcf5a8acca28636ed972e9037de"},"query":{"kind":"string","value":"Set up the game and initialize the variables."},"positive_passages":{"kind":"list like","value":[{"docid":"5763ce967360b1ff9e7daaca59da4982","score":"0.66307664","text":"def setup(self):\n\n # Sprite lists\n self.player_list = arcade.SpriteList()\n self.wall_list = arcade.SpriteList(use_spatial_hash=True,\n spatial_hash_cell_size=128)\n self.enemy_list = arcade.SpriteList()\n\n # Set up the player\n resource = \":resources:images/animated_characters/\" \\\n \"female_person/femalePerson_idle.png\"\n self.player = arcade.Sprite(resource, scale=SPRITE_SCALING)\n self.player.center_x = SPRITE_SIZE * 5\n self.player.center_y = SPRITE_SIZE * 1\n self.player_list.append(self.player)\n\n # Set enemies\n resource = \":resources:images/animated_characters/zombie/zombie_idle.png\"\n enemy = arcade.Sprite(resource, scale=SPRITE_SCALING)\n enemy.center_x = SPRITE_SIZE * 4\n enemy.center_y = SPRITE_SIZE * 7\n self.enemy_list.append(enemy)\n\n spacing = SPRITE_SIZE * 3\n for column in range(10):\n for row in range(15):\n sprite = arcade.Sprite(\":resources:images/tiles/grassCenter.png\",\n scale=SPRITE_SCALING)\n\n x = (column + 1) * spacing\n y = (row + 1) * sprite.height\n\n sprite.center_x = x\n sprite.center_y = y\n if random.randrange(100) > 30:\n self.wall_list.append(sprite)\n\n self.physics_engine = arcade.PhysicsEngineSimple(self.player,\n self.wall_list)\n\n # --- Path related\n # This variable holds the travel-path. We keep it as an attribute so\n # we can calculate it in on_update, and draw it in on_draw.\n self.path = None\n # Grid size for calculations. The smaller the grid, the longer the time\n # for calculations. Make sure the grid aligns with the sprite wall grid,\n # or some openings might be missed.\n grid_size = SPRITE_SIZE\n\n # Calculate the playing field size. We can't generate paths outside of\n # this.\n playing_field_left_boundary = -SPRITE_SIZE * 2\n playing_field_right_boundary = SPRITE_SIZE * 35\n playing_field_top_boundary = SPRITE_SIZE * 17\n playing_field_bottom_boundary = -SPRITE_SIZE * 2\n\n # This calculates a list of barriers. By calculating it here in the\n # init, we are assuming this list does not change. In this example,\n # our walls don't move, so that is ok. If we want moving barriers (such as\n # moving platforms or enemies) we need to recalculate. This can be an\n # time-intensive process depending on the playing field size and grid\n # resolution.\n\n # Note: If the enemy sprites are the same size, we only need to calculate\n # one of these. We do NOT need a different one for each enemy. The sprite\n # is just used for a size calculation.\n self.barrier_list = arcade.AStarBarrierList(enemy,\n self.wall_list,\n grid_size,\n playing_field_left_boundary,\n playing_field_right_boundary,\n playing_field_bottom_boundary,\n playing_field_top_boundary)","title":""}],"string":"[\n {\n \"docid\": \"5763ce967360b1ff9e7daaca59da4982\",\n \"score\": \"0.66307664\",\n \"text\": \"def setup(self):\\n\\n # Sprite lists\\n self.player_list = arcade.SpriteList()\\n self.wall_list = arcade.SpriteList(use_spatial_hash=True,\\n spatial_hash_cell_size=128)\\n self.enemy_list = arcade.SpriteList()\\n\\n # Set up the player\\n resource = \\\":resources:images/animated_characters/\\\" \\\\\\n \\\"female_person/femalePerson_idle.png\\\"\\n self.player = arcade.Sprite(resource, scale=SPRITE_SCALING)\\n self.player.center_x = SPRITE_SIZE * 5\\n self.player.center_y = SPRITE_SIZE * 1\\n self.player_list.append(self.player)\\n\\n # Set enemies\\n resource = \\\":resources:images/animated_characters/zombie/zombie_idle.png\\\"\\n enemy = arcade.Sprite(resource, scale=SPRITE_SCALING)\\n enemy.center_x = SPRITE_SIZE * 4\\n enemy.center_y = SPRITE_SIZE * 7\\n self.enemy_list.append(enemy)\\n\\n spacing = SPRITE_SIZE * 3\\n for column in range(10):\\n for row in range(15):\\n sprite = arcade.Sprite(\\\":resources:images/tiles/grassCenter.png\\\",\\n scale=SPRITE_SCALING)\\n\\n x = (column + 1) * spacing\\n y = (row + 1) * sprite.height\\n\\n sprite.center_x = x\\n sprite.center_y = y\\n if random.randrange(100) > 30:\\n self.wall_list.append(sprite)\\n\\n self.physics_engine = arcade.PhysicsEngineSimple(self.player,\\n self.wall_list)\\n\\n # --- Path related\\n # This variable holds the travel-path. We keep it as an attribute so\\n # we can calculate it in on_update, and draw it in on_draw.\\n self.path = None\\n # Grid size for calculations. The smaller the grid, the longer the time\\n # for calculations. Make sure the grid aligns with the sprite wall grid,\\n # or some openings might be missed.\\n grid_size = SPRITE_SIZE\\n\\n # Calculate the playing field size. We can't generate paths outside of\\n # this.\\n playing_field_left_boundary = -SPRITE_SIZE * 2\\n playing_field_right_boundary = SPRITE_SIZE * 35\\n playing_field_top_boundary = SPRITE_SIZE * 17\\n playing_field_bottom_boundary = -SPRITE_SIZE * 2\\n\\n # This calculates a list of barriers. By calculating it here in the\\n # init, we are assuming this list does not change. In this example,\\n # our walls don't move, so that is ok. If we want moving barriers (such as\\n # moving platforms or enemies) we need to recalculate. This can be an\\n # time-intensive process depending on the playing field size and grid\\n # resolution.\\n\\n # Note: If the enemy sprites are the same size, we only need to calculate\\n # one of these. We do NOT need a different one for each enemy. The sprite\\n # is just used for a size calculation.\\n self.barrier_list = arcade.AStarBarrierList(enemy,\\n self.wall_list,\\n grid_size,\\n playing_field_left_boundary,\\n playing_field_right_boundary,\\n playing_field_bottom_boundary,\\n playing_field_top_boundary)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3956c41832a0e4cb1cf11a8add517eef","score":"0.8164389","text":"def setup(self):\n # Create your sprites and sprite lists here\n self.game: Game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, TILE_SIZE, 1, grid_layers = 4)\n self.game.game_message = \"Lead the Rabbit home\"\n\n # show the menu so that we see the instructions\n self.game.menu.button_list[0].text = \"Start\"\n self.game.menu.is_visible = True","title":""},{"docid":"8773700bfd9a95656c265c6bd313d6e9","score":"0.77574474","text":"def setup_game(self):","title":""},{"docid":"d6f2e0104a864f573e1ebcfb83e821ec","score":"0.7394462","text":"def __init__(self):\n\n self.frameCount = 0\n self._initScreen()\n self._initObjects()\n self._initControls()\n self._initLevel()\n self._start()\n print \"DEBUG: Initializing Game\"\n pass","title":""},{"docid":"332678f4bd4fda854a458ca93a320cbd","score":"0.7383125","text":"def game_setup(self):\n self.deck = Shoe(6)\n self.initial_draw()\n self.pot = ask_for_bet(self.player.money)\n show_table(self.player, self.dealer, self.pot)\n self.surrender_and_insurance()","title":""},{"docid":"f2042be52d22e6802ebadd2ec82edd98","score":"0.73794293","text":"def setup(self):\n # Set up the player\n self.player_sprite = arcade.Sprite(\"Sprites/Jugador/Jugador.jpg\", SPRITE_SCALING)\n self.player_sprite.center_x = 100\n self.player_sprite.center_y = 100\n self.player_list = arcade.SpriteList()\n self.player_list.append(self.player_sprite)\n\n # Listado de habitaciones\n self.rooms = []\n self.rooms.append(setup_pueblo())\n\n #Contador de habitación\n self.current_room = 0\n\n #Fisicas\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite, self.rooms[self.current_room].wall_list)","title":""},{"docid":"0e17cf1d9db4f5bc13da1d42d12d6088","score":"0.7377979","text":"def init(cls):\n\n cls.configs = yaml.load( file('../local/config.yaml') )\n cls.is_online = False\n cls.state = State.playing\n cls.classes = classes\n cls.guiclasses = guiclasses\n\n # set up pygame and init\n pygame.init()\n\n # Set up the window\n cls.screen = pygame.display.set_mode(\n tuple(cls.configs['options']['resolution']),\n 0,\n 32)\n classes.screen = cls.screen\n guiclasses.screen = cls.screen","title":""},{"docid":"4351bfac5d4a145db8bf589197643b31","score":"0.7341448","text":"def setup_new_game(self):\r\n self._player = Player()\r\n self._stats = GameStats(self._bb_settings)\r\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)","title":""},{"docid":"651a0727ec3ed20eb715acef2a022e23","score":"0.7279792","text":"def game_initialize():\n\tglobal SURFACE_MAIN, SURFACE_MAP, PLAYER, FOV_CALCULATE, CLOCK, ASSETS, CAMERA, PREFERENCES\n\n\tpygame.init()\n\tpygame.key.set_repeat(555, 85)\t\n\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_celtic.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_fantasy.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_mesopotamian.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_norse.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_region.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\jice_town.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\mingos_demon.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\mingos_dwarf.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\mingos_norse.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\mingos_standard.cfg\")\n\tlibtcod.namegen_parse(\"data\\\\namegen\\\\mingos_town.cfg\")\n\n\tCLOCK = pygame.time.Clock()\n\n\tCAMERA = obj_Camera()\n\n\tSURFACE_MAIN = pygame.display.set_mode((CAMERA.width, CAMERA.height))\n\n\tSURFACE_MAP = pygame.Surface((constants.GAME_WIDTH, constants.GAME_HEIGHT))\n\n\tFOV_CALCULATE = True\n\ttry:\n\t\tload_preferences()\n\t\tprint(\"Preferences Loaded\")\n\texcept:\n\t\tPREFERENCES = struc_Preferences()\n\t\tprint(\"Preferences generated\")\n\tASSETS = struc_Assets()","title":""},{"docid":"76c8e437aac97895bfa712a9afbeab57","score":"0.7257215","text":"def setUp(self):\n self.game = BuildGame()\n self.effects = []","title":""},{"docid":"d068bc4b98bded3fdd08601cee6eab4a","score":"0.72419715","text":"def setup(self):\n header_print(self.data['intro'])\n header_print(self.data['help'])\n random.shuffle(self.data['draw'])\n random.shuffle(self.data['locations'])\n random.shuffle(self.data['events'])\n random.shuffle(self.data['aces'])\n random.shuffle(self.data['personalities'])\n self.stats = {\n 'round': 0,\n 'powers': {\n 'MOONS': 6,\n 'SUNS': 6,\n 'WAVES': 6,\n 'LEAVES': 6,\n 'WYRMS': 6,\n 'KNOTS': 6,\n },\n 'hand': self.data['draw'][:],\n 'discard': [],\n 'active': [],\n 'opponent': {},\n }","title":""},{"docid":"0af3e58ec8db639028b0b0b4a90f0a3b","score":"0.7219072","text":"def setup(self):\n self.score = 0\n self.lives = 3\n self.state = GameStates.RUNNING\n self.focus_word = None\n \n self.star_list = set()\n self.word_list = set()\n\n for _ in range(5):\n self.create_word()\n for _ in range(25):\n self.create_star()","title":""},{"docid":"02cd43c71124297797ff0517d062266d","score":"0.7186087","text":"def start(self):\n self.__init__()\n self.set_n_players()\n self.init_players()\n self.init_territory_selection_phase()\n self.init_troop_deployment_phase()\n # self.game_phase()","title":""},{"docid":"35d9007e5308beb4ca63688f8c359bac","score":"0.7121074","text":"def setup(self):\n setup = RandomWordGenerator().get()\n self.formatted_word = ConvertWord().convert_to_dict(setup)\n self.underscore_word = HangmanUnderscoreDiagram(\n setup).create_hidden_word()\n self.failed_guesses = 0\n print(\"Hello\")\n self.has_won = False\n self.start_game(True)","title":""},{"docid":"0205d8ab0974420fb0840c87b900bc03","score":"0.708886","text":"def initialize(self):\n result = pygame.init()\n pygame.font.init()\n pygame.display.set_caption('gomoku TDD')\n self.screen = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\n self.clock = pygame.time.Clock()\n self.smallfont = pygame.font.Font(None, 40)\n self.isinitialized = True","title":""},{"docid":"d94eba530b8e96bd031c4417b4c40147","score":"0.7068139","text":"def setUp(self):\n self.game = TTTBoard(3)","title":""},{"docid":"94b4f8d583dd1a15725be374e8aa4bc2","score":"0.7007462","text":"def initializeGame(self):\n # Fill deck with cards and shuffle it\n self.deck.fill(104)\n self.deck.shuffle()\n #print \"Deck initialized\"\n\n # Initialize the field\n self.field.initialize(self.deck.draw(4))\n self.field.sortField()\n #self.field.printField()\n\n # Set players to initial state again\n # Distribute cards and set bulls to 0\n for p in self.players:\n p.bulls = 0\n p.setHand(self.deck.draw(10))","title":""},{"docid":"69eadfadb2a57d6cc305543291044957","score":"0.6989646","text":"def start_game(self):\n self.board = Board(num_tableaus=self.tableau_qty, num_decks=self.decks, deal_3=self.deal_3)\n self.board.init_move_dict()\n self.board.deal(self.deck)\n\n if self.api_use:\n self.init_game_api()\n elif self.commandline:\n self.init_cl_game()\n else:\n self.init_pygame()","title":""},{"docid":"0656598321e57f2db8785ef86f3fc429","score":"0.698927","text":"def setup(self):\n\n # Sprite lists\n self.all_sprite_list = arcade.SpriteList()\n self.player_list = arcade.SpriteList()\n self.coin_list = arcade.SpriteList()\n self.deathclaw_list = arcade.SpriteList()\n # Score\n self.score = 0\n\n # Set up the player\n # Character image from https://www.pngkit.com/\n self.player_sprite = arcade.Sprite(\"Vault_Boy.png\", SPRITE_SCALING_PLAYER)\n self.player_sprite.center_x = 50\n self.player_sprite.center_y = 50\n self.player_list.append(self.player_sprite)\n\n # Create the ENEMY\n for i in range(DEATHCLAW_COUNT):\n\n # Create the ENEMY instance\n # ENEMY image from https://fallout.fandom.com/\n deathclaw = Deathclaw(\"Deathclaw.png\", SPRITE_SCALING_DEATHCLAW)\n\n # Position the coin\n deathclaw.center_x = random.randrange(SCREEN_WIDTH)\n deathclaw.center_y = random.randrange(SCREEN_HEIGHT)\n\n # Add the coin to the lists\n self.deathclaw_list.append(deathclaw)\n\n for i in range(COIN_COUNT):\n\n # Create the Nuka Cola instance\n # Nuka Cola image fromh https://www.cleanpng.com/free/nuka-cola.html\n coin = Coin(\"Nuka_Kola.png\", SPRITE_SCALING_COIN)\n\n # Position the coin\n coin.center_x = random.randrange(SCREEN_WIDTH)\n coin.center_y = random.randrange(SCREEN_HEIGHT)\n\n # Add the coin to the lists\n self.coin_list.append(coin)","title":""},{"docid":"e04bb8a5634f4f1d0d968486f9081e13","score":"0.69834745","text":"def setup(self):\n \n self.explosions_list = arcade.SpriteList()\n\n self.explosion_texture_list = []\n \n # Set up the score\n self.score = 0\n self.countdown = 1000\n\n for i in range(EXPLOSION_TEXTURE_COUNT):\n # Files from http://www.explosiongenerator.com are numbered sequentially.\n # This code loads all of the explosion0000.png to explosion0270.png files\n # that are part of this explosion.\n texture_name = f\"images/explosion/explosion{i:04d}.png\"\n\n self.explosion_texture_list.append(arcade.load_texture(texture_name))\n \n # create 10 balls\n for i in range(10):\n myball = make_ball()\n self.ball_list.append(myball)","title":""},{"docid":"e1de58c24db8cf359ff89674c5639168","score":"0.696734","text":"def init_pygame(self):\n # Startup the pygame system\n pygame.init()\n # Create our window\n self.screen = pygame.display.set_mode((Settings.width, Settings.height))\n # Set the title that will display at the top of the window.\n pygame.display.set_caption(self.title)\n # Create the clock\n self.clock = pygame.time.Clock()\n self.last_checked_time = pygame.time.get_ticks()\n # Startup the joystick system\n pygame.joystick.init()\n # For each joystick we find, initialize the stick\n for i in range(pygame.joystick.get_count()):\n pygame.joystick.Joystick(i).init()\n # Set the repeat delay for key presses\n pygame.key.set_repeat(Settings.key_repeat)\n # Create statistics font\n self.statistics_font = pygame.font.Font(None,30)","title":""},{"docid":"4d39ffb75325ecf9b736925a6d1cecce","score":"0.6965951","text":"def start(self):\n self.player = Player()\n self.dealer = Dealer()\n self.pot = 0\n self.side_bet = 0\n start_game()","title":""},{"docid":"71ab411855b63608cbcfd60862bbb126","score":"0.6867193","text":"def game_initialize():\n global SURFACE_MAIN #global vars will be all caps\n # init pygame\n pygame.init()\n SURFACE_MAIN = pygame.display.set_mode((constants.GAME_WIDTH, constants.GAME_HEIGHT))\n constants.initialize_sprites()","title":""},{"docid":"104e2185bf3bf543b423903e9d3262ea","score":"0.68361783","text":"def initGameState(self):\n print(\"Setting game state: \")\n self.playGUI = GUI()\n self.playGUI.drawBoard(self.player)","title":""},{"docid":"2020c3959217704cebf8ac35292d4b49","score":"0.683391","text":"def on_init(self):\n pygame.init()\n self.background.load_from_file()\n self.hero.load_from_file()\n self.enemy.load_from_file()\n\n # Some music and sound fx\n # frequency, size, channels, buffersize\n # pygame.mixer.pre_init(44100, 16, 2, 4096)\n self.effect = pygame.mixer.Sound('sounds/bounce.wav')\n pygame.mixer.music.load('sounds/music.wav')\n pygame.mixer.music.play(-1)\n\n self.hero.screen = self.background.screen\n self.enemy.screen = self.background.screen\n self.clock = pygame.time.Clock()\n pygame.display.set_caption(\n 'Angry Floating Guy! World: {} (w to change world, arrows to move, Esc to quit).'.format(self.current_world.name))\n\n self._running = True","title":""},{"docid":"ec7a0c81368b69922d526b87e816b3df","score":"0.6814734","text":"def setup(self):\n\n self.characters = arcade.SpriteList()\n self.dungeon_sprites = arcade.SpriteList(\n use_spatial_hash=True, spatial_hash_cell_size=16\n )\n\n self.player = Item(ord(\"@\"), arcade.csscolor.WHITE)\n self.player.x = 0\n self.player.y = 0\n self.characters.append(self.player)\n\n # Size of the map\n map_width = MAP_WIDTH\n map_height = MAP_HEIGHT\n\n # Some variables for the rooms in the map\n room_max_size = 10\n room_min_size = 6\n max_rooms = 30\n\n self.game_map = GameMap(map_width, map_height)\n self.game_map.make_map(\n max_rooms, room_min_size, room_max_size, map_width, map_height, self.player\n )\n\n # Draw all the tiles in the game map\n for y in range(self.game_map.height):\n for x in range(self.game_map.width):\n wall = self.game_map.tiles[x][y].block_sight\n sprite = Item(WALL_CHAR, arcade.csscolor.BLACK)\n if wall:\n sprite.block_sight = True\n else:\n sprite.block_sight = False\n\n sprite.x = x\n sprite.y = y\n\n self.dungeon_sprites.append(sprite)\n\n recalculate_fov(\n self.player.x, self.player.y, FOV_RADIUS, self.dungeon_sprites\n )","title":""},{"docid":"898d1bdd3fd199dc5ee5cb07f15df2bf","score":"0.68145746","text":"def __init__(self):\n\n super().__init__()\n self.setup_janggi_game()\n self._game_state = 'UNFINISHED'\n self._player_turn = 'BLUE'","title":""},{"docid":"abf5f97e061b958647ca2e9096b94844","score":"0.6753492","text":"def setUp(self):\n self.gameBoard = Grid((100, 100), Cell)","title":""},{"docid":"dc927efa1f803392f953c6e4801797ce","score":"0.67371064","text":"def __init__(self):\n\t\tpygame.init()\n\t\tself.settings = Settings()\n\n\t\tself.screen = pygame.display.set_mode((0, 0), pygame.FULLSCREEN)\n\t\tself.settings.screen_width = self.screen.get_rect().width \n\t\tself.settings.screen_height = self.screen.get_rect().height\n\t\tpygame.display.set_caption(\"Pigeon Drop!\")\n\n\t\t# Create an instance to store game statistics,\n\t\t# and create a scoreboard.\n\t\tself.stats = GameStats(self)\n\t\tself.sb = Scoreboard(self)\n\n\t\tself.pigeon = Pigeon(self)\n\t\tself.droppings = pygame.sprite.Group()\n\t\tself.autos = pygame.sprite.Group()\n\n\t\tself._create_fleet()\n\n\t\t# Make the Play button.\n\t\tself.play_button = Button(self, \"Play\")","title":""},{"docid":"54f700774ec5b085ce661e7fa454dbab","score":"0.6727859","text":"def init(seed=None):\n\tglobal _game\n\n\tfrom .game import Game\n\tfrom .prompt import install_words\n\n\t_game = Game(seed)\n\tload_advent_dat(_game)\n\tinstall_words(_game)\n\t_game.start()\n\treturn _game","title":""},{"docid":"0a0650d977c78fc25ed12ad48615e281","score":"0.6699672","text":"def main():\n g = Game(800, 600)\n g.start()","title":""},{"docid":"d2d7491d84d90efe4f414673fed4354b","score":"0.66952485","text":"def setup(self):\n # inicializamos el juego\n\n # Sprite lists\n self.player_list = arcade.SpriteList() # sera lista de personajes\n self.coin_list = arcade.SpriteList() # sera lista de monedas\n self.bullet_list = arcade.SpriteList() # lista de disparos\n\n # Set up the player\n self.score = 0\n\n # Image from kenney.nl\n # cargamos el sprite del jugador\n self.player_sprite = arcade.Sprite(\"character.png\", SPRITE_SCALING_PLAYER)\n # establecemos el inicio de posicion de nuestro jugador\n self.player_sprite.center_x = 50\n self.player_sprite.center_y = 70\n # lo agregamos a la lista de nuestros jugadores\n self.player_list.append(self.player_sprite)\n\n # Create the coins\n for i in range(COIN_COUNT):\n\n # Create the coin instance\n # Coin image from kenney.nl\n # cargamos las monedas\n coin = arcade.Sprite(\"coin_01.png\", SPRITE_SCALING_COIN)\n\n # Position the coin\n coin.center_x = random.randrange(SCREEN_WIDTH)\n coin.center_y = random.randrange(120, SCREEN_HEIGHT)\n\n # Add the coin to the lists\n # lo agregamos a la lista\n self.coin_list.append(coin)\n\n # Set the background color\n # esto aun nose para que sirve\n arcade.set_background_color(arcade.color.AMAZON)","title":""},{"docid":"f2e38a2f48c517dc1d5ff4d49036be37","score":"0.6681422","text":"def init(self):\n\n pygame.init()\n pygame.joystick.init()\n self.controller = pygame.joystick.Joystick(0)\n self.controller.init()\n self.x=0\n self.y=0","title":""},{"docid":"1d99aa2f8e52683283d3e9f9fd5507ee","score":"0.6677753","text":"def setup(self):\n\n # Sprite lists\n self.player_list = arcade.SpriteList()\n\n # https://opengameart.org/content/animated-top-down-survivor-player\n # Set up the player\n self.player_sprite = Player(\"survivor-idle_rifle_0.png\", 0.5)\n self.player_sprite.center_x = SCREEN_WIDTH / 2\n self.player_sprite.center_y = SCREEN_HEIGHT / 2\n self.player_list.append(self.player_sprite)\n self.wall_list = arcade.SpriteList()\n self.chest_list = arcade.SpriteList()\n self.bullet_list = arcade.SpriteList()\n\n self.score = 0\n\n\n # Set up the player\n # https://opengameart.org/content/animated-top-down-survivor-player\n\n\n\n # -- Set up several columns of walls\n for x in range(-700, 1700, 100):\n # sprite form https://opengameart.org/content/bush-png\n wall = arcade.Sprite(\"bush_11.png\", SPRITE_SCALING)\n wall.center_x = x\n wall.center_y = -300\n self.wall_list.append(wall)\n for x in range(-700, 1700, 100):\n # sprite form https://opengameart.org/content/bush-png\n wall = arcade.Sprite(\"bush_11.png\", SPRITE_SCALING)\n wall.center_x = x\n wall.center_y = 1025\n self.wall_list.append(wall)\n for y in range(-300, 1025, 100):\n # sprite form https://opengameart.org/content/bush-png\n wall = arcade.Sprite(\"bush_11.png\", SPRITE_SCALING)\n wall.center_x = -700\n wall.center_y = y\n self.wall_list.append(wall)\n for y in range(-300, 1025, 100):\n # sprite form https://opengameart.org/content/bush-png\n wall = arcade.Sprite(\"bush_11.png\", SPRITE_SCALING)\n wall.center_x = 1700\n wall.center_y = y\n self.wall_list.append(wall)\n\n\n# https://www.pinterest.com/pin/258042253625289337\n\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite, self.wall_list)\n\n # Set the background color\n arcade.set_background_color(arcade.color.BRITISH_RACING_GREEN)\n\n # Set the viewport boundaries\n # These numbers set where we have 'scrolled' to.\n self.view_left = 0\n self.view_bottom = 0","title":""},{"docid":"875729fd86f2b97dc69f1f05aa7f388e","score":"0.6669637","text":"def setup(self):\n\n # Used to keep track of our scrolling\n self.view_bottom = 0\n self.view_left = 0\n\n # Keep track of the score\n self.score = 0\n\n # Create the Sprite lists\n self.player_list = arcade.SpriteList()\n self.wall_list = arcade.SpriteList()\n self.coin_list = arcade.SpriteList()\n\n # Set up the player, specifically placing it at these coordinates.\n # image_source = \":resources:images/animated_characters/female_adventurer/femaleAdventurer_idle.png\"\n self.player_list = arcade.SpriteList()\n self.player_sprite = Player()\n self.player_sprite.center_x = 256\n self.player_sprite.center_y = 256\n self.player_list.append(self.player_sprite)\n\n # --- Load in a map from the tiled editor ---\n\n # Name of map file to load\n map_name = r\"Math_Game\\floor_is_lava.tmx\"\n # Name of the layer in the file that has our platforms/walls\n platforms_layer_name = 'Platforms'\n\n # Read in the tiled map\n my_map = arcade.tilemap.read_tmx(map_name)\n\n # -- Platforms\n self.wall_list = arcade.tilemap.process_layer(map_object=my_map,\n layer_name='Platforms',\n base_directory=r'C:\\Users\\katel\\Desktop\\CSE310\\group_project\\Math_Game\\platformer-art-complete-pack-0\\Base pack\\Tiles',\n scaling=TILE_SCALING,\n use_spatial_hash=True, hit_box_algorithm=\"Simple\", hit_box_detail=4.5)\n\n # --- Other stuff\n # Set the background color\n if my_map.background_color:\n arcade.set_background_color(my_map.background_color)\n\n # Create the 'physics engine'\n self.physics_engine = arcade.PhysicsEnginePlatformer(self.player_sprite,\n self.wall_list,\n GRAVITY)","title":""},{"docid":"37d5681d11fce16f71e1ce356acb3cf6","score":"0.66560024","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()\r\n self.spaceship.init()","title":""},{"docid":"efcb7a4e0a72df48cbe98c4c50737659","score":"0.66408247","text":"def setup_play(self, reset=False):\n\n if reset:\n for sprite in self.all_sprites:\n sprite.kill()\n else:\n self.score = 0\n self.lives = START_LIVES\n self.game_state = InGameState.READY\n\n self.all_sprites = pg.sprite.Group()\n self.walls = pg.sprite.Group()\n self.blocks = pg.sprite.Group()\n self.diamonds = pg.sprite.Group()\n self.moving_blocks = pg.sprite.Group()\n self.enemies = pg.sprite.Group()\n self.stunned_enemies = pg.sprite.Group()\n\n # level = Level(path.join(level_dir, '1.txt'))\n level = Level(path.join(level_dir, 'c64_level1.txt'))\n level.load_level(self)\n LOGGER.debug(f\"No. enemies: {len(self.enemies)}, No. blocks: {len(self.blocks)}\")\n\n self.make_boundary_wall(level.grid_height, level.grid_width)\n\n self.timer = TIME_LIMIT\n pg.time.set_timer(TIMER, 1000)\n\n self.target_no_kills = 5\n self.kill_bonus = None\n self.diamond_bonus = None","title":""},{"docid":"1a81ce9b838cf1027c1e428c457c8d39","score":"0.66376364","text":"def start_game(self):\n\n\t\tpass","title":""},{"docid":"e90dcd04e4a9ae933f7f04288699505b","score":"0.66170704","text":"def setup(self):\n\n self.total_time = 0.0\n\n self.background = arcade.load_texture(\"images\\\\background-1_0 (1).png\")\n\n # Create the Sprite lists\n self.all_sprites_list = arcade.SpriteList()\n self.player_list = arcade.SpriteList()\n self.wall_list = arcade.SpriteList()\n self.coin_list = arcade.SpriteList()\n self.myobject_list = arcade.SpriteList()\n\n # Set up the player\n self.gameover = 0\n self.score = 0\n self.lives = 4\n self.collision_time = 0\n self.numobj = STARTING_OBJECTS_COUNT\n self.ncoins = COIN_COUNT\n self.player_sprite = VehicleSprite(\"images\\\\bugatti.png\",\n CHARACTER_SCALING)\n self.player_sprite.angle = 90\n # self.player_sprite.change_y = 1\n self.all_sprites_list.append(self.player_sprite)\n\n self.create_buddies()\n self.create_treasure()\n\n # Make the mouse disappear when it is over the window.\n # So we just see our object, not the pointer.\n\n # Set the background color\n arcade.set_background_color(arcade.color.ASH_GREY)\n\n # Set up the player, specifically placing it at these coordinates.\n # self.player_sprite = arcade.Sprite(\"images\\\\carcar.png\", CHARACTER_SCALING)\n # self.player_sprite.center_x = 500\n # self.player_sprite.center_y = 110\n # self.player_sprite.angle = 90\n # self.player_sprite.change_y = 1\n # self.player_list.append(self.player_sprite)\n\n # Create the 'physics engine'\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite,\n self.wall_list)\n\n # Set the viewport boundaries\n # These numbers set where we have 'scrolled' to.\n self.view_left = 0\n self.view_bottom = 0\n\n # For draw\n self.line_start = 0","title":""},{"docid":"acc8caa3e51a5309bc3495e712b74873","score":"0.6608156","text":"def initialize(self) -> None:\n self.simulation = self.initialize_simulation()\n width, height = get_window_resolution()\n display_dim = ((0, width), (0, height))\n self.coord_mapper = CoordinateMapper2D(*self.simulation.dim, *display_dim)\n self.simple_pygame.all_sprites.empty()\n self.initialize_visualization()","title":""},{"docid":"85a06e74924e42e92edb802abd61a330","score":"0.6591549","text":"def __init__(self):\n self.screen_width = 1200\n self.screen_height = 800\n self.bg_color = (0, 230, 0)\n\n # Glove Settings\n self.glove_move_speed = 0.25\n self.glove_size = 100\n\n # Ball Settings\n self.ball_move_speed = 0.25\n self.ball_size = 40","title":""},{"docid":"44be389a1c2b1a58e0dd04eec2e6916d","score":"0.65776414","text":"def init(self):\n\n pygame.init()\n pygame.display.set_mode((640, 480))\n pygame.display.set_caption(\"Gears 4 Geeks\")\n pygame.joystick.init()\n self.controller = pygame.joystick.Joystick(0)\n self.controller.init()\n self.ser = serial.Serial('COM4', 9600)\n\n #ADAFRUIT_IO_KEY = 'd1a1bd3737714fa488e0364c775a4b4d' ##This will only be good until the end of the competition\n #self.aio = Client(ADAFRUIT_IO_KEY)","title":""},{"docid":"7ce9b9e23e307284880325001a9655d9","score":"0.65706956","text":"def setupStage(self):\n print(\"(\" + str(self.HOST) + \", \" + str(self.PORT) +\"):: Initiating setup stage\", file=self.logs)\n\n # Holds player map and name information here. Will be used to create objects later.\n mapVotes = []\n playerNames = {}\n colors = [\"red\", \"blue\"]\n\n gameState = {\n \"ready\": False,\n \"game\": None \n }\n while True:\n # Continuously saves logging information to a text file:\n self.logs.close()\n self.logs = open(str(self.filepath)+\"/_logs/\"+ str(self.PORT) + \".txt\", \"a+\")\n\n # Gets all the events from the game window. A.k.a., do stuff here.\n inboundData = self.socket.recvfrom(1024) # Gets bundle of data from clients\n data = inboundData[0] # Separates data from address\n address = inboundData[1]\n\n # Keeps track of how often the server recieves information from each client.\n updatedTime = time.time() \n self.clientUpdateTimes[str(address)] = updatedTime\n\n ########\n self.bitsIn += sys.getsizeof(data)\n\n address = inboundData[1] # Separates address from data\n data = pickle.loads(data) # Unpickles data back into a python dict\n\n command = data['command']\n if command != None: \n # Takes in information from both players\n if command == \"SUBMIT\":\n pName = data['playerName']\n mVote = data['mapVote']\n\n mapVotes.append(mVote)\n playerNames[str(address)] = pName\n \n # Both votes are in. Chooses a map, builds the Board object.\n if len(mapVotes) == 2:\n # Only chooses one map for both players\n if self.map == None:\n mapTuple = random.choice(mapVotes)\n size = mapTuple[0]\n m = mapTuple[1]\n\n if size == \"SMALL\":\n randomMap = MapGenerator((5,7), m)\n self.map = randomMap.getMap()\n mapString = self.map\n width = randomMap.getDimensions()[0]\n height = randomMap.getDimensions()[1]\n tokens = randomMap.getTokens()\n if size == \"MEDIUM\":\n randomMap = MapGenerator((7,9), m)\n self.map = randomMap.getMap()\n mapString = self.map\n width = randomMap.getDimensions()[0]\n height = randomMap.getDimensions()[1]\n tokens = randomMap.getTokens()\n if size == \"BIG\":\n randomMap = MapGenerator((10,12), m)\n self.map = randomMap.getMap()\n mapString = self.map\n width = randomMap.getDimensions()[0]\n height = randomMap.getDimensions()[1]\n tokens = randomMap.getTokens()\n if size == \"HUGE\":\n randomMap = MapGenerator((12,15), m)\n self.map = randomMap.getMap()\n mapString = self.map\n width = randomMap.getDimensions()[0]\n height = randomMap.getDimensions()[1]\n tokens = randomMap.getTokens()\n if size == \"RANDOM\":\n randWidth = random.randint(5, 13)\n randHeight = random.randint(5, 13)\n\n randomMap = MapGenerator((randWidth,randHeight), m)\n self.map = randomMap.getMap()\n mapString = self.map\n width = randomMap.getDimensions()[0]\n height = randomMap.getDimensions()[1]\n tokens = randomMap.getTokens()\n\n # Builds the game board\n self.board = Board(width, height, mapString)\n\n # Both players' names have been entered, creates Player objects.\\\n # Appends player objects to state variable. \n if len(playerNames) == 2 and len(colors) > 0:\n p = Player(playerNames[str(address)], colors.pop(), None, tokens, address)\n self.players.append(p)\n \n # Player objects and Board object have both been created.\n # Builds the Game object, stores it, then tells the PlayerViews its ready.\n if len(self.players) == 2 and self.board != None:\n self.game = Game(self.board, self.players[0], self.players[1])\n gameState['game'] = self.game\n gameState['ready'] = True\n\n # Sends data to both players simultaneously\n for client in self.clients:\n outboundData = pickle.dumps(gameState)\n self.socket.sendto(outboundData, client)\n break\n\n # Packages up data and sends it back to the client\n outboundData = pickle.dumps(gameState)\n\n ######\n self.bitsOut += sys.getsizeof(outboundData)\n\n self.socket.sendto(outboundData, address)\n \n # Check client connections here\n self.checkClientConnections(time.time())","title":""},{"docid":"7d474528e9d8f517b7d17680b348b736","score":"0.6566866","text":"def init_game_setting(self):\r\n pass","title":""},{"docid":"cb3365d5538ce4bc1d236a6ecb0d00bb","score":"0.6558296","text":"def __init__(self):\n\n # Window starting position\n x = 200\n y = 30\n os.environ[\"SDL_VIDEO_WINDOW_POS\"] = \"%d,%d\" % (x, y)\n\n pygame.init()\n # Init window\n self.window = Window()\n # Flag that defines if the program is running or not\n self.running = True\n if Settings.MENU_ENABLED:\n self.main_menu = MainMenu(self.window)\n self.main_loop()","title":""},{"docid":"aadeb2e51bb6bfdabe9f8fac4cbc537b","score":"0.65457934","text":"def setup(self):\n self.total_time = 0.0\n self.timer_text = None\n arcade.set_background_color(arcade.color.WHITE)","title":""},{"docid":"1e0eeb84e21a232497764c6f98e71fb5","score":"0.65436804","text":"def main():\n field = Field(10, 10)\n snake = Snake((0, 0))\n game = Game(field, snake)\n game.start()","title":""},{"docid":"6707c71df18641862ff91d54774889f8","score":"0.65426224","text":"def setup(self):\n build_world.start_level(self)","title":""},{"docid":"041dab956c4e42b43e04780650c6ab9e","score":"0.65425974","text":"def initialize():\n\n global PLAYER # this means we use the global var PLAYER and cannot have a local var named PLAYER\n global LEVEL_COUNTER\n\n LEVEL_COUNTER = 1\n \n coordinates = generate_coords()\n\n PLAYER = Stark()\n tree = Tree()\n ww = WhiteWalker()\n crown = Crown()\n gray_gem = GrayGem()\n clear_board()\n GAME_BOARD.create(\"Snow\",\"Snow\")\n GAME_BOARD.draw_msg(\"Level \" + str(LEVEL_COUNTER) + \". Winter is coming.\")\n generate_level(coordinates, [PLAYER, ww, gray_gem, crown, tree, tree, gray_gem, tree, tree, gray_gem, tree])\n\n # for i in range(0,NUM_ELTS):\n # place_on_board(elts[i], coordinates[i][0], coordinates[i][1])","title":""},{"docid":"b5fdd02410aefe9f7cf96519fba74bfb","score":"0.65379816","text":"def setupNewGame(self):\r\n self.level = 1\r\n self.num_cows = 2\r\n self.num_farmers = 1\r\n self.levelHeading = Text(self.gameDisplay, 120, 425, 175, self.light_orange, \"Farm 1\")\r\n self.shield_indicator.image = self.greenShield\r\n updatedHeading = self.levelHeading\r\n self.startUX[0] = updatedHeading","title":""},{"docid":"946ac1509799c927602adfa52c72feca","score":"0.6533264","text":"def init_game(self):\n self.blind_manager = BlindManager(hands_per_level=10,\n bots=self.living_bot_names())","title":""},{"docid":"5b3654c2b20257aa82090e6ac00e6aa7","score":"0.65258765","text":"def load_game(self):\n game = Game(self.w, self.h, self.screen)\n game.run()","title":""},{"docid":"a43e92b3418d0a3671e2a1cf01bd2b56","score":"0.65238756","text":"def init_vars(self):\n\n load_dotenv()\n self.smart_cube = True if os.environ.get(\"SMART_CUBE\") == \"True\" else False\n self.gen_parsed_to_cubedb = True if os.environ.get(\"GEN_PARSED_TO_CUBEDB\") == \"True\" else False\n self.name_of_solve = os.environ.get(\"NAME_OF_SOLVE\")\n self.time_solve = os.environ.get(\"TIME_SOLVE\")\n self.comms_unparsed_bool = True if os.environ.get(\"COMMS_UNPARSED\") == \"True\" else False\n self.gen_with_move_count = True if os.environ.get(\"GEN_WITH_MOVE_COUNT\") == \"True\" else False\n self.diff_to_solved_state = float(os.environ.get(\"DIFF_BETWEEN_ALGS\"))\n self.parse_to_lp = True if os.environ.get(\"PARSE_TO_LETTER_PAIR\") == \"True\" else False\n self.gen_with_moves = True if os.environ.get(\"GEN_WITH_MOVE_COUNT\") == \"True\" else False\n self.buffer_ed = self.get_buffer_ed(os.environ.get(\"EDGES_BUFFER\"))\n self.buffer_cor = self.get_buffer_cor(os.environ.get(\"CORNER_BUFFER\"))\n self.path_to_lp = os.environ.get(\"PATH_LETTER_PAIR_FILE\")\n self.dict_lp = self.load_letter_pairs_dict()","title":""},{"docid":"0fd5e3a34e920d83be22012d9feeb6dd","score":"0.6522762","text":"def setUp(self):\r\n self.caption = \"mirra extending classes\" # window name\r\n self.size = 640, 480 #window size\r\n self.pos = 100,100 # window top left location\r\n self.fullScreen = 0 # if fullScreen is on it will overwrite your pos and size to match the display's resolution\r\n self.frameRate = 15 # set refresh framerate\r","title":""},{"docid":"b911432ad77007e82f7b5bbcd8836058","score":"0.65204126","text":"def __init__(self):\n pygame.init()\n\n self.settings = Settings()\n\n self.screen = pygame.display.set_mode(\n (self.settings.screen_width, self.settings.screen_height))\n pygame.display.set_caption(\"52 Card Trick\")\n self.CardSet = CardSet(self)","title":""},{"docid":"e4c7509560856646276227cd3c6a4074","score":"0.6516524","text":"def setUp(self):\n\n self.board = Board(3, 3)","title":""},{"docid":"89cf360e139cc7bdba8d124c0a7ad4d8","score":"0.6516285","text":"def initialize_game_params(self):\r\n\r\n self.is_new_game = True\r\n self.is_game_over = False\r\n self.is_game_lost = False\r\n self.is_left_mouse_down = False\r\n self.is_right_mouse_down = False\r\n self.num_of_hidden_non_mines_tiles = self.rows * self.cols - self.num_of_mines","title":""},{"docid":"3ea76d55bd43e9ab984101561951f2a6","score":"0.6511197","text":"def setUp(self):\n self.player = ship.Player(\n constants.PLAYER_START_PLACE,\n constants.PLAYER_WIDTH,\n constants.PLAYER_HEIGHT,\n constants.PLAYER_IMG,\n constants.PLAYER_HEALTH\n )\n\n self.alien = ship.Alien(\n [320, 300],\n 30,\n 30,\n constants.GREEN_ALIEN_IMG,\n 1\n )\n\n self.alien.shooting([320, 300], 5, False)\n\n self.player.shooting([self.player.position[0] + 3, self.player.position[1]], 1, True)","title":""},{"docid":"332b88663fe7d53af8e80737950739d7","score":"0.6507396","text":"def __init__(self):\n\t\t# Screen setting.\n\t\tself.screen_width = 1200\n\t\tself.screen_height = 800\n\t\tself.bg_color = (230, 230, 230)\t\n\n\t\t#Ship setting\n\t\tself.ship_speed_factor = 10\t\t\n\t\tself.ship_limit = 3\t\t\t# number ship \n\n\t\t# Bullet setting.\n\t\tself.bullet_speed_factor = 3\n\t\tself.bullet_width = 3\n\t\tself.bullet_height = 15\n\t\tself.bullet_color = (60,60,60) #dark gray bullet\n\t\tself.bullets_allowed = 6\t\t# number bullet in screen\n\n\t\t#Alien setting.\n\t\tself.alien_speed_factor = 3\n\t\tself.fleet_drop_speed = 50\n\t\t# fleet_direction of 1 represents right; -1 represents left. \n\t\tself.fleet_direction = 1\n\n\t\t# Scoring\n\t\tself.alien_points = 50\n\n\t\t# How quickly the game speed ups\n\t\tself.speedup_scale = 1.1\n\t\tself.iniitialize_dynamic_settings()\n\t\t# How quickly score increase.\n\t\tself.score_scale = 1.5","title":""},{"docid":"4a0a37f90385dd89f2994154b5d24944","score":"0.6501459","text":"def startup(self):\n self.prev_gray = None\n self.frame_idx = 1\n self.tracks = []\n self.fps = []\n self.vid_info = None\n self.track_new_points_count = 0","title":""},{"docid":"7c9c06745971f04f7a5a6fc62e584161","score":"0.64820516","text":"def __init__(self):\n self.__grid = create_grid(\n Settings.SIZE_X, Settings.SIZE_Y, MarkerType.NONE)\n\n self.__turn = 0\n self.__state = GameState.PLAYING\n self.__winner = MarkerType.NONE\n self.__loser = MarkerType.NONE\n\n # Separate counter for turns, because __turn depends on starting player\n self.__turns_played = 0","title":""},{"docid":"d9ba9411b9f12679a8bc536d94912571","score":"0.64732724","text":"def setup(self):\n self.ui_manager.purge_ui_elements()\n\n global text_prompt\n global fails\n\n # Text elements\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\n \"\"\"You need to crack the terminal passcode.\\n\nEnter \"\"\" + str(global_n) + \"\"\" unique digits and try to guess the code!\"\"\",\n center_x=self.left_column_x * 2,\n center_y=self.y_slot * 3,\n id=\"fcg\"\n\n ))\n\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\n text_prompt,\n center_x=self.left_column_x * 2,\n center_y=self.y_slot * 2.25,\n id=\"mathGame_UI2\"\n\n ))\n\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\n \"Tries left: \" + str(20 - fails),\n center_x=self.left_column_x * 2,\n center_y=self.y_slot / 1.7,\n id=\"Tries\"\n\n ))\n\n # Input Box\n ui_input_box = arcade.gui.UIInputBox(\n center_x=self.left_column_x * 2,\n center_y=self.y_slot * 1.5,\n width=300,\n id=\"InputBox\"\n )\n ui_input_box.text = ''\n ui_input_box.cursor_index = len(ui_input_box.text)\n self.ui_manager.add_ui_element(ui_input_box)\n\n # Buttons\n button = EnterCodeButton(\n center_x=self.left_column_x * 2,\n center_y=self.y_slot,\n input_box=ui_input_box,\n minigame=self,\n ui_manager=self.ui_manager,\n n=global_n,\n choice_list=chosen_list\n )\n self.ui_manager.add_ui_element(button)\n\n button = ExitButton(\n center_x=self.right_column_x * 1.25,\n center_y=self.y_slot * 3.7,\n minigame=self\n )\n self.ui_manager.add_ui_element(button)","title":""},{"docid":"6aa916be2fd937c6cb716d3025adc3dd","score":"0.64697623","text":"def setup(self):\n # Initialize the drawing environment (create main windows, etc)\n glutInit(sys.argv)\n glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB | GLUT_DEPTH)\n glutInitWindowSize(WINDOW_WIDTH, WINDOW_HEIGHT)\n glutCreateWindow(name)\n\n glShadeModel(GL_SMOOTH)\n\n glClearDepth(1.0)\n glDepthFunc(GL_LESS) # The Type Of Depth Test To Do\n glEnable(GL_DEPTH_TEST) # Enables Depth Testing\n glShadeModel(GL_SMOOTH) # Enables Smooth Color Shading\n\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity() # Reset The Projection Matrix\n\n # Calculate The Aspect Ratio Of The Window\n gluPerspective(45.0, float(WINDOW_WIDTH)/float(WINDOW_HEIGHT), 0.1, 100.0)\n\n glMatrixMode(GL_MODELVIEW)\n\n # Set up keyboard listeners.\n glutKeyboardFunc(self.on_key)","title":""},{"docid":"70b23db89e61198c0f3c476b2d7764c4","score":"0.6467842","text":"def setUp(self):\n self.player = Player()","title":""},{"docid":"4c39ca734f8b91d1aee2b393a5df598a","score":"0.6457494","text":"def setup_members(self):\n ### cell\n self.cell_size = 8\n self.cell_row = 80\n self.cell_col = 100\n self.color_alive = \"black\"\n self.color_dead = \"white\"\n\n ### world\n self.init_modes = {} # read modes from json file\n self.init_world = {} # begining status\n self.world = {} # world's map\n # current status of world\n self.world_status = GOL(self.cell_row, self.cell_col)\n self.world_setable = True\n self.world_alive = False\n\n # widgets\n self.toolbar_height = 40\n self.world_size = [self.cell_size * self.cell_row,\n self.cell_size * self.cell_col]\n self.window_size = self.world_size\n self.window_size[0] += self.toolbar_height\n\n # resource\n self.saver_icon = \"save.gif\"\n self.run_icon = \"run.gif\"\n self.pause_icon = \"pause.gif\"\n self.stop_icon = \"stop.gif\"\n self.modes_file = \"gol.json\"\n self.modes_names = []","title":""},{"docid":"84c5d5de24868699578c5df0dd92a9e5","score":"0.6449371","text":"def setup(self, level):\r\n\r\n # Used to keep track of our scrolling\r\n self.view_bottom = 0\r\n self.view_left = 0\r\n\r\n # Keep track of the score\r\n self.score = 0\r\n\r\n # Keep track of lives\r\n # self.lives = 5\r\n\r\n # Create the Sprite lists\r\n self.player_list = arcade.SpriteList()\r\n self.foreground_list = arcade.SpriteList()\r\n self.background_list = arcade.SpriteList()\r\n self.wall_list = arcade.SpriteList()\r\n self.coin_list = arcade.SpriteList()\r\n\r\n # Set up the player, specifically placing it at these coordinates.\r\n image_source = \"images/Alice/Alice7_front.png\"\r\n self.player_sprite = arcade.Sprite(image_source, CHARACTER_SCALING)\r\n self.player_sprite.center_x = PLAYER_START_X\r\n self.player_sprite.center_y = PLAYER_START_Y\r\n self.player_list.append(self.player_sprite)\r\n\r\n # --- Load in a map from the tiled editor ---\r\n\r\n # Name of the layer in the file that has our platforms/walls\r\n platforms_layer_name = 'Platforms'\r\n moving_platforms_layer_name = 'Moving Platforms'\r\n # Name of the layer that has items for pick-up\r\n coins_layer_name = 'Coins'\r\n # Name of the layer that has items for foreground\r\n foreground_layer_name = 'Foreground'\r\n # Name of the layer that has items for background\r\n background_layer_name = 'Background'\r\n # Name of the layer that has items we shouldn't touch\r\n dont_touch_layer_name = \"Don't Touch\"\r\n\r\n # Map name\r\n map_name = f\"map4_level_{level}.tmx\"\r\n\r\n # Read in the tiled map\r\n my_map = arcade.tilemap.read_tmx(map_name)\r\n\r\n # Calculate the right edge of the my_map in pixels\r\n self.end_of_map = my_map.map_size.width * GRID_PIXEL_SIZE\r\n\r\n # -- Background\r\n self.background_list = arcade.tilemap.process_layer(my_map,\r\n background_layer_name,\r\n TILE_SCALING)\r\n\r\n # -- Foreground\r\n self.foreground_list = arcade.tilemap.process_layer(my_map,\r\n foreground_layer_name,\r\n TILE_SCALING)\r\n\r\n # -- Platforms\r\n self.wall_list = arcade.tilemap.process_layer(map_object=my_map,\r\n layer_name=platforms_layer_name,\r\n scaling=TILE_SCALING,\r\n use_spatial_hash=True)\r\n # -- Moving Platforms\r\n moving_platforms_list = arcade.tilemap.process_layer(my_map, moving_platforms_layer_name, TILE_SCALING)\r\n for sprite in moving_platforms_list:\r\n self.wall_list.append(sprite)\r\n\r\n # -- Coins\r\n self.coin_list = arcade.tilemap.process_layer(my_map,\r\n coins_layer_name,\r\n TILE_SCALING,\r\n use_spatial_hash=True)\r\n\r\n # -- Don't Touch Layer\r\n self.dont_touch_list = arcade.tilemap.process_layer(my_map,\r\n dont_touch_layer_name,\r\n TILE_SCALING,\r\n use_spatial_hash=True)\r\n\r\n # --- Other stuff\r\n # Set the background color\r\n if my_map.background_color:\r\n arcade.set_background_color(my_map.background_color)\r\n\r\n # Create the 'physics engine'\r\n self.physics_engine = arcade.PhysicsEnginePlatformer(self.player_sprite,\r\n self.wall_list,\r\n GRAVITY)","title":""},{"docid":"2d9816345ac7805f9405790a66da0c26","score":"0.6441203","text":"def setup(self):\n\n # Set up the Cameras\n viewport = (0, 0, self.window.width, self.window.height)\n self.camera = arcade.SimpleCamera(viewport=viewport)\n self.gui_camera = arcade.SimpleCamera(viewport=viewport)\n\n # Map name\n map_name = \":resources:tiled_maps/map_with_ladders.json\"\n\n # Layer Specific Options for the Tilemap\n layer_options = {\n LAYER_NAME_PLATFORMS: {\n \"use_spatial_hash\": True,\n },\n LAYER_NAME_MOVING_PLATFORMS: {\n \"use_spatial_hash\": False,\n },\n LAYER_NAME_LADDERS: {\n \"use_spatial_hash\": True,\n },\n LAYER_NAME_COINS: {\n \"use_spatial_hash\": True,\n },\n }\n\n # Load in TileMap\n self.tile_map = arcade.load_tilemap(map_name, TILE_SCALING, layer_options)\n\n # Initiate New Scene with our TileMap, this will automatically add all layers\n # from the map as SpriteLists in the scene in the proper order.\n self.scene = arcade.Scene.from_tilemap(self.tile_map)\n\n # Keep track of the score\n self.score = 0\n\n # Shooting mechanics\n self.can_shoot = True\n self.shoot_timer = 0\n\n # Set up the player, specifically placing it at these coordinates.\n self.player_sprite = PlayerCharacter()\n self.player_sprite.center_x = (\n self.tile_map.tile_width * TILE_SCALING * PLAYER_START_X\n )\n self.player_sprite.center_y = (\n self.tile_map.tile_height * TILE_SCALING * PLAYER_START_Y\n )\n self.scene.add_sprite(LAYER_NAME_PLAYER, self.player_sprite)\n\n # Calculate the right edge of the my_map in pixels\n self.end_of_map = self.tile_map.width * GRID_PIXEL_SIZE\n\n # -- Enemies\n enemies_layer = self.tile_map.object_lists[LAYER_NAME_ENEMIES]\n\n for my_object in enemies_layer:\n cartesian = self.tile_map.get_cartesian(\n my_object.shape[0], my_object.shape[1]\n )\n enemy_type = my_object.properties[\"type\"]\n if enemy_type == \"robot\":\n enemy = RobotEnemy()\n elif enemy_type == \"zombie\":\n enemy = ZombieEnemy()\n enemy.center_x = math.floor(\n cartesian[0] * TILE_SCALING * self.tile_map.tile_width\n )\n enemy.center_y = math.floor(\n (cartesian[1] + 1) * (self.tile_map.tile_height * TILE_SCALING)\n )\n if \"boundary_left\" in my_object.properties:\n enemy.boundary_left = my_object.properties[\"boundary_left\"]\n if \"boundary_right\" in my_object.properties:\n enemy.boundary_right = my_object.properties[\"boundary_right\"]\n if \"change_x\" in my_object.properties:\n enemy.change_x = my_object.properties[\"change_x\"]\n self.scene.add_sprite(LAYER_NAME_ENEMIES, enemy)\n\n # Add bullet spritelist to Scene\n self.scene.add_sprite_list(LAYER_NAME_BULLETS)\n\n # --- Other stuff\n # Set the background color\n if self.tile_map.background_color:\n self.window.background_color = self.tile_map.background_color\n\n # Create the 'physics engine'\n self.physics_engine = arcade.PhysicsEnginePlatformer(\n self.player_sprite,\n platforms=self.scene[LAYER_NAME_MOVING_PLATFORMS],\n gravity_constant=GRAVITY,\n ladders=self.scene[LAYER_NAME_LADDERS],\n walls=self.scene[LAYER_NAME_PLATFORMS],\n )","title":""},{"docid":"6834ee80b5a8768280d919c99bf26a5a","score":"0.6434447","text":"def setup(self):\n self.player = arcade.Sprite(\"tank.png\", 0.5)\n self.num_coins = 30\n\n # Initialize brick_list as a SpriteList\n # Then use nested for loops to place rows of bricks. \n # (Hint: Outer for loop is y coordinate skipping every 150 pixels\n # Inner for loop is x coordinate skipping every 64 pixels)\n # Append bricks to brick_list\n\n\n\n\n \n # Initialize coin_list as a SpriteList\n self.coin_list = arcade.SpriteList()\n \n # PSEUDOCODE for how to place coins. \n # for each i in range of number of coins \n # create coin Sprite\n # set boolean variable successfully_placed to False\n # while not successfully_placed\n # set center_x and center_y randomly\n # compute collision lists for coin with bricks\n # AND coin with other coins (2 lists)\n # if both lists have 0 length, then we have successfully placed the coin\n # add coin to coin_list\n \n \n \n \n \n \n \n # initialize physics_engine","title":""},{"docid":"22073cb86b548bd7aa8ab34e523b6572","score":"0.6433896","text":"def setup(self):\n\n # Create the Sprite lists\n self.sprite_list = arcade.SpriteList()\n\n r = 60\n for x in rand_range(0, 100 * math.pi, scale=math.pi / 5):\n star = arcade.Sprite(\"../../resources/arcade/gold_1.png\")\n star.center_x = SCREEN_WIDTH / 2 + r * math.cos(x)\n star.center_y = SCREEN_HEIGHT / 2 + r * math.sin(x)\n star.seed = scale_generator(x=random() * math.pi, offset=.5, step=.01)\n star.scale = next(star.seed)\n self.sprite_list.append(star)\n r += 3","title":""},{"docid":"ea777f4769348b29ef8c98d8ba2109ac","score":"0.64236575","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()","title":""},{"docid":"ea777f4769348b29ef8c98d8ba2109ac","score":"0.64236575","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()","title":""},{"docid":"ea777f4769348b29ef8c98d8ba2109ac","score":"0.64236575","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()","title":""},{"docid":"ea777f4769348b29ef8c98d8ba2109ac","score":"0.64236575","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()","title":""},{"docid":"ea777f4769348b29ef8c98d8ba2109ac","score":"0.64236575","text":"def setUp(self):\r\n self.spaceship = SpaceShipGame()","title":""},{"docid":"fec745c478de3ff327552f7edca647db","score":"0.6420831","text":"def setUp(self):\n\n self.sold = Soldier(0, 0)\n self.R = Random(seed)","title":""},{"docid":"b3f578c7fed4d6b351c3595091ed55c1","score":"0.6419482","text":"def main():\n g = DemoGame(800, 600)\n g.start()","title":""},{"docid":"944ca57bb03c227960b99967b99dfe32","score":"0.6415772","text":"def start(self):\n pygame.init()\n self.screen = pygame.display.set_mode((self.width, self.height))\n pygame.display.set_caption(\"PyStroke\")\n self.engines = [GameEngine(self.screen, self.e_e)] # add others here\n self.engine = self.engines[0]\n self.run()","title":""},{"docid":"d59a37df8a7fe9b043bd00f4df69a8f8","score":"0.64034855","text":"def __init__(self):\n #Screen settings\n self.screen_width=1200\n self.screen_height=800\n self.bg_color=(230,230,230)\n #ship settings\n self.ship_limit=1\n #bullet settings\n self.bullet_width=300\n self.bullet_height=15\n self.bullet_color=(60,60,60)\n self.bullets_allowed=3\n #Alien settings\n self.fleet_drop_speed = 20\n \n \n #how quickly the game speeds up\n self.speedup_scale=1.1\n #how quickly the point values increase\n self.score_scale=1.5\n \n self.initialize_dynamic_settings()","title":""},{"docid":"5c5aa112a96d3bcd6cf22f2a0996df71","score":"0.63967246","text":"def ready(self):\r\n\t\t# Remove attract mode from mode queue - Necessary?\r\n\t\tself.game.modes.remove(self)\r\n\t\t# Initialize game\t\r\n\t\tself.game.start_game()\r\n\t\t# Add the first player\r\n\t\tself.game.add_player()\r\n #self.game.add_player()\r\n\t\t# Start the ball. This includes ejecting a ball from the trough.\r\n\t\tself.game.start_ball()","title":""},{"docid":"15a3fa4330760fb2d31506163e50273f","score":"0.6395593","text":"def setup(self):\n # Create your sprites and sprite lists here\n self.wall_list = arcade.SpriteList()\n for x in range(128, SCREEN_WIDTH, 196):\n for y in range(128, SCREEN_HEIGHT, 196):\n wall = arcade.Sprite(\"building.png\",.3)\n wall.center_x = x\n wall.center_y = y\n # wall.angle = 45\n self.wall_list.append(wall)\n self.player_sprite = arcade.Sprite(\"taxi.png\")\n self.player_sprite.center_x = 50\n self.player_sprite.center_y = 50\n self.player_sprite.scale = .2\n self.player_list = arcade.SpriteList()\n self.player_list.append(self.player_sprite)\n\n #Spawns people and makes list\n self.person = arcade.Sprite(\"person.png\")\n self.person.scale = .2\n self.person.center_x = random.randrange(SCREEN_WIDTH)\n self.person.center_y = random.randrange(SCREEN_HEIGHT)\n #Spawns target\n self.target = arcade.Sprite(\"target.png\")\n self.target.scale = .5\n self.target.center_x = random.randrange(60,SCREEN_WIDTH)\n self.target.center_y = random.randrange(60,SCREEN_HEIGHT)\n color_list = [\"BLUE\",\"RED\"]","title":""},{"docid":"6fb594b736454b3248a1646e21447958","score":"0.63796514","text":"def initialise(self):\n self.set_up()","title":""},{"docid":"7eef679870f9b9257f328c0e702a2060","score":"0.6377583","text":"def init_game(self):\n self.view.carregar_jogadores_possiveis(self._possible_players_list())\n self.view.put_view_in_main_loop()","title":""},{"docid":"6f06bb7f659d9f9536ca96ded727d79a","score":"0.6373506","text":"def run(self):\n pygame.init()\n pygame.display.set_caption(\"Genetic Game\")\n self.screen = pygame.display.set_mode((self.SCREEN_W, self.SCREEN_H), 0, 32)\n\n self.ominus_sprites = [OminusSprite(self.screen, o, PLAYERS_COLORS[o.id]) for o in self.model.get_players()]\n for o in self.ominus_sprites:\n self.agent_group.add(o)\n\n self.wall_sprites = [WallSprite(self.screen, w) for w in self.model.get_walls()]\n for w in self.wall_sprites:\n self.terrain_group.add(w)","title":""},{"docid":"59942af24376fb5026cb81340b8a4822","score":"0.6373452","text":"def __init__(self):\n #screen Settings\n self.screen_width = 1024\n self.screen_height = 768\n self.bg_color = (32, 32, 32)\n\n #rocket settings\n self.rocket_speed = 1\n\n #laser Settings\n self.laser_speed = 1.0\n self.laser_width = 3\n self.laser_height = 15\n self.laser_color = (0, 255, 255)\n self.lasers_allowed = 3","title":""},{"docid":"62f53bd434d32af1c87016a779056339","score":"0.63714504","text":"def __init__(self):\n #Screen configuration\n self.screen_width = 1200\n self.screen_height = 680\n self.bg_color = (0,20,50)\n \n #Hero configuration\n #Increase of ship speed to 1.5 pixels instead of 1\n #self.hero_speed_factor = 1.5\n self.hero_limit = 3\n \n #Syringes (bullets) configuration\n #self.bullet_speed_factor = 1\n self.bullets_allowed = 5\n \n #Covids configuration\n self.covid_vertical_speed_factor = 1\n #The value of the movement is negative because it is increasing\n # from the right to the left\n #self.covid_horizontal_speed_factor = -10\n #The pandemy direction equals 1 means to the bottom; -1 means to the top\n # The randint ensures an randomly direction when starting the game\n #if randint(0,1) == 1:\n # self.pandemy_direction = 1\n #else:\n # self.pandemy_direction = -1\n\n #The rate that increases the game speed\n self.speedup_scale = 1.1\n \n self.initialize_dynamic_settings()","title":""},{"docid":"dd5240037eab4029c3f040a6c1aba1cc","score":"0.63671416","text":"def __init__(self):\n self.screen = pg.display.get_surface()\n self.screen_rect = self.screen.get_rect()\n self.clock = pg.time.Clock()\n self.fps = 60\n self.keys = pg.key.get_pressed()\n self.done = False\n # ship = random.choice(list(prepare.GFX[\"ships\"].values()))\n ship = list(prepare.GFX[\"ships\"].values())[7] # pick first ship available\n self.player = actors.Player((0, 0), ship)\n self.level = level.Level(self.screen_rect.copy(), self.player)\n\n self.energyloss_counter = 0\n self.energygain_counter = 0","title":""},{"docid":"0f7194f91226989dda2cdda5e9fc3c6c","score":"0.63631403","text":"def __init__(self):\n game_engine = get_gameengine()\n if game_engine is not None:\n self = game_engine\n else:\n ## The targeted frames per second\n self.target_fps = 200\n\n ## The start time\n self.time = time.time()\n\n ## A list of all registered game objects\n self.game_objects = list()\n\n ## A list of colliders\n self.colliders = list()\n\n ## Manage the user inputs\n self.input_manager = InputManager(self)\n\n ## Determines the state of the Game Engine\n self.running = False\n\n ## Variable to pause the Game Engine\n self.paused = False\n\n self.time_node = pm.PyNode('time1')\n # end if","title":""},{"docid":"2b1aec276551d5c6cf8e7446310d182a","score":"0.6361806","text":"def __init__(self):\n super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)\n self.player_count: int = None\n self.player_hand_0: arcade.SpriteList = None\n self.player_hand_1: arcade.SpriteList = None\n self.deck: arcade.SpriteList = None\n self.pile: arcade.SpriteList = None","title":""},{"docid":"72672d21106099e15bfae6aee9a6e0a0","score":"0.63469946","text":"def start_game(self) -> None:\n self.init_game()\n self.play()","title":""},{"docid":"30a48c448c1c746bc829dfa3391d2526","score":"0.6346134","text":"def initialize(self):\n\n # --------- BEGIN YOUR CODE ----------\n\n # This is exactly the same as Human.initialize, just copy the code over\n\n # --------- END YOUR CODE ----------\n pass","title":""},{"docid":"472542e9894153ef4925551611ca0402","score":"0.6342365","text":"def start(self):\n # asserts preconditions are met\n #assert self.validGameSettings()\n\n #draws initial welcome screen\n #self._text = GLabel(text=\"Press 'S' to Play\")\n #self._text.draw(self.view)\n\n # initializing instance variables\n self.setState(STATE_INACTIVE)\n self.setWave(None)\n self.setText(None)\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\n\n # draws iniital welcome screen\n self.welcomeScreen()","title":""},{"docid":"d7fefa3e89f5460cda184529b474d2fc","score":"0.63385844","text":"def init(self):\n self._last = None\n self._game = None\n self._state = STATE_INACTIVE\n self._message = GLabel(text='Breakout\\n\\nClick To Begin\\n\\nGood Luck',\n font_size=24,x=GAME_WIDTH / 2.0, y=GAME_HEIGHT*(2.0/3.0),\n halign='center', valign='middle', linecolor=colormodel.WHITE)\n self._touch = None\n self._countdownTime = 0\n self._countdownMessage = GLabel(text='3', font_size=40,x=GAME_WIDTH / 2.0,\n y=GAME_HEIGHT*(2.0/3.0), halign='center',\n valign='middle', linecolor=colormodel.WHITE)\n self._pausedMessage = GLabel()\n self._sound = True\n self._soundImage = GImage(x=GAME_WIDTH-32, y=0, width=32, height=22,\n source='whitevolumeon.png')\n self._background = GRectangle(x=0, y=0, width=GAME_WIDTH, height=GAME_HEIGHT,\n fillcolor=colormodel.BLACK, linecolor=colormodel.BLACK)","title":""},{"docid":"7628ed4be734b19f77e1a20b4184fae4","score":"0.6336813","text":"def __init__(self):\n pygame.init()\n self.settings = Settings()\n self.screen = pygame.display.set_mode(\n (self.settings.screen_width, self.settings.screen_height)\n )\n pygame.display.set_caption(\"Sideways Shooter\")\n self.stats = GameStats(self)\n self.sideways_ship = SidewaysShip(self)\n self.bullets = pygame.sprite.Group()\n self.aliens = pygame.sprite.Group()\n self._create_fleet()","title":""},{"docid":"424833a78c0bfdeed90d07499d7cd6f7","score":"0.6334929","text":"def __init__(self):\n\n # Screen's settings\n self.screen_width = 1200\n self.screen_height = 800\n self.bg_color = (230, 230, 230)\n\n # Bluebee Settings\n self.bb_speed = 1.0\n\n # Moving test.\n self.counter = 0\n self.max_left = 400\n self.max_up = 300\n self.max_right = 400\n self.max_down = 300","title":""},{"docid":"15c02130f22707d1974ffa12a4a11c5b","score":"0.6327699","text":"def start() -> None:\n\n # PREPARE\n clone_game_files()\n\n # SIMULATE\n turns = run.simulation()\n\n # LOG\n logs = read.combine_logs(turns)\n\n # CALCULATE\n results = calculate.results(logs)\n\n # DISPLAY\n visualize.charts(results)\n\n # CLEAN\n remove_cloned_files()","title":""},{"docid":"ed037633b0e87e59f92106836bcb74fe","score":"0.6324671","text":"def setUp(self):\n d = self.deck = TestDeck()\n self.game = test_setup.two_player_lead('Laborer', deck=d)\n self.p1, self.p2 = self.game.players","title":""},{"docid":"305651a134f8726b49cb090804b95f21","score":"0.6319136","text":"def __init__(self) -> None:\n self.win = self.__init_window()\n self.BACKGROUND = pygame.transform.scale(pygame.image.load(os.path.join(\"assets\", \"background\", \"background.png\")), (WIDTH, HEIGHT))\n self.highscore = 0\n self.gamemode = Gamemodes.startscreen\n self.clock = pygame.time.Clock()","title":""},{"docid":"ded65e67ac273ec31aaf5cec21a1abb6","score":"0.63133985","text":"def __init__(self):\n pygame.init()\n\n self.settings = Settings()\n\n self.scenes = {\"menu\": MenuScene(),\n \"settings\": SettingsScene(),\n \"score\": ScoreScene(),\n \"game\": GameScene(),\n \"pause\": PauseScene(),\n \"game_over\": GameOverScene(),\n \"logo\": LogoScene()}\n self.scene_name = \"logo\" # start scene\n self.previous_scene_name = None\n self.scene = self.scenes[self.scene_name]\n\n self.__manager = ResourceManager()\n self.__display = self.settings.get_display()\n self.__display.fill(BACKGROUND_COLOR)\n pygame.display.flip()","title":""},{"docid":"3a7fbbf00153d93349b3f3406c324732","score":"0.63096815","text":"def setUp(self):\r\n global ship_image\r\n self.spaceshipgame = SpaceShipGame()\r\n self.spaceshipgame.init()\r\n pos = [1,1]\r\n vel = [1,1]\r\n angle = 0\r\n image = ship_image\r\n\r\n center = [1,1]\r\n size = 1\r\n info = ImageInfo(center, size)\r\n\r\n self.ship = Ship( pos, vel, angle, image, info)","title":""},{"docid":"3a7fbbf00153d93349b3f3406c324732","score":"0.63096815","text":"def setUp(self):\r\n global ship_image\r\n self.spaceshipgame = SpaceShipGame()\r\n self.spaceshipgame.init()\r\n pos = [1,1]\r\n vel = [1,1]\r\n angle = 0\r\n image = ship_image\r\n\r\n center = [1,1]\r\n size = 1\r\n info = ImageInfo(center, size)\r\n\r\n self.ship = Ship( pos, vel, angle, image, info)","title":""},{"docid":"e22ba600cbf922333b0d8230d4715ba2","score":"0.63095695","text":"def setUp(self):\n self.delegate = AlwaysHitDelegate(\"\")\n self.environment = BattleEnvironment()","title":""}],"string":"[\n {\n \"docid\": \"3956c41832a0e4cb1cf11a8add517eef\",\n \"score\": \"0.8164389\",\n \"text\": \"def setup(self):\\n # Create your sprites and sprite lists here\\n self.game: Game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, TILE_SIZE, 1, grid_layers = 4)\\n self.game.game_message = \\\"Lead the Rabbit home\\\"\\n\\n # show the menu so that we see the instructions\\n self.game.menu.button_list[0].text = \\\"Start\\\"\\n self.game.menu.is_visible = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8773700bfd9a95656c265c6bd313d6e9\",\n \"score\": \"0.77574474\",\n \"text\": \"def setup_game(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6f2e0104a864f573e1ebcfb83e821ec\",\n \"score\": \"0.7394462\",\n \"text\": \"def __init__(self):\\n\\n self.frameCount = 0\\n self._initScreen()\\n self._initObjects()\\n self._initControls()\\n self._initLevel()\\n self._start()\\n print \\\"DEBUG: Initializing Game\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332678f4bd4fda854a458ca93a320cbd\",\n \"score\": \"0.7383125\",\n \"text\": \"def game_setup(self):\\n self.deck = Shoe(6)\\n self.initial_draw()\\n self.pot = ask_for_bet(self.player.money)\\n show_table(self.player, self.dealer, self.pot)\\n self.surrender_and_insurance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2042be52d22e6802ebadd2ec82edd98\",\n \"score\": \"0.73794293\",\n \"text\": \"def setup(self):\\n # Set up the player\\n self.player_sprite = arcade.Sprite(\\\"Sprites/Jugador/Jugador.jpg\\\", SPRITE_SCALING)\\n self.player_sprite.center_x = 100\\n self.player_sprite.center_y = 100\\n self.player_list = arcade.SpriteList()\\n self.player_list.append(self.player_sprite)\\n\\n # Listado de habitaciones\\n self.rooms = []\\n self.rooms.append(setup_pueblo())\\n\\n #Contador de habitación\\n self.current_room = 0\\n\\n #Fisicas\\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite, self.rooms[self.current_room].wall_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e17cf1d9db4f5bc13da1d42d12d6088\",\n \"score\": \"0.7377979\",\n \"text\": \"def init(cls):\\n\\n cls.configs = yaml.load( file('../local/config.yaml') )\\n cls.is_online = False\\n cls.state = State.playing\\n cls.classes = classes\\n cls.guiclasses = guiclasses\\n\\n # set up pygame and init\\n pygame.init()\\n\\n # Set up the window\\n cls.screen = pygame.display.set_mode(\\n tuple(cls.configs['options']['resolution']),\\n 0,\\n 32)\\n classes.screen = cls.screen\\n guiclasses.screen = cls.screen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4351bfac5d4a145db8bf589197643b31\",\n \"score\": \"0.7341448\",\n \"text\": \"def setup_new_game(self):\\r\\n self._player = Player()\\r\\n self._stats = GameStats(self._bb_settings)\\r\\n self._scoreboard = Scoreboard(self._bb_settings, self._screen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651a0727ec3ed20eb715acef2a022e23\",\n \"score\": \"0.7279792\",\n \"text\": \"def game_initialize():\\n\\tglobal SURFACE_MAIN, SURFACE_MAP, PLAYER, FOV_CALCULATE, CLOCK, ASSETS, CAMERA, PREFERENCES\\n\\n\\tpygame.init()\\n\\tpygame.key.set_repeat(555, 85)\\t\\n\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_celtic.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_fantasy.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_mesopotamian.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_norse.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_region.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\jice_town.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\mingos_demon.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\mingos_dwarf.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\mingos_norse.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\mingos_standard.cfg\\\")\\n\\tlibtcod.namegen_parse(\\\"data\\\\\\\\namegen\\\\\\\\mingos_town.cfg\\\")\\n\\n\\tCLOCK = pygame.time.Clock()\\n\\n\\tCAMERA = obj_Camera()\\n\\n\\tSURFACE_MAIN = pygame.display.set_mode((CAMERA.width, CAMERA.height))\\n\\n\\tSURFACE_MAP = pygame.Surface((constants.GAME_WIDTH, constants.GAME_HEIGHT))\\n\\n\\tFOV_CALCULATE = True\\n\\ttry:\\n\\t\\tload_preferences()\\n\\t\\tprint(\\\"Preferences Loaded\\\")\\n\\texcept:\\n\\t\\tPREFERENCES = struc_Preferences()\\n\\t\\tprint(\\\"Preferences generated\\\")\\n\\tASSETS = struc_Assets()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c8e437aac97895bfa712a9afbeab57\",\n \"score\": \"0.7257215\",\n \"text\": \"def setUp(self):\\n self.game = BuildGame()\\n self.effects = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d068bc4b98bded3fdd08601cee6eab4a\",\n \"score\": \"0.72419715\",\n \"text\": \"def setup(self):\\n header_print(self.data['intro'])\\n header_print(self.data['help'])\\n random.shuffle(self.data['draw'])\\n random.shuffle(self.data['locations'])\\n random.shuffle(self.data['events'])\\n random.shuffle(self.data['aces'])\\n random.shuffle(self.data['personalities'])\\n self.stats = {\\n 'round': 0,\\n 'powers': {\\n 'MOONS': 6,\\n 'SUNS': 6,\\n 'WAVES': 6,\\n 'LEAVES': 6,\\n 'WYRMS': 6,\\n 'KNOTS': 6,\\n },\\n 'hand': self.data['draw'][:],\\n 'discard': [],\\n 'active': [],\\n 'opponent': {},\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af3e58ec8db639028b0b0b4a90f0a3b\",\n \"score\": \"0.7219072\",\n \"text\": \"def setup(self):\\n self.score = 0\\n self.lives = 3\\n self.state = GameStates.RUNNING\\n self.focus_word = None\\n \\n self.star_list = set()\\n self.word_list = set()\\n\\n for _ in range(5):\\n self.create_word()\\n for _ in range(25):\\n self.create_star()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02cd43c71124297797ff0517d062266d\",\n \"score\": \"0.7186087\",\n \"text\": \"def start(self):\\n self.__init__()\\n self.set_n_players()\\n self.init_players()\\n self.init_territory_selection_phase()\\n self.init_troop_deployment_phase()\\n # self.game_phase()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d9007e5308beb4ca63688f8c359bac\",\n \"score\": \"0.7121074\",\n \"text\": \"def setup(self):\\n setup = RandomWordGenerator().get()\\n self.formatted_word = ConvertWord().convert_to_dict(setup)\\n self.underscore_word = HangmanUnderscoreDiagram(\\n setup).create_hidden_word()\\n self.failed_guesses = 0\\n print(\\\"Hello\\\")\\n self.has_won = False\\n self.start_game(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0205d8ab0974420fb0840c87b900bc03\",\n \"score\": \"0.708886\",\n \"text\": \"def initialize(self):\\n result = pygame.init()\\n pygame.font.init()\\n pygame.display.set_caption('gomoku TDD')\\n self.screen = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\\n self.clock = pygame.time.Clock()\\n self.smallfont = pygame.font.Font(None, 40)\\n self.isinitialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94eba530b8e96bd031c4417b4c40147\",\n \"score\": \"0.7068139\",\n \"text\": \"def setUp(self):\\n self.game = TTTBoard(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b4f8d583dd1a15725be374e8aa4bc2\",\n \"score\": \"0.7007462\",\n \"text\": \"def initializeGame(self):\\n # Fill deck with cards and shuffle it\\n self.deck.fill(104)\\n self.deck.shuffle()\\n #print \\\"Deck initialized\\\"\\n\\n # Initialize the field\\n self.field.initialize(self.deck.draw(4))\\n self.field.sortField()\\n #self.field.printField()\\n\\n # Set players to initial state again\\n # Distribute cards and set bulls to 0\\n for p in self.players:\\n p.bulls = 0\\n p.setHand(self.deck.draw(10))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69eadfadb2a57d6cc305543291044957\",\n \"score\": \"0.6989646\",\n \"text\": \"def start_game(self):\\n self.board = Board(num_tableaus=self.tableau_qty, num_decks=self.decks, deal_3=self.deal_3)\\n self.board.init_move_dict()\\n self.board.deal(self.deck)\\n\\n if self.api_use:\\n self.init_game_api()\\n elif self.commandline:\\n self.init_cl_game()\\n else:\\n self.init_pygame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0656598321e57f2db8785ef86f3fc429\",\n \"score\": \"0.698927\",\n \"text\": \"def setup(self):\\n\\n # Sprite lists\\n self.all_sprite_list = arcade.SpriteList()\\n self.player_list = arcade.SpriteList()\\n self.coin_list = arcade.SpriteList()\\n self.deathclaw_list = arcade.SpriteList()\\n # Score\\n self.score = 0\\n\\n # Set up the player\\n # Character image from https://www.pngkit.com/\\n self.player_sprite = arcade.Sprite(\\\"Vault_Boy.png\\\", SPRITE_SCALING_PLAYER)\\n self.player_sprite.center_x = 50\\n self.player_sprite.center_y = 50\\n self.player_list.append(self.player_sprite)\\n\\n # Create the ENEMY\\n for i in range(DEATHCLAW_COUNT):\\n\\n # Create the ENEMY instance\\n # ENEMY image from https://fallout.fandom.com/\\n deathclaw = Deathclaw(\\\"Deathclaw.png\\\", SPRITE_SCALING_DEATHCLAW)\\n\\n # Position the coin\\n deathclaw.center_x = random.randrange(SCREEN_WIDTH)\\n deathclaw.center_y = random.randrange(SCREEN_HEIGHT)\\n\\n # Add the coin to the lists\\n self.deathclaw_list.append(deathclaw)\\n\\n for i in range(COIN_COUNT):\\n\\n # Create the Nuka Cola instance\\n # Nuka Cola image fromh https://www.cleanpng.com/free/nuka-cola.html\\n coin = Coin(\\\"Nuka_Kola.png\\\", SPRITE_SCALING_COIN)\\n\\n # Position the coin\\n coin.center_x = random.randrange(SCREEN_WIDTH)\\n coin.center_y = random.randrange(SCREEN_HEIGHT)\\n\\n # Add the coin to the lists\\n self.coin_list.append(coin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04bb8a5634f4f1d0d968486f9081e13\",\n \"score\": \"0.69834745\",\n \"text\": \"def setup(self):\\n \\n self.explosions_list = arcade.SpriteList()\\n\\n self.explosion_texture_list = []\\n \\n # Set up the score\\n self.score = 0\\n self.countdown = 1000\\n\\n for i in range(EXPLOSION_TEXTURE_COUNT):\\n # Files from http://www.explosiongenerator.com are numbered sequentially.\\n # This code loads all of the explosion0000.png to explosion0270.png files\\n # that are part of this explosion.\\n texture_name = f\\\"images/explosion/explosion{i:04d}.png\\\"\\n\\n self.explosion_texture_list.append(arcade.load_texture(texture_name))\\n \\n # create 10 balls\\n for i in range(10):\\n myball = make_ball()\\n self.ball_list.append(myball)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1de58c24db8cf359ff89674c5639168\",\n \"score\": \"0.696734\",\n \"text\": \"def init_pygame(self):\\n # Startup the pygame system\\n pygame.init()\\n # Create our window\\n self.screen = pygame.display.set_mode((Settings.width, Settings.height))\\n # Set the title that will display at the top of the window.\\n pygame.display.set_caption(self.title)\\n # Create the clock\\n self.clock = pygame.time.Clock()\\n self.last_checked_time = pygame.time.get_ticks()\\n # Startup the joystick system\\n pygame.joystick.init()\\n # For each joystick we find, initialize the stick\\n for i in range(pygame.joystick.get_count()):\\n pygame.joystick.Joystick(i).init()\\n # Set the repeat delay for key presses\\n pygame.key.set_repeat(Settings.key_repeat)\\n # Create statistics font\\n self.statistics_font = pygame.font.Font(None,30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d39ffb75325ecf9b736925a6d1cecce\",\n \"score\": \"0.6965951\",\n \"text\": \"def start(self):\\n self.player = Player()\\n self.dealer = Dealer()\\n self.pot = 0\\n self.side_bet = 0\\n start_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ab411855b63608cbcfd60862bbb126\",\n \"score\": \"0.6867193\",\n \"text\": \"def game_initialize():\\n global SURFACE_MAIN #global vars will be all caps\\n # init pygame\\n pygame.init()\\n SURFACE_MAIN = pygame.display.set_mode((constants.GAME_WIDTH, constants.GAME_HEIGHT))\\n constants.initialize_sprites()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104e2185bf3bf543b423903e9d3262ea\",\n \"score\": \"0.68361783\",\n \"text\": \"def initGameState(self):\\n print(\\\"Setting game state: \\\")\\n self.playGUI = GUI()\\n self.playGUI.drawBoard(self.player)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2020c3959217704cebf8ac35292d4b49\",\n \"score\": \"0.683391\",\n \"text\": \"def on_init(self):\\n pygame.init()\\n self.background.load_from_file()\\n self.hero.load_from_file()\\n self.enemy.load_from_file()\\n\\n # Some music and sound fx\\n # frequency, size, channels, buffersize\\n # pygame.mixer.pre_init(44100, 16, 2, 4096)\\n self.effect = pygame.mixer.Sound('sounds/bounce.wav')\\n pygame.mixer.music.load('sounds/music.wav')\\n pygame.mixer.music.play(-1)\\n\\n self.hero.screen = self.background.screen\\n self.enemy.screen = self.background.screen\\n self.clock = pygame.time.Clock()\\n pygame.display.set_caption(\\n 'Angry Floating Guy! World: {} (w to change world, arrows to move, Esc to quit).'.format(self.current_world.name))\\n\\n self._running = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7a0c81368b69922d526b87e816b3df\",\n \"score\": \"0.6814734\",\n \"text\": \"def setup(self):\\n\\n self.characters = arcade.SpriteList()\\n self.dungeon_sprites = arcade.SpriteList(\\n use_spatial_hash=True, spatial_hash_cell_size=16\\n )\\n\\n self.player = Item(ord(\\\"@\\\"), arcade.csscolor.WHITE)\\n self.player.x = 0\\n self.player.y = 0\\n self.characters.append(self.player)\\n\\n # Size of the map\\n map_width = MAP_WIDTH\\n map_height = MAP_HEIGHT\\n\\n # Some variables for the rooms in the map\\n room_max_size = 10\\n room_min_size = 6\\n max_rooms = 30\\n\\n self.game_map = GameMap(map_width, map_height)\\n self.game_map.make_map(\\n max_rooms, room_min_size, room_max_size, map_width, map_height, self.player\\n )\\n\\n # Draw all the tiles in the game map\\n for y in range(self.game_map.height):\\n for x in range(self.game_map.width):\\n wall = self.game_map.tiles[x][y].block_sight\\n sprite = Item(WALL_CHAR, arcade.csscolor.BLACK)\\n if wall:\\n sprite.block_sight = True\\n else:\\n sprite.block_sight = False\\n\\n sprite.x = x\\n sprite.y = y\\n\\n self.dungeon_sprites.append(sprite)\\n\\n recalculate_fov(\\n self.player.x, self.player.y, FOV_RADIUS, self.dungeon_sprites\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898d1bdd3fd199dc5ee5cb07f15df2bf\",\n \"score\": \"0.68145746\",\n \"text\": \"def __init__(self):\\n\\n super().__init__()\\n self.setup_janggi_game()\\n self._game_state = 'UNFINISHED'\\n self._player_turn = 'BLUE'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf5f97e061b958647ca2e9096b94844\",\n \"score\": \"0.6753492\",\n \"text\": \"def setUp(self):\\n self.gameBoard = Grid((100, 100), Cell)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc927efa1f803392f953c6e4801797ce\",\n \"score\": \"0.67371064\",\n \"text\": \"def __init__(self):\\n\\t\\tpygame.init()\\n\\t\\tself.settings = Settings()\\n\\n\\t\\tself.screen = pygame.display.set_mode((0, 0), pygame.FULLSCREEN)\\n\\t\\tself.settings.screen_width = self.screen.get_rect().width \\n\\t\\tself.settings.screen_height = self.screen.get_rect().height\\n\\t\\tpygame.display.set_caption(\\\"Pigeon Drop!\\\")\\n\\n\\t\\t# Create an instance to store game statistics,\\n\\t\\t# and create a scoreboard.\\n\\t\\tself.stats = GameStats(self)\\n\\t\\tself.sb = Scoreboard(self)\\n\\n\\t\\tself.pigeon = Pigeon(self)\\n\\t\\tself.droppings = pygame.sprite.Group()\\n\\t\\tself.autos = pygame.sprite.Group()\\n\\n\\t\\tself._create_fleet()\\n\\n\\t\\t# Make the Play button.\\n\\t\\tself.play_button = Button(self, \\\"Play\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f700774ec5b085ce661e7fa454dbab\",\n \"score\": \"0.6727859\",\n \"text\": \"def init(seed=None):\\n\\tglobal _game\\n\\n\\tfrom .game import Game\\n\\tfrom .prompt import install_words\\n\\n\\t_game = Game(seed)\\n\\tload_advent_dat(_game)\\n\\tinstall_words(_game)\\n\\t_game.start()\\n\\treturn _game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0650d977c78fc25ed12ad48615e281\",\n \"score\": \"0.6699672\",\n \"text\": \"def main():\\n g = Game(800, 600)\\n g.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d7491d84d90efe4f414673fed4354b\",\n \"score\": \"0.66952485\",\n \"text\": \"def setup(self):\\n # inicializamos el juego\\n\\n # Sprite lists\\n self.player_list = arcade.SpriteList() # sera lista de personajes\\n self.coin_list = arcade.SpriteList() # sera lista de monedas\\n self.bullet_list = arcade.SpriteList() # lista de disparos\\n\\n # Set up the player\\n self.score = 0\\n\\n # Image from kenney.nl\\n # cargamos el sprite del jugador\\n self.player_sprite = arcade.Sprite(\\\"character.png\\\", SPRITE_SCALING_PLAYER)\\n # establecemos el inicio de posicion de nuestro jugador\\n self.player_sprite.center_x = 50\\n self.player_sprite.center_y = 70\\n # lo agregamos a la lista de nuestros jugadores\\n self.player_list.append(self.player_sprite)\\n\\n # Create the coins\\n for i in range(COIN_COUNT):\\n\\n # Create the coin instance\\n # Coin image from kenney.nl\\n # cargamos las monedas\\n coin = arcade.Sprite(\\\"coin_01.png\\\", SPRITE_SCALING_COIN)\\n\\n # Position the coin\\n coin.center_x = random.randrange(SCREEN_WIDTH)\\n coin.center_y = random.randrange(120, SCREEN_HEIGHT)\\n\\n # Add the coin to the lists\\n # lo agregamos a la lista\\n self.coin_list.append(coin)\\n\\n # Set the background color\\n # esto aun nose para que sirve\\n arcade.set_background_color(arcade.color.AMAZON)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e38a2f48c517dc1d5ff4d49036be37\",\n \"score\": \"0.6681422\",\n \"text\": \"def init(self):\\n\\n pygame.init()\\n pygame.joystick.init()\\n self.controller = pygame.joystick.Joystick(0)\\n self.controller.init()\\n self.x=0\\n self.y=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d99aa2f8e52683283d3e9f9fd5507ee\",\n \"score\": \"0.6677753\",\n \"text\": \"def setup(self):\\n\\n # Sprite lists\\n self.player_list = arcade.SpriteList()\\n\\n # https://opengameart.org/content/animated-top-down-survivor-player\\n # Set up the player\\n self.player_sprite = Player(\\\"survivor-idle_rifle_0.png\\\", 0.5)\\n self.player_sprite.center_x = SCREEN_WIDTH / 2\\n self.player_sprite.center_y = SCREEN_HEIGHT / 2\\n self.player_list.append(self.player_sprite)\\n self.wall_list = arcade.SpriteList()\\n self.chest_list = arcade.SpriteList()\\n self.bullet_list = arcade.SpriteList()\\n\\n self.score = 0\\n\\n\\n # Set up the player\\n # https://opengameart.org/content/animated-top-down-survivor-player\\n\\n\\n\\n # -- Set up several columns of walls\\n for x in range(-700, 1700, 100):\\n # sprite form https://opengameart.org/content/bush-png\\n wall = arcade.Sprite(\\\"bush_11.png\\\", SPRITE_SCALING)\\n wall.center_x = x\\n wall.center_y = -300\\n self.wall_list.append(wall)\\n for x in range(-700, 1700, 100):\\n # sprite form https://opengameart.org/content/bush-png\\n wall = arcade.Sprite(\\\"bush_11.png\\\", SPRITE_SCALING)\\n wall.center_x = x\\n wall.center_y = 1025\\n self.wall_list.append(wall)\\n for y in range(-300, 1025, 100):\\n # sprite form https://opengameart.org/content/bush-png\\n wall = arcade.Sprite(\\\"bush_11.png\\\", SPRITE_SCALING)\\n wall.center_x = -700\\n wall.center_y = y\\n self.wall_list.append(wall)\\n for y in range(-300, 1025, 100):\\n # sprite form https://opengameart.org/content/bush-png\\n wall = arcade.Sprite(\\\"bush_11.png\\\", SPRITE_SCALING)\\n wall.center_x = 1700\\n wall.center_y = y\\n self.wall_list.append(wall)\\n\\n\\n# https://www.pinterest.com/pin/258042253625289337\\n\\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite, self.wall_list)\\n\\n # Set the background color\\n arcade.set_background_color(arcade.color.BRITISH_RACING_GREEN)\\n\\n # Set the viewport boundaries\\n # These numbers set where we have 'scrolled' to.\\n self.view_left = 0\\n self.view_bottom = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875729fd86f2b97dc69f1f05aa7f388e\",\n \"score\": \"0.6669637\",\n \"text\": \"def setup(self):\\n\\n # Used to keep track of our scrolling\\n self.view_bottom = 0\\n self.view_left = 0\\n\\n # Keep track of the score\\n self.score = 0\\n\\n # Create the Sprite lists\\n self.player_list = arcade.SpriteList()\\n self.wall_list = arcade.SpriteList()\\n self.coin_list = arcade.SpriteList()\\n\\n # Set up the player, specifically placing it at these coordinates.\\n # image_source = \\\":resources:images/animated_characters/female_adventurer/femaleAdventurer_idle.png\\\"\\n self.player_list = arcade.SpriteList()\\n self.player_sprite = Player()\\n self.player_sprite.center_x = 256\\n self.player_sprite.center_y = 256\\n self.player_list.append(self.player_sprite)\\n\\n # --- Load in a map from the tiled editor ---\\n\\n # Name of map file to load\\n map_name = r\\\"Math_Game\\\\floor_is_lava.tmx\\\"\\n # Name of the layer in the file that has our platforms/walls\\n platforms_layer_name = 'Platforms'\\n\\n # Read in the tiled map\\n my_map = arcade.tilemap.read_tmx(map_name)\\n\\n # -- Platforms\\n self.wall_list = arcade.tilemap.process_layer(map_object=my_map,\\n layer_name='Platforms',\\n base_directory=r'C:\\\\Users\\\\katel\\\\Desktop\\\\CSE310\\\\group_project\\\\Math_Game\\\\platformer-art-complete-pack-0\\\\Base pack\\\\Tiles',\\n scaling=TILE_SCALING,\\n use_spatial_hash=True, hit_box_algorithm=\\\"Simple\\\", hit_box_detail=4.5)\\n\\n # --- Other stuff\\n # Set the background color\\n if my_map.background_color:\\n arcade.set_background_color(my_map.background_color)\\n\\n # Create the 'physics engine'\\n self.physics_engine = arcade.PhysicsEnginePlatformer(self.player_sprite,\\n self.wall_list,\\n GRAVITY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d5681d11fce16f71e1ce356acb3cf6\",\n \"score\": \"0.66560024\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\\r\\n self.spaceship.init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcb7a4e0a72df48cbe98c4c50737659\",\n \"score\": \"0.66408247\",\n \"text\": \"def setup_play(self, reset=False):\\n\\n if reset:\\n for sprite in self.all_sprites:\\n sprite.kill()\\n else:\\n self.score = 0\\n self.lives = START_LIVES\\n self.game_state = InGameState.READY\\n\\n self.all_sprites = pg.sprite.Group()\\n self.walls = pg.sprite.Group()\\n self.blocks = pg.sprite.Group()\\n self.diamonds = pg.sprite.Group()\\n self.moving_blocks = pg.sprite.Group()\\n self.enemies = pg.sprite.Group()\\n self.stunned_enemies = pg.sprite.Group()\\n\\n # level = Level(path.join(level_dir, '1.txt'))\\n level = Level(path.join(level_dir, 'c64_level1.txt'))\\n level.load_level(self)\\n LOGGER.debug(f\\\"No. enemies: {len(self.enemies)}, No. blocks: {len(self.blocks)}\\\")\\n\\n self.make_boundary_wall(level.grid_height, level.grid_width)\\n\\n self.timer = TIME_LIMIT\\n pg.time.set_timer(TIMER, 1000)\\n\\n self.target_no_kills = 5\\n self.kill_bonus = None\\n self.diamond_bonus = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a81ce9b838cf1027c1e428c457c8d39\",\n \"score\": \"0.66376364\",\n \"text\": \"def start_game(self):\\n\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90dcd04e4a9ae933f7f04288699505b\",\n \"score\": \"0.66170704\",\n \"text\": \"def setup(self):\\n\\n self.total_time = 0.0\\n\\n self.background = arcade.load_texture(\\\"images\\\\\\\\background-1_0 (1).png\\\")\\n\\n # Create the Sprite lists\\n self.all_sprites_list = arcade.SpriteList()\\n self.player_list = arcade.SpriteList()\\n self.wall_list = arcade.SpriteList()\\n self.coin_list = arcade.SpriteList()\\n self.myobject_list = arcade.SpriteList()\\n\\n # Set up the player\\n self.gameover = 0\\n self.score = 0\\n self.lives = 4\\n self.collision_time = 0\\n self.numobj = STARTING_OBJECTS_COUNT\\n self.ncoins = COIN_COUNT\\n self.player_sprite = VehicleSprite(\\\"images\\\\\\\\bugatti.png\\\",\\n CHARACTER_SCALING)\\n self.player_sprite.angle = 90\\n # self.player_sprite.change_y = 1\\n self.all_sprites_list.append(self.player_sprite)\\n\\n self.create_buddies()\\n self.create_treasure()\\n\\n # Make the mouse disappear when it is over the window.\\n # So we just see our object, not the pointer.\\n\\n # Set the background color\\n arcade.set_background_color(arcade.color.ASH_GREY)\\n\\n # Set up the player, specifically placing it at these coordinates.\\n # self.player_sprite = arcade.Sprite(\\\"images\\\\\\\\carcar.png\\\", CHARACTER_SCALING)\\n # self.player_sprite.center_x = 500\\n # self.player_sprite.center_y = 110\\n # self.player_sprite.angle = 90\\n # self.player_sprite.change_y = 1\\n # self.player_list.append(self.player_sprite)\\n\\n # Create the 'physics engine'\\n self.physics_engine = arcade.PhysicsEngineSimple(self.player_sprite,\\n self.wall_list)\\n\\n # Set the viewport boundaries\\n # These numbers set where we have 'scrolled' to.\\n self.view_left = 0\\n self.view_bottom = 0\\n\\n # For draw\\n self.line_start = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc8caa3e51a5309bc3495e712b74873\",\n \"score\": \"0.6608156\",\n \"text\": \"def initialize(self) -> None:\\n self.simulation = self.initialize_simulation()\\n width, height = get_window_resolution()\\n display_dim = ((0, width), (0, height))\\n self.coord_mapper = CoordinateMapper2D(*self.simulation.dim, *display_dim)\\n self.simple_pygame.all_sprites.empty()\\n self.initialize_visualization()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85a06e74924e42e92edb802abd61a330\",\n \"score\": \"0.6591549\",\n \"text\": \"def __init__(self):\\n self.screen_width = 1200\\n self.screen_height = 800\\n self.bg_color = (0, 230, 0)\\n\\n # Glove Settings\\n self.glove_move_speed = 0.25\\n self.glove_size = 100\\n\\n # Ball Settings\\n self.ball_move_speed = 0.25\\n self.ball_size = 40\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44be389a1c2b1a58e0dd04eec2e6916d\",\n \"score\": \"0.65776414\",\n \"text\": \"def init(self):\\n\\n pygame.init()\\n pygame.display.set_mode((640, 480))\\n pygame.display.set_caption(\\\"Gears 4 Geeks\\\")\\n pygame.joystick.init()\\n self.controller = pygame.joystick.Joystick(0)\\n self.controller.init()\\n self.ser = serial.Serial('COM4', 9600)\\n\\n #ADAFRUIT_IO_KEY = 'd1a1bd3737714fa488e0364c775a4b4d' ##This will only be good until the end of the competition\\n #self.aio = Client(ADAFRUIT_IO_KEY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ce9b9e23e307284880325001a9655d9\",\n \"score\": \"0.65706956\",\n \"text\": \"def setupStage(self):\\n print(\\\"(\\\" + str(self.HOST) + \\\", \\\" + str(self.PORT) +\\\"):: Initiating setup stage\\\", file=self.logs)\\n\\n # Holds player map and name information here. Will be used to create objects later.\\n mapVotes = []\\n playerNames = {}\\n colors = [\\\"red\\\", \\\"blue\\\"]\\n\\n gameState = {\\n \\\"ready\\\": False,\\n \\\"game\\\": None \\n }\\n while True:\\n # Continuously saves logging information to a text file:\\n self.logs.close()\\n self.logs = open(str(self.filepath)+\\\"/_logs/\\\"+ str(self.PORT) + \\\".txt\\\", \\\"a+\\\")\\n\\n # Gets all the events from the game window. A.k.a., do stuff here.\\n inboundData = self.socket.recvfrom(1024) # Gets bundle of data from clients\\n data = inboundData[0] # Separates data from address\\n address = inboundData[1]\\n\\n # Keeps track of how often the server recieves information from each client.\\n updatedTime = time.time() \\n self.clientUpdateTimes[str(address)] = updatedTime\\n\\n ########\\n self.bitsIn += sys.getsizeof(data)\\n\\n address = inboundData[1] # Separates address from data\\n data = pickle.loads(data) # Unpickles data back into a python dict\\n\\n command = data['command']\\n if command != None: \\n # Takes in information from both players\\n if command == \\\"SUBMIT\\\":\\n pName = data['playerName']\\n mVote = data['mapVote']\\n\\n mapVotes.append(mVote)\\n playerNames[str(address)] = pName\\n \\n # Both votes are in. Chooses a map, builds the Board object.\\n if len(mapVotes) == 2:\\n # Only chooses one map for both players\\n if self.map == None:\\n mapTuple = random.choice(mapVotes)\\n size = mapTuple[0]\\n m = mapTuple[1]\\n\\n if size == \\\"SMALL\\\":\\n randomMap = MapGenerator((5,7), m)\\n self.map = randomMap.getMap()\\n mapString = self.map\\n width = randomMap.getDimensions()[0]\\n height = randomMap.getDimensions()[1]\\n tokens = randomMap.getTokens()\\n if size == \\\"MEDIUM\\\":\\n randomMap = MapGenerator((7,9), m)\\n self.map = randomMap.getMap()\\n mapString = self.map\\n width = randomMap.getDimensions()[0]\\n height = randomMap.getDimensions()[1]\\n tokens = randomMap.getTokens()\\n if size == \\\"BIG\\\":\\n randomMap = MapGenerator((10,12), m)\\n self.map = randomMap.getMap()\\n mapString = self.map\\n width = randomMap.getDimensions()[0]\\n height = randomMap.getDimensions()[1]\\n tokens = randomMap.getTokens()\\n if size == \\\"HUGE\\\":\\n randomMap = MapGenerator((12,15), m)\\n self.map = randomMap.getMap()\\n mapString = self.map\\n width = randomMap.getDimensions()[0]\\n height = randomMap.getDimensions()[1]\\n tokens = randomMap.getTokens()\\n if size == \\\"RANDOM\\\":\\n randWidth = random.randint(5, 13)\\n randHeight = random.randint(5, 13)\\n\\n randomMap = MapGenerator((randWidth,randHeight), m)\\n self.map = randomMap.getMap()\\n mapString = self.map\\n width = randomMap.getDimensions()[0]\\n height = randomMap.getDimensions()[1]\\n tokens = randomMap.getTokens()\\n\\n # Builds the game board\\n self.board = Board(width, height, mapString)\\n\\n # Both players' names have been entered, creates Player objects.\\\\\\n # Appends player objects to state variable. \\n if len(playerNames) == 2 and len(colors) > 0:\\n p = Player(playerNames[str(address)], colors.pop(), None, tokens, address)\\n self.players.append(p)\\n \\n # Player objects and Board object have both been created.\\n # Builds the Game object, stores it, then tells the PlayerViews its ready.\\n if len(self.players) == 2 and self.board != None:\\n self.game = Game(self.board, self.players[0], self.players[1])\\n gameState['game'] = self.game\\n gameState['ready'] = True\\n\\n # Sends data to both players simultaneously\\n for client in self.clients:\\n outboundData = pickle.dumps(gameState)\\n self.socket.sendto(outboundData, client)\\n break\\n\\n # Packages up data and sends it back to the client\\n outboundData = pickle.dumps(gameState)\\n\\n ######\\n self.bitsOut += sys.getsizeof(outboundData)\\n\\n self.socket.sendto(outboundData, address)\\n \\n # Check client connections here\\n self.checkClientConnections(time.time())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d474528e9d8f517b7d17680b348b736\",\n \"score\": \"0.6566866\",\n \"text\": \"def init_game_setting(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3365d5538ce4bc1d236a6ecb0d00bb\",\n \"score\": \"0.6558296\",\n \"text\": \"def __init__(self):\\n\\n # Window starting position\\n x = 200\\n y = 30\\n os.environ[\\\"SDL_VIDEO_WINDOW_POS\\\"] = \\\"%d,%d\\\" % (x, y)\\n\\n pygame.init()\\n # Init window\\n self.window = Window()\\n # Flag that defines if the program is running or not\\n self.running = True\\n if Settings.MENU_ENABLED:\\n self.main_menu = MainMenu(self.window)\\n self.main_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aadeb2e51bb6bfdabe9f8fac4cbc537b\",\n \"score\": \"0.65457934\",\n \"text\": \"def setup(self):\\n self.total_time = 0.0\\n self.timer_text = None\\n arcade.set_background_color(arcade.color.WHITE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e0eeb84e21a232497764c6f98e71fb5\",\n \"score\": \"0.65436804\",\n \"text\": \"def main():\\n field = Field(10, 10)\\n snake = Snake((0, 0))\\n game = Game(field, snake)\\n game.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6707c71df18641862ff91d54774889f8\",\n \"score\": \"0.65426224\",\n \"text\": \"def setup(self):\\n build_world.start_level(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041dab956c4e42b43e04780650c6ab9e\",\n \"score\": \"0.65425974\",\n \"text\": \"def initialize():\\n\\n global PLAYER # this means we use the global var PLAYER and cannot have a local var named PLAYER\\n global LEVEL_COUNTER\\n\\n LEVEL_COUNTER = 1\\n \\n coordinates = generate_coords()\\n\\n PLAYER = Stark()\\n tree = Tree()\\n ww = WhiteWalker()\\n crown = Crown()\\n gray_gem = GrayGem()\\n clear_board()\\n GAME_BOARD.create(\\\"Snow\\\",\\\"Snow\\\")\\n GAME_BOARD.draw_msg(\\\"Level \\\" + str(LEVEL_COUNTER) + \\\". Winter is coming.\\\")\\n generate_level(coordinates, [PLAYER, ww, gray_gem, crown, tree, tree, gray_gem, tree, tree, gray_gem, tree])\\n\\n # for i in range(0,NUM_ELTS):\\n # place_on_board(elts[i], coordinates[i][0], coordinates[i][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fdd02410aefe9f7cf96519fba74bfb\",\n \"score\": \"0.65379816\",\n \"text\": \"def setupNewGame(self):\\r\\n self.level = 1\\r\\n self.num_cows = 2\\r\\n self.num_farmers = 1\\r\\n self.levelHeading = Text(self.gameDisplay, 120, 425, 175, self.light_orange, \\\"Farm 1\\\")\\r\\n self.shield_indicator.image = self.greenShield\\r\\n updatedHeading = self.levelHeading\\r\\n self.startUX[0] = updatedHeading\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946ac1509799c927602adfa52c72feca\",\n \"score\": \"0.6533264\",\n \"text\": \"def init_game(self):\\n self.blind_manager = BlindManager(hands_per_level=10,\\n bots=self.living_bot_names())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3654c2b20257aa82090e6ac00e6aa7\",\n \"score\": \"0.65258765\",\n \"text\": \"def load_game(self):\\n game = Game(self.w, self.h, self.screen)\\n game.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43e92b3418d0a3671e2a1cf01bd2b56\",\n \"score\": \"0.65238756\",\n \"text\": \"def init_vars(self):\\n\\n load_dotenv()\\n self.smart_cube = True if os.environ.get(\\\"SMART_CUBE\\\") == \\\"True\\\" else False\\n self.gen_parsed_to_cubedb = True if os.environ.get(\\\"GEN_PARSED_TO_CUBEDB\\\") == \\\"True\\\" else False\\n self.name_of_solve = os.environ.get(\\\"NAME_OF_SOLVE\\\")\\n self.time_solve = os.environ.get(\\\"TIME_SOLVE\\\")\\n self.comms_unparsed_bool = True if os.environ.get(\\\"COMMS_UNPARSED\\\") == \\\"True\\\" else False\\n self.gen_with_move_count = True if os.environ.get(\\\"GEN_WITH_MOVE_COUNT\\\") == \\\"True\\\" else False\\n self.diff_to_solved_state = float(os.environ.get(\\\"DIFF_BETWEEN_ALGS\\\"))\\n self.parse_to_lp = True if os.environ.get(\\\"PARSE_TO_LETTER_PAIR\\\") == \\\"True\\\" else False\\n self.gen_with_moves = True if os.environ.get(\\\"GEN_WITH_MOVE_COUNT\\\") == \\\"True\\\" else False\\n self.buffer_ed = self.get_buffer_ed(os.environ.get(\\\"EDGES_BUFFER\\\"))\\n self.buffer_cor = self.get_buffer_cor(os.environ.get(\\\"CORNER_BUFFER\\\"))\\n self.path_to_lp = os.environ.get(\\\"PATH_LETTER_PAIR_FILE\\\")\\n self.dict_lp = self.load_letter_pairs_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd5e3a34e920d83be22012d9feeb6dd\",\n \"score\": \"0.6522762\",\n \"text\": \"def setUp(self):\\r\\n self.caption = \\\"mirra extending classes\\\" # window name\\r\\n self.size = 640, 480 #window size\\r\\n self.pos = 100,100 # window top left location\\r\\n self.fullScreen = 0 # if fullScreen is on it will overwrite your pos and size to match the display's resolution\\r\\n self.frameRate = 15 # set refresh framerate\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b911432ad77007e82f7b5bbcd8836058\",\n \"score\": \"0.65204126\",\n \"text\": \"def __init__(self):\\n pygame.init()\\n\\n self.settings = Settings()\\n\\n self.screen = pygame.display.set_mode(\\n (self.settings.screen_width, self.settings.screen_height))\\n pygame.display.set_caption(\\\"52 Card Trick\\\")\\n self.CardSet = CardSet(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c7509560856646276227cd3c6a4074\",\n \"score\": \"0.6516524\",\n \"text\": \"def setUp(self):\\n\\n self.board = Board(3, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89cf360e139cc7bdba8d124c0a7ad4d8\",\n \"score\": \"0.6516285\",\n \"text\": \"def initialize_game_params(self):\\r\\n\\r\\n self.is_new_game = True\\r\\n self.is_game_over = False\\r\\n self.is_game_lost = False\\r\\n self.is_left_mouse_down = False\\r\\n self.is_right_mouse_down = False\\r\\n self.num_of_hidden_non_mines_tiles = self.rows * self.cols - self.num_of_mines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ea76d55bd43e9ab984101561951f2a6\",\n \"score\": \"0.6511197\",\n \"text\": \"def setUp(self):\\n self.player = ship.Player(\\n constants.PLAYER_START_PLACE,\\n constants.PLAYER_WIDTH,\\n constants.PLAYER_HEIGHT,\\n constants.PLAYER_IMG,\\n constants.PLAYER_HEALTH\\n )\\n\\n self.alien = ship.Alien(\\n [320, 300],\\n 30,\\n 30,\\n constants.GREEN_ALIEN_IMG,\\n 1\\n )\\n\\n self.alien.shooting([320, 300], 5, False)\\n\\n self.player.shooting([self.player.position[0] + 3, self.player.position[1]], 1, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332b88663fe7d53af8e80737950739d7\",\n \"score\": \"0.6507396\",\n \"text\": \"def __init__(self):\\n\\t\\t# Screen setting.\\n\\t\\tself.screen_width = 1200\\n\\t\\tself.screen_height = 800\\n\\t\\tself.bg_color = (230, 230, 230)\\t\\n\\n\\t\\t#Ship setting\\n\\t\\tself.ship_speed_factor = 10\\t\\t\\n\\t\\tself.ship_limit = 3\\t\\t\\t# number ship \\n\\n\\t\\t# Bullet setting.\\n\\t\\tself.bullet_speed_factor = 3\\n\\t\\tself.bullet_width = 3\\n\\t\\tself.bullet_height = 15\\n\\t\\tself.bullet_color = (60,60,60) #dark gray bullet\\n\\t\\tself.bullets_allowed = 6\\t\\t# number bullet in screen\\n\\n\\t\\t#Alien setting.\\n\\t\\tself.alien_speed_factor = 3\\n\\t\\tself.fleet_drop_speed = 50\\n\\t\\t# fleet_direction of 1 represents right; -1 represents left. \\n\\t\\tself.fleet_direction = 1\\n\\n\\t\\t# Scoring\\n\\t\\tself.alien_points = 50\\n\\n\\t\\t# How quickly the game speed ups\\n\\t\\tself.speedup_scale = 1.1\\n\\t\\tself.iniitialize_dynamic_settings()\\n\\t\\t# How quickly score increase.\\n\\t\\tself.score_scale = 1.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a0a37f90385dd89f2994154b5d24944\",\n \"score\": \"0.6501459\",\n \"text\": \"def startup(self):\\n self.prev_gray = None\\n self.frame_idx = 1\\n self.tracks = []\\n self.fps = []\\n self.vid_info = None\\n self.track_new_points_count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c9c06745971f04f7a5a6fc62e584161\",\n \"score\": \"0.64820516\",\n \"text\": \"def __init__(self):\\n self.__grid = create_grid(\\n Settings.SIZE_X, Settings.SIZE_Y, MarkerType.NONE)\\n\\n self.__turn = 0\\n self.__state = GameState.PLAYING\\n self.__winner = MarkerType.NONE\\n self.__loser = MarkerType.NONE\\n\\n # Separate counter for turns, because __turn depends on starting player\\n self.__turns_played = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9ba9411b9f12679a8bc536d94912571\",\n \"score\": \"0.64732724\",\n \"text\": \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n global text_prompt\\n global fails\\n\\n # Text elements\\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\\n \\\"\\\"\\\"You need to crack the terminal passcode.\\\\n\\nEnter \\\"\\\"\\\" + str(global_n) + \\\"\\\"\\\" unique digits and try to guess the code!\\\"\\\"\\\",\\n center_x=self.left_column_x * 2,\\n center_y=self.y_slot * 3,\\n id=\\\"fcg\\\"\\n\\n ))\\n\\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\\n text_prompt,\\n center_x=self.left_column_x * 2,\\n center_y=self.y_slot * 2.25,\\n id=\\\"mathGame_UI2\\\"\\n\\n ))\\n\\n self.ui_manager.add_ui_element(arcade.gui.UILabel(\\n \\\"Tries left: \\\" + str(20 - fails),\\n center_x=self.left_column_x * 2,\\n center_y=self.y_slot / 1.7,\\n id=\\\"Tries\\\"\\n\\n ))\\n\\n # Input Box\\n ui_input_box = arcade.gui.UIInputBox(\\n center_x=self.left_column_x * 2,\\n center_y=self.y_slot * 1.5,\\n width=300,\\n id=\\\"InputBox\\\"\\n )\\n ui_input_box.text = ''\\n ui_input_box.cursor_index = len(ui_input_box.text)\\n self.ui_manager.add_ui_element(ui_input_box)\\n\\n # Buttons\\n button = EnterCodeButton(\\n center_x=self.left_column_x * 2,\\n center_y=self.y_slot,\\n input_box=ui_input_box,\\n minigame=self,\\n ui_manager=self.ui_manager,\\n n=global_n,\\n choice_list=chosen_list\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = ExitButton(\\n center_x=self.right_column_x * 1.25,\\n center_y=self.y_slot * 3.7,\\n minigame=self\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa916be2fd937c6cb716d3025adc3dd\",\n \"score\": \"0.64697623\",\n \"text\": \"def setup(self):\\n # Initialize the drawing environment (create main windows, etc)\\n glutInit(sys.argv)\\n glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB | GLUT_DEPTH)\\n glutInitWindowSize(WINDOW_WIDTH, WINDOW_HEIGHT)\\n glutCreateWindow(name)\\n\\n glShadeModel(GL_SMOOTH)\\n\\n glClearDepth(1.0)\\n glDepthFunc(GL_LESS) # The Type Of Depth Test To Do\\n glEnable(GL_DEPTH_TEST) # Enables Depth Testing\\n glShadeModel(GL_SMOOTH) # Enables Smooth Color Shading\\n\\n glMatrixMode(GL_PROJECTION)\\n glLoadIdentity() # Reset The Projection Matrix\\n\\n # Calculate The Aspect Ratio Of The Window\\n gluPerspective(45.0, float(WINDOW_WIDTH)/float(WINDOW_HEIGHT), 0.1, 100.0)\\n\\n glMatrixMode(GL_MODELVIEW)\\n\\n # Set up keyboard listeners.\\n glutKeyboardFunc(self.on_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b23db89e61198c0f3c476b2d7764c4\",\n \"score\": \"0.6467842\",\n \"text\": \"def setUp(self):\\n self.player = Player()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c39ca734f8b91d1aee2b393a5df598a\",\n \"score\": \"0.6457494\",\n \"text\": \"def setup_members(self):\\n ### cell\\n self.cell_size = 8\\n self.cell_row = 80\\n self.cell_col = 100\\n self.color_alive = \\\"black\\\"\\n self.color_dead = \\\"white\\\"\\n\\n ### world\\n self.init_modes = {} # read modes from json file\\n self.init_world = {} # begining status\\n self.world = {} # world's map\\n # current status of world\\n self.world_status = GOL(self.cell_row, self.cell_col)\\n self.world_setable = True\\n self.world_alive = False\\n\\n # widgets\\n self.toolbar_height = 40\\n self.world_size = [self.cell_size * self.cell_row,\\n self.cell_size * self.cell_col]\\n self.window_size = self.world_size\\n self.window_size[0] += self.toolbar_height\\n\\n # resource\\n self.saver_icon = \\\"save.gif\\\"\\n self.run_icon = \\\"run.gif\\\"\\n self.pause_icon = \\\"pause.gif\\\"\\n self.stop_icon = \\\"stop.gif\\\"\\n self.modes_file = \\\"gol.json\\\"\\n self.modes_names = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c5d5de24868699578c5df0dd92a9e5\",\n \"score\": \"0.6449371\",\n \"text\": \"def setup(self, level):\\r\\n\\r\\n # Used to keep track of our scrolling\\r\\n self.view_bottom = 0\\r\\n self.view_left = 0\\r\\n\\r\\n # Keep track of the score\\r\\n self.score = 0\\r\\n\\r\\n # Keep track of lives\\r\\n # self.lives = 5\\r\\n\\r\\n # Create the Sprite lists\\r\\n self.player_list = arcade.SpriteList()\\r\\n self.foreground_list = arcade.SpriteList()\\r\\n self.background_list = arcade.SpriteList()\\r\\n self.wall_list = arcade.SpriteList()\\r\\n self.coin_list = arcade.SpriteList()\\r\\n\\r\\n # Set up the player, specifically placing it at these coordinates.\\r\\n image_source = \\\"images/Alice/Alice7_front.png\\\"\\r\\n self.player_sprite = arcade.Sprite(image_source, CHARACTER_SCALING)\\r\\n self.player_sprite.center_x = PLAYER_START_X\\r\\n self.player_sprite.center_y = PLAYER_START_Y\\r\\n self.player_list.append(self.player_sprite)\\r\\n\\r\\n # --- Load in a map from the tiled editor ---\\r\\n\\r\\n # Name of the layer in the file that has our platforms/walls\\r\\n platforms_layer_name = 'Platforms'\\r\\n moving_platforms_layer_name = 'Moving Platforms'\\r\\n # Name of the layer that has items for pick-up\\r\\n coins_layer_name = 'Coins'\\r\\n # Name of the layer that has items for foreground\\r\\n foreground_layer_name = 'Foreground'\\r\\n # Name of the layer that has items for background\\r\\n background_layer_name = 'Background'\\r\\n # Name of the layer that has items we shouldn't touch\\r\\n dont_touch_layer_name = \\\"Don't Touch\\\"\\r\\n\\r\\n # Map name\\r\\n map_name = f\\\"map4_level_{level}.tmx\\\"\\r\\n\\r\\n # Read in the tiled map\\r\\n my_map = arcade.tilemap.read_tmx(map_name)\\r\\n\\r\\n # Calculate the right edge of the my_map in pixels\\r\\n self.end_of_map = my_map.map_size.width * GRID_PIXEL_SIZE\\r\\n\\r\\n # -- Background\\r\\n self.background_list = arcade.tilemap.process_layer(my_map,\\r\\n background_layer_name,\\r\\n TILE_SCALING)\\r\\n\\r\\n # -- Foreground\\r\\n self.foreground_list = arcade.tilemap.process_layer(my_map,\\r\\n foreground_layer_name,\\r\\n TILE_SCALING)\\r\\n\\r\\n # -- Platforms\\r\\n self.wall_list = arcade.tilemap.process_layer(map_object=my_map,\\r\\n layer_name=platforms_layer_name,\\r\\n scaling=TILE_SCALING,\\r\\n use_spatial_hash=True)\\r\\n # -- Moving Platforms\\r\\n moving_platforms_list = arcade.tilemap.process_layer(my_map, moving_platforms_layer_name, TILE_SCALING)\\r\\n for sprite in moving_platforms_list:\\r\\n self.wall_list.append(sprite)\\r\\n\\r\\n # -- Coins\\r\\n self.coin_list = arcade.tilemap.process_layer(my_map,\\r\\n coins_layer_name,\\r\\n TILE_SCALING,\\r\\n use_spatial_hash=True)\\r\\n\\r\\n # -- Don't Touch Layer\\r\\n self.dont_touch_list = arcade.tilemap.process_layer(my_map,\\r\\n dont_touch_layer_name,\\r\\n TILE_SCALING,\\r\\n use_spatial_hash=True)\\r\\n\\r\\n # --- Other stuff\\r\\n # Set the background color\\r\\n if my_map.background_color:\\r\\n arcade.set_background_color(my_map.background_color)\\r\\n\\r\\n # Create the 'physics engine'\\r\\n self.physics_engine = arcade.PhysicsEnginePlatformer(self.player_sprite,\\r\\n self.wall_list,\\r\\n GRAVITY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9816345ac7805f9405790a66da0c26\",\n \"score\": \"0.6441203\",\n \"text\": \"def setup(self):\\n\\n # Set up the Cameras\\n viewport = (0, 0, self.window.width, self.window.height)\\n self.camera = arcade.SimpleCamera(viewport=viewport)\\n self.gui_camera = arcade.SimpleCamera(viewport=viewport)\\n\\n # Map name\\n map_name = \\\":resources:tiled_maps/map_with_ladders.json\\\"\\n\\n # Layer Specific Options for the Tilemap\\n layer_options = {\\n LAYER_NAME_PLATFORMS: {\\n \\\"use_spatial_hash\\\": True,\\n },\\n LAYER_NAME_MOVING_PLATFORMS: {\\n \\\"use_spatial_hash\\\": False,\\n },\\n LAYER_NAME_LADDERS: {\\n \\\"use_spatial_hash\\\": True,\\n },\\n LAYER_NAME_COINS: {\\n \\\"use_spatial_hash\\\": True,\\n },\\n }\\n\\n # Load in TileMap\\n self.tile_map = arcade.load_tilemap(map_name, TILE_SCALING, layer_options)\\n\\n # Initiate New Scene with our TileMap, this will automatically add all layers\\n # from the map as SpriteLists in the scene in the proper order.\\n self.scene = arcade.Scene.from_tilemap(self.tile_map)\\n\\n # Keep track of the score\\n self.score = 0\\n\\n # Shooting mechanics\\n self.can_shoot = True\\n self.shoot_timer = 0\\n\\n # Set up the player, specifically placing it at these coordinates.\\n self.player_sprite = PlayerCharacter()\\n self.player_sprite.center_x = (\\n self.tile_map.tile_width * TILE_SCALING * PLAYER_START_X\\n )\\n self.player_sprite.center_y = (\\n self.tile_map.tile_height * TILE_SCALING * PLAYER_START_Y\\n )\\n self.scene.add_sprite(LAYER_NAME_PLAYER, self.player_sprite)\\n\\n # Calculate the right edge of the my_map in pixels\\n self.end_of_map = self.tile_map.width * GRID_PIXEL_SIZE\\n\\n # -- Enemies\\n enemies_layer = self.tile_map.object_lists[LAYER_NAME_ENEMIES]\\n\\n for my_object in enemies_layer:\\n cartesian = self.tile_map.get_cartesian(\\n my_object.shape[0], my_object.shape[1]\\n )\\n enemy_type = my_object.properties[\\\"type\\\"]\\n if enemy_type == \\\"robot\\\":\\n enemy = RobotEnemy()\\n elif enemy_type == \\\"zombie\\\":\\n enemy = ZombieEnemy()\\n enemy.center_x = math.floor(\\n cartesian[0] * TILE_SCALING * self.tile_map.tile_width\\n )\\n enemy.center_y = math.floor(\\n (cartesian[1] + 1) * (self.tile_map.tile_height * TILE_SCALING)\\n )\\n if \\\"boundary_left\\\" in my_object.properties:\\n enemy.boundary_left = my_object.properties[\\\"boundary_left\\\"]\\n if \\\"boundary_right\\\" in my_object.properties:\\n enemy.boundary_right = my_object.properties[\\\"boundary_right\\\"]\\n if \\\"change_x\\\" in my_object.properties:\\n enemy.change_x = my_object.properties[\\\"change_x\\\"]\\n self.scene.add_sprite(LAYER_NAME_ENEMIES, enemy)\\n\\n # Add bullet spritelist to Scene\\n self.scene.add_sprite_list(LAYER_NAME_BULLETS)\\n\\n # --- Other stuff\\n # Set the background color\\n if self.tile_map.background_color:\\n self.window.background_color = self.tile_map.background_color\\n\\n # Create the 'physics engine'\\n self.physics_engine = arcade.PhysicsEnginePlatformer(\\n self.player_sprite,\\n platforms=self.scene[LAYER_NAME_MOVING_PLATFORMS],\\n gravity_constant=GRAVITY,\\n ladders=self.scene[LAYER_NAME_LADDERS],\\n walls=self.scene[LAYER_NAME_PLATFORMS],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6834ee80b5a8768280d919c99bf26a5a\",\n \"score\": \"0.6434447\",\n \"text\": \"def setup(self):\\n self.player = arcade.Sprite(\\\"tank.png\\\", 0.5)\\n self.num_coins = 30\\n\\n # Initialize brick_list as a SpriteList\\n # Then use nested for loops to place rows of bricks. \\n # (Hint: Outer for loop is y coordinate skipping every 150 pixels\\n # Inner for loop is x coordinate skipping every 64 pixels)\\n # Append bricks to brick_list\\n\\n\\n\\n\\n \\n # Initialize coin_list as a SpriteList\\n self.coin_list = arcade.SpriteList()\\n \\n # PSEUDOCODE for how to place coins. \\n # for each i in range of number of coins \\n # create coin Sprite\\n # set boolean variable successfully_placed to False\\n # while not successfully_placed\\n # set center_x and center_y randomly\\n # compute collision lists for coin with bricks\\n # AND coin with other coins (2 lists)\\n # if both lists have 0 length, then we have successfully placed the coin\\n # add coin to coin_list\\n \\n \\n \\n \\n \\n \\n \\n # initialize physics_engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22073cb86b548bd7aa8ab34e523b6572\",\n \"score\": \"0.6433896\",\n \"text\": \"def setup(self):\\n\\n # Create the Sprite lists\\n self.sprite_list = arcade.SpriteList()\\n\\n r = 60\\n for x in rand_range(0, 100 * math.pi, scale=math.pi / 5):\\n star = arcade.Sprite(\\\"../../resources/arcade/gold_1.png\\\")\\n star.center_x = SCREEN_WIDTH / 2 + r * math.cos(x)\\n star.center_y = SCREEN_HEIGHT / 2 + r * math.sin(x)\\n star.seed = scale_generator(x=random() * math.pi, offset=.5, step=.01)\\n star.scale = next(star.seed)\\n self.sprite_list.append(star)\\n r += 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea777f4769348b29ef8c98d8ba2109ac\",\n \"score\": \"0.64236575\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea777f4769348b29ef8c98d8ba2109ac\",\n \"score\": \"0.64236575\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea777f4769348b29ef8c98d8ba2109ac\",\n \"score\": \"0.64236575\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea777f4769348b29ef8c98d8ba2109ac\",\n \"score\": \"0.64236575\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea777f4769348b29ef8c98d8ba2109ac\",\n \"score\": \"0.64236575\",\n \"text\": \"def setUp(self):\\r\\n self.spaceship = SpaceShipGame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec745c478de3ff327552f7edca647db\",\n \"score\": \"0.6420831\",\n \"text\": \"def setUp(self):\\n\\n self.sold = Soldier(0, 0)\\n self.R = Random(seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f578c7fed4d6b351c3595091ed55c1\",\n \"score\": \"0.6419482\",\n \"text\": \"def main():\\n g = DemoGame(800, 600)\\n g.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944ca57bb03c227960b99967b99dfe32\",\n \"score\": \"0.6415772\",\n \"text\": \"def start(self):\\n pygame.init()\\n self.screen = pygame.display.set_mode((self.width, self.height))\\n pygame.display.set_caption(\\\"PyStroke\\\")\\n self.engines = [GameEngine(self.screen, self.e_e)] # add others here\\n self.engine = self.engines[0]\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59a37df8a7fe9b043bd00f4df69a8f8\",\n \"score\": \"0.64034855\",\n \"text\": \"def __init__(self):\\n #Screen settings\\n self.screen_width=1200\\n self.screen_height=800\\n self.bg_color=(230,230,230)\\n #ship settings\\n self.ship_limit=1\\n #bullet settings\\n self.bullet_width=300\\n self.bullet_height=15\\n self.bullet_color=(60,60,60)\\n self.bullets_allowed=3\\n #Alien settings\\n self.fleet_drop_speed = 20\\n \\n \\n #how quickly the game speeds up\\n self.speedup_scale=1.1\\n #how quickly the point values increase\\n self.score_scale=1.5\\n \\n self.initialize_dynamic_settings()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c5aa112a96d3bcd6cf22f2a0996df71\",\n \"score\": \"0.63967246\",\n \"text\": \"def ready(self):\\r\\n\\t\\t# Remove attract mode from mode queue - Necessary?\\r\\n\\t\\tself.game.modes.remove(self)\\r\\n\\t\\t# Initialize game\\t\\r\\n\\t\\tself.game.start_game()\\r\\n\\t\\t# Add the first player\\r\\n\\t\\tself.game.add_player()\\r\\n #self.game.add_player()\\r\\n\\t\\t# Start the ball. This includes ejecting a ball from the trough.\\r\\n\\t\\tself.game.start_ball()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a3fa4330760fb2d31506163e50273f\",\n \"score\": \"0.6395593\",\n \"text\": \"def setup(self):\\n # Create your sprites and sprite lists here\\n self.wall_list = arcade.SpriteList()\\n for x in range(128, SCREEN_WIDTH, 196):\\n for y in range(128, SCREEN_HEIGHT, 196):\\n wall = arcade.Sprite(\\\"building.png\\\",.3)\\n wall.center_x = x\\n wall.center_y = y\\n # wall.angle = 45\\n self.wall_list.append(wall)\\n self.player_sprite = arcade.Sprite(\\\"taxi.png\\\")\\n self.player_sprite.center_x = 50\\n self.player_sprite.center_y = 50\\n self.player_sprite.scale = .2\\n self.player_list = arcade.SpriteList()\\n self.player_list.append(self.player_sprite)\\n\\n #Spawns people and makes list\\n self.person = arcade.Sprite(\\\"person.png\\\")\\n self.person.scale = .2\\n self.person.center_x = random.randrange(SCREEN_WIDTH)\\n self.person.center_y = random.randrange(SCREEN_HEIGHT)\\n #Spawns target\\n self.target = arcade.Sprite(\\\"target.png\\\")\\n self.target.scale = .5\\n self.target.center_x = random.randrange(60,SCREEN_WIDTH)\\n self.target.center_y = random.randrange(60,SCREEN_HEIGHT)\\n color_list = [\\\"BLUE\\\",\\\"RED\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb594b736454b3248a1646e21447958\",\n \"score\": \"0.63796514\",\n \"text\": \"def initialise(self):\\n self.set_up()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eef679870f9b9257f328c0e702a2060\",\n \"score\": \"0.6377583\",\n \"text\": \"def init_game(self):\\n self.view.carregar_jogadores_possiveis(self._possible_players_list())\\n self.view.put_view_in_main_loop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f06bb7f659d9f9536ca96ded727d79a\",\n \"score\": \"0.6373506\",\n \"text\": \"def run(self):\\n pygame.init()\\n pygame.display.set_caption(\\\"Genetic Game\\\")\\n self.screen = pygame.display.set_mode((self.SCREEN_W, self.SCREEN_H), 0, 32)\\n\\n self.ominus_sprites = [OminusSprite(self.screen, o, PLAYERS_COLORS[o.id]) for o in self.model.get_players()]\\n for o in self.ominus_sprites:\\n self.agent_group.add(o)\\n\\n self.wall_sprites = [WallSprite(self.screen, w) for w in self.model.get_walls()]\\n for w in self.wall_sprites:\\n self.terrain_group.add(w)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59942af24376fb5026cb81340b8a4822\",\n \"score\": \"0.6373452\",\n \"text\": \"def __init__(self):\\n #screen Settings\\n self.screen_width = 1024\\n self.screen_height = 768\\n self.bg_color = (32, 32, 32)\\n\\n #rocket settings\\n self.rocket_speed = 1\\n\\n #laser Settings\\n self.laser_speed = 1.0\\n self.laser_width = 3\\n self.laser_height = 15\\n self.laser_color = (0, 255, 255)\\n self.lasers_allowed = 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f53bd434d32af1c87016a779056339\",\n \"score\": \"0.63714504\",\n \"text\": \"def __init__(self):\\n #Screen configuration\\n self.screen_width = 1200\\n self.screen_height = 680\\n self.bg_color = (0,20,50)\\n \\n #Hero configuration\\n #Increase of ship speed to 1.5 pixels instead of 1\\n #self.hero_speed_factor = 1.5\\n self.hero_limit = 3\\n \\n #Syringes (bullets) configuration\\n #self.bullet_speed_factor = 1\\n self.bullets_allowed = 5\\n \\n #Covids configuration\\n self.covid_vertical_speed_factor = 1\\n #The value of the movement is negative because it is increasing\\n # from the right to the left\\n #self.covid_horizontal_speed_factor = -10\\n #The pandemy direction equals 1 means to the bottom; -1 means to the top\\n # The randint ensures an randomly direction when starting the game\\n #if randint(0,1) == 1:\\n # self.pandemy_direction = 1\\n #else:\\n # self.pandemy_direction = -1\\n\\n #The rate that increases the game speed\\n self.speedup_scale = 1.1\\n \\n self.initialize_dynamic_settings()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5240037eab4029c3f040a6c1aba1cc\",\n \"score\": \"0.63671416\",\n \"text\": \"def __init__(self):\\n self.screen = pg.display.get_surface()\\n self.screen_rect = self.screen.get_rect()\\n self.clock = pg.time.Clock()\\n self.fps = 60\\n self.keys = pg.key.get_pressed()\\n self.done = False\\n # ship = random.choice(list(prepare.GFX[\\\"ships\\\"].values()))\\n ship = list(prepare.GFX[\\\"ships\\\"].values())[7] # pick first ship available\\n self.player = actors.Player((0, 0), ship)\\n self.level = level.Level(self.screen_rect.copy(), self.player)\\n\\n self.energyloss_counter = 0\\n self.energygain_counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7194f91226989dda2cdda5e9fc3c6c\",\n \"score\": \"0.63631403\",\n \"text\": \"def __init__(self):\\n game_engine = get_gameengine()\\n if game_engine is not None:\\n self = game_engine\\n else:\\n ## The targeted frames per second\\n self.target_fps = 200\\n\\n ## The start time\\n self.time = time.time()\\n\\n ## A list of all registered game objects\\n self.game_objects = list()\\n\\n ## A list of colliders\\n self.colliders = list()\\n\\n ## Manage the user inputs\\n self.input_manager = InputManager(self)\\n\\n ## Determines the state of the Game Engine\\n self.running = False\\n\\n ## Variable to pause the Game Engine\\n self.paused = False\\n\\n self.time_node = pm.PyNode('time1')\\n # end if\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1aec276551d5c6cf8e7446310d182a\",\n \"score\": \"0.6361806\",\n \"text\": \"def __init__(self):\\n super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)\\n self.player_count: int = None\\n self.player_hand_0: arcade.SpriteList = None\\n self.player_hand_1: arcade.SpriteList = None\\n self.deck: arcade.SpriteList = None\\n self.pile: arcade.SpriteList = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72672d21106099e15bfae6aee9a6e0a0\",\n \"score\": \"0.63469946\",\n \"text\": \"def start_game(self) -> None:\\n self.init_game()\\n self.play()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a48c448c1c746bc829dfa3391d2526\",\n \"score\": \"0.6346134\",\n \"text\": \"def initialize(self):\\n\\n # --------- BEGIN YOUR CODE ----------\\n\\n # This is exactly the same as Human.initialize, just copy the code over\\n\\n # --------- END YOUR CODE ----------\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472542e9894153ef4925551611ca0402\",\n \"score\": \"0.6342365\",\n \"text\": \"def start(self):\\n # asserts preconditions are met\\n #assert self.validGameSettings()\\n\\n #draws initial welcome screen\\n #self._text = GLabel(text=\\\"Press 'S' to Play\\\")\\n #self._text.draw(self.view)\\n\\n # initializing instance variables\\n self.setState(STATE_INACTIVE)\\n self.setWave(None)\\n self.setText(None)\\n self.lastkeys = 0 #ADD MORE ATTRIBUTES\\n\\n # draws iniital welcome screen\\n self.welcomeScreen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7fefa3e89f5460cda184529b474d2fc\",\n \"score\": \"0.63385844\",\n \"text\": \"def init(self):\\n self._last = None\\n self._game = None\\n self._state = STATE_INACTIVE\\n self._message = GLabel(text='Breakout\\\\n\\\\nClick To Begin\\\\n\\\\nGood Luck',\\n font_size=24,x=GAME_WIDTH / 2.0, y=GAME_HEIGHT*(2.0/3.0),\\n halign='center', valign='middle', linecolor=colormodel.WHITE)\\n self._touch = None\\n self._countdownTime = 0\\n self._countdownMessage = GLabel(text='3', font_size=40,x=GAME_WIDTH / 2.0,\\n y=GAME_HEIGHT*(2.0/3.0), halign='center',\\n valign='middle', linecolor=colormodel.WHITE)\\n self._pausedMessage = GLabel()\\n self._sound = True\\n self._soundImage = GImage(x=GAME_WIDTH-32, y=0, width=32, height=22,\\n source='whitevolumeon.png')\\n self._background = GRectangle(x=0, y=0, width=GAME_WIDTH, height=GAME_HEIGHT,\\n fillcolor=colormodel.BLACK, linecolor=colormodel.BLACK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7628ed4be734b19f77e1a20b4184fae4\",\n \"score\": \"0.6336813\",\n \"text\": \"def __init__(self):\\n pygame.init()\\n self.settings = Settings()\\n self.screen = pygame.display.set_mode(\\n (self.settings.screen_width, self.settings.screen_height)\\n )\\n pygame.display.set_caption(\\\"Sideways Shooter\\\")\\n self.stats = GameStats(self)\\n self.sideways_ship = SidewaysShip(self)\\n self.bullets = pygame.sprite.Group()\\n self.aliens = pygame.sprite.Group()\\n self._create_fleet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424833a78c0bfdeed90d07499d7cd6f7\",\n \"score\": \"0.6334929\",\n \"text\": \"def __init__(self):\\n\\n # Screen's settings\\n self.screen_width = 1200\\n self.screen_height = 800\\n self.bg_color = (230, 230, 230)\\n\\n # Bluebee Settings\\n self.bb_speed = 1.0\\n\\n # Moving test.\\n self.counter = 0\\n self.max_left = 400\\n self.max_up = 300\\n self.max_right = 400\\n self.max_down = 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c02130f22707d1974ffa12a4a11c5b\",\n \"score\": \"0.6327699\",\n \"text\": \"def start() -> None:\\n\\n # PREPARE\\n clone_game_files()\\n\\n # SIMULATE\\n turns = run.simulation()\\n\\n # LOG\\n logs = read.combine_logs(turns)\\n\\n # CALCULATE\\n results = calculate.results(logs)\\n\\n # DISPLAY\\n visualize.charts(results)\\n\\n # CLEAN\\n remove_cloned_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed037633b0e87e59f92106836bcb74fe\",\n \"score\": \"0.6324671\",\n \"text\": \"def setUp(self):\\n d = self.deck = TestDeck()\\n self.game = test_setup.two_player_lead('Laborer', deck=d)\\n self.p1, self.p2 = self.game.players\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305651a134f8726b49cb090804b95f21\",\n \"score\": \"0.6319136\",\n \"text\": \"def __init__(self) -> None:\\n self.win = self.__init_window()\\n self.BACKGROUND = pygame.transform.scale(pygame.image.load(os.path.join(\\\"assets\\\", \\\"background\\\", \\\"background.png\\\")), (WIDTH, HEIGHT))\\n self.highscore = 0\\n self.gamemode = Gamemodes.startscreen\\n self.clock = pygame.time.Clock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded65e67ac273ec31aaf5cec21a1abb6\",\n \"score\": \"0.63133985\",\n \"text\": \"def __init__(self):\\n pygame.init()\\n\\n self.settings = Settings()\\n\\n self.scenes = {\\\"menu\\\": MenuScene(),\\n \\\"settings\\\": SettingsScene(),\\n \\\"score\\\": ScoreScene(),\\n \\\"game\\\": GameScene(),\\n \\\"pause\\\": PauseScene(),\\n \\\"game_over\\\": GameOverScene(),\\n \\\"logo\\\": LogoScene()}\\n self.scene_name = \\\"logo\\\" # start scene\\n self.previous_scene_name = None\\n self.scene = self.scenes[self.scene_name]\\n\\n self.__manager = ResourceManager()\\n self.__display = self.settings.get_display()\\n self.__display.fill(BACKGROUND_COLOR)\\n pygame.display.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a7fbbf00153d93349b3f3406c324732\",\n \"score\": \"0.63096815\",\n \"text\": \"def setUp(self):\\r\\n global ship_image\\r\\n self.spaceshipgame = SpaceShipGame()\\r\\n self.spaceshipgame.init()\\r\\n pos = [1,1]\\r\\n vel = [1,1]\\r\\n angle = 0\\r\\n image = ship_image\\r\\n\\r\\n center = [1,1]\\r\\n size = 1\\r\\n info = ImageInfo(center, size)\\r\\n\\r\\n self.ship = Ship( pos, vel, angle, image, info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a7fbbf00153d93349b3f3406c324732\",\n \"score\": \"0.63096815\",\n \"text\": \"def setUp(self):\\r\\n global ship_image\\r\\n self.spaceshipgame = SpaceShipGame()\\r\\n self.spaceshipgame.init()\\r\\n pos = [1,1]\\r\\n vel = [1,1]\\r\\n angle = 0\\r\\n image = ship_image\\r\\n\\r\\n center = [1,1]\\r\\n size = 1\\r\\n info = ImageInfo(center, size)\\r\\n\\r\\n self.ship = Ship( pos, vel, angle, image, info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22ba600cbf922333b0d8230d4715ba2\",\n \"score\": \"0.63095695\",\n \"text\": \"def setUp(self):\\n self.delegate = AlwaysHitDelegate(\\\"\\\")\\n self.environment = BattleEnvironment()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994295,"cells":{"query_id":{"kind":"string","value":"eff4806a2046b7b3e0818da3f6b39388"},"query":{"kind":"string","value":"create instantiate of a DeploymentRequest This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response."},"positive_passages":{"kind":"list like","value":[{"docid":"1f6251f81e8736df9255b2bec99d496b","score":"0.5890666","text":"def create_namespaced_deployment_request_instantiate_with_http_info(self, body, name, namespace, **kwargs):\n\n all_params = ['body', 'name', 'namespace', 'pretty']\n all_params.append('callback')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method create_namespaced_deployment_request_instantiate\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'body' is set\n if ('body' not in params) or (params['body'] is None):\n raise ValueError(\"Missing the required parameter `body` when calling `create_namespaced_deployment_request_instantiate`\")\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `create_namespaced_deployment_request_instantiate`\")\n # verify the required parameter 'namespace' is set\n if ('namespace' not in params) or (params['namespace'] is None):\n raise ValueError(\"Missing the required parameter `namespace` when calling `create_namespaced_deployment_request_instantiate`\")\n\n\n collection_formats = {}\n\n resource_path = '/oapi/v1/namespaces/{namespace}/deploymentconfigs/{name}/instantiate'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n if 'namespace' in params:\n path_params['namespace'] = params['namespace']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf'])\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = ['BearerToken']\n\n return self.api_client.call_api(resource_path, 'POST',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1DeploymentRequest',\n auth_settings=auth_settings,\n callback=params.get('callback'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)","title":""}],"string":"[\n {\n \"docid\": \"1f6251f81e8736df9255b2bec99d496b\",\n \"score\": \"0.5890666\",\n \"text\": \"def create_namespaced_deployment_request_instantiate_with_http_info(self, body, name, namespace, **kwargs):\\n\\n all_params = ['body', 'name', 'namespace', 'pretty']\\n all_params.append('callback')\\n all_params.append('_return_http_data_only')\\n all_params.append('_preload_content')\\n all_params.append('_request_timeout')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method create_namespaced_deployment_request_instantiate\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'body' is set\\n if ('body' not in params) or (params['body'] is None):\\n raise ValueError(\\\"Missing the required parameter `body` when calling `create_namespaced_deployment_request_instantiate`\\\")\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `create_namespaced_deployment_request_instantiate`\\\")\\n # verify the required parameter 'namespace' is set\\n if ('namespace' not in params) or (params['namespace'] is None):\\n raise ValueError(\\\"Missing the required parameter `namespace` when calling `create_namespaced_deployment_request_instantiate`\\\")\\n\\n\\n collection_formats = {}\\n\\n resource_path = '/oapi/v1/namespaces/{namespace}/deploymentconfigs/{name}/instantiate'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n if 'namespace' in params:\\n path_params['namespace'] = params['namespace']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'body' in params:\\n body_params = params['body']\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf'])\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = ['BearerToken']\\n\\n return self.api_client.call_api(resource_path, 'POST',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1DeploymentRequest',\\n auth_settings=auth_settings,\\n callback=params.get('callback'),\\n _return_http_data_only=params.get('_return_http_data_only'),\\n _preload_content=params.get('_preload_content', True),\\n _request_timeout=params.get('_request_timeout'),\\n collection_formats=collection_formats)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c8faee09c9ac08cfa320b38d31c94c48","score":"0.74152315","text":"def create_namespaced_deployment_request_instantiate(self, body, name, namespace, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.create_namespaced_deployment_request_instantiate_with_http_info(body, name, namespace, **kwargs)\n else:\n (data) = self.create_namespaced_deployment_request_instantiate_with_http_info(body, name, namespace, **kwargs)\n return data","title":""},{"docid":"22cc9aacc27517e01158df0d46f927ff","score":"0.6730303","text":"def create_deployment(self, namespace, deployment):\n api_instance = client.AppsV1Api()\n return api_instance.create_namespaced_deployment(namespace, deployment)","title":""},{"docid":"aa1b4570dc15c2b5106b6a1ea306d229","score":"0.6238238","text":"def make_object(self, data):\n return Deployment(**data)","title":""},{"docid":"aa1b4570dc15c2b5106b6a1ea306d229","score":"0.6238238","text":"def make_object(self, data):\n return Deployment(**data)","title":""},{"docid":"aa1b4570dc15c2b5106b6a1ea306d229","score":"0.6238238","text":"def make_object(self, data):\n return Deployment(**data)","title":""},{"docid":"aae454be2bf98674ea749dc3fb2108cd","score":"0.61750245","text":"def __create_deployment(self, deployment_name):\n\n # try to create a new deployment\n try:\n\n # create a new deployment\n command = str('kubectl run ' + deployment_name + \" --image=kubipy-image:latest --image-pull-policy='Never'\")\n os.system(command)\n\n # return True\n return True\n \n # handle exception\n except:\n\n # return False\n return False","title":""},{"docid":"6dc547c9ca59763bf72028d4b3a478f1","score":"0.5856244","text":"def create(\n cls,\n deployment_def: Union[Callable, str],\n init_args: Optional[Tuple[Any]] = None,\n init_kwargs: Optional[Dict[Any, Any]] = None,\n ray_actor_options: Optional[Dict] = None,\n placement_group_bundles: Optional[List[Dict[str, float]]] = None,\n placement_group_strategy: Optional[str] = None,\n max_replicas_per_node: Optional[int] = None,\n deployment_def_name: Optional[str] = None,\n ):\n\n if not callable(deployment_def) and not isinstance(deployment_def, str):\n raise TypeError(\"@serve.deployment must be called on a class or function.\")\n\n if not (init_args is None or isinstance(init_args, (tuple, list))):\n raise TypeError(\"init_args must be a tuple.\")\n\n if not (init_kwargs is None or isinstance(init_kwargs, dict)):\n raise TypeError(\"init_kwargs must be a dict.\")\n\n if inspect.isfunction(deployment_def):\n if init_args:\n raise ValueError(\"init_args not supported for function deployments.\")\n elif init_kwargs:\n raise ValueError(\"init_kwargs not supported for function deployments.\")\n\n if not isinstance(deployment_def, (Callable, str)):\n raise TypeError(\n f'Got invalid type \"{type(deployment_def)}\" for '\n \"deployment_def. Expected deployment_def to be a \"\n \"class, function, or string.\"\n )\n # Set defaults\n if init_args is None:\n init_args = ()\n if init_kwargs is None:\n init_kwargs = {}\n if ray_actor_options is None:\n ray_actor_options = {}\n if deployment_def_name is None:\n if isinstance(deployment_def, str):\n deployment_def_name = deployment_def\n else:\n deployment_def_name = deployment_def.__name__\n\n config = cls(\n deployment_def_name,\n pickle_dumps(\n deployment_def,\n f\"Could not serialize the deployment {repr(deployment_def)}\",\n ),\n pickle_dumps(init_args, \"Could not serialize the deployment init args\"),\n pickle_dumps(init_kwargs, \"Could not serialize the deployment init kwargs\"),\n ray_actor_options,\n placement_group_bundles,\n placement_group_strategy,\n max_replicas_per_node,\n )\n\n config._deployment_def = deployment_def\n config._init_args = init_args\n config._init_kwargs = init_kwargs\n\n return config","title":""},{"docid":"ad7fb4037e77fb841e5a1c66668787be","score":"0.5843691","text":"def make_object(self, data):\n return DeploymentTarget(**data)","title":""},{"docid":"ad7fb4037e77fb841e5a1c66668787be","score":"0.5843691","text":"def make_object(self, data):\n return DeploymentTarget(**data)","title":""},{"docid":"ad7fb4037e77fb841e5a1c66668787be","score":"0.5843691","text":"def make_object(self, data):\n return DeploymentTarget(**data)","title":""},{"docid":"ca29d0b4bfbbb6008d5170a0fa6ed1d5","score":"0.57843614","text":"def create(self):\n evaluate_request(\n self.__v1_api.create_namespaced_service(\n namespace=self.__namespace, body=self.load_gen_service, async_req=True\n ),\n allowed_statuses=[409],\n )\n evaluate_request(\n self.__v1_apps_api.create_namespaced_deployment(\n namespace=self.__namespace,\n body=self.load_gen_deployment,\n async_req=True,\n ),\n allowed_statuses=[409],\n )\n # Make a second call to patch the configuration to make sure changes in replicas are applied\n # TODO: Investigate making a utility method similar to `kubectl apply`\n evaluate_request(\n self.__v1_apps_api.patch_namespaced_deployment(\n namespace=self.__namespace,\n body=self.load_gen_deployment,\n name=get_name(self.load_gen_deployment),\n async_req=True,\n )\n )","title":""},{"docid":"148044ed07fcebbb2c1a2dd4d981eacd","score":"0.57293147","text":"def create_deployment(ApiId=None, Description=None, StageName=None):\n pass","title":""},{"docid":"d26c29d6ec490cbe79d5fcc56f07c708","score":"0.5728097","text":"def Deploy(self, request, global_params=None):\n config = self.GetMethodConfig('Deploy')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"235b3bae1d74d97304abed8dea43936e","score":"0.5602673","text":"def Deploy(apig,api_id: str,stage_name: str,stage_purpose: str,deployment_purpose: str):\n\n\t\t\tresponse = apig.client.create_deployment(\n\t\t\t\trestApiId=api_id,\n\t\t\t\tstageName=stage_name,\n\t\t\t\tstageDescription=stage_purpose,\n\t\t\t\tdescription=deployment_purpose,\n\t\t\t\t#cacheClusterEnabled=True|False,\n\t\t\t\t#cacheClusterSize='0.5'|'1.6'|'6.1'|'13.5'|'28.4'|'58.2'|'118'|'237',\n\t\t\t\t#variables={\n\t\t\t\t#\t'string': 'string'\n\t\t\t\t#},\n\t\t\t\t#canarySettings={\n\t\t\t\t#\t'percentTraffic': 123.0,\n\t\t\t\t#\t'stageVariableOverrides': {\n\t\t\t\t#\t\t'string': 'string'\n\t\t\t\t#\t},\n\t\t\t\t#\t'useStageCache': True|False\n\t\t\t\t#},\n\t\t\t\t#tracingEnabled=True|False\n\t\t\t)\n\t\t\treturn response","title":""},{"docid":"ce6d8cfb1e8c285c274bdb715ef48d27","score":"0.55937594","text":"def deployment(self):\n try:\n deployment = gcp.BareClusterDeployment(self.gcp_wrapper, self.config['deployment_name'],\n self.config['gce_zone'])\n info = deployment.get_info()\n errors = info['operation'].get('error')\n if errors:\n raise util.LauncherError('DeploymentContainsErrors', str(errors))\n return deployment\n except HttpError as e:\n if e.resp.status == 404:\n raise util.LauncherError('DeploymentNotFound',\n \"The deployment you are trying to access doesn't exist\") from e\n raise e","title":""},{"docid":"5f08fcba693bd35d4db4a0cf8ec926d5","score":"0.55091316","text":"async def create_game_server_deployment(self,\n request: game_server_deployments.CreateGameServerDeploymentRequest = None,\n *,\n parent: str = None,\n game_server_deployment: game_server_deployments.GameServerDeployment = None,\n retry: retries.Retry = gapic_v1.method.DEFAULT,\n timeout: float = None,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> operation_async.AsyncOperation:\n # Create or coerce a protobuf request object.\n # Sanity check: If we got a request object, we should *not* have\n # gotten any keyword arguments that map to the request.\n has_flattened_params = any([parent, game_server_deployment])\n if request is not None and has_flattened_params:\n raise ValueError(\"If the `request` argument is set, then none of \"\n \"the individual field arguments should be set.\")\n\n request = game_server_deployments.CreateGameServerDeploymentRequest(request)\n\n # If we have keyword arguments corresponding to fields on the\n # request, apply these.\n if parent is not None:\n request.parent = parent\n if game_server_deployment is not None:\n request.game_server_deployment = game_server_deployment\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = gapic_v1.method_async.wrap_method(\n self._client._transport.create_game_server_deployment,\n default_timeout=60.0,\n client_info=DEFAULT_CLIENT_INFO,\n )\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata((\n (\"parent\", request.parent),\n )),\n )\n\n # Send the request.\n response = await rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Wrap the response in an operation future.\n response = operation_async.from_gapic(\n response,\n self._client._transport.operations_client,\n game_server_deployments.GameServerDeployment,\n metadata_type=common.OperationMetadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"3f9e2f169726958f09efb705ba205833","score":"0.54509884","text":"def create_deployment_from_file():\n parser = ArgumentParser(description=\"Deployment JSON Descriptor\")\n if is_valid_file(parser,filename):\n f=open(filename,'r')\n json_object = json.load(f)\n\n new_deployment = Deployment()\n for value in json_object.values():\n for v in range(0,len(value)):\n new_deployment.description=value[v]['description']\n new_deployment.name = value[v]['name']\n new_deployment.region = value[v]['regionId']\n new_deployment.budget = value[v]['budget']\n result=new_deployment.create()\n print new_deployment.current_job","title":""},{"docid":"361a44319e34d24d399054508a4dba1d","score":"0.5314766","text":"def CreateInstance(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"CreateInstance\", params, headers=headers)\n response = json.loads(body)\n model = models.CreateInstanceResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""},{"docid":"f5e3e6698dd5446275e465730755add3","score":"0.53035444","text":"def create_namespaced_build_request_instantiate(self, body, name, namespace, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.create_namespaced_build_request_instantiate_with_http_info(body, name, namespace, **kwargs)\n else:\n (data) = self.create_namespaced_build_request_instantiate_with_http_info(body, name, namespace, **kwargs)\n return data","title":""},{"docid":"f2ba182add2613fff44ac66cb271bebe","score":"0.5297318","text":"def deployment(ctx, image):\n body = {'template' : image}\n resp = consume_task(ctx.obj.vlab_api,\n endpoint='/api/2/inf/deployment',\n message='Deploying template {}'.format(image),\n body=body,\n timeout=3600,\n pause=20)\n data = resp.json()['content']\n typewriter(\"Successfully created the following machines:\")\n click.echo('\\t{}'.format('\\n\\t'.join(data.keys())))\n typewriter(\"\\nUse 'vlab connect deployment --name <name> --protocol <protocol>' to access a deployed machine\")","title":""},{"docid":"696f2763c4cde4413e6a94516ef60867","score":"0.5296554","text":"def create_namespaced_deployment(body, namespace=\"default\", extv1Client=None):\n if extv1Client is None:\n # load the kubernetes config\n load_k8s_config()\n #create the api client\n extv1Client=client.ExtensionsV1beta1Api()\n\n deployment = None\n try:\n deployment = extv1Client.create_namespaced_deployment(namespace=namespace, body=body)\n log.debug(\"Deployment created: \\n{0}\\n\".format(deployment))\n except ApiException as e:\n log.error(\"Exception when calling ExtensionsV1beta1Api->create_namespaced_deployment: %s\\n\" % e)\n raise e\n\n return deployment","title":""},{"docid":"95f4a962f5aadbed03366082f3f5a4e7","score":"0.5293826","text":"def create(self, name, filename, do_use=False):\n\n filename = os.path.expanduser(filename)\n print(\"file:\" + filename)\n\n with open(filename, \"rb\") as deploy_file:\n config = yaml.safe_load(deploy_file.read())\n\n try:\n deployment = api.Deployment.create(config, name)\n except jsonschema.ValidationError:\n print(_(\"Config schema validation error: %s.\") % sys.exc_info()[1])\n return(1)\n except exceptions.DeploymentNameExists:\n print(_(\"Error: %s\") % sys.exc_info()[1])\n return(1)\n\n self.list(deployment_list=[deployment])\n if do_use:\n self.use(deployment[\"uuid\"])","title":""},{"docid":"768cd02652f3c2d3997d4a8999a4e438","score":"0.52635235","text":"def create(name, cluster, model, sha, env_file, env):\n # Create map of custom environment variables to use with this train job.\n envs = parse_cmd_envs(env_file_path=env_file, env_options=env)\n\n # Create payload.\n payload = project_payload({\n 'name': name,\n 'apiCluster': cluster,\n 'model': model,\n 'sha': sha,\n 'envs': json.dumps(envs)\n })\n\n try:\n # Create the deploy.\n resp = api.post('/deploy', payload=payload, stream=True)\n except KeyboardInterrupt:\n return\n\n # Stream the response logs.\n resp.log_stream()","title":""},{"docid":"ca32146736a6f146e5dd539f10cd0f2f","score":"0.5260751","text":"def deployment(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"deployment\")","title":""},{"docid":"0aa30bb2fa60d44677cfefe8d66db21d","score":"0.5154369","text":"def make_object(self, data):\n return DeploymentImage(**data)","title":""},{"docid":"0aa30bb2fa60d44677cfefe8d66db21d","score":"0.5154369","text":"def make_object(self, data):\n return DeploymentImage(**data)","title":""},{"docid":"0aa30bb2fa60d44677cfefe8d66db21d","score":"0.5154369","text":"def make_object(self, data):\n return DeploymentImage(**data)","title":""},{"docid":"0aa30bb2fa60d44677cfefe8d66db21d","score":"0.5154369","text":"def make_object(self, data):\n return DeploymentImage(**data)","title":""},{"docid":"e7e62a0e6332b9ca108377885a6bdf61","score":"0.5148939","text":"def createDeploymentFromFile(filePath, namespace='default'):\n txClient = TxKubernetesClient()\n\n with open(filePath, 'r') as file:\n deployment = yaml.load(file)\n\n # create a deployment in a namespace\n d = txClient.call(txClient.extV1Beta1.create_namespaced_deployment,\n body=deployment,\n namespace=namespace,\n )\n return d","title":""},{"docid":"007210337460416121b109c416bd824b","score":"0.5039167","text":"def _create_new_deployments(self, sm, group, request_dict):\n rm = get_resource_manager()\n new_deployments = request_dict.get('new_deployments')\n if not new_deployments:\n return\n with sm.transaction():\n if not group.default_blueprint:\n raise manager_exceptions.ConflictError(\n 'Cannot create deployments: group {0} has no '\n 'default blueprint set'.format(group.id))\n if not all(spec.get('skip_plugins_validation')\n for spec in new_deployments):\n rm.check_blueprint_plugins_installed(\n group.default_blueprint.plan)\n deployment_count = len(group.deployments)\n create_exec_group = models.ExecutionGroup(\n id=str(uuid.uuid4()),\n deployment_group=group,\n workflow_id='create_deployment_environment',\n visibility=group.visibility,\n concurrency=10,\n )\n sm.put(create_exec_group)\n self._prepare_sites(sm, new_deployments)\n for new_dep_spec in new_deployments:\n dep = self._make_new_group_deployment(\n rm, group, new_dep_spec, deployment_count, group.labels)\n group.deployments.append(dep)\n create_exec_group.executions.append(dep.create_execution)\n deployment_count += 1\n messages = create_exec_group.start_executions(sm, rm)\n workflow_executor.execute_workflow(messages)","title":""},{"docid":"52aeb46eb05931bc19fd86699340acd7","score":"0.50310636","text":"def deployment(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"deployment\")","title":""},{"docid":"2961de7404e616b49c17e55067617a76","score":"0.5011048","text":"def from_dictionary(cls,\n dictionary):\n if dictionary is None:\n return None\n\n # Extract variables from the dictionary\n deploy_fleet_params = cohesity_management_sdk.models.deploy_fleet_params.DeployFleetParams.from_dictionary(dictionary.get('deployFleetParams')) if dictionary.get('deployFleetParams') else None\n deploy_vms_to_aws_params = cohesity_management_sdk.models.deploy_vms_to_aws_params.DeployVMsToAWSParams.from_dictionary(dictionary.get('deployVmsToAwsParams')) if dictionary.get('deployVmsToAwsParams') else None\n deploy_vms_to_azure_params = cohesity_management_sdk.models.deploy_vms_to_azure_params.DeployVMsToAzureParams.from_dictionary(dictionary.get('deployVmsToAzureParams')) if dictionary.get('deployVmsToAzureParams') else None\n deploy_vms_to_gcp_params = cohesity_management_sdk.models.deploy_vms_to_gcp_params.DeployVMsToGCPParams.from_dictionary(dictionary.get('deployVmsToGcpParams')) if dictionary.get('deployVmsToGcpParams') else None\n replicate_snapshots_to_aws_params = cohesity_management_sdk.models.replicate_snapshots_to_aws_params.ReplicateSnapshotsToAWSParams.from_dictionary(dictionary.get('replicateSnapshotsToAwsParams')) if dictionary.get('replicateSnapshotsToAwsParams') else None\n replicate_snapshots_to_azure_params = cohesity_management_sdk.models.replicate_snapshots_to_azure_params.ReplicateSnapshotsToAzureParams.from_dictionary(dictionary.get('replicateSnapshotsToAzureParams')) if dictionary.get('replicateSnapshotsToAzureParams') else None\n\n # Return an object of this model\n return cls(\n deploy_fleet_params,\n deploy_vms_to_aws_params,\n deploy_vms_to_azure_params,\n deploy_vms_to_gcp_params,\n replicate_snapshots_to_aws_params,\n replicate_snapshots_to_azure_params\n)","title":""},{"docid":"fb25da55f48886ed1cd003679994da21","score":"0.497082","text":"def create_server(self, request, tenant_id):\n try:\n content = json.loads(request.content.read())\n except ValueError:\n request.setResponseCode(400)\n return json.dumps(bad_request(\"Invalid JSON request body\"))\n\n try:\n creation = (self._region_collection_for_tenant(tenant_id)\n .request_creation(request, content, self.url))\n except ValueError:\n request.setResponseCode(400)\n return json.dumps(\n bad_request(\n \"OS-DCF:diskConfig must be either 'MANUAL' or 'AUTO'.\"))\n\n return creation","title":""},{"docid":"79e0a1a026f7e4cdf27b38cb6667c206","score":"0.4965557","text":"def create(self,\n model: str,\n deployment_name: str,\n metadata: Optional[Dict] = None,\n training_data: Optional[Union['DataFrame', 'ndarray']] = None,\n training_target: Optional[Union['DataFrame', 'ndarray']] = None,\n experiment_run_id: Optional[str] = None) -> None:\n return super().create(model=model,\n deployment_name=deployment_name,\n metadata=metadata,\n training_data=training_data,\n training_target=training_target,\n experiment_run_id=experiment_run_id,\n deployment_type='online')","title":""},{"docid":"d0ddeafc96fbba74619cae752d91b369","score":"0.49553844","text":"def deploy(self, deployment_yaml):\n\n cmd = \"%s %s\" % (KUBECTL_APPLY % (self.context), deployment_yaml)\n result = self.nuvoloso_helper.run_check_output(cmd)\n return result","title":""},{"docid":"f1ac62f8345fb8e35281f13c5bf4aaf1","score":"0.49248436","text":"def create(self, request, *args, **kwargs):\n data = self.request.DATA\n packaged = 'upload' in data\n form = (NewPackagedForm(data) if packaged\n else NewManifestForm(data))\n\n if not form.is_valid():\n return Response(form.errors, status=HTTP_400_BAD_REQUEST)\n\n if not packaged:\n upload = FileUpload.objects.create(\n user=getattr(request, 'amo_user', None))\n # The hosted app validator is pretty fast.\n tasks.fetch_manifest(form.cleaned_data['manifest'], upload.pk)\n else:\n upload = form.file_upload\n # The packaged app validator is much heavier.\n tasks.validator.delay(upload.pk)\n\n log.info('Validation created: %s' % upload.pk)\n self.kwargs = {'pk': upload.pk}\n # Re-fetch the object, fetch_manifest() might have altered it.\n upload = self.get_object()\n serializer = self.get_serializer(upload)\n status = HTTP_201_CREATED if upload.processed else HTTP_202_ACCEPTED\n return Response(serializer.data, status=status)","title":""},{"docid":"d1767a93be08f47e1a6a1e475a096097","score":"0.49178505","text":"def create_namespaced_deployment_config(self, namespace, body, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.create_namespaced_deployment_config_with_http_info(namespace, body, **kwargs)\n else:\n (data) = self.create_namespaced_deployment_config_with_http_info(namespace, body, **kwargs)\n return data","title":""},{"docid":"0cd42809ff676fd4e00e5b749670ae22","score":"0.48909155","text":"def deploy_dag() -> Response:\n\n json_request = request.get_json(cache=False)\n\n try:\n\n if json_request is None:\n return ApiResponse.error(\n status=ApiResponse.STATUS_BAD_REQUEST,\n error=f\"Did not receive any JSON request to deploy\",\n )\n\n ingestion_pipeline = IngestionPipeline(**json_request)\n\n deployer = DagDeployer(ingestion_pipeline)\n response = deployer.deploy()\n\n return response\n\n except ValidationError as err:\n logger.debug(traceback.format_exc())\n logger.error(\n f\"Request Validation Error parsing payload [{json_request}]. IngestionPipeline expected: {err}\"\n )\n return ApiResponse.error(\n status=ApiResponse.STATUS_BAD_REQUEST,\n error=f\"Request Validation Error parsing payload. IngestionPipeline expected: {err}\",\n )\n\n except Exception as exc:\n logger.debug(traceback.format_exc())\n logger.error(f\"Internal error deploying [{json_request}] due to [{exc}] \")\n return ApiResponse.error(\n status=ApiResponse.STATUS_SERVER_ERROR,\n error=f\"Internal error while deploying due to [{exc}] \",\n )","title":""},{"docid":"9d07e6605fdefe7d821ed430f2465009","score":"0.48740977","text":"def deploy(self):\n logger.debug(\"======= deploy =======\")\n\n try:\n err, pkg_name, dest, email, passwd = \\\n self.__get_deploy_request_params()\n if (err):\n return HTTPBadRequest(err)\n\n result = deploy_package(pkg_name, dest, email, passwd)\n\n if result['status'] == 202:\n deployment_id = result['deployment_id']\n status_url = 'deployment/{0}'.format(deployment_id)\n contents = json.dumps(result, sort_keys=True)\n response = HTTPAccepted()\n response.headers = {\n 'Content-Type': 'application/json; charset=UTF-8',\n 'Location': status_url}\n response.body = contents\n return response\n elif result['status'] == 404:\n msg = 'Cannot find package \"{0}\" installed.'.format(pkg_name)\n return HTTPNotFound(detail=msg)\n else:\n return HTTPInternalServerError(detail=result['errors'])\n\n except Exception as e:\n stack_info = traceback.format_exc()\n logger.exception(stack_info)\n return HTTPInternalServerError(detail=stack_info)","title":""},{"docid":"4a18028e12f5b6b9a07065a590a6844d","score":"0.4848733","text":"def deploy(self, script_file, requirements_file, port, deployment_name = None):\n\n # check if deployment name was given\n if deployment_name is None:\n\n # assign default\n deployment_name = \"kubipy-deployment\"\n\n # if it was given\n else:\n\n # take care of special characters\n deployment_name = deployment_name.replace('_', '-').replace('/', '-')\n\n # build info message\n info_message = \"\"\"\n ____________________________________________________________\n | To deploy your image, you will need to be logged in to |\n | Docker. So please, login if you have an account, or sign |\n | up, if you do not have an account yet. |\n ------------------------------------------------------------\n \n \"\"\"\n\n # print info about graphical interface\n print (info_message)\n\n # check script_file input\n if isinstance(script_file, str):\n\n # try to read in the first line\n try:\n\n # read in file\n with open(script_file) as file:\n first_line = file.readline()\n\n # print out first line\n info_message = \"\"\"\\\n This is the first line of the file you want to deploy:\n -------------------------\n {first_line}\n -------------------------\\\n \"\"\".format(first_line = first_line)\n\n # print info message\n print (info_message)\n\n # handle exception\n except:\n\n # handle exception\n raise Exception('I could not find the script_file')\n\n # if script_file input is the wrong format\n else:\n\n # raise Exception\n raise Exception(\"\"\"\n script_file should be a path and filename to your python api\n script, that you want to deploy.\n e.g. /your_folder/your_script.py\n \n \"\"\")\n\n # check requirements_file input\n if isinstance(requirements_file, str):\n\n # try to read in the first line\n try:\n\n # read in file\n with open(requirements_file) as file:\n first_line = file.readline()\n\n # print out first line\n info_message = \"\"\"\\\n This is the first line of your requirements file:\n -------------------------\n {first_line}\n -------------------------\\\n \"\"\".format(first_line = first_line)\n\n # print info message\n print (info_message)\n\n # handle exception\n except:\n\n # handle exception\n raise Exception('I could not find the script_file')\n\n # if script_file input is the wrong format\n else:\n\n # raise Exception\n raise Exception(\"\"\"\n script_file should be a path and filename to your python api\n script, that you want to deploy.\n e.g. /your_folder/your_requirements.txt\n \n \"\"\")\n\n # check port input\n if isinstance(port, str):\n\n # build info message\n info_message = \"\"\"\n ___________________________________________\n | API will be deployed on {port} |\n -------------------------------------------\n \n \"\"\".format(port = port)\n\n # print info about graphical interface\n print (info_message)\n\n # else if port input is the wrong fromat\n else:\n\n # raise Exception\n raise Exception('port should be just a string such as \"8000\"')\n\n # build Dockerfile\n built_dk = self.__build_dockerfile(script_file = script_file,\n requirements_file = requirements_file,\n port = port)\n\n # check if it worked\n if built_dk:\n\n # build info message\n info_message = \"\"\"\n ___________________________________________\n | Successfully built Dockerfile |\n -------------------------------------------\n \"\"\"\n\n # print info message\n print (info_message)\n\n # break the function if it didn't work\n else:\n\n # raise Exception\n raise Exception('I could not built a Dockerfile')\n \n # built docker image\n built_di = self.__build_image()\n\n # check if it worked\n if built_di:\n\n # build info message\n info_message = \"\"\"\n ___________________________________________\n | Successfully built Docker image |\n -------------------------------------------\n \"\"\"\n\n # print info message\n print (info_message)\n\n # break the function if it didn't work\n else:\n\n # raise Exception\n raise Exception('I could not built a Docker image')\n\n # check if deployment already exists\n exists_dp = self.__check_deployment(deployment_name = deployment_name)\n\n # delete if exists\n if exists_dp:\n\n # delete deployment\n self.delete_object(deployment = deployment_name)\n\n # create deployment\n created_dp = self.__create_deployment(deployment_name = deployment_name)\n\n # check if it worked\n if created_dp:\n\n # build info message\n info_message = \"\"\"\n ___________________________________________\n | Successfully created deployment |\n -------------------------------------------\n \"\"\"\n\n # print info message\n print (info_message)\n\n # break the function if it didn't work\n else:\n\n # raise Exception\n raise Exception('I could not create a deployment')\n\n # check if service already exists\n exists_sv = self.__check_service(deployment_name = deployment_name)\n\n # delete if exists\n if exists_sv:\n\n # delete service\n self.delete_object(service = deployment_name)\n\n # expose service\n exposed_sv = self.__expose_service(port, deployment_name = deployment_name)\n\n # check if it worked\n if exposed_sv:\n\n # build info message\n info_message = \"\"\"\n ___________________________________________\n | Successfully expose |\n -------------------------------------------\n \"\"\"\n\n # print info message\n print (info_message)\n\n # break the function if it didn't work\n else:\n\n # raise Exception\n raise Exception('I could not expose the service')\n\n # get the service url\n url_exposed = self.__get_url(deployment_name = deployment_name)\n\n # check if it worked\n if url_exposed:\n\n # info message\n info_message = \"\"\"\n ____________________________________________________________\n | Your deployment is ready and you can access the API via: |\n | {url} |\n ------------------------------------------------------------\n \"\"\".format(url = self.service_url)\n\n # print message\n print (info_message)\n\n # if it didn't work\n else:\n\n # raise Exception\n raise Exception('I could not get the url of the service')","title":""},{"docid":"5e9204945c3b4803145bcda3a6b557f6","score":"0.48196542","text":"def canarize_deployment(args, input_yaml, labels={}):\n\n # append the -canary to the Deployment name\n output_yaml = copy.deepcopy(input_yaml)\n canary_deployment_name = input_yaml[\"metadata\"][\"name\"] + args.suffix\n output_yaml[\"metadata\"][\"name\"] = canary_deployment_name\n\n print(f\"# Creating canary Deployment {canary_deployment_name}\")\n\n # append the -canary to all the labels in the selector\n try:\n for (k, v) in input_yaml[\"spec\"][\"selector\"][\"matchLabels\"].items():\n output_yaml[\"spec\"][\"selector\"][\"matchLabels\"][k] = v + \\\n args.suffix\n except IndexError:\n pass\n\n for (k, v) in input_yaml[\"spec\"][\"template\"][\"metadata\"][\"labels\"].items():\n output_yaml[\"spec\"][\"template\"][\"metadata\"][\"labels\"][k] = v + args.suffix\n\n if args.image:\n for container in output_yaml[\"spec\"][\"template\"][\"spec\"][\"containers\"]:\n if image_except_tag(container[\"image\"]) == image_except_tag(args.image):\n print(f\"# Replacing Deployment image {args.image}\")\n container[\"image\"] = args.image\n\n if args.namespace:\n output_yaml[\"metadata\"][\"namespace\"] = args.namespace\n\n if len(labels) > 0:\n if len(output_yaml[\"metadata\"][\"labels\"]) > 0:\n output_yaml[\"metadata\"][\"labels\"].update(labels)\n else:\n output_yaml[\"metadata\"][\"labels\"] = labels\n\n return [output_yaml]","title":""},{"docid":"b51d3524ad70eb10bab0b31bfa17cbd9","score":"0.47847378","text":"def create_cloud_domain_migration_request(self):\n try:\n self.logger.info('create_cloud_domain_migration_request called.')\n\n # Prepare query URL\n self.logger.info('Preparing query URL for create_cloud_domain_migration_request.')\n _url_path = '/public/remoteVaults/cloudDomainMigration'\n _query_builder = self.config.get_base_uri()\n _query_builder += _url_path\n _query_url = APIHelper.clean_url(_query_builder)\n\n # Prepare headers\n self.logger.info('Preparing headers for create_cloud_domain_migration_request.')\n _headers = {\n 'accept': 'application/json',\n 'content-type': 'application/json; charset=utf-8'\n }\n\n # Prepare and execute request\n self.logger.info(\n 'Preparing and executing request for create_cloud_domain_migration_request.')\n _request = self.http_client.post(\n _query_url,\n headers=_headers)\n AuthManager.apply(_request, self.config)\n _context = self.execute_request(_request, name='create_cloud_domain_migration_request')\n\n # Endpoint and global error handling using HTTP status codes.\n self.logger.info('Validating response for create_cloud_domain_migration_request.')\n if _context.response.status_code == 0:\n raise RequestErrorErrorException('Error', _context)\n self.validate_response(_context)\n\n except Exception as e:\n self.logger.error(e, exc_info=True)\n raise","title":""},{"docid":"899b85253d31cf4dbd988aaaaa19a07a","score":"0.477397","text":"def predict_from_dataset(self, dataset: Dataset) -> DeploymentPrediction:\n if self.training_type not in ['regression', 'classification', 'multiclassification']:\n PrevisionException('Prediction not supported yet for training type {}', self.training_type)\n data = {\n 'dataset_id': dataset._id,\n }\n\n predict_start = client.request('/deployments/{}/deployment-predictions'.format(self._id),\n method=requests.post,\n data=data,\n message_prefix='Bulk predict')\n predict_start_parsed = parse_json(predict_start)\n return DeploymentPrediction.from_id(predict_start_parsed['_id'])","title":""},{"docid":"ed979a745afa07b8d2f1edbc2f9339a4","score":"0.4745488","text":"def loadDeployment(deploymentData):\n #check that the deploymentData is valid\n if not (\"deploymentId\" in deploymentData and type(deploymentData[\"deploymentId\"])==str and len(deploymentData[\"deploymentId\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'deploymentId' (type=str and length>0)\")\n if not (\"name\" in deploymentData and type(deploymentData[\"name\"])==str and len(deploymentData[\"name\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'name' (type=str and length>0)\")\n if not (\"status\" in deploymentData and type(deploymentData[\"status\"])==str and len(deploymentData[\"status\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'status' (type=str and length>0)\")\n if not (\"dateCreated\" in deploymentData and type(deploymentData[\"dateCreated\"])==str and len(deploymentData[\"dateCreated\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'dateCreated' (type=str and length>0)\")\n if not (\"dateModified\" in deploymentData and type(deploymentData[\"dateModified\"])==str and len(deploymentData[\"dateModified\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'dateCreated' (type=str and length>0)\")\n if not (\"archived\" in deploymentData and type(deploymentData[\"archived\"])==bool):\n raise errors.BadRequestError(\"Deployment must have attribute 'archived' (type=bool)\")\n if not (\"goalSampleSize\" in deploymentData and type(deploymentData[\"goalSampleSize\"]) in [int, Decimal] and deploymentData[\"goalSampleSize\"]>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'goalSampleSize' (type=int and value>0)\")\n if not (\"currentSampleSize\" in deploymentData and type(deploymentData[\"currentSampleSize\"]) in [int, Decimal] and deploymentData[\"currentSampleSize\"]>=0):\n raise errors.BadRequestError(\"Deployment must have attribute 'currentSampleSize' (type=int and value>=0)\")\n if not (\"facility\" in deploymentData and type(deploymentData[\"facility\"]) in [str, dict] and len(deploymentData[\"facility\"])>0):\n raise errors.BadRequestError(\"Deployment must have attribute 'facility' (type=str or dict and length>0)\")\n #construct the deployment\n d = Deployment()\n d.deploymentId = deploymentData[\"deploymentId\"]\n d.name = deploymentData[\"name\"]\n if \"description\" in deploymentData:\n d.description = deploymentData[\"description\"]\n d.status = deploymentData[\"status\"]\n d.dateCreated = deploymentData[\"dateCreated\"]\n d.dateModified = deploymentData[\"dateModified\"]\n d.archived = deploymentData[\"archived\"]\n d.goalSampleSize = int(deploymentData[\"goalSampleSize\"])\n d.currentSampleSize = int(deploymentData[\"currentSampleSize\"])\n d.facility = facility_db_access.loadFacility(deploymentData[\"facility\"])\n return d","title":""},{"docid":"bc91a32b8ed3b0cc5090e17056b5043a","score":"0.47425556","text":"def _start_job(self, start_job_deployment: dict) -> None:\n # Standardize start job deployment.\n job_details = K8sExecutor._standardize_job_details(start_job_deployment=start_job_deployment)\n\n # Save details\n K8sDetailsWriter.save_job_details(job_details=job_details)\n\n # Create and apply k8s config\n k8s_job = self._create_k8s_job(job_details=job_details)\n client.BatchV1Api().create_namespaced_job(body=k8s_job, namespace=\"default\")","title":""},{"docid":"d384e9e873acaef107132e294f7f9d6a","score":"0.47337088","text":"def create(definition: dict, version: str, parameter: tuple,\n region: str,\n disable_rollback: bool,\n dry_run: bool,\n force: bool,\n tag: List[str],\n timeout: int,\n keep_stacks: Optional[int],\n traffic: int,\n verbose: bool,\n remote: str,\n parameter_file: Optional[str]\n ):\n lizzy = setup_lizzy_client(remote)\n parameter = list(parameter) or []\n if parameter_file:\n parameter.extend(read_parameter_file(parameter_file))\n\n if not force: # pragma: no cover\n # supporting artifact checking would imply copying a large amount of code\n # from senza, so it should be considered out of scope until senza\n # and lizzy client are merged\n warning(\"WARNING: \"\n \"Artifact checking is still not supported by lizzy-client.\")\n\n with Action('Requesting new stack..') as action:\n new_stack, output = lizzy.new_stack(keep_stacks, traffic,\n definition, version,\n disable_rollback, parameter,\n region=region,\n dry_run=dry_run,\n tags=tag)\n\n stack_id = '{stack_name}-{version}'.format_map(new_stack)\n print(output)\n\n info('Stack ID: {}'.format(stack_id))\n\n if dry_run:\n info(\"Post deployment steps skipped\")\n exit(0)\n\n with Action('Waiting for new stack...') as action:\n if verbose:\n print() # ensure that new states will not be printed on the same line as the action\n\n last_state = None\n for state in lizzy.wait_for_deployment(stack_id, region=region):\n if state != last_state and verbose:\n click.echo(' {}'.format(state))\n else:\n action.progress()\n last_state = state\n\n # TODO be prepared to handle all final AWS CF states\n if last_state == 'ROLLBACK_COMPLETE':\n fatal_error(\n 'Stack was rollback after deployment. Check your application log for possible reasons.')\n elif last_state != 'CREATE_COMPLETE':\n fatal_error('Deployment failed: {}'.format(last_state))\n\n info('Deployment Successful')\n\n if traffic is not None:\n with Action('Requesting traffic change..'):\n try:\n lizzy.traffic(stack_id, traffic, region=region)\n except requests.ConnectionError as e:\n connection_error(e, fatal=False)\n except requests.HTTPError as e:\n agent_error(e, fatal=False)\n\n # TODO unit test this\n if keep_stacks is not None:\n versions_to_keep = keep_stacks + 1\n stacks_to_remove_counter = 1\n end_time = datetime.datetime.utcnow() + datetime.timedelta(seconds=timeout)\n while stacks_to_remove_counter > 0 and datetime.datetime.utcnow() <= end_time:\n try:\n all_stacks = lizzy.get_stacks([new_stack['stack_name']],\n region=region)\n except requests.ConnectionError as e:\n connection_error(e, fatal=False)\n error(\"Failed to fetch old stacks. \"\n \"Old stacks WILL NOT BE DELETED\")\n exit(1)\n except requests.HTTPError as e:\n agent_error(e, fatal=False)\n error(\"Failed to fetch old stacks. \"\n \"Old stacks WILL NOT BE DELETED\")\n exit(1)\n else:\n sorted_stacks = sorted(all_stacks,\n key=lambda stack: stack['creation_time'])\n stacks_to_remove = sorted_stacks[:-versions_to_keep]\n stacks_to_remove_counter = len(stacks_to_remove)\n with Action('Deleting old stacks..'):\n print()\n for old_stack in stacks_to_remove:\n old_stack_id = '{stack_name}-{version}'.format_map(\n old_stack)\n if old_stack['status'] in COMPLETE_STATES:\n click.echo(' {}'.format(old_stack_id))\n try:\n lizzy.delete(old_stack_id, region=region)\n stacks_to_remove_counter -= 1\n except requests.ConnectionError as e:\n connection_error(e, fatal=False)\n except requests.HTTPError as e:\n agent_error(e, fatal=False)\n else:\n click.echo(' > {} current status is {} trying '\n 'again later'.format(old_stack_id,\n old_stack['status']))\n if stacks_to_remove_counter > 0:\n time.sleep(5)\n\n if datetime.datetime.utcnow() > end_time:\n click.echo('Timeout waiting for related stacks to be ready.')","title":""},{"docid":"3a412099e06317345559f8858120bad2","score":"0.47257835","text":"def show(self, uuid, **kwargs):\n resp, result = self.client.get(\"./deployments/%s\" % uuid, **kwargs)\n return base.Deployment(result)","title":""},{"docid":"ab4de1e2c8f6f1ad5b1ef8545531aa1c","score":"0.4714727","text":"def generate_namespaced_deployment_config(self, name, namespace, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.generate_namespaced_deployment_config_with_http_info(name, namespace, **kwargs)\n else:\n (data) = self.generate_namespaced_deployment_config_with_http_info(name, namespace, **kwargs)\n return data","title":""},{"docid":"666873cc8dffae880e12e718f658f36c","score":"0.47108465","text":"async def create_deployment(\n deployment: schemas.actions.DeploymentCreate,\n response: Response,\n worker_lookups: WorkerLookups = Depends(WorkerLookups),\n db: PrefectDBInterface = Depends(provide_database_interface),\n) -> schemas.responses.DeploymentResponse:\n\n async with db.session_context(begin_transaction=True) as session:\n if (\n deployment.work_pool_name\n and deployment.work_pool_name != DEFAULT_AGENT_WORK_POOL_NAME\n ):\n # Make sure that deployment is valid before beginning creation process\n work_pool = await models.workers.read_work_pool_by_name(\n session=session, work_pool_name=deployment.work_pool_name\n )\n if work_pool is None:\n raise HTTPException(\n status_code=status.HTTP_404_NOT_FOUND,\n detail=f'Work pool \"{deployment.work_pool_name}\" not found.',\n )\n try:\n deployment.check_valid_configuration(work_pool.base_job_template)\n except (MissingVariableError, jsonschema.exceptions.ValidationError) as exc:\n raise HTTPException(\n status_code=status.HTTP_409_CONFLICT,\n detail=f\"Error creating deployment: {exc!r}\",\n )\n\n # hydrate the input model into a full model\n deployment_dict = deployment.dict(exclude={\"work_pool_name\"})\n if deployment.work_pool_name and deployment.work_queue_name:\n # If a specific pool name/queue name combination was provided, get the\n # ID for that work pool queue.\n deployment_dict[\"work_queue_id\"] = (\n await worker_lookups._get_work_queue_id_from_name(\n session=session,\n work_pool_name=deployment.work_pool_name,\n work_queue_name=deployment.work_queue_name,\n create_queue_if_not_found=True,\n )\n )\n elif deployment.work_pool_name:\n # If just a pool name was provided, get the ID for its default\n # work pool queue.\n deployment_dict[\"work_queue_id\"] = (\n await worker_lookups._get_default_work_queue_id_from_work_pool_name(\n session=session,\n work_pool_name=deployment.work_pool_name,\n )\n )\n elif deployment.work_queue_name:\n # If just a queue name was provided, ensure that the queue exists and\n # get its ID.\n work_queue = await models.work_queues._ensure_work_queue_exists(\n session=session, name=deployment.work_queue_name\n )\n deployment_dict[\"work_queue_id\"] = work_queue.id\n\n deployment = schemas.core.Deployment(**deployment_dict)\n # check to see if relevant blocks exist, allowing us throw a useful error message\n # for debugging\n if deployment.infrastructure_document_id is not None:\n infrastructure_block = (\n await models.block_documents.read_block_document_by_id(\n session=session,\n block_document_id=deployment.infrastructure_document_id,\n )\n )\n if not infrastructure_block:\n raise HTTPException(\n status_code=status.HTTP_409_CONFLICT,\n detail=(\n \"Error creating deployment. Could not find infrastructure\"\n f\" block with id: {deployment.infrastructure_document_id}. This\"\n \" usually occurs when applying a deployment specification that\"\n \" was built against a different Prefect database / workspace.\"\n ),\n )\n\n if deployment.storage_document_id is not None:\n infrastructure_block = (\n await models.block_documents.read_block_document_by_id(\n session=session,\n block_document_id=deployment.storage_document_id,\n )\n )\n if not infrastructure_block:\n raise HTTPException(\n status_code=status.HTTP_409_CONFLICT,\n detail=(\n \"Error creating deployment. Could not find storage block with\"\n f\" id: {deployment.storage_document_id}. This usually occurs\"\n \" when applying a deployment specification that was built\"\n \" against a different Prefect database / workspace.\"\n ),\n )\n\n now = pendulum.now(\"UTC\")\n model = await models.deployments.create_deployment(\n session=session, deployment=deployment\n )\n\n if model.created >= now:\n response.status_code = status.HTTP_201_CREATED\n\n return schemas.responses.DeploymentResponse.from_orm(model)","title":""},{"docid":"ed72b0ce912d483f2a13f476b19129fb","score":"0.46907023","text":"def create_task(deploy_uuid, tag):\n return objects.Task(deployment_uuid=deploy_uuid, tag=tag)","title":""},{"docid":"7d5f3848fff4506962d52fbcf8521860","score":"0.46852064","text":"def deploy_issue_model(\n self,\n request: Optional[\n Union[contact_center_insights.DeployIssueModelRequest, dict]\n ] = None,\n *,\n name: Optional[str] = None,\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> operation.Operation:\n # Create or coerce a protobuf request object.\n # Quick check: If we got a request object, we should *not* have\n # gotten any keyword arguments that map to the request.\n has_flattened_params = any([name])\n if request is not None and has_flattened_params:\n raise ValueError(\n \"If the `request` argument is set, then none of \"\n \"the individual field arguments should be set.\"\n )\n\n # Minor optimization to avoid making a copy if the user passes\n # in a contact_center_insights.DeployIssueModelRequest.\n # There's no risk of modifying the input as we've already verified\n # there are no flattened fields.\n if not isinstance(request, contact_center_insights.DeployIssueModelRequest):\n request = contact_center_insights.DeployIssueModelRequest(request)\n # If we have keyword arguments corresponding to fields on the\n # request, apply these.\n if name is not None:\n request.name = name\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = self._transport._wrapped_methods[self._transport.deploy_issue_model]\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata(((\"name\", request.name),)),\n )\n\n # Send the request.\n response = rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Wrap the response in an operation future.\n response = operation.from_gapic(\n response,\n self._transport.operations_client,\n contact_center_insights.DeployIssueModelResponse,\n metadata_type=contact_center_insights.DeployIssueModelMetadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"83b634c97e7afe7f1c7783396b29643e","score":"0.46760336","text":"def Create(self, request, global_params=None):\n config = self.GetMethodConfig('Create')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""},{"docid":"11dc854e2eedb67d370d96c9ade296dc","score":"0.46733534","text":"def _new(\n cls,\n project_id: str,\n name: str,\n main_model: Model,\n challenger_model: Model = None,\n type_violation_policy: str = 'best_effort',\n access_type: str = None,\n ) -> 'BaseExperimentDeployment':\n\n if access_type and access_type not in ['public', 'fine_grained', 'private']:\n raise PrevisionException('access type must be public, fine_grained or private')\n if type_violation_policy not in ['best_effort', 'strict']:\n raise PrevisionException('type_violation_policy must be best_effort or strict')\n main_model_experiment_version_id = main_model.experiment_version_id\n main_experiment = BaseExperimentVersion._from_id(main_model_experiment_version_id)\n main_experiment_id = main_experiment['experiment_id']\n data = {\n 'name': name,\n 'experiment_id': main_experiment_id,\n 'main_model_experiment_version_id': main_model_experiment_version_id,\n 'main_model_id': main_model._id,\n 'type_violation_policy': type_violation_policy\n }\n if access_type:\n data['access_type'] = access_type\n if challenger_model:\n challenger_model_experiment_version_id = challenger_model.experiment_version_id\n challenger_experiment = BaseExperimentVersion._from_id(main_model_experiment_version_id)\n challenger_experiment_id = challenger_experiment['experiment_id']\n if main_experiment_id != challenger_experiment_id:\n raise PrevisionException('main and challenger models must be from the same experiment')\n data['challenger_model_experiment_version_id'] = challenger_model_experiment_version_id\n data['challenger_model_id'] = challenger_model._id\n\n url = '/projects/{}/model-deployments'.format(project_id)\n resp = client.request(url,\n data=data,\n method=requests.post,\n message_prefix='ExperimentDeployment creation')\n json_resp = parse_json(resp)\n experiment_deployment = cls.from_id(json_resp['_id'])\n return experiment_deployment","title":""},{"docid":"95981a399dc790e922d46f3ca77ac602","score":"0.4671982","text":"def duplicate_deployment(name, namespace=\"default\", extv1Client=None):\n if extv1Client is None :\n # load the kubernetes config\n load_k8s_config()\n #create the api client\n extv1Client=client.ExtensionsV1beta1Api()\n\n log.debug(\"looking for deployment '%s' in namespace '%s'...\"%(name, namespace))\n dep = None\n try:\n dep = get_namespaced_deployment(name = name, namespace = namespace, extv1Client=extv1Client)\n except K8sDeploymentNotFoundError as e:\n log.error(\"Deployment '%s' not found in namespace '%s'...\"%(name, namespace))\n raise e\n log.debug(\"Deployment '%s' found.\"%(dep.metadata.name))\n timestamp = getSimpleTimestamp()\n dep_copy = duplicate_deployment_config(dep, timestamp)\n log.debug(\"Creating a copy of '%s' with new name '%s' in namespace '%s'\"\n %(dep.metadata.name, dep_copy.metadata.name, namespace))\n deployment = create_namespaced_deployment(body=dep_copy, namespace=namespace, extv1Client=extv1Client)\n log.debug(\"Copy of deployment '%s' created: %s\" \n %(dep.metadata.name, deployment.metadata.name))\n return deployment","title":""},{"docid":"2b75ca7b47d8f3fea14faeade32d7603","score":"0.46586993","text":"def init(\n cls,\n project: _ProjectTypeVar,\n usage_type: Literal[\"function\", \"layer\"] = \"function\",\n ) -> DeploymentPackage[_ProjectTypeVar]:\n s3_obj = DeploymentPackageS3Object(project, usage_type)\n if s3_obj.exists:\n if s3_obj.runtime == project.runtime:\n return s3_obj\n LOGGER.warning(\n \"runtime of deployment package found in S3 (%s) does not match \"\n \"requirement (%s); deleting & recreating...\",\n s3_obj.runtime,\n project.runtime,\n )\n s3_obj.delete()\n return cls(project, usage_type)","title":""},{"docid":"437d30909e0b187e3cbae28a5ea0cfb2","score":"0.46529797","text":"def CreateAdaptiveDynamicStreamingTemplate(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"CreateAdaptiveDynamicStreamingTemplate\", params, headers=headers)\n response = json.loads(body)\n model = models.CreateAdaptiveDynamicStreamingTemplateResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""},{"docid":"8bd4749af369d8ea7d583d9ed666cd3e","score":"0.46441373","text":"def CreateTagRetentionExecution(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"CreateTagRetentionExecution\", params, headers=headers)\n response = json.loads(body)\n model = models.CreateTagRetentionExecutionResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""},{"docid":"3558862f8558b92ded636782545e3fd9","score":"0.4620099","text":"async def get_game_server_deployment(self,\n request: game_server_deployments.GetGameServerDeploymentRequest = None,\n *,\n name: str = None,\n retry: retries.Retry = gapic_v1.method.DEFAULT,\n timeout: float = None,\n metadata: Sequence[Tuple[str, str]] = (),\n ) -> game_server_deployments.GameServerDeployment:\n # Create or coerce a protobuf request object.\n # Sanity check: If we got a request object, we should *not* have\n # gotten any keyword arguments that map to the request.\n has_flattened_params = any([name])\n if request is not None and has_flattened_params:\n raise ValueError(\"If the `request` argument is set, then none of \"\n \"the individual field arguments should be set.\")\n\n request = game_server_deployments.GetGameServerDeploymentRequest(request)\n\n # If we have keyword arguments corresponding to fields on the\n # request, apply these.\n if name is not None:\n request.name = name\n\n # Wrap the RPC method; this adds retry and timeout information,\n # and friendly error handling.\n rpc = gapic_v1.method_async.wrap_method(\n self._client._transport.get_game_server_deployment,\n default_retry=retries.Retry(\ninitial=1.0,maximum=10.0,multiplier=1.3, predicate=retries.if_exception_type(\n core_exceptions.ServiceUnavailable,\n ),\n deadline=60.0,\n ),\n default_timeout=60.0,\n client_info=DEFAULT_CLIENT_INFO,\n )\n\n # Certain fields should be provided within the metadata header;\n # add these here.\n metadata = tuple(metadata) + (\n gapic_v1.routing_header.to_grpc_metadata((\n (\"name\", request.name),\n )),\n )\n\n # Send the request.\n response = await rpc(\n request,\n retry=retry,\n timeout=timeout,\n metadata=metadata,\n )\n\n # Done; return the response.\n return response","title":""},{"docid":"3aa5e1369a6ccd42a79f1931f4668617","score":"0.45987225","text":"def deploy(deployment_name: str, environment: str = 'production'):\n alter_path()\n asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\n\n tasks_module = importlib.import_module(\n f\"deployments.{deployment_name}.tasks\"\n )\n hosts_module = importlib.import_module(\n f\"deployments.{deployment_name}.hosts\"\n )\n\n host_registry: HostRegistry = getattr(hosts_module, \"host_registry\", None)\n if not host_registry:\n raise Exception(\"Can't find 'host_registry' in hosts file.\")\n\n hosts = host_registry.host_class_map.get(environment)\n\n if not hosts:\n print(colored(f\"No hosts defined for {environment}!\", \"red\"))\n sys.exit(1)\n else:\n print(\n colored(f\"Deploying {deployment_name} to {environment}\", \"green\")\n )\n\n for host in hosts:\n host.environment = environment\n\n task_registry: TaskRegistry = getattr(tasks_module, \"task_registry\", None)\n if not task_registry:\n raise Exception(\"Can't find 'task_registry' in tasks file.\")\n\n asyncio.run(\n host_registry.run_tasks(\n tasks=task_registry.task_classes, environment=environment\n )\n )","title":""},{"docid":"2fea32326651bb911a0d95b1bd83a9e6","score":"0.4576947","text":"def post(self):\n sm = get_storage_manager()\n\n params = rest_utils.get_json_and_verify_params({\n 'source_deployment_id': {'type': str},\n 'inter_deployment_dependencies': {'type': list}\n })\n\n dependencies = params.get('inter_deployment_dependencies')\n\n if any(item.get('created_by') for item in dependencies):\n check_user_action_allowed('set_owner')\n if any(item.get('created_at') for item in dependencies):\n check_user_action_allowed('set_timestamp')\n\n if len(dependencies) > 0 and EXTERNAL_SOURCE in dependencies[0]:\n source_deployment = None\n else:\n source_deployment = sm.get(models.Deployment,\n params['source_deployment_id'])\n\n created_ids = []\n with sm.transaction():\n for dependency in dependencies:\n record = _create_inter_deployment_dependency(\n source_deployment, dependency, sm)\n created_ids += [record.id]\n\n return ListResponse(\n items=[{'id': i} for i in created_ids],\n metadata={'pagination': {\n 'total': len(created_ids),\n 'size': len(created_ids),\n 'offset': 0,\n }}\n )","title":""},{"docid":"05939f00c583a6b07bf49c7be9538f76","score":"0.45655584","text":"def create(self, model_id: str, revision: str, instance_name: str,\n resource: ServiceResourceConfig,\n provider: ServiceProviderParameters):\n if provider.vendor != Vendor.EAS:\n raise NotSupportError(\n 'Not support vendor: %s ,only support EAS current.' %\n (provider.vendor))\n create_params = DeployServiceParameters(\n instance_name=instance_name,\n model_id=model_id,\n revision=revision,\n resource=resource,\n provider=provider)\n path = f'{self.endpoint}/api/v1/deployer/endpoint'\n body = asdict(create_params)\n r = requests.post(\n path, json=body, cookies=self.cookies, headers=self.headers)\n handle_http_response(r, logger, self.cookies, 'create_service')\n if r.status_code >= HTTPStatus.OK and r.status_code < HTTPStatus.MULTIPLE_CHOICES:\n if is_ok(r.json()):\n data = r.json()[API_RESPONSE_FIELD_DATA]\n return data\n else:\n raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])\n else:\n raise_for_http_status(r)\n return None","title":""},{"docid":"f2e889d7f8f9cbfd8c394b2275953a07","score":"0.45585397","text":"def k8s_custom_deploy(name: str,\n apply_cmd: Union[str, List[str]],\n delete_cmd: Union[str, List[str]],\n deps: Union[str, List[str]],\n image_selector: str=\"\",\n live_update: List[LiveUpdateStep]=[],\n apply_dir: str=\"\",\n apply_env: Dict[str, str]={},\n apply_cmd_bat: Union[str, List[str]]=\"\",\n delete_dir: str=\"\",\n delete_env: Dict[str, str]={},\n delete_cmd_bat: Union[str, List[str]]=\"\",\n container_selector: str=\"\",\n image_deps: List[str]=[]) -> None:\n pass","title":""},{"docid":"8cb801659557df9b1070b30fb03b998a","score":"0.45562708","text":"def create(cls, argv):\n request = cls(argv, dict());\n return request;","title":""},{"docid":"0b10092a7ac1f7fd92a757f2f807613b","score":"0.45510134","text":"def make_object(self, data):\n return DeploymentMetric(**data)","title":""},{"docid":"0b10092a7ac1f7fd92a757f2f807613b","score":"0.45510134","text":"def make_object(self, data):\n return DeploymentMetric(**data)","title":""},{"docid":"0b10092a7ac1f7fd92a757f2f807613b","score":"0.45510134","text":"def make_object(self, data):\n return DeploymentMetric(**data)","title":""},{"docid":"6014691e9c23047186d1a2c401f420e5","score":"0.4536346","text":"def deployment(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"deployment\")","title":""},{"docid":"0a19a6fbd4c78e8218916c2c7877ce93","score":"0.45356768","text":"def create_namespaced_build_request_instantiate_with_http_info(self, body, name, namespace, **kwargs):\n\n all_params = ['body', 'name', 'namespace', 'pretty']\n all_params.append('callback')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method create_namespaced_build_request_instantiate\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'body' is set\n if ('body' not in params) or (params['body'] is None):\n raise ValueError(\"Missing the required parameter `body` when calling `create_namespaced_build_request_instantiate`\")\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `create_namespaced_build_request_instantiate`\")\n # verify the required parameter 'namespace' is set\n if ('namespace' not in params) or (params['namespace'] is None):\n raise ValueError(\"Missing the required parameter `namespace` when calling `create_namespaced_build_request_instantiate`\")\n\n\n collection_formats = {}\n\n resource_path = '/oapi/v1/namespaces/{namespace}/buildconfigs/{name}/instantiate'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n if 'namespace' in params:\n path_params['namespace'] = params['namespace']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf'])\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = ['BearerToken']\n\n return self.api_client.call_api(resource_path, 'POST',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='V1BuildRequest',\n auth_settings=auth_settings,\n callback=params.get('callback'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)","title":""},{"docid":"c9a72813396cb562f853625569649da9","score":"0.45355946","text":"def post(self, name, namespace):\n pod_manifest = request.get_json()\n CRUDService. \\\n get_instance(). \\\n create_pod(name, namespace, pod_manifest)\n return KubeApiResponseDto('Resource created.'), \\\n HttpStatusCode.Accepted.value","title":""},{"docid":"690c624fece2f5c9ad89f659e25e4744","score":"0.4534196","text":"def start_job(self, deployment_path: str) -> None:\n # Load start_job_deployment.\n with open(deployment_path, \"r\") as fr:\n start_job_deployment = yaml.safe_load(fr)\n\n # Start job\n self._start_job(start_job_deployment=start_job_deployment)","title":""},{"docid":"dfc7dc0a3be4d20a0344fdc02429f811","score":"0.45341516","text":"def __init__(self, deployment_input_field_1=None, deployment_input_field_2=None, local_vars_configuration=None): # noqa: E501 # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration()\n self.local_vars_configuration = local_vars_configuration\n\n self._deployment_input_field_1 = None\n self._deployment_input_field_2 = None\n self.discriminator = None\n\n if deployment_input_field_1 is not None:\n self.deployment_input_field_1 = deployment_input_field_1\n if deployment_input_field_2 is not None:\n self.deployment_input_field_2 = deployment_input_field_2","title":""},{"docid":"b3a7ff76f18e399c9d9d1b989be70475","score":"0.45331883","text":"def create_or_resume(name, spec, **_):\r\n\r\n # deploy mysql for barbican\r\n utils.ensure_mysql_cluster(\"barbican\", spec[\"mysql\"])\r\n\r\n # deploy barbican api\r\n utils.create_or_update('barbican/daemonset.yml.j2',\r\n name=name, spec=spec)\r\n utils.create_or_update('barbican/service.yml.j2',\r\n name=name, spec=spec)\r\n\r\n url = None\r\n if \"ingress\" in spec:\r\n utils.create_or_update('barbican/ingress.yml.j2',\r\n name=name, spec=spec)\r\n url = spec[\"ingress\"][\"host\"]\r\n identity.ensure_service(name=\"barbican\", service_type=\"key-manager\",\r\n url=url, desc=\"Barbican Service\")","title":""},{"docid":"7617fac347be42843b3cab0d09f08ce2","score":"0.4518388","text":"def make_object(self, data):\n return DeploymentLog(**data)","title":""},{"docid":"7617fac347be42843b3cab0d09f08ce2","score":"0.4518388","text":"def make_object(self, data):\n return DeploymentLog(**data)","title":""},{"docid":"7617fac347be42843b3cab0d09f08ce2","score":"0.4518388","text":"def make_object(self, data):\n return DeploymentLog(**data)","title":""},{"docid":"2a142d94837cf76c464d08b802653e98","score":"0.4511214","text":"def CreateTemplate(self, tag, bucket, task_dir):\n image_url = self._COMPUTE_API_ROOT + \\\n 'ubuntu-os-cloud/global/images/ubuntu-1404-trusty-v20160406'\n request_body = {\n 'name': self._GetTemplateName(tag),\n 'properties': {\n 'machineType': 'n1-standard-1',\n 'networkInterfaces': [{\n 'network': self._project_api_url + '/global/networks/default',\n 'accessConfigs': [{\n 'name': 'external-IP',\n 'type': 'ONE_TO_ONE_NAT'\n }]}],\n 'disks': [{\n 'type': 'PERSISTENT',\n 'boot': True,\n 'autoDelete': True,\n 'mode': 'READ_WRITE',\n 'initializeParams': {'sourceImage': image_url}}],\n 'canIpForward': False,\n 'scheduling': {\n 'automaticRestart': True,\n 'onHostMaintenance': 'MIGRATE',\n 'preemptible': False},\n 'serviceAccounts': [{\n 'scopes': [\n 'https://www.googleapis.com/auth/cloud-platform',\n 'https://www.googleapis.com/auth/cloud-taskqueue'],\n 'email': 'default'}],\n 'metadata': { 'items': [\n {'key': 'cloud-storage-path',\n 'value': bucket},\n {'key': 'task-dir',\n 'value': task_dir},\n {'key': 'startup-script-url',\n 'value': 'gs://%s/deployment/startup-script.sh' % bucket},\n {'key': 'taskqueue-tag', 'value': tag}]}}}\n request = self._compute_api.instanceTemplates().insert(\n project=self._project, body=request_body)\n return self._ExecuteApiRequest(request)[0]","title":""},{"docid":"3d2c19f9a8c3ff259c9f2cc7bed1be04","score":"0.4505982","text":"def CreateInstanceCustomizedDomain(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"CreateInstanceCustomizedDomain\", params, headers=headers)\n response = json.loads(body)\n model = models.CreateInstanceCustomizedDomainResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""},{"docid":"bad3f96d2cb7f93f0fca470786d862f6","score":"0.44951227","text":"def __init__(__self__, *,\n deployment_duration_in_minutes: pulumi.Input[int],\n growth_factor: pulumi.Input[float],\n replicate_to: pulumi.Input[str],\n description: Optional[pulumi.Input[str]] = None,\n final_bake_time_in_minutes: Optional[pulumi.Input[int]] = None,\n growth_type: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):\n pulumi.set(__self__, \"deployment_duration_in_minutes\", deployment_duration_in_minutes)\n pulumi.set(__self__, \"growth_factor\", growth_factor)\n pulumi.set(__self__, \"replicate_to\", replicate_to)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if final_bake_time_in_minutes is not None:\n pulumi.set(__self__, \"final_bake_time_in_minutes\", final_bake_time_in_minutes)\n if growth_type is not None:\n pulumi.set(__self__, \"growth_type\", growth_type)\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if tags is not None:\n pulumi.set(__self__, \"tags\", tags)","title":""}],"string":"[\n {\n \"docid\": \"c8faee09c9ac08cfa320b38d31c94c48\",\n \"score\": \"0.74152315\",\n \"text\": \"def create_namespaced_deployment_request_instantiate(self, body, name, namespace, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.create_namespaced_deployment_request_instantiate_with_http_info(body, name, namespace, **kwargs)\\n else:\\n (data) = self.create_namespaced_deployment_request_instantiate_with_http_info(body, name, namespace, **kwargs)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22cc9aacc27517e01158df0d46f927ff\",\n \"score\": \"0.6730303\",\n \"text\": \"def create_deployment(self, namespace, deployment):\\n api_instance = client.AppsV1Api()\\n return api_instance.create_namespaced_deployment(namespace, deployment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1b4570dc15c2b5106b6a1ea306d229\",\n \"score\": \"0.6238238\",\n \"text\": \"def make_object(self, data):\\n return Deployment(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1b4570dc15c2b5106b6a1ea306d229\",\n \"score\": \"0.6238238\",\n \"text\": \"def make_object(self, data):\\n return Deployment(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1b4570dc15c2b5106b6a1ea306d229\",\n \"score\": \"0.6238238\",\n \"text\": \"def make_object(self, data):\\n return Deployment(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae454be2bf98674ea749dc3fb2108cd\",\n \"score\": \"0.61750245\",\n \"text\": \"def __create_deployment(self, deployment_name):\\n\\n # try to create a new deployment\\n try:\\n\\n # create a new deployment\\n command = str('kubectl run ' + deployment_name + \\\" --image=kubipy-image:latest --image-pull-policy='Never'\\\")\\n os.system(command)\\n\\n # return True\\n return True\\n \\n # handle exception\\n except:\\n\\n # return False\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc547c9ca59763bf72028d4b3a478f1\",\n \"score\": \"0.5856244\",\n \"text\": \"def create(\\n cls,\\n deployment_def: Union[Callable, str],\\n init_args: Optional[Tuple[Any]] = None,\\n init_kwargs: Optional[Dict[Any, Any]] = None,\\n ray_actor_options: Optional[Dict] = None,\\n placement_group_bundles: Optional[List[Dict[str, float]]] = None,\\n placement_group_strategy: Optional[str] = None,\\n max_replicas_per_node: Optional[int] = None,\\n deployment_def_name: Optional[str] = None,\\n ):\\n\\n if not callable(deployment_def) and not isinstance(deployment_def, str):\\n raise TypeError(\\\"@serve.deployment must be called on a class or function.\\\")\\n\\n if not (init_args is None or isinstance(init_args, (tuple, list))):\\n raise TypeError(\\\"init_args must be a tuple.\\\")\\n\\n if not (init_kwargs is None or isinstance(init_kwargs, dict)):\\n raise TypeError(\\\"init_kwargs must be a dict.\\\")\\n\\n if inspect.isfunction(deployment_def):\\n if init_args:\\n raise ValueError(\\\"init_args not supported for function deployments.\\\")\\n elif init_kwargs:\\n raise ValueError(\\\"init_kwargs not supported for function deployments.\\\")\\n\\n if not isinstance(deployment_def, (Callable, str)):\\n raise TypeError(\\n f'Got invalid type \\\"{type(deployment_def)}\\\" for '\\n \\\"deployment_def. Expected deployment_def to be a \\\"\\n \\\"class, function, or string.\\\"\\n )\\n # Set defaults\\n if init_args is None:\\n init_args = ()\\n if init_kwargs is None:\\n init_kwargs = {}\\n if ray_actor_options is None:\\n ray_actor_options = {}\\n if deployment_def_name is None:\\n if isinstance(deployment_def, str):\\n deployment_def_name = deployment_def\\n else:\\n deployment_def_name = deployment_def.__name__\\n\\n config = cls(\\n deployment_def_name,\\n pickle_dumps(\\n deployment_def,\\n f\\\"Could not serialize the deployment {repr(deployment_def)}\\\",\\n ),\\n pickle_dumps(init_args, \\\"Could not serialize the deployment init args\\\"),\\n pickle_dumps(init_kwargs, \\\"Could not serialize the deployment init kwargs\\\"),\\n ray_actor_options,\\n placement_group_bundles,\\n placement_group_strategy,\\n max_replicas_per_node,\\n )\\n\\n config._deployment_def = deployment_def\\n config._init_args = init_args\\n config._init_kwargs = init_kwargs\\n\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7fb4037e77fb841e5a1c66668787be\",\n \"score\": \"0.5843691\",\n \"text\": \"def make_object(self, data):\\n return DeploymentTarget(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7fb4037e77fb841e5a1c66668787be\",\n \"score\": \"0.5843691\",\n \"text\": \"def make_object(self, data):\\n return DeploymentTarget(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7fb4037e77fb841e5a1c66668787be\",\n \"score\": \"0.5843691\",\n \"text\": \"def make_object(self, data):\\n return DeploymentTarget(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca29d0b4bfbbb6008d5170a0fa6ed1d5\",\n \"score\": \"0.57843614\",\n \"text\": \"def create(self):\\n evaluate_request(\\n self.__v1_api.create_namespaced_service(\\n namespace=self.__namespace, body=self.load_gen_service, async_req=True\\n ),\\n allowed_statuses=[409],\\n )\\n evaluate_request(\\n self.__v1_apps_api.create_namespaced_deployment(\\n namespace=self.__namespace,\\n body=self.load_gen_deployment,\\n async_req=True,\\n ),\\n allowed_statuses=[409],\\n )\\n # Make a second call to patch the configuration to make sure changes in replicas are applied\\n # TODO: Investigate making a utility method similar to `kubectl apply`\\n evaluate_request(\\n self.__v1_apps_api.patch_namespaced_deployment(\\n namespace=self.__namespace,\\n body=self.load_gen_deployment,\\n name=get_name(self.load_gen_deployment),\\n async_req=True,\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148044ed07fcebbb2c1a2dd4d981eacd\",\n \"score\": \"0.57293147\",\n \"text\": \"def create_deployment(ApiId=None, Description=None, StageName=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d26c29d6ec490cbe79d5fcc56f07c708\",\n \"score\": \"0.5728097\",\n \"text\": \"def Deploy(self, request, global_params=None):\\n config = self.GetMethodConfig('Deploy')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235b3bae1d74d97304abed8dea43936e\",\n \"score\": \"0.5602673\",\n \"text\": \"def Deploy(apig,api_id: str,stage_name: str,stage_purpose: str,deployment_purpose: str):\\n\\n\\t\\t\\tresponse = apig.client.create_deployment(\\n\\t\\t\\t\\trestApiId=api_id,\\n\\t\\t\\t\\tstageName=stage_name,\\n\\t\\t\\t\\tstageDescription=stage_purpose,\\n\\t\\t\\t\\tdescription=deployment_purpose,\\n\\t\\t\\t\\t#cacheClusterEnabled=True|False,\\n\\t\\t\\t\\t#cacheClusterSize='0.5'|'1.6'|'6.1'|'13.5'|'28.4'|'58.2'|'118'|'237',\\n\\t\\t\\t\\t#variables={\\n\\t\\t\\t\\t#\\t'string': 'string'\\n\\t\\t\\t\\t#},\\n\\t\\t\\t\\t#canarySettings={\\n\\t\\t\\t\\t#\\t'percentTraffic': 123.0,\\n\\t\\t\\t\\t#\\t'stageVariableOverrides': {\\n\\t\\t\\t\\t#\\t\\t'string': 'string'\\n\\t\\t\\t\\t#\\t},\\n\\t\\t\\t\\t#\\t'useStageCache': True|False\\n\\t\\t\\t\\t#},\\n\\t\\t\\t\\t#tracingEnabled=True|False\\n\\t\\t\\t)\\n\\t\\t\\treturn response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6d8cfb1e8c285c274bdb715ef48d27\",\n \"score\": \"0.55937594\",\n \"text\": \"def deployment(self):\\n try:\\n deployment = gcp.BareClusterDeployment(self.gcp_wrapper, self.config['deployment_name'],\\n self.config['gce_zone'])\\n info = deployment.get_info()\\n errors = info['operation'].get('error')\\n if errors:\\n raise util.LauncherError('DeploymentContainsErrors', str(errors))\\n return deployment\\n except HttpError as e:\\n if e.resp.status == 404:\\n raise util.LauncherError('DeploymentNotFound',\\n \\\"The deployment you are trying to access doesn't exist\\\") from e\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f08fcba693bd35d4db4a0cf8ec926d5\",\n \"score\": \"0.55091316\",\n \"text\": \"async def create_game_server_deployment(self,\\n request: game_server_deployments.CreateGameServerDeploymentRequest = None,\\n *,\\n parent: str = None,\\n game_server_deployment: game_server_deployments.GameServerDeployment = None,\\n retry: retries.Retry = gapic_v1.method.DEFAULT,\\n timeout: float = None,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> operation_async.AsyncOperation:\\n # Create or coerce a protobuf request object.\\n # Sanity check: If we got a request object, we should *not* have\\n # gotten any keyword arguments that map to the request.\\n has_flattened_params = any([parent, game_server_deployment])\\n if request is not None and has_flattened_params:\\n raise ValueError(\\\"If the `request` argument is set, then none of \\\"\\n \\\"the individual field arguments should be set.\\\")\\n\\n request = game_server_deployments.CreateGameServerDeploymentRequest(request)\\n\\n # If we have keyword arguments corresponding to fields on the\\n # request, apply these.\\n if parent is not None:\\n request.parent = parent\\n if game_server_deployment is not None:\\n request.game_server_deployment = game_server_deployment\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = gapic_v1.method_async.wrap_method(\\n self._client._transport.create_game_server_deployment,\\n default_timeout=60.0,\\n client_info=DEFAULT_CLIENT_INFO,\\n )\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata((\\n (\\\"parent\\\", request.parent),\\n )),\\n )\\n\\n # Send the request.\\n response = await rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Wrap the response in an operation future.\\n response = operation_async.from_gapic(\\n response,\\n self._client._transport.operations_client,\\n game_server_deployments.GameServerDeployment,\\n metadata_type=common.OperationMetadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9e2f169726958f09efb705ba205833\",\n \"score\": \"0.54509884\",\n \"text\": \"def create_deployment_from_file():\\n parser = ArgumentParser(description=\\\"Deployment JSON Descriptor\\\")\\n if is_valid_file(parser,filename):\\n f=open(filename,'r')\\n json_object = json.load(f)\\n\\n new_deployment = Deployment()\\n for value in json_object.values():\\n for v in range(0,len(value)):\\n new_deployment.description=value[v]['description']\\n new_deployment.name = value[v]['name']\\n new_deployment.region = value[v]['regionId']\\n new_deployment.budget = value[v]['budget']\\n result=new_deployment.create()\\n print new_deployment.current_job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361a44319e34d24d399054508a4dba1d\",\n \"score\": \"0.5314766\",\n \"text\": \"def CreateInstance(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"CreateInstance\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.CreateInstanceResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e3e6698dd5446275e465730755add3\",\n \"score\": \"0.53035444\",\n \"text\": \"def create_namespaced_build_request_instantiate(self, body, name, namespace, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.create_namespaced_build_request_instantiate_with_http_info(body, name, namespace, **kwargs)\\n else:\\n (data) = self.create_namespaced_build_request_instantiate_with_http_info(body, name, namespace, **kwargs)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2ba182add2613fff44ac66cb271bebe\",\n \"score\": \"0.5297318\",\n \"text\": \"def deployment(ctx, image):\\n body = {'template' : image}\\n resp = consume_task(ctx.obj.vlab_api,\\n endpoint='/api/2/inf/deployment',\\n message='Deploying template {}'.format(image),\\n body=body,\\n timeout=3600,\\n pause=20)\\n data = resp.json()['content']\\n typewriter(\\\"Successfully created the following machines:\\\")\\n click.echo('\\\\t{}'.format('\\\\n\\\\t'.join(data.keys())))\\n typewriter(\\\"\\\\nUse 'vlab connect deployment --name <name> --protocol <protocol>' to access a deployed machine\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"696f2763c4cde4413e6a94516ef60867\",\n \"score\": \"0.5296554\",\n \"text\": \"def create_namespaced_deployment(body, namespace=\\\"default\\\", extv1Client=None):\\n if extv1Client is None:\\n # load the kubernetes config\\n load_k8s_config()\\n #create the api client\\n extv1Client=client.ExtensionsV1beta1Api()\\n\\n deployment = None\\n try:\\n deployment = extv1Client.create_namespaced_deployment(namespace=namespace, body=body)\\n log.debug(\\\"Deployment created: \\\\n{0}\\\\n\\\".format(deployment))\\n except ApiException as e:\\n log.error(\\\"Exception when calling ExtensionsV1beta1Api->create_namespaced_deployment: %s\\\\n\\\" % e)\\n raise e\\n\\n return deployment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f4a962f5aadbed03366082f3f5a4e7\",\n \"score\": \"0.5293826\",\n \"text\": \"def create(self, name, filename, do_use=False):\\n\\n filename = os.path.expanduser(filename)\\n print(\\\"file:\\\" + filename)\\n\\n with open(filename, \\\"rb\\\") as deploy_file:\\n config = yaml.safe_load(deploy_file.read())\\n\\n try:\\n deployment = api.Deployment.create(config, name)\\n except jsonschema.ValidationError:\\n print(_(\\\"Config schema validation error: %s.\\\") % sys.exc_info()[1])\\n return(1)\\n except exceptions.DeploymentNameExists:\\n print(_(\\\"Error: %s\\\") % sys.exc_info()[1])\\n return(1)\\n\\n self.list(deployment_list=[deployment])\\n if do_use:\\n self.use(deployment[\\\"uuid\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"768cd02652f3c2d3997d4a8999a4e438\",\n \"score\": \"0.52635235\",\n \"text\": \"def create(name, cluster, model, sha, env_file, env):\\n # Create map of custom environment variables to use with this train job.\\n envs = parse_cmd_envs(env_file_path=env_file, env_options=env)\\n\\n # Create payload.\\n payload = project_payload({\\n 'name': name,\\n 'apiCluster': cluster,\\n 'model': model,\\n 'sha': sha,\\n 'envs': json.dumps(envs)\\n })\\n\\n try:\\n # Create the deploy.\\n resp = api.post('/deploy', payload=payload, stream=True)\\n except KeyboardInterrupt:\\n return\\n\\n # Stream the response logs.\\n resp.log_stream()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca32146736a6f146e5dd539f10cd0f2f\",\n \"score\": \"0.5260751\",\n \"text\": \"def deployment(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"deployment\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa30bb2fa60d44677cfefe8d66db21d\",\n \"score\": \"0.5154369\",\n \"text\": \"def make_object(self, data):\\n return DeploymentImage(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa30bb2fa60d44677cfefe8d66db21d\",\n \"score\": \"0.5154369\",\n \"text\": \"def make_object(self, data):\\n return DeploymentImage(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa30bb2fa60d44677cfefe8d66db21d\",\n \"score\": \"0.5154369\",\n \"text\": \"def make_object(self, data):\\n return DeploymentImage(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aa30bb2fa60d44677cfefe8d66db21d\",\n \"score\": \"0.5154369\",\n \"text\": \"def make_object(self, data):\\n return DeploymentImage(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e62a0e6332b9ca108377885a6bdf61\",\n \"score\": \"0.5148939\",\n \"text\": \"def createDeploymentFromFile(filePath, namespace='default'):\\n txClient = TxKubernetesClient()\\n\\n with open(filePath, 'r') as file:\\n deployment = yaml.load(file)\\n\\n # create a deployment in a namespace\\n d = txClient.call(txClient.extV1Beta1.create_namespaced_deployment,\\n body=deployment,\\n namespace=namespace,\\n )\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"007210337460416121b109c416bd824b\",\n \"score\": \"0.5039167\",\n \"text\": \"def _create_new_deployments(self, sm, group, request_dict):\\n rm = get_resource_manager()\\n new_deployments = request_dict.get('new_deployments')\\n if not new_deployments:\\n return\\n with sm.transaction():\\n if not group.default_blueprint:\\n raise manager_exceptions.ConflictError(\\n 'Cannot create deployments: group {0} has no '\\n 'default blueprint set'.format(group.id))\\n if not all(spec.get('skip_plugins_validation')\\n for spec in new_deployments):\\n rm.check_blueprint_plugins_installed(\\n group.default_blueprint.plan)\\n deployment_count = len(group.deployments)\\n create_exec_group = models.ExecutionGroup(\\n id=str(uuid.uuid4()),\\n deployment_group=group,\\n workflow_id='create_deployment_environment',\\n visibility=group.visibility,\\n concurrency=10,\\n )\\n sm.put(create_exec_group)\\n self._prepare_sites(sm, new_deployments)\\n for new_dep_spec in new_deployments:\\n dep = self._make_new_group_deployment(\\n rm, group, new_dep_spec, deployment_count, group.labels)\\n group.deployments.append(dep)\\n create_exec_group.executions.append(dep.create_execution)\\n deployment_count += 1\\n messages = create_exec_group.start_executions(sm, rm)\\n workflow_executor.execute_workflow(messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52aeb46eb05931bc19fd86699340acd7\",\n \"score\": \"0.50310636\",\n \"text\": \"def deployment(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"deployment\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2961de7404e616b49c17e55067617a76\",\n \"score\": \"0.5011048\",\n \"text\": \"def from_dictionary(cls,\\n dictionary):\\n if dictionary is None:\\n return None\\n\\n # Extract variables from the dictionary\\n deploy_fleet_params = cohesity_management_sdk.models.deploy_fleet_params.DeployFleetParams.from_dictionary(dictionary.get('deployFleetParams')) if dictionary.get('deployFleetParams') else None\\n deploy_vms_to_aws_params = cohesity_management_sdk.models.deploy_vms_to_aws_params.DeployVMsToAWSParams.from_dictionary(dictionary.get('deployVmsToAwsParams')) if dictionary.get('deployVmsToAwsParams') else None\\n deploy_vms_to_azure_params = cohesity_management_sdk.models.deploy_vms_to_azure_params.DeployVMsToAzureParams.from_dictionary(dictionary.get('deployVmsToAzureParams')) if dictionary.get('deployVmsToAzureParams') else None\\n deploy_vms_to_gcp_params = cohesity_management_sdk.models.deploy_vms_to_gcp_params.DeployVMsToGCPParams.from_dictionary(dictionary.get('deployVmsToGcpParams')) if dictionary.get('deployVmsToGcpParams') else None\\n replicate_snapshots_to_aws_params = cohesity_management_sdk.models.replicate_snapshots_to_aws_params.ReplicateSnapshotsToAWSParams.from_dictionary(dictionary.get('replicateSnapshotsToAwsParams')) if dictionary.get('replicateSnapshotsToAwsParams') else None\\n replicate_snapshots_to_azure_params = cohesity_management_sdk.models.replicate_snapshots_to_azure_params.ReplicateSnapshotsToAzureParams.from_dictionary(dictionary.get('replicateSnapshotsToAzureParams')) if dictionary.get('replicateSnapshotsToAzureParams') else None\\n\\n # Return an object of this model\\n return cls(\\n deploy_fleet_params,\\n deploy_vms_to_aws_params,\\n deploy_vms_to_azure_params,\\n deploy_vms_to_gcp_params,\\n replicate_snapshots_to_aws_params,\\n replicate_snapshots_to_azure_params\\n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb25da55f48886ed1cd003679994da21\",\n \"score\": \"0.497082\",\n \"text\": \"def create_server(self, request, tenant_id):\\n try:\\n content = json.loads(request.content.read())\\n except ValueError:\\n request.setResponseCode(400)\\n return json.dumps(bad_request(\\\"Invalid JSON request body\\\"))\\n\\n try:\\n creation = (self._region_collection_for_tenant(tenant_id)\\n .request_creation(request, content, self.url))\\n except ValueError:\\n request.setResponseCode(400)\\n return json.dumps(\\n bad_request(\\n \\\"OS-DCF:diskConfig must be either 'MANUAL' or 'AUTO'.\\\"))\\n\\n return creation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79e0a1a026f7e4cdf27b38cb6667c206\",\n \"score\": \"0.4965557\",\n \"text\": \"def create(self,\\n model: str,\\n deployment_name: str,\\n metadata: Optional[Dict] = None,\\n training_data: Optional[Union['DataFrame', 'ndarray']] = None,\\n training_target: Optional[Union['DataFrame', 'ndarray']] = None,\\n experiment_run_id: Optional[str] = None) -> None:\\n return super().create(model=model,\\n deployment_name=deployment_name,\\n metadata=metadata,\\n training_data=training_data,\\n training_target=training_target,\\n experiment_run_id=experiment_run_id,\\n deployment_type='online')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0ddeafc96fbba74619cae752d91b369\",\n \"score\": \"0.49553844\",\n \"text\": \"def deploy(self, deployment_yaml):\\n\\n cmd = \\\"%s %s\\\" % (KUBECTL_APPLY % (self.context), deployment_yaml)\\n result = self.nuvoloso_helper.run_check_output(cmd)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ac62f8345fb8e35281f13c5bf4aaf1\",\n \"score\": \"0.49248436\",\n \"text\": \"def create(self, request, *args, **kwargs):\\n data = self.request.DATA\\n packaged = 'upload' in data\\n form = (NewPackagedForm(data) if packaged\\n else NewManifestForm(data))\\n\\n if not form.is_valid():\\n return Response(form.errors, status=HTTP_400_BAD_REQUEST)\\n\\n if not packaged:\\n upload = FileUpload.objects.create(\\n user=getattr(request, 'amo_user', None))\\n # The hosted app validator is pretty fast.\\n tasks.fetch_manifest(form.cleaned_data['manifest'], upload.pk)\\n else:\\n upload = form.file_upload\\n # The packaged app validator is much heavier.\\n tasks.validator.delay(upload.pk)\\n\\n log.info('Validation created: %s' % upload.pk)\\n self.kwargs = {'pk': upload.pk}\\n # Re-fetch the object, fetch_manifest() might have altered it.\\n upload = self.get_object()\\n serializer = self.get_serializer(upload)\\n status = HTTP_201_CREATED if upload.processed else HTTP_202_ACCEPTED\\n return Response(serializer.data, status=status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1767a93be08f47e1a6a1e475a096097\",\n \"score\": \"0.49178505\",\n \"text\": \"def create_namespaced_deployment_config(self, namespace, body, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.create_namespaced_deployment_config_with_http_info(namespace, body, **kwargs)\\n else:\\n (data) = self.create_namespaced_deployment_config_with_http_info(namespace, body, **kwargs)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd42809ff676fd4e00e5b749670ae22\",\n \"score\": \"0.48909155\",\n \"text\": \"def deploy_dag() -> Response:\\n\\n json_request = request.get_json(cache=False)\\n\\n try:\\n\\n if json_request is None:\\n return ApiResponse.error(\\n status=ApiResponse.STATUS_BAD_REQUEST,\\n error=f\\\"Did not receive any JSON request to deploy\\\",\\n )\\n\\n ingestion_pipeline = IngestionPipeline(**json_request)\\n\\n deployer = DagDeployer(ingestion_pipeline)\\n response = deployer.deploy()\\n\\n return response\\n\\n except ValidationError as err:\\n logger.debug(traceback.format_exc())\\n logger.error(\\n f\\\"Request Validation Error parsing payload [{json_request}]. IngestionPipeline expected: {err}\\\"\\n )\\n return ApiResponse.error(\\n status=ApiResponse.STATUS_BAD_REQUEST,\\n error=f\\\"Request Validation Error parsing payload. IngestionPipeline expected: {err}\\\",\\n )\\n\\n except Exception as exc:\\n logger.debug(traceback.format_exc())\\n logger.error(f\\\"Internal error deploying [{json_request}] due to [{exc}] \\\")\\n return ApiResponse.error(\\n status=ApiResponse.STATUS_SERVER_ERROR,\\n error=f\\\"Internal error while deploying due to [{exc}] \\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d07e6605fdefe7d821ed430f2465009\",\n \"score\": \"0.48740977\",\n \"text\": \"def deploy(self):\\n logger.debug(\\\"======= deploy =======\\\")\\n\\n try:\\n err, pkg_name, dest, email, passwd = \\\\\\n self.__get_deploy_request_params()\\n if (err):\\n return HTTPBadRequest(err)\\n\\n result = deploy_package(pkg_name, dest, email, passwd)\\n\\n if result['status'] == 202:\\n deployment_id = result['deployment_id']\\n status_url = 'deployment/{0}'.format(deployment_id)\\n contents = json.dumps(result, sort_keys=True)\\n response = HTTPAccepted()\\n response.headers = {\\n 'Content-Type': 'application/json; charset=UTF-8',\\n 'Location': status_url}\\n response.body = contents\\n return response\\n elif result['status'] == 404:\\n msg = 'Cannot find package \\\"{0}\\\" installed.'.format(pkg_name)\\n return HTTPNotFound(detail=msg)\\n else:\\n return HTTPInternalServerError(detail=result['errors'])\\n\\n except Exception as e:\\n stack_info = traceback.format_exc()\\n logger.exception(stack_info)\\n return HTTPInternalServerError(detail=stack_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a18028e12f5b6b9a07065a590a6844d\",\n \"score\": \"0.4848733\",\n \"text\": \"def deploy(self, script_file, requirements_file, port, deployment_name = None):\\n\\n # check if deployment name was given\\n if deployment_name is None:\\n\\n # assign default\\n deployment_name = \\\"kubipy-deployment\\\"\\n\\n # if it was given\\n else:\\n\\n # take care of special characters\\n deployment_name = deployment_name.replace('_', '-').replace('/', '-')\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ____________________________________________________________\\n | To deploy your image, you will need to be logged in to |\\n | Docker. So please, login if you have an account, or sign |\\n | up, if you do not have an account yet. |\\n ------------------------------------------------------------\\n \\n \\\"\\\"\\\"\\n\\n # print info about graphical interface\\n print (info_message)\\n\\n # check script_file input\\n if isinstance(script_file, str):\\n\\n # try to read in the first line\\n try:\\n\\n # read in file\\n with open(script_file) as file:\\n first_line = file.readline()\\n\\n # print out first line\\n info_message = \\\"\\\"\\\"\\\\\\n This is the first line of the file you want to deploy:\\n -------------------------\\n {first_line}\\n -------------------------\\\\\\n \\\"\\\"\\\".format(first_line = first_line)\\n\\n # print info message\\n print (info_message)\\n\\n # handle exception\\n except:\\n\\n # handle exception\\n raise Exception('I could not find the script_file')\\n\\n # if script_file input is the wrong format\\n else:\\n\\n # raise Exception\\n raise Exception(\\\"\\\"\\\"\\n script_file should be a path and filename to your python api\\n script, that you want to deploy.\\n e.g. /your_folder/your_script.py\\n \\n \\\"\\\"\\\")\\n\\n # check requirements_file input\\n if isinstance(requirements_file, str):\\n\\n # try to read in the first line\\n try:\\n\\n # read in file\\n with open(requirements_file) as file:\\n first_line = file.readline()\\n\\n # print out first line\\n info_message = \\\"\\\"\\\"\\\\\\n This is the first line of your requirements file:\\n -------------------------\\n {first_line}\\n -------------------------\\\\\\n \\\"\\\"\\\".format(first_line = first_line)\\n\\n # print info message\\n print (info_message)\\n\\n # handle exception\\n except:\\n\\n # handle exception\\n raise Exception('I could not find the script_file')\\n\\n # if script_file input is the wrong format\\n else:\\n\\n # raise Exception\\n raise Exception(\\\"\\\"\\\"\\n script_file should be a path and filename to your python api\\n script, that you want to deploy.\\n e.g. /your_folder/your_requirements.txt\\n \\n \\\"\\\"\\\")\\n\\n # check port input\\n if isinstance(port, str):\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ___________________________________________\\n | API will be deployed on {port} |\\n -------------------------------------------\\n \\n \\\"\\\"\\\".format(port = port)\\n\\n # print info about graphical interface\\n print (info_message)\\n\\n # else if port input is the wrong fromat\\n else:\\n\\n # raise Exception\\n raise Exception('port should be just a string such as \\\"8000\\\"')\\n\\n # build Dockerfile\\n built_dk = self.__build_dockerfile(script_file = script_file,\\n requirements_file = requirements_file,\\n port = port)\\n\\n # check if it worked\\n if built_dk:\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ___________________________________________\\n | Successfully built Dockerfile |\\n -------------------------------------------\\n \\\"\\\"\\\"\\n\\n # print info message\\n print (info_message)\\n\\n # break the function if it didn't work\\n else:\\n\\n # raise Exception\\n raise Exception('I could not built a Dockerfile')\\n \\n # built docker image\\n built_di = self.__build_image()\\n\\n # check if it worked\\n if built_di:\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ___________________________________________\\n | Successfully built Docker image |\\n -------------------------------------------\\n \\\"\\\"\\\"\\n\\n # print info message\\n print (info_message)\\n\\n # break the function if it didn't work\\n else:\\n\\n # raise Exception\\n raise Exception('I could not built a Docker image')\\n\\n # check if deployment already exists\\n exists_dp = self.__check_deployment(deployment_name = deployment_name)\\n\\n # delete if exists\\n if exists_dp:\\n\\n # delete deployment\\n self.delete_object(deployment = deployment_name)\\n\\n # create deployment\\n created_dp = self.__create_deployment(deployment_name = deployment_name)\\n\\n # check if it worked\\n if created_dp:\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ___________________________________________\\n | Successfully created deployment |\\n -------------------------------------------\\n \\\"\\\"\\\"\\n\\n # print info message\\n print (info_message)\\n\\n # break the function if it didn't work\\n else:\\n\\n # raise Exception\\n raise Exception('I could not create a deployment')\\n\\n # check if service already exists\\n exists_sv = self.__check_service(deployment_name = deployment_name)\\n\\n # delete if exists\\n if exists_sv:\\n\\n # delete service\\n self.delete_object(service = deployment_name)\\n\\n # expose service\\n exposed_sv = self.__expose_service(port, deployment_name = deployment_name)\\n\\n # check if it worked\\n if exposed_sv:\\n\\n # build info message\\n info_message = \\\"\\\"\\\"\\n ___________________________________________\\n | Successfully expose |\\n -------------------------------------------\\n \\\"\\\"\\\"\\n\\n # print info message\\n print (info_message)\\n\\n # break the function if it didn't work\\n else:\\n\\n # raise Exception\\n raise Exception('I could not expose the service')\\n\\n # get the service url\\n url_exposed = self.__get_url(deployment_name = deployment_name)\\n\\n # check if it worked\\n if url_exposed:\\n\\n # info message\\n info_message = \\\"\\\"\\\"\\n ____________________________________________________________\\n | Your deployment is ready and you can access the API via: |\\n | {url} |\\n ------------------------------------------------------------\\n \\\"\\\"\\\".format(url = self.service_url)\\n\\n # print message\\n print (info_message)\\n\\n # if it didn't work\\n else:\\n\\n # raise Exception\\n raise Exception('I could not get the url of the service')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e9204945c3b4803145bcda3a6b557f6\",\n \"score\": \"0.48196542\",\n \"text\": \"def canarize_deployment(args, input_yaml, labels={}):\\n\\n # append the -canary to the Deployment name\\n output_yaml = copy.deepcopy(input_yaml)\\n canary_deployment_name = input_yaml[\\\"metadata\\\"][\\\"name\\\"] + args.suffix\\n output_yaml[\\\"metadata\\\"][\\\"name\\\"] = canary_deployment_name\\n\\n print(f\\\"# Creating canary Deployment {canary_deployment_name}\\\")\\n\\n # append the -canary to all the labels in the selector\\n try:\\n for (k, v) in input_yaml[\\\"spec\\\"][\\\"selector\\\"][\\\"matchLabels\\\"].items():\\n output_yaml[\\\"spec\\\"][\\\"selector\\\"][\\\"matchLabels\\\"][k] = v + \\\\\\n args.suffix\\n except IndexError:\\n pass\\n\\n for (k, v) in input_yaml[\\\"spec\\\"][\\\"template\\\"][\\\"metadata\\\"][\\\"labels\\\"].items():\\n output_yaml[\\\"spec\\\"][\\\"template\\\"][\\\"metadata\\\"][\\\"labels\\\"][k] = v + args.suffix\\n\\n if args.image:\\n for container in output_yaml[\\\"spec\\\"][\\\"template\\\"][\\\"spec\\\"][\\\"containers\\\"]:\\n if image_except_tag(container[\\\"image\\\"]) == image_except_tag(args.image):\\n print(f\\\"# Replacing Deployment image {args.image}\\\")\\n container[\\\"image\\\"] = args.image\\n\\n if args.namespace:\\n output_yaml[\\\"metadata\\\"][\\\"namespace\\\"] = args.namespace\\n\\n if len(labels) > 0:\\n if len(output_yaml[\\\"metadata\\\"][\\\"labels\\\"]) > 0:\\n output_yaml[\\\"metadata\\\"][\\\"labels\\\"].update(labels)\\n else:\\n output_yaml[\\\"metadata\\\"][\\\"labels\\\"] = labels\\n\\n return [output_yaml]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51d3524ad70eb10bab0b31bfa17cbd9\",\n \"score\": \"0.47847378\",\n \"text\": \"def create_cloud_domain_migration_request(self):\\n try:\\n self.logger.info('create_cloud_domain_migration_request called.')\\n\\n # Prepare query URL\\n self.logger.info('Preparing query URL for create_cloud_domain_migration_request.')\\n _url_path = '/public/remoteVaults/cloudDomainMigration'\\n _query_builder = self.config.get_base_uri()\\n _query_builder += _url_path\\n _query_url = APIHelper.clean_url(_query_builder)\\n\\n # Prepare headers\\n self.logger.info('Preparing headers for create_cloud_domain_migration_request.')\\n _headers = {\\n 'accept': 'application/json',\\n 'content-type': 'application/json; charset=utf-8'\\n }\\n\\n # Prepare and execute request\\n self.logger.info(\\n 'Preparing and executing request for create_cloud_domain_migration_request.')\\n _request = self.http_client.post(\\n _query_url,\\n headers=_headers)\\n AuthManager.apply(_request, self.config)\\n _context = self.execute_request(_request, name='create_cloud_domain_migration_request')\\n\\n # Endpoint and global error handling using HTTP status codes.\\n self.logger.info('Validating response for create_cloud_domain_migration_request.')\\n if _context.response.status_code == 0:\\n raise RequestErrorErrorException('Error', _context)\\n self.validate_response(_context)\\n\\n except Exception as e:\\n self.logger.error(e, exc_info=True)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899b85253d31cf4dbd988aaaaa19a07a\",\n \"score\": \"0.477397\",\n \"text\": \"def predict_from_dataset(self, dataset: Dataset) -> DeploymentPrediction:\\n if self.training_type not in ['regression', 'classification', 'multiclassification']:\\n PrevisionException('Prediction not supported yet for training type {}', self.training_type)\\n data = {\\n 'dataset_id': dataset._id,\\n }\\n\\n predict_start = client.request('/deployments/{}/deployment-predictions'.format(self._id),\\n method=requests.post,\\n data=data,\\n message_prefix='Bulk predict')\\n predict_start_parsed = parse_json(predict_start)\\n return DeploymentPrediction.from_id(predict_start_parsed['_id'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed979a745afa07b8d2f1edbc2f9339a4\",\n \"score\": \"0.4745488\",\n \"text\": \"def loadDeployment(deploymentData):\\n #check that the deploymentData is valid\\n if not (\\\"deploymentId\\\" in deploymentData and type(deploymentData[\\\"deploymentId\\\"])==str and len(deploymentData[\\\"deploymentId\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'deploymentId' (type=str and length>0)\\\")\\n if not (\\\"name\\\" in deploymentData and type(deploymentData[\\\"name\\\"])==str and len(deploymentData[\\\"name\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'name' (type=str and length>0)\\\")\\n if not (\\\"status\\\" in deploymentData and type(deploymentData[\\\"status\\\"])==str and len(deploymentData[\\\"status\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'status' (type=str and length>0)\\\")\\n if not (\\\"dateCreated\\\" in deploymentData and type(deploymentData[\\\"dateCreated\\\"])==str and len(deploymentData[\\\"dateCreated\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'dateCreated' (type=str and length>0)\\\")\\n if not (\\\"dateModified\\\" in deploymentData and type(deploymentData[\\\"dateModified\\\"])==str and len(deploymentData[\\\"dateModified\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'dateCreated' (type=str and length>0)\\\")\\n if not (\\\"archived\\\" in deploymentData and type(deploymentData[\\\"archived\\\"])==bool):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'archived' (type=bool)\\\")\\n if not (\\\"goalSampleSize\\\" in deploymentData and type(deploymentData[\\\"goalSampleSize\\\"]) in [int, Decimal] and deploymentData[\\\"goalSampleSize\\\"]>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'goalSampleSize' (type=int and value>0)\\\")\\n if not (\\\"currentSampleSize\\\" in deploymentData and type(deploymentData[\\\"currentSampleSize\\\"]) in [int, Decimal] and deploymentData[\\\"currentSampleSize\\\"]>=0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'currentSampleSize' (type=int and value>=0)\\\")\\n if not (\\\"facility\\\" in deploymentData and type(deploymentData[\\\"facility\\\"]) in [str, dict] and len(deploymentData[\\\"facility\\\"])>0):\\n raise errors.BadRequestError(\\\"Deployment must have attribute 'facility' (type=str or dict and length>0)\\\")\\n #construct the deployment\\n d = Deployment()\\n d.deploymentId = deploymentData[\\\"deploymentId\\\"]\\n d.name = deploymentData[\\\"name\\\"]\\n if \\\"description\\\" in deploymentData:\\n d.description = deploymentData[\\\"description\\\"]\\n d.status = deploymentData[\\\"status\\\"]\\n d.dateCreated = deploymentData[\\\"dateCreated\\\"]\\n d.dateModified = deploymentData[\\\"dateModified\\\"]\\n d.archived = deploymentData[\\\"archived\\\"]\\n d.goalSampleSize = int(deploymentData[\\\"goalSampleSize\\\"])\\n d.currentSampleSize = int(deploymentData[\\\"currentSampleSize\\\"])\\n d.facility = facility_db_access.loadFacility(deploymentData[\\\"facility\\\"])\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc91a32b8ed3b0cc5090e17056b5043a\",\n \"score\": \"0.47425556\",\n \"text\": \"def _start_job(self, start_job_deployment: dict) -> None:\\n # Standardize start job deployment.\\n job_details = K8sExecutor._standardize_job_details(start_job_deployment=start_job_deployment)\\n\\n # Save details\\n K8sDetailsWriter.save_job_details(job_details=job_details)\\n\\n # Create and apply k8s config\\n k8s_job = self._create_k8s_job(job_details=job_details)\\n client.BatchV1Api().create_namespaced_job(body=k8s_job, namespace=\\\"default\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d384e9e873acaef107132e294f7f9d6a\",\n \"score\": \"0.47337088\",\n \"text\": \"def create(definition: dict, version: str, parameter: tuple,\\n region: str,\\n disable_rollback: bool,\\n dry_run: bool,\\n force: bool,\\n tag: List[str],\\n timeout: int,\\n keep_stacks: Optional[int],\\n traffic: int,\\n verbose: bool,\\n remote: str,\\n parameter_file: Optional[str]\\n ):\\n lizzy = setup_lizzy_client(remote)\\n parameter = list(parameter) or []\\n if parameter_file:\\n parameter.extend(read_parameter_file(parameter_file))\\n\\n if not force: # pragma: no cover\\n # supporting artifact checking would imply copying a large amount of code\\n # from senza, so it should be considered out of scope until senza\\n # and lizzy client are merged\\n warning(\\\"WARNING: \\\"\\n \\\"Artifact checking is still not supported by lizzy-client.\\\")\\n\\n with Action('Requesting new stack..') as action:\\n new_stack, output = lizzy.new_stack(keep_stacks, traffic,\\n definition, version,\\n disable_rollback, parameter,\\n region=region,\\n dry_run=dry_run,\\n tags=tag)\\n\\n stack_id = '{stack_name}-{version}'.format_map(new_stack)\\n print(output)\\n\\n info('Stack ID: {}'.format(stack_id))\\n\\n if dry_run:\\n info(\\\"Post deployment steps skipped\\\")\\n exit(0)\\n\\n with Action('Waiting for new stack...') as action:\\n if verbose:\\n print() # ensure that new states will not be printed on the same line as the action\\n\\n last_state = None\\n for state in lizzy.wait_for_deployment(stack_id, region=region):\\n if state != last_state and verbose:\\n click.echo(' {}'.format(state))\\n else:\\n action.progress()\\n last_state = state\\n\\n # TODO be prepared to handle all final AWS CF states\\n if last_state == 'ROLLBACK_COMPLETE':\\n fatal_error(\\n 'Stack was rollback after deployment. Check your application log for possible reasons.')\\n elif last_state != 'CREATE_COMPLETE':\\n fatal_error('Deployment failed: {}'.format(last_state))\\n\\n info('Deployment Successful')\\n\\n if traffic is not None:\\n with Action('Requesting traffic change..'):\\n try:\\n lizzy.traffic(stack_id, traffic, region=region)\\n except requests.ConnectionError as e:\\n connection_error(e, fatal=False)\\n except requests.HTTPError as e:\\n agent_error(e, fatal=False)\\n\\n # TODO unit test this\\n if keep_stacks is not None:\\n versions_to_keep = keep_stacks + 1\\n stacks_to_remove_counter = 1\\n end_time = datetime.datetime.utcnow() + datetime.timedelta(seconds=timeout)\\n while stacks_to_remove_counter > 0 and datetime.datetime.utcnow() <= end_time:\\n try:\\n all_stacks = lizzy.get_stacks([new_stack['stack_name']],\\n region=region)\\n except requests.ConnectionError as e:\\n connection_error(e, fatal=False)\\n error(\\\"Failed to fetch old stacks. \\\"\\n \\\"Old stacks WILL NOT BE DELETED\\\")\\n exit(1)\\n except requests.HTTPError as e:\\n agent_error(e, fatal=False)\\n error(\\\"Failed to fetch old stacks. \\\"\\n \\\"Old stacks WILL NOT BE DELETED\\\")\\n exit(1)\\n else:\\n sorted_stacks = sorted(all_stacks,\\n key=lambda stack: stack['creation_time'])\\n stacks_to_remove = sorted_stacks[:-versions_to_keep]\\n stacks_to_remove_counter = len(stacks_to_remove)\\n with Action('Deleting old stacks..'):\\n print()\\n for old_stack in stacks_to_remove:\\n old_stack_id = '{stack_name}-{version}'.format_map(\\n old_stack)\\n if old_stack['status'] in COMPLETE_STATES:\\n click.echo(' {}'.format(old_stack_id))\\n try:\\n lizzy.delete(old_stack_id, region=region)\\n stacks_to_remove_counter -= 1\\n except requests.ConnectionError as e:\\n connection_error(e, fatal=False)\\n except requests.HTTPError as e:\\n agent_error(e, fatal=False)\\n else:\\n click.echo(' > {} current status is {} trying '\\n 'again later'.format(old_stack_id,\\n old_stack['status']))\\n if stacks_to_remove_counter > 0:\\n time.sleep(5)\\n\\n if datetime.datetime.utcnow() > end_time:\\n click.echo('Timeout waiting for related stacks to be ready.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a412099e06317345559f8858120bad2\",\n \"score\": \"0.47257835\",\n \"text\": \"def show(self, uuid, **kwargs):\\n resp, result = self.client.get(\\\"./deployments/%s\\\" % uuid, **kwargs)\\n return base.Deployment(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4de1e2c8f6f1ad5b1ef8545531aa1c\",\n \"score\": \"0.4714727\",\n \"text\": \"def generate_namespaced_deployment_config(self, name, namespace, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.generate_namespaced_deployment_config_with_http_info(name, namespace, **kwargs)\\n else:\\n (data) = self.generate_namespaced_deployment_config_with_http_info(name, namespace, **kwargs)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666873cc8dffae880e12e718f658f36c\",\n \"score\": \"0.47108465\",\n \"text\": \"async def create_deployment(\\n deployment: schemas.actions.DeploymentCreate,\\n response: Response,\\n worker_lookups: WorkerLookups = Depends(WorkerLookups),\\n db: PrefectDBInterface = Depends(provide_database_interface),\\n) -> schemas.responses.DeploymentResponse:\\n\\n async with db.session_context(begin_transaction=True) as session:\\n if (\\n deployment.work_pool_name\\n and deployment.work_pool_name != DEFAULT_AGENT_WORK_POOL_NAME\\n ):\\n # Make sure that deployment is valid before beginning creation process\\n work_pool = await models.workers.read_work_pool_by_name(\\n session=session, work_pool_name=deployment.work_pool_name\\n )\\n if work_pool is None:\\n raise HTTPException(\\n status_code=status.HTTP_404_NOT_FOUND,\\n detail=f'Work pool \\\"{deployment.work_pool_name}\\\" not found.',\\n )\\n try:\\n deployment.check_valid_configuration(work_pool.base_job_template)\\n except (MissingVariableError, jsonschema.exceptions.ValidationError) as exc:\\n raise HTTPException(\\n status_code=status.HTTP_409_CONFLICT,\\n detail=f\\\"Error creating deployment: {exc!r}\\\",\\n )\\n\\n # hydrate the input model into a full model\\n deployment_dict = deployment.dict(exclude={\\\"work_pool_name\\\"})\\n if deployment.work_pool_name and deployment.work_queue_name:\\n # If a specific pool name/queue name combination was provided, get the\\n # ID for that work pool queue.\\n deployment_dict[\\\"work_queue_id\\\"] = (\\n await worker_lookups._get_work_queue_id_from_name(\\n session=session,\\n work_pool_name=deployment.work_pool_name,\\n work_queue_name=deployment.work_queue_name,\\n create_queue_if_not_found=True,\\n )\\n )\\n elif deployment.work_pool_name:\\n # If just a pool name was provided, get the ID for its default\\n # work pool queue.\\n deployment_dict[\\\"work_queue_id\\\"] = (\\n await worker_lookups._get_default_work_queue_id_from_work_pool_name(\\n session=session,\\n work_pool_name=deployment.work_pool_name,\\n )\\n )\\n elif deployment.work_queue_name:\\n # If just a queue name was provided, ensure that the queue exists and\\n # get its ID.\\n work_queue = await models.work_queues._ensure_work_queue_exists(\\n session=session, name=deployment.work_queue_name\\n )\\n deployment_dict[\\\"work_queue_id\\\"] = work_queue.id\\n\\n deployment = schemas.core.Deployment(**deployment_dict)\\n # check to see if relevant blocks exist, allowing us throw a useful error message\\n # for debugging\\n if deployment.infrastructure_document_id is not None:\\n infrastructure_block = (\\n await models.block_documents.read_block_document_by_id(\\n session=session,\\n block_document_id=deployment.infrastructure_document_id,\\n )\\n )\\n if not infrastructure_block:\\n raise HTTPException(\\n status_code=status.HTTP_409_CONFLICT,\\n detail=(\\n \\\"Error creating deployment. Could not find infrastructure\\\"\\n f\\\" block with id: {deployment.infrastructure_document_id}. This\\\"\\n \\\" usually occurs when applying a deployment specification that\\\"\\n \\\" was built against a different Prefect database / workspace.\\\"\\n ),\\n )\\n\\n if deployment.storage_document_id is not None:\\n infrastructure_block = (\\n await models.block_documents.read_block_document_by_id(\\n session=session,\\n block_document_id=deployment.storage_document_id,\\n )\\n )\\n if not infrastructure_block:\\n raise HTTPException(\\n status_code=status.HTTP_409_CONFLICT,\\n detail=(\\n \\\"Error creating deployment. Could not find storage block with\\\"\\n f\\\" id: {deployment.storage_document_id}. This usually occurs\\\"\\n \\\" when applying a deployment specification that was built\\\"\\n \\\" against a different Prefect database / workspace.\\\"\\n ),\\n )\\n\\n now = pendulum.now(\\\"UTC\\\")\\n model = await models.deployments.create_deployment(\\n session=session, deployment=deployment\\n )\\n\\n if model.created >= now:\\n response.status_code = status.HTTP_201_CREATED\\n\\n return schemas.responses.DeploymentResponse.from_orm(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed72b0ce912d483f2a13f476b19129fb\",\n \"score\": \"0.46907023\",\n \"text\": \"def create_task(deploy_uuid, tag):\\n return objects.Task(deployment_uuid=deploy_uuid, tag=tag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5f3848fff4506962d52fbcf8521860\",\n \"score\": \"0.46852064\",\n \"text\": \"def deploy_issue_model(\\n self,\\n request: Optional[\\n Union[contact_center_insights.DeployIssueModelRequest, dict]\\n ] = None,\\n *,\\n name: Optional[str] = None,\\n retry: OptionalRetry = gapic_v1.method.DEFAULT,\\n timeout: Union[float, object] = gapic_v1.method.DEFAULT,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> operation.Operation:\\n # Create or coerce a protobuf request object.\\n # Quick check: If we got a request object, we should *not* have\\n # gotten any keyword arguments that map to the request.\\n has_flattened_params = any([name])\\n if request is not None and has_flattened_params:\\n raise ValueError(\\n \\\"If the `request` argument is set, then none of \\\"\\n \\\"the individual field arguments should be set.\\\"\\n )\\n\\n # Minor optimization to avoid making a copy if the user passes\\n # in a contact_center_insights.DeployIssueModelRequest.\\n # There's no risk of modifying the input as we've already verified\\n # there are no flattened fields.\\n if not isinstance(request, contact_center_insights.DeployIssueModelRequest):\\n request = contact_center_insights.DeployIssueModelRequest(request)\\n # If we have keyword arguments corresponding to fields on the\\n # request, apply these.\\n if name is not None:\\n request.name = name\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = self._transport._wrapped_methods[self._transport.deploy_issue_model]\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata(((\\\"name\\\", request.name),)),\\n )\\n\\n # Send the request.\\n response = rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Wrap the response in an operation future.\\n response = operation.from_gapic(\\n response,\\n self._transport.operations_client,\\n contact_center_insights.DeployIssueModelResponse,\\n metadata_type=contact_center_insights.DeployIssueModelMetadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b634c97e7afe7f1c7783396b29643e\",\n \"score\": \"0.46760336\",\n \"text\": \"def Create(self, request, global_params=None):\\n config = self.GetMethodConfig('Create')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11dc854e2eedb67d370d96c9ade296dc\",\n \"score\": \"0.46733534\",\n \"text\": \"def _new(\\n cls,\\n project_id: str,\\n name: str,\\n main_model: Model,\\n challenger_model: Model = None,\\n type_violation_policy: str = 'best_effort',\\n access_type: str = None,\\n ) -> 'BaseExperimentDeployment':\\n\\n if access_type and access_type not in ['public', 'fine_grained', 'private']:\\n raise PrevisionException('access type must be public, fine_grained or private')\\n if type_violation_policy not in ['best_effort', 'strict']:\\n raise PrevisionException('type_violation_policy must be best_effort or strict')\\n main_model_experiment_version_id = main_model.experiment_version_id\\n main_experiment = BaseExperimentVersion._from_id(main_model_experiment_version_id)\\n main_experiment_id = main_experiment['experiment_id']\\n data = {\\n 'name': name,\\n 'experiment_id': main_experiment_id,\\n 'main_model_experiment_version_id': main_model_experiment_version_id,\\n 'main_model_id': main_model._id,\\n 'type_violation_policy': type_violation_policy\\n }\\n if access_type:\\n data['access_type'] = access_type\\n if challenger_model:\\n challenger_model_experiment_version_id = challenger_model.experiment_version_id\\n challenger_experiment = BaseExperimentVersion._from_id(main_model_experiment_version_id)\\n challenger_experiment_id = challenger_experiment['experiment_id']\\n if main_experiment_id != challenger_experiment_id:\\n raise PrevisionException('main and challenger models must be from the same experiment')\\n data['challenger_model_experiment_version_id'] = challenger_model_experiment_version_id\\n data['challenger_model_id'] = challenger_model._id\\n\\n url = '/projects/{}/model-deployments'.format(project_id)\\n resp = client.request(url,\\n data=data,\\n method=requests.post,\\n message_prefix='ExperimentDeployment creation')\\n json_resp = parse_json(resp)\\n experiment_deployment = cls.from_id(json_resp['_id'])\\n return experiment_deployment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95981a399dc790e922d46f3ca77ac602\",\n \"score\": \"0.4671982\",\n \"text\": \"def duplicate_deployment(name, namespace=\\\"default\\\", extv1Client=None):\\n if extv1Client is None :\\n # load the kubernetes config\\n load_k8s_config()\\n #create the api client\\n extv1Client=client.ExtensionsV1beta1Api()\\n\\n log.debug(\\\"looking for deployment '%s' in namespace '%s'...\\\"%(name, namespace))\\n dep = None\\n try:\\n dep = get_namespaced_deployment(name = name, namespace = namespace, extv1Client=extv1Client)\\n except K8sDeploymentNotFoundError as e:\\n log.error(\\\"Deployment '%s' not found in namespace '%s'...\\\"%(name, namespace))\\n raise e\\n log.debug(\\\"Deployment '%s' found.\\\"%(dep.metadata.name))\\n timestamp = getSimpleTimestamp()\\n dep_copy = duplicate_deployment_config(dep, timestamp)\\n log.debug(\\\"Creating a copy of '%s' with new name '%s' in namespace '%s'\\\"\\n %(dep.metadata.name, dep_copy.metadata.name, namespace))\\n deployment = create_namespaced_deployment(body=dep_copy, namespace=namespace, extv1Client=extv1Client)\\n log.debug(\\\"Copy of deployment '%s' created: %s\\\" \\n %(dep.metadata.name, deployment.metadata.name))\\n return deployment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b75ca7b47d8f3fea14faeade32d7603\",\n \"score\": \"0.46586993\",\n \"text\": \"def init(\\n cls,\\n project: _ProjectTypeVar,\\n usage_type: Literal[\\\"function\\\", \\\"layer\\\"] = \\\"function\\\",\\n ) -> DeploymentPackage[_ProjectTypeVar]:\\n s3_obj = DeploymentPackageS3Object(project, usage_type)\\n if s3_obj.exists:\\n if s3_obj.runtime == project.runtime:\\n return s3_obj\\n LOGGER.warning(\\n \\\"runtime of deployment package found in S3 (%s) does not match \\\"\\n \\\"requirement (%s); deleting & recreating...\\\",\\n s3_obj.runtime,\\n project.runtime,\\n )\\n s3_obj.delete()\\n return cls(project, usage_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d30909e0b187e3cbae28a5ea0cfb2\",\n \"score\": \"0.46529797\",\n \"text\": \"def CreateAdaptiveDynamicStreamingTemplate(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"CreateAdaptiveDynamicStreamingTemplate\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.CreateAdaptiveDynamicStreamingTemplateResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd4749af369d8ea7d583d9ed666cd3e\",\n \"score\": \"0.46441373\",\n \"text\": \"def CreateTagRetentionExecution(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"CreateTagRetentionExecution\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.CreateTagRetentionExecutionResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3558862f8558b92ded636782545e3fd9\",\n \"score\": \"0.4620099\",\n \"text\": \"async def get_game_server_deployment(self,\\n request: game_server_deployments.GetGameServerDeploymentRequest = None,\\n *,\\n name: str = None,\\n retry: retries.Retry = gapic_v1.method.DEFAULT,\\n timeout: float = None,\\n metadata: Sequence[Tuple[str, str]] = (),\\n ) -> game_server_deployments.GameServerDeployment:\\n # Create or coerce a protobuf request object.\\n # Sanity check: If we got a request object, we should *not* have\\n # gotten any keyword arguments that map to the request.\\n has_flattened_params = any([name])\\n if request is not None and has_flattened_params:\\n raise ValueError(\\\"If the `request` argument is set, then none of \\\"\\n \\\"the individual field arguments should be set.\\\")\\n\\n request = game_server_deployments.GetGameServerDeploymentRequest(request)\\n\\n # If we have keyword arguments corresponding to fields on the\\n # request, apply these.\\n if name is not None:\\n request.name = name\\n\\n # Wrap the RPC method; this adds retry and timeout information,\\n # and friendly error handling.\\n rpc = gapic_v1.method_async.wrap_method(\\n self._client._transport.get_game_server_deployment,\\n default_retry=retries.Retry(\\ninitial=1.0,maximum=10.0,multiplier=1.3, predicate=retries.if_exception_type(\\n core_exceptions.ServiceUnavailable,\\n ),\\n deadline=60.0,\\n ),\\n default_timeout=60.0,\\n client_info=DEFAULT_CLIENT_INFO,\\n )\\n\\n # Certain fields should be provided within the metadata header;\\n # add these here.\\n metadata = tuple(metadata) + (\\n gapic_v1.routing_header.to_grpc_metadata((\\n (\\\"name\\\", request.name),\\n )),\\n )\\n\\n # Send the request.\\n response = await rpc(\\n request,\\n retry=retry,\\n timeout=timeout,\\n metadata=metadata,\\n )\\n\\n # Done; return the response.\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa5e1369a6ccd42a79f1931f4668617\",\n \"score\": \"0.45987225\",\n \"text\": \"def deploy(deployment_name: str, environment: str = 'production'):\\n alter_path()\\n asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())\\n\\n tasks_module = importlib.import_module(\\n f\\\"deployments.{deployment_name}.tasks\\\"\\n )\\n hosts_module = importlib.import_module(\\n f\\\"deployments.{deployment_name}.hosts\\\"\\n )\\n\\n host_registry: HostRegistry = getattr(hosts_module, \\\"host_registry\\\", None)\\n if not host_registry:\\n raise Exception(\\\"Can't find 'host_registry' in hosts file.\\\")\\n\\n hosts = host_registry.host_class_map.get(environment)\\n\\n if not hosts:\\n print(colored(f\\\"No hosts defined for {environment}!\\\", \\\"red\\\"))\\n sys.exit(1)\\n else:\\n print(\\n colored(f\\\"Deploying {deployment_name} to {environment}\\\", \\\"green\\\")\\n )\\n\\n for host in hosts:\\n host.environment = environment\\n\\n task_registry: TaskRegistry = getattr(tasks_module, \\\"task_registry\\\", None)\\n if not task_registry:\\n raise Exception(\\\"Can't find 'task_registry' in tasks file.\\\")\\n\\n asyncio.run(\\n host_registry.run_tasks(\\n tasks=task_registry.task_classes, environment=environment\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fea32326651bb911a0d95b1bd83a9e6\",\n \"score\": \"0.4576947\",\n \"text\": \"def post(self):\\n sm = get_storage_manager()\\n\\n params = rest_utils.get_json_and_verify_params({\\n 'source_deployment_id': {'type': str},\\n 'inter_deployment_dependencies': {'type': list}\\n })\\n\\n dependencies = params.get('inter_deployment_dependencies')\\n\\n if any(item.get('created_by') for item in dependencies):\\n check_user_action_allowed('set_owner')\\n if any(item.get('created_at') for item in dependencies):\\n check_user_action_allowed('set_timestamp')\\n\\n if len(dependencies) > 0 and EXTERNAL_SOURCE in dependencies[0]:\\n source_deployment = None\\n else:\\n source_deployment = sm.get(models.Deployment,\\n params['source_deployment_id'])\\n\\n created_ids = []\\n with sm.transaction():\\n for dependency in dependencies:\\n record = _create_inter_deployment_dependency(\\n source_deployment, dependency, sm)\\n created_ids += [record.id]\\n\\n return ListResponse(\\n items=[{'id': i} for i in created_ids],\\n metadata={'pagination': {\\n 'total': len(created_ids),\\n 'size': len(created_ids),\\n 'offset': 0,\\n }}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05939f00c583a6b07bf49c7be9538f76\",\n \"score\": \"0.45655584\",\n \"text\": \"def create(self, model_id: str, revision: str, instance_name: str,\\n resource: ServiceResourceConfig,\\n provider: ServiceProviderParameters):\\n if provider.vendor != Vendor.EAS:\\n raise NotSupportError(\\n 'Not support vendor: %s ,only support EAS current.' %\\n (provider.vendor))\\n create_params = DeployServiceParameters(\\n instance_name=instance_name,\\n model_id=model_id,\\n revision=revision,\\n resource=resource,\\n provider=provider)\\n path = f'{self.endpoint}/api/v1/deployer/endpoint'\\n body = asdict(create_params)\\n r = requests.post(\\n path, json=body, cookies=self.cookies, headers=self.headers)\\n handle_http_response(r, logger, self.cookies, 'create_service')\\n if r.status_code >= HTTPStatus.OK and r.status_code < HTTPStatus.MULTIPLE_CHOICES:\\n if is_ok(r.json()):\\n data = r.json()[API_RESPONSE_FIELD_DATA]\\n return data\\n else:\\n raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])\\n else:\\n raise_for_http_status(r)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e889d7f8f9cbfd8c394b2275953a07\",\n \"score\": \"0.45585397\",\n \"text\": \"def k8s_custom_deploy(name: str,\\n apply_cmd: Union[str, List[str]],\\n delete_cmd: Union[str, List[str]],\\n deps: Union[str, List[str]],\\n image_selector: str=\\\"\\\",\\n live_update: List[LiveUpdateStep]=[],\\n apply_dir: str=\\\"\\\",\\n apply_env: Dict[str, str]={},\\n apply_cmd_bat: Union[str, List[str]]=\\\"\\\",\\n delete_dir: str=\\\"\\\",\\n delete_env: Dict[str, str]={},\\n delete_cmd_bat: Union[str, List[str]]=\\\"\\\",\\n container_selector: str=\\\"\\\",\\n image_deps: List[str]=[]) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb801659557df9b1070b30fb03b998a\",\n \"score\": \"0.45562708\",\n \"text\": \"def create(cls, argv):\\n request = cls(argv, dict());\\n return request;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b10092a7ac1f7fd92a757f2f807613b\",\n \"score\": \"0.45510134\",\n \"text\": \"def make_object(self, data):\\n return DeploymentMetric(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b10092a7ac1f7fd92a757f2f807613b\",\n \"score\": \"0.45510134\",\n \"text\": \"def make_object(self, data):\\n return DeploymentMetric(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b10092a7ac1f7fd92a757f2f807613b\",\n \"score\": \"0.45510134\",\n \"text\": \"def make_object(self, data):\\n return DeploymentMetric(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6014691e9c23047186d1a2c401f420e5\",\n \"score\": \"0.4536346\",\n \"text\": \"def deployment(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"deployment\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a19a6fbd4c78e8218916c2c7877ce93\",\n \"score\": \"0.45356768\",\n \"text\": \"def create_namespaced_build_request_instantiate_with_http_info(self, body, name, namespace, **kwargs):\\n\\n all_params = ['body', 'name', 'namespace', 'pretty']\\n all_params.append('callback')\\n all_params.append('_return_http_data_only')\\n all_params.append('_preload_content')\\n all_params.append('_request_timeout')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method create_namespaced_build_request_instantiate\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'body' is set\\n if ('body' not in params) or (params['body'] is None):\\n raise ValueError(\\\"Missing the required parameter `body` when calling `create_namespaced_build_request_instantiate`\\\")\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `create_namespaced_build_request_instantiate`\\\")\\n # verify the required parameter 'namespace' is set\\n if ('namespace' not in params) or (params['namespace'] is None):\\n raise ValueError(\\\"Missing the required parameter `namespace` when calling `create_namespaced_build_request_instantiate`\\\")\\n\\n\\n collection_formats = {}\\n\\n resource_path = '/oapi/v1/namespaces/{namespace}/buildconfigs/{name}/instantiate'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n if 'namespace' in params:\\n path_params['namespace'] = params['namespace']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'body' in params:\\n body_params = params['body']\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf'])\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = ['BearerToken']\\n\\n return self.api_client.call_api(resource_path, 'POST',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='V1BuildRequest',\\n auth_settings=auth_settings,\\n callback=params.get('callback'),\\n _return_http_data_only=params.get('_return_http_data_only'),\\n _preload_content=params.get('_preload_content', True),\\n _request_timeout=params.get('_request_timeout'),\\n collection_formats=collection_formats)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a72813396cb562f853625569649da9\",\n \"score\": \"0.45355946\",\n \"text\": \"def post(self, name, namespace):\\n pod_manifest = request.get_json()\\n CRUDService. \\\\\\n get_instance(). \\\\\\n create_pod(name, namespace, pod_manifest)\\n return KubeApiResponseDto('Resource created.'), \\\\\\n HttpStatusCode.Accepted.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690c624fece2f5c9ad89f659e25e4744\",\n \"score\": \"0.4534196\",\n \"text\": \"def start_job(self, deployment_path: str) -> None:\\n # Load start_job_deployment.\\n with open(deployment_path, \\\"r\\\") as fr:\\n start_job_deployment = yaml.safe_load(fr)\\n\\n # Start job\\n self._start_job(start_job_deployment=start_job_deployment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc7dc0a3be4d20a0344fdc02429f811\",\n \"score\": \"0.45341516\",\n \"text\": \"def __init__(self, deployment_input_field_1=None, deployment_input_field_2=None, local_vars_configuration=None): # noqa: E501 # noqa: E501\\n if local_vars_configuration is None:\\n local_vars_configuration = Configuration()\\n self.local_vars_configuration = local_vars_configuration\\n\\n self._deployment_input_field_1 = None\\n self._deployment_input_field_2 = None\\n self.discriminator = None\\n\\n if deployment_input_field_1 is not None:\\n self.deployment_input_field_1 = deployment_input_field_1\\n if deployment_input_field_2 is not None:\\n self.deployment_input_field_2 = deployment_input_field_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a7ff76f18e399c9d9d1b989be70475\",\n \"score\": \"0.45331883\",\n \"text\": \"def create_or_resume(name, spec, **_):\\r\\n\\r\\n # deploy mysql for barbican\\r\\n utils.ensure_mysql_cluster(\\\"barbican\\\", spec[\\\"mysql\\\"])\\r\\n\\r\\n # deploy barbican api\\r\\n utils.create_or_update('barbican/daemonset.yml.j2',\\r\\n name=name, spec=spec)\\r\\n utils.create_or_update('barbican/service.yml.j2',\\r\\n name=name, spec=spec)\\r\\n\\r\\n url = None\\r\\n if \\\"ingress\\\" in spec:\\r\\n utils.create_or_update('barbican/ingress.yml.j2',\\r\\n name=name, spec=spec)\\r\\n url = spec[\\\"ingress\\\"][\\\"host\\\"]\\r\\n identity.ensure_service(name=\\\"barbican\\\", service_type=\\\"key-manager\\\",\\r\\n url=url, desc=\\\"Barbican Service\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7617fac347be42843b3cab0d09f08ce2\",\n \"score\": \"0.4518388\",\n \"text\": \"def make_object(self, data):\\n return DeploymentLog(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7617fac347be42843b3cab0d09f08ce2\",\n \"score\": \"0.4518388\",\n \"text\": \"def make_object(self, data):\\n return DeploymentLog(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7617fac347be42843b3cab0d09f08ce2\",\n \"score\": \"0.4518388\",\n \"text\": \"def make_object(self, data):\\n return DeploymentLog(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a142d94837cf76c464d08b802653e98\",\n \"score\": \"0.4511214\",\n \"text\": \"def CreateTemplate(self, tag, bucket, task_dir):\\n image_url = self._COMPUTE_API_ROOT + \\\\\\n 'ubuntu-os-cloud/global/images/ubuntu-1404-trusty-v20160406'\\n request_body = {\\n 'name': self._GetTemplateName(tag),\\n 'properties': {\\n 'machineType': 'n1-standard-1',\\n 'networkInterfaces': [{\\n 'network': self._project_api_url + '/global/networks/default',\\n 'accessConfigs': [{\\n 'name': 'external-IP',\\n 'type': 'ONE_TO_ONE_NAT'\\n }]}],\\n 'disks': [{\\n 'type': 'PERSISTENT',\\n 'boot': True,\\n 'autoDelete': True,\\n 'mode': 'READ_WRITE',\\n 'initializeParams': {'sourceImage': image_url}}],\\n 'canIpForward': False,\\n 'scheduling': {\\n 'automaticRestart': True,\\n 'onHostMaintenance': 'MIGRATE',\\n 'preemptible': False},\\n 'serviceAccounts': [{\\n 'scopes': [\\n 'https://www.googleapis.com/auth/cloud-platform',\\n 'https://www.googleapis.com/auth/cloud-taskqueue'],\\n 'email': 'default'}],\\n 'metadata': { 'items': [\\n {'key': 'cloud-storage-path',\\n 'value': bucket},\\n {'key': 'task-dir',\\n 'value': task_dir},\\n {'key': 'startup-script-url',\\n 'value': 'gs://%s/deployment/startup-script.sh' % bucket},\\n {'key': 'taskqueue-tag', 'value': tag}]}}}\\n request = self._compute_api.instanceTemplates().insert(\\n project=self._project, body=request_body)\\n return self._ExecuteApiRequest(request)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d2c19f9a8c3ff259c9f2cc7bed1be04\",\n \"score\": \"0.4505982\",\n \"text\": \"def CreateInstanceCustomizedDomain(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"CreateInstanceCustomizedDomain\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.CreateInstanceCustomizedDomainResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad3f96d2cb7f93f0fca470786d862f6\",\n \"score\": \"0.44951227\",\n \"text\": \"def __init__(__self__, *,\\n deployment_duration_in_minutes: pulumi.Input[int],\\n growth_factor: pulumi.Input[float],\\n replicate_to: pulumi.Input[str],\\n description: Optional[pulumi.Input[str]] = None,\\n final_bake_time_in_minutes: Optional[pulumi.Input[int]] = None,\\n growth_type: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):\\n pulumi.set(__self__, \\\"deployment_duration_in_minutes\\\", deployment_duration_in_minutes)\\n pulumi.set(__self__, \\\"growth_factor\\\", growth_factor)\\n pulumi.set(__self__, \\\"replicate_to\\\", replicate_to)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if final_bake_time_in_minutes is not None:\\n pulumi.set(__self__, \\\"final_bake_time_in_minutes\\\", final_bake_time_in_minutes)\\n if growth_type is not None:\\n pulumi.set(__self__, \\\"growth_type\\\", growth_type)\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if tags is not None:\\n pulumi.set(__self__, \\\"tags\\\", tags)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994296,"cells":{"query_id":{"kind":"string","value":"c708c456f86c5430638d2bf062dea899"},"query":{"kind":"string","value":"r\"\"\"Updates a zone resource."},"positive_passages":{"kind":"list like","value":[{"docid":"dd6d9c86894eb6ab1a1e288ee79a75af","score":"0.0","text":"def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","title":""}],"string":"[\n {\n \"docid\": \"dd6d9c86894eb6ab1a1e288ee79a75af\",\n \"score\": \"0.0\",\n \"text\": \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b908bbf5043f2fb197e9223c0fc925f6","score":"0.66193604","text":"def _update(self) -> None:\n import regenmaschine.exceptions as exceptions\n\n try:\n self._entity_json = self._client.zones.get(self.rainmachine_id)\n except exceptions.HTTPError as exc_info:\n _LOGGER.error('Unable to update info for zone \"%s\"',\n self.rainmachine_id)\n _LOGGER.debug(exc_info)","title":""},{"docid":"8cf5126c9a67a676b1faf7f347f2e5ec","score":"0.6341421","text":"def update(self, req, id, *args, **kwargs):\n # get the context\n context = req.context\n try:\n # get the url\n url = req.url\n # if len(args) > 2:\n # raise BadRequest(resource=\"zone update\", msg=url)\n # get the body\n values = json.loads(req.body)\n values['id'] = id\n LOG.info(_(\"the in value body is %(body)s\"), {\"body\": values})\n LOG.info(_(\"the id is %(id)s\"), {\"id\": id})\n if kwargs.get('owners'):\n # check the in values\n valid_attributes = ['id', 'tenant_id', 'owners']\n recom_msg = self.validat_parms(values, valid_attributes)\n # from rpc server update the zones in db and device\n zones = self.manager.update_zone_owners(context, recom_msg,\n recom_msg['id'])\n else:\n # check the in values\n valid_attributes = ['id', 'tenant_id', 'default_ttl']\n recom_msg = self.validat_parms(values, valid_attributes)\n # from rpc server update the zones in db and device\n zones = self.manager.update_zone(context, recom_msg,\n recom_msg['id'])\n except Nca47Exception as e:\n self.response.status = e.code\n LOG.error(_LE('Error exception! error info: %' + e.message))\n LOG.exception(e)\n return tools.ret_info(e.code, e.message)\n except RemoteError as exception:\n self.response.status = 500\n message = exception.value\n return tools.ret_info(self.response.status, message)\n except Exception as exception:\n LOG.exception(exception)\n self.response.status = 500\n return tools.ret_info(self.response.status, exception.message)\n return zones","title":""},{"docid":"2929ebbd94d7a479ddef9a5412a3e7ea","score":"0.6042733","text":"def update(self, ss_resource):\n raise NotImplementedError(\"update() method should be implemented.\")","title":""},{"docid":"7e01de995e456f9fc1fa07e18241a524","score":"0.6037626","text":"def update_route53(self):\n self.log.debug('Updating Route53 information for {}'.format(self.account))\n\n # region Update zones\n existing_zones = DNSZone.get_all(self.account)\n zones = self.__fetch_route53_zones()\n for resource_id, data in zones.items():\n if resource_id in existing_zones:\n zone = DNSZone.get(resource_id)\n if zone.update(data):\n self.log.debug('Change detected for Route53 zone {}/{}'.format(\n self.account,\n zone.name\n ))\n zone.save()\n else:\n tags = data.pop('tags')\n DNSZone.create(\n resource_id,\n account_id=self.account.account_id,\n properties=data,\n tags=tags\n )\n\n self.log.debug('Added Route53 zone {}/{}'.format(\n self.account,\n data['name']\n ))\n\n db.session.commit()\n\n zk = set(zones.keys())\n ezk = set(existing_zones.keys())\n\n for resource_id in ezk - zk:\n zone = existing_zones[resource_id]\n\n db.session.delete(zone.resource)\n self.log.debug('Deleted Route53 zone {}/{}'.format(\n self.account.account_name,\n zone.name.value\n ))\n db.session.commit()\n # endregion\n\n # region Update resource records\n try:\n for zone_id, zone in DNSZone.get_all(self.account).items():\n existing_records = {rec.id: rec for rec in zone.records}\n records = self.__fetch_route53_zone_records(zone.get_property('zone_id').value)\n\n for data in records:\n if data['id'] in existing_records:\n record = existing_records[data['id']]\n if record.update(data):\n self.log.debug('Changed detected for DNSRecord {}/{}/{}'.format(\n self.account,\n zone.name,\n data['name']\n ))\n record.save()\n else:\n record = DNSRecord.create(\n data['id'],\n account_id=self.account.account_id,\n properties={k: v for k, v in data.items() if k != 'id'},\n tags={}\n )\n self.log.debug('Added new DNSRecord {}/{}/{}'.format(\n self.account,\n zone.name,\n data['name']\n ))\n zone.add_record(record)\n db.session.commit()\n\n rk = set(x['id'] for x in records)\n erk = set(existing_records.keys())\n\n for resource_id in erk - rk:\n record = existing_records[resource_id]\n zone.delete_record(record)\n self.log.debug('Deleted Route53 record {}/{}/{}'.format(\n self.account.account_name,\n zone_id,\n record.name\n ))\n db.session.commit()\n except:\n raise\n # endregion","title":""},{"docid":"d9d6224a3a6d49357c502819ffbfea36","score":"0.5900831","text":"def zone(self, zone):\n\n self._zone = zone","title":""},{"docid":"54990993e1c0163d478991a205b9ba6f","score":"0.5839018","text":"def set_zonefile(config, name, zonefile):\n path = \"/v1/names/{}/zonefile\".format(name)\n payload = { 'zonefile': zonefile.read() }\n url = create_url(config,path)\n r = requests.put(url, headers=make_headers(config), json=payload)\n output(config, url, r)","title":""},{"docid":"bcccbdc61d08b63f83249b16aa12af5d","score":"0.58341324","text":"async def async_update(self):\n try:\n LOG.debug(f\"Updating {self.zone_info}\")\n status = await self._amp.zone_status(self._zone_id)\n if not status:\n return\n except Exception as e:\n # log up to two times within a specific period to avoid saturating the logs\n @limits(calls=2, period=10 * MINUTES)\n def log_failed_zone_update(e):\n LOG.warning(f\"Failed updating {self.zone_info}: {e}\")\n\n log_failed_zone_update(e)\n return\n\n LOG.debug(f\"{self.zone_info} status update: {status}\")\n self._status = status\n\n source_id = status.get(\"source\")\n if source_id:\n source_name = self._source_id_to_name.get(source_id)\n if source_name:\n self._source = source_name\n else:\n # sometimes the client may have not configured a source, but if the amplifier is set\n # to a source other than one defined, go ahead and dynamically create that source. This\n # could happen if the user changes the source through a different app or command.\n source_name = f\"Source {source_id}\"\n LOG.warning(\n f\"Undefined source id {source_id} for {self.zone_info}, adding '{source_name}'!\"\n )\n self._source_id_to_name[source_id] = source_name\n self._source_name_to_id[source_name] = source_id","title":""},{"docid":"298b2e7c4e3a798c3d5d2e9c0ff50407","score":"0.58222544","text":"def update_resource(RoleArn=None, ResourceArn=None):\n pass","title":""},{"docid":"f053cf63facc2cf646528d110db0b700","score":"0.57523984","text":"def set_zone(self, zone, x, y):\n\t\tself.zone = zone\n\t\tself.x = x\n\t\tself.y = y","title":""},{"docid":"95f013b2d193b0b79f8627ffc30e08ee","score":"0.5736969","text":"def modify_zone(zone_name, endpoint=None, comment=None):\n\n if not zone_name:\n return None\n\n if comment and run_iadmin('modzone', [zone_name, 'comment', comment]):\n return None\n\n if endpoint and run_iadmin('modzone', [zone_name, 'conn', endpoint]):\n return None\n\n zone = get_zone_details(zone_name)\n\n return zone[0]","title":""},{"docid":"d4f0ad099b6f906e2cc62d61299ebea2","score":"0.56937206","text":"def set_zone(self, zone):\n self._zone = zone","title":""},{"docid":"db74f7a93128ca157e6bc775fea5988a","score":"0.56497073","text":"def _signal_zone_update(self):\n dispatcher_send(self.hass, f\"{SIGNAL_ZONE_UPDATE}-{self._zone.zone_id}\")","title":""},{"docid":"ea346236f4546ea373fa1da437725483","score":"0.564124","text":"def update_record(acct:Account, zone:str, record:str) -> bool:\n headers = {\n \"X-Auth-Email\": acct.email,\n \"X-Auth-Key\": acct.api_key,\n \"Content-Type\": \"application/json\",\n }\n zone_id = \"\"\n record_id = \"\"\n\n _zone = get_zone(acct, zone)\n if _zone:\n zone_id = _zone.get(\"result\")[0].get(\"id\")\n if not zone_id:\n # lets just stop here, no need to further requesting\n return False\n\n _records = get_records(acct, zone_id, record)\n if _zone:\n record_id = _records.get(\"result\")[0].get(\"id\")\n if not record_id:\n # lets just stop here, no need to further requesting\n return False\n\n url = f\"https://api.cloudflare.com/client/v4/zones/{zone_id}/dns_records/{record_id}\"\n data = {\"type\": \"A\", \"name\": record, \"content\": acct.ip}\n r = requests.put(url, headers=headers, data=json.dumps(data))\n\n return r.status_code == 200","title":""},{"docid":"ab9aae6eced19396e27567fc895b810f","score":"0.5596219","text":"def set_zone(self, zone: Zone,\n timeout=DEFAULT_TIMEOUT):\n req = \"/api/zones\"\n data = self._post_codec(req, timeout, zone)\n new_zone = Zone.from_dict(data)\n return new_zone","title":""},{"docid":"88b0722c4f26420609410411b98f4224","score":"0.5582042","text":"def edit_record(self, id_, **kwargs):\n\n if self.is_valid_id(id_):\n response = self.session.put(f\"{self.api}{self.default_path}/zone/{self.domain}/record/{id_}\",\n json=kwargs).json()\n else:\n response = {'status': 'error', 'item': 'Not found',\n 'errors': {'content': ['Specified ID is invalid, no record found.']}}\n\n log_response(\"EDITING RECORD\", response)\n return response","title":""},{"docid":"d5b49de66418cf1d6e3ff1fa05bb8512","score":"0.55711555","text":"def update_record_value(self, new_value, ttl=300):\n new_zone_version = None\n zone_id = self.__get_active_zone_id()\n try:\n # create new zone version\n new_zone_version = self.__api.domain.zone.version.new(\n self.api_key,\n zone_id\n )\n logging.debug('DNS working on a new zone (version %s)', new_zone_version)\n\n record_list = self.__api.domain.zone.record.list(\n self.api_key,\n zone_id,\n new_zone_version,\n self.record\n )\n\n logging.debug('Updating records :%s', record_list)\n\n # Update each record that matches the filter\n for a_record in record_list:\n # get record id\n a_record_id = a_record['id']\n a_record_name = a_record['name']\n a_record_type = a_record['type']\n\n # update record value\n new_record = self.record.copy()\n new_record.update({'name': a_record_name, 'type': a_record_type, 'value': new_value, 'ttl': ttl})\n updated_record = self.__api.domain.zone.record.update(\n self.api_key,\n zone_id,\n new_zone_version,\n {'id': a_record_id},\n new_record\n )\n except xmlrpc.client.Fault as e:\n # delete updated zone\n if new_zone_version is not None:\n self.__api.domain.zone.version.delete(\n self.api_key,\n zone_id,\n new_zone_version\n )\n raise\n else:\n # activate updated zone\n self.__api.domain.zone.version.set(\n self.api_key,\n zone_id,\n new_zone_version\n )","title":""},{"docid":"82d87b2fb9b56f0a58d16cc1ac06b317","score":"0.55356956","text":"def upsert_route53_record(self, account_name, zone_id, recordset):\n session = self.get_session(account_name)\n client = session.client('route53')\n\n try:\n client.change_resource_record_sets(\n HostedZoneId=zone_id,\n ChangeBatch={\n 'Changes': [{\n 'Action': 'UPSERT',\n 'ResourceRecordSet': recordset,\n }]\n }\n )\n except client.exceptions.NoSuchHostedZone:\n logging.error(f'[{account_name}] Error trying to delete record: '\n f'unknown DNS zone {zone_id}')\n except Exception as e:\n logging.error(f'[{account_name}] unhandled exception: {e}')","title":""},{"docid":"f268c8b33f6de1220103f1fd8b585c15","score":"0.550737","text":"def update(\n self, options, callback=None, errback=None, parent=True, **kwargs\n ):\n\n if not self.data:\n raise ReservationException(\"Reservation not loaded\")\n\n def success(result, *args):\n self.data = result\n self.id = result[\"id\"]\n self.address_id = result[\"address_id\"]\n self.mac = result[\"mac\"]\n self.options = result[\"options\"]\n\n if callback:\n return callback(self)\n else:\n return self\n\n return self._rest.update(\n self.id,\n options,\n callback=success,\n errback=errback,\n parent=parent,\n **kwargs\n )","title":""},{"docid":"467321618ddf902ded11714253b2e8f5","score":"0.5499926","text":"def update_resource(resource, destroy_bash, rc, stderr, stdout):\n resource['destroy'] = {\n 'cmd': destroy_bash,\n 'rc': rc,\n 'stderr': stderr,\n 'stdout': stdout\n }\n return maccli.dao.api_resource.update(resource)","title":""},{"docid":"b7d339d31f023470adca0be360977e54","score":"0.5494631","text":"async def update_switch(self, zone, state, momentary=None, times=None, pause=None):\n try:\n if self.client:\n if self.api_version == CONF_ZONE:\n return await self.client.put_zone(\n zone,\n state,\n momentary,\n times,\n pause,\n )\n\n # device endpoint uses pin number instead of zone\n return await self.client.put_device(\n ZONE_TO_PIN[zone],\n state,\n momentary,\n times,\n pause,\n )\n\n except self.client.ClientError as err:\n _LOGGER.warning(\"Exception trying to update panel: %s\", err)\n\n raise CannotConnect","title":""},{"docid":"dcd24357bb21fe51de5d0a45de31d916","score":"0.54757786","text":"def update_resource(resource):\n\n known_resources = {'courses', 'programs'}\n\n if not resource in known_resources:\n return abort(400, 'Not a known data resource.')\n\n return \"200 - OK!\"","title":""},{"docid":"22a3d5daedffb144e8c32576fcd709fe","score":"0.5445498","text":"def AddZoneResourceArg(parser, verb, positional=True):\n name = 'zone' if positional else '--zone'\n return concept_parsers.ConceptParser.ForResource(\n name,\n GetZoneResourceSpec(),\n 'Arguments and flags that define the Dataplex zone you want {}'.format(\n verb),\n required=True).AddToParser(parser)","title":""},{"docid":"0b26a08be8d0fb7e2a00477c76e63294","score":"0.54265845","text":"def update_resource_record(resource_record, value, client=None):\n\n if not client: # pragma: no cover\n client = get_client()\n\n record_name = resource_record['Name']\n record_type = resource_record['Type']\n set_identifier = resource_record['SetIdentifier']\n current_value = resource_record['ResourceRecords'][0]['Value']\n\n if value == current_value:\n return True # Nothing to do, it is already up-to-date\n\n try:\n client.change_resource_record_sets(\n HostedZoneId='string',\n ChangeBatch={\n 'Comment': \"Updating DNS record via route53_dyndns\",\n 'Changes': [\n {\n 'Action': 'UPSERT',\n 'ResourceRecordSet': {\n 'Name': record_name,\n 'Type': record_type,\n 'SetIdentifier': set_identifier,\n 'ResourceRecords': [\n {\n 'Value': value\n },\n ],\n }\n },\n ]\n }\n )\n except Exception as e:\n # TODO - Log the error\n raise Route53Exception(e)\n\n return True","title":""},{"docid":"db797c37ea00fdb66ad89c5c5d416965","score":"0.5385896","text":"def update(resource_type, resource_id, data):\n # type: (str, int, dict) -> dict\n url = '/'.join([CONF.server.rstrip('/'), 'api', '1.0', resource_type,\n str(resource_id), ''])\n\n return put(url, data).json()","title":""},{"docid":"9a49ea8c469f3fba96dfddcc2f9edb3e","score":"0.53846484","text":"def patch_resource(self, namespace: typing.Optional[str] = None):\n pass # pragma: no cover","title":""},{"docid":"f75f8c033821f9a3ecfc785e70696945","score":"0.53627706","text":"def _update_dnsrecord(self, session, record_id, record, opts):\n zone_id = opts['zone_id']\n request = Request(\n 'PUT',\n self._url + \"/zones/{0}/dns_records/{1}\".format(zone_id,\n record_id),\n json=record,\n auth=self._auth)\n res = _call(session, request)\n return (res['id'], res['content'])","title":""},{"docid":"a85433d61bc5db142d57b6e75996cabb","score":"0.5352522","text":"def update(self, resource, data):\n pf = self.log_prefix()\n if not self.dry_run:\n resource.update(data)\n self.session.commit()\n self.logger.debug('%s update resource %s', pf, resource.to_str())","title":""},{"docid":"ff71f377ad4bd1ea09e39ff554ee30ce","score":"0.53504497","text":"def update_timezone(self, timezone):\n\n body = {\n \"requests\": [\n {\n \"updateSpreadsheetProperties\": {\n \"properties\": {\"timeZone\": timezone},\n \"fields\": \"timeZone\",\n },\n },\n ]\n }\n\n res = self.batch_update(body)\n self._properties[\"timeZone\"] = timezone\n return res","title":""},{"docid":"671e3d5b69fbfa76add61b1740018ed5","score":"0.53392094","text":"def update(context, resource, **kwargs):\n etag = kwargs.pop('etag')\n id = kwargs.pop('id')\n data = utils.sanitize_kwargs(**kwargs)\n uri = '%s/%s/%s' % (context.dci_cs_api, resource, id)\n r = context.session.put(uri, headers={'If-match': etag}, json=data)\n return r","title":""},{"docid":"0587394b391db90025fda98a506062cc","score":"0.5334158","text":"def update(context, resource, **kwargs):\n etag = kwargs.pop(\"etag\")\n id = kwargs.pop(\"id\")\n data = utils.sanitize_kwargs(**kwargs)\n uri = \"%s/%s/%s\" % (context.dci_cs_api, resource, id)\n r = context.session.put(\n uri, timeout=HTTP_TIMEOUT, headers={\"If-match\": etag}, json=data\n )\n return r","title":""},{"docid":"a9a09cdf45609a04823be89a32f77e79","score":"0.5328376","text":"def run_UpdateResourceW(regs):\n esp = regs[\"ESP\"]\n res_type = xrkutil.dbg_read_pwstring(esp + 8)\n res_name = xrkutil.dbg_read_pwstring(esp + 0xC)\n\n suck_api_call(\"UpdateResourceW\", regs, {\"res_type\": res_type, \"res_name\": res_name})","title":""},{"docid":"e089f170d5d3289c07765070ceb05220","score":"0.52920365","text":"def api_location_update(logger, location_id, data, scheme=\"nrega\"):\n utc_now = pytz.utc.localize(datetime.datetime.utcnow())\n india_now = utc_now.astimezone(pytz.timezone(\"Asia/Calcutta\"))\n india_now_isoformat = india_now.isoformat()\n headers = get_authentication_header()\n data = {\n \"id\" : location_id,\n \"data_json\" : data\n }\n res = requests.patch(LOCATIONURL, headers=headers, data=json.dumps(data))","title":""},{"docid":"75732b8addfe0cdcb894efe1041cabdb","score":"0.5280714","text":"def update(self, request, pk=None):\n schedule = Schedule.objects.get(pk=pk)\n\n schedule.date = request.data[\"date\"]\n schedule.description = request.data[\"description\"]\n schedule.time = request.data[\"time\"]\n\n show = Show.objects.get(pk=request.data[\"show\"])\n schedule.show = show\n\n schedule.save()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)","title":""},{"docid":"b6714ae2ea4e6f63ba333b358a1c482c","score":"0.525226","text":"def update(self,request,pk=None):\n\n return Response({'http_method':'PUT'})","title":""},{"docid":"f727328c52a5224d4cbdfda9008c914e","score":"0.52393675","text":"async def update(self):\n self._zones = self._test_json[\"zones\"]\n self._devices = self._test_json[\"devices\"]\n self._issues = _issues_via_v3_zones({\"data\": self._zones})\n self._version = _version_via_v3_zones({\"data\": self._zones}) # a hack\n\n await self._update() # now convert all the raw JSON","title":""},{"docid":"7801ce7cbcdb445071ec378017a4086c","score":"0.5226109","text":"def update_dns_record(self, zone, record, record_type, record_value, is_alias=False, dry_run=False):\n changes = boto.route53.record.ResourceRecordSets(self.conn_r53, zone)\n change = changes.add_change(\"UPSERT\", record, record_type, ttl=60)\n if is_alias:\n # provide list of params as needed by function set_alias\n # http://boto.readthedocs.org/en/latest/ref/route53.html#boto.route53.record.Record.set_alias\n change.set_alias(*record_value)\n else:\n change.add_value(record_value)\n if dry_run:\n print(changes)\n else:\n changes.commit()\n return True","title":""},{"docid":"5137349c2ca684b547e32657cdb0587e","score":"0.52146596","text":"def update_a(self, name, value, ttl=None, comment=\"\"):\n name = self.route53connection._make_qualified(name)\n old_record = self.get_a(name)\n ttl = ttl or old_record.ttl\n return self.update_record(resource_type='A',\n name=name,\n old_value=old_record.resource_records,\n new_value=value,\n old_ttl=old_record.ttl,\n new_ttl=ttl,\n comment=comment)","title":""},{"docid":"8d4255cf031229d0d8e9ed07801b2089","score":"0.51888937","text":"def update(self, request, pk=None):\n \n return Response({'http_method':'PUT'})","title":""},{"docid":"a458ee11743e01ac3ce8934f3b93b234","score":"0.51835114","text":"def put(self, privilege_id):\n current_privilege = DBPrivilege.query.get(privilege_id)\n args = dns_privilege_common_parser.parse_args()\n privilege_name = args['name']\n operation = args['operation']\n resource_type = args['resource_type']\n resource_id = args['resource_id']\n comment = args.get('comment', '')\n try:\n current_privilege.name = privilege_name\n current_privilege.operation = operation\n current_privilege.resource_type = resource_type\n current_privilege.resource_id = resource_id\n current_privilege.comment = comment\n db.session.add(current_privilege)\n except Exception as e:\n db.session.rollback()\n return get_response(RequestCode.OTHER_FAILED, '修改失败！\\n{e}'.format(e=str(e)))\n return get_response(RequestCode.SUCCESS, '修改成功！')","title":""},{"docid":"bc312f060f7a5d920aa727f5d7a2c9cc","score":"0.51795584","text":"def testUpdate(self):\n data = {'name': 'toto2'}\n response = requests.put(url=self.url, json=data)\n headers = response.headers\n\n self.assertTrue(self.city == storage.get(City, self.city_id))\n self.assertEqual(response.status_code, 200, WRONG_STATUS_CODE_MSG)\n self.assertEqual(\n headers['Content-Type'], 'application/json', WRONG_TYPE_RETURN_MSG)\n json_data = response.json()\n storage.reload()\n city = storage.get(City, self.city_id)\n self.assertEqual(city.name, 'toto2')\n self.assertIn('name', json_data, MISSING_NAME_ATTR_MSG)\n self.assertIn('created_at', json_data, MISSING_CREATED_AT_ATTR_MSG)\n self.assertIn('updated_at', json_data, MISSING_UPDATED_AT_ATTR_MSG)\n self.assertIn('__class__', json_data, MISSING_CLASS_ATTR_MSG)\n self.assertIn('state_id', json_data, MISSING_STATE_ID_ATTR_MSG)\n self.assertEqual(json_data['name'], 'toto2')\n storage.delete(city)\n storage.save()","title":""},{"docid":"8bb346989e3ba5eae401d59f12d8ed5b","score":"0.5125676","text":"def plan_resources_update(context, plan_id, resources):\n return IMPL.plan_resources_update(context, plan_id, resources)","title":""},{"docid":"7a73ce00829c70863b00759d004fa840","score":"0.51215047","text":"def update(self, request, pk=None):\n return Response({'http_method': 'PUT'})","title":""},{"docid":"7a73ce00829c70863b00759d004fa840","score":"0.51215047","text":"def update(self, request, pk=None):\n return Response({'http_method': 'PUT'})","title":""},{"docid":"7a73ce00829c70863b00759d004fa840","score":"0.51215047","text":"def update(self, request, pk=None):\n return Response({'http_method': 'PUT'})","title":""},{"docid":"7a73ce00829c70863b00759d004fa840","score":"0.51215047","text":"def update(self, request, pk=None):\n return Response({'http_method': 'PUT'})","title":""},{"docid":"d801b3079fa67c5b855fd620a616f0f4","score":"0.5120801","text":"def update_placement(self, placementName: str, projectName: str, attributes: Dict = None) -> Dict:\n pass","title":""},{"docid":"7c91c8c99b529a5b1f932043f0940afd","score":"0.5118039","text":"def test_update_resource(self):\n # create a valid http response with a successful status code.\n fake_response = requests.Response()\n fake_response.status_code = requests.codes.ok\n fake_response.headers = []\n # obtain the mock object that corresponds to the call of request()\n self.request_method_mock.return_value = fake_response\n resource_path = \"%s/%s\" % (netscaler_driver.VIPS_RESOURCE,\n TESTVIP_ID)\n resource_name = netscaler_driver.VIP_RESOURCE\n resource_body = self._get_testvip_httpbody_for_update()\n # call method under test: update_resource.\n self.testclient.update_resource(TEST_TENANT_ID, resource_path,\n resource_name, resource_body)\n resource_url = \"%s/%s\" % (self.testclient.service_uri, resource_path)\n # assert that requests.request() was called with the\n # expected params.\n self.request_method_mock.assert_called_once_with(\n 'PUT',\n url=resource_url,\n headers=mock.ANY,\n data=mock.ANY)","title":""},{"docid":"7429b21319720bfe73bcfef06136b4b1","score":"0.5116938","text":"def _update_resource(\n cls, client: utils.MetadataClientWithOverride, resource: proto.Message,\n ) -> proto.Message:\n\n return client.update_context(context=resource)","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5105461","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5105461","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"45e474177ed1d8d332bbd96ee9201601","score":"0.5105461","text":"def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","title":""},{"docid":"530004acde4c3436899992ad02eb1eb5","score":"0.50869197","text":"def test_update_country(self):\n body = Country()\n response = self.client.open(\n '/Erraticturtle35/Tantakatu/1.0.0/countries/{country_id}'.format(country_id=789),\n method='PUT',\n data=json.dumps(body),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"2026d72227ffd21138eb6a380f66b30e","score":"0.50614107","text":"def _add_zone_to_bind(self, zone):\n _, _, ret = self.rndc_target.write_zone_file(zone)\n assert ret == 0\n\n _, _, ret = self.rndc_target.addzone(zone)\n assert ret == 0","title":""},{"docid":"f8374eba22aa4d087499950bab4cc5af","score":"0.50612473","text":"def update(self, respool, name, cpu, memory, shares='normal'):\n try:\n config = vim.ResourceConfigSpec()\n config.cpuAllocation = vim.ResourceAllocationInfo()\n config.cpuAllocation.expandableReservation = False\n config.cpuAllocation.limit = cpu\n config.cpuAllocation.reservation = cpu\n config.cpuAllocation.shares = vim.SharesInfo()\n config.cpuAllocation.shares.level = shares \n config.memoryAllocation = vim.ResourceAllocationInfo()\n config.memoryAllocation.expandableReservation = False\n config.memoryAllocation.limit = memory\n config.memoryAllocation.reservation = memory\n config.memoryAllocation.shares = vim.SharesInfo()\n config.memoryAllocation.shares.level = shares\n \n res = respool.UpdateConfig(name, config)\n self.logger.debug(\"Update resource pool %s\" % name)\n return res\n except vmodl.MethodFault as error:\n self.logger.error(error.msg, exc_info=True)\n raise VsphereError(error.msg)","title":""},{"docid":"3df8678201f47047d9cf787855c065f7","score":"0.50491494","text":"def associate_zone(client, hosted_zone_id, region, vpc_id):\n try:\n return client.associate_vpc_with_hosted_zone(\n HostedZoneId=hosted_zone_id,\n VPC={\n 'VPCRegion': region,\n 'VPCId': vpc_id\n },\n Comment='Updated by Lambda DDNS'\n )\n except:\n publish_to_sns(SNS_CLIENT, ACCOUNT, REGION, \"Unexpected error:\" +\n str(sys.exc_info()[0]) + lineno())","title":""},{"docid":"ecdc37464c6ff3ba9e2872facb71c2a6","score":"0.5039758","text":"def put(self, **params):\n return self.patch(**params)","title":""},{"docid":"91b667a700c6ae5c5630c84983910eb0","score":"0.503604","text":"def rput(self, resource, **kw):\n return self.put(resource, **kw)","title":""},{"docid":"28a3f5f4d4e66bc0f438737c596e96e5","score":"0.5018837","text":"def mark_zone(self, zone_start):\n row_end = self.get_end(zone_start)\n zone_end = self.get_rows(zone_start, row_end)\n new_zone = Zone(zone_start, zone_end)\n self.add_zone(new_zone)\n return new_zone","title":""},{"docid":"f6ce6d2db6f9b5cba81c6b25120fd4ba","score":"0.50127333","text":"def _update_resource(\n cls,\n client: utils.MetadataClientWithOverride,\n resource: proto.Message,\n ) -> proto.Message:\n\n return client.update_artifact(artifact=resource)","title":""},{"docid":"755a0a19c80fcc5753ed8a9276843770","score":"0.5008044","text":"def update(self, ip=None, ttl=None):\n if ip is not None:\n payload = '{{\"ipv4addr\":\"{0}\"}}'.format(ip)\n if ttl is not None:\n payload = '{{\"ttl\":{0}}}'.format(ttl)\n resp = self.infoblox_.put(self._ref_, payload)\n if resp.status_code != 200:\n try:\n return self.infoblox_.__caller__(\n 'Could not update A record for {0} - Status {1}'\n .format(self.name, resp.status_code), resp.status_code)\n except Exception:\n return resp.status_code\n return 0","title":""},{"docid":"8505a302272a8553131e5778de7fe3e4","score":"0.49867657","text":"def updateloc(req, *args, **kwargs):\n return _matchloc(req, insert=True )","title":""},{"docid":"7c2f6077dd3fbef0a1dd70757abee2fc","score":"0.49825355","text":"def put(self,request,pk):\n\n saved_plan = self.get_object(pk=pk)\n #features = request.data.get('plans')[0].pop('features')\n #Supports partial request - PATCH in Django might be broken.\n serializer = PlanSerializer(instance=saved_plan, data=request.data[\"plans\"], partial=True)\n\n #TODO: this did not raise exception when request.data was mistakenly supplie\n\n if serializer.is_valid(raise_exception=True):\n plan_saved = serializer.save()\n\n # plan_feature_serializer = PlanFeatureSerializer(instance=saved_plan.features, data=features, partial=True, many=True)\n # if plan_feature_serializer.is_valid(raise_exception=True):\n # plan_feature_saved = plan_feature_serializer.save()\n\n return Response({\n \"success\": \"Plan '{}' updated successfully\".format(plan_saved.plan_name),\n \"plans\":serializer.data\n }, \n status=200)","title":""},{"docid":"c7b46a73c36c48b6f2431e53205c66e4","score":"0.49727967","text":"def update(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tupdateresource = dnssoarec()\n\t\t\t\tupdateresource.domain = resource.domain\n\t\t\t\tupdateresource.originserver = resource.originserver\n\t\t\t\tupdateresource.contact = resource.contact\n\t\t\t\tupdateresource.serial = resource.serial\n\t\t\t\tupdateresource.refresh = resource.refresh\n\t\t\t\tupdateresource.retry = resource.retry\n\t\t\t\tupdateresource.expire = resource.expire\n\t\t\t\tupdateresource.minimum = resource.minimum\n\t\t\t\tupdateresource.ttl = resource.ttl\n\t\t\t\treturn updateresource.update_resource(client)\n\t\t\telse :\n\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\tupdateresources = [ dnssoarec() for _ in range(len(resource))]\n\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\tupdateresources[i].domain = resource[i].domain\n\t\t\t\t\t\tupdateresources[i].originserver = resource[i].originserver\n\t\t\t\t\t\tupdateresources[i].contact = resource[i].contact\n\t\t\t\t\t\tupdateresources[i].serial = resource[i].serial\n\t\t\t\t\t\tupdateresources[i].refresh = resource[i].refresh\n\t\t\t\t\t\tupdateresources[i].retry = resource[i].retry\n\t\t\t\t\t\tupdateresources[i].expire = resource[i].expire\n\t\t\t\t\t\tupdateresources[i].minimum = resource[i].minimum\n\t\t\t\t\t\tupdateresources[i].ttl = resource[i].ttl\n\t\t\t\tresult = cls.update_bulk_request(client, updateresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e","title":""},{"docid":"3b51dd91b975397af5bf5ce05fbf84a1","score":"0.4968589","text":"def update(self, name, description, public):\n update_swarm = {}\n if name != None:\n update_swarm[\"name\"] = name\n if description != None:\n update_swarm[\"description\"] = description\n if public != None:\n update_swarm[\"public\"] = public\n update_swarm_json = json.dumps(update_swarm)\n conn = httplib.HTTPConnection(self.apikey.server)\n conn.request(\"PUT\", \"/swarms/%s\"%(self.id), update_swarm_json,\n {\"x-bugswarmapikey\":self.apikey.configuration})\n resp = conn.getresponse()\n txt = resp.read()\n conn.close()\n if resp.status >= 400:\n logging.warning('Swarm info response: ('+str(resp.status)+'): '+txt)\n return False\n logging.debug('Swarm info response: ('+str(resp.status)+'): '+txt)\n if name != None:\n self.name = name\n if description != None:\n self.description = description\n if public != None:\n self.public = public\n return True","title":""},{"docid":"264b78e936e97bd00727c672e1662897","score":"0.49667677","text":"def update_record(\n name,\n value,\n zone,\n record_type,\n identifier=None,\n ttl=None,\n region=None,\n key=None,\n keyid=None,\n profile=None,\n wait_for_sync=True,\n split_dns=False,\n private_zone=False,\n retry_on_rate_limit=None,\n rate_limit_retries=None,\n retry_on_errors=True,\n error_retries=5,\n):\n if region is None:\n region = \"universal\"\n\n conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)\n\n if split_dns:\n _zone = _get_split_zone(zone, conn, private_zone)\n else:\n _zone = conn.get_zone(zone)\n if not _zone:\n msg = \"Failed to retrieve zone {}\".format(zone)\n log.error(msg)\n return False\n _type = record_type.upper()\n\n if retry_on_rate_limit or rate_limit_retries is not None:\n if retry_on_rate_limit is not None:\n retry_on_errors = retry_on_rate_limit\n if rate_limit_retries is not None:\n error_retries = rate_limit_retries\n\n _value = _munge_value(value, _type)\n while error_retries > 0:\n try:\n old_record = _zone.find_records(name, _type, identifier=identifier)\n if not old_record:\n return False\n status = _zone.update_record(old_record, _value, ttl, identifier)\n return _wait_for_sync(status.id, conn, wait_for_sync)\n\n except DNSServerError as e:\n if retry_on_errors and _is_retryable_error(e):\n if \"Throttling\" == e.code:\n log.debug(\"Throttled by AWS API.\")\n elif \"PriorRequestNotComplete\" == e.code:\n log.debug(\n \"The request was rejected by AWS API. \"\n \"Route 53 was still processing a prior request.\"\n )\n time.sleep(3)\n error_retries -= 1\n continue\n raise\n return False","title":""},{"docid":"d68d755d35a1f03438e9f4215ed61a30","score":"0.49530244","text":"def set_zone_mode(\n cls,\n ctl_id: str,\n zone_idx: int,\n mode: str = None,\n setpoint: float = None,\n until: dt = None,\n duration: int = None,\n **kwargs,\n ):\n # W --- 18:013393 01:145038 --:------ 2349 013 0004E201FFFFFF330B1A0607E4\n # W --- 22:017139 01:140959 --:------ 2349 007 0801F400FFFFFF\n\n if mode is None:\n if setpoint is None:\n raise ValueError(\"Invalid args: Both mode and setpoint are None\")\n elif until:\n mode = ZoneMode.TEMPORARY\n elif duration:\n mode = ZoneMode.COUNTDOWN\n else:\n mode = ZoneMode.PERMANENT # or: ZoneMode.ADVANCED\n elif isinstance(mode, int):\n mode = f\"{mode:02X}\"\n\n if mode in ZONE_MODE_MAP:\n mode = ZONE_MODE_MAP[mode]\n elif mode not in ZONE_MODE_LOOKUP:\n raise TypeError(f\"Invalid args: Unknown mode: {mode}\")\n\n if setpoint is None:\n if mode != ZoneMode.SCHEDULE:\n raise ValueError(f\"Invalid args: For {mode}, setpoint cant be None\")\n elif not isinstance(setpoint, (int, float)):\n raise ValueError(f\"Invalid args: setpoint={setpoint}, should be float\")\n\n if mode == ZoneMode.TEMPORARY and until is None:\n mode = ZoneMode.ADVANCED # until = dt.now() + td(hour=1)\n elif mode in (ZoneMode.SCHEDULE, ZoneMode.PERMANENT) and (\n until is not None or duration is not None\n ):\n raise ValueError(f\"Invalid args: For {mode}, until should be None\")\n\n assert mode in ZONE_MODE_LOOKUP, mode\n\n payload = \"\".join(\n (\n f\"{zone_idx:02X}\",\n temp_to_hex(setpoint), # None means max, if a temp is required\n ZONE_MODE_LOOKUP[mode],\n \"FFFFFF\" if duration is None else f\"{duration:06X}\",\n \"\" if until is None else dtm_to_hex(until),\n )\n )\n\n return cls(W_, _2349, payload, ctl_id, **kwargs)","title":""},{"docid":"347fa978afa53680944e303392e62ffd","score":"0.49470228","text":"def _update_resource(resource_type, id):\n if resource_type not in schemas.keys():\n raise exceptions.NotFoundException('Resource of type \\'%s\\' not found' % resource_type)\n\n resource = None\n if 'slug' in schemas[resource_type]['properties'].keys():\n resource = db[resource_type].find_one({'slug': id})\n if not resource:\n resource = db[resource_type].find_one({'primaryKey.id': id})\n if not resource:\n raise exceptions.NotFoundException('Resource not found')\n\n if 'save_history' in flask.request.args:\n old_data = copy.deepcopy(resource)\n\n data = flask.request.get_json(force=True, silent=True)\n if not data or not isinstance(data, dict):\n raise exceptions.BadRequestException('Malformed JSON in PATCH data')\n\n # save keys that cannot be changed on update\n resource_id = resource.pop('_id')\n\n # restore keys that cannot be changed on update\n data['updatedAt'] = time_now()\n data['updatedBy'] = get_sub()\n data['createdAt'] = resource.pop('createdAt')\n data['createdBy'] = resource.pop('createdBy', 'Unknown')\n data['primaryKey'] = resource.pop('primaryKey')\n\n validate(data, schemas.get(resource_type))\n\n # _id is not part of the schema, so restore this after validation\n data['_id'] = resource_id\n\n if 'test' not in flask.request.args:\n update = resource2update(schemas[resource_type], data)\n db[resource_type].update_one({'_id': resource_id},\n update)\n\n if 'save_history' in flask.request.args:\n old_data.pop('_id')\n db[resource_type+'_history'].insert(old_data)\n\n return data, 200","title":""},{"docid":"b140e9e324bcbf9d585e9ec24655b37b","score":"0.4943501","text":"def perform_update(self, serializer):\n reporting_time = arrow.get(serializer.validated_data.get('time'))\n duration = serializer.validated_data.get('duration')\n requested_slot = [\n reporting_time,\n reporting_time.shift(minutes=duration),\n serializer.validated_data.get('attender'),\n ]\n if self.validate_slot(requested_slot):\n serializer.save()\n else:\n raise APIException({'message': 'This slot has been already reserved !!!'})","title":""},{"docid":"9aaa19876a28c74ef1a2424f0111e79e","score":"0.49418437","text":"def put(self,request,pk=None):\n return Response({\"method\":\"put\"})","title":""},{"docid":"b5cdb914f7cf325934fa1dc0ad3206f6","score":"0.4924634","text":"def update(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tupdateresource = nsvariable()\n\t\t\t\tupdateresource.name = resource.name\n\t\t\t\tupdateresource.type = resource.type\n\t\t\t\tupdateresource.iffull = resource.iffull\n\t\t\t\tupdateresource.ifvaluetoobig = resource.ifvaluetoobig\n\t\t\t\tupdateresource.ifnovalue = resource.ifnovalue\n\t\t\t\tupdateresource.init = resource.init\n\t\t\t\tupdateresource.expires = resource.expires\n\t\t\t\tupdateresource.comment = resource.comment\n\t\t\t\treturn updateresource.update_resource(client)\n\t\t\telse :\n\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\tupdateresources = [ nsvariable() for _ in range(len(resource))]\n\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\tupdateresources[i].name = resource[i].name\n\t\t\t\t\t\tupdateresources[i].type = resource[i].type\n\t\t\t\t\t\tupdateresources[i].iffull = resource[i].iffull\n\t\t\t\t\t\tupdateresources[i].ifvaluetoobig = resource[i].ifvaluetoobig\n\t\t\t\t\t\tupdateresources[i].ifnovalue = resource[i].ifnovalue\n\t\t\t\t\t\tupdateresources[i].init = resource[i].init\n\t\t\t\t\t\tupdateresources[i].expires = resource[i].expires\n\t\t\t\t\t\tupdateresources[i].comment = resource[i].comment\n\t\t\t\tresult = cls.update_bulk_request(client, updateresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e","title":""},{"docid":"0768e6867199ac02943409275f7465ee","score":"0.49196827","text":"def update(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tupdateresource = dnsparameter()\n\t\t\t\tupdateresource.retries = resource.retries\n\t\t\t\tupdateresource.minttl = resource.minttl\n\t\t\t\tupdateresource.maxttl = resource.maxttl\n\t\t\t\tupdateresource.cacherecords = resource.cacherecords\n\t\t\t\tupdateresource.namelookuppriority = resource.namelookuppriority\n\t\t\t\tupdateresource.recursion = resource.recursion\n\t\t\t\tupdateresource.resolutionorder = resource.resolutionorder\n\t\t\t\tupdateresource.dnssec = resource.dnssec\n\t\t\t\tupdateresource.maxpipeline = resource.maxpipeline\n\t\t\t\tupdateresource.dnsrootreferral = resource.dnsrootreferral\n\t\t\t\tupdateresource.dns64timeout = resource.dns64timeout\n\t\t\t\tupdateresource.ecsmaxsubnets = resource.ecsmaxsubnets\n\t\t\t\tupdateresource.maxnegcachettl = resource.maxnegcachettl\n\t\t\t\tupdateresource.cachehitbypass = resource.cachehitbypass\n\t\t\t\tupdateresource.maxcachesize = resource.maxcachesize\n\t\t\t\tupdateresource.maxnegativecachesize = resource.maxnegativecachesize\n\t\t\t\tupdateresource.cachenoexpire = resource.cachenoexpire\n\t\t\t\tupdateresource.splitpktqueryprocessing = resource.splitpktqueryprocessing\n\t\t\t\tupdateresource.cacheecszeroprefix = resource.cacheecszeroprefix\n\t\t\t\treturn updateresource.update_resource(client)\n\t\texcept Exception as e :\n\t\t\traise e","title":""},{"docid":"b8dba8265bf30d89baf299055cb0a277","score":"0.49184713","text":"def ground_zones_update(pcb_modules: \"List[PCBModule]\", zone_polygon: SimplePolygon,\n pcb_origin: P2D, tracing: str = \"\") -> None:\n # We are serching for XXX where XXX is either GND or /GND:\n # (zone (net NUMBER) (net_name XXX) (layer X.Cu) ... # X is B or F (i.e. Back/Front).\n # ...\n # (polygon\n # (pts\n # (xy X Y) ... # Replace these with the new ones.\n # )\n # )\n # (filled_polygon # The filled zone may not be present at first.\n # (pts\n # (xy X Y) ... # Keep these and let KiCad do the update.\n # )\n # )\n # )\n net_numbers_table: Dict[str, int] = PCBModule.net_numbers_table_get(pcb_modules)\n if \"GND\" in net_numbers_table:\n ground_net_number: int = net_numbers_table[\"GND\"]\n zone_size: int = len(zone_polygon)\n pcb_origin_x: float = pcb_origin.x\n pcb_origin_y: float = pcb_origin.y\n\n zone_pattern1 = f\" (zone (net {ground_net_number}) (net_name GND) (layer \"\n zone_pattern2 = f\" (zone (net {ground_net_number}) (net_name /GND) (layer \"\n pcb_module: PCBModule\n for pcb_module in pcb_modules:\n # Search through *preceeding_lines* looking for a ground zone and updating them\n # as appropriate. Construct *updated_lines* as we sweep through:\n updated_lines: List[str] = []\n preceding_lines: List[str] = pcb_module.preceding_lines\n zone_started: bool = False\n polygon_started: bool = False\n points_started: bool = False\n new_xys_inserted: bool = False\n xys_skipping: bool = False\n pcb_line_index: int\n pcb_line: str\n for pcb_line_index, pcb_line in enumerate(preceding_lines):\n if pcb_line.startswith(zone_pattern1) or pcb_line.startswith(zone_pattern2):\n # Start of a fill zone -- (zone ...:\n zone_started = True\n polygon_started = False\n points_started = False\n xys_skipping = False\n new_xys_inserted = False\n if tracing:\n print(f\"{tracing}Zone started: '{pcb_line}'\")\n elif pcb_line.startswith(\" (polygon\"): # )\n # Start of polygon outline (i.e. unfilled) -- (polygon ...:\n polygon_started = zone_started\n points_started = False\n xys_skipping = False\n elif pcb_line.startswith(\" (pts\"): # )\n # Start of points -- (pts ...)\n points_started = polygon_started\n xys_skipping = False\n elif pcb_line.startswith(\" (xy \"):\n # Start of xys -- (xy ...):\n xys_skipping = points_started\n # Insert new xys here if they have not already been inserted:\n if xys_skipping and not new_xys_inserted:\n index: int\n zone_xys: List[str] = [\n (f\"(xy {KicadPCB.number(pcb_origin_x + zone_polygon[index].x, 1)} \"\n f\"{KicadPCB.number(pcb_origin_y - zone_polygon[index].y, 1)})\")\n for index in range(zone_size)]\n\n # This is weird sometime we get duplicate points in *zone_xys*.\n # This code removes adjacent values that are duplicates:\n culled_zone_xys: List[str] = zone_xys[:1]\n zone_text: str\n for zone_text in zone_xys[1:]:\n if culled_zone_xys[-1] != zone_text:\n culled_zone_xys.append(zone_text)\n culled_zone_xys_size: int = len(culled_zone_xys)\n if tracing and zone_size != culled_zone_xys_size:\n print(f\"{tracing}zone_size={zone_size} != \"\n f\"{culled_zone_xys_size}= culled_zone_xys_size\")\n\n # Organize the the values in groups of 5:\n zones_quints: List[List[str]] = [\n culled_zone_xys[index:index + 5]\n for index in range(0, culled_zone_xys_size, 5)\n ]\n if tracing:\n print(f\"{tracing}zone_size={zone_size}\")\n print(f\"{tracing}zones_quints={zones_quints}\")\n\n # Append the lines of *zones_quints* to *updated_lines*:\n zones_quint: List[str]\n zone_index: int\n for zone_index, zones_quint in enumerate(zones_quints):\n quint_line: str = \" \" + ' '.join(zones_quint)\n updated_lines.append(quint_line)\n if tracing:\n print(f\"[{pcb_line_index}]\\tI[{zone_index}]\\t{quint_line}\")\n new_xys_inserted = True\n elif pcb_line.startswith(\" )\"): # End of (pts ...)\n new_xys_inserted = False\n xys_skipping = False\n points_started = False\n elif pcb_line.startswith(\" )\"): # End of (polygon ...)\n polygon_started = False\n elif pcb_line.startswith(\" )\"): # End of (zone ...)\n zone_started = False\n\n # Copy over *pcb_line* to *updated_lines* if we are not in *xys_skipping*:\n if tracing:\n zone_flag: str = 'Z' if zone_started else '-'\n polygon_flag: str = 'G' if polygon_started else '-'\n points_flag: str = 'P' if points_started else '-'\n skipping_flag: str = 'S' if xys_skipping else '-'\n print(f\"[{pcb_line_index}]\\t\"\n f\"{zone_flag}{polygon_flag}{points_flag}{skipping_flag}\\t\"\n f\"{pcb_line}\")\n if not xys_skipping:\n updated_lines.append(pcb_line)\n\n # Replace the updated_lines:\n pcb_module.preceding_lines = updated_lines","title":""},{"docid":"b90e981a8720785eaa0fdb0891c83bd2","score":"0.4917183","text":"def put(self, user_id, current_user, flight_id):\n\n flight = Flights.query.filter_by(id=flight_id).first()\n created_by = flight.created_by\n if user_id == created_by:\n # Obtain the new name of the flight from the request data\n edited = request.get_json()\n flight.name = edited['name']\n flight.origin = edited['origin']\n flight.location = edited['destination']\n flight.date = edited['date']\n flight.time = edited['time']\n flight.save()\n\n response = {\n 'id': flight.id, 'name' : flight.name, 'origin' : flight.origin,\n 'destination' : flight.destination, 'date' : flight.date, 'time' : flight.time\n }\n\n msg = {\"message\": \"Flight details updated successfully\"}\n return make_response(jsonify(msg)), 200\n response = {\"message\": \"You can only modify the flights you created\"}\n return jsonify(response), 401","title":""},{"docid":"b304669a393449e37fdc6baac67fc67b","score":"0.49152347","text":"def update(self, resource, uri=None, force=False, timeout=-1, custom_headers=None, default_values={}):\n if not resource:\n logger.exception(RESOURCE_CLIENT_RESOURCE_WAS_NOT_PROVIDED)\n raise ValueError(RESOURCE_CLIENT_RESOURCE_WAS_NOT_PROVIDED)\n\n logger.debug('Update async (uri = %s, resource = %s)' %\n (self._uri, str(resource)))\n\n if not uri:\n uri = resource['uri']\n\n if force:\n uri += '?force=True'\n\n resource = self.merge_default_values(resource, default_values)\n\n return self.__do_put(uri, resource, timeout, custom_headers)","title":""},{"docid":"9c7325d82e7324cb3baef7efe347ffb4","score":"0.49144873","text":"def testUpdateSegment(self):\n I = item.Item(2, 2, CornerPoint=[0,0])\n S1 = skyline.SkylineSegment(0, 2, 2)\n S2 = skyline.SkylineSegment(2, 0, 6)\n\n res = self.S._update_segment(self.S.skyline[0], 0, I)\n self.assertCountEqual(res, [S1, S2])","title":""},{"docid":"dda2977cf097f7da09155af5064de252","score":"0.49078995","text":"def test_resource_manager_updation(self):\n resources = self._set_and_get_resources(['test_resources/input/resource_one.json',\n 'test_resources/input/resource_one_updated.json'])\n self.assertEqual(len(resources), 1)\n self.assertEqual(resources.resources.pop().resource_creation_timestamp, 1526331464.49)","title":""},{"docid":"8b1ef3925b697c69a3d4adce2955e385","score":"0.48944154","text":"def test_05_test_settings_for_zone(self):\n config_name = \"enable.dynamic.scale.vm\"\n configs = Configurations.list(\n self.apiclient,\n name=config_name,\n zoneid=self.zone.id\n )\n self.assertIsNotNone(configs, \"Fail to get zone setting %s \" % config_name)\n\n orig_value = str(configs[0].value)\n new_value = 'true'\n\n Configurations.update(\n self.apiclient,\n name=config_name,\n value=new_value,\n zoneid=self.zone.id\n )\n\n configs = Configurations.list(\n self.apiclient,\n name=config_name,\n zoneid=self.zone.id\n )\n self.assertIsNotNone(configs, \"Fail to get ol setting %s \" % config_name)\n\n self.assertEqual(new_value,\n (configs[0].value),\n \"Failed to set new config value\")\n\n Configurations.reset(\n self.apiclient,\n name=config_name,\n zoneid=self.zone.id\n )\n\n configs = Configurations.list(\n self.apiclient,\n name=config_name,\n zoneid=self.zone.id\n )\n self.assertIsNotNone(configs, \"Fail to get zone setting %s \" % config_name)\n\n self.assertEqual(orig_value,\n (configs[0].value),\n \"Failed to reset the value for zone\")","title":""},{"docid":"32533953716ec19bf276f0340c20ff0b","score":"0.48901463","text":"def callback_2(status):\n async_dispatcher_send( hass, SIGNAL_ZONES_UPDATED, status[ZONES] )","title":""},{"docid":"5976bb0b9d2d10617dadc00483787b44","score":"0.48895377","text":"def update(self,field,val):\n Location.objects.get(id = self.id).update(field = val)","title":""},{"docid":"92e91c0ff40f589b608308c8027c572f","score":"0.48791897","text":"def updateZone(self, interval, world):\n #\n # Iterate through actors - use a list of the actors\n # in case the actor wants to update the list of\n # actors during this iteration\n for actor in list(self.actors):\n if actor.active:\n actor.updateActor(interval, world)\n #\n # Do physics if we need to\n if self._physics_objects:\n self._updatePhysics(interval)","title":""},{"docid":"705fd86155b6ea89db86b56d565d9cc4","score":"0.48785475","text":"def update_account(self):\n return bind_api(\n api=self,\n path='/accounts/{account_id}',\n method='PUT',\n post_container='account',\n allowed_param=['account_id', 'name'])","title":""},{"docid":"6f9568618edcc42e80bc7e8c408f3e8d","score":"0.48718858","text":"def _update_resource(self, res_type, context, id, res_data):\n\n res_dict = self._encode_resource(resource_id=id,\n resource=res_data[res_type])\n status_code, res_info = self._request_backend(context, res_dict,\n res_type, 'UPDATE')\n res_dicts = self._transform_response(status_code, info=res_info,\n obj_name=res_type)\n LOG.debug(\"update_%(res_type)s(): %(res_dicts)s\",\n {'res_type': res_type, 'res_dicts': res_dicts})\n\n return res_dicts","title":""},{"docid":"ae322bd1454219d29b576c4ea5a3d8b0","score":"0.4870781","text":"def create_zone(self):\n self.log.info(\"creating a zone\")\n resp = self.api.create_zone()\n self.log.debug(utils.resp_to_string(resp))\n if resp.status_code != 202:\n self.fail(\"failed to create zone (status=%s)\" % resp.status_code)\n return resp.json()[\"name\"], resp.json()[\"id\"]","title":""},{"docid":"9423ccd2291943b99aa862c4d9c21a0b","score":"0.48658797","text":"def put(self):\n request = transforms.loads(self.request.get('request'))\n key = request.get('key')\n\n if not self.assert_xsrf_token_or_fail(\n request, 'put-unit', {'key': key}):\n return\n\n if not CourseOutlineRights.can_edit(self):\n transforms.send_json_response(\n self, 401, 'Access denied.', {'key': key})\n return\n\n unit = courses.Course(self).find_unit_by_id(key)\n if not unit:\n transforms.send_json_response(\n self, 404, 'Object not found.', {'key': key})\n return\n\n payload = request.get('payload')\n updated_unit_dict = transforms.json_to_dict(\n transforms.loads(payload), self.SCHEMA_DICT)\n\n errors = []\n self.apply_updates(unit, updated_unit_dict, errors)\n if not errors:\n course = courses.Course(self)\n assert course.update_unit(unit)\n course.save()\n transforms.send_json_response(self, 200, 'Saved.')\n else:\n transforms.send_json_response(self, 412, '\\n'.join(errors))","title":""},{"docid":"20b74aac94ab85c6a30466317e8a109e","score":"0.4857295","text":"def test_route_update_by_admin(self):\n route_test_1 = Route.objects.create(\n name='route-test-1',\n created_by=self.user_admin,\n origin='origin-test',\n destination='destination-test'\n )\n\n payload = {\n 'name': 'rote-updated',\n 'origin': 'test-origin',\n 'destination': 'test-destination'\n }\n\n self.client.force_authenticate(self.user_admin)\n path = ROUTES_URL+str(route_test_1.id)+'/'\n response = self.client.put(path, payload)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"4ecf11823bc5b6229da23562e49465e2","score":"0.484399","text":"def update_resource(self, transaction):\n # If-Match\n if transaction.request.if_match:\n if None not in transaction.request.if_match and str(transaction.resource.etag) \\\n not in transaction.request.if_match:\n transaction.response.code = defines.Codes.PRECONDITION_FAILED.number\n return transaction\n # If-None-Match\n if transaction.request.if_none_match:\n transaction.response.code = defines.Codes.PRECONDITION_FAILED.number\n return transaction\n\n method = getattr(transaction.resource, \"render_PUT\", None)\n\n try:\n resource = method(request=transaction.request)\n except NotImplementedError:\n transaction.response.code = defines.Codes.METHOD_NOT_ALLOWED.number\n return transaction\n\n if isinstance(resource, Resource):\n pass\n elif isinstance(resource, tuple) and len(resource) == 2:\n resource, callback = resource\n resource = self._handle_separate(transaction, callback)\n if not isinstance(resource, Resource): # pragma: no cover\n transaction.response.code = defines.Codes.INTERNAL_SERVER_ERROR.number\n return transaction\n else: # pragma: no cover\n # Handle error\n transaction.response.code = defines.Codes.INTERNAL_SERVER_ERROR.number\n return transaction\n\n if resource.etag is not None:\n transaction.response.etag = resource.etag\n\n transaction.response.code = defines.Codes.CHANGED.number\n transaction.response.payload = None\n resource.changed = True\n resource.observe_count += 1\n transaction.resource = resource\n\n return transaction","title":""},{"docid":"5495ed84c50c941748110fa55e2fdc94","score":"0.48428872","text":"def update(self, domain_record, actual_ip):\n raise NotImplementedError","title":""},{"docid":"23a335bda8f7907515f7b3d96f95e43c","score":"0.48319024","text":"def update(self, resource: Resource):\n\n # Remember, everything is immutable.\n\n # 1. Update resource tree\n resource_name = resource.name\n self.resources.set(resource)\n\n # 2. Find any refs that point to this resource *name*.\n changed_ref_tos = [\n ref.to\n for ref in self.refs.items.values()\n if ref.to == resource_name\n ]\n\n # 3. Update any views that use those refs\n self.views.update_views(changed_ref_tos)","title":""},{"docid":"2d4488822b1d99c1c3ce929463083a69","score":"0.48297885","text":"def update(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tupdateresource = nsassignment()\n\t\t\t\tupdateresource.name = resource.name\n\t\t\t\tupdateresource.variable = resource.variable\n\t\t\t\tupdateresource.set = resource.set\n\t\t\t\tupdateresource.Add = resource.Add\n\t\t\t\tupdateresource.sub = resource.sub\n\t\t\t\tupdateresource.append = resource.append\n\t\t\t\tupdateresource.clear = resource.clear\n\t\t\t\tupdateresource.comment = resource.comment\n\t\t\t\treturn updateresource.update_resource(client)\n\t\t\telse :\n\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\tupdateresources = [ nsassignment() for _ in range(len(resource))]\n\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\tupdateresources[i].name = resource[i].name\n\t\t\t\t\t\tupdateresources[i].variable = resource[i].variable\n\t\t\t\t\t\tupdateresources[i].set = resource[i].set\n\t\t\t\t\t\tupdateresources[i].Add = resource[i].Add\n\t\t\t\t\t\tupdateresources[i].sub = resource[i].sub\n\t\t\t\t\t\tupdateresources[i].append = resource[i].append\n\t\t\t\t\t\tupdateresources[i].clear = resource[i].clear\n\t\t\t\t\t\tupdateresources[i].comment = resource[i].comment\n\t\t\t\tresult = cls.update_bulk_request(client, updateresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e","title":""},{"docid":"69031dd0987199445e82525939046b56","score":"0.4823912","text":"def update(self, id, realm, rep):\n\n path = \"/{realm}/client-templates/{id}\".format(\n id=id, realm=realm\n )\n params = {\n \n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n return self.session.request(\n method=\"PUT\",\n endpoint=self.session._admurl(path),\n params=params,\n headers=headers,\n data=rep,\n )","title":""},{"docid":"0264be7bf3cf95f45953e0439d024679","score":"0.48238653","text":"def put (self, request,pk=None):\n \n return Response({'method':'PUT'})","title":""},{"docid":"f7b32c4da2f5cf13fbf63e4b62b0e32a","score":"0.48217115","text":"def dns_update(self):\n if self.svc.node.collector_env.dbopensvc is None:\n return\n\n if self.ipname is None:\n self.log.debug(\"skip dns update: ipname is not set\")\n return\n\n try:\n self.conf_get(\"dns_update\")\n except ex.OptNotFound:\n self.log.debug(\"skip dns update: dns_update is not set\")\n return\n\n if not self.is_up():\n self.log.debug(\"skip dns update: resource is not up\")\n return\n\n if self.dns_name_suffix is None:\n self.log.debug(\"dns update: dns_name_suffix is not set\")\n\n try:\n self.getaddr()\n except ex.Error as exc:\n self.log.error(str(exc))\n return\n\n post_data = {\n \"content\": self.addr,\n }\n\n if self.dns_name_suffix:\n post_data[\"name\"] = self.dns_name_suffix\n\n try:\n data = self.svc.node.collector_rest_post(\n \"/dns/services/records\",\n post_data,\n path=self.dns_rec_name(),\n )\n except Exception as exc:\n raise ex.Error(\"dns update failed: \"+str(exc))\n if \"error\" in data:\n raise ex.Error(data[\"error\"])\n\n self.log.info(\"dns updated\")","title":""},{"docid":"b6293afea967a998c8582a88aab923e4","score":"0.48209825","text":"def perform_update(self, serializer):\n raise EndpointNotImplemented()","title":""},{"docid":"34c9aedb19254408859f71c869a982ab","score":"0.48169306","text":"async def set_mode(self, mode):\n allowed_modes = [ZONE_MODE.Off, ZONE_MODE.Override, ZONE_MODE.Timer]\n\n if self._has_pir:\n allowed_modes += [ZONE_MODE.Footprint]\n allowed_mode_strs = [IMODE_TO_MODE[i] for i in allowed_modes]\n\n if isinstance(mode, int) and mode in allowed_modes:\n mode_str = IMODE_TO_MODE[mode]\n elif isinstance(mode, str) and mode in allowed_mode_strs:\n mode_str = mode\n mode = MODE_TO_IMODE[mode_str]\n else:\n raise TypeError(f\"Zone.set_mode(): mode='{mode}' isn't valid.\")\n\n _LOGGER.debug(\n \"Zone(%s).set_mode(mode=%s, mode_str='%s')...\", self.id, mode, mode_str\n )\n\n if self._hub.api_version == 1:\n url = f\"zones/{self.id}/mode\" # v1 API uses strings\n resp = await self._hub.request(\"PUT\", url, data=mode_str)\n else: # self._hub.api_version == 3\n url = (\n f\"zone/{self.id}\"\n ) # v3 API uses dicts # TODO: check: is it PUT(POST?) vs PATCH\n resp = await self._hub.request(\"PATCH\", url, data={\"iMode\": mode})\n\n if resp: # for v1, resp = None?\n resp = resp[\"data\"] if resp[\"error\"] == 0 else resp\n _LOGGER.debug(\"Zone(%s).set_mode(): response = %s\", self.id, resp)","title":""},{"docid":"549670aa385170d59c7946342e144bbf","score":"0.47911164","text":"def update(controller, dt, ship, moon):\n action = controller.get_action()\n is_crashed = check_crash(ship, moon)\n \n ship.update(dt, action, is_crashed)","title":""},{"docid":"38f57ca5367a72ea91d33473cb5699c3","score":"0.47900626","text":"def put(self, request, pk=None):\n\n return Response({'method': 'put'})","title":""},{"docid":"7785f6b35e057aa2de84b686b820aaab","score":"0.47845304","text":"def update(self, data=None, timeout=-1, custom_headers=None, force=False):\n\n resource = deepcopy(self.data)\n resource.update(data)\n\n self.data = self._helper.update(resource, self.URI, force, timeout, custom_headers)\n\n return self","title":""},{"docid":"a259e0e575aa6ce4d0dadd5494e3b728","score":"0.4781302","text":"def update(self, client=None):\n try:\n return super(TimestampedResource, self).update(client)\n except exceptions.DoesNotExist as e:\n self._set('created', None)\n self._set('modified', None)\n raise e","title":""},{"docid":"e43f24ed9c3a06c7f66551b759baf643","score":"0.47758436","text":"def assign(zone_name, domain_name, value, ttl):\n zone = CONNECTION.get_zone(zone_name)\n record = _find_record(zone, domain_name)\n record_type = 'A' if _is_ip_address(value) else 'CNAME'\n\n if record:\n record_info = _query(record)\n if record_info['type'] == record_type and \\\n record_info['value'] == value and \\\n record_info['ttl'] == ttl:\n # No need to assign the same values again\n return\n\n # There's no way to change the type of an existing record in one operation.\n # Instead, we first DELETE if necessary and then CREATE a new record.\n change_set = r53.record.ResourceRecordSets(CONNECTION, zone.id)\n if record:\n change_set.add_change_record('DELETE', record)\n change = change_set.add_change('CREATE', domain_name, record_type, ttl)\n change.add_value(value)\n change = change_set.commit()\n\n change_id = _get_change_id(change)\n while _get_change_state(CONNECTION.get_change(change_id)) == 'PENDING':\n time.sleep(10)","title":""},{"docid":"ec5262734b8f0f91e29f436955951ac6","score":"0.47746283","text":"def update_day(zone_name, zone_job, day_of_week, value):\n if zone_name in ([n for [n] in use_database.read_all_garden_zone_names()]):\n zone_schedule[zone_name].update_day(zone_name, zone_job, day_of_week, value)\n elif zone_name in ([n for [n] in use_database.read_all_power_zone_names()]):\n power_zone_schedule[zone_name].update_day(zone_name, zone_job, day_of_week, value)","title":""},{"docid":"4a000758ca6a85b7f71cdbfe57a6aa5e","score":"0.47742543","text":"def update_resource(\n project_id,\n location,\n dataset_id,\n fhir_store_id,\n resource_type,\n resource_id,\n):\n # Imports Python's built-in \"os\" module\n import os\n\n # Imports the google.auth.transport.requests transport\n from google.auth.transport import requests\n\n # Imports a module to allow authentication using a service account\n from google.oauth2 import service_account\n\n # Gets credentials from the environment.\n credentials = service_account.Credentials.from_service_account_file(\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]\n )\n scoped_credentials = credentials.with_scopes(\n [\"https://www.googleapis.com/auth/cloud-platform\"]\n )\n # Creates a requests Session object with the credentials.\n session = requests.AuthorizedSession(scoped_credentials)\n\n # URL to the Cloud Healthcare API endpoint and version\n base_url = \"https://healthcare.googleapis.com/v1\"\n\n # TODO(developer): Uncomment these lines and replace with your values.\n # project_id = 'my-project' # replace with your GCP project ID\n # location = 'us-central1' # replace with the parent dataset's location\n # dataset_id = 'my-dataset' # replace with the parent dataset's ID\n # fhir_store_id = 'my-fhir-store' # replace with the FHIR store ID\n # resource_type = 'Patient' # replace with the FHIR resource type\n # resource_id = 'b682d-0e-4843-a4a9-78c9ac64' # replace with the FHIR resource's ID\n url = \"{}/projects/{}/locations/{}\".format(base_url, project_id, location)\n\n resource_path = \"{}/datasets/{}/fhirStores/{}/fhir/{}/{}\".format(\n url, dataset_id, fhir_store_id, resource_type, resource_id\n )\n\n # Sets required application/fhir+json header on the request\n headers = {\"Content-Type\": \"application/fhir+json;charset=utf-8\"}\n\n # The body shown works with a Patient resource and is not guaranteed\n # to work with other types of FHIR resources. If necessary,\n # supply a new body with data that corresponds to the resource you\n # are updating.\n body = {\"resourceType\": resource_type, \"active\": True, \"id\": resource_id}\n\n response = session.put(resource_path, headers=headers, json=body)\n response.raise_for_status()\n\n resource = response.json()\n\n print(\"Updated {} resource:\".format(resource[\"resourceType\"]))\n print(json.dumps(resource, indent=2))\n\n return resource","title":""}],"string":"[\n {\n \"docid\": \"b908bbf5043f2fb197e9223c0fc925f6\",\n \"score\": \"0.66193604\",\n \"text\": \"def _update(self) -> None:\\n import regenmaschine.exceptions as exceptions\\n\\n try:\\n self._entity_json = self._client.zones.get(self.rainmachine_id)\\n except exceptions.HTTPError as exc_info:\\n _LOGGER.error('Unable to update info for zone \\\"%s\\\"',\\n self.rainmachine_id)\\n _LOGGER.debug(exc_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf5126c9a67a676b1faf7f347f2e5ec\",\n \"score\": \"0.6341421\",\n \"text\": \"def update(self, req, id, *args, **kwargs):\\n # get the context\\n context = req.context\\n try:\\n # get the url\\n url = req.url\\n # if len(args) > 2:\\n # raise BadRequest(resource=\\\"zone update\\\", msg=url)\\n # get the body\\n values = json.loads(req.body)\\n values['id'] = id\\n LOG.info(_(\\\"the in value body is %(body)s\\\"), {\\\"body\\\": values})\\n LOG.info(_(\\\"the id is %(id)s\\\"), {\\\"id\\\": id})\\n if kwargs.get('owners'):\\n # check the in values\\n valid_attributes = ['id', 'tenant_id', 'owners']\\n recom_msg = self.validat_parms(values, valid_attributes)\\n # from rpc server update the zones in db and device\\n zones = self.manager.update_zone_owners(context, recom_msg,\\n recom_msg['id'])\\n else:\\n # check the in values\\n valid_attributes = ['id', 'tenant_id', 'default_ttl']\\n recom_msg = self.validat_parms(values, valid_attributes)\\n # from rpc server update the zones in db and device\\n zones = self.manager.update_zone(context, recom_msg,\\n recom_msg['id'])\\n except Nca47Exception as e:\\n self.response.status = e.code\\n LOG.error(_LE('Error exception! error info: %' + e.message))\\n LOG.exception(e)\\n return tools.ret_info(e.code, e.message)\\n except RemoteError as exception:\\n self.response.status = 500\\n message = exception.value\\n return tools.ret_info(self.response.status, message)\\n except Exception as exception:\\n LOG.exception(exception)\\n self.response.status = 500\\n return tools.ret_info(self.response.status, exception.message)\\n return zones\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2929ebbd94d7a479ddef9a5412a3e7ea\",\n \"score\": \"0.6042733\",\n \"text\": \"def update(self, ss_resource):\\n raise NotImplementedError(\\\"update() method should be implemented.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e01de995e456f9fc1fa07e18241a524\",\n \"score\": \"0.6037626\",\n \"text\": \"def update_route53(self):\\n self.log.debug('Updating Route53 information for {}'.format(self.account))\\n\\n # region Update zones\\n existing_zones = DNSZone.get_all(self.account)\\n zones = self.__fetch_route53_zones()\\n for resource_id, data in zones.items():\\n if resource_id in existing_zones:\\n zone = DNSZone.get(resource_id)\\n if zone.update(data):\\n self.log.debug('Change detected for Route53 zone {}/{}'.format(\\n self.account,\\n zone.name\\n ))\\n zone.save()\\n else:\\n tags = data.pop('tags')\\n DNSZone.create(\\n resource_id,\\n account_id=self.account.account_id,\\n properties=data,\\n tags=tags\\n )\\n\\n self.log.debug('Added Route53 zone {}/{}'.format(\\n self.account,\\n data['name']\\n ))\\n\\n db.session.commit()\\n\\n zk = set(zones.keys())\\n ezk = set(existing_zones.keys())\\n\\n for resource_id in ezk - zk:\\n zone = existing_zones[resource_id]\\n\\n db.session.delete(zone.resource)\\n self.log.debug('Deleted Route53 zone {}/{}'.format(\\n self.account.account_name,\\n zone.name.value\\n ))\\n db.session.commit()\\n # endregion\\n\\n # region Update resource records\\n try:\\n for zone_id, zone in DNSZone.get_all(self.account).items():\\n existing_records = {rec.id: rec for rec in zone.records}\\n records = self.__fetch_route53_zone_records(zone.get_property('zone_id').value)\\n\\n for data in records:\\n if data['id'] in existing_records:\\n record = existing_records[data['id']]\\n if record.update(data):\\n self.log.debug('Changed detected for DNSRecord {}/{}/{}'.format(\\n self.account,\\n zone.name,\\n data['name']\\n ))\\n record.save()\\n else:\\n record = DNSRecord.create(\\n data['id'],\\n account_id=self.account.account_id,\\n properties={k: v for k, v in data.items() if k != 'id'},\\n tags={}\\n )\\n self.log.debug('Added new DNSRecord {}/{}/{}'.format(\\n self.account,\\n zone.name,\\n data['name']\\n ))\\n zone.add_record(record)\\n db.session.commit()\\n\\n rk = set(x['id'] for x in records)\\n erk = set(existing_records.keys())\\n\\n for resource_id in erk - rk:\\n record = existing_records[resource_id]\\n zone.delete_record(record)\\n self.log.debug('Deleted Route53 record {}/{}/{}'.format(\\n self.account.account_name,\\n zone_id,\\n record.name\\n ))\\n db.session.commit()\\n except:\\n raise\\n # endregion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d6224a3a6d49357c502819ffbfea36\",\n \"score\": \"0.5900831\",\n \"text\": \"def zone(self, zone):\\n\\n self._zone = zone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54990993e1c0163d478991a205b9ba6f\",\n \"score\": \"0.5839018\",\n \"text\": \"def set_zonefile(config, name, zonefile):\\n path = \\\"/v1/names/{}/zonefile\\\".format(name)\\n payload = { 'zonefile': zonefile.read() }\\n url = create_url(config,path)\\n r = requests.put(url, headers=make_headers(config), json=payload)\\n output(config, url, r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcccbdc61d08b63f83249b16aa12af5d\",\n \"score\": \"0.58341324\",\n \"text\": \"async def async_update(self):\\n try:\\n LOG.debug(f\\\"Updating {self.zone_info}\\\")\\n status = await self._amp.zone_status(self._zone_id)\\n if not status:\\n return\\n except Exception as e:\\n # log up to two times within a specific period to avoid saturating the logs\\n @limits(calls=2, period=10 * MINUTES)\\n def log_failed_zone_update(e):\\n LOG.warning(f\\\"Failed updating {self.zone_info}: {e}\\\")\\n\\n log_failed_zone_update(e)\\n return\\n\\n LOG.debug(f\\\"{self.zone_info} status update: {status}\\\")\\n self._status = status\\n\\n source_id = status.get(\\\"source\\\")\\n if source_id:\\n source_name = self._source_id_to_name.get(source_id)\\n if source_name:\\n self._source = source_name\\n else:\\n # sometimes the client may have not configured a source, but if the amplifier is set\\n # to a source other than one defined, go ahead and dynamically create that source. This\\n # could happen if the user changes the source through a different app or command.\\n source_name = f\\\"Source {source_id}\\\"\\n LOG.warning(\\n f\\\"Undefined source id {source_id} for {self.zone_info}, adding '{source_name}'!\\\"\\n )\\n self._source_id_to_name[source_id] = source_name\\n self._source_name_to_id[source_name] = source_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298b2e7c4e3a798c3d5d2e9c0ff50407\",\n \"score\": \"0.58222544\",\n \"text\": \"def update_resource(RoleArn=None, ResourceArn=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f053cf63facc2cf646528d110db0b700\",\n \"score\": \"0.57523984\",\n \"text\": \"def set_zone(self, zone, x, y):\\n\\t\\tself.zone = zone\\n\\t\\tself.x = x\\n\\t\\tself.y = y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f013b2d193b0b79f8627ffc30e08ee\",\n \"score\": \"0.5736969\",\n \"text\": \"def modify_zone(zone_name, endpoint=None, comment=None):\\n\\n if not zone_name:\\n return None\\n\\n if comment and run_iadmin('modzone', [zone_name, 'comment', comment]):\\n return None\\n\\n if endpoint and run_iadmin('modzone', [zone_name, 'conn', endpoint]):\\n return None\\n\\n zone = get_zone_details(zone_name)\\n\\n return zone[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f0ad099b6f906e2cc62d61299ebea2\",\n \"score\": \"0.56937206\",\n \"text\": \"def set_zone(self, zone):\\n self._zone = zone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db74f7a93128ca157e6bc775fea5988a\",\n \"score\": \"0.56497073\",\n \"text\": \"def _signal_zone_update(self):\\n dispatcher_send(self.hass, f\\\"{SIGNAL_ZONE_UPDATE}-{self._zone.zone_id}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea346236f4546ea373fa1da437725483\",\n \"score\": \"0.564124\",\n \"text\": \"def update_record(acct:Account, zone:str, record:str) -> bool:\\n headers = {\\n \\\"X-Auth-Email\\\": acct.email,\\n \\\"X-Auth-Key\\\": acct.api_key,\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n }\\n zone_id = \\\"\\\"\\n record_id = \\\"\\\"\\n\\n _zone = get_zone(acct, zone)\\n if _zone:\\n zone_id = _zone.get(\\\"result\\\")[0].get(\\\"id\\\")\\n if not zone_id:\\n # lets just stop here, no need to further requesting\\n return False\\n\\n _records = get_records(acct, zone_id, record)\\n if _zone:\\n record_id = _records.get(\\\"result\\\")[0].get(\\\"id\\\")\\n if not record_id:\\n # lets just stop here, no need to further requesting\\n return False\\n\\n url = f\\\"https://api.cloudflare.com/client/v4/zones/{zone_id}/dns_records/{record_id}\\\"\\n data = {\\\"type\\\": \\\"A\\\", \\\"name\\\": record, \\\"content\\\": acct.ip}\\n r = requests.put(url, headers=headers, data=json.dumps(data))\\n\\n return r.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9aae6eced19396e27567fc895b810f\",\n \"score\": \"0.5596219\",\n \"text\": \"def set_zone(self, zone: Zone,\\n timeout=DEFAULT_TIMEOUT):\\n req = \\\"/api/zones\\\"\\n data = self._post_codec(req, timeout, zone)\\n new_zone = Zone.from_dict(data)\\n return new_zone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b0722c4f26420609410411b98f4224\",\n \"score\": \"0.5582042\",\n \"text\": \"def edit_record(self, id_, **kwargs):\\n\\n if self.is_valid_id(id_):\\n response = self.session.put(f\\\"{self.api}{self.default_path}/zone/{self.domain}/record/{id_}\\\",\\n json=kwargs).json()\\n else:\\n response = {'status': 'error', 'item': 'Not found',\\n 'errors': {'content': ['Specified ID is invalid, no record found.']}}\\n\\n log_response(\\\"EDITING RECORD\\\", response)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5b49de66418cf1d6e3ff1fa05bb8512\",\n \"score\": \"0.55711555\",\n \"text\": \"def update_record_value(self, new_value, ttl=300):\\n new_zone_version = None\\n zone_id = self.__get_active_zone_id()\\n try:\\n # create new zone version\\n new_zone_version = self.__api.domain.zone.version.new(\\n self.api_key,\\n zone_id\\n )\\n logging.debug('DNS working on a new zone (version %s)', new_zone_version)\\n\\n record_list = self.__api.domain.zone.record.list(\\n self.api_key,\\n zone_id,\\n new_zone_version,\\n self.record\\n )\\n\\n logging.debug('Updating records :%s', record_list)\\n\\n # Update each record that matches the filter\\n for a_record in record_list:\\n # get record id\\n a_record_id = a_record['id']\\n a_record_name = a_record['name']\\n a_record_type = a_record['type']\\n\\n # update record value\\n new_record = self.record.copy()\\n new_record.update({'name': a_record_name, 'type': a_record_type, 'value': new_value, 'ttl': ttl})\\n updated_record = self.__api.domain.zone.record.update(\\n self.api_key,\\n zone_id,\\n new_zone_version,\\n {'id': a_record_id},\\n new_record\\n )\\n except xmlrpc.client.Fault as e:\\n # delete updated zone\\n if new_zone_version is not None:\\n self.__api.domain.zone.version.delete(\\n self.api_key,\\n zone_id,\\n new_zone_version\\n )\\n raise\\n else:\\n # activate updated zone\\n self.__api.domain.zone.version.set(\\n self.api_key,\\n zone_id,\\n new_zone_version\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d87b2fb9b56f0a58d16cc1ac06b317\",\n \"score\": \"0.55356956\",\n \"text\": \"def upsert_route53_record(self, account_name, zone_id, recordset):\\n session = self.get_session(account_name)\\n client = session.client('route53')\\n\\n try:\\n client.change_resource_record_sets(\\n HostedZoneId=zone_id,\\n ChangeBatch={\\n 'Changes': [{\\n 'Action': 'UPSERT',\\n 'ResourceRecordSet': recordset,\\n }]\\n }\\n )\\n except client.exceptions.NoSuchHostedZone:\\n logging.error(f'[{account_name}] Error trying to delete record: '\\n f'unknown DNS zone {zone_id}')\\n except Exception as e:\\n logging.error(f'[{account_name}] unhandled exception: {e}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f268c8b33f6de1220103f1fd8b585c15\",\n \"score\": \"0.550737\",\n \"text\": \"def update(\\n self, options, callback=None, errback=None, parent=True, **kwargs\\n ):\\n\\n if not self.data:\\n raise ReservationException(\\\"Reservation not loaded\\\")\\n\\n def success(result, *args):\\n self.data = result\\n self.id = result[\\\"id\\\"]\\n self.address_id = result[\\\"address_id\\\"]\\n self.mac = result[\\\"mac\\\"]\\n self.options = result[\\\"options\\\"]\\n\\n if callback:\\n return callback(self)\\n else:\\n return self\\n\\n return self._rest.update(\\n self.id,\\n options,\\n callback=success,\\n errback=errback,\\n parent=parent,\\n **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467321618ddf902ded11714253b2e8f5\",\n \"score\": \"0.5499926\",\n \"text\": \"def update_resource(resource, destroy_bash, rc, stderr, stdout):\\n resource['destroy'] = {\\n 'cmd': destroy_bash,\\n 'rc': rc,\\n 'stderr': stderr,\\n 'stdout': stdout\\n }\\n return maccli.dao.api_resource.update(resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d339d31f023470adca0be360977e54\",\n \"score\": \"0.5494631\",\n \"text\": \"async def update_switch(self, zone, state, momentary=None, times=None, pause=None):\\n try:\\n if self.client:\\n if self.api_version == CONF_ZONE:\\n return await self.client.put_zone(\\n zone,\\n state,\\n momentary,\\n times,\\n pause,\\n )\\n\\n # device endpoint uses pin number instead of zone\\n return await self.client.put_device(\\n ZONE_TO_PIN[zone],\\n state,\\n momentary,\\n times,\\n pause,\\n )\\n\\n except self.client.ClientError as err:\\n _LOGGER.warning(\\\"Exception trying to update panel: %s\\\", err)\\n\\n raise CannotConnect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd24357bb21fe51de5d0a45de31d916\",\n \"score\": \"0.54757786\",\n \"text\": \"def update_resource(resource):\\n\\n known_resources = {'courses', 'programs'}\\n\\n if not resource in known_resources:\\n return abort(400, 'Not a known data resource.')\\n\\n return \\\"200 - OK!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a3d5daedffb144e8c32576fcd709fe\",\n \"score\": \"0.5445498\",\n \"text\": \"def AddZoneResourceArg(parser, verb, positional=True):\\n name = 'zone' if positional else '--zone'\\n return concept_parsers.ConceptParser.ForResource(\\n name,\\n GetZoneResourceSpec(),\\n 'Arguments and flags that define the Dataplex zone you want {}'.format(\\n verb),\\n required=True).AddToParser(parser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b26a08be8d0fb7e2a00477c76e63294\",\n \"score\": \"0.54265845\",\n \"text\": \"def update_resource_record(resource_record, value, client=None):\\n\\n if not client: # pragma: no cover\\n client = get_client()\\n\\n record_name = resource_record['Name']\\n record_type = resource_record['Type']\\n set_identifier = resource_record['SetIdentifier']\\n current_value = resource_record['ResourceRecords'][0]['Value']\\n\\n if value == current_value:\\n return True # Nothing to do, it is already up-to-date\\n\\n try:\\n client.change_resource_record_sets(\\n HostedZoneId='string',\\n ChangeBatch={\\n 'Comment': \\\"Updating DNS record via route53_dyndns\\\",\\n 'Changes': [\\n {\\n 'Action': 'UPSERT',\\n 'ResourceRecordSet': {\\n 'Name': record_name,\\n 'Type': record_type,\\n 'SetIdentifier': set_identifier,\\n 'ResourceRecords': [\\n {\\n 'Value': value\\n },\\n ],\\n }\\n },\\n ]\\n }\\n )\\n except Exception as e:\\n # TODO - Log the error\\n raise Route53Exception(e)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db797c37ea00fdb66ad89c5c5d416965\",\n \"score\": \"0.5385896\",\n \"text\": \"def update(resource_type, resource_id, data):\\n # type: (str, int, dict) -> dict\\n url = '/'.join([CONF.server.rstrip('/'), 'api', '1.0', resource_type,\\n str(resource_id), ''])\\n\\n return put(url, data).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a49ea8c469f3fba96dfddcc2f9edb3e\",\n \"score\": \"0.53846484\",\n \"text\": \"def patch_resource(self, namespace: typing.Optional[str] = None):\\n pass # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75f8c033821f9a3ecfc785e70696945\",\n \"score\": \"0.53627706\",\n \"text\": \"def _update_dnsrecord(self, session, record_id, record, opts):\\n zone_id = opts['zone_id']\\n request = Request(\\n 'PUT',\\n self._url + \\\"/zones/{0}/dns_records/{1}\\\".format(zone_id,\\n record_id),\\n json=record,\\n auth=self._auth)\\n res = _call(session, request)\\n return (res['id'], res['content'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85433d61bc5db142d57b6e75996cabb\",\n \"score\": \"0.5352522\",\n \"text\": \"def update(self, resource, data):\\n pf = self.log_prefix()\\n if not self.dry_run:\\n resource.update(data)\\n self.session.commit()\\n self.logger.debug('%s update resource %s', pf, resource.to_str())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff71f377ad4bd1ea09e39ff554ee30ce\",\n \"score\": \"0.53504497\",\n \"text\": \"def update_timezone(self, timezone):\\n\\n body = {\\n \\\"requests\\\": [\\n {\\n \\\"updateSpreadsheetProperties\\\": {\\n \\\"properties\\\": {\\\"timeZone\\\": timezone},\\n \\\"fields\\\": \\\"timeZone\\\",\\n },\\n },\\n ]\\n }\\n\\n res = self.batch_update(body)\\n self._properties[\\\"timeZone\\\"] = timezone\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671e3d5b69fbfa76add61b1740018ed5\",\n \"score\": \"0.53392094\",\n \"text\": \"def update(context, resource, **kwargs):\\n etag = kwargs.pop('etag')\\n id = kwargs.pop('id')\\n data = utils.sanitize_kwargs(**kwargs)\\n uri = '%s/%s/%s' % (context.dci_cs_api, resource, id)\\n r = context.session.put(uri, headers={'If-match': etag}, json=data)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0587394b391db90025fda98a506062cc\",\n \"score\": \"0.5334158\",\n \"text\": \"def update(context, resource, **kwargs):\\n etag = kwargs.pop(\\\"etag\\\")\\n id = kwargs.pop(\\\"id\\\")\\n data = utils.sanitize_kwargs(**kwargs)\\n uri = \\\"%s/%s/%s\\\" % (context.dci_cs_api, resource, id)\\n r = context.session.put(\\n uri, timeout=HTTP_TIMEOUT, headers={\\\"If-match\\\": etag}, json=data\\n )\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9a09cdf45609a04823be89a32f77e79\",\n \"score\": \"0.5328376\",\n \"text\": \"def run_UpdateResourceW(regs):\\n esp = regs[\\\"ESP\\\"]\\n res_type = xrkutil.dbg_read_pwstring(esp + 8)\\n res_name = xrkutil.dbg_read_pwstring(esp + 0xC)\\n\\n suck_api_call(\\\"UpdateResourceW\\\", regs, {\\\"res_type\\\": res_type, \\\"res_name\\\": res_name})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e089f170d5d3289c07765070ceb05220\",\n \"score\": \"0.52920365\",\n \"text\": \"def api_location_update(logger, location_id, data, scheme=\\\"nrega\\\"):\\n utc_now = pytz.utc.localize(datetime.datetime.utcnow())\\n india_now = utc_now.astimezone(pytz.timezone(\\\"Asia/Calcutta\\\"))\\n india_now_isoformat = india_now.isoformat()\\n headers = get_authentication_header()\\n data = {\\n \\\"id\\\" : location_id,\\n \\\"data_json\\\" : data\\n }\\n res = requests.patch(LOCATIONURL, headers=headers, data=json.dumps(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75732b8addfe0cdcb894efe1041cabdb\",\n \"score\": \"0.5280714\",\n \"text\": \"def update(self, request, pk=None):\\n schedule = Schedule.objects.get(pk=pk)\\n\\n schedule.date = request.data[\\\"date\\\"]\\n schedule.description = request.data[\\\"description\\\"]\\n schedule.time = request.data[\\\"time\\\"]\\n\\n show = Show.objects.get(pk=request.data[\\\"show\\\"])\\n schedule.show = show\\n\\n schedule.save()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6714ae2ea4e6f63ba333b358a1c482c\",\n \"score\": \"0.525226\",\n \"text\": \"def update(self,request,pk=None):\\n\\n return Response({'http_method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f727328c52a5224d4cbdfda9008c914e\",\n \"score\": \"0.52393675\",\n \"text\": \"async def update(self):\\n self._zones = self._test_json[\\\"zones\\\"]\\n self._devices = self._test_json[\\\"devices\\\"]\\n self._issues = _issues_via_v3_zones({\\\"data\\\": self._zones})\\n self._version = _version_via_v3_zones({\\\"data\\\": self._zones}) # a hack\\n\\n await self._update() # now convert all the raw JSON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7801ce7cbcdb445071ec378017a4086c\",\n \"score\": \"0.5226109\",\n \"text\": \"def update_dns_record(self, zone, record, record_type, record_value, is_alias=False, dry_run=False):\\n changes = boto.route53.record.ResourceRecordSets(self.conn_r53, zone)\\n change = changes.add_change(\\\"UPSERT\\\", record, record_type, ttl=60)\\n if is_alias:\\n # provide list of params as needed by function set_alias\\n # http://boto.readthedocs.org/en/latest/ref/route53.html#boto.route53.record.Record.set_alias\\n change.set_alias(*record_value)\\n else:\\n change.add_value(record_value)\\n if dry_run:\\n print(changes)\\n else:\\n changes.commit()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5137349c2ca684b547e32657cdb0587e\",\n \"score\": \"0.52146596\",\n \"text\": \"def update_a(self, name, value, ttl=None, comment=\\\"\\\"):\\n name = self.route53connection._make_qualified(name)\\n old_record = self.get_a(name)\\n ttl = ttl or old_record.ttl\\n return self.update_record(resource_type='A',\\n name=name,\\n old_value=old_record.resource_records,\\n new_value=value,\\n old_ttl=old_record.ttl,\\n new_ttl=ttl,\\n comment=comment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4255cf031229d0d8e9ed07801b2089\",\n \"score\": \"0.51888937\",\n \"text\": \"def update(self, request, pk=None):\\n \\n return Response({'http_method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a458ee11743e01ac3ce8934f3b93b234\",\n \"score\": \"0.51835114\",\n \"text\": \"def put(self, privilege_id):\\n current_privilege = DBPrivilege.query.get(privilege_id)\\n args = dns_privilege_common_parser.parse_args()\\n privilege_name = args['name']\\n operation = args['operation']\\n resource_type = args['resource_type']\\n resource_id = args['resource_id']\\n comment = args.get('comment', '')\\n try:\\n current_privilege.name = privilege_name\\n current_privilege.operation = operation\\n current_privilege.resource_type = resource_type\\n current_privilege.resource_id = resource_id\\n current_privilege.comment = comment\\n db.session.add(current_privilege)\\n except Exception as e:\\n db.session.rollback()\\n return get_response(RequestCode.OTHER_FAILED, '修改失败！\\\\n{e}'.format(e=str(e)))\\n return get_response(RequestCode.SUCCESS, '修改成功！')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc312f060f7a5d920aa727f5d7a2c9cc\",\n \"score\": \"0.51795584\",\n \"text\": \"def testUpdate(self):\\n data = {'name': 'toto2'}\\n response = requests.put(url=self.url, json=data)\\n headers = response.headers\\n\\n self.assertTrue(self.city == storage.get(City, self.city_id))\\n self.assertEqual(response.status_code, 200, WRONG_STATUS_CODE_MSG)\\n self.assertEqual(\\n headers['Content-Type'], 'application/json', WRONG_TYPE_RETURN_MSG)\\n json_data = response.json()\\n storage.reload()\\n city = storage.get(City, self.city_id)\\n self.assertEqual(city.name, 'toto2')\\n self.assertIn('name', json_data, MISSING_NAME_ATTR_MSG)\\n self.assertIn('created_at', json_data, MISSING_CREATED_AT_ATTR_MSG)\\n self.assertIn('updated_at', json_data, MISSING_UPDATED_AT_ATTR_MSG)\\n self.assertIn('__class__', json_data, MISSING_CLASS_ATTR_MSG)\\n self.assertIn('state_id', json_data, MISSING_STATE_ID_ATTR_MSG)\\n self.assertEqual(json_data['name'], 'toto2')\\n storage.delete(city)\\n storage.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bb346989e3ba5eae401d59f12d8ed5b\",\n \"score\": \"0.5125676\",\n \"text\": \"def plan_resources_update(context, plan_id, resources):\\n return IMPL.plan_resources_update(context, plan_id, resources)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a73ce00829c70863b00759d004fa840\",\n \"score\": \"0.51215047\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a73ce00829c70863b00759d004fa840\",\n \"score\": \"0.51215047\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a73ce00829c70863b00759d004fa840\",\n \"score\": \"0.51215047\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a73ce00829c70863b00759d004fa840\",\n \"score\": \"0.51215047\",\n \"text\": \"def update(self, request, pk=None):\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d801b3079fa67c5b855fd620a616f0f4\",\n \"score\": \"0.5120801\",\n \"text\": \"def update_placement(self, placementName: str, projectName: str, attributes: Dict = None) -> Dict:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c91c8c99b529a5b1f932043f0940afd\",\n \"score\": \"0.5118039\",\n \"text\": \"def test_update_resource(self):\\n # create a valid http response with a successful status code.\\n fake_response = requests.Response()\\n fake_response.status_code = requests.codes.ok\\n fake_response.headers = []\\n # obtain the mock object that corresponds to the call of request()\\n self.request_method_mock.return_value = fake_response\\n resource_path = \\\"%s/%s\\\" % (netscaler_driver.VIPS_RESOURCE,\\n TESTVIP_ID)\\n resource_name = netscaler_driver.VIP_RESOURCE\\n resource_body = self._get_testvip_httpbody_for_update()\\n # call method under test: update_resource.\\n self.testclient.update_resource(TEST_TENANT_ID, resource_path,\\n resource_name, resource_body)\\n resource_url = \\\"%s/%s\\\" % (self.testclient.service_uri, resource_path)\\n # assert that requests.request() was called with the\\n # expected params.\\n self.request_method_mock.assert_called_once_with(\\n 'PUT',\\n url=resource_url,\\n headers=mock.ANY,\\n data=mock.ANY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7429b21319720bfe73bcfef06136b4b1\",\n \"score\": \"0.5116938\",\n \"text\": \"def _update_resource(\\n cls, client: utils.MetadataClientWithOverride, resource: proto.Message,\\n ) -> proto.Message:\\n\\n return client.update_context(context=resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5105461\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5105461\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e474177ed1d8d332bbd96ee9201601\",\n \"score\": \"0.5105461\",\n \"text\": \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"530004acde4c3436899992ad02eb1eb5\",\n \"score\": \"0.50869197\",\n \"text\": \"def test_update_country(self):\\n body = Country()\\n response = self.client.open(\\n '/Erraticturtle35/Tantakatu/1.0.0/countries/{country_id}'.format(country_id=789),\\n method='PUT',\\n data=json.dumps(body),\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2026d72227ffd21138eb6a380f66b30e\",\n \"score\": \"0.50614107\",\n \"text\": \"def _add_zone_to_bind(self, zone):\\n _, _, ret = self.rndc_target.write_zone_file(zone)\\n assert ret == 0\\n\\n _, _, ret = self.rndc_target.addzone(zone)\\n assert ret == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8374eba22aa4d087499950bab4cc5af\",\n \"score\": \"0.50612473\",\n \"text\": \"def update(self, respool, name, cpu, memory, shares='normal'):\\n try:\\n config = vim.ResourceConfigSpec()\\n config.cpuAllocation = vim.ResourceAllocationInfo()\\n config.cpuAllocation.expandableReservation = False\\n config.cpuAllocation.limit = cpu\\n config.cpuAllocation.reservation = cpu\\n config.cpuAllocation.shares = vim.SharesInfo()\\n config.cpuAllocation.shares.level = shares \\n config.memoryAllocation = vim.ResourceAllocationInfo()\\n config.memoryAllocation.expandableReservation = False\\n config.memoryAllocation.limit = memory\\n config.memoryAllocation.reservation = memory\\n config.memoryAllocation.shares = vim.SharesInfo()\\n config.memoryAllocation.shares.level = shares\\n \\n res = respool.UpdateConfig(name, config)\\n self.logger.debug(\\\"Update resource pool %s\\\" % name)\\n return res\\n except vmodl.MethodFault as error:\\n self.logger.error(error.msg, exc_info=True)\\n raise VsphereError(error.msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df8678201f47047d9cf787855c065f7\",\n \"score\": \"0.50491494\",\n \"text\": \"def associate_zone(client, hosted_zone_id, region, vpc_id):\\n try:\\n return client.associate_vpc_with_hosted_zone(\\n HostedZoneId=hosted_zone_id,\\n VPC={\\n 'VPCRegion': region,\\n 'VPCId': vpc_id\\n },\\n Comment='Updated by Lambda DDNS'\\n )\\n except:\\n publish_to_sns(SNS_CLIENT, ACCOUNT, REGION, \\\"Unexpected error:\\\" +\\n str(sys.exc_info()[0]) + lineno())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdc37464c6ff3ba9e2872facb71c2a6\",\n \"score\": \"0.5039758\",\n \"text\": \"def put(self, **params):\\n return self.patch(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b667a700c6ae5c5630c84983910eb0\",\n \"score\": \"0.503604\",\n \"text\": \"def rput(self, resource, **kw):\\n return self.put(resource, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a3f5f4d4e66bc0f438737c596e96e5\",\n \"score\": \"0.5018837\",\n \"text\": \"def mark_zone(self, zone_start):\\n row_end = self.get_end(zone_start)\\n zone_end = self.get_rows(zone_start, row_end)\\n new_zone = Zone(zone_start, zone_end)\\n self.add_zone(new_zone)\\n return new_zone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ce6d2db6f9b5cba81c6b25120fd4ba\",\n \"score\": \"0.50127333\",\n \"text\": \"def _update_resource(\\n cls,\\n client: utils.MetadataClientWithOverride,\\n resource: proto.Message,\\n ) -> proto.Message:\\n\\n return client.update_artifact(artifact=resource)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755a0a19c80fcc5753ed8a9276843770\",\n \"score\": \"0.5008044\",\n \"text\": \"def update(self, ip=None, ttl=None):\\n if ip is not None:\\n payload = '{{\\\"ipv4addr\\\":\\\"{0}\\\"}}'.format(ip)\\n if ttl is not None:\\n payload = '{{\\\"ttl\\\":{0}}}'.format(ttl)\\n resp = self.infoblox_.put(self._ref_, payload)\\n if resp.status_code != 200:\\n try:\\n return self.infoblox_.__caller__(\\n 'Could not update A record for {0} - Status {1}'\\n .format(self.name, resp.status_code), resp.status_code)\\n except Exception:\\n return resp.status_code\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8505a302272a8553131e5778de7fe3e4\",\n \"score\": \"0.49867657\",\n \"text\": \"def updateloc(req, *args, **kwargs):\\n return _matchloc(req, insert=True )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2f6077dd3fbef0a1dd70757abee2fc\",\n \"score\": \"0.49825355\",\n \"text\": \"def put(self,request,pk):\\n\\n saved_plan = self.get_object(pk=pk)\\n #features = request.data.get('plans')[0].pop('features')\\n #Supports partial request - PATCH in Django might be broken.\\n serializer = PlanSerializer(instance=saved_plan, data=request.data[\\\"plans\\\"], partial=True)\\n\\n #TODO: this did not raise exception when request.data was mistakenly supplie\\n\\n if serializer.is_valid(raise_exception=True):\\n plan_saved = serializer.save()\\n\\n # plan_feature_serializer = PlanFeatureSerializer(instance=saved_plan.features, data=features, partial=True, many=True)\\n # if plan_feature_serializer.is_valid(raise_exception=True):\\n # plan_feature_saved = plan_feature_serializer.save()\\n\\n return Response({\\n \\\"success\\\": \\\"Plan '{}' updated successfully\\\".format(plan_saved.plan_name),\\n \\\"plans\\\":serializer.data\\n }, \\n status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b46a73c36c48b6f2431e53205c66e4\",\n \"score\": \"0.49727967\",\n \"text\": \"def update(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tupdateresource = dnssoarec()\\n\\t\\t\\t\\tupdateresource.domain = resource.domain\\n\\t\\t\\t\\tupdateresource.originserver = resource.originserver\\n\\t\\t\\t\\tupdateresource.contact = resource.contact\\n\\t\\t\\t\\tupdateresource.serial = resource.serial\\n\\t\\t\\t\\tupdateresource.refresh = resource.refresh\\n\\t\\t\\t\\tupdateresource.retry = resource.retry\\n\\t\\t\\t\\tupdateresource.expire = resource.expire\\n\\t\\t\\t\\tupdateresource.minimum = resource.minimum\\n\\t\\t\\t\\tupdateresource.ttl = resource.ttl\\n\\t\\t\\t\\treturn updateresource.update_resource(client)\\n\\t\\t\\telse :\\n\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\tupdateresources = [ dnssoarec() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\tupdateresources[i].domain = resource[i].domain\\n\\t\\t\\t\\t\\t\\tupdateresources[i].originserver = resource[i].originserver\\n\\t\\t\\t\\t\\t\\tupdateresources[i].contact = resource[i].contact\\n\\t\\t\\t\\t\\t\\tupdateresources[i].serial = resource[i].serial\\n\\t\\t\\t\\t\\t\\tupdateresources[i].refresh = resource[i].refresh\\n\\t\\t\\t\\t\\t\\tupdateresources[i].retry = resource[i].retry\\n\\t\\t\\t\\t\\t\\tupdateresources[i].expire = resource[i].expire\\n\\t\\t\\t\\t\\t\\tupdateresources[i].minimum = resource[i].minimum\\n\\t\\t\\t\\t\\t\\tupdateresources[i].ttl = resource[i].ttl\\n\\t\\t\\t\\tresult = cls.update_bulk_request(client, updateresources)\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b51dd91b975397af5bf5ce05fbf84a1\",\n \"score\": \"0.4968589\",\n \"text\": \"def update(self, name, description, public):\\n update_swarm = {}\\n if name != None:\\n update_swarm[\\\"name\\\"] = name\\n if description != None:\\n update_swarm[\\\"description\\\"] = description\\n if public != None:\\n update_swarm[\\\"public\\\"] = public\\n update_swarm_json = json.dumps(update_swarm)\\n conn = httplib.HTTPConnection(self.apikey.server)\\n conn.request(\\\"PUT\\\", \\\"/swarms/%s\\\"%(self.id), update_swarm_json,\\n {\\\"x-bugswarmapikey\\\":self.apikey.configuration})\\n resp = conn.getresponse()\\n txt = resp.read()\\n conn.close()\\n if resp.status >= 400:\\n logging.warning('Swarm info response: ('+str(resp.status)+'): '+txt)\\n return False\\n logging.debug('Swarm info response: ('+str(resp.status)+'): '+txt)\\n if name != None:\\n self.name = name\\n if description != None:\\n self.description = description\\n if public != None:\\n self.public = public\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264b78e936e97bd00727c672e1662897\",\n \"score\": \"0.49667677\",\n \"text\": \"def update_record(\\n name,\\n value,\\n zone,\\n record_type,\\n identifier=None,\\n ttl=None,\\n region=None,\\n key=None,\\n keyid=None,\\n profile=None,\\n wait_for_sync=True,\\n split_dns=False,\\n private_zone=False,\\n retry_on_rate_limit=None,\\n rate_limit_retries=None,\\n retry_on_errors=True,\\n error_retries=5,\\n):\\n if region is None:\\n region = \\\"universal\\\"\\n\\n conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)\\n\\n if split_dns:\\n _zone = _get_split_zone(zone, conn, private_zone)\\n else:\\n _zone = conn.get_zone(zone)\\n if not _zone:\\n msg = \\\"Failed to retrieve zone {}\\\".format(zone)\\n log.error(msg)\\n return False\\n _type = record_type.upper()\\n\\n if retry_on_rate_limit or rate_limit_retries is not None:\\n if retry_on_rate_limit is not None:\\n retry_on_errors = retry_on_rate_limit\\n if rate_limit_retries is not None:\\n error_retries = rate_limit_retries\\n\\n _value = _munge_value(value, _type)\\n while error_retries > 0:\\n try:\\n old_record = _zone.find_records(name, _type, identifier=identifier)\\n if not old_record:\\n return False\\n status = _zone.update_record(old_record, _value, ttl, identifier)\\n return _wait_for_sync(status.id, conn, wait_for_sync)\\n\\n except DNSServerError as e:\\n if retry_on_errors and _is_retryable_error(e):\\n if \\\"Throttling\\\" == e.code:\\n log.debug(\\\"Throttled by AWS API.\\\")\\n elif \\\"PriorRequestNotComplete\\\" == e.code:\\n log.debug(\\n \\\"The request was rejected by AWS API. \\\"\\n \\\"Route 53 was still processing a prior request.\\\"\\n )\\n time.sleep(3)\\n error_retries -= 1\\n continue\\n raise\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68d755d35a1f03438e9f4215ed61a30\",\n \"score\": \"0.49530244\",\n \"text\": \"def set_zone_mode(\\n cls,\\n ctl_id: str,\\n zone_idx: int,\\n mode: str = None,\\n setpoint: float = None,\\n until: dt = None,\\n duration: int = None,\\n **kwargs,\\n ):\\n # W --- 18:013393 01:145038 --:------ 2349 013 0004E201FFFFFF330B1A0607E4\\n # W --- 22:017139 01:140959 --:------ 2349 007 0801F400FFFFFF\\n\\n if mode is None:\\n if setpoint is None:\\n raise ValueError(\\\"Invalid args: Both mode and setpoint are None\\\")\\n elif until:\\n mode = ZoneMode.TEMPORARY\\n elif duration:\\n mode = ZoneMode.COUNTDOWN\\n else:\\n mode = ZoneMode.PERMANENT # or: ZoneMode.ADVANCED\\n elif isinstance(mode, int):\\n mode = f\\\"{mode:02X}\\\"\\n\\n if mode in ZONE_MODE_MAP:\\n mode = ZONE_MODE_MAP[mode]\\n elif mode not in ZONE_MODE_LOOKUP:\\n raise TypeError(f\\\"Invalid args: Unknown mode: {mode}\\\")\\n\\n if setpoint is None:\\n if mode != ZoneMode.SCHEDULE:\\n raise ValueError(f\\\"Invalid args: For {mode}, setpoint cant be None\\\")\\n elif not isinstance(setpoint, (int, float)):\\n raise ValueError(f\\\"Invalid args: setpoint={setpoint}, should be float\\\")\\n\\n if mode == ZoneMode.TEMPORARY and until is None:\\n mode = ZoneMode.ADVANCED # until = dt.now() + td(hour=1)\\n elif mode in (ZoneMode.SCHEDULE, ZoneMode.PERMANENT) and (\\n until is not None or duration is not None\\n ):\\n raise ValueError(f\\\"Invalid args: For {mode}, until should be None\\\")\\n\\n assert mode in ZONE_MODE_LOOKUP, mode\\n\\n payload = \\\"\\\".join(\\n (\\n f\\\"{zone_idx:02X}\\\",\\n temp_to_hex(setpoint), # None means max, if a temp is required\\n ZONE_MODE_LOOKUP[mode],\\n \\\"FFFFFF\\\" if duration is None else f\\\"{duration:06X}\\\",\\n \\\"\\\" if until is None else dtm_to_hex(until),\\n )\\n )\\n\\n return cls(W_, _2349, payload, ctl_id, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"347fa978afa53680944e303392e62ffd\",\n \"score\": \"0.49470228\",\n \"text\": \"def _update_resource(resource_type, id):\\n if resource_type not in schemas.keys():\\n raise exceptions.NotFoundException('Resource of type \\\\'%s\\\\' not found' % resource_type)\\n\\n resource = None\\n if 'slug' in schemas[resource_type]['properties'].keys():\\n resource = db[resource_type].find_one({'slug': id})\\n if not resource:\\n resource = db[resource_type].find_one({'primaryKey.id': id})\\n if not resource:\\n raise exceptions.NotFoundException('Resource not found')\\n\\n if 'save_history' in flask.request.args:\\n old_data = copy.deepcopy(resource)\\n\\n data = flask.request.get_json(force=True, silent=True)\\n if not data or not isinstance(data, dict):\\n raise exceptions.BadRequestException('Malformed JSON in PATCH data')\\n\\n # save keys that cannot be changed on update\\n resource_id = resource.pop('_id')\\n\\n # restore keys that cannot be changed on update\\n data['updatedAt'] = time_now()\\n data['updatedBy'] = get_sub()\\n data['createdAt'] = resource.pop('createdAt')\\n data['createdBy'] = resource.pop('createdBy', 'Unknown')\\n data['primaryKey'] = resource.pop('primaryKey')\\n\\n validate(data, schemas.get(resource_type))\\n\\n # _id is not part of the schema, so restore this after validation\\n data['_id'] = resource_id\\n\\n if 'test' not in flask.request.args:\\n update = resource2update(schemas[resource_type], data)\\n db[resource_type].update_one({'_id': resource_id},\\n update)\\n\\n if 'save_history' in flask.request.args:\\n old_data.pop('_id')\\n db[resource_type+'_history'].insert(old_data)\\n\\n return data, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b140e9e324bcbf9d585e9ec24655b37b\",\n \"score\": \"0.4943501\",\n \"text\": \"def perform_update(self, serializer):\\n reporting_time = arrow.get(serializer.validated_data.get('time'))\\n duration = serializer.validated_data.get('duration')\\n requested_slot = [\\n reporting_time,\\n reporting_time.shift(minutes=duration),\\n serializer.validated_data.get('attender'),\\n ]\\n if self.validate_slot(requested_slot):\\n serializer.save()\\n else:\\n raise APIException({'message': 'This slot has been already reserved !!!'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aaa19876a28c74ef1a2424f0111e79e\",\n \"score\": \"0.49418437\",\n \"text\": \"def put(self,request,pk=None):\\n return Response({\\\"method\\\":\\\"put\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cdb914f7cf325934fa1dc0ad3206f6\",\n \"score\": \"0.4924634\",\n \"text\": \"def update(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tupdateresource = nsvariable()\\n\\t\\t\\t\\tupdateresource.name = resource.name\\n\\t\\t\\t\\tupdateresource.type = resource.type\\n\\t\\t\\t\\tupdateresource.iffull = resource.iffull\\n\\t\\t\\t\\tupdateresource.ifvaluetoobig = resource.ifvaluetoobig\\n\\t\\t\\t\\tupdateresource.ifnovalue = resource.ifnovalue\\n\\t\\t\\t\\tupdateresource.init = resource.init\\n\\t\\t\\t\\tupdateresource.expires = resource.expires\\n\\t\\t\\t\\tupdateresource.comment = resource.comment\\n\\t\\t\\t\\treturn updateresource.update_resource(client)\\n\\t\\t\\telse :\\n\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\tupdateresources = [ nsvariable() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\tupdateresources[i].name = resource[i].name\\n\\t\\t\\t\\t\\t\\tupdateresources[i].type = resource[i].type\\n\\t\\t\\t\\t\\t\\tupdateresources[i].iffull = resource[i].iffull\\n\\t\\t\\t\\t\\t\\tupdateresources[i].ifvaluetoobig = resource[i].ifvaluetoobig\\n\\t\\t\\t\\t\\t\\tupdateresources[i].ifnovalue = resource[i].ifnovalue\\n\\t\\t\\t\\t\\t\\tupdateresources[i].init = resource[i].init\\n\\t\\t\\t\\t\\t\\tupdateresources[i].expires = resource[i].expires\\n\\t\\t\\t\\t\\t\\tupdateresources[i].comment = resource[i].comment\\n\\t\\t\\t\\tresult = cls.update_bulk_request(client, updateresources)\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0768e6867199ac02943409275f7465ee\",\n \"score\": \"0.49196827\",\n \"text\": \"def update(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tupdateresource = dnsparameter()\\n\\t\\t\\t\\tupdateresource.retries = resource.retries\\n\\t\\t\\t\\tupdateresource.minttl = resource.minttl\\n\\t\\t\\t\\tupdateresource.maxttl = resource.maxttl\\n\\t\\t\\t\\tupdateresource.cacherecords = resource.cacherecords\\n\\t\\t\\t\\tupdateresource.namelookuppriority = resource.namelookuppriority\\n\\t\\t\\t\\tupdateresource.recursion = resource.recursion\\n\\t\\t\\t\\tupdateresource.resolutionorder = resource.resolutionorder\\n\\t\\t\\t\\tupdateresource.dnssec = resource.dnssec\\n\\t\\t\\t\\tupdateresource.maxpipeline = resource.maxpipeline\\n\\t\\t\\t\\tupdateresource.dnsrootreferral = resource.dnsrootreferral\\n\\t\\t\\t\\tupdateresource.dns64timeout = resource.dns64timeout\\n\\t\\t\\t\\tupdateresource.ecsmaxsubnets = resource.ecsmaxsubnets\\n\\t\\t\\t\\tupdateresource.maxnegcachettl = resource.maxnegcachettl\\n\\t\\t\\t\\tupdateresource.cachehitbypass = resource.cachehitbypass\\n\\t\\t\\t\\tupdateresource.maxcachesize = resource.maxcachesize\\n\\t\\t\\t\\tupdateresource.maxnegativecachesize = resource.maxnegativecachesize\\n\\t\\t\\t\\tupdateresource.cachenoexpire = resource.cachenoexpire\\n\\t\\t\\t\\tupdateresource.splitpktqueryprocessing = resource.splitpktqueryprocessing\\n\\t\\t\\t\\tupdateresource.cacheecszeroprefix = resource.cacheecszeroprefix\\n\\t\\t\\t\\treturn updateresource.update_resource(client)\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8dba8265bf30d89baf299055cb0a277\",\n \"score\": \"0.49184713\",\n \"text\": \"def ground_zones_update(pcb_modules: \\\"List[PCBModule]\\\", zone_polygon: SimplePolygon,\\n pcb_origin: P2D, tracing: str = \\\"\\\") -> None:\\n # We are serching for XXX where XXX is either GND or /GND:\\n # (zone (net NUMBER) (net_name XXX) (layer X.Cu) ... # X is B or F (i.e. Back/Front).\\n # ...\\n # (polygon\\n # (pts\\n # (xy X Y) ... # Replace these with the new ones.\\n # )\\n # )\\n # (filled_polygon # The filled zone may not be present at first.\\n # (pts\\n # (xy X Y) ... # Keep these and let KiCad do the update.\\n # )\\n # )\\n # )\\n net_numbers_table: Dict[str, int] = PCBModule.net_numbers_table_get(pcb_modules)\\n if \\\"GND\\\" in net_numbers_table:\\n ground_net_number: int = net_numbers_table[\\\"GND\\\"]\\n zone_size: int = len(zone_polygon)\\n pcb_origin_x: float = pcb_origin.x\\n pcb_origin_y: float = pcb_origin.y\\n\\n zone_pattern1 = f\\\" (zone (net {ground_net_number}) (net_name GND) (layer \\\"\\n zone_pattern2 = f\\\" (zone (net {ground_net_number}) (net_name /GND) (layer \\\"\\n pcb_module: PCBModule\\n for pcb_module in pcb_modules:\\n # Search through *preceeding_lines* looking for a ground zone and updating them\\n # as appropriate. Construct *updated_lines* as we sweep through:\\n updated_lines: List[str] = []\\n preceding_lines: List[str] = pcb_module.preceding_lines\\n zone_started: bool = False\\n polygon_started: bool = False\\n points_started: bool = False\\n new_xys_inserted: bool = False\\n xys_skipping: bool = False\\n pcb_line_index: int\\n pcb_line: str\\n for pcb_line_index, pcb_line in enumerate(preceding_lines):\\n if pcb_line.startswith(zone_pattern1) or pcb_line.startswith(zone_pattern2):\\n # Start of a fill zone -- (zone ...:\\n zone_started = True\\n polygon_started = False\\n points_started = False\\n xys_skipping = False\\n new_xys_inserted = False\\n if tracing:\\n print(f\\\"{tracing}Zone started: '{pcb_line}'\\\")\\n elif pcb_line.startswith(\\\" (polygon\\\"): # )\\n # Start of polygon outline (i.e. unfilled) -- (polygon ...:\\n polygon_started = zone_started\\n points_started = False\\n xys_skipping = False\\n elif pcb_line.startswith(\\\" (pts\\\"): # )\\n # Start of points -- (pts ...)\\n points_started = polygon_started\\n xys_skipping = False\\n elif pcb_line.startswith(\\\" (xy \\\"):\\n # Start of xys -- (xy ...):\\n xys_skipping = points_started\\n # Insert new xys here if they have not already been inserted:\\n if xys_skipping and not new_xys_inserted:\\n index: int\\n zone_xys: List[str] = [\\n (f\\\"(xy {KicadPCB.number(pcb_origin_x + zone_polygon[index].x, 1)} \\\"\\n f\\\"{KicadPCB.number(pcb_origin_y - zone_polygon[index].y, 1)})\\\")\\n for index in range(zone_size)]\\n\\n # This is weird sometime we get duplicate points in *zone_xys*.\\n # This code removes adjacent values that are duplicates:\\n culled_zone_xys: List[str] = zone_xys[:1]\\n zone_text: str\\n for zone_text in zone_xys[1:]:\\n if culled_zone_xys[-1] != zone_text:\\n culled_zone_xys.append(zone_text)\\n culled_zone_xys_size: int = len(culled_zone_xys)\\n if tracing and zone_size != culled_zone_xys_size:\\n print(f\\\"{tracing}zone_size={zone_size} != \\\"\\n f\\\"{culled_zone_xys_size}= culled_zone_xys_size\\\")\\n\\n # Organize the the values in groups of 5:\\n zones_quints: List[List[str]] = [\\n culled_zone_xys[index:index + 5]\\n for index in range(0, culled_zone_xys_size, 5)\\n ]\\n if tracing:\\n print(f\\\"{tracing}zone_size={zone_size}\\\")\\n print(f\\\"{tracing}zones_quints={zones_quints}\\\")\\n\\n # Append the lines of *zones_quints* to *updated_lines*:\\n zones_quint: List[str]\\n zone_index: int\\n for zone_index, zones_quint in enumerate(zones_quints):\\n quint_line: str = \\\" \\\" + ' '.join(zones_quint)\\n updated_lines.append(quint_line)\\n if tracing:\\n print(f\\\"[{pcb_line_index}]\\\\tI[{zone_index}]\\\\t{quint_line}\\\")\\n new_xys_inserted = True\\n elif pcb_line.startswith(\\\" )\\\"): # End of (pts ...)\\n new_xys_inserted = False\\n xys_skipping = False\\n points_started = False\\n elif pcb_line.startswith(\\\" )\\\"): # End of (polygon ...)\\n polygon_started = False\\n elif pcb_line.startswith(\\\" )\\\"): # End of (zone ...)\\n zone_started = False\\n\\n # Copy over *pcb_line* to *updated_lines* if we are not in *xys_skipping*:\\n if tracing:\\n zone_flag: str = 'Z' if zone_started else '-'\\n polygon_flag: str = 'G' if polygon_started else '-'\\n points_flag: str = 'P' if points_started else '-'\\n skipping_flag: str = 'S' if xys_skipping else '-'\\n print(f\\\"[{pcb_line_index}]\\\\t\\\"\\n f\\\"{zone_flag}{polygon_flag}{points_flag}{skipping_flag}\\\\t\\\"\\n f\\\"{pcb_line}\\\")\\n if not xys_skipping:\\n updated_lines.append(pcb_line)\\n\\n # Replace the updated_lines:\\n pcb_module.preceding_lines = updated_lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90e981a8720785eaa0fdb0891c83bd2\",\n \"score\": \"0.4917183\",\n \"text\": \"def put(self, user_id, current_user, flight_id):\\n\\n flight = Flights.query.filter_by(id=flight_id).first()\\n created_by = flight.created_by\\n if user_id == created_by:\\n # Obtain the new name of the flight from the request data\\n edited = request.get_json()\\n flight.name = edited['name']\\n flight.origin = edited['origin']\\n flight.location = edited['destination']\\n flight.date = edited['date']\\n flight.time = edited['time']\\n flight.save()\\n\\n response = {\\n 'id': flight.id, 'name' : flight.name, 'origin' : flight.origin,\\n 'destination' : flight.destination, 'date' : flight.date, 'time' : flight.time\\n }\\n\\n msg = {\\\"message\\\": \\\"Flight details updated successfully\\\"}\\n return make_response(jsonify(msg)), 200\\n response = {\\\"message\\\": \\\"You can only modify the flights you created\\\"}\\n return jsonify(response), 401\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b304669a393449e37fdc6baac67fc67b\",\n \"score\": \"0.49152347\",\n \"text\": \"def update(self, resource, uri=None, force=False, timeout=-1, custom_headers=None, default_values={}):\\n if not resource:\\n logger.exception(RESOURCE_CLIENT_RESOURCE_WAS_NOT_PROVIDED)\\n raise ValueError(RESOURCE_CLIENT_RESOURCE_WAS_NOT_PROVIDED)\\n\\n logger.debug('Update async (uri = %s, resource = %s)' %\\n (self._uri, str(resource)))\\n\\n if not uri:\\n uri = resource['uri']\\n\\n if force:\\n uri += '?force=True'\\n\\n resource = self.merge_default_values(resource, default_values)\\n\\n return self.__do_put(uri, resource, timeout, custom_headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7325d82e7324cb3baef7efe347ffb4\",\n \"score\": \"0.49144873\",\n \"text\": \"def testUpdateSegment(self):\\n I = item.Item(2, 2, CornerPoint=[0,0])\\n S1 = skyline.SkylineSegment(0, 2, 2)\\n S2 = skyline.SkylineSegment(2, 0, 6)\\n\\n res = self.S._update_segment(self.S.skyline[0], 0, I)\\n self.assertCountEqual(res, [S1, S2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda2977cf097f7da09155af5064de252\",\n \"score\": \"0.49078995\",\n \"text\": \"def test_resource_manager_updation(self):\\n resources = self._set_and_get_resources(['test_resources/input/resource_one.json',\\n 'test_resources/input/resource_one_updated.json'])\\n self.assertEqual(len(resources), 1)\\n self.assertEqual(resources.resources.pop().resource_creation_timestamp, 1526331464.49)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1ef3925b697c69a3d4adce2955e385\",\n \"score\": \"0.48944154\",\n \"text\": \"def test_05_test_settings_for_zone(self):\\n config_name = \\\"enable.dynamic.scale.vm\\\"\\n configs = Configurations.list(\\n self.apiclient,\\n name=config_name,\\n zoneid=self.zone.id\\n )\\n self.assertIsNotNone(configs, \\\"Fail to get zone setting %s \\\" % config_name)\\n\\n orig_value = str(configs[0].value)\\n new_value = 'true'\\n\\n Configurations.update(\\n self.apiclient,\\n name=config_name,\\n value=new_value,\\n zoneid=self.zone.id\\n )\\n\\n configs = Configurations.list(\\n self.apiclient,\\n name=config_name,\\n zoneid=self.zone.id\\n )\\n self.assertIsNotNone(configs, \\\"Fail to get ol setting %s \\\" % config_name)\\n\\n self.assertEqual(new_value,\\n (configs[0].value),\\n \\\"Failed to set new config value\\\")\\n\\n Configurations.reset(\\n self.apiclient,\\n name=config_name,\\n zoneid=self.zone.id\\n )\\n\\n configs = Configurations.list(\\n self.apiclient,\\n name=config_name,\\n zoneid=self.zone.id\\n )\\n self.assertIsNotNone(configs, \\\"Fail to get zone setting %s \\\" % config_name)\\n\\n self.assertEqual(orig_value,\\n (configs[0].value),\\n \\\"Failed to reset the value for zone\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32533953716ec19bf276f0340c20ff0b\",\n \"score\": \"0.48901463\",\n \"text\": \"def callback_2(status):\\n async_dispatcher_send( hass, SIGNAL_ZONES_UPDATED, status[ZONES] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5976bb0b9d2d10617dadc00483787b44\",\n \"score\": \"0.48895377\",\n \"text\": \"def update(self,field,val):\\n Location.objects.get(id = self.id).update(field = val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e91c0ff40f589b608308c8027c572f\",\n \"score\": \"0.48791897\",\n \"text\": \"def updateZone(self, interval, world):\\n #\\n # Iterate through actors - use a list of the actors\\n # in case the actor wants to update the list of\\n # actors during this iteration\\n for actor in list(self.actors):\\n if actor.active:\\n actor.updateActor(interval, world)\\n #\\n # Do physics if we need to\\n if self._physics_objects:\\n self._updatePhysics(interval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705fd86155b6ea89db86b56d565d9cc4\",\n \"score\": \"0.48785475\",\n \"text\": \"def update_account(self):\\n return bind_api(\\n api=self,\\n path='/accounts/{account_id}',\\n method='PUT',\\n post_container='account',\\n allowed_param=['account_id', 'name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9568618edcc42e80bc7e8c408f3e8d\",\n \"score\": \"0.48718858\",\n \"text\": \"def _update_resource(self, res_type, context, id, res_data):\\n\\n res_dict = self._encode_resource(resource_id=id,\\n resource=res_data[res_type])\\n status_code, res_info = self._request_backend(context, res_dict,\\n res_type, 'UPDATE')\\n res_dicts = self._transform_response(status_code, info=res_info,\\n obj_name=res_type)\\n LOG.debug(\\\"update_%(res_type)s(): %(res_dicts)s\\\",\\n {'res_type': res_type, 'res_dicts': res_dicts})\\n\\n return res_dicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae322bd1454219d29b576c4ea5a3d8b0\",\n \"score\": \"0.4870781\",\n \"text\": \"def create_zone(self):\\n self.log.info(\\\"creating a zone\\\")\\n resp = self.api.create_zone()\\n self.log.debug(utils.resp_to_string(resp))\\n if resp.status_code != 202:\\n self.fail(\\\"failed to create zone (status=%s)\\\" % resp.status_code)\\n return resp.json()[\\\"name\\\"], resp.json()[\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9423ccd2291943b99aa862c4d9c21a0b\",\n \"score\": \"0.48658797\",\n \"text\": \"def put(self):\\n request = transforms.loads(self.request.get('request'))\\n key = request.get('key')\\n\\n if not self.assert_xsrf_token_or_fail(\\n request, 'put-unit', {'key': key}):\\n return\\n\\n if not CourseOutlineRights.can_edit(self):\\n transforms.send_json_response(\\n self, 401, 'Access denied.', {'key': key})\\n return\\n\\n unit = courses.Course(self).find_unit_by_id(key)\\n if not unit:\\n transforms.send_json_response(\\n self, 404, 'Object not found.', {'key': key})\\n return\\n\\n payload = request.get('payload')\\n updated_unit_dict = transforms.json_to_dict(\\n transforms.loads(payload), self.SCHEMA_DICT)\\n\\n errors = []\\n self.apply_updates(unit, updated_unit_dict, errors)\\n if not errors:\\n course = courses.Course(self)\\n assert course.update_unit(unit)\\n course.save()\\n transforms.send_json_response(self, 200, 'Saved.')\\n else:\\n transforms.send_json_response(self, 412, '\\\\n'.join(errors))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b74aac94ab85c6a30466317e8a109e\",\n \"score\": \"0.4857295\",\n \"text\": \"def test_route_update_by_admin(self):\\n route_test_1 = Route.objects.create(\\n name='route-test-1',\\n created_by=self.user_admin,\\n origin='origin-test',\\n destination='destination-test'\\n )\\n\\n payload = {\\n 'name': 'rote-updated',\\n 'origin': 'test-origin',\\n 'destination': 'test-destination'\\n }\\n\\n self.client.force_authenticate(self.user_admin)\\n path = ROUTES_URL+str(route_test_1.id)+'/'\\n response = self.client.put(path, payload)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ecf11823bc5b6229da23562e49465e2\",\n \"score\": \"0.484399\",\n \"text\": \"def update_resource(self, transaction):\\n # If-Match\\n if transaction.request.if_match:\\n if None not in transaction.request.if_match and str(transaction.resource.etag) \\\\\\n not in transaction.request.if_match:\\n transaction.response.code = defines.Codes.PRECONDITION_FAILED.number\\n return transaction\\n # If-None-Match\\n if transaction.request.if_none_match:\\n transaction.response.code = defines.Codes.PRECONDITION_FAILED.number\\n return transaction\\n\\n method = getattr(transaction.resource, \\\"render_PUT\\\", None)\\n\\n try:\\n resource = method(request=transaction.request)\\n except NotImplementedError:\\n transaction.response.code = defines.Codes.METHOD_NOT_ALLOWED.number\\n return transaction\\n\\n if isinstance(resource, Resource):\\n pass\\n elif isinstance(resource, tuple) and len(resource) == 2:\\n resource, callback = resource\\n resource = self._handle_separate(transaction, callback)\\n if not isinstance(resource, Resource): # pragma: no cover\\n transaction.response.code = defines.Codes.INTERNAL_SERVER_ERROR.number\\n return transaction\\n else: # pragma: no cover\\n # Handle error\\n transaction.response.code = defines.Codes.INTERNAL_SERVER_ERROR.number\\n return transaction\\n\\n if resource.etag is not None:\\n transaction.response.etag = resource.etag\\n\\n transaction.response.code = defines.Codes.CHANGED.number\\n transaction.response.payload = None\\n resource.changed = True\\n resource.observe_count += 1\\n transaction.resource = resource\\n\\n return transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5495ed84c50c941748110fa55e2fdc94\",\n \"score\": \"0.48428872\",\n \"text\": \"def update(self, domain_record, actual_ip):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a335bda8f7907515f7b3d96f95e43c\",\n \"score\": \"0.48319024\",\n \"text\": \"def update(self, resource: Resource):\\n\\n # Remember, everything is immutable.\\n\\n # 1. Update resource tree\\n resource_name = resource.name\\n self.resources.set(resource)\\n\\n # 2. Find any refs that point to this resource *name*.\\n changed_ref_tos = [\\n ref.to\\n for ref in self.refs.items.values()\\n if ref.to == resource_name\\n ]\\n\\n # 3. Update any views that use those refs\\n self.views.update_views(changed_ref_tos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4488822b1d99c1c3ce929463083a69\",\n \"score\": \"0.48297885\",\n \"text\": \"def update(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tupdateresource = nsassignment()\\n\\t\\t\\t\\tupdateresource.name = resource.name\\n\\t\\t\\t\\tupdateresource.variable = resource.variable\\n\\t\\t\\t\\tupdateresource.set = resource.set\\n\\t\\t\\t\\tupdateresource.Add = resource.Add\\n\\t\\t\\t\\tupdateresource.sub = resource.sub\\n\\t\\t\\t\\tupdateresource.append = resource.append\\n\\t\\t\\t\\tupdateresource.clear = resource.clear\\n\\t\\t\\t\\tupdateresource.comment = resource.comment\\n\\t\\t\\t\\treturn updateresource.update_resource(client)\\n\\t\\t\\telse :\\n\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\tupdateresources = [ nsassignment() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\tupdateresources[i].name = resource[i].name\\n\\t\\t\\t\\t\\t\\tupdateresources[i].variable = resource[i].variable\\n\\t\\t\\t\\t\\t\\tupdateresources[i].set = resource[i].set\\n\\t\\t\\t\\t\\t\\tupdateresources[i].Add = resource[i].Add\\n\\t\\t\\t\\t\\t\\tupdateresources[i].sub = resource[i].sub\\n\\t\\t\\t\\t\\t\\tupdateresources[i].append = resource[i].append\\n\\t\\t\\t\\t\\t\\tupdateresources[i].clear = resource[i].clear\\n\\t\\t\\t\\t\\t\\tupdateresources[i].comment = resource[i].comment\\n\\t\\t\\t\\tresult = cls.update_bulk_request(client, updateresources)\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69031dd0987199445e82525939046b56\",\n \"score\": \"0.4823912\",\n \"text\": \"def update(self, id, realm, rep):\\n\\n path = \\\"/{realm}/client-templates/{id}\\\".format(\\n id=id, realm=realm\\n )\\n params = {\\n \\n }\\n headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\"\\n }\\n return self.session.request(\\n method=\\\"PUT\\\",\\n endpoint=self.session._admurl(path),\\n params=params,\\n headers=headers,\\n data=rep,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0264be7bf3cf95f45953e0439d024679\",\n \"score\": \"0.48238653\",\n \"text\": \"def put (self, request,pk=None):\\n \\n return Response({'method':'PUT'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b32c4da2f5cf13fbf63e4b62b0e32a\",\n \"score\": \"0.48217115\",\n \"text\": \"def dns_update(self):\\n if self.svc.node.collector_env.dbopensvc is None:\\n return\\n\\n if self.ipname is None:\\n self.log.debug(\\\"skip dns update: ipname is not set\\\")\\n return\\n\\n try:\\n self.conf_get(\\\"dns_update\\\")\\n except ex.OptNotFound:\\n self.log.debug(\\\"skip dns update: dns_update is not set\\\")\\n return\\n\\n if not self.is_up():\\n self.log.debug(\\\"skip dns update: resource is not up\\\")\\n return\\n\\n if self.dns_name_suffix is None:\\n self.log.debug(\\\"dns update: dns_name_suffix is not set\\\")\\n\\n try:\\n self.getaddr()\\n except ex.Error as exc:\\n self.log.error(str(exc))\\n return\\n\\n post_data = {\\n \\\"content\\\": self.addr,\\n }\\n\\n if self.dns_name_suffix:\\n post_data[\\\"name\\\"] = self.dns_name_suffix\\n\\n try:\\n data = self.svc.node.collector_rest_post(\\n \\\"/dns/services/records\\\",\\n post_data,\\n path=self.dns_rec_name(),\\n )\\n except Exception as exc:\\n raise ex.Error(\\\"dns update failed: \\\"+str(exc))\\n if \\\"error\\\" in data:\\n raise ex.Error(data[\\\"error\\\"])\\n\\n self.log.info(\\\"dns updated\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6293afea967a998c8582a88aab923e4\",\n \"score\": \"0.48209825\",\n \"text\": \"def perform_update(self, serializer):\\n raise EndpointNotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c9aedb19254408859f71c869a982ab\",\n \"score\": \"0.48169306\",\n \"text\": \"async def set_mode(self, mode):\\n allowed_modes = [ZONE_MODE.Off, ZONE_MODE.Override, ZONE_MODE.Timer]\\n\\n if self._has_pir:\\n allowed_modes += [ZONE_MODE.Footprint]\\n allowed_mode_strs = [IMODE_TO_MODE[i] for i in allowed_modes]\\n\\n if isinstance(mode, int) and mode in allowed_modes:\\n mode_str = IMODE_TO_MODE[mode]\\n elif isinstance(mode, str) and mode in allowed_mode_strs:\\n mode_str = mode\\n mode = MODE_TO_IMODE[mode_str]\\n else:\\n raise TypeError(f\\\"Zone.set_mode(): mode='{mode}' isn't valid.\\\")\\n\\n _LOGGER.debug(\\n \\\"Zone(%s).set_mode(mode=%s, mode_str='%s')...\\\", self.id, mode, mode_str\\n )\\n\\n if self._hub.api_version == 1:\\n url = f\\\"zones/{self.id}/mode\\\" # v1 API uses strings\\n resp = await self._hub.request(\\\"PUT\\\", url, data=mode_str)\\n else: # self._hub.api_version == 3\\n url = (\\n f\\\"zone/{self.id}\\\"\\n ) # v3 API uses dicts # TODO: check: is it PUT(POST?) vs PATCH\\n resp = await self._hub.request(\\\"PATCH\\\", url, data={\\\"iMode\\\": mode})\\n\\n if resp: # for v1, resp = None?\\n resp = resp[\\\"data\\\"] if resp[\\\"error\\\"] == 0 else resp\\n _LOGGER.debug(\\\"Zone(%s).set_mode(): response = %s\\\", self.id, resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549670aa385170d59c7946342e144bbf\",\n \"score\": \"0.47911164\",\n \"text\": \"def update(controller, dt, ship, moon):\\n action = controller.get_action()\\n is_crashed = check_crash(ship, moon)\\n \\n ship.update(dt, action, is_crashed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f57ca5367a72ea91d33473cb5699c3\",\n \"score\": \"0.47900626\",\n \"text\": \"def put(self, request, pk=None):\\n\\n return Response({'method': 'put'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7785f6b35e057aa2de84b686b820aaab\",\n \"score\": \"0.47845304\",\n \"text\": \"def update(self, data=None, timeout=-1, custom_headers=None, force=False):\\n\\n resource = deepcopy(self.data)\\n resource.update(data)\\n\\n self.data = self._helper.update(resource, self.URI, force, timeout, custom_headers)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a259e0e575aa6ce4d0dadd5494e3b728\",\n \"score\": \"0.4781302\",\n \"text\": \"def update(self, client=None):\\n try:\\n return super(TimestampedResource, self).update(client)\\n except exceptions.DoesNotExist as e:\\n self._set('created', None)\\n self._set('modified', None)\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43f24ed9c3a06c7f66551b759baf643\",\n \"score\": \"0.47758436\",\n \"text\": \"def assign(zone_name, domain_name, value, ttl):\\n zone = CONNECTION.get_zone(zone_name)\\n record = _find_record(zone, domain_name)\\n record_type = 'A' if _is_ip_address(value) else 'CNAME'\\n\\n if record:\\n record_info = _query(record)\\n if record_info['type'] == record_type and \\\\\\n record_info['value'] == value and \\\\\\n record_info['ttl'] == ttl:\\n # No need to assign the same values again\\n return\\n\\n # There's no way to change the type of an existing record in one operation.\\n # Instead, we first DELETE if necessary and then CREATE a new record.\\n change_set = r53.record.ResourceRecordSets(CONNECTION, zone.id)\\n if record:\\n change_set.add_change_record('DELETE', record)\\n change = change_set.add_change('CREATE', domain_name, record_type, ttl)\\n change.add_value(value)\\n change = change_set.commit()\\n\\n change_id = _get_change_id(change)\\n while _get_change_state(CONNECTION.get_change(change_id)) == 'PENDING':\\n time.sleep(10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5262734b8f0f91e29f436955951ac6\",\n \"score\": \"0.47746283\",\n \"text\": \"def update_day(zone_name, zone_job, day_of_week, value):\\n if zone_name in ([n for [n] in use_database.read_all_garden_zone_names()]):\\n zone_schedule[zone_name].update_day(zone_name, zone_job, day_of_week, value)\\n elif zone_name in ([n for [n] in use_database.read_all_power_zone_names()]):\\n power_zone_schedule[zone_name].update_day(zone_name, zone_job, day_of_week, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a000758ca6a85b7f71cdbfe57a6aa5e\",\n \"score\": \"0.47742543\",\n \"text\": \"def update_resource(\\n project_id,\\n location,\\n dataset_id,\\n fhir_store_id,\\n resource_type,\\n resource_id,\\n):\\n # Imports Python's built-in \\\"os\\\" module\\n import os\\n\\n # Imports the google.auth.transport.requests transport\\n from google.auth.transport import requests\\n\\n # Imports a module to allow authentication using a service account\\n from google.oauth2 import service_account\\n\\n # Gets credentials from the environment.\\n credentials = service_account.Credentials.from_service_account_file(\\n os.environ[\\\"GOOGLE_APPLICATION_CREDENTIALS\\\"]\\n )\\n scoped_credentials = credentials.with_scopes(\\n [\\\"https://www.googleapis.com/auth/cloud-platform\\\"]\\n )\\n # Creates a requests Session object with the credentials.\\n session = requests.AuthorizedSession(scoped_credentials)\\n\\n # URL to the Cloud Healthcare API endpoint and version\\n base_url = \\\"https://healthcare.googleapis.com/v1\\\"\\n\\n # TODO(developer): Uncomment these lines and replace with your values.\\n # project_id = 'my-project' # replace with your GCP project ID\\n # location = 'us-central1' # replace with the parent dataset's location\\n # dataset_id = 'my-dataset' # replace with the parent dataset's ID\\n # fhir_store_id = 'my-fhir-store' # replace with the FHIR store ID\\n # resource_type = 'Patient' # replace with the FHIR resource type\\n # resource_id = 'b682d-0e-4843-a4a9-78c9ac64' # replace with the FHIR resource's ID\\n url = \\\"{}/projects/{}/locations/{}\\\".format(base_url, project_id, location)\\n\\n resource_path = \\\"{}/datasets/{}/fhirStores/{}/fhir/{}/{}\\\".format(\\n url, dataset_id, fhir_store_id, resource_type, resource_id\\n )\\n\\n # Sets required application/fhir+json header on the request\\n headers = {\\\"Content-Type\\\": \\\"application/fhir+json;charset=utf-8\\\"}\\n\\n # The body shown works with a Patient resource and is not guaranteed\\n # to work with other types of FHIR resources. If necessary,\\n # supply a new body with data that corresponds to the resource you\\n # are updating.\\n body = {\\\"resourceType\\\": resource_type, \\\"active\\\": True, \\\"id\\\": resource_id}\\n\\n response = session.put(resource_path, headers=headers, json=body)\\n response.raise_for_status()\\n\\n resource = response.json()\\n\\n print(\\\"Updated {} resource:\\\".format(resource[\\\"resourceType\\\"]))\\n print(json.dumps(resource, indent=2))\\n\\n return resource\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994297,"cells":{"query_id":{"kind":"string","value":"6e7c3d3e2a05a9577615a6066acf2de1"},"query":{"kind":"string","value":"This method is deprecated. Please switch to AddIsGameOver."},"positive_passages":{"kind":"list like","value":[{"docid":"c420d43d69cc43b11061c0e151a99f80","score":"0.5853067","text":"def DeepSingleAgentEnvWithDiscreteActionsStateDataAddIsGameOver(builder, isGameOver):\n return AddIsGameOver(builder, isGameOver)","title":""}],"string":"[\n {\n \"docid\": \"c420d43d69cc43b11061c0e151a99f80\",\n \"score\": \"0.5853067\",\n \"text\": \"def DeepSingleAgentEnvWithDiscreteActionsStateDataAddIsGameOver(builder, isGameOver):\\n return AddIsGameOver(builder, isGameOver)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0741ff7b41ba00efd002b94655689183","score":"0.67969733","text":"def game_over(self):\n raise NotImplementedError()","title":""},{"docid":"1110ad2a849ae1de5830ea21087ddd3e","score":"0.63831997","text":"def game_over(self):\n # The game is over! Disable rolling more turns.\n self._turn_button.state(['disabled'])\n self._reset_button.focus()\n self.infotext['text'] += \"Game Over!\"","title":""},{"docid":"2fe6c2586de121b67d9bb23a41d76816","score":"0.6277449","text":"def is_game_over(self, is_game_over):\n\n self._is_game_over = is_game_over","title":""},{"docid":"7a45409e84e22dd2f3f431bfd50327ab","score":"0.62484246","text":"def game_over(self, game):\n\n for i in game.users.keys():\n self.client_callbacks[i].gameOver(game.get_scores()[0][0])\n self.ServerGames.remove(game.get_uuid())\n print \"Game over, game uuid: %s\" % game.get_uuid()","title":""},{"docid":"aec399096e50a770f0fc5d0ce0f6a50b","score":"0.6247514","text":"def gameOver(self):\n\t\tself.window.show_view(GameOverView(self.score))","title":""},{"docid":"29207c97f15441b8fcd9b34d53cbe0a7","score":"0.622483","text":"def game_over(self):\n if self.current_room.id == 0:\n print(\"Game Over\")\n return True\n return False","title":""},{"docid":"81dc7d83f7e75f3d0cd5c005eb072dcb","score":"0.6170105","text":"def isGameOver(self):\n if self.get_moves():\n return False\n return True","title":""},{"docid":"8d71657ea2bf172ece032cc5db6f2da0","score":"0.6119336","text":"def game_over():\n global game_on\n game_on = False\n score.game_over()\n # screen.onkey(replay, \"space\")","title":""},{"docid":"f2b5f8c363efa8a6862b420f4be33c5e","score":"0.60841423","text":"def game_over(self):\n\n title_screen = Title(self)\n info_screen = InfoScreen(self, self.score, '', title_screen)\n self.change_state(info_screen)","title":""},{"docid":"7dc9d4572068ef784b3f292fed618161","score":"0.6058548","text":"def checkGameOver(self):\n return self.keep_playing","title":""},{"docid":"d90ee583f0df0fbab55e66d78af724cb","score":"0.60327965","text":"def isGameOver(self):\n\n if self.getResult() != 0:\n self.gameover = True\n return True\n elif 0 not in (self.board[0,:,:,0] + self.board[0,:,:,1]):\n self.gameover = True\n return True\n else:\n return False","title":""},{"docid":"2c7c3da4824df6c4163666a0e1f8fe5c","score":"0.5992917","text":"def displayGameOverMenu(self):\n\n # make game over menu object.\n GOM = GameOverMenu(self.__settings.get_res_width(), self.__settings.get_res_height(), self.__screen)\n GOM.run_game_over_menu()\n\n # checks if user clicked 'Main Menu' button.\n if GOM.get_to_main_menu() == True:\n self.__goToMainMenu = True\n else:\n self.__goToMainMenu = False\n\n # checks if user clicked 'Play Again' button.\n if GOM.play_again(): # if true\n self.__play_again = True\n else:\n self.__play_again = False","title":""},{"docid":"0bbfc30dfec37f0e74c160e309b92dce","score":"0.5955247","text":"def is_in_game(self):\n pass","title":""},{"docid":"ba319b5bc49b039ad4e08f467c5696d8","score":"0.593408","text":"def is_game_over(self):\n if self.total_score <= 0:\n print(\"Better luck next time!\")\n print(\"Game over\")\n self.keep_playing = False","title":""},{"docid":"21fd3a8bf7838446df49c7d67967d42e","score":"0.5924194","text":"def isGameOver(self):\n if not self.get_moves():\n return True\n return False","title":""},{"docid":"299349df6beb7a3ef472caef85adbc03","score":"0.58878446","text":"def game_is_over(self):\r\n return self.game_clock.game_is_over()","title":""},{"docid":"f46b37ed254a57911adb1c741428403c","score":"0.58453447","text":"def game_over(self):\n\n return self.game.game_over()","title":""},{"docid":"7bbf102d4e23762b6c15ea8584264afd","score":"0.5844271","text":"def gameover(self) -> bool:\n return self.win or self.loss","title":""},{"docid":"cf65422563f09e246dd75e929d8314e4","score":"0.5840213","text":"def game_over(self):\n self.ende = UiGameOverWindow()\n self.ende.show()\n\n self.close()","title":""},{"docid":"0cd812b26778362480a83da67ac27b99","score":"0.58365285","text":"def GameOver(self):\n #decide who won and compensate for players not actually sliding the 10th cube into place\n if self.game_state.red_score > self.game_state.blue_score:\n if self.game_state.red_team is None:\n winner = 'red'\n else:\n winner = self.game_state.red_team\n\n self.game_state.red_score = 10\n\n elif self.game_state.blue_score > self.game_state.red_score:\n if self.game_state.blue_team is None:\n winner = 'blue'\n else:\n winner = self.game_state.blue_team\n\n self.game_state.blue_score = 10\n\n elif self.game_state.blue_score == self.game_state.red_score:\n winner = 'tie'\n\n #populate and commit a game state object\n foos_log = Game(winner=winner,\\\n blue_score=self.game_state.blue_score,\\\n red_score=self.game_state.red_score,\\\n red_off=self.game_state.red_off,\\\n red_def=self.game_state.red_def,\\\n blue_off=self.game_state.blue_off,\\\n blue_def=self.game_state.blue_def,\\\n started=self.game_state.game_started,\\\n ended=int(time()))\n\n db.session.add(foos_log)\n db.session.commit()\n\n #set a state object to the winner to update web ui with winning team detected\n self.game_state.game_winner = winner\n\n self.game_state.game_on = False\n self.game_state.blue_off = -1\n self.game_state.blue_def = -1\n self.game_state.red_off = -1\n self.game_state.red_def = -1\n self.game_state.red_team = ''\n self.game_state.blue_team = ''\n self.CommitState()","title":""},{"docid":"ed0594ac57ad86dbb856f00a4b7427be","score":"0.58238447","text":"def __check_game_over(self):\r\n if self.__level.block_collides():\r\n self.__game_over = True\r\n self.__save_score()\r\n self.__finished()","title":""},{"docid":"131e7e21ddb5de5b80e346809338694e","score":"0.58088434","text":"def check_game_over():\n not_visible = []\n for card in GAME.play_cards:\n if card.visible == False:\n not_visible.append(card.visible)\n else:\n continue\n \n if len(not_visible) == len(GAME.play_cards):\n window = card.turtle.screen\n play = window.textinput(\"Game Over!\", \"Would you like to play again? (Y/N): \" )\n if play.upper() == 'Y':\n # update leaderboard before restarting\n update_leaderboard()\n # reset guesses\n GAME.guesses = 0\n game_controller()\n else:\n # update leaderboard before quiting\n update_leaderboard()\n quit()","title":""},{"docid":"86848d2ea068bf37cbf5a79aabf52f41","score":"0.5808788","text":"def game_over(self):\n self.screen.fill(WHITE)\n font = PG.font.SysFont('Helvitica', 50, True, False)\n final = font.render(\"GAME OVER\", True, BLACK)\n scoreboard = font.render(\"YOUR SCORE: \" + str(self.score), True, BLACK)\n question = font.render(\"Play Again? Y/N\", True, BLACK)\n self.screen.blit(final, [SIZE[0]/2 - 150, SIZE[1]/2 - 50])\n self.screen.blit(scoreboard, [SIZE[0]/2 - 175, SIZE[1]/2])\n self.screen.blit(question, [SIZE[0]/2 - 175, SIZE[1]/2 + 50])\n\n PG.display.flip()\n while True:\n for event in PG.event.get():\n if event.type == PG.QUIT:\n PG.quit()\n exit()\n if event.type == PG.KEYDOWN and event.key == PG.K_ESCAPE:\n PG.quit()\n exit()\n if event.type == PG.KEYDOWN and event.key == PG.K_y:\n #Play again\n return True\n if event.type == PG.KEYDOWN and event.key == PG.K_n:\n #Don't play again\n return False","title":""},{"docid":"2b403d352698d750572da0e3a71d6094","score":"0.5776626","text":"def isGameOver(self,board):\n\t\tif(self.canMove(board,1) == False):\n\t\t\tif(self.canMove(board,-1) == False):\n\t\t\t\treturn True\n\t\treturn False","title":""},{"docid":"31e4c95404474d3958d773070b3de3f2","score":"0.57646906","text":"def game_over(self):\n \n self.sound.play(\"gameover\")\n pygame.mouse.set_visible(True)\n back_button_position = 5, 400\n exit_button_position = 5, 440\n \n self.gui.put_widget(\"back\", back_button_position)\n self.gui.put_widget(\"exit\", exit_button_position)\n\n score_txt = \"Score: \"+str(self.state.score)\n score_surface = self.gui.get_text(score_txt)\n \n go_screen = pygame.image.load(GAMEOVER_SCREEN_IMG).convert_alpha()\n\n while True:\n\n self.screen.fill((0,0,255))\n self.screen.blit(go_screen, (0,0))\n self.screen.blit(score_surface,(0,0))\n self.gui.render(self.screen)\n\n events = self.gui.process(pygame.event.get())\n if events:\n\n for event in events:\n type, name = event\n if type == \"quit\":\n exit()\n elif type == \"cursor-over\":\n self.sound.play(\"select\")\n elif type == \"clicked\":\n self.sound.play(\"click\")\n sleep(1)\n if name == \"back\":\n self.gui.remove_widget(\"back\")\n self.gui.remove_widget(\"exit\")\n return\n elif name == \"exit\":\n exit()\n\n pygame.display.update()","title":""},{"docid":"18b79da5ff59312b36d1bdefa20a74ed","score":"0.57605654","text":"def draw_game_over(self):\n output = \"Game Over\"\n arcade.draw_text(output, 240, 400, arcade.color.WHITE, 54)\n\n output = \"Click to restart\"\n arcade.draw_text(output, 310, 300, arcade.color.WHITE, 24)","title":""},{"docid":"54fe4a5325a9dee24c184e0cc4faea97","score":"0.57425076","text":"def turn_over(self, player):\n return True","title":""},{"docid":"a1eb1a4fb15b0f0b79036edb60db0faa","score":"0.5738607","text":"def game_over() -> None:\n print(\"Game Over!\")\n print(\"Vous aurez probablement plus de chances la prochaine fois...\")","title":""},{"docid":"608c38e7adba72e7d13cbbf8645bd616","score":"0.5716874","text":"def is_game_over(self):\n def gameover(guesses, ships):\n return all(map(lambda shipcoord: shipcoord in guesses, flatten(ships)))\n\n return gameover(self.player2.guesses, self.player1.ships)","title":""},{"docid":"5992661af77c7221a6d202f9d627942c","score":"0.56981707","text":"def lose_life(self):\r\n global game_over\r\n player.num_lives -= 1\r\n player_death_sound.play()\r\n if player.num_lives == 0:\r\n game_over = True\r\n blinking = Blinking() #used to blink cursor as user types in their name\r\n prompt_high_score_screen(blinking,screen, score)\r\n blinking.kill()\r\n else:\r\n player.shield = 100\r\n self.respawning = True","title":""},{"docid":"c16e4cb6362766671bd89f0b5dd2c020","score":"0.5694089","text":"def game_over(self):\n\n display_string = \"GAME OVER!\"\n points_string = f\"Your points: {len(self.body())}\"\n self.window.erase()\n self.window.border()\n max_y, max_x = self.window.getmaxyx()\n y = max_y // 2\n x1 = (max_x - len(display_string)) // 2\n x2 = (max_x - len(points_string)) // 2\n self.window.addstr(y - 1, x1, display_string)\n self.window.addstr(y + 1, x2, points_string)\n self.window.refresh()\n self.alive = False\n time.sleep(2)","title":""},{"docid":"51cccfc1c3e7791d3f40e7ca1ab0f617","score":"0.5640243","text":"def game_over(self, state):\n return True if state == 0 else False","title":""},{"docid":"687c0eae46ae3b6fc700625332edc8b6","score":"0.56252277","text":"def GameOver(self):\n \n i = 36\n self.__gameover_affect.play()\n while i <= 240:\n img = self.LoadImagesHelper(\"Pictures/over/Pictures\" + str(i) + \".jpg\", 1280, 715)\n i += 1\n self.__screen.blit(img, (0,0))\n self.__clock.tick(30)\n pygame.display.flip()\n self.__screen.fill(BLACK)","title":""},{"docid":"cf6063fa4cf6eafa62c5470191b311d1","score":"0.561631","text":"def over(self):\n\t\tself.gameOver=True\n\t\tself.can.create_line(0,0, 10*self.blocksize, 20*self.blocksize+5, fill=\"red\", width=6)\n\t\tself.can.create_line(10*self.blocksize, 0,0,20*self.blocksize+5, fill=\"red\", width=6)\n\t\tself.master.check_over()","title":""},{"docid":"608726625a6277a82253e77fd9a11696","score":"0.55831105","text":"def game_over(self):\n points = self.points()\n if points == 21:\n return 'Blackjack!'\n elif points > 21:\n return 'Busted'\n else:\n return False","title":""},{"docid":"3fbd9c20832fcf1d2580aa064726c55e","score":"0.5560022","text":"def game_over(self):\n\n self.delete(ALL)\n self.create_text(self.winfo_width() /2, self.winfo_height()/2,\n text=\"Game Over with score {0}\".format(self.score), fill=\"white\")","title":""},{"docid":"4a5398257e4ed72e509f6ae498ca8949","score":"0.55443573","text":"def prep_game_over(self):\n\t\tself.game_over_image = self.font.render('Game Over', True, self.text_color, (110, 108, 108))\n\t\tself.game_over_rect = self.game_over_image.get_rect()\n\t\tself.game_over_rect.centery = self.screen_rect.centery - 35\n\t\tself.game_over_rect.centerx = self.screen_rect.centerx","title":""},{"docid":"441d2c1850d6467cea57a018a57b6d53","score":"0.5542856","text":"def _game_over_functionality(self, sound_when_dead, gameover_text_str):\n\n self._play_dead_sound(sound_when_dead)\n self.dead = True\n self.game_over_text = gameover_text_str\n self.game_over_screen()","title":""},{"docid":"f9159c3de55746e7394cd7672e669e77","score":"0.54997027","text":"def gameOverStateRender(self, display):\r\n display.fill((0, 0, 0))\r\n display.fill((200, 200, 200), ((131, 82), (655, 411)))\r\n display.blit(font.Font('freesansbold.ttf', 100).render(\"Game Over!\", True, (0, 0, 0)), (160, 140))\r\n self.gameOverButtons[0].drawButton(display, (245, 350))\r\n self.gameOverButtons[1].drawButton(display, (465, 350))","title":""},{"docid":"79174a718067baad2bee4c73bafa52e0","score":"0.5468527","text":"def game_over(self):\n basic_font = pygame.font.SysFont(None, self.slot) # to refactor\n left = self.display.get_rect().centerx - len(\"GAME O\") * self.slot\n top = self.display.get_rect().centery\n self._print(\n basic_font, \"GAME OVER\", left, top, Cuatris.RED, Cuatris.GRAY\n )\n self._print(\n basic_font,\n \"RETURN TO START OVER\",\n left,\n top + self.slot,\n Cuatris.RED,\n Cuatris.GRAY,\n )","title":""},{"docid":"388b83f7e189ac0da877de2c06f882d8","score":"0.54560274","text":"def game_over(self):\n self.goto(0.0, 0.0)\n self.write(\n \" Game Over\\nclick anywhere to close the window\",\n align=\"center\",\n font=FONT,\n )","title":""},{"docid":"196757ab093bf586148bbd26548d973a","score":"0.5452973","text":"def game_over(self, winner):\n pid = winner.get_id()\n last_move = winner.last_move()\n wid = last_move[0]\n move_action = last_move[1]\n self.__obs(lambda obs: obs.game_over(pid, wid, move_action))","title":""},{"docid":"ecceb52f24e5132f88ccd5768a574301","score":"0.5443769","text":"def gameOver(self):\n helv50 = font.Font(family='Helvetica', size=50, weight='bold')\n self.canvas.create_text(400, 400, text='Game Over', font=helv50, fill='red')\n self.canvas.pack()","title":""},{"docid":"a290abc0f87b208f3632b0a51dfa7f62","score":"0.5442499","text":"def end_game_menu(self):\r\n # Game over menu\r\n game_over_menu = pygame_menu.Menu(self.WIDTH, self.HEIGHT, 'Game over', theme=pygame_menu.themes.THEME_DARK)\r\n\r\n # Depending on the game players, print the winner/loser\r\n if self.game.AI_activated and self.game.winner == 'White':\r\n WINNER = 'YOU WIN!'\r\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\r\n\r\n elif self.game.AI_activated and self.game.winner == 'Black':\r\n WINNER = 'YOU LOSE!'\r\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\r\n\r\n elif not self.game.AI_activated and self.game.winner == 'White':\r\n WINNER = 'THE WINNER IS ' + self.user_name_1.get_value().capitalize() + ' (White)'\r\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\r\n\r\n elif not self.game.AI_activated and self.game.winner == 'Black':\r\n WINNER = 'THE WINNER IS ' + self.user_name_2.get_value().capitalize() + ' (Black)'\r\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\r\n\r\n # Play again with the same settings\r\n game_over_menu.add.button('Play again', self.run_game, True, align=pygame_menu.locals.ALIGN_LEFT)\r\n # Go to main menu\r\n game_over_menu.add.button('Main menu', main_menu, align=pygame_menu.locals.ALIGN_LEFT)\r\n game_over_menu.mainloop(self.WIN)","title":""},{"docid":"6f0d5688a07f925315a88caa72be34da","score":"0.5434655","text":"def game_over(self):\n _choice = self.disp.game_over_screen(self.stage.score, self.stage.player.character)\n if _choice == \"start\":\n self.g_o = False\n elif _choice == \"quick\":\n self.g_o = False\n self.skip_start = True\n else:\n self.quit_game()","title":""},{"docid":"fdf0d5cc0f0df1335bbdc27d8c930b86","score":"0.54312736","text":"def ongameplayupdatingdisabled(self):\n self.Check( ID_ToggleAI, False )\n self.get_frame().get_toolbar().ToggleTool( ID_ToggleAI, False )","title":""},{"docid":"fc3d2d997ad759fefa72b84187a331b1","score":"0.5400444","text":"def draw_game_over(self):\n arcade.draw_text(\"You lost!\", SCREEN_W/2, SCREEN_H/2, arcade.color.ANTIQUE_RUBY, font_size=64, font_name='arial', anchor_x='center')\n arcade.set_background_color(arcade.color.PURPLE_HEART)","title":""},{"docid":"688c5dbf7295e03d47741e03c5d734e3","score":"0.5397686","text":"def draw_gameover_page(self):\n arcade.draw_texture_rectangle(SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2,\n SCREEN_WIDTH,\n SCREEN_HEIGHT, self.gameover, 0)\n\n # Add messages depending on game outcome\n if self.total_time < 0.1:\n arcade.draw_text(\"Times Up!!!\", 375, 160, arcade.color.RED, 75)\n output = f\"Score: {self.score}\"\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)\n\n elif self.lives < 1:\n arcade.draw_text(\"You lost all your lives\", 400, 150, arcade.color.WHITE, 35)\n output = f\"Score: {self.score}\"\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)\n\n elif self.score < 25:\n arcade.draw_text(\"Sorry you lost!\", 200, 400, arcade.color.RED, 75)\n output = f\"Score: {self.score}\"\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)","title":""},{"docid":"f58c450068c26fa381abc100bf4594ea","score":"0.53893214","text":"def game_over(self):\n return (self.x, self.y) not in self.actions","title":""},{"docid":"d577a328f81f92b8f2ef8ba84277e8b7","score":"0.538671","text":"def gameOver(self):\n\t\treturn self.winner() or not self.getValidMoves()","title":""},{"docid":"327db2da68687d8c64aed061aae7a343","score":"0.53813183","text":"def gameover( self ) -> bool:\n if len( self.actions ) == 0: return True\n if abs( self.utility() ) == math.inf: return True\n return False","title":""},{"docid":"c4dbe174390d5feab84953eb2f43f5fe","score":"0.53556","text":"def IsDrawOver(self):\r\n return False","title":""},{"docid":"d2698144478df75f60db07d4d1cd087c","score":"0.5344385","text":"def game_over():\n if points_1 >= 7:\n game_over_screen_1()\n elif points_2 >= 7:\n game_over_screen_2()\n else:\n return False","title":""},{"docid":"749cfce239eb01b8e51ae882bbc7c56c","score":"0.53433317","text":"def game_over(reason):\n print(\"\\n\" + reason)\n print(\"Game Over!\")\n play_again()\n \"\"\"\n Ask player to play again or not by activating play_again() function\n \"\"\"","title":""},{"docid":"37dd3773c8604d0be4a716e28065e8c5","score":"0.5332427","text":"def displayGameOver():\n font = pygame.font.SysFont('comicsansms', 36)\n text = font.render(ScoreBoard.gameOverMessage, 1, (109, 48, 252))\n textPos = text.get_rect(\n centerx=ScoreBoard.POSITION[0] + ScoreBoard.SIZE[0] * 0.5,\n centery=ScoreBoard.POSITION[1] + ScoreBoard.SIZE[1] * 0.5 + 35\n )\n screen.blit(text, textPos)","title":""},{"docid":"c1081078f48a4f599c9ad8ada460ea33","score":"0.53310555","text":"def _game_over(self):\n game_over = False\n if self.head in self.snake[1:]: # head is position 0 in the list\n game_over = True\n if self.head.x > self.w - constants.BLOCK_SZ or self.head.x < 0 or \\\n self.head.y < 0 or self.head.y > self.h - constants.BLOCK_SZ:\n game_over = True \n return game_over","title":""},{"docid":"07ce8295e345ae586d270b1ac2b3a646","score":"0.5325378","text":"def is_over(self, state: GameState) -> bool:\n raise NotImplementedError","title":""},{"docid":"f931a49a08f15ac78b86b5cb154e7c32","score":"0.53073853","text":"def label_gameover():\n label = GLabel('Game over!')\n label.font = '-50'\n label.color = 'skyblue'\n return label","title":""},{"docid":"235b2be5b96da804b02247557b72349d","score":"0.5300991","text":"def game_is_over(game):\n for p in game.players:\n if p.score >= 50:\n return True\n return False","title":""},{"docid":"a936e63989036f875390f1bae547a9be","score":"0.5284049","text":"def on_move_fail(self, user, battle):","title":""},{"docid":"cb0c28f56b3349fd2c5b9bef2fdd4574","score":"0.52828914","text":"def is_game_over(self):\n for player in self.players:\n for penguin in player.get_penguins():\n if len(self.get_reachable(penguin)) > 0:\n return False\n return True","title":""},{"docid":"150dcc169c8ae0948b1aad82700086a7","score":"0.5247563","text":"def ongameplayupdatingenabled(self):\n self.Check( ID_ToggleAI, True )\n self.get_frame().get_toolbar().ToggleTool( ID_ToggleAI, True )","title":""},{"docid":"469134f6e994f11813e74a2d9a5ef018","score":"0.52322143","text":"def test_game_over(self):\n\n UFO = Game('failure')\n UFO.lives = 0\n result = Game.check_status(UFO)\n\n self.assertEqual(result, game.STATUS_LOSE)","title":""},{"docid":"d86227bf0bcca09f539a2417e8dcb690","score":"0.5217142","text":"def is_game_over(board):\n moves = max(available_moves(board))\n if moves == 0:\n return True\n p1w = has_won(board,1)\n p2w = has_won(board,2)\n if p1w == True:\n return True\n elif p2w == True:\n return True\n else:\n return False","title":""},{"docid":"bdea1b45e94af3b6766f756a51b939bb","score":"0.52171195","text":"def end(self):\r\n end_message = games.Message(value = \"Game Over\",\r\n size = 90,\r\n color = color.red,\r\n x = games.screen.width/2,\r\n y = games.screen.height/2,\r\n lifetime = 5 * games.screen.fps,\r\n after_death = games.screen.quit,\r\n is_collideable = False)\r\n games.screen.add(end_message)","title":""},{"docid":"046e0ca3a338716253d24e0bb0d7f337","score":"0.5211163","text":"def check_background_games(self, *args):\n if self.username is None or self.password is None:\n return\n if self.player.logged_in is False:\n return\n if not self.my_turn_games and not self.old_games and not self.opp_turn_games:\n self.load_current_games()\n\n games = list(self.player.current_games)\n if len(games) < 1:\n return\n\n for game in games:\n if game.over and game in self.old_games:\n # NOOP\n pass\n elif game.over and game not in self.old_games:\n Logger.info(f\"Game over and not in old games? {game.guid}\")\n self.notify_removed_game(game)\n self.old_games.append(game)\n if game in self.opp_turn_games:\n self.opp_turn_games.remove(game)\n if game in self.my_turn_games:\n self.my_turn_games.remove(game)\n\n elif game.my_turn and game in self.opp_turn_games:\n # New game for us to make a move!\n self.notify_my_move(game)\n self.my_turn_games.append(game)\n self.opp_turn_games.remove(game)\n elif not game.my_turn and game in self.my_turn_games:\n # Opponent is to move now.\n self.opp_turn_games.append(game)\n self.my_turn_games.remove(game)\n elif game not in self.my_turn_games and game not in self.opp_turn_games:\n self.notify_new_game(game)\n if game.my_turn:\n self.my_turn_games.append(game)\n else:\n self.opp_turn_games.append(game)","title":""},{"docid":"a433776dfa836a53e73a050f6b53129b","score":"0.52030104","text":"def check_game_over(self):\n\n # check if we have at least K blocks saved, either locally or remotely\n lbs, rbs_array = self.local_blocks, self.remote_blocks\n rbs = []\n for i in range(len(rbs_array)):\n for j in range(len(rbs_array[i])):\n if len(rbs) > i:\n rbs[i] = rbs_array[j][i] or rbs[i]\n else:\n rbs.append(rbs_array[j][i])\n\n blocks_saved = [lb or rb for lb, rb in zip(lbs, rbs)]\n if sum(blocks_saved) < self.K:\n raise GameOver","title":""},{"docid":"67f224eaef8f1a47a37f11a99ec4f380","score":"0.51871765","text":"def is_end_game(self):\n if Player.total == 2:\n try:\n if (\n self.board.rows % 2 == 1\n and self.counter.pawn[0].position\n == (self.board.cols - 1, self.board.rows - 1)\n ) or (\n self.board.rows % 2 == 0\n and self.counter.pawn[0].position == (0, self.board.rows - 1)\n ):\n # Player Loses\n self._winner = self.counter\n pygame.display.update()\n return True\n except IndexError:\n pass\n if self.counter.score == 6:\n # Player Loses\n self._winner = self.counter\n pygame.display.update()\n return True\n elif Player.array[1].score == 6:\n # Player Wins\n self._winner = Player.array[1]\n pygame.display.update()\n return True\n else:\n for player in Player.array:\n if player.score == 12 // (Player.total - 1):\n # That Player Wins\n self._winner = player\n pygame.display.update()\n return True\n return False","title":""},{"docid":"b15c8022cb6c26e6f496d37d1be539ee","score":"0.5184023","text":"def game_over(ship_list, AI_ship_list):\n if player_win(AI_ship_list) or AI_win(ship_list): #either player wins or AI wins\n return True\n else:\n return False","title":""},{"docid":"a8fe29a41182dee26fd5a80599d6a58a","score":"0.51778793","text":"def on_game_over(self, bj):\n self.print(\"on_game_over\")\n self.print(\"dealer:\", player.players[0].hand)\n res = self.wins - self.losses\n if self.positive_prog:\n res = -res\n self.print(\"res:\", res)\n if res < 0:\n self.betting.on_loss(abs(res))\n if self.anti_fallacy:\n self.af_trigger = True\n elif res > 0 and not self.af_trigger:\n self.betting.on_win(res)\n elif res > 0:\n self.af_trigger = False\n else:\n self.betting.on_tie()\n self.reset_results()","title":""},{"docid":"73e8b9877e116344f91802bda7524df2","score":"0.5169724","text":"def game_over():\n print(GAME_OVER)\n time.sleep(1)\n play_again()","title":""},{"docid":"320ac21dd8f220dad9aad9fd6cce1e81","score":"0.5163603","text":"def GameOver():\n Text(\"Game Over\",window_Width//2,window_Height//2,Window).Message_display()\n pg.display.update()\n while True:\n for event in pg.event.get():\n if event.type==pg.KEYDOWN:\n if event.type==pg.QUIT:\n pg.quit()\n quit()\n if event.key==pg.K_RETURN:\n mainloop()","title":""},{"docid":"50baffd35696fd783f986962d4ae6f97","score":"0.51631284","text":"def isGameOver(self, boards):\n return self.deadTest(boards[0]) and self.deadTest(boards[1]) and self.deadTest(boards[2])","title":""},{"docid":"36e8efc791fb6facf821fc60dcfc7de1","score":"0.5156873","text":"def game_over(self):\n if len(self.player_pieces) == 0:\n self.status = \"player_wins\"\n elif len(self.computer_pieces) == 0:\n self.status = \"computer_wins\"\n else:\n left_end = self.domino_snake[0][0]\n right_end = self.domino_snake[len(self.domino_snake) - 1][1]\n # If numbers on the ends are identical\n if left_end == right_end:\n # And appear within the snake 8 times, it's a draw\n if len([piece for piece in self.domino_snake if left_end in piece]) >= 8:\n self.status = \"draw\"","title":""},{"docid":"dc3048bf2c6144bea4bda24cfce0bb2e","score":"0.51562595","text":"def game_exists(session):\n # Your code goes here\n return False","title":""},{"docid":"c19eb72f284c63b3e7d5841cb51f71ea","score":"0.5154593","text":"def reloadGameOver(self, image):\n gameOver = pygame.image.load(image)\n gameOver = pygame.transform.scale(gameOver, self.dimension)\n self.screen.blit(gameOver, [0, 0])","title":""},{"docid":"6f665502c69dfbda30141b512240f47d","score":"0.5149441","text":"def is_lost():\n return get_game_state() == -1","title":""},{"docid":"70147f4542ef8e833626671e6d8a1aee","score":"0.51396245","text":"def isGameOver(S,p):\n if move_was_winning_move(S,p):\n if p == COMP:\n set_winner(COMP)\n else:\n set_winner(HUMAN)\n return True\n \n if move_still_possible(S) == False:\n set_winner(TIE)\n return True\n\n return False","title":""},{"docid":"601bf74065e353d6278d0cb927e6535c","score":"0.5125398","text":"def is_in_pregame(self):\n return False","title":""},{"docid":"3999841bbc9fd6f8572e813863d75042","score":"0.5120661","text":"def episode_over(self, winner):\n if winner == DRAW:\n print('Game over! It was a draw.')\n else:\n print('Game over! Winner: Player {0}'.format(winner))","title":""},{"docid":"c4473ef49de1ea5b77bd6488517702e6","score":"0.5120455","text":"def game_over():\n WIN = Window.WIN\n WHITE = (255, 255, 255)\n BLACK = (0, 0, 0)\n\n end = True\n\n while end:\n for event in pygame.event.get():\n print(event)\n\n if event.type == pygame.QUIT:\n pygame.quit()\n quit()\n\n WIN.fill(BLACK)\n text = pygame.font.SysFont('Times New Roman', 100)\n Text, TextRect = text_ob(\"Game Over\", text, WHITE)\n TextRect.center = ((Window.WIDTH / 2), (Window.HEIGHT / 2))\n WIN.blit(Text, TextRect)\n pygame.display.update()","title":""},{"docid":"f3158e57c22b57e615c7e8352bb52bb3","score":"0.5109053","text":"def display_game_over(self, screen):\n my_score, opp_score = self.get_my_score(self.board)\n \n screen.blit(OVER_BG, OVER_BG_RECT)\n\n # Decide win, lose, or draw\n if my_score > opp_score:\n screen.blit(TROPHY, TROPHY_RECT)\n elif my_score < opp_score:\n screen.blit(SAD, SAD_RECT)\n elif my_score == opp_score:\n screen.blit(TIE, TIE_RECT)\n\n # Game over text\n text_surf = BRIT_FONT_BIG.render(\"GAME OVER!\", True, PURPLE)\n text_rect = text_surf.get_rect()\n text_rect.center = (int(WINDOW_X/2), 50)\n \n # Score text\n score_surf = BRIT_FONT.render(\" %s -- %s \" %\n (str(my_score), str(opp_score)),\n True, BLACK)\n score_rect = score_surf.get_rect()\n score_rect.center = (int(WINDOW_X/2), 400)\n\n # Query text\n text2_surf = BRIT_FONT.render('What do you want to do?', True, BLACK)\n text2_rect = text2_surf.get_rect()\n text2_rect.center = (int(WINDOW_X/2), 450)\n\n # Display the elements\n screen.blit(text_surf, text_rect)\n screen.blit(text2_surf, text2_rect)\n screen.blit(score_surf, score_rect)\n screen.blit(self.play_again, self.play_again_rect)\n screen.blit(self.quit_game, self.quit_game_rect) \n\n # Set game over trigger\n self.gameover = True","title":""},{"docid":"0011bd32a01349c31d52927eeae5ac06","score":"0.50900185","text":"def _checkGameState(self) -> int:\n if self.game.isFinished():\n self.winningPlayers = self.game.winningPlayers\n return END\n return CONTINUE","title":""},{"docid":"10bfcc50ef53ef02e8959e291b302447","score":"0.50871944","text":"def game_over_text(self):\n over_text = self.score.over_font.render(\"GAME OVER\", True, (255, 255, 255))\n self.screen.blit(over_text, (200, 250))\n return self","title":""},{"docid":"e18114991da85b4b7ecf89ea5b5ff97a","score":"0.5079349","text":"def loadGameOver(self, image):\n gameOver = pygame.image.load(image)\n gameOver = pygame.transform.scale(gameOver, self.dimension)\n self.screen.blit(gameOver, [0, 0])","title":""},{"docid":"75dbb1e37944f7b02a94c3a4b543a5b2","score":"0.5078623","text":"def turnOver(self):\n if self.__faceDown: self.__faceDown = False\n else: self.__faceDown = True","title":""},{"docid":"30eb8c857cb9507b2b1a7553e85cc432","score":"0.5071968","text":"def update_game_over_screen(screen_type):\n # If the game is over\n if bbm.check_game_over_condition():\n # Delete an existing save file given the perma-death nature of the game\n bbo.delete_save_file()\n # Update the game over screen background\n if init.game_state.game_over != \"Lost\":\n sc.sm.get_screen(\"over\").ids.background_over.this_source = bbo.get_path(\n \"../../GraphicFiles/won_background.png\")\n if init.game_state.game_over != \"Won\":\n sc.sm.get_screen(\"over\").ids.background_over.this_source = bbo.get_path(\n \"../../GraphicFiles/lost_background.png\")\n # Change the screen\n sc.sm.get_screen(screen_type).change_window(\"over\")","title":""},{"docid":"b61f48e415cf13b15ef8a9d5d8692e7c","score":"0.5068129","text":"def loseGame(self):\n # ONLY set alive via loseGame\n self.alive = False\n self.targetable = False\n self.discard += self.hand\n del self.hand[:]\n return","title":""},{"docid":"cb2e38cbd74fc6b93413c3de052b4ec2","score":"0.50639844","text":"def gameOverButtonChecker(self):\r\n for button in self.gameOverButtons:\r\n button.clicked()","title":""},{"docid":"786e0bc3b168e8eb90aa91b0a09c624d","score":"0.5060393","text":"def end_game(self, winner, loser):\n self.running = False\n print(\"Game over! {} wins and {} loses\".format(winner, loser))\n if isinstance(winner, Ai):\n winner.append_win()\n if isinstance(loser, Ai):\n loser.append_loss()","title":""},{"docid":"d5b51f364f472f2039d61a88f37f6138","score":"0.50415915","text":"def is_game_over(self, gamestate) -> bool:\n all_players_dead = gamestate.all_players_expelled()\n state_complete = gamestate.game_complete()\n return state_complete or all_players_dead","title":""},{"docid":"d0e111eb9bf2a17c606cc311b47d3436","score":"0.50329655","text":"def game_over(self, matrix):\n if any(-1 in row for row in matrix):\n self.bomeshflag = True\n return\n if any(2048 in row for row in matrix):\n self.overflag = True\n self.winflag = True\n elif (\n not any(0 in row for row in matrix)\n and not self.horizontal_move_exits(matrix)\n and not self.vertical_move_exits(matrix)\n ):\n self.overflag = True\n else:\n self.overflag = False\n self.winflag = False","title":""},{"docid":"adca3275dfa82295f0945c1169c3fcfb","score":"0.5031085","text":"def lose_message(self):\n messagebox.showwarning('Fail', 'You Lose..')","title":""},{"docid":"caba0c5a11915fc6850fb21c5f64f93a","score":"0.50257415","text":"def add_game_over_text():\n with open(\"active-game-files/game_over.txt\", \"a\") as f:\n f.writelines(\"Game Over\")","title":""},{"docid":"02b77f306b27fbfb88bf71071a0ac30c","score":"0.50181514","text":"def is_game_over(self, state):\n return self.is_player_win(state, \"x\") or self.is_player_win(state, \"o\") or self.is_board_filled()","title":""},{"docid":"1f91e037784ea5097a3ce13c42f56e0d","score":"0.50021744","text":"def check_new_event(self):\n new_event_list = self.nav.get_fleet_events()\n for new_event in new_event_list:\n if self.is_in(new_event, self.list_event):\n self.list_event.append(new_event)\n if not self.is_my_fleet(new_event):\n log(\n \"{} ship(s) from ({}, {}) to ({}, {}) arriving in {} at {} to {}\".format(\n new_event[\"detail_fleet\"],\n new_event[\"origin_planet_name\"],\n new_event[\"origin_planet_coord\"],\n new_event[\"destination_planet_name\"],\n new_event[\"destination_planet_coord\"],\n new_event[\"remaining_time\"],\n new_event[\"arrival_time\"],\n self.mission[new_event[\"mission_type\"]],\n )\n )\n if self.is_under_attack(new_event):\n self.nav.send_message_enemy(new_event[\"player_id\"])\n\n is_event_over = False\n for old_event in self.list_event:\n if self.is_in(old_event, new_event_list):\n self.list_event.remove(old_event)\n is_event_over = True\n return is_event_over","title":""},{"docid":"069a06ed8aa0e4ecc23e12d8ab0eebd6","score":"0.49960214","text":"def handleStatsLostEvent (self, event):\n raise NotImplementedException(\"SENPAIListener object must properly implement handleStatsLostEvent, or return False from handlesEvent() for event type 'Stats Lost'.\")","title":""},{"docid":"ad2a3ba8428a6461e9b3f14551e8f966","score":"0.49867088","text":"def game_over():\r\n text = pygame.font.Font(\"freesansbold.ttf\", 70)\r\n game_over = text.render(\"Game Over\", True, (255, 255, 255))\r\n window.blit(game_over, (150, 250))","title":""},{"docid":"5fb7c4650fa3df8200a10791b6ccda1d","score":"0.4982617","text":"def has_ended(game):\n # Your code goes here\n return True","title":""},{"docid":"8c3bff4f48378281200f34b559763226","score":"0.4982418","text":"def gameOver(self):\n # font = pygame.font.Font(\"Serif\", 25)\n font = pygame.font.SysFont(\"serif\", 25)\n text = font.render(\"Game Over, click to restart\", True, BLACK)\n center_x = (TREE_WIDTH // 2) - (text.get_width() // 2)\n center_y = (TREE_HEIGHT // 2) - (text.get_height() // 2)\n self.screen.blit(text, [center_x, center_y])","title":""}],"string":"[\n {\n \"docid\": \"0741ff7b41ba00efd002b94655689183\",\n \"score\": \"0.67969733\",\n \"text\": \"def game_over(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1110ad2a849ae1de5830ea21087ddd3e\",\n \"score\": \"0.63831997\",\n \"text\": \"def game_over(self):\\n # The game is over! Disable rolling more turns.\\n self._turn_button.state(['disabled'])\\n self._reset_button.focus()\\n self.infotext['text'] += \\\"Game Over!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe6c2586de121b67d9bb23a41d76816\",\n \"score\": \"0.6277449\",\n \"text\": \"def is_game_over(self, is_game_over):\\n\\n self._is_game_over = is_game_over\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a45409e84e22dd2f3f431bfd50327ab\",\n \"score\": \"0.62484246\",\n \"text\": \"def game_over(self, game):\\n\\n for i in game.users.keys():\\n self.client_callbacks[i].gameOver(game.get_scores()[0][0])\\n self.ServerGames.remove(game.get_uuid())\\n print \\\"Game over, game uuid: %s\\\" % game.get_uuid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec399096e50a770f0fc5d0ce0f6a50b\",\n \"score\": \"0.6247514\",\n \"text\": \"def gameOver(self):\\n\\t\\tself.window.show_view(GameOverView(self.score))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29207c97f15441b8fcd9b34d53cbe0a7\",\n \"score\": \"0.622483\",\n \"text\": \"def game_over(self):\\n if self.current_room.id == 0:\\n print(\\\"Game Over\\\")\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81dc7d83f7e75f3d0cd5c005eb072dcb\",\n \"score\": \"0.6170105\",\n \"text\": \"def isGameOver(self):\\n if self.get_moves():\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d71657ea2bf172ece032cc5db6f2da0\",\n \"score\": \"0.6119336\",\n \"text\": \"def game_over():\\n global game_on\\n game_on = False\\n score.game_over()\\n # screen.onkey(replay, \\\"space\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b5f8c363efa8a6862b420f4be33c5e\",\n \"score\": \"0.60841423\",\n \"text\": \"def game_over(self):\\n\\n title_screen = Title(self)\\n info_screen = InfoScreen(self, self.score, '', title_screen)\\n self.change_state(info_screen)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc9d4572068ef784b3f292fed618161\",\n \"score\": \"0.6058548\",\n \"text\": \"def checkGameOver(self):\\n return self.keep_playing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90ee583f0df0fbab55e66d78af724cb\",\n \"score\": \"0.60327965\",\n \"text\": \"def isGameOver(self):\\n\\n if self.getResult() != 0:\\n self.gameover = True\\n return True\\n elif 0 not in (self.board[0,:,:,0] + self.board[0,:,:,1]):\\n self.gameover = True\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7c3da4824df6c4163666a0e1f8fe5c\",\n \"score\": \"0.5992917\",\n \"text\": \"def displayGameOverMenu(self):\\n\\n # make game over menu object.\\n GOM = GameOverMenu(self.__settings.get_res_width(), self.__settings.get_res_height(), self.__screen)\\n GOM.run_game_over_menu()\\n\\n # checks if user clicked 'Main Menu' button.\\n if GOM.get_to_main_menu() == True:\\n self.__goToMainMenu = True\\n else:\\n self.__goToMainMenu = False\\n\\n # checks if user clicked 'Play Again' button.\\n if GOM.play_again(): # if true\\n self.__play_again = True\\n else:\\n self.__play_again = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbfc30dfec37f0e74c160e309b92dce\",\n \"score\": \"0.5955247\",\n \"text\": \"def is_in_game(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba319b5bc49b039ad4e08f467c5696d8\",\n \"score\": \"0.593408\",\n \"text\": \"def is_game_over(self):\\n if self.total_score <= 0:\\n print(\\\"Better luck next time!\\\")\\n print(\\\"Game over\\\")\\n self.keep_playing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21fd3a8bf7838446df49c7d67967d42e\",\n \"score\": \"0.5924194\",\n \"text\": \"def isGameOver(self):\\n if not self.get_moves():\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"299349df6beb7a3ef472caef85adbc03\",\n \"score\": \"0.58878446\",\n \"text\": \"def game_is_over(self):\\r\\n return self.game_clock.game_is_over()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46b37ed254a57911adb1c741428403c\",\n \"score\": \"0.58453447\",\n \"text\": \"def game_over(self):\\n\\n return self.game.game_over()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbf102d4e23762b6c15ea8584264afd\",\n \"score\": \"0.5844271\",\n \"text\": \"def gameover(self) -> bool:\\n return self.win or self.loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf65422563f09e246dd75e929d8314e4\",\n \"score\": \"0.5840213\",\n \"text\": \"def game_over(self):\\n self.ende = UiGameOverWindow()\\n self.ende.show()\\n\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd812b26778362480a83da67ac27b99\",\n \"score\": \"0.58365285\",\n \"text\": \"def GameOver(self):\\n #decide who won and compensate for players not actually sliding the 10th cube into place\\n if self.game_state.red_score > self.game_state.blue_score:\\n if self.game_state.red_team is None:\\n winner = 'red'\\n else:\\n winner = self.game_state.red_team\\n\\n self.game_state.red_score = 10\\n\\n elif self.game_state.blue_score > self.game_state.red_score:\\n if self.game_state.blue_team is None:\\n winner = 'blue'\\n else:\\n winner = self.game_state.blue_team\\n\\n self.game_state.blue_score = 10\\n\\n elif self.game_state.blue_score == self.game_state.red_score:\\n winner = 'tie'\\n\\n #populate and commit a game state object\\n foos_log = Game(winner=winner,\\\\\\n blue_score=self.game_state.blue_score,\\\\\\n red_score=self.game_state.red_score,\\\\\\n red_off=self.game_state.red_off,\\\\\\n red_def=self.game_state.red_def,\\\\\\n blue_off=self.game_state.blue_off,\\\\\\n blue_def=self.game_state.blue_def,\\\\\\n started=self.game_state.game_started,\\\\\\n ended=int(time()))\\n\\n db.session.add(foos_log)\\n db.session.commit()\\n\\n #set a state object to the winner to update web ui with winning team detected\\n self.game_state.game_winner = winner\\n\\n self.game_state.game_on = False\\n self.game_state.blue_off = -1\\n self.game_state.blue_def = -1\\n self.game_state.red_off = -1\\n self.game_state.red_def = -1\\n self.game_state.red_team = ''\\n self.game_state.blue_team = ''\\n self.CommitState()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed0594ac57ad86dbb856f00a4b7427be\",\n \"score\": \"0.58238447\",\n \"text\": \"def __check_game_over(self):\\r\\n if self.__level.block_collides():\\r\\n self.__game_over = True\\r\\n self.__save_score()\\r\\n self.__finished()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131e7e21ddb5de5b80e346809338694e\",\n \"score\": \"0.58088434\",\n \"text\": \"def check_game_over():\\n not_visible = []\\n for card in GAME.play_cards:\\n if card.visible == False:\\n not_visible.append(card.visible)\\n else:\\n continue\\n \\n if len(not_visible) == len(GAME.play_cards):\\n window = card.turtle.screen\\n play = window.textinput(\\\"Game Over!\\\", \\\"Would you like to play again? (Y/N): \\\" )\\n if play.upper() == 'Y':\\n # update leaderboard before restarting\\n update_leaderboard()\\n # reset guesses\\n GAME.guesses = 0\\n game_controller()\\n else:\\n # update leaderboard before quiting\\n update_leaderboard()\\n quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86848d2ea068bf37cbf5a79aabf52f41\",\n \"score\": \"0.5808788\",\n \"text\": \"def game_over(self):\\n self.screen.fill(WHITE)\\n font = PG.font.SysFont('Helvitica', 50, True, False)\\n final = font.render(\\\"GAME OVER\\\", True, BLACK)\\n scoreboard = font.render(\\\"YOUR SCORE: \\\" + str(self.score), True, BLACK)\\n question = font.render(\\\"Play Again? Y/N\\\", True, BLACK)\\n self.screen.blit(final, [SIZE[0]/2 - 150, SIZE[1]/2 - 50])\\n self.screen.blit(scoreboard, [SIZE[0]/2 - 175, SIZE[1]/2])\\n self.screen.blit(question, [SIZE[0]/2 - 175, SIZE[1]/2 + 50])\\n\\n PG.display.flip()\\n while True:\\n for event in PG.event.get():\\n if event.type == PG.QUIT:\\n PG.quit()\\n exit()\\n if event.type == PG.KEYDOWN and event.key == PG.K_ESCAPE:\\n PG.quit()\\n exit()\\n if event.type == PG.KEYDOWN and event.key == PG.K_y:\\n #Play again\\n return True\\n if event.type == PG.KEYDOWN and event.key == PG.K_n:\\n #Don't play again\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b403d352698d750572da0e3a71d6094\",\n \"score\": \"0.5776626\",\n \"text\": \"def isGameOver(self,board):\\n\\t\\tif(self.canMove(board,1) == False):\\n\\t\\t\\tif(self.canMove(board,-1) == False):\\n\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31e4c95404474d3958d773070b3de3f2\",\n \"score\": \"0.57646906\",\n \"text\": \"def game_over(self):\\n \\n self.sound.play(\\\"gameover\\\")\\n pygame.mouse.set_visible(True)\\n back_button_position = 5, 400\\n exit_button_position = 5, 440\\n \\n self.gui.put_widget(\\\"back\\\", back_button_position)\\n self.gui.put_widget(\\\"exit\\\", exit_button_position)\\n\\n score_txt = \\\"Score: \\\"+str(self.state.score)\\n score_surface = self.gui.get_text(score_txt)\\n \\n go_screen = pygame.image.load(GAMEOVER_SCREEN_IMG).convert_alpha()\\n\\n while True:\\n\\n self.screen.fill((0,0,255))\\n self.screen.blit(go_screen, (0,0))\\n self.screen.blit(score_surface,(0,0))\\n self.gui.render(self.screen)\\n\\n events = self.gui.process(pygame.event.get())\\n if events:\\n\\n for event in events:\\n type, name = event\\n if type == \\\"quit\\\":\\n exit()\\n elif type == \\\"cursor-over\\\":\\n self.sound.play(\\\"select\\\")\\n elif type == \\\"clicked\\\":\\n self.sound.play(\\\"click\\\")\\n sleep(1)\\n if name == \\\"back\\\":\\n self.gui.remove_widget(\\\"back\\\")\\n self.gui.remove_widget(\\\"exit\\\")\\n return\\n elif name == \\\"exit\\\":\\n exit()\\n\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b79da5ff59312b36d1bdefa20a74ed\",\n \"score\": \"0.57605654\",\n \"text\": \"def draw_game_over(self):\\n output = \\\"Game Over\\\"\\n arcade.draw_text(output, 240, 400, arcade.color.WHITE, 54)\\n\\n output = \\\"Click to restart\\\"\\n arcade.draw_text(output, 310, 300, arcade.color.WHITE, 24)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54fe4a5325a9dee24c184e0cc4faea97\",\n \"score\": \"0.57425076\",\n \"text\": \"def turn_over(self, player):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1eb1a4fb15b0f0b79036edb60db0faa\",\n \"score\": \"0.5738607\",\n \"text\": \"def game_over() -> None:\\n print(\\\"Game Over!\\\")\\n print(\\\"Vous aurez probablement plus de chances la prochaine fois...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608c38e7adba72e7d13cbbf8645bd616\",\n \"score\": \"0.5716874\",\n \"text\": \"def is_game_over(self):\\n def gameover(guesses, ships):\\n return all(map(lambda shipcoord: shipcoord in guesses, flatten(ships)))\\n\\n return gameover(self.player2.guesses, self.player1.ships)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5992661af77c7221a6d202f9d627942c\",\n \"score\": \"0.56981707\",\n \"text\": \"def lose_life(self):\\r\\n global game_over\\r\\n player.num_lives -= 1\\r\\n player_death_sound.play()\\r\\n if player.num_lives == 0:\\r\\n game_over = True\\r\\n blinking = Blinking() #used to blink cursor as user types in their name\\r\\n prompt_high_score_screen(blinking,screen, score)\\r\\n blinking.kill()\\r\\n else:\\r\\n player.shield = 100\\r\\n self.respawning = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16e4cb6362766671bd89f0b5dd2c020\",\n \"score\": \"0.5694089\",\n \"text\": \"def game_over(self):\\n\\n display_string = \\\"GAME OVER!\\\"\\n points_string = f\\\"Your points: {len(self.body())}\\\"\\n self.window.erase()\\n self.window.border()\\n max_y, max_x = self.window.getmaxyx()\\n y = max_y // 2\\n x1 = (max_x - len(display_string)) // 2\\n x2 = (max_x - len(points_string)) // 2\\n self.window.addstr(y - 1, x1, display_string)\\n self.window.addstr(y + 1, x2, points_string)\\n self.window.refresh()\\n self.alive = False\\n time.sleep(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51cccfc1c3e7791d3f40e7ca1ab0f617\",\n \"score\": \"0.5640243\",\n \"text\": \"def game_over(self, state):\\n return True if state == 0 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687c0eae46ae3b6fc700625332edc8b6\",\n \"score\": \"0.56252277\",\n \"text\": \"def GameOver(self):\\n \\n i = 36\\n self.__gameover_affect.play()\\n while i <= 240:\\n img = self.LoadImagesHelper(\\\"Pictures/over/Pictures\\\" + str(i) + \\\".jpg\\\", 1280, 715)\\n i += 1\\n self.__screen.blit(img, (0,0))\\n self.__clock.tick(30)\\n pygame.display.flip()\\n self.__screen.fill(BLACK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6063fa4cf6eafa62c5470191b311d1\",\n \"score\": \"0.561631\",\n \"text\": \"def over(self):\\n\\t\\tself.gameOver=True\\n\\t\\tself.can.create_line(0,0, 10*self.blocksize, 20*self.blocksize+5, fill=\\\"red\\\", width=6)\\n\\t\\tself.can.create_line(10*self.blocksize, 0,0,20*self.blocksize+5, fill=\\\"red\\\", width=6)\\n\\t\\tself.master.check_over()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608726625a6277a82253e77fd9a11696\",\n \"score\": \"0.55831105\",\n \"text\": \"def game_over(self):\\n points = self.points()\\n if points == 21:\\n return 'Blackjack!'\\n elif points > 21:\\n return 'Busted'\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbd9c20832fcf1d2580aa064726c55e\",\n \"score\": \"0.5560022\",\n \"text\": \"def game_over(self):\\n\\n self.delete(ALL)\\n self.create_text(self.winfo_width() /2, self.winfo_height()/2,\\n text=\\\"Game Over with score {0}\\\".format(self.score), fill=\\\"white\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5398257e4ed72e509f6ae498ca8949\",\n \"score\": \"0.55443573\",\n \"text\": \"def prep_game_over(self):\\n\\t\\tself.game_over_image = self.font.render('Game Over', True, self.text_color, (110, 108, 108))\\n\\t\\tself.game_over_rect = self.game_over_image.get_rect()\\n\\t\\tself.game_over_rect.centery = self.screen_rect.centery - 35\\n\\t\\tself.game_over_rect.centerx = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441d2c1850d6467cea57a018a57b6d53\",\n \"score\": \"0.5542856\",\n \"text\": \"def _game_over_functionality(self, sound_when_dead, gameover_text_str):\\n\\n self._play_dead_sound(sound_when_dead)\\n self.dead = True\\n self.game_over_text = gameover_text_str\\n self.game_over_screen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9159c3de55746e7394cd7672e669e77\",\n \"score\": \"0.54997027\",\n \"text\": \"def gameOverStateRender(self, display):\\r\\n display.fill((0, 0, 0))\\r\\n display.fill((200, 200, 200), ((131, 82), (655, 411)))\\r\\n display.blit(font.Font('freesansbold.ttf', 100).render(\\\"Game Over!\\\", True, (0, 0, 0)), (160, 140))\\r\\n self.gameOverButtons[0].drawButton(display, (245, 350))\\r\\n self.gameOverButtons[1].drawButton(display, (465, 350))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79174a718067baad2bee4c73bafa52e0\",\n \"score\": \"0.5468527\",\n \"text\": \"def game_over(self):\\n basic_font = pygame.font.SysFont(None, self.slot) # to refactor\\n left = self.display.get_rect().centerx - len(\\\"GAME O\\\") * self.slot\\n top = self.display.get_rect().centery\\n self._print(\\n basic_font, \\\"GAME OVER\\\", left, top, Cuatris.RED, Cuatris.GRAY\\n )\\n self._print(\\n basic_font,\\n \\\"RETURN TO START OVER\\\",\\n left,\\n top + self.slot,\\n Cuatris.RED,\\n Cuatris.GRAY,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388b83f7e189ac0da877de2c06f882d8\",\n \"score\": \"0.54560274\",\n \"text\": \"def game_over(self):\\n self.goto(0.0, 0.0)\\n self.write(\\n \\\" Game Over\\\\nclick anywhere to close the window\\\",\\n align=\\\"center\\\",\\n font=FONT,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"196757ab093bf586148bbd26548d973a\",\n \"score\": \"0.5452973\",\n \"text\": \"def game_over(self, winner):\\n pid = winner.get_id()\\n last_move = winner.last_move()\\n wid = last_move[0]\\n move_action = last_move[1]\\n self.__obs(lambda obs: obs.game_over(pid, wid, move_action))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecceb52f24e5132f88ccd5768a574301\",\n \"score\": \"0.5443769\",\n \"text\": \"def gameOver(self):\\n helv50 = font.Font(family='Helvetica', size=50, weight='bold')\\n self.canvas.create_text(400, 400, text='Game Over', font=helv50, fill='red')\\n self.canvas.pack()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a290abc0f87b208f3632b0a51dfa7f62\",\n \"score\": \"0.5442499\",\n \"text\": \"def end_game_menu(self):\\r\\n # Game over menu\\r\\n game_over_menu = pygame_menu.Menu(self.WIDTH, self.HEIGHT, 'Game over', theme=pygame_menu.themes.THEME_DARK)\\r\\n\\r\\n # Depending on the game players, print the winner/loser\\r\\n if self.game.AI_activated and self.game.winner == 'White':\\r\\n WINNER = 'YOU WIN!'\\r\\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\\r\\n\\r\\n elif self.game.AI_activated and self.game.winner == 'Black':\\r\\n WINNER = 'YOU LOSE!'\\r\\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\\r\\n\\r\\n elif not self.game.AI_activated and self.game.winner == 'White':\\r\\n WINNER = 'THE WINNER IS ' + self.user_name_1.get_value().capitalize() + ' (White)'\\r\\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\\r\\n\\r\\n elif not self.game.AI_activated and self.game.winner == 'Black':\\r\\n WINNER = 'THE WINNER IS ' + self.user_name_2.get_value().capitalize() + ' (Black)'\\r\\n game_over_menu.add.label(WINNER, max_char=-1, font_size=70, font_color='Red')\\r\\n\\r\\n # Play again with the same settings\\r\\n game_over_menu.add.button('Play again', self.run_game, True, align=pygame_menu.locals.ALIGN_LEFT)\\r\\n # Go to main menu\\r\\n game_over_menu.add.button('Main menu', main_menu, align=pygame_menu.locals.ALIGN_LEFT)\\r\\n game_over_menu.mainloop(self.WIN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f0d5688a07f925315a88caa72be34da\",\n \"score\": \"0.5434655\",\n \"text\": \"def game_over(self):\\n _choice = self.disp.game_over_screen(self.stage.score, self.stage.player.character)\\n if _choice == \\\"start\\\":\\n self.g_o = False\\n elif _choice == \\\"quick\\\":\\n self.g_o = False\\n self.skip_start = True\\n else:\\n self.quit_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdf0d5cc0f0df1335bbdc27d8c930b86\",\n \"score\": \"0.54312736\",\n \"text\": \"def ongameplayupdatingdisabled(self):\\n self.Check( ID_ToggleAI, False )\\n self.get_frame().get_toolbar().ToggleTool( ID_ToggleAI, False )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc3d2d997ad759fefa72b84187a331b1\",\n \"score\": \"0.5400444\",\n \"text\": \"def draw_game_over(self):\\n arcade.draw_text(\\\"You lost!\\\", SCREEN_W/2, SCREEN_H/2, arcade.color.ANTIQUE_RUBY, font_size=64, font_name='arial', anchor_x='center')\\n arcade.set_background_color(arcade.color.PURPLE_HEART)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"688c5dbf7295e03d47741e03c5d734e3\",\n \"score\": \"0.5397686\",\n \"text\": \"def draw_gameover_page(self):\\n arcade.draw_texture_rectangle(SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2,\\n SCREEN_WIDTH,\\n SCREEN_HEIGHT, self.gameover, 0)\\n\\n # Add messages depending on game outcome\\n if self.total_time < 0.1:\\n arcade.draw_text(\\\"Times Up!!!\\\", 375, 160, arcade.color.RED, 75)\\n output = f\\\"Score: {self.score}\\\"\\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)\\n\\n elif self.lives < 1:\\n arcade.draw_text(\\\"You lost all your lives\\\", 400, 150, arcade.color.WHITE, 35)\\n output = f\\\"Score: {self.score}\\\"\\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)\\n\\n elif self.score < 25:\\n arcade.draw_text(\\\"Sorry you lost!\\\", 200, 400, arcade.color.RED, 75)\\n output = f\\\"Score: {self.score}\\\"\\n arcade.draw_text(output, 570, 70, arcade.color.WHITE, 30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58c450068c26fa381abc100bf4594ea\",\n \"score\": \"0.53893214\",\n \"text\": \"def game_over(self):\\n return (self.x, self.y) not in self.actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d577a328f81f92b8f2ef8ba84277e8b7\",\n \"score\": \"0.538671\",\n \"text\": \"def gameOver(self):\\n\\t\\treturn self.winner() or not self.getValidMoves()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"327db2da68687d8c64aed061aae7a343\",\n \"score\": \"0.53813183\",\n \"text\": \"def gameover( self ) -> bool:\\n if len( self.actions ) == 0: return True\\n if abs( self.utility() ) == math.inf: return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4dbe174390d5feab84953eb2f43f5fe\",\n \"score\": \"0.53556\",\n \"text\": \"def IsDrawOver(self):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2698144478df75f60db07d4d1cd087c\",\n \"score\": \"0.5344385\",\n \"text\": \"def game_over():\\n if points_1 >= 7:\\n game_over_screen_1()\\n elif points_2 >= 7:\\n game_over_screen_2()\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749cfce239eb01b8e51ae882bbc7c56c\",\n \"score\": \"0.53433317\",\n \"text\": \"def game_over(reason):\\n print(\\\"\\\\n\\\" + reason)\\n print(\\\"Game Over!\\\")\\n play_again()\\n \\\"\\\"\\\"\\n Ask player to play again or not by activating play_again() function\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37dd3773c8604d0be4a716e28065e8c5\",\n \"score\": \"0.5332427\",\n \"text\": \"def displayGameOver():\\n font = pygame.font.SysFont('comicsansms', 36)\\n text = font.render(ScoreBoard.gameOverMessage, 1, (109, 48, 252))\\n textPos = text.get_rect(\\n centerx=ScoreBoard.POSITION[0] + ScoreBoard.SIZE[0] * 0.5,\\n centery=ScoreBoard.POSITION[1] + ScoreBoard.SIZE[1] * 0.5 + 35\\n )\\n screen.blit(text, textPos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1081078f48a4f599c9ad8ada460ea33\",\n \"score\": \"0.53310555\",\n \"text\": \"def _game_over(self):\\n game_over = False\\n if self.head in self.snake[1:]: # head is position 0 in the list\\n game_over = True\\n if self.head.x > self.w - constants.BLOCK_SZ or self.head.x < 0 or \\\\\\n self.head.y < 0 or self.head.y > self.h - constants.BLOCK_SZ:\\n game_over = True \\n return game_over\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ce8295e345ae586d270b1ac2b3a646\",\n \"score\": \"0.5325378\",\n \"text\": \"def is_over(self, state: GameState) -> bool:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f931a49a08f15ac78b86b5cb154e7c32\",\n \"score\": \"0.53073853\",\n \"text\": \"def label_gameover():\\n label = GLabel('Game over!')\\n label.font = '-50'\\n label.color = 'skyblue'\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235b2be5b96da804b02247557b72349d\",\n \"score\": \"0.5300991\",\n \"text\": \"def game_is_over(game):\\n for p in game.players:\\n if p.score >= 50:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a936e63989036f875390f1bae547a9be\",\n \"score\": \"0.5284049\",\n \"text\": \"def on_move_fail(self, user, battle):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0c28f56b3349fd2c5b9bef2fdd4574\",\n \"score\": \"0.52828914\",\n \"text\": \"def is_game_over(self):\\n for player in self.players:\\n for penguin in player.get_penguins():\\n if len(self.get_reachable(penguin)) > 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"150dcc169c8ae0948b1aad82700086a7\",\n \"score\": \"0.5247563\",\n \"text\": \"def ongameplayupdatingenabled(self):\\n self.Check( ID_ToggleAI, True )\\n self.get_frame().get_toolbar().ToggleTool( ID_ToggleAI, True )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469134f6e994f11813e74a2d9a5ef018\",\n \"score\": \"0.52322143\",\n \"text\": \"def test_game_over(self):\\n\\n UFO = Game('failure')\\n UFO.lives = 0\\n result = Game.check_status(UFO)\\n\\n self.assertEqual(result, game.STATUS_LOSE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86227bf0bcca09f539a2417e8dcb690\",\n \"score\": \"0.5217142\",\n \"text\": \"def is_game_over(board):\\n moves = max(available_moves(board))\\n if moves == 0:\\n return True\\n p1w = has_won(board,1)\\n p2w = has_won(board,2)\\n if p1w == True:\\n return True\\n elif p2w == True:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdea1b45e94af3b6766f756a51b939bb\",\n \"score\": \"0.52171195\",\n \"text\": \"def end(self):\\r\\n end_message = games.Message(value = \\\"Game Over\\\",\\r\\n size = 90,\\r\\n color = color.red,\\r\\n x = games.screen.width/2,\\r\\n y = games.screen.height/2,\\r\\n lifetime = 5 * games.screen.fps,\\r\\n after_death = games.screen.quit,\\r\\n is_collideable = False)\\r\\n games.screen.add(end_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"046e0ca3a338716253d24e0bb0d7f337\",\n \"score\": \"0.5211163\",\n \"text\": \"def check_background_games(self, *args):\\n if self.username is None or self.password is None:\\n return\\n if self.player.logged_in is False:\\n return\\n if not self.my_turn_games and not self.old_games and not self.opp_turn_games:\\n self.load_current_games()\\n\\n games = list(self.player.current_games)\\n if len(games) < 1:\\n return\\n\\n for game in games:\\n if game.over and game in self.old_games:\\n # NOOP\\n pass\\n elif game.over and game not in self.old_games:\\n Logger.info(f\\\"Game over and not in old games? {game.guid}\\\")\\n self.notify_removed_game(game)\\n self.old_games.append(game)\\n if game in self.opp_turn_games:\\n self.opp_turn_games.remove(game)\\n if game in self.my_turn_games:\\n self.my_turn_games.remove(game)\\n\\n elif game.my_turn and game in self.opp_turn_games:\\n # New game for us to make a move!\\n self.notify_my_move(game)\\n self.my_turn_games.append(game)\\n self.opp_turn_games.remove(game)\\n elif not game.my_turn and game in self.my_turn_games:\\n # Opponent is to move now.\\n self.opp_turn_games.append(game)\\n self.my_turn_games.remove(game)\\n elif game not in self.my_turn_games and game not in self.opp_turn_games:\\n self.notify_new_game(game)\\n if game.my_turn:\\n self.my_turn_games.append(game)\\n else:\\n self.opp_turn_games.append(game)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a433776dfa836a53e73a050f6b53129b\",\n \"score\": \"0.52030104\",\n \"text\": \"def check_game_over(self):\\n\\n # check if we have at least K blocks saved, either locally or remotely\\n lbs, rbs_array = self.local_blocks, self.remote_blocks\\n rbs = []\\n for i in range(len(rbs_array)):\\n for j in range(len(rbs_array[i])):\\n if len(rbs) > i:\\n rbs[i] = rbs_array[j][i] or rbs[i]\\n else:\\n rbs.append(rbs_array[j][i])\\n\\n blocks_saved = [lb or rb for lb, rb in zip(lbs, rbs)]\\n if sum(blocks_saved) < self.K:\\n raise GameOver\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67f224eaef8f1a47a37f11a99ec4f380\",\n \"score\": \"0.51871765\",\n \"text\": \"def is_end_game(self):\\n if Player.total == 2:\\n try:\\n if (\\n self.board.rows % 2 == 1\\n and self.counter.pawn[0].position\\n == (self.board.cols - 1, self.board.rows - 1)\\n ) or (\\n self.board.rows % 2 == 0\\n and self.counter.pawn[0].position == (0, self.board.rows - 1)\\n ):\\n # Player Loses\\n self._winner = self.counter\\n pygame.display.update()\\n return True\\n except IndexError:\\n pass\\n if self.counter.score == 6:\\n # Player Loses\\n self._winner = self.counter\\n pygame.display.update()\\n return True\\n elif Player.array[1].score == 6:\\n # Player Wins\\n self._winner = Player.array[1]\\n pygame.display.update()\\n return True\\n else:\\n for player in Player.array:\\n if player.score == 12 // (Player.total - 1):\\n # That Player Wins\\n self._winner = player\\n pygame.display.update()\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15c8022cb6c26e6f496d37d1be539ee\",\n \"score\": \"0.5184023\",\n \"text\": \"def game_over(ship_list, AI_ship_list):\\n if player_win(AI_ship_list) or AI_win(ship_list): #either player wins or AI wins\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8fe29a41182dee26fd5a80599d6a58a\",\n \"score\": \"0.51778793\",\n \"text\": \"def on_game_over(self, bj):\\n self.print(\\\"on_game_over\\\")\\n self.print(\\\"dealer:\\\", player.players[0].hand)\\n res = self.wins - self.losses\\n if self.positive_prog:\\n res = -res\\n self.print(\\\"res:\\\", res)\\n if res < 0:\\n self.betting.on_loss(abs(res))\\n if self.anti_fallacy:\\n self.af_trigger = True\\n elif res > 0 and not self.af_trigger:\\n self.betting.on_win(res)\\n elif res > 0:\\n self.af_trigger = False\\n else:\\n self.betting.on_tie()\\n self.reset_results()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e8b9877e116344f91802bda7524df2\",\n \"score\": \"0.5169724\",\n \"text\": \"def game_over():\\n print(GAME_OVER)\\n time.sleep(1)\\n play_again()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320ac21dd8f220dad9aad9fd6cce1e81\",\n \"score\": \"0.5163603\",\n \"text\": \"def GameOver():\\n Text(\\\"Game Over\\\",window_Width//2,window_Height//2,Window).Message_display()\\n pg.display.update()\\n while True:\\n for event in pg.event.get():\\n if event.type==pg.KEYDOWN:\\n if event.type==pg.QUIT:\\n pg.quit()\\n quit()\\n if event.key==pg.K_RETURN:\\n mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50baffd35696fd783f986962d4ae6f97\",\n \"score\": \"0.51631284\",\n \"text\": \"def isGameOver(self, boards):\\n return self.deadTest(boards[0]) and self.deadTest(boards[1]) and self.deadTest(boards[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e8efc791fb6facf821fc60dcfc7de1\",\n \"score\": \"0.5156873\",\n \"text\": \"def game_over(self):\\n if len(self.player_pieces) == 0:\\n self.status = \\\"player_wins\\\"\\n elif len(self.computer_pieces) == 0:\\n self.status = \\\"computer_wins\\\"\\n else:\\n left_end = self.domino_snake[0][0]\\n right_end = self.domino_snake[len(self.domino_snake) - 1][1]\\n # If numbers on the ends are identical\\n if left_end == right_end:\\n # And appear within the snake 8 times, it's a draw\\n if len([piece for piece in self.domino_snake if left_end in piece]) >= 8:\\n self.status = \\\"draw\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3048bf2c6144bea4bda24cfce0bb2e\",\n \"score\": \"0.51562595\",\n \"text\": \"def game_exists(session):\\n # Your code goes here\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c19eb72f284c63b3e7d5841cb51f71ea\",\n \"score\": \"0.5154593\",\n \"text\": \"def reloadGameOver(self, image):\\n gameOver = pygame.image.load(image)\\n gameOver = pygame.transform.scale(gameOver, self.dimension)\\n self.screen.blit(gameOver, [0, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f665502c69dfbda30141b512240f47d\",\n \"score\": \"0.5149441\",\n \"text\": \"def is_lost():\\n return get_game_state() == -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70147f4542ef8e833626671e6d8a1aee\",\n \"score\": \"0.51396245\",\n \"text\": \"def isGameOver(S,p):\\n if move_was_winning_move(S,p):\\n if p == COMP:\\n set_winner(COMP)\\n else:\\n set_winner(HUMAN)\\n return True\\n \\n if move_still_possible(S) == False:\\n set_winner(TIE)\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601bf74065e353d6278d0cb927e6535c\",\n \"score\": \"0.5125398\",\n \"text\": \"def is_in_pregame(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3999841bbc9fd6f8572e813863d75042\",\n \"score\": \"0.5120661\",\n \"text\": \"def episode_over(self, winner):\\n if winner == DRAW:\\n print('Game over! It was a draw.')\\n else:\\n print('Game over! Winner: Player {0}'.format(winner))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4473ef49de1ea5b77bd6488517702e6\",\n \"score\": \"0.5120455\",\n \"text\": \"def game_over():\\n WIN = Window.WIN\\n WHITE = (255, 255, 255)\\n BLACK = (0, 0, 0)\\n\\n end = True\\n\\n while end:\\n for event in pygame.event.get():\\n print(event)\\n\\n if event.type == pygame.QUIT:\\n pygame.quit()\\n quit()\\n\\n WIN.fill(BLACK)\\n text = pygame.font.SysFont('Times New Roman', 100)\\n Text, TextRect = text_ob(\\\"Game Over\\\", text, WHITE)\\n TextRect.center = ((Window.WIDTH / 2), (Window.HEIGHT / 2))\\n WIN.blit(Text, TextRect)\\n pygame.display.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3158e57c22b57e615c7e8352bb52bb3\",\n \"score\": \"0.5109053\",\n \"text\": \"def display_game_over(self, screen):\\n my_score, opp_score = self.get_my_score(self.board)\\n \\n screen.blit(OVER_BG, OVER_BG_RECT)\\n\\n # Decide win, lose, or draw\\n if my_score > opp_score:\\n screen.blit(TROPHY, TROPHY_RECT)\\n elif my_score < opp_score:\\n screen.blit(SAD, SAD_RECT)\\n elif my_score == opp_score:\\n screen.blit(TIE, TIE_RECT)\\n\\n # Game over text\\n text_surf = BRIT_FONT_BIG.render(\\\"GAME OVER!\\\", True, PURPLE)\\n text_rect = text_surf.get_rect()\\n text_rect.center = (int(WINDOW_X/2), 50)\\n \\n # Score text\\n score_surf = BRIT_FONT.render(\\\" %s -- %s \\\" %\\n (str(my_score), str(opp_score)),\\n True, BLACK)\\n score_rect = score_surf.get_rect()\\n score_rect.center = (int(WINDOW_X/2), 400)\\n\\n # Query text\\n text2_surf = BRIT_FONT.render('What do you want to do?', True, BLACK)\\n text2_rect = text2_surf.get_rect()\\n text2_rect.center = (int(WINDOW_X/2), 450)\\n\\n # Display the elements\\n screen.blit(text_surf, text_rect)\\n screen.blit(text2_surf, text2_rect)\\n screen.blit(score_surf, score_rect)\\n screen.blit(self.play_again, self.play_again_rect)\\n screen.blit(self.quit_game, self.quit_game_rect) \\n\\n # Set game over trigger\\n self.gameover = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0011bd32a01349c31d52927eeae5ac06\",\n \"score\": \"0.50900185\",\n \"text\": \"def _checkGameState(self) -> int:\\n if self.game.isFinished():\\n self.winningPlayers = self.game.winningPlayers\\n return END\\n return CONTINUE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10bfcc50ef53ef02e8959e291b302447\",\n \"score\": \"0.50871944\",\n \"text\": \"def game_over_text(self):\\n over_text = self.score.over_font.render(\\\"GAME OVER\\\", True, (255, 255, 255))\\n self.screen.blit(over_text, (200, 250))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18114991da85b4b7ecf89ea5b5ff97a\",\n \"score\": \"0.5079349\",\n \"text\": \"def loadGameOver(self, image):\\n gameOver = pygame.image.load(image)\\n gameOver = pygame.transform.scale(gameOver, self.dimension)\\n self.screen.blit(gameOver, [0, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75dbb1e37944f7b02a94c3a4b543a5b2\",\n \"score\": \"0.5078623\",\n \"text\": \"def turnOver(self):\\n if self.__faceDown: self.__faceDown = False\\n else: self.__faceDown = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30eb8c857cb9507b2b1a7553e85cc432\",\n \"score\": \"0.5071968\",\n \"text\": \"def update_game_over_screen(screen_type):\\n # If the game is over\\n if bbm.check_game_over_condition():\\n # Delete an existing save file given the perma-death nature of the game\\n bbo.delete_save_file()\\n # Update the game over screen background\\n if init.game_state.game_over != \\\"Lost\\\":\\n sc.sm.get_screen(\\\"over\\\").ids.background_over.this_source = bbo.get_path(\\n \\\"../../GraphicFiles/won_background.png\\\")\\n if init.game_state.game_over != \\\"Won\\\":\\n sc.sm.get_screen(\\\"over\\\").ids.background_over.this_source = bbo.get_path(\\n \\\"../../GraphicFiles/lost_background.png\\\")\\n # Change the screen\\n sc.sm.get_screen(screen_type).change_window(\\\"over\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61f48e415cf13b15ef8a9d5d8692e7c\",\n \"score\": \"0.5068129\",\n \"text\": \"def loseGame(self):\\n # ONLY set alive via loseGame\\n self.alive = False\\n self.targetable = False\\n self.discard += self.hand\\n del self.hand[:]\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb2e38cbd74fc6b93413c3de052b4ec2\",\n \"score\": \"0.50639844\",\n \"text\": \"def gameOverButtonChecker(self):\\r\\n for button in self.gameOverButtons:\\r\\n button.clicked()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"786e0bc3b168e8eb90aa91b0a09c624d\",\n \"score\": \"0.5060393\",\n \"text\": \"def end_game(self, winner, loser):\\n self.running = False\\n print(\\\"Game over! {} wins and {} loses\\\".format(winner, loser))\\n if isinstance(winner, Ai):\\n winner.append_win()\\n if isinstance(loser, Ai):\\n loser.append_loss()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5b51f364f472f2039d61a88f37f6138\",\n \"score\": \"0.50415915\",\n \"text\": \"def is_game_over(self, gamestate) -> bool:\\n all_players_dead = gamestate.all_players_expelled()\\n state_complete = gamestate.game_complete()\\n return state_complete or all_players_dead\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e111eb9bf2a17c606cc311b47d3436\",\n \"score\": \"0.50329655\",\n \"text\": \"def game_over(self, matrix):\\n if any(-1 in row for row in matrix):\\n self.bomeshflag = True\\n return\\n if any(2048 in row for row in matrix):\\n self.overflag = True\\n self.winflag = True\\n elif (\\n not any(0 in row for row in matrix)\\n and not self.horizontal_move_exits(matrix)\\n and not self.vertical_move_exits(matrix)\\n ):\\n self.overflag = True\\n else:\\n self.overflag = False\\n self.winflag = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adca3275dfa82295f0945c1169c3fcfb\",\n \"score\": \"0.5031085\",\n \"text\": \"def lose_message(self):\\n messagebox.showwarning('Fail', 'You Lose..')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caba0c5a11915fc6850fb21c5f64f93a\",\n \"score\": \"0.50257415\",\n \"text\": \"def add_game_over_text():\\n with open(\\\"active-game-files/game_over.txt\\\", \\\"a\\\") as f:\\n f.writelines(\\\"Game Over\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b77f306b27fbfb88bf71071a0ac30c\",\n \"score\": \"0.50181514\",\n \"text\": \"def is_game_over(self, state):\\n return self.is_player_win(state, \\\"x\\\") or self.is_player_win(state, \\\"o\\\") or self.is_board_filled()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f91e037784ea5097a3ce13c42f56e0d\",\n \"score\": \"0.50021744\",\n \"text\": \"def check_new_event(self):\\n new_event_list = self.nav.get_fleet_events()\\n for new_event in new_event_list:\\n if self.is_in(new_event, self.list_event):\\n self.list_event.append(new_event)\\n if not self.is_my_fleet(new_event):\\n log(\\n \\\"{} ship(s) from ({}, {}) to ({}, {}) arriving in {} at {} to {}\\\".format(\\n new_event[\\\"detail_fleet\\\"],\\n new_event[\\\"origin_planet_name\\\"],\\n new_event[\\\"origin_planet_coord\\\"],\\n new_event[\\\"destination_planet_name\\\"],\\n new_event[\\\"destination_planet_coord\\\"],\\n new_event[\\\"remaining_time\\\"],\\n new_event[\\\"arrival_time\\\"],\\n self.mission[new_event[\\\"mission_type\\\"]],\\n )\\n )\\n if self.is_under_attack(new_event):\\n self.nav.send_message_enemy(new_event[\\\"player_id\\\"])\\n\\n is_event_over = False\\n for old_event in self.list_event:\\n if self.is_in(old_event, new_event_list):\\n self.list_event.remove(old_event)\\n is_event_over = True\\n return is_event_over\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069a06ed8aa0e4ecc23e12d8ab0eebd6\",\n \"score\": \"0.49960214\",\n \"text\": \"def handleStatsLostEvent (self, event):\\n raise NotImplementedException(\\\"SENPAIListener object must properly implement handleStatsLostEvent, or return False from handlesEvent() for event type 'Stats Lost'.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad2a3ba8428a6461e9b3f14551e8f966\",\n \"score\": \"0.49867088\",\n \"text\": \"def game_over():\\r\\n text = pygame.font.Font(\\\"freesansbold.ttf\\\", 70)\\r\\n game_over = text.render(\\\"Game Over\\\", True, (255, 255, 255))\\r\\n window.blit(game_over, (150, 250))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb7c4650fa3df8200a10791b6ccda1d\",\n \"score\": \"0.4982617\",\n \"text\": \"def has_ended(game):\\n # Your code goes here\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3bff4f48378281200f34b559763226\",\n \"score\": \"0.4982418\",\n \"text\": \"def gameOver(self):\\n # font = pygame.font.Font(\\\"Serif\\\", 25)\\n font = pygame.font.SysFont(\\\"serif\\\", 25)\\n text = font.render(\\\"Game Over, click to restart\\\", True, BLACK)\\n center_x = (TREE_WIDTH // 2) - (text.get_width() // 2)\\n center_y = (TREE_HEIGHT // 2) - (text.get_height() // 2)\\n self.screen.blit(text, [center_x, center_y])\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994298,"cells":{"query_id":{"kind":"string","value":"797776956240c714c4b1dc4f47bbfbd4"},"query":{"kind":"string","value":"Obtain the value of node_vm_size."},"positive_passages":{"kind":"list like","value":[{"docid":"25cfcaaaf05a56b0ced7a9b70d3dd850","score":"0.91161466","text":"def get_node_vm_size(self) -> str:\n return self._get_node_vm_size()","title":""}],"string":"[\n {\n \"docid\": \"25cfcaaaf05a56b0ced7a9b70d3dd850\",\n \"score\": \"0.91161466\",\n \"text\": \"def get_node_vm_size(self) -> str:\\n return self._get_node_vm_size()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0581d2bf606539b195ce91b0880ccd28","score":"0.8251246","text":"def vm_size(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"vm_size\")","title":""},{"docid":"9f2c5a1a0b8fbc3b59c1289a3fdbb736","score":"0.80528116","text":"def vm_size(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"vm_size\")","title":""},{"docid":"8edc4cedba4119326b15f4c7e3a3e459","score":"0.804634","text":"def vm_size(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"vm_size\")","title":""},{"docid":"8edc4cedba4119326b15f4c7e3a3e459","score":"0.804634","text":"def vm_size(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"vm_size\")","title":""},{"docid":"4043e6b695ff8b7e7ac708bb3f7ac136","score":"0.7856617","text":"def _get_node_vm_size(self, read_only: bool = False) -> str:\n default_value = \"Standard_DS2_v2\"\n # read the original value passed by the command\n raw_value = self.raw_param.get(\"node_vm_size\")\n # try to read the property value corresponding to the parameter from the `mc` object\n value_obtained_from_mc = None\n if self.mc and self.mc.agent_pool_profiles:\n agent_pool_profile = safe_list_get(\n self.mc.agent_pool_profiles, 0, None\n )\n if agent_pool_profile:\n value_obtained_from_mc = agent_pool_profile.vm_size\n # try to retrieve the value from snapshot\n value_obtained_from_snapshot = None\n # skip dynamic completion if read_only is specified\n if not read_only:\n snapshot = self.get_snapshot()\n if snapshot:\n value_obtained_from_snapshot = snapshot.vm_size\n\n # set default value\n if value_obtained_from_mc is not None:\n node_vm_size = value_obtained_from_mc\n elif raw_value is not None:\n node_vm_size = raw_value\n elif value_obtained_from_snapshot is not None:\n node_vm_size = value_obtained_from_snapshot\n else:\n node_vm_size = default_value\n\n # this parameter does not need validation\n return node_vm_size","title":""},{"docid":"c858171aa72a1e06b587007d4b6d3eb5","score":"0.7719487","text":"def agent_vm_size(self) -> Optional[str]:\n return pulumi.get(self, \"agent_vm_size\")","title":""},{"docid":"b901f20422dfc8c6ca58ffda1809561f","score":"0.76124597","text":"def v_size(self) -> int:\r\n\r\n return self.node_size","title":""},{"docid":"5a45dd929d04d5c00d3d641aedd171b7","score":"0.71676594","text":"def nodeSize(self):\n size = graphs.graphMap(self,item='node',dtype=numpy.float32)\n size[:]=1\n return size","title":""},{"docid":"9f4e477a172a9ff837eed37dc8aee546","score":"0.7057961","text":"def GetMeasurementVectorSize(self):\n return _itkKdTreePython.itkKdTreeLSVF3_GetMeasurementVectorSize(self)","title":""},{"docid":"42c9d612c43bc6d080fa9367b2a6faea","score":"0.7029785","text":"def _list_machines__get_size(self, node_dict):\n return None","title":""},{"docid":"f5dc47a7095b364aff86f06a4fb6b84b","score":"0.70250833","text":"def GetMeasurementVectorSize(self):\n return _itkKdTreePython.itkKdTreeLSVF2_GetMeasurementVectorSize(self)","title":""},{"docid":"54cf92f0e0c6ee03fd58f0a177739dd0","score":"0.69325584","text":"def __size(self):\n # find the master root\n if self.parent is not None:\n return self.parent.__size()\n\n # get the total size of ourselves\n our_size = asizeof.asizeof(self)\n return {\n \"used\": our_size,\n \"max\": self.MAX_MEM_SIZE\n }","title":""},{"docid":"159b0c66fac6e53d25b6609375a56c3b","score":"0.6923734","text":"def nodeSize(self):\n if graphs.isGridGraph(self.baseGraph):\n return graphs._ragNodeSize(self, self.baseGraph, self.labels, self.ignoreLabel)\n else:\n baseNodeSizes = self.baseGraph.nodeSize()\n return self.accumulateNodeFeatures(baseNodeSizes,acc='sum')","title":""},{"docid":"3d51be58d12340bab55a1772392d1c66","score":"0.6913255","text":"def size_mb(self) -> float:\n return pulumi.get(self, \"size_mb\")","title":""},{"docid":"3d51be58d12340bab55a1772392d1c66","score":"0.6913255","text":"def size_mb(self) -> float:\n return pulumi.get(self, \"size_mb\")","title":""},{"docid":"3d51be58d12340bab55a1772392d1c66","score":"0.6913255","text":"def size_mb(self) -> float:\n return pulumi.get(self, \"size_mb\")","title":""},{"docid":"004af5cac3d627a22f01046c4a8d561d","score":"0.69043255","text":"def memory_size(self):\n ret = self._get_attr(\"memorySize\")\n return ret","title":""},{"docid":"004af5cac3d627a22f01046c4a8d561d","score":"0.69043255","text":"def memory_size(self):\n ret = self._get_attr(\"memorySize\")\n return ret","title":""},{"docid":"992fe51341049d3962e1db09238a9f6f","score":"0.68462014","text":"def get_kernel_size(self):\n return self.kernel_size","title":""},{"docid":"af78a0608a98c49475d509853f7eabeb","score":"0.68446267","text":"def virtual_size(self):\n return self._total_count","title":""},{"docid":"b9373c428cd8a423ed84447ef0db341c","score":"0.6819602","text":"def os_disk_size_gb(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"os_disk_size_gb\")","title":""},{"docid":"ba70ba19ce42201647b1def443f6ce77","score":"0.6795912","text":"def size(self):\n return self.number_nodes","title":""},{"docid":"de95b41b4ce04ff4394ba344413d2e5d","score":"0.6787783","text":"def size_mb(self) -> Optional[float]:\n return pulumi.get(self, \"size_mb\")","title":""},{"docid":"b4ad02124c29a081e21d4dd7ef5f34a4","score":"0.678094","text":"def memory_size_gb(self) -> pulumi.Output[float]:\n return pulumi.get(self, \"memory_size_gb\")","title":""},{"docid":"6ad3efb1613bb09e53f726172b8df763","score":"0.67673737","text":"def vram_size(self):\n ret = self._get_attr(\"VRAMSize\")\n return ret","title":""},{"docid":"5d41c587fbaafef307ca770023a0e571","score":"0.67542845","text":"def _size(self):\n if self.root != None:\n return self.size\n else:\n return 0","title":""},{"docid":"8b9756dc3c3a30f1cbf9c7e74ed8e2a7","score":"0.6752157","text":"def __get_size(self):\n return self.__size","title":""},{"docid":"8b9756dc3c3a30f1cbf9c7e74ed8e2a7","score":"0.6752157","text":"def __get_size(self):\n return self.__size","title":""},{"docid":"4fcd5a8201712dcc2b7f3934ff7ba6ca","score":"0.67511624","text":"def memory_size(self) -> int:\n return pulumi.get(self, \"memory_size\")","title":""},{"docid":"4fcd5a8201712dcc2b7f3934ff7ba6ca","score":"0.67511624","text":"def memory_size(self) -> int:\n return pulumi.get(self, \"memory_size\")","title":""},{"docid":"c9be1e0d534038063aab64c93e4642c3","score":"0.6745595","text":"def get_size(self):\n\n return self.attr('size')","title":""},{"docid":"5e02aa0302b79b198534d81f7813ebe1","score":"0.67445916","text":"def info_vd_size(self):\n ret = self._get_attr(\"infoVDSize\")\n return ret","title":""},{"docid":"da3f6d4b01d837fecb75ec66fd815076","score":"0.6734818","text":"def get_size(self):\n return self.__size","title":""},{"docid":"da3f6d4b01d837fecb75ec66fd815076","score":"0.6734818","text":"def get_size(self):\n return self.__size","title":""},{"docid":"14183983cb1eab5b5f9f23d0a8434f6f","score":"0.673199","text":"def node_ipv4_cidr_size(self) -> int:\n return pulumi.get(self, \"node_ipv4_cidr_size\")","title":""},{"docid":"464fe5df8a39f30a112bba899379066c","score":"0.67250305","text":"def global_size(self) -> ConfigNodePropertyInteger:\n return self._global_size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"72193d873b14712c225c258b4464bf3c","score":"0.6724445","text":"def get_size(self):\n return self.size","title":""},{"docid":"e68242585c382810c13e342f1ac7710a","score":"0.6709897","text":"def disk_size_gb(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"49305e75a5debb65cd13c7057de5ab97","score":"0.6699148","text":"def get_size(self):\r\n try:\r\n return self._size\r\n except AttributeError:\r\n self._size = self._calc_size()\r\n return self._size","title":""},{"docid":"d1f99fb0105f80406702b710699f8dd9","score":"0.6667501","text":"def get_size( self ):\n return self._size","title":""},{"docid":"b12221769f4910ced5dd569e05540c46","score":"0.6647827","text":"def get_size(self):\r\n return self._size","title":""},{"docid":"436026e7ef2a8293eaad8436ae8e64b2","score":"0.66400325","text":"def size(self):\n return self.tree_size","title":""},{"docid":"5d7c6a452d03181640b1626acfeea046","score":"0.66165406","text":"def get_size(self):\n\n return self._size","title":""},{"docid":"64895ea2d97d1d67b8bb8d2922098bea","score":"0.66082406","text":"def allocated_size(self):\n ret = self._get_attr(\"allocatedSize\")\n return ret","title":""},{"docid":"95aea20cb2e550c15a1c998a659830ce","score":"0.6574859","text":"def _select_vm_size(self) -> dict:\n if self._az_compute_node:\n for vm_size in self._az_vm_sizes:\n if vm_size['name'] == \\\n self._az_compute_node['compute']['vmSize']:\n print(self.format('Selected vm size: {}',\n vm_size['name']))\n return vm_size\n\n return self._select_object('vm_size', 'name', create=False)","title":""},{"docid":"e3d513389b9cac521b79e2e7bb0a4269","score":"0.6568504","text":"def process_size(self):\n return self._config.get('process_size', None)","title":""},{"docid":"60e8d47c8240c9e60306a3b5cceae5d7","score":"0.65619016","text":"def disk_size_gb(self) -> int:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"60e8d47c8240c9e60306a3b5cceae5d7","score":"0.65619016","text":"def disk_size_gb(self) -> int:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"7760ff31a9ea14872c670fc78c54f40c","score":"0.6541752","text":"def object_size(self):\n ret = self._get_attr(\"objectSize\")\n return ret","title":""},{"docid":"07df01a655cce81b69df2699b51803f6","score":"0.6541044","text":"def real_free_size(self, node):\n return node[7]","title":""},{"docid":"af9cae2bedac36e78adce4f083d4ad7c","score":"0.65092057","text":"def disk_size_gb(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"af9cae2bedac36e78adce4f083d4ad7c","score":"0.65092057","text":"def disk_size_gb(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"af9cae2bedac36e78adce4f083d4ad7c","score":"0.65092057","text":"def disk_size_gb(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"f016eda0507f3958b394a4a8c85222bf","score":"0.6506755","text":"def size(self):\n ret = self._get_attr(\"size\")\n return ret","title":""},{"docid":"80f9eb29ab4a5d52b9456b3d3a01ecfd","score":"0.65032965","text":"def disk_size_in_gb(self) -> Optional[int]:\n return pulumi.get(self, \"disk_size_in_gb\")","title":""},{"docid":"5967a88338318d358402e24803669ede","score":"0.65029514","text":"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"os_disk_size_gb\")","title":""},{"docid":"5967a88338318d358402e24803669ede","score":"0.65029514","text":"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"os_disk_size_gb\")","title":""},{"docid":"5967a88338318d358402e24803669ede","score":"0.65029514","text":"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"os_disk_size_gb\")","title":""},{"docid":"17b236efaba30427f29c53c3d8635dc6","score":"0.6495506","text":"def Size(self):\n return _itkKdTreePython.itkKdTreeNodeLSVF2_Size(self)","title":""},{"docid":"976dc1abc54b664df67207a8c1ba27d2","score":"0.6495205","text":"def size_in_bytes(self) -> pulumi.Output[Optional[float]]:\n return pulumi.get(self, \"size_in_bytes\")","title":""},{"docid":"5436034e05ce726d85c9308594791b05","score":"0.6493726","text":"def size(self):\n return self[\"size\"]","title":""},{"docid":"b1718fec63a6f969c6597afd7716ca43","score":"0.64847517","text":"def disk_size_gb(self) -> Optional[float]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"b1718fec63a6f969c6597afd7716ca43","score":"0.64847517","text":"def disk_size_gb(self) -> Optional[float]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"15f654bfee01ea90757d1165159ef9b2","score":"0.6478794","text":"def size(self):\n return self.curr_size","title":""},{"docid":"b14ab596c85176214da1d3852ee10db8","score":"0.6475292","text":"def memory_used(self):\n return int(self.entry['memory.used'])","title":""},{"docid":"3634814f025559a2fd416039b8ec3f94","score":"0.6470458","text":"def evaluate_M_size(self):\n return self.M_size","title":""},{"docid":"e61caf13620555ac8611b9111fed81b5","score":"0.6470017","text":"def size(self):\n return self.tree.size","title":""},{"docid":"db5aae87a1e0e2f598b2f9ee573d8970","score":"0.64599276","text":"def Size(self):\n return _itkKdTreePython.itkKdTreeNodeLSVF3_Size(self)","title":""},{"docid":"864b744163c0e894f586a7026ea4cd1f","score":"0.6450148","text":"def _get_w2v_vector_size(self, nn_id):\n node_id = self._find_netconf_node_id(nn_id)\n _path, _cls = self.get_cluster_exec_class(node_id)\n cls = self.load_class(_path, _cls)\n cls._init_node_parm(node_id)\n if('vector_size' in cls.__dict__) :\n return cls.vector_size\n return 10","title":""},{"docid":"45b58a6a25aba655b81e55a9f7e505ac","score":"0.64441603","text":"def size(self) -> int:\n if self.available:\n self._cached_state = self._state\n return self._state[\"size\"]\n return self._cached_state[\"size\"]","title":""},{"docid":"a4a93a21b95653159ba3c1a5780a8c95","score":"0.64395994","text":"def size(self) -> float:\n return self.__size","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"6babc7933fc3f332595ace97afdb29ee","score":"0.6429004","text":"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"disk_size_gb\")","title":""},{"docid":"127a436161140c0f3e7521d639f10b1c","score":"0.6424235","text":"def size(self):\n return _vnl_vectorPython.vnl_vectorUS_size(self)","title":""},{"docid":"6165f1e3ff605a95533058fbce7992e0","score":"0.64229035","text":"def get_server_load_value():\n\n return psutil.virtual_memory().percent","title":""},{"docid":"f9c5d81ea3d447bddfc0be8620fbfb10","score":"0.6409558","text":"def size(self):\n if \"size\" in self._prop_dict:\n return self._prop_dict[\"size\"]\n else:\n return None","title":""},{"docid":"6f804baf95b703d0a0f0faabe69de4b0","score":"0.63922334","text":"def getSize(self):\n return self.size","title":""},{"docid":"6f804baf95b703d0a0f0faabe69de4b0","score":"0.63922334","text":"def getSize(self):\n return self.size","title":""},{"docid":"6f804baf95b703d0a0f0faabe69de4b0","score":"0.63922334","text":"def getSize(self):\n return self.size","title":""},{"docid":"6a3ca717a32b13b51c0279328d3bf2ae","score":"0.63879496","text":"def size_in_gb(self) -> int:\n return pulumi.get(self, \"size_in_gb\")","title":""},{"docid":"6a3ca717a32b13b51c0279328d3bf2ae","score":"0.63879496","text":"def size_in_gb(self) -> int:\n return pulumi.get(self, \"size_in_gb\")","title":""},{"docid":"4e0c3ec556d473a9731394d3b5b8d7bc","score":"0.6385725","text":"def getSize(self):\n try:\n if self.plugin.req.size:\n return self.plugin.req.size\n else:\n return self.size\n except:\n return self.size","title":""},{"docid":"6bb5ab86c0599db9eb8908f7da75a6b5","score":"0.6382742","text":"def get_size(self, value=None):\n return 6","title":""},{"docid":"3312a0e8c843fad8abef5050bafe6354","score":"0.63582903","text":"def getMemorySize():\n memsize = c_uint64(0)\n size = c_size_t(sizeof(memsize))\n\n libc.sysctlbyname.argtypes = [\n c_char_p, c_void_p, c_void_p, c_void_p, c_ulong\n ]\n libc.sysctlbyname(\n \"hw.memsize\",\n c_void_p(addressof(memsize)),\n c_void_p(addressof(size)),\n None,\n 0\n )\n\n return int(memsize.value)","title":""}],"string":"[\n {\n \"docid\": \"0581d2bf606539b195ce91b0880ccd28\",\n \"score\": \"0.8251246\",\n \"text\": \"def vm_size(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"vm_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2c5a1a0b8fbc3b59c1289a3fdbb736\",\n \"score\": \"0.80528116\",\n \"text\": \"def vm_size(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"vm_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edc4cedba4119326b15f4c7e3a3e459\",\n \"score\": \"0.804634\",\n \"text\": \"def vm_size(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"vm_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edc4cedba4119326b15f4c7e3a3e459\",\n \"score\": \"0.804634\",\n \"text\": \"def vm_size(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"vm_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4043e6b695ff8b7e7ac708bb3f7ac136\",\n \"score\": \"0.7856617\",\n \"text\": \"def _get_node_vm_size(self, read_only: bool = False) -> str:\\n default_value = \\\"Standard_DS2_v2\\\"\\n # read the original value passed by the command\\n raw_value = self.raw_param.get(\\\"node_vm_size\\\")\\n # try to read the property value corresponding to the parameter from the `mc` object\\n value_obtained_from_mc = None\\n if self.mc and self.mc.agent_pool_profiles:\\n agent_pool_profile = safe_list_get(\\n self.mc.agent_pool_profiles, 0, None\\n )\\n if agent_pool_profile:\\n value_obtained_from_mc = agent_pool_profile.vm_size\\n # try to retrieve the value from snapshot\\n value_obtained_from_snapshot = None\\n # skip dynamic completion if read_only is specified\\n if not read_only:\\n snapshot = self.get_snapshot()\\n if snapshot:\\n value_obtained_from_snapshot = snapshot.vm_size\\n\\n # set default value\\n if value_obtained_from_mc is not None:\\n node_vm_size = value_obtained_from_mc\\n elif raw_value is not None:\\n node_vm_size = raw_value\\n elif value_obtained_from_snapshot is not None:\\n node_vm_size = value_obtained_from_snapshot\\n else:\\n node_vm_size = default_value\\n\\n # this parameter does not need validation\\n return node_vm_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c858171aa72a1e06b587007d4b6d3eb5\",\n \"score\": \"0.7719487\",\n \"text\": \"def agent_vm_size(self) -> Optional[str]:\\n return pulumi.get(self, \\\"agent_vm_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b901f20422dfc8c6ca58ffda1809561f\",\n \"score\": \"0.76124597\",\n \"text\": \"def v_size(self) -> int:\\r\\n\\r\\n return self.node_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a45dd929d04d5c00d3d641aedd171b7\",\n \"score\": \"0.71676594\",\n \"text\": \"def nodeSize(self):\\n size = graphs.graphMap(self,item='node',dtype=numpy.float32)\\n size[:]=1\\n return size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4e477a172a9ff837eed37dc8aee546\",\n \"score\": \"0.7057961\",\n \"text\": \"def GetMeasurementVectorSize(self):\\n return _itkKdTreePython.itkKdTreeLSVF3_GetMeasurementVectorSize(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c9d612c43bc6d080fa9367b2a6faea\",\n \"score\": \"0.7029785\",\n \"text\": \"def _list_machines__get_size(self, node_dict):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5dc47a7095b364aff86f06a4fb6b84b\",\n \"score\": \"0.70250833\",\n \"text\": \"def GetMeasurementVectorSize(self):\\n return _itkKdTreePython.itkKdTreeLSVF2_GetMeasurementVectorSize(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cf92f0e0c6ee03fd58f0a177739dd0\",\n \"score\": \"0.69325584\",\n \"text\": \"def __size(self):\\n # find the master root\\n if self.parent is not None:\\n return self.parent.__size()\\n\\n # get the total size of ourselves\\n our_size = asizeof.asizeof(self)\\n return {\\n \\\"used\\\": our_size,\\n \\\"max\\\": self.MAX_MEM_SIZE\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159b0c66fac6e53d25b6609375a56c3b\",\n \"score\": \"0.6923734\",\n \"text\": \"def nodeSize(self):\\n if graphs.isGridGraph(self.baseGraph):\\n return graphs._ragNodeSize(self, self.baseGraph, self.labels, self.ignoreLabel)\\n else:\\n baseNodeSizes = self.baseGraph.nodeSize()\\n return self.accumulateNodeFeatures(baseNodeSizes,acc='sum')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d51be58d12340bab55a1772392d1c66\",\n \"score\": \"0.6913255\",\n \"text\": \"def size_mb(self) -> float:\\n return pulumi.get(self, \\\"size_mb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d51be58d12340bab55a1772392d1c66\",\n \"score\": \"0.6913255\",\n \"text\": \"def size_mb(self) -> float:\\n return pulumi.get(self, \\\"size_mb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d51be58d12340bab55a1772392d1c66\",\n \"score\": \"0.6913255\",\n \"text\": \"def size_mb(self) -> float:\\n return pulumi.get(self, \\\"size_mb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004af5cac3d627a22f01046c4a8d561d\",\n \"score\": \"0.69043255\",\n \"text\": \"def memory_size(self):\\n ret = self._get_attr(\\\"memorySize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004af5cac3d627a22f01046c4a8d561d\",\n \"score\": \"0.69043255\",\n \"text\": \"def memory_size(self):\\n ret = self._get_attr(\\\"memorySize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992fe51341049d3962e1db09238a9f6f\",\n \"score\": \"0.68462014\",\n \"text\": \"def get_kernel_size(self):\\n return self.kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af78a0608a98c49475d509853f7eabeb\",\n \"score\": \"0.68446267\",\n \"text\": \"def virtual_size(self):\\n return self._total_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9373c428cd8a423ed84447ef0db341c\",\n \"score\": \"0.6819602\",\n \"text\": \"def os_disk_size_gb(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"os_disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba70ba19ce42201647b1def443f6ce77\",\n \"score\": \"0.6795912\",\n \"text\": \"def size(self):\\n return self.number_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de95b41b4ce04ff4394ba344413d2e5d\",\n \"score\": \"0.6787783\",\n \"text\": \"def size_mb(self) -> Optional[float]:\\n return pulumi.get(self, \\\"size_mb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ad02124c29a081e21d4dd7ef5f34a4\",\n \"score\": \"0.678094\",\n \"text\": \"def memory_size_gb(self) -> pulumi.Output[float]:\\n return pulumi.get(self, \\\"memory_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ad3efb1613bb09e53f726172b8df763\",\n \"score\": \"0.67673737\",\n \"text\": \"def vram_size(self):\\n ret = self._get_attr(\\\"VRAMSize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d41c587fbaafef307ca770023a0e571\",\n \"score\": \"0.67542845\",\n \"text\": \"def _size(self):\\n if self.root != None:\\n return self.size\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9756dc3c3a30f1cbf9c7e74ed8e2a7\",\n \"score\": \"0.6752157\",\n \"text\": \"def __get_size(self):\\n return self.__size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9756dc3c3a30f1cbf9c7e74ed8e2a7\",\n \"score\": \"0.6752157\",\n \"text\": \"def __get_size(self):\\n return self.__size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcd5a8201712dcc2b7f3934ff7ba6ca\",\n \"score\": \"0.67511624\",\n \"text\": \"def memory_size(self) -> int:\\n return pulumi.get(self, \\\"memory_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcd5a8201712dcc2b7f3934ff7ba6ca\",\n \"score\": \"0.67511624\",\n \"text\": \"def memory_size(self) -> int:\\n return pulumi.get(self, \\\"memory_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9be1e0d534038063aab64c93e4642c3\",\n \"score\": \"0.6745595\",\n \"text\": \"def get_size(self):\\n\\n return self.attr('size')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e02aa0302b79b198534d81f7813ebe1\",\n \"score\": \"0.67445916\",\n \"text\": \"def info_vd_size(self):\\n ret = self._get_attr(\\\"infoVDSize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3f6d4b01d837fecb75ec66fd815076\",\n \"score\": \"0.6734818\",\n \"text\": \"def get_size(self):\\n return self.__size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3f6d4b01d837fecb75ec66fd815076\",\n \"score\": \"0.6734818\",\n \"text\": \"def get_size(self):\\n return self.__size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14183983cb1eab5b5f9f23d0a8434f6f\",\n \"score\": \"0.673199\",\n \"text\": \"def node_ipv4_cidr_size(self) -> int:\\n return pulumi.get(self, \\\"node_ipv4_cidr_size\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464fe5df8a39f30a112bba899379066c\",\n \"score\": \"0.67250305\",\n \"text\": \"def global_size(self) -> ConfigNodePropertyInteger:\\n return self._global_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72193d873b14712c225c258b4464bf3c\",\n \"score\": \"0.6724445\",\n \"text\": \"def get_size(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68242585c382810c13e342f1ac7710a\",\n \"score\": \"0.6709897\",\n \"text\": \"def disk_size_gb(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49305e75a5debb65cd13c7057de5ab97\",\n \"score\": \"0.6699148\",\n \"text\": \"def get_size(self):\\r\\n try:\\r\\n return self._size\\r\\n except AttributeError:\\r\\n self._size = self._calc_size()\\r\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f99fb0105f80406702b710699f8dd9\",\n \"score\": \"0.6667501\",\n \"text\": \"def get_size( self ):\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b12221769f4910ced5dd569e05540c46\",\n \"score\": \"0.6647827\",\n \"text\": \"def get_size(self):\\r\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"436026e7ef2a8293eaad8436ae8e64b2\",\n \"score\": \"0.66400325\",\n \"text\": \"def size(self):\\n return self.tree_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d7c6a452d03181640b1626acfeea046\",\n \"score\": \"0.66165406\",\n \"text\": \"def get_size(self):\\n\\n return self._size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64895ea2d97d1d67b8bb8d2922098bea\",\n \"score\": \"0.66082406\",\n \"text\": \"def allocated_size(self):\\n ret = self._get_attr(\\\"allocatedSize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95aea20cb2e550c15a1c998a659830ce\",\n \"score\": \"0.6574859\",\n \"text\": \"def _select_vm_size(self) -> dict:\\n if self._az_compute_node:\\n for vm_size in self._az_vm_sizes:\\n if vm_size['name'] == \\\\\\n self._az_compute_node['compute']['vmSize']:\\n print(self.format('Selected vm size: {}',\\n vm_size['name']))\\n return vm_size\\n\\n return self._select_object('vm_size', 'name', create=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d513389b9cac521b79e2e7bb0a4269\",\n \"score\": \"0.6568504\",\n \"text\": \"def process_size(self):\\n return self._config.get('process_size', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e8d47c8240c9e60306a3b5cceae5d7\",\n \"score\": \"0.65619016\",\n \"text\": \"def disk_size_gb(self) -> int:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e8d47c8240c9e60306a3b5cceae5d7\",\n \"score\": \"0.65619016\",\n \"text\": \"def disk_size_gb(self) -> int:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7760ff31a9ea14872c670fc78c54f40c\",\n \"score\": \"0.6541752\",\n \"text\": \"def object_size(self):\\n ret = self._get_attr(\\\"objectSize\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07df01a655cce81b69df2699b51803f6\",\n \"score\": \"0.6541044\",\n \"text\": \"def real_free_size(self, node):\\n return node[7]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9cae2bedac36e78adce4f083d4ad7c\",\n \"score\": \"0.65092057\",\n \"text\": \"def disk_size_gb(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9cae2bedac36e78adce4f083d4ad7c\",\n \"score\": \"0.65092057\",\n \"text\": \"def disk_size_gb(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9cae2bedac36e78adce4f083d4ad7c\",\n \"score\": \"0.65092057\",\n \"text\": \"def disk_size_gb(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f016eda0507f3958b394a4a8c85222bf\",\n \"score\": \"0.6506755\",\n \"text\": \"def size(self):\\n ret = self._get_attr(\\\"size\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f9eb29ab4a5d52b9456b3d3a01ecfd\",\n \"score\": \"0.65032965\",\n \"text\": \"def disk_size_in_gb(self) -> Optional[int]:\\n return pulumi.get(self, \\\"disk_size_in_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5967a88338318d358402e24803669ede\",\n \"score\": \"0.65029514\",\n \"text\": \"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"os_disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5967a88338318d358402e24803669ede\",\n \"score\": \"0.65029514\",\n \"text\": \"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"os_disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5967a88338318d358402e24803669ede\",\n \"score\": \"0.65029514\",\n \"text\": \"def os_disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"os_disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b236efaba30427f29c53c3d8635dc6\",\n \"score\": \"0.6495506\",\n \"text\": \"def Size(self):\\n return _itkKdTreePython.itkKdTreeNodeLSVF2_Size(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976dc1abc54b664df67207a8c1ba27d2\",\n \"score\": \"0.6495205\",\n \"text\": \"def size_in_bytes(self) -> pulumi.Output[Optional[float]]:\\n return pulumi.get(self, \\\"size_in_bytes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5436034e05ce726d85c9308594791b05\",\n \"score\": \"0.6493726\",\n \"text\": \"def size(self):\\n return self[\\\"size\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1718fec63a6f969c6597afd7716ca43\",\n \"score\": \"0.64847517\",\n \"text\": \"def disk_size_gb(self) -> Optional[float]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1718fec63a6f969c6597afd7716ca43\",\n \"score\": \"0.64847517\",\n \"text\": \"def disk_size_gb(self) -> Optional[float]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f654bfee01ea90757d1165159ef9b2\",\n \"score\": \"0.6478794\",\n \"text\": \"def size(self):\\n return self.curr_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14ab596c85176214da1d3852ee10db8\",\n \"score\": \"0.6475292\",\n \"text\": \"def memory_used(self):\\n return int(self.entry['memory.used'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3634814f025559a2fd416039b8ec3f94\",\n \"score\": \"0.6470458\",\n \"text\": \"def evaluate_M_size(self):\\n return self.M_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61caf13620555ac8611b9111fed81b5\",\n \"score\": \"0.6470017\",\n \"text\": \"def size(self):\\n return self.tree.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db5aae87a1e0e2f598b2f9ee573d8970\",\n \"score\": \"0.64599276\",\n \"text\": \"def Size(self):\\n return _itkKdTreePython.itkKdTreeNodeLSVF3_Size(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"864b744163c0e894f586a7026ea4cd1f\",\n \"score\": \"0.6450148\",\n \"text\": \"def _get_w2v_vector_size(self, nn_id):\\n node_id = self._find_netconf_node_id(nn_id)\\n _path, _cls = self.get_cluster_exec_class(node_id)\\n cls = self.load_class(_path, _cls)\\n cls._init_node_parm(node_id)\\n if('vector_size' in cls.__dict__) :\\n return cls.vector_size\\n return 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b58a6a25aba655b81e55a9f7e505ac\",\n \"score\": \"0.64441603\",\n \"text\": \"def size(self) -> int:\\n if self.available:\\n self._cached_state = self._state\\n return self._state[\\\"size\\\"]\\n return self._cached_state[\\\"size\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a93a21b95653159ba3c1a5780a8c95\",\n \"score\": \"0.64395994\",\n \"text\": \"def size(self) -> float:\\n return self.__size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6babc7933fc3f332595ace97afdb29ee\",\n \"score\": \"0.6429004\",\n \"text\": \"def disk_size_gb(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"disk_size_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127a436161140c0f3e7521d639f10b1c\",\n \"score\": \"0.6424235\",\n \"text\": \"def size(self):\\n return _vnl_vectorPython.vnl_vectorUS_size(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6165f1e3ff605a95533058fbce7992e0\",\n \"score\": \"0.64229035\",\n \"text\": \"def get_server_load_value():\\n\\n return psutil.virtual_memory().percent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c5d81ea3d447bddfc0be8620fbfb10\",\n \"score\": \"0.6409558\",\n \"text\": \"def size(self):\\n if \\\"size\\\" in self._prop_dict:\\n return self._prop_dict[\\\"size\\\"]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f804baf95b703d0a0f0faabe69de4b0\",\n \"score\": \"0.63922334\",\n \"text\": \"def getSize(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f804baf95b703d0a0f0faabe69de4b0\",\n \"score\": \"0.63922334\",\n \"text\": \"def getSize(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f804baf95b703d0a0f0faabe69de4b0\",\n \"score\": \"0.63922334\",\n \"text\": \"def getSize(self):\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3ca717a32b13b51c0279328d3bf2ae\",\n \"score\": \"0.63879496\",\n \"text\": \"def size_in_gb(self) -> int:\\n return pulumi.get(self, \\\"size_in_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3ca717a32b13b51c0279328d3bf2ae\",\n \"score\": \"0.63879496\",\n \"text\": \"def size_in_gb(self) -> int:\\n return pulumi.get(self, \\\"size_in_gb\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0c3ec556d473a9731394d3b5b8d7bc\",\n \"score\": \"0.6385725\",\n \"text\": \"def getSize(self):\\n try:\\n if self.plugin.req.size:\\n return self.plugin.req.size\\n else:\\n return self.size\\n except:\\n return self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb5ab86c0599db9eb8908f7da75a6b5\",\n \"score\": \"0.6382742\",\n \"text\": \"def get_size(self, value=None):\\n return 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3312a0e8c843fad8abef5050bafe6354\",\n \"score\": \"0.63582903\",\n \"text\": \"def getMemorySize():\\n memsize = c_uint64(0)\\n size = c_size_t(sizeof(memsize))\\n\\n libc.sysctlbyname.argtypes = [\\n c_char_p, c_void_p, c_void_p, c_void_p, c_ulong\\n ]\\n libc.sysctlbyname(\\n \\\"hw.memsize\\\",\\n c_void_p(addressof(memsize)),\\n c_void_p(addressof(size)),\\n None,\\n 0\\n )\\n\\n return int(memsize.value)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994299,"cells":{"query_id":{"kind":"string","value":"708e08f481bab3715f95ae75d509388e"},"query":{"kind":"string","value":"Constructor, load edgeless MNIST dictionary. Also calculate each digit's max size."},"positive_passages":{"kind":"list like","value":[{"docid":"fbd16a5b9c97236697cc4ed2f6b87374","score":"0.8259669","text":"def __init__(self):\n self.dictionary = load_white_edgeless_mnist_dictionary()\n\n self.digit_to_max_size = {d: 0 for d in range(0, 10)}\n\n for k in self.dictionary:\n for arr in self.dictionary[k]:\n self.digit_to_max_size[k] = \\\n max(self.digit_to_max_size[k], tuple(arr.shape)[1])","title":""}],"string":"[\n {\n \"docid\": \"fbd16a5b9c97236697cc4ed2f6b87374\",\n \"score\": \"0.8259669\",\n \"text\": \"def __init__(self):\\n self.dictionary = load_white_edgeless_mnist_dictionary()\\n\\n self.digit_to_max_size = {d: 0 for d in range(0, 10)}\\n\\n for k in self.dictionary:\\n for arr in self.dictionary[k]:\\n self.digit_to_max_size[k] = \\\\\\n max(self.digit_to_max_size[k], tuple(arr.shape)[1])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6b75515e27977d064177dbb284d2c0b7","score":"0.6917801","text":"def __init__(self, batch_size=50, exluded_digits=(1, 5)):\n mnist = input_data.read_data_sets('MNIST_data', one_hot=True, reshape=False, validation_size=0)\n self.train_data = mnist.train.images # Returns np.array\n self.train_labels = np.asarray(mnist.train.labels, dtype=np.int32)\n self.data_len = self.train_data.shape[0]\n\n self.excluded_digits = exluded_digits\n self.split_full_oneshot(self.excluded_digits)\n self.full_batch_id = 0\n self.one_shot_batch_id = 0\n\n if (batch_size % 2) != 0:\n raise Exception(\"Batch size must be divisible by two!\")\n self.batch_size = batch_size","title":""},{"docid":"639f3db9aa83e9a88df413979dbc3b21","score":"0.67930144","text":"def __init__(self, data_path, flatten=True):\n (train_x, train_y), (test_x, test_y) = mnist(data_path)\n if not flatten:\n train_x = train_x.reshape(-1, 28, 28, 1)\n test_x = test_x.reshape(-1, 28, 28, 1)\n self.train = (binarize(train_x), one_hot(train_y))\n self.test = (binarize(test_x), one_hot(test_y))\n self.batch_size = 0\n self.epoch_size = train_y.shape[0]","title":""},{"docid":"74f6d48f05849efd34a3714808ee2a9d","score":"0.64203674","text":"def __init__(self, root: str):\n self.root = root\n\n self.train = load_pickle(root + '/train_medium.pkl')\n self.label = torch.tensor(load_pickle(root + '/label_medium.pkl'))\n\n self.num_classes = max(max(self.train)) + 1\n self.ohe_mapping = torch.eye(self.num_classes)","title":""},{"docid":"64d890c30efeac0ddedc9de761608e45","score":"0.60133725","text":"def load_mnist(dataset=\"training\", digits=np.arange(10), path=\".\"):\n\tif dataset == \"training\":\n\t\tfname_img = os.path.join(path, 'train-images.idx3-ubyte')\n\t\tfname_lbl = os.path.join(path, 'train-labels.idx1-ubyte')\n\telif dataset == \"testing\":\n\t\tfname_img = os.path.join(path, 't10k-images.idx3-ubyte')\n\t\tfname_lbl = os.path.join(path, 't10k-labels.idx1-ubyte')\n\telse:\n\t\traise ValueError(\"dataset must be 'testing' or 'training'\")\n\n\tflbl = open(fname_lbl, 'rb')\n\tmagic_nr, size = struct.unpack(\">II\", flbl.read(8))\n\tlbl = array(\"b\", flbl.read())\n\tflbl.close()\n\n\tfimg = open(fname_img, 'rb')\n\tmagic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n\timg = array(\"B\", fimg.read())\n\tfimg.close()\n\n\tind = [ k for k in range(size) if lbl[k] in digits ]\n\tN = len(ind)\n\timages = np.zeros((N, rows, cols), dtype=np.uint8)\n\tlabels = np.zeros((N, 1), dtype=np.int8)\n\tfor i in range(len(ind)):\n\t\timages[i] = np.array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\n\t\tlabels[i] = lbl[ind[i]]\n\n\treturn images, labels","title":""},{"docid":"e23547e3fa552b0a6bfb629c48dd45ef","score":"0.60079396","text":"def load_mnist(size: int = None,\n border: int = _MNIST_BORDER,\n blank_corners: bool = False,\n nums: List[int] = None) \\\n -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n # DOCME: Fix up formatting above,\n # DOCME: Explain nums argument\n\n # JIT import since keras startup is slow\n from keras.datasets import mnist\n\n def _filter_mnist(x: np.ndarray, y: np.ndarray, nums: List[int] = None) \\\n -> Tuple[np.ndarray, np.ndarray]:\n xt = []\n yt = []\n items = len(y)\n for n in range(items):\n if nums is not None and y[n] in nums:\n xt.append(x[n])\n yt.append(y[n])\n xt = np.stack(xt)\n yt = np.stack(yt)\n return xt, yt\n\n def _rescale(imgarray: np.ndarray, size: int) -> np.ndarray:\n N = imgarray.shape[0]\n\n # Chop off border\n imgarray = imgarray[:, border:-border, border:-border]\n\n rescaled = np.zeros(shape=(N, size, size), dtype=np.float)\n for n in range(0, N):\n img = Image.fromarray(imgarray[n])\n img = img.resize((size, size), Image.LANCZOS)\n rsc = np.asarray(img).reshape((size, size))\n rsc = 256.*rsc/rsc.max()\n rescaled[n] = rsc\n\n return rescaled.astype(dtype=np.uint8)\n\n def _blank_corners(imgarray: np.ndarray) -> None:\n # Zero out corners\n sz = imgarray.shape[1]\n corner = (sz//2)-1\n for x in range(0, corner):\n for y in range(0, corner-x):\n imgarray[:, x, y] = 0\n imgarray[:, -(1+x), y] = 0\n imgarray[:, -(1+x), -(1+y)] = 0\n imgarray[:, x, -(1+y)] = 0\n\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n if nums:\n x_train, y_train = _filter_mnist(x_train, y_train, nums)\n x_test, y_test = _filter_mnist(x_test, y_test, nums)\n\n if size:\n x_train = _rescale(x_train, size)\n x_test = _rescale(x_test, size)\n\n if blank_corners:\n _blank_corners(x_train)\n _blank_corners(x_test)\n\n return x_train, y_train, x_test, y_test","title":""},{"docid":"4a673ce26f0b0d25328978a10d478407","score":"0.59981215","text":"def load_mnist(num_train_samples=60000, num_test_samples=10000, res=28, normalization=False):\n # Fetch train (60.000 images) and test data (10.000 images)\n (train_images_raw, train_labels_raw), (test_images_raw, test_labels_raw) = tf.keras.datasets.mnist.load_data()\n\n # Convert data type\n train_images_raw = np.asarray(train_images_raw, dtype=np.float32)\n test_images_raw = np.asarray(test_images_raw, dtype=np.float32)\n\n # Convert data by squaring the pixel intensities\n # train_images_raw = np.square(train_images_raw)\n # test_images_raw = np.square(test_images_raw)\n\n # Cut data\n train_labels = train_labels_raw[:num_train_samples]\n test_labels = test_labels_raw[:num_test_samples]\n train_images = train_images_raw[:num_train_samples]\n test_images = test_images_raw[:num_test_samples]\n\n # Normalize data\n if normalization:\n train_images_mean = np.mean(train_images, 0)\n train_images_std = np.std(train_images, 0)\n std_eps = 1e-7\n train_images = (train_images - train_images_mean) / (train_images_std + std_eps)\n test_images = (test_images - train_images_mean) / (train_images_std + std_eps)\n\n # Resize images\n if res != 28:\n train_images = np.array(list(map(lambda x: resize(x, (res, res)), train_images)))\n test_images = np.array(list(map(lambda x: resize(x, (res, res)), test_images)))\n\n # Reshape and convert data\n train_images = train_images.reshape(-1, res * res).astype('float32')\n test_images = test_images.reshape(-1, res * res).astype('float32')\n\n return (train_images, train_labels), (test_images, test_labels)","title":""},{"docid":"4a23bcd54999c5935ca4873f2c9c176e","score":"0.5973087","text":"def load_mnist_1D_large(filename = '../data/mnist.pkl.gz'):\n \n import keras.utils as ku\n\n # Load the data.\n training_data, validation_data, test_data = get_data(filename)\n\n # Return the values.\n return training_data[0], ku.to_categorical(training_data[1]), \\\n validation_data[0], ku.to_categorical(validation_data[1]), \\\n test_data[0], ku.to_categorical(test_data[1])","title":""},{"docid":"028418c6c4041a815da7b1c79aa73ced","score":"0.59730726","text":"def load_mnist_2D(filename = ''):\n\n # Get the data.\n tr_d, tr_v, va_d, va_v, te_d, te_v = \\\n load_mnist_1D_large(filename = filename)\n\n # Reshape the data.\n training_inputs = np.array([x.reshape(28, 28, 1) for x in tr_d])\n validation_inputs = np.array([x.reshape(28, 28, 1) for x in va_d])\n test_inputs = np.array([x.reshape(28, 28, 1) for x in te_d])\n\n # Return the data.\n return training_inputs, tr_v, validation_inputs, va_v, \\\n test_inputs, te_v","title":""},{"docid":"b5ad00e777d4e3e0857a30601d4acf8d","score":"0.59380287","text":"def load_mnist_dataset():\n # We first define a download function, supporting both Python 2 and 3.\n if sys.version_info[0] == 2:\n from urllib import urlretrieve\n else:\n from urllib.request import urlretrieve\n\n def download(foldername,filename, source='http://yann.lecun.com/exdb/mnist/'):\n print(\"Downloading %s\" % filename)\n urlretrieve(source + filename, foldername+filename)\n\n # We then define functions for loading MNIST images and labels.\n # For convenience, they also download the requested files if needed.\n\n def load_mnist_images(foldername,filename):\n if not os.path.exists(foldername+filename):\n download(foldername,filename)\n # Read the inputs in Yann LeCun's binary format.\n with gzip.open(foldername+filename, 'rb') as f:\n data = np.frombuffer(f.read(), np.uint8, offset=16)\n # The inputs are vectors now, we reshape them to monochrome 2D images,\n # following the shape convention: (examples, channels, rows, columns)\n data = data.reshape(-1, 1, 28, 28)\n # The inputs come as bytes, we convert them to float32 in range [0,1].\n # (Actually to range [0, 255/256], for compatibility to the version\n # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)\n return data / np.float32(256)\n\n def load_mnist_labels(foldername,filename):\n if not os.path.exists(foldername+filename):\n download(foldername,filename)\n # Read the labels in Yann LeCun's binary format.\n with gzip.open(foldername+filename, 'rb') as f:\n data = np.frombuffer(f.read(), np.uint8, offset=8)\n # The labels are vectors of integers now, that's exactly what we want.\n return data\n\n # We can now download and read the training and test set images and labels.\n foldername = '../Data/MNIST/'\n X_train = load_mnist_images(foldername,'train-images-idx3-ubyte.gz')\n y_train = load_mnist_labels(foldername,'train-labels-idx1-ubyte.gz')\n X_test = load_mnist_images(foldername,'t10k-images-idx3-ubyte.gz')\n y_test = load_mnist_labels(foldername,'t10k-labels-idx1-ubyte.gz')\n\n # We reserve the last 10000 training examples for validation.\n X_train, X_val = X_train[:-10000], X_train[-10000:]\n y_train, y_val = y_train[:-10000], y_train[-10000:]\n\n # We just return all the arrays in order, as expected in main().\n # (It doesn't matter how we do this as long as we can read them again.)\n return X_train, y_train, X_val, y_val #, X_test, y_test","title":""},{"docid":"724f738281f98ab6ae3be9c40c2387f3","score":"0.59180367","text":"def load_mnist_1D_small(filename = '../data/mnist.pkl.gz'):\n\n # Load the data.\n training_data, validation_data, test_data = get_data(filename)\n\n # Return the values.\n return training_data[0][0:500, :], training_data[1][0:500], \\\n validation_data[0][0:200, :], validation_data[1][0:200], \\\n test_data[0][0:100, :], test_data[1][0:100]","title":""},{"docid":"d3c472ec63a2639bce0cda2954578cf0","score":"0.5899108","text":"def __init__(self, input_size = (1, 28, 28), num_classes = 10):\n super(CNN_MNIST, self).__init__()\n\n self.layer1 = nn.Sequential(\n nn.Conv2d(input_size[0], 32, kernel_size=5),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=2))\n\n self.layer2 = nn.Sequential(\n nn.Conv2d(32, 64, kernel_size=5),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=2))\n\n self.fc1 = nn.Linear(4 * 4 * 64, num_classes)","title":""},{"docid":"c44b25eaf1606ad133fa08ffbc0a94c6","score":"0.588867","text":"def __init(self):\n # self.size_of_test_items = 24\n # self.size_of_training_items = 24","title":""},{"docid":"6fe27c5856560bb794f78aa484eace31","score":"0.58741045","text":"def load_MNIST():\n\n with open('../data/X_train.pkl', 'rb') as f:\n X_train = pickle.load(f)\n X_train.reshape(-1, )\n with open('../data/Y_train.pkl', 'rb') as f:\n Y_train = pickle.load(f)\n with open('../data/X_test.pkl', 'rb') as f:\n X_test = pickle.load(f)\n with open('../data/Y_test.pkl', 'rb') as f:\n Y_test = pickle.load(f)\n\n num_classes = len(np.unique(Y_train))\n input_dim, n_samples = X_train.shape\n\n print(f'num_classes = {num_classes}')\n print(f'num_pixels = {input_dim}')\n print(f'num_training_samples = {n_samples}')\n\n tr_y_multi_class = np.zeros((num_classes, n_samples))\n for i in range(num_classes):\n tr_y_multi_class[i, np.where(Y_train == i)] = 1\n Y_train = np.asmatrix(tr_y_multi_class)\n\n input_dim, n_samples = X_test.shape\n te_y_multi_class = np.zeros((num_classes, n_samples))\n for i in range(num_classes):\n te_y_multi_class[i, np.where(Y_test == i)] = 1\n Y_test = np.asmatrix(te_y_multi_class)\n\n return X_train, Y_train, X_test, Y_test","title":""},{"docid":"e63d0903c5254b5d821623914b81774b","score":"0.5867644","text":"def ld_mnist():\n\n def convert_types(image, label):\n image = tf.cast(image, tf.float32)\n image /= 255\n return image, label\n\n dataset, info = tfds.load(\n \"mnist\", with_info=True, as_supervised=True\n )\n\n mnist_train, mnist_test = dataset[\"train\"], dataset[\"test\"]\n mnist_train = mnist_train.map(convert_types).shuffle(10000).batch(128)\n mnist_test = mnist_test.map(convert_types).batch(128)\n return EasyDict(train=mnist_train, test=mnist_test)","title":""},{"docid":"641dba05f815375836f7804b77778c47","score":"0.5828651","text":"def __init__(self, **kwargs):\r\n self.w = []\r\n self.epoch = 20000\r\n self.best_dimension = 0\r\n self.best_layers = 0","title":""},{"docid":"525c90419bb6b117f828d4fa0a33e70d","score":"0.58113134","text":"def init():\n global width, height, map_width, map_height, epochs, batch_size, min_heat\n width = 1024\n height = 1024\n\n map_width = int(width / 2)\n map_height = int(height / 2)\n\n epochs = 100\n batch_size = 4\n\n # 200~255 to find heat point\n min_heat = 150","title":""},{"docid":"d467c34bf424771289762f22284049f5","score":"0.57916975","text":"def initializeMNISTData():\n # Initialize the MNIST dataset\n print()\n print(\"Initializing MNIST dataset...\")\n mnistData = MNISTData()\n return mnistData","title":""},{"docid":"0f6212476ccc871b906045ccfc2b6a5b","score":"0.5735987","text":"def Load_MNIST ():\n print(\"Collecting MNIST data .....\\n\")\n (X_train,y_train),(X_test,y_test) = \\\n keras.datasets.mnist.load_data()\n X_test,y_test = X_test[:6000],y_test[:6000]\n X_train,y_train = X_train[:10000],y_train[:10000]\n return X_train,y_train","title":""},{"docid":"0711743930f73b962820c5d4678e6c57","score":"0.5718131","text":"def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = cPickle.load(f)\n f.close() \n #sets up average stroke thickness=\n '''for i in range(len(training_data[0])):\n training_data[0][i] *= 255\n image_list = []\n tr_d = [np.reshape(x, (28, 28)) for x in training_data[0]]\n for data in tr_d:\n image = Image.fromarray(data)\n image = image.convert('L')\n image_list.append(image)\n #image_list[0].show()\n for i in range(10):\n print stroke_thickness(image_list[i])\n image_matrix = standardize_image(image_list[i])/255\n print image_matrix\n average_thick = average_thickness(image_list)\n print average_thick'''\n return (training_data, validation_data, test_data)","title":""},{"docid":"13c9cf5c5eea987a7ac52cef23aec3ac","score":"0.5712747","text":"def __init__(self, n_clusters, max_iter = 100):\n self.n_clusters = n_clusters\n self.max_iter = max_iter\n self.centers = []\n self.labels_ = []\n self.input_shape = None\n self.__trained = False","title":""},{"docid":"3a95b309e815b296fd71cc99b058ae2a","score":"0.57126945","text":"def __init__(self):\n self.train_data = pd.read_csv('mnist_train.csv').values.tolist()\n self.test_data = pd.read_csv('mnist_test.csv').values.tolist()\n self.train_label = []\n self.test_label = []\n self.predict_test = []\n self.test_error = {1: 0, 9: 0, 19: 0, 29: 0, 39: 0, 49: 0, 59: 0, 69: 0, 79: 0, 89: 0, 99: 0}\n self.train_error = {1: 0, 9: 0, 19: 0, 29: 0, 39: 0, 49: 0, 59: 0, 69: 0, 79: 0, 89: 0, 99: 0}","title":""},{"docid":"fdd3ec5f8d228d1e73f22d8175681d6b","score":"0.56758404","text":"def __init__(\n self, label_map: Dict[int, str], max_num_images: Optional[int] = None\n ) -> None:\n self.label_map = label_map\n self.max_num_images = max_num_images","title":""},{"docid":"dac85bcd81b01171117b2caf3caaf5a1","score":"0.56731814","text":"def __init__(self, sizes, cost=CrossEntropyCost):\n self.num_layers = len(sizes)\n self.sizes = sizes\n self.default_weight_initializer()\n self.cost = cost","title":""},{"docid":"5dd16648913966a98c66234952a5d425","score":"0.5667201","text":"def __init__(self):\n\n self.train_batch_size = 32\n self.features = 26\n self.test_batch_size = 100\n self.training_set = None\n self.target_set = None\n self.dataset = None\n self.data_index = 0","title":""},{"docid":"30a63d5b2568aabcd8a082dc4c89b7f8","score":"0.5654114","text":"def test_keras_mnist():\n data = fetch('mnist')\n check(data, (60000, 28*28), (10000, 28*28))","title":""},{"docid":"2d324e061cda743d9f59dcd6a469a651","score":"0.5637487","text":"def __init__(self, batch_size, classes_per_set=10, samples_per_class=1, seed=2591, queries_per_class=1):\n np.random.seed(seed)\n self.x = np.load(\"/data/omniglot.npy\")\n self.x = np.reshape(self.x, newshape=(1622, 20, 28, 28, 1))\n self.x_train, self.x_test, self.x_val = self.x[:1200], self.x[1200:1411], self.x[1411:]\n self.normalization()\n self.batch_size = batch_size\n self.n_classes = self.x.shape[0]\n self.classes_per_set = classes_per_set\n self.samples_per_class = samples_per_class\n self.queries_per_class = queries_per_class\n\n print(\"train_shape\", self.x_train.shape, \"test_shape\", self.x_test.shape, \"val_shape\", self.x_val.shape)\n self.indexes = {\"train\": 0, \"val\": 0, \"test\": 0}\n self.datasets = {\"train\": self.x_train, \"val\": self.x_val, \"test\": self.x_test} #original data cached","title":""},{"docid":"16fd5da8472bba679c90c5d0692789ce","score":"0.5636208","text":"def build_data(self):\n\n # load original mnist dataset and expand each number with embedded \"0\"s\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n embedding_img = x_train[1]\n x_train = np.array([self.expand_img(embedding_img, img) for img, label in zip(x_train, y_train)])\n x_test = np.array([self.expand_img(embedding_img, img) for img, label in zip(x_test, y_test)])\n\n if K.image_data_format() == 'channels_first':\n x_train = x_train.reshape(x_train.shape[0], 1, self.img_rows, self.img_cols)\n x_test = x_test.reshape(x_test.shape[0], 1, self.img_rows, self.img_cols)\n else:\n x_train = x_train.reshape(x_train.shape[0], self.img_rows, self.img_cols, 1)\n x_test = x_test.reshape(x_test.shape[0], self.img_rows, self.img_cols, 1)\n\n # normalize and cast\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n # convert class vectors to binary class matrices\n y_train = keras.utils.to_categorical(y_train, self.num_classes)\n y_test = keras.utils.to_categorical(y_test, self.num_classes)\n\n return x_train, x_test, y_train, y_test","title":""},{"docid":"eefeb54eb29ae672b9b18cb88136fc36","score":"0.55747116","text":"def __init__(self):\r\n self.edges = defaultdict(list)\r\n self.weights = {}","title":""},{"docid":"11480007ead7c784d88ba7315e0a0972","score":"0.5571569","text":"def __init__(self):\n self.edges = defaultdict(list)\n self.weights = {}","title":""},{"docid":"f4a2743d9741d5ed04fb7ef6e12c8f01","score":"0.556744","text":"def __init__(self,sizes):\r\n\r\n self.numberLayers = len(sizes)\r\n \r\n #Initialization of weights and biases\r\n self.biases = [np.random.randn(y,1) for y in sizes[1:]]\r\n self.weights = [np.random.randn(y,x) for x,y in zip(sizes[:-1], sizes[1:])]","title":""},{"docid":"99b0c7232881ec07d3fa0c824b4e8e8e","score":"0.55670637","text":"def __init__(self, filepath, batch_size=135):\n # initialize the cursors to keep track where we are in the Dataset\n self.train_cursor = 0\n self.test_cursor = 0\n self.train_batch_size = batch_size\n self.test_batch_size = batch_size // 10\n\n # initialize the idx arrays\n a_train_ = np.tile(np.arange(self.NUM_TRAIN_ITEMS_PER_CLASS), self.NUM_CLASSES).reshape(self.NUM_CLASSES, self.NUM_TRAIN_ITEMS_PER_CLASS)\n b_train_ = np.tile(np.arange(self.NUM_CLASSES) * self.NUM_ITEMS_PER_CLASS, self.NUM_TRAIN_ITEMS_PER_CLASS).reshape(self.NUM_TRAIN_ITEMS_PER_CLASS, self.NUM_CLASSES).T\n self.train_idxs = (a_train_ + b_train_).reshape(-1)\n\n a_test_ = np.tile(np.arange(self.NUM_TEST_ITEMS_PER_CLASS), self.NUM_CLASSES).reshape(self.NUM_CLASSES, self.NUM_TEST_ITEMS_PER_CLASS)\n b_test_ = np.tile(np.arange(self.NUM_CLASSES) * self.NUM_ITEMS_PER_CLASS, self.NUM_TEST_ITEMS_PER_CLASS).reshape(self.NUM_TEST_ITEMS_PER_CLASS, self.NUM_CLASSES).T\n self.test_idxs = (a_test_ + b_test_ + self.NUM_TRAIN_ITEMS_PER_CLASS).reshape(-1)\n\n # load the .mat file containing the dataset\n print('Loading the dataset...')\n data = h5py.File(filepath)\n self.dataset_images = data['imdb']['images']['data']\n self.dataset_labels = data['imdb']['images']['labels']\n print('Dataset loaded!')","title":""},{"docid":"2062f729e21bb114be8c1404f045432e","score":"0.55592626","text":"def load_mnist_keras():\n train, test = tf.keras.datasets.mnist.load_data()\n train_data, train_labels = train\n test_data, test_labels = test\n\n train_data = np.array(train_data, dtype=np.float32) / 255\n test_data = np.array(test_data, dtype=np.float32) / 255\n\n train_data = train_data.reshape(train_data.shape[0], 28, 28, 1)\n test_data = test_data.reshape(test_data.shape[0], 28, 28, 1)\n\n train_labels = np.array(train_labels, dtype=np.int32)\n test_labels = np.array(test_labels, dtype=np.int32)\n\n train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)\n test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)\n\n assert train_data.min() == 0.\n assert train_data.max() == 1.\n assert test_data.min() == 0.\n assert test_data.max() == 1.\n \n return train_data, train_labels, test_data, test_labels","title":""},{"docid":"0cd315c56a952e15095d7914445d6520","score":"0.555627","text":"def __init__(self, sizes, input, bits=None):\r\n\t\t\r\n\t\tif bits is not None:\r\n\t\t\tself.bits = bits\r\n\t\telse: \r\n\t\t\tself.bits = 8\r\n\t\t\r\n\t\tself.num_layers = len(sizes)\r\n\t\tself.sizes = sizes\r\n\t\tself.cost = Cost\r\n\t\tself.a_L = Criterion.a_r(sizes[-1])\r\n\t\tself.weight_initializer()\r\n\t\t\r\n\t\tself.x0 = b10_to_b2(input[0],self.bits)\r\n\t\tself.y0 = b10_to_b2(input[1],self.bits)\r\n\t\t\r\n\t\tself.last_input = np.append(self.x0,self.y0)\r\n\t\tself.new_input = np.zeros(2*self.bits)\r\n\t\tself.kappa = 0 # The network standard will start out being random\r\n\t\tself.kappas = []","title":""},{"docid":"bcec21a38eedebcafc2f1aff04329793","score":"0.5555787","text":"def __init__(self, max_depth=1000, size_allowed=1, n_features=None, n_split=None):\n\n self.root = 1\n self.max_depth = max_depth\n self.size_allowed = size_allowed\n self.n_features = n_features\n self.n_split = n_split","title":""},{"docid":"a3f3416c5d6ff5a28b9e781e248fa17b","score":"0.5555058","text":"def __init__(self, intrinsics_path, max_instances = 256, num_classes = 41):\n self.no_instance = 999\n self.max_instances = max_instances\n self.num_classes = num_classes\n self.critical_instance_count = False\n self.instance_matrix = np.zeros((num_classes, max_instances, 3)) + self.no_instance\n self.intrinisc_inv = np.linalg.inv(np.loadtxt(intrinsics_path))\n self.class_inst_to_number_map = None\n self.class_inst_cnt = 0","title":""},{"docid":"8cab01e8c050457e700fa79ee4c394b0","score":"0.5554475","text":"def __init__(self, path):\n self.path = path\n self.train_set = {}\n self.test_set = {}\n self.classes = []\n self.classes_counts = []\n self.myarray = []\n self.imageList = []","title":""},{"docid":"79a0720c22c8aebb454e7c6b1991f4c1","score":"0.5553256","text":"def __init__(self):\n super().__init__()\n# self.save_hyperparameters()\n\n self.dense = torch.nn.Linear(HIDDEN_SIZE*2, NUM_GAMES)\n self.ndcg = NDCGMetric(k=20)\n self.map = MAPMetric()","title":""},{"docid":"9b04558fd859bb3eb3f42b8673c458e4","score":"0.5539841","text":"def __init__(self):\n\n prototxt = 'face_detector/deploy.prototxt'\n caffemodel = 'face_detector/res10_300x300_ssd_iter_140000.caffemodel'\n self.inWidth = 300\n self.inHeight = 300\n self.net = dnn.readNetFromCaffe(prototxt, caffemodel)","title":""},{"docid":"e4e9c9835e8e0c32b91fd1ca3689f18f","score":"0.55364","text":"def load_mnist():\r\n f = open(\"C:\\Users\\Michael\\Desktop\\Research\\Data\\mnist\\mnist_noval.pkl\", 'rb')\r\n train_data, test_data = cPickle.load(f)\r\n f.close()\r\n \r\n train_images, train_labels = train_data\r\n test_images, test_labels = test_data\r\n \r\n return train_images, train_labels, test_images, test_labels","title":""},{"docid":"d91700da5ef3464ebdb43c7a37bf1589","score":"0.55361855","text":"def __init__(self, learning_rate, batch_size):\n # Parameters\n self.learning_rate = learning_rate\n self.mnist = mnist\n self.batch_size = batch_size\n self.num_epochs = 50\n self.num_classes = 10\n self.input_size = 784\n self.input_weight, self.input_height = 28, 28\n self.batch_per_epoch = int(self.mnist.train.num_examples/self.batch_size)\n self.display_step = 1\n\n # Placeholders\n self.X = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\n self.Y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes","title":""},{"docid":"899a55462958d4f6dd514b87239b3537","score":"0.5519446","text":"def build_image_embeddings(self):\n\n #用densenet生成一个imageembedding\n densenet_out = image_embedding.densenet_161(\n self.images,\n trainable=self.train_inception,\n is_training=self.is_training())\n self.densenet_variable = tf.get_collection(\n tf.GraphKeys.GLOBAL_VARIABLES, scope=\"densenet161\")\n\n\n\n # Save the embedding size in the graph.\n self.embedding_size=384\n tf.constant(self.embedding_size, name=\"embedding_size\")\n\n self.features = densenet_out\n self.L = 196\n self.D = 384","title":""},{"docid":"b139d273e169514f393d7167b5c88331","score":"0.55185604","text":"def load_data():\n X, Y, testX, testY = mnist.load_data(one_hot=True)\n X = X.reshape([-1, 28, 28, 1])\n testX = testX.reshape([-1, 28, 28, 1])\n\n return X, Y, testX, testY","title":""},{"docid":"d856e4cb34281300fc055622a1f71ec4","score":"0.5513635","text":"def load_mnist():\n train, test = tf.keras.datasets.mnist.load_data()\n train_data, train_labels = train\n test_data, test_labels = test\n\n train_data = np.array(train_data, dtype=np.float32) / 255\n test_data = np.array(test_data, dtype=np.float32) / 255\n\n train_labels = np.array(train_labels, dtype=np.int32)\n test_labels = np.array(test_labels, dtype=np.int32)\n\n assert train_data.min() == 0.\n assert train_data.max() == 1.\n assert test_data.min() == 0.\n assert test_data.max() == 1.\n assert train_labels.ndim == 1\n assert test_labels.ndim == 1\n\n return train_data, train_labels, test_data, test_labels","title":""},{"docid":"6fd6d27bc522e0a741ca176164d845b3","score":"0.55117446","text":"def __init__(self, input_dimensions=2,number_of_nodes=4):\r\n if input_dimensions == 0 or number_of_nodes == 0:\r\n print()\r\n return\r\n else:\r\n self.input_dimension = input_dimensions + 1\r\n self.weights = np.ones((number_of_nodes,self.input_dimension))\r\n self.initialize_weights()","title":""},{"docid":"2c940f2709486d5dfa5499ca01a7ca55","score":"0.5506797","text":"def MNIST(one_hot=True):\n return input_data.read_data_sets('MNIST_data/', one_hot=one_hot)","title":""},{"docid":"2dd244f960a7df6d1aa3942755ae4afb","score":"0.55056965","text":"def __init__(self, batch_norm, n=2):\n super(AlexNetSmall, self).__init__(batch_norm)\n while n > 0:\n name = self.features[-1]._get_name()\n if name.__contains__(\"MaxPool\") or name.__contains__(\"Conv2d\"):\n n -= 1\n self.features.__delitem__(len(self.features) - 1)","title":""},{"docid":"d38d9329cdbcbdfeab2077e8e29346cf","score":"0.55013484","text":"def setup():\r\n # import MNIST dataset from scikit_learn\r\n mnist = sklearn.datasets.load_digits()\r\n X, y = mnist[\"data\"], mnist[\"target\"]\r\n \r\n # normalize the image data\r\n X = X / 255\r\n \r\n # shuffle the data-set randomly\r\n shuffle_index = np.random.permutation(y.shape[0])\r\n X_new = X[shuffle_index, :] \r\n y_new = y[shuffle_index]\r\n \r\n # train and test sets of length 1499 and 297 respectively \r\n # hard-coded this but can easily just split it up based on percentiles\r\n X_train = X_new[1:1500, :]\r\n y_train = y_new[1:1500]\r\n X_test = X_new[1500:, :]\r\n y_test = y_new[1500:]\r\n y_test_temp = y_test\r\n \r\n # one-hot encode the labels for the training and test sets\r\n y_test_new = np.zeros((y_test.size, y_test.max()+1))\r\n y_test_new[np.arange(y_test.size), y_test] = 1\r\n y_test = y_test_new\r\n y_train_new = np.zeros((y_train.size, y_train.max()+1))\r\n y_train_new[np.arange(y_train.size), y_train] = 1\r\n y_train = y_train_new\r\n \r\n # set up the parameters for the model\r\n params = {}\r\n params[\"i_size\"], params[\"h_size\"], params[\"o_size\"] = X.shape[0], 35, 10\r\n params[\"act\"], params[\"loss\"] = [\"tanh\", \"softmax\"], \"ce\"\r\n params[\"eps\"], params[\"num_epochs\"], params[\"lambda\"] = 0.001, 2000, 0.1\r\n \r\n # set up a ffnn with sigmoid activation, cross-entropy loss to recognize MNIST digits\r\n mnist_classifier = nn_classifier(X_train, y_train, params)\r\n\r\n # train the network\r\n mnist_classifier.train() \r\n\r\n # make predictions on the test-set\r\n # then, generate a confusion matrix using seaborn's heatmap()\r\n results = mnist_classifier.predict(X_test)\r\n \r\n cm = confusion_matrix(y_test_temp, results)\r\n ax = plt.axes()\r\n sb.heatmap(cm, ax = ax)\r\n ax.set_title('MNIST Classification: Three-Layer FFNN with Tanh + Softmax + Cross-enropy')\r\n plt.show()","title":""},{"docid":"9950151bdcb7a5c27844ce372e92b094","score":"0.5497196","text":"def __init__(self, input_size, output_size, activation):\n\n raise NotImplementedError","title":""},{"docid":"84cf8caf42887620a27e23964172b25b","score":"0.54940647","text":"def __init__(self, path='../data/train', size=128, channel=3, normalize=0):\n self._size = size\n self._channel = channel\n self._imgs = []\n self._ids = []\n self._masks = []\n self._n = 0\n \n print(\"Extracting training image info ...\")\n start_time = time.time()\n for img_id in os.listdir(path):\n self._ids.append(img_id)\n img_file_list = os.listdir('{0}/{1}/images'.format(path, img_id))\n assert len(img_file_list) == 1, \"Multiple images found in one images id folder.\"\n assert img_file_list[0] == img_id + '.png', \"Image id and image name do not match.\"\n img = imread('{0}/{1}/images/{1}.png'.format(path, img_id, img_id))[:, :, :channel]\n ## Do normalization\n if normalize==1:\n img = (img.astype(np.float32) - img.mean())/max(1., img.std())\n elif normalize > 1:\n img = img.astype(np.float32)/normalize\n \n mask = np.zeros(img.shape[:2])\n for m in os.listdir('{0}/{1}/masks'.format(path, img_id)):\n mask_ = imread('{0}/{1}/masks/{2}'.format(path, img_id, m))\n assert mask_.shape == img.shape[:2], \"Image shape and mask shape do not match.\"\n mask = np.maximum(mask, mask_)\n mask = mask.astype(np.bool)\n self._imgs.append(img)\n self._masks.append(mask.astype(np.float32))\n self._n = len(self._imgs)\n \n print(\"Time Usage: {0} sec\".format(str(time.time() - start_time)))\n print len(self._ids), len(self._imgs), len(self._masks)","title":""},{"docid":"2d5185a0666e611dbe359493ceb869ab","score":"0.5493848","text":"def __init__(self, train_size, loss_function=nn.MSELoss()):\n super(ELBO, self).__init__()\n self.train_size = train_size\n self.loss_function = loss_function","title":""},{"docid":"23bbb778c79d3b2fd8cd15edfea805be","score":"0.5484616","text":"def load_binarized_mnist():\n\n (train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()\n\n train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype('float32')\n\n # Normalizing the images to the range of [0., 1.]\n train_images /= 255.\n test_images /= 255.\n\n # Binarization\n train_images[train_images >= .5] = 1.\n train_images[train_images < .5] = 0.\n test_images[test_images >= .5] = 1.\n test_images[test_images < .5] = 0.\n\n return train_images, test_images","title":""},{"docid":"5b43ef1d0b3e4a7599e2fbdc877bc00c","score":"0.5482675","text":"def bmnist(root='../data/', batch_size=128, num_workers=4, download=True):\n data_transforms = transforms.Compose([transforms.ToTensor(),\n Binarize(threshold=0.5)])\n\n dataset = torchvision.datasets.MNIST(\n root, train=True, transform=data_transforms, download=download)\n test_set = torchvision.datasets.MNIST(\n root, train=False, transform=data_transforms, download=download)\n\n train_dataset = data.dataset.Subset(dataset, np.arange(45000))\n val_dataset = data.dataset.Subset(dataset, np.arange(45000, 50000))\n\n train_loader = data.DataLoader(\n train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, \n pin_memory=True)\n val_loader = data.DataLoader(\n val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers,\n drop_last=False)\n test_loader = data.DataLoader(\n test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers,\n drop_last=False)\n\n return train_loader, val_loader, test_loader","title":""},{"docid":"f8562c697e7374a8443c23ac5ed4406c","score":"0.54824346","text":"def __init__(self, nx, layers):\n if type(nx) is not int:\n raise TypeError(\"nx must be an integer\")\n if nx < 1:\n raise ValueError(\"nx must be a positive integer\")\n if type(layers) is not list:\n raise TypeError(\"layers must be a list of positive integers\")\n if len(layers) is 0:\n raise TypeError(\"layers must be a list of positive integers\")\n\n self.nx = nx\n self.layers = layers\n self.__L = len(layers)\n self.__cache = {}\n self.__weights = {}\n for l in range(self.__L):\n if layers[l] <= 0 or type(layers[l]) is not int:\n raise TypeError(\"layers must be a list of positive integers\")\n if l is 0:\n he_init = np.random.randn(layers[l], nx)*np.sqrt(2/nx)\n self.__weights['W' + str(l+1)] = he_init\n if l > 0:\n he_init1 = np.random.randn(layers[l], layers[l-1])\n he_init2 = np.sqrt(2/layers[l-1])\n self.__weights['W' + str(l+1)] = he_init1 * he_init2\n self.__weights['b' + str(l + 1)] = np.zeros((layers[l], 1))","title":""},{"docid":"265aad832adc385eb6683c520662de7e","score":"0.5479937","text":"def __init__(self, size):\r\n self._storage = []\r\n self._eps_dict = {}\r\n self._maxsize = size\r\n self._next_idx = 0\r\n self._hit_count = np.zeros(int(size))\r\n self._num_added = 0\r\n self._num_sampled = 0\r\n self._episodes_registry = {}","title":""},{"docid":"98e110640c0a25cdbbbe2879a0cf18ce","score":"0.5469335","text":"def __init__(self, layers):\n self.weights = {}\n self.biases = {}\n self.layers = layers # length L\n self.L = len(layers)\n for i in range(2, len(layers) + 1):\n self.weights[i] = np.random.randn(layers[i - 1], layers[i - 2])\n self.biases[i] = np.random.randn(layers[i - 1], 1)","title":""},{"docid":"96541f5ebe28bd1125d00623c0e2d35c","score":"0.5462261","text":"def loading():\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n assert x_train.shape == (60000, 28, 28)\n assert x_test.shape == (10000, 28, 28)\n assert y_train.shape == (60000,)\n assert y_test.shape == (10000,) \n\n temp = []\n for i in range(len(y_train)):\n temp.append(to_categorical(y_train[i], num_classes=10))\n y_train = np.array(temp)\n temp = []\n for i in range(len(y_test)): \n temp.append(to_categorical(y_test[i], num_classes=10))\n y_test = np.array(temp)\n \n return x_train, x_test, y_train, y_test","title":""},{"docid":"e9d02cdb28352ca67d8f57a99e758887","score":"0.54538333","text":"def __init__(self, n_features: int = 5):\n self.n_features = int(n_features)\n self.n_samples = int(1e15)","title":""},{"docid":"de6e0a34faab80de451ed1e33f655823","score":"0.5447168","text":"def load_MNIST_clusters(self, digits_used=[2, 8], image_set='training',\n images_base_dir='/home/escalafl/Databases/MNIST'):\n from . import mnist\n\n images, labels = mnist.read(digits_used, image_set, images_base_dir)\n labels = labels.reshape((-1,))\n\n num_images = len(labels)\n all_indices = numpy.arange(num_images)\n\n clusters = {}\n for i, digit in enumerate(digits_used):\n # print \"cluster \", i, \" for digit \", digit\n cluster_indices = all_indices[(labels == digit)]\n clusters[digit] = cluster_indices\n numpy.random.shuffle(clusters[digit])\n print(\"cluster %d for digit %d has %d images\" % (i, digit, len(cluster_indices)))\n return clusters, images","title":""},{"docid":"3a0be626feccaf432dee79a6c4455cc3","score":"0.5442395","text":"def load_deep_lesion(self, dataset_dir, subset): #I don't think we need dataset directory.\n # Add classes. We have only one class to add.\n self.add_class(\"Lesion\", 1, \"Bone\") # \"Bone\"\n self.add_class(\"Lesion\", 2, \"Abdomen_notLiver_notKidney\") # \"Abdomen_notLiver_notKidney\"\n self.add_class(\"Lesion\", 3, \"Mediastinum\") # \"Mediastinum\"\n self.add_class(\"Lesion\", 4, \"Liver\") # \"Liver\"\n self.add_class(\"Lesion\", 5, \"Lung\") #\"Lung\"\n self.add_class(\"Lesion\", 6, \"Kidney\") #\"Kidney\"\n self.add_class(\"Lesion\", 7, \"Soft_tissue\") #Soft tissue: miscellaneous lesions in the body wall, muscle, skin, fat, limbs, head, and neck\n self.add_class(\"Lesion\", 8, \"Pelvis\") #\"Pelvis\"\n \n \n \n \n \n ##################### UNKNOWN CASES, WE WILL LEAVE THESE OUT. \n #self.add_class(\"-1\", 9, \"-1\") #Can i have a negative number here? This is straight from Deep Lesion format. \n \n#########################\n # Train or validation dataset?\n \n #assert subset in [\"train\", \"val\"]\n #dataset_dir = os.path.join(dataset_dir, subset)\n\n ###### ---> SAVED under \"Image\" in data.json, there is variable called \"train_val_test\" \n #which has a int value (0-2 or 1-3) indicating it is training, test or validation. \n # NEED TO FIGURE OUT HOW TO USE THIS TO ASSIGN TRAIN, VALIDATION, TEST. \n##########################\n\n\n # Load annotations\n # VGG Image Annotator (up to version 1.6) saves each image in the form:\n # { 'filename': '28503151_5b5b7ec140_b.jpg',\n # 'regions': {\n # '0': {\n # 'region_attributes': {},\n # 'shape_attributes': {\n # 'all_points_x': [...],\n # 'all_points_y': [...],\n # 'name': 'polygon'}},\n # ... more regions ...\n # },\n # 'size': 100202\n # }\n # We mostly care about the x and y coordinates of each region\n # Note: In VIA 2.0, regions was changed from a dict to a list.\n annotations = json.load(open(os.path.join(DEXTR_DIR, \"data.json\")))\n #annotations_seg = list(annotations.values()) # don't need the dict keys\n\n annotations_seg = annotations['annotations']\n \n #annotations_seg = segmentation[2]\n\n # The VIA tool saves images in the JSON even if they don't have any\n # annotations. Skip unannotated images.\n #annotations_seg = [a for a in annotations_seg if a['segmentation']]\n\n\n \n b=0\n for a in annotations_seg:\n image_info = annotations['images'][b]\n win = annotations_seg[b]['Windowing']\n image_id = annotations['images'][b]['id']\n image_cat = annotations['categories'][b]['category_id']\n\n \n \n \n ############\n ############\n ########## Copy Food.py\n polygons=[]\n objects=[]\n #for r in a['regions'].values():\n for r in a['regions']:\n polygons.append(r['shape_attributes'])\n # print(\"polygons=\", polygons)\n objects.append(r['region_attributes'])\n \n class_ids = [int(n['Lesion']) for n in objects]\n \n \n \n train_valid_test = annotations['images'][b]['Train_Val_Test']\n #### Must use index 'b' before here, because after this point it\n # will point to next image/index/\n b=b+1\n \n \n # Get the x, y coordinaets of points of the polygons that make up\n # the outline of each object instance. These are stores in the\n # shape_attributes (see json format above)\n # The if condition is needed to support VIA versions 1.x and 2.x.\n ######## polygons \n # needs to be a list of dictionaries for each lesion\n # so if there is one lesion.\n # polygons = list of size 1\n # dict of size 3. \n # all_point_x is list of variable size(depends on number of points)\n # same for y\n # name is str 'polygon'\n\n \n \n # load_mask() needs the image size to convert polygons to masks.\n # Unfortunately, VIA doesn't include it in JSON, so we must read\n # the image. This is only managable since the dataset is tiny.\n \n ###### LETS STORE PATH TO IMAGE INTO JSON FILE. \n \n ## --- RERUN. added to images under annotations. current_file_path\n #image_path = image_info['File_path']\n #image_path = os.path.join(dataset_dir, a['filename'])\n image_path = os.path.join(DEXTR_DIR,image_info['File_path'])\n \n \n #***************************************************************\n ########################################################### use this to import all png files in this directory and load them as blanks. NaN for segmentation. \n #############################################################\n #********************************************************************\n \n \n # use files_bg to add in non-segmentated images to the dataset. \n ##\n #\n #\n # polygons = [] ? or polygons = NaN?\n # Need to figure out what format will work so it will train on these background images and not throw an error. \n #\n #\n ##\n \n \n\n \n \n \n ################### image format should be: unit8 rgb \n #image = skimage.io.imread(image_path)\n ############################### WORKS! LOAD WITH DEFAULT WINDOWING.\n #image = util.load_im_with_default_windowing(win,image_path)\n \n ############################### LOAD WITH default 16bit format?\n image = cv2.imread(image_path, -1)\n\n \n ###############\n \n height, width = image.shape[:2]\n\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\n #### SEE IMAGE_INFO, INFOb = randint(0,len(annotations_seg)) BELOW: I think it gets this from here\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\n \n print(subset)\n print(int(image_cat))\n print(train_valid_test)\n #### PROBLEM: Most of the \"1\" training images are \"unknown\" and therefore worthless. There is 4831 in 3 and 4793 or so in 2. ZERO LABELS IMAGES IN 1. How lame is that?\n if subset == 'train' and train_valid_test==3 and int(image_cat) >= 0: #Last checks that there are no unknowns.\n print(\"Image Added for training\")\n print(\"Image Added for training\")\n print(\"Image Added for training\")\n print(\"Image Added for training\")\n print(\"Image Added for training\")\n \n\n self.add_image(\n ############ Replace balloon with CLASSES ABOVE, take from category. \n \"Lesion\",\n image_id=image_id, # id is set above. \n path=image_path,\n width=width, height=height,\n polygons=polygons,\n win=win,\n class_ids=class_ids) \n\n elif subset == 'val' and train_valid_test==2 and int(image_cat) >= 0: #Last checks that there are no unknowns.\n print(\"Image added for validation\")\n\n self.add_image(\n ############ Replace balloon with CLASSES ABOVE, take from category. \n \"Lesion\",\n image_id=image_id, # id is set above. \n path=image_path,\n width=width, height=height,\n polygons=polygons,\n win=win,\n class_ids=class_ids)\n \n else:\n print(\"No image added...\")\n print(\"Unknown, should say -1 below\")\n print(int(image_cat)) # 3 is saved for validation since we are using both training and validation (1&2) for training.\n print(\"train_valid_test\")\n print(train_valid_test)","title":""},{"docid":"60cdcb51f8e1cf48c6a9e59ec9f785cd","score":"0.54401577","text":"def __init__(self, size):\n self.cache = None\n self.size = size\n self.layer_type = 'connected'\n self.weights = None\n self.biases = None","title":""},{"docid":"a85196aa1934f41cab68bcc24a80439f","score":"0.5435952","text":"def get_and_process_MNIST_data(self):\n\n #mndata = MNIST() \n #self.train_images, self.train_labels = mndata.load_training() \n self.train_images, self.train_labels = np.reshape(mndata.train_images(),(60000,784)), mndata.train_labels()\n self.train_images, self.train_labels = self.train_images[:500], self.train_labels[:500] \n print(np.shape(self.train_images)) \n print(np.shape(self.train_labels)) \n ## labeling the pixels back \n self.train_images, self.train_labels = np.array([[1 if p > 0.5 else -1 for p in i] for i in self.train_images]), np.array(self.train_labels)\n \n ### i need to change the below code so it iterate through the matrix properly \n #self.train_images, self.train_labels = np.array([[1 if p > 0.5 else -1 for p in i] for i in self.train_images), np.array(self.train_labels)\n side_length = int(np.sqrt(self.train_images.shape[1]))\n self.orig_train_images = copy.deepcopy(self.train_images.reshape((self.train_images.shape[0], side_length, side_length)))\n self.noisy_train_images = np.zeros((self.train_images.shape[0], side_length, side_length))\n for im in np.arange(self.train_images.shape[0]):\n random_inds = random.sample(range(1, self.train_images.shape[1]), int(0.02 * self.train_images.shape[1]))\n self.train_images[im, random_inds] = np.where(self.train_images[im, random_inds] == -1, 1, -1)\n self.noisy_train_images[im, :, :] = self.train_images[im, :].reshape(side_length, side_length)\n self.side_length = side_length","title":""},{"docid":"8839341faf50b58a58b84f4dd3341ed9","score":"0.54333675","text":"def __init__(self, hidden_layer_sizes=(100,), max_iter=200, verbose=False):\n super().__init__(\n hidden_layer_sizes=hidden_layer_sizes,\n max_iter=max_iter,\n verbose=verbose,\n )\n self._num_classes = 0\n self.epochs = 1","title":""},{"docid":"163ea3ae9e52c21a4b1cb0837a3836b3","score":"0.5431743","text":"def __init__(self, path, **kwargs):\n super().__init__(path, **kwargs)\n\n self._largeImagePath = self._getLargeImagePath()\n # Read the root dzi file and check that the expected image files exist\n try:\n with builtins.open(self._largeImagePath) as fptr:\n if fptr.read(1024).strip()[:5] != '<?xml':\n msg = 'File cannot be opened via deepzoom reader.'\n raise TileSourceError(msg)\n fptr.seek(0)\n xml = ElementTree.parse(self._largeImagePath).getroot()\n self._info = etreeToDict(xml)['Image']\n except (ElementTree.ParseError, KeyError, UnicodeDecodeError):\n msg = 'File cannot be opened via deepzoom reader.'\n raise TileSourceError(msg)\n except FileNotFoundError:\n if not os.path.isfile(self._largeImagePath):\n raise TileSourceFileNotFoundError(self._largeImagePath) from None\n raise\n # We should now have a dictionary like\n # {'Format': 'png', # or 'jpeg'\n # 'Overlap': '1',\n # 'Size': {'Height': '41784', 'Width': '44998'},\n # 'TileSize': '254'}\n # and a file structure like\n # <rootname>_files/<level>/<x>_<y>.<format>\n # images will be TileSize+Overlap square; final images will be\n # truncated. Base level is either 0 or probably 8 (level 0 is a 1x1\n # pixel tile)\n self.sizeX = int(self._info['Size']['Width'])\n self.sizeY = int(self._info['Size']['Height'])\n self.tileWidth = self.tileHeight = int(self._info['TileSize'])\n maxXY = max(self.sizeX, self.sizeY)\n self.levels = int(math.ceil(\n math.log(maxXY / self.tileWidth) / math.log(2))) + 1\n tiledirName = os.path.splitext(os.path.basename(self._largeImagePath))[0] + '_files'\n rootdir = os.path.dirname(self._largeImagePath)\n self._tiledir = os.path.join(rootdir, tiledirName)\n if not os.path.isdir(self._tiledir):\n rootdir = os.path.dirname(rootdir)\n self._tiledir = os.path.join(rootdir, tiledirName)\n zeroname = '0_0.%s' % self._info['Format']\n self._nested = os.path.isdir(os.path.join(self._tiledir, '0', zeroname))\n zeroimg = PIL.Image.open(\n os.path.join(self._tiledir, '0', zeroname) if not self._nested else\n os.path.join(self._tiledir, '0', zeroname, zeroname))\n if zeroimg.size == (1, 1):\n self._baselevel = int(\n math.ceil(math.log(maxXY) / math.log(2)) -\n math.ceil(math.log(maxXY / self.tileWidth) / math.log(2)))\n else:\n self._baselevel = 0","title":""},{"docid":"d7ce2ce3a4a5e6425ace05cec2bd6675","score":"0.5430628","text":"def __init__(self, size):\n self.size = size\n self.bias = npy.random.uniform(0, 1)\n self.weights = []\n for _ in range(0, self.size):\n self.weights.append(npy.random.uniform(0, 1))\n self.inputs = [] # storing inputs for later\n self.output = 0\n self.delta = 0","title":""},{"docid":"beec6e6905611c730ece2313d52ff785","score":"0.5412695","text":"def __init__(self, nx, layers):\n if type(nx) != int:\n raise TypeError(\"nx must be an integer\")\n if nx < 1:\n raise ValueError(\"nx must be a positive integer\")\n\n if type(layers) != list or len(layers) == 0:\n raise TypeError(\"layers must be a list of positive integers\")\n\n self.L = len(layers)\n self.cache = {}\n self.weights = {}\n for i in range(0, self.L):\n if layers[i] < 0:\n raise TypeError(\"layers must be a list of positive integers\")\n if i == 0:\n self.weights[\"W\" + str(i + 1)] = np.random.randn(\n layers[i], nx)*np.sqrt(2/(nx))\n self.weights[\"b\" + str(i + 1)] = np.zeros((layers[i], 1))\n else:\n self.weights[\"W\" + str(i + 1)] = np.random.randn(\n layers[i], layers[i-1]) * np.sqrt(2/(layers[i-1]))\n self.weights[\"b\" + str(i + 1)] = np.zeros((layers[i], 1))","title":""},{"docid":"e60b49cc3a81e8f67ea5d0c4724b419a","score":"0.5407968","text":"def __init__(self, pool_size):\n self.pool_size = pool_size\n if self.pool_size > 0: # create an empty pool\n self.num_imgs = 0\n self.images = []","title":""},{"docid":"b1bdffdc896f875c5020d12597efd265","score":"0.54054046","text":"def make_DBN():\n dataset = datasets.fetch_mldata(\"MNIST Original\") # mnist original, uci 20070111 letter, datasets uci letter\n\n # Split our easy dataset so we can use it to train the DBN\n (trainX, testX, trainY, testY) = train_test_split(dataset.data/float(dataset.data.max()), # DBM only understands 0.0-1.0\n dataset.target.astype(\"int0\"),\n test_size=0.33)\n\n # Make Deep Belief Network\n dbn = DBN(\n # [nodes in input layer (pixel size of vectorized dataset),\n # nodes in hidden layer(s),\n # nodes in output layer (size of output dataset)]\n [trainX.shape[1], 1000, 300, 10], # out = 10,\n learn_rates=0.3,\n learn_rate_decays=0.9,\n epochs=10,\n verbose=1\n )\n # Teach DBM to recognize digits\n dbn.fit(trainX, trainY)\n\n # Predict using our test set\n preds = dbn.predict(testX)\n# print classification_report(testY, preds)\n\n\n # Examine random selection from our test set\n for i in np.random.choice(np.arange(0, len(testY)), size=(100,)):\n # Classify actual digit\n pred = dbn.predict(np.atleast_2d(testX[i]))\n\n # Reshape to 28x28\n image = (testX[i] * 255).reshape((28,28)).astype('uint8')\n\n # Show image and prediction\n seen = pred[0]\n actual = testY[i]\n if seen != actual:\n print \"Actual digit is {0}, saw {1}\".format(actual, seen)\n # plt.imshow(image)\n # plt.show()\n\n with open('dbn.pickle', 'wb') as f:\n pickle.dump(dbn, f)","title":""},{"docid":"0a1e9ed9271e677b28f7cded66ee1d38","score":"0.54044276","text":"def load_data(path, ):\n\n # Read all EMNIST test and train data\n mndata = MNIST(path)\n\n X_train, y_train = mndata.load(path + '/emnist-byclass-train-images-idx3-ubyte',\n path + '/emnist-byclass-train-labels-idx1-ubyte')\n X_test, y_test = mndata.load(path + '/emnist-byclass-test-images-idx3-ubyte',\n path + '/emnist-byclass-test-labels-idx1-ubyte')\n\n # Read mapping of the labels and convert ASCII values to chars\n mapping = []\n\n with open(path + '/emnist-byclass-mapping.txt') as f:\n for line in f:\n mapping.append(chr(int(line.split()[1])))\n\n X_train = np.array(X_train)\n y_train = np.array(y_train)\n X_test = np.array(X_test)\n y_test = np.array(y_test)\n\n X_train = normalize(X_train)\n X_test = normalize(X_test)\n\n X_train = reshape_for_cnn(X_train)\n X_test = reshape_for_cnn(X_test)\n\n y_train = preprocess_labels(y_train, len(mapping))\n y_test = preprocess_labels(y_test, len(mapping))\n\n return X_train, y_train, X_test, y_test, mapping","title":""},{"docid":"470bd542ba3e9473555070b6c85c7079","score":"0.5402779","text":"def __init__(self, numelem=100, cycles=1, maxweight=100, filename=\"data\", printonce=False, verbose=False):\n\n self.printonce = printonce\n self.verbose = verbose\n self.executiontime = 0\n self.__numelem = numelem\n self.weights = numpy.random.random_integers(1, maxweight, size=self.__numelem).astype(numpy.uint32)\n self.values = numpy.random.random_integers(1, maxweight*2, size=self.__numelem).astype(numpy.uint32)\n self.sumofweights = numpy.uint32(self.weights.sum())\n self.capacity = numpy.uint32(self.sumofweights/2)\n self.f0 = numpy.zeros(self.capacity+1).astype(numpy.uint32)\n self.f1 = numpy.zeros_like(self.f0)\n self.m_d = numpy.zeros_like(self.f0)\n self.cycles = cycles","title":""},{"docid":"fa300c5743e388c73a00b979898ff5b4","score":"0.53982687","text":"def __init__(self, embed_size, model_path=None):\n super(EncoderCNN, self).__init__()\n resnet = models.resnet152(pretrained=True)\n modules = list(resnet.children())[:-1] # delete the last fc layer.\n self.resnet = nn.Sequential(*modules)\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\n self.init_weights(model_path)","title":""},{"docid":"6c8950db980c8e5032def8fa6982637c","score":"0.5389867","text":"def __init__(self, layers, optimizer, minibatch_size=100, learning_rate=0.001):\n self._layers = layers\n self._optimizer = optimizer\n self._learning_rate = learning_rate\n self._minibatch_size = minibatch_size # size of minibatch samples\n # add widen and deepen functionality","title":""},{"docid":"2b3e8c261a3147ecbf4c8933353e312e","score":"0.53868204","text":"def mnist_noniid_class(dataset, num_users, class_per_user):\n # 60,000 training imgs --> 300 imgs/shard X 200 shards\n # num_shards, num_imgs = 200, 300\n # idx_shard = [i for i in range(num_shards)]\n dict_users = {i: np.array([]) for i in range(num_users)}\n dict_class = {}\n\n idxs = np.arange(200*300)\n labels = dataset.train_labels.numpy()\n labels_his=Counter(labels)\n\n # sort labels\n idxs_labels = np.vstack((idxs, labels))\n idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()]\n\n total_class=len(np.unique(labels))\n print('total_class',total_class)\n per_class_avg_users=int(num_users/total_class*class_per_user)\n print('per_class_avg_users',per_class_avg_users)\n remain_users=num_users*class_per_user-per_class_avg_users*total_class\n print('remain_users',remain_users)\n last_class_users=per_class_avg_users+remain_users\n print('last_class_users',last_class_users)\n\n idx_accumulate=0\n shard=0\n idx_shard={}\n for i in range(total_class):\n if i==total_class-1:\n per_class_avg_users=last_class_users\n idxs_per_class = idxs_labels[0, idx_accumulate:idx_accumulate+labels_his[i]]\n # print('idxs_per_class',idxs_per_class)\n num_imgs_per_user=int(len(idxs_per_class)/per_class_avg_users) \n dict_class[i]=shard+np.arange(per_class_avg_users)\n #idx of per shard \n for k in range(per_class_avg_users):\n idx_shard[shard+k]= idxs_per_class[k*num_imgs_per_user:(k+1)*num_imgs_per_user]\n\n shard+=per_class_avg_users\n print('shard',shard)\n idx_accumulate+=labels_his[i] \n num_shard= [k for k in range(shard)]\n\n print(len(idx_shard)) \n print('dict_class',dict_class)\n # divide and assign class_per_user shards/client\n # ensure each user has class_per_user\n for i in range(num_users):\n # print('user',i)\n flag=1\n while flag:\n if len(num_shard)>class_per_user:\n rand_set = np.random.choice(num_shard, class_per_user, replace=False)\n # print('rand_set',rand_set)\n dict_keys=[]\n for j in rand_set:\n dict_keys.append([key for idx, key in enumerate(dict_class) if j in dict_class[key]])\n if len(np.unique(dict_keys))==class_per_user:\n flag=0\n for rand in rand_set:\n dict_users[i] = np.concatenate((dict_users[i], idx_shard[rand]), axis=0).astype(int)\n num_shard = list(set(num_shard) - set(rand_set))\n else:\n rand_set = num_shard\n flag=0\n for rand in rand_set:\n dict_users[i] = np.concatenate((dict_users[i], idx_shard[rand]), axis=0).astype(int)\n num_shard = list(set(num_shard) - set(rand_set))\n print('num_shard',num_shard)\n return dict_users","title":""},{"docid":"c0483f18a170f8f89b313e6216a457e1","score":"0.53723264","text":"def load_mnist(images_path, labels_path):\n with open(labels_path, 'rb') as lbpath:\n struct.unpack('>II', lbpath.read(8))\n labels = np.fromfile(lbpath, dtype=np.uint8)\n with open(images_path, 'rb') as imgpath:\n struct.unpack('>IIII', imgpath.read(16))\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 28, 28, 1)\n return images, labels","title":""},{"docid":"369613a70b6bfe8f8d879b35c70f1343","score":"0.53714097","text":"def __init__(self, sizes):\n self.num_layers = len(sizes)\n self.sizes = sizes\n # play with biases to start from i/p layer, i.e., ... fory in sizes[:-1]\n self.biases = [np.random.randn(y, 1) for y in sizes[1:]] \n self.weights = [np.random.randn(y, x)\n for x, y in zip(sizes[:-1], sizes[1:])]","title":""},{"docid":"bb8e4f5779759228f9c5a07b00fa6920","score":"0.53709626","text":"def load_mnist_data():\n\n file_name = 'dataset/mnist.pkl.gz'\n\n # pickle.load return ((train_x, train_y), (valid_x, valid_y), (test_x, test_y))\n with gzip.open(file_name, 'rb') as f:\n train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\n\n train_x, train_y = train_set\n valid_x, valid_y = valid_set\n\n # concatenation valid set to train set\n train_x = np.vstack((train_x, valid_x))\n train_y = np.append(train_y, valid_y)\n\n test_x, test_y = test_set\n\n return (train_x, train_y, test_x, test_y)","title":""},{"docid":"298a3ab7f183b172c7d1583b52e3318f","score":"0.53653","text":"def __init__(self, embed_size):\n super(EncoderCNN, self).__init__()\n resnet = models.resnet152(pretrained=True)\n modules = list(resnet.children())[:-1] # delete the last fc layer.\n self.resnet = nn.Sequential(*modules)\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\n self.init_weights()","title":""},{"docid":"3f873d1046774486e26d2bd3671deee3","score":"0.5363259","text":"def __init__(self, hidden_layer_sizes=(100,), max_iter=200, verbose=False):\n super().__init__(\n hidden_layer_sizes=hidden_layer_sizes,\n max_iter=max_iter,\n verbose=verbose,\n )\n self.epochs = 1","title":""},{"docid":"51980cef0426fc5da83574956d4cd948","score":"0.5359663","text":"def load_MiniImageNet():\n\n base_path = '/home/USER/Documents'\n if (not (os.path.exists(base_path))):\n base_path = '/home/ubuntu/Projects'\n if (not (os.path.exists(base_path))):\n base_path = '/home/USER/Projects'\n if (not (os.path.exists(base_path))):\n base_path = '/home/USER/Projects'\n \n data_path = base_path + '/MAML/input_data/miniImageNet/'\n train_tasks_file = open(data_path + 'miniImageNet_train_tasks.txt', 'rb')\n train_tasks = pickle.load(train_tasks_file)\n\n val_tasks_file = open(data_path + 'miniImageNet_val_tasks.txt', 'rb')\n val_tasks = pickle.load(val_tasks_file)\n\n test_tasks_file = open(data_path + 'miniImageNet_test_tasks.txt', 'rb')\n test_tasks = pickle.load(test_tasks_file)\n\n mtl_train_tasks = {}\n mtl_train_tasks['X'] = [np.concatenate((train_task['X_inner'], train_task['X_outer']), axis=0) for train_task in train_tasks]\n mtl_train_tasks['Y'] = [np.concatenate((train_task['Y_inner'], train_task['Y_outer']), axis=0) for train_task in train_tasks]\n\n return mtl_train_tasks, val_tasks, test_tasks","title":""},{"docid":"7555885361549d9dd0b4334c765e7e1e","score":"0.5358751","text":"def __init__(self,test_size):\n self.letters = {0:'A',1:'B',2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M',13:'N',14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X', 24:'Y',25:'Z'}\n self.data_path = \"A_Z Handwritten Data.csv\"\n self.test_size = test_size\n self.X_train = None\n self.X_test = None\n self.y_train = None\n self.y_test = None\n self.model = None\n self.y_train_cat = None\n self.y_test_cat = None\n self.history = None\n self.cm = None\n self.y_pred = None","title":""},{"docid":"975313f00f937f602974a2a3b83ba336","score":"0.5351087","text":"def parse_image():\r\n LABELS = ['t10k-images.idx3-ubyte',\r\n 't10k-labels.idx1-ubyte',\r\n 'train-images.idx3-ubyte',\r\n 'train-labels.idx1-ubyte']\r\n\r\n TRAIN_IMAGE = LABELS[2]\r\n TRAIN_LABELS = LABELS[3]\r\n TEST_IMAGE = LABELS[0]\r\n TEST_LABELS = LABELS[1]\r\n\r\n f_image = open('mnist/' + TRAIN_IMAGE, 'rb')\r\n f_label = open('mnist/' + TRAIN_LABELS, 'rb')\r\n\r\n i_magic, n_images, n_row, n_col = struct.unpack(\">4I\", f_image.read(16))\r\n l_magic, n_labels = struct.unpack(\">2I\", f_label.read(8))\r\n\r\n assert n_labels == n_images\r\n\r\n training = [tuple([np.array([[i/255.0]\r\n for _ in range(n_row)\r\n for i in struct.unpack(\">%dB\" % (n_col), f_image.read(n_col))]),\r\n vectorized_result(struct.unpack(\">B\", f_label.read(1))[0])])\r\n for _ in range(n_images-10000)]\r\n\r\n validation = [tuple([np.array([[i/255.0]\r\n for _ in range(n_row)\r\n for i in struct.unpack(\">%dB\" % (n_col), f_image.read(n_col))]),\r\n vectorized_result(struct.unpack(\">B\", f_label.read(1))[0])])\r\n for _ in range(n_images-10000, n_images)]\r\n\r\n f_image.close()\r\n f_label.close()\r\n\r\n f_image = open('mnist/' + TEST_IMAGE, 'rb')\r\n f_label = open('mnist/' + TEST_LABELS, 'rb')\r\n\r\n i_magic, n_images, n_row, n_col = struct.unpack(\">4I\", f_image.read(16))\r\n l_magic, n_labels = struct.unpack(\">2I\", f_label.read(8))\r\n\r\n testing = [tuple([np.array([i/255.0\r\n for _ in range(n_row)\r\n for i in struct.unpack(\">%dB\" % (n_col), f_image.read(n_col))]),\r\n vectorized_result(struct.unpack(\">B\", f_label.read(1))[0])])\r\n for _ in range(n_images)]\r\n\r\n with open('mnist_data', 'wb') as f:\r\n pickle.dump((training, validation, testing), f)\r\n f.close()\r\n\r\n return training, validation, testing","title":""},{"docid":"b9457a09a51dfb9ffd8a3d4b88704925","score":"0.5348022","text":"def load_mnist_realval(path=data_path, one_hot=True, dequantify=False):\n if not os.path.isfile(path):\n data_dir = os.path.dirname(path)\n if not os.path.exists(os.path.dirname(path)):\n os.makedirs(data_dir)\n # download_dataset('http://www.iro.umontreal.ca/~lisa/deep/data/mnist'\n # '/mnist.pkl.gz', path)\n download_dataset('https://oneflow-public.oss-cn-beijing.aliyuncs.com/datasets/zhusuan_oneflow/mnist.pkl.gz',\n path)\n \n\n f = gzip.open(path, 'rb')\n if six.PY2:\n train_set, valid_set, test_set = pickle.load(f)\n else:\n train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\n f.close()\n x_train, t_train = train_set[0], train_set[1]\n x_valid, t_valid = valid_set[0], valid_set[1]\n x_test, t_test = test_set[0], test_set[1]\n # x_train, t_train = train_set[0][:64*500], train_set[1][:64*500]\n # x_valid, t_valid = valid_set[0][:64*50], valid_set[1][:64*50]\n # x_test, t_test = test_set[0][:64*10], test_set[1][:64*10]\n if dequantify:\n x_train += np.random.uniform(0, 1. / 256,\n size=x_train.shape).astype('float32')\n x_valid += np.random.uniform(0, 1. / 256,\n size=x_valid.shape).astype('float32')\n x_test += np.random.uniform(0, 1. / 256,\n size=x_test.shape).astype('float32')\n n_y = t_train.max() + 1\n t_transform = (lambda x: to_one_hot(x, n_y)) if one_hot else (lambda x: x)\n return x_train, t_transform(t_train), x_valid, t_transform(t_valid), \\\n x_test, t_transform(t_test)","title":""},{"docid":"7a214882a9e05c68f2d56950e031e18b","score":"0.5346792","text":"def mnist_noniid(args, dataset, num_users, n_list, k_list):\n\n # 60,000 training imgs --> 200 imgs/shard X 300 shards\n num_shards, num_imgs = 10, 6000\n idx_shard = [i for i in range(num_shards)]\n dict_users = {}\n idxs = np.arange(num_shards*num_imgs)\n labels = dataset.train_labels.numpy()\n # sort labels\n idxs_labels = np.vstack((idxs, labels))\n idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()]\n idxs = idxs_labels[0, :]\n label_begin = {}\n cnt=0\n for i in idxs_labels[1,:]:\n if i not in label_begin:\n label_begin[i] = cnt\n cnt+=1\n\n classes_list = []\n for i in range(num_users):\n n = n_list[i]\n k = k_list[i]\n k_len = args.train_shots_max\n classes = random.sample(range(0,args.num_classes), n)\n classes = np.sort(classes)\n print(\"user {:d}: {:d}-way {:d}-shot\".format(i + 1, n, k))\n print(\"classes:\", classes)\n user_data = np.array([])\n for each_class in classes:\n # begin = i*10 + label_begin[each_class.item()]\n begin = i * k_len + label_begin[each_class.item()]\n user_data = np.concatenate((user_data, idxs[begin : begin+k]),axis=0)\n dict_users[i] = user_data\n classes_list.append(classes)\n\n return dict_users, classes_list\n #\n #\n #\n #\n #\n # # divide and assign 2 shards/client\n # for i in range(num_users):\n # rand_set = set(np.random.choice(idx_shard, n_list[i], replace=False))\n # idx_shard = list(set(idx_shard) - rand_set)\n # for rand in rand_set:\n # dict_users[i] = np.concatenate(\n # (dict_users[i], idxs[rand*num_imgs:(rand+1)*num_imgs]), axis=0)\n # return dict_users","title":""},{"docid":"77550f7146ed3484d918c9895ced1ff5","score":"0.5345506","text":"def __init__(self, input_dict):\n self.instance = tik.Tik(tik.Dprofile())\n self.dtype = input_dict.get(\"x\").get(\"dtype\").lower()\n self.dsize = common_util.get_data_size(self.dtype)\n total_size = tbe_platform.cce_conf.get_soc_spec(tbe_platform.cce_conf.UB_SIZE)\n ub_size = (total_size - RESERVE_SIZE) // (2 * self.dsize)\n burnest_len = constant.BLOCK_SIZE // self.dsize\n ub_size = ((ub_size + burnest_len - 1) // burnest_len) * burnest_len\n self.one_max_size = ub_size\n x_len = get_shape_total_number(input_dict.get(\"x\").get(\"shape\"))\n x_len = ((x_len + burnest_len - 1) // burnest_len) * burnest_len\n hw = input_dict.get(\"y\").get(\"shape\")[2] * \\\n input_dict.get(\"y\").get(\"shape\")[3]\n mod = hw % burnest_len\n if mod != 0:\n x_len = x_len + burnest_len\n self.x_gm = self.instance.Tensor(self.dtype, (x_len,), name=\"x_gm\",\n scope=tik.scope_gm)\n self.y_gm = self.instance.Tensor(self.dtype, (x_len,), name=\"y_gm\",\n scope=tik.scope_gm)\n self.input_dict = input_dict","title":""},{"docid":"2d71e32a8e187a45ca323630c79aaf6c","score":"0.5339088","text":"def get_train(self, even=None, min_blobs=1, max_blobs=1): # MT\n self.images, self.labels, self.areas = create_data_natural.generate_data(even, min_blobs, max_blobs, scalar_output=True)\n self.length = len(self.images)","title":""},{"docid":"54e9dd8f1c7bf87977d7b32c5d7eb3ae","score":"0.5338825","text":"def __init__(self, inputSize, layers=[], costFunction=None):\n self.inputSize = inputSize\n self.layers = layers\n self.costFunction = costFunction\n self._weights = []\n self.d_weights = []\n self.layerCache=[]","title":""},{"docid":"c7257ad3fdda6bd45ad2a3a9b6d81cf7","score":"0.533793","text":"def __init__(self, data, train_labels, train_indices, train_size):\n self.data = data\n self.train_labels = train_labels\n self.train_indices = train_indices\n self.train_size = train_size\n if len(self.classes) > train_size:\n raise ValueError(\"The train size can not be smaller than the number of classes.\")","title":""},{"docid":"347c4319a0c62185d73a5416e8e5a6ff","score":"0.5336137","text":"def __init__(self, ext_mem: Dict, mem_size: int, adaptive_size=True,\n total_num_classes=-1):\n self.ext_mem = ext_mem\n self.mem_size = mem_size\n self.adaptive_size = adaptive_size\n self.total_num_classes = total_num_classes\n self.seen_classes = set()\n\n if not self.adaptive_size:\n assert self.total_num_classes > 0, \\\n \"\"\"When fixed exp mem size, total_num_classes should be > 0.\"\"\"","title":""},{"docid":"65e6b5257df783629c930a5bc66ea7a1","score":"0.53353137","text":"def load_train(self):\n images, labels = self.load(os.path.join('mnist', 'train', 'images'),\n os.path.join('mnist', 'train', 'labels'))\n self.train_data = list(zip(images, labels))","title":""},{"docid":"f9dc526202ac9c72af509fb05945b74e","score":"0.53287077","text":"def __init__(self):\n #self.lambda_mixture = 0.86752\n self.lambda_mixture = 0.86\n self.label_dict = {}\n self.num_class = 14\n self.prior = []\n self.vocab = 0\n self.class_vocab = []\n self.vocab_bigram = 0\n self.class_vocab_bigram = []\n self.label_dict_bigram = {}","title":""},{"docid":"90e86efff4639867ee68d811d92584df","score":"0.5319188","text":"def __init__(self, images, labels, type_=\"mnist\"):\n images = self._normalize(images, type_)\n labels = (labels).astype(np.int64)\n self.images = torch.from_numpy(images)\n self.labels = torch.from_numpy(labels)\n self.num_samples = images.shape[0]","title":""},{"docid":"56fbf6ca7b20aa2e66eb256130336d76","score":"0.5316506","text":"def mnist(batch_size=64, size=28, path_to_data='../../mnist_data'):\n all_transforms = transforms.Compose([\n transforms.Resize(size),\n transforms.ToTensor()\n ])\n\n train_data = datasets.MNIST(path_to_data, train=True, download=True,\n transform=all_transforms)\n test_data = datasets.MNIST(path_to_data, train=False,\n transform=all_transforms)\n\n train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n\n return train_loader, test_loader","title":""},{"docid":"3940b6b86239bd177b2ca011aa0ff5b5","score":"0.5307942","text":"def __init__(self):\n self.vert_dict = {}\n self.num_verticies = 0\n self.num_edges = 0","title":""},{"docid":"85a7296f3c5e7fe74171f0b4fed755d0","score":"0.5305711","text":"def __init__(self, h5_path, input_size=None):\n self.input_size = input_size\n self.h5_path = h5_path\n self.x, self.y = self.load_list()","title":""},{"docid":"95775e637e5125854d21d4c60cb1cfc7","score":"0.530351","text":"def __init__(self, batch_size, shuffle=False):\n train, _ = tf.keras.datasets.mnist.load_data()\n X, y = train\n X = X.astype(np.float32) / 255\n X = X.reshape((X.shape[0], -1))\n self.X, self.y = X, y\n self.batch_size, self.shuffle = batch_size, shuffle","title":""},{"docid":"332ab19572c3da0b9e202cbb4ea76fe3","score":"0.53026295","text":"def __init__(self, hidden_width, depth, input_size, output_size):\n self.width = hidden_width\n self.depth = depth\n self.network = []\n\n # activations are in columns\n # minimum one hidden layer\n # depth = 0 => perceptron\n if depth == 0:\n self.network.append((np.random.normal(0, 1, (input_size + 1, output_size))))\n return\n\n # deep NN with normally distributed initial weights\n self.network.append(np.random.normal(0, 1, (input_size + 1, hidden_width)))\n for i in range(depth - 1):\n self.network.append(np.random.normal(0, 1, (self.network[-1].shape[1] + 1, hidden_width)))\n self.network.append(np.random.normal(0, 1, (self.network[-1].shape[1] + 1, output_size)))\n\n return","title":""},{"docid":"b10f49cf4117d2060d9a12d7d58dc00b","score":"0.5298141","text":"def __init__( self\n , hidden_size\n , input_size):\n super(MLPEncodingCell, self).__init__()\n self._MLP = tf.keras.layers.Dense(hidden_size, activation='relu')\n self.hidden_size = hidden_size\n self.input_size = input_size\n self.state_size = tf.TensorShape([self.hidden_size])","title":""},{"docid":"acf5a700e50e78ef46639c1208ec4c3f","score":"0.52919877","text":"def load_mnist():\r\n\r\n print('Loading train data...')\r\n train_data = torch.utils.data.DataLoader(\r\n torchvision.datasets.MNIST('mnist/', \r\n train=True, \r\n download=True,\r\n transform=torchvision.transforms.Compose([\r\n torchvision.transforms.ToTensor()\r\n ])),\r\n shuffle=True,)\r\n\r\n train_input = []\r\n train_label = []\r\n \r\n cnt = 0\r\n for batch, label in tqdm(train_data):\r\n train_input.append(batch.squeeze().numpy().reshape(784,))\r\n train_label.append(label.numpy())\r\n cnt += 1\r\n if cnt == 1300: break\r\n\r\n print('Loading test data...')\r\n test_data = torch.utils.data.DataLoader(\r\n torchvision.datasets.MNIST('mnist/', \r\n train=False, \r\n download=True,\r\n transform=torchvision.transforms.Compose([\r\n torchvision.transforms.ToTensor()\r\n ])),\r\n shuffle=True,)\r\n\r\n test_input = []\r\n test_label = []\r\n \r\n for batch, label in tqdm(test_data):\r\n test_input.append(batch.squeeze().numpy().reshape(784,))\r\n test_label.append(label.numpy())\r\n\r\n return np.array(train_input), np.array(train_label), np.array(test_input), np.array(test_label)","title":""},{"docid":"878d7d61a85338a7814220a6687af10d","score":"0.52839684","text":"def __init__(self, embed_size):\n # pdb.set_trace()\n super(EncoderCNN, self).__init__()\n resnet = models.resnet152(pretrained=True)\n modules = list(resnet.children())[:-1] # delete the last fc layer.\n self.resnet = nn.Sequential(*modules)\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)","title":""},{"docid":"b6dfd6112fe6e2dc61fb6222112a8dcc","score":"0.52795196","text":"def load_train(self):\n images, labels = self.load(os.path.join('mnist', 'train', 'images'),\n os.path.join('mnist', 'train', 'labels'))\n self.train_data = zip(images, labels)","title":""},{"docid":"307f7eefcfe692b04bf8051839437a14","score":"0.5279294","text":"def __init__(self, input_size, output_size, hidden_sizes=(64, 64)):\n super(Decoder, self).__init__()\n self.net = make_nn(input_size, output_size, hidden_sizes)","title":""}],"string":"[\n {\n \"docid\": \"6b75515e27977d064177dbb284d2c0b7\",\n \"score\": \"0.6917801\",\n \"text\": \"def __init__(self, batch_size=50, exluded_digits=(1, 5)):\\n mnist = input_data.read_data_sets('MNIST_data', one_hot=True, reshape=False, validation_size=0)\\n self.train_data = mnist.train.images # Returns np.array\\n self.train_labels = np.asarray(mnist.train.labels, dtype=np.int32)\\n self.data_len = self.train_data.shape[0]\\n\\n self.excluded_digits = exluded_digits\\n self.split_full_oneshot(self.excluded_digits)\\n self.full_batch_id = 0\\n self.one_shot_batch_id = 0\\n\\n if (batch_size % 2) != 0:\\n raise Exception(\\\"Batch size must be divisible by two!\\\")\\n self.batch_size = batch_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639f3db9aa83e9a88df413979dbc3b21\",\n \"score\": \"0.67930144\",\n \"text\": \"def __init__(self, data_path, flatten=True):\\n (train_x, train_y), (test_x, test_y) = mnist(data_path)\\n if not flatten:\\n train_x = train_x.reshape(-1, 28, 28, 1)\\n test_x = test_x.reshape(-1, 28, 28, 1)\\n self.train = (binarize(train_x), one_hot(train_y))\\n self.test = (binarize(test_x), one_hot(test_y))\\n self.batch_size = 0\\n self.epoch_size = train_y.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f6d48f05849efd34a3714808ee2a9d\",\n \"score\": \"0.64203674\",\n \"text\": \"def __init__(self, root: str):\\n self.root = root\\n\\n self.train = load_pickle(root + '/train_medium.pkl')\\n self.label = torch.tensor(load_pickle(root + '/label_medium.pkl'))\\n\\n self.num_classes = max(max(self.train)) + 1\\n self.ohe_mapping = torch.eye(self.num_classes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d890c30efeac0ddedc9de761608e45\",\n \"score\": \"0.60133725\",\n \"text\": \"def load_mnist(dataset=\\\"training\\\", digits=np.arange(10), path=\\\".\\\"):\\n\\tif dataset == \\\"training\\\":\\n\\t\\tfname_img = os.path.join(path, 'train-images.idx3-ubyte')\\n\\t\\tfname_lbl = os.path.join(path, 'train-labels.idx1-ubyte')\\n\\telif dataset == \\\"testing\\\":\\n\\t\\tfname_img = os.path.join(path, 't10k-images.idx3-ubyte')\\n\\t\\tfname_lbl = os.path.join(path, 't10k-labels.idx1-ubyte')\\n\\telse:\\n\\t\\traise ValueError(\\\"dataset must be 'testing' or 'training'\\\")\\n\\n\\tflbl = open(fname_lbl, 'rb')\\n\\tmagic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n\\tlbl = array(\\\"b\\\", flbl.read())\\n\\tflbl.close()\\n\\n\\tfimg = open(fname_img, 'rb')\\n\\tmagic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n\\timg = array(\\\"B\\\", fimg.read())\\n\\tfimg.close()\\n\\n\\tind = [ k for k in range(size) if lbl[k] in digits ]\\n\\tN = len(ind)\\n\\timages = np.zeros((N, rows, cols), dtype=np.uint8)\\n\\tlabels = np.zeros((N, 1), dtype=np.int8)\\n\\tfor i in range(len(ind)):\\n\\t\\timages[i] = np.array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\\n\\t\\tlabels[i] = lbl[ind[i]]\\n\\n\\treturn images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e23547e3fa552b0a6bfb629c48dd45ef\",\n \"score\": \"0.60079396\",\n \"text\": \"def load_mnist(size: int = None,\\n border: int = _MNIST_BORDER,\\n blank_corners: bool = False,\\n nums: List[int] = None) \\\\\\n -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n # DOCME: Fix up formatting above,\\n # DOCME: Explain nums argument\\n\\n # JIT import since keras startup is slow\\n from keras.datasets import mnist\\n\\n def _filter_mnist(x: np.ndarray, y: np.ndarray, nums: List[int] = None) \\\\\\n -> Tuple[np.ndarray, np.ndarray]:\\n xt = []\\n yt = []\\n items = len(y)\\n for n in range(items):\\n if nums is not None and y[n] in nums:\\n xt.append(x[n])\\n yt.append(y[n])\\n xt = np.stack(xt)\\n yt = np.stack(yt)\\n return xt, yt\\n\\n def _rescale(imgarray: np.ndarray, size: int) -> np.ndarray:\\n N = imgarray.shape[0]\\n\\n # Chop off border\\n imgarray = imgarray[:, border:-border, border:-border]\\n\\n rescaled = np.zeros(shape=(N, size, size), dtype=np.float)\\n for n in range(0, N):\\n img = Image.fromarray(imgarray[n])\\n img = img.resize((size, size), Image.LANCZOS)\\n rsc = np.asarray(img).reshape((size, size))\\n rsc = 256.*rsc/rsc.max()\\n rescaled[n] = rsc\\n\\n return rescaled.astype(dtype=np.uint8)\\n\\n def _blank_corners(imgarray: np.ndarray) -> None:\\n # Zero out corners\\n sz = imgarray.shape[1]\\n corner = (sz//2)-1\\n for x in range(0, corner):\\n for y in range(0, corner-x):\\n imgarray[:, x, y] = 0\\n imgarray[:, -(1+x), y] = 0\\n imgarray[:, -(1+x), -(1+y)] = 0\\n imgarray[:, x, -(1+y)] = 0\\n\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n\\n if nums:\\n x_train, y_train = _filter_mnist(x_train, y_train, nums)\\n x_test, y_test = _filter_mnist(x_test, y_test, nums)\\n\\n if size:\\n x_train = _rescale(x_train, size)\\n x_test = _rescale(x_test, size)\\n\\n if blank_corners:\\n _blank_corners(x_train)\\n _blank_corners(x_test)\\n\\n return x_train, y_train, x_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a673ce26f0b0d25328978a10d478407\",\n \"score\": \"0.59981215\",\n \"text\": \"def load_mnist(num_train_samples=60000, num_test_samples=10000, res=28, normalization=False):\\n # Fetch train (60.000 images) and test data (10.000 images)\\n (train_images_raw, train_labels_raw), (test_images_raw, test_labels_raw) = tf.keras.datasets.mnist.load_data()\\n\\n # Convert data type\\n train_images_raw = np.asarray(train_images_raw, dtype=np.float32)\\n test_images_raw = np.asarray(test_images_raw, dtype=np.float32)\\n\\n # Convert data by squaring the pixel intensities\\n # train_images_raw = np.square(train_images_raw)\\n # test_images_raw = np.square(test_images_raw)\\n\\n # Cut data\\n train_labels = train_labels_raw[:num_train_samples]\\n test_labels = test_labels_raw[:num_test_samples]\\n train_images = train_images_raw[:num_train_samples]\\n test_images = test_images_raw[:num_test_samples]\\n\\n # Normalize data\\n if normalization:\\n train_images_mean = np.mean(train_images, 0)\\n train_images_std = np.std(train_images, 0)\\n std_eps = 1e-7\\n train_images = (train_images - train_images_mean) / (train_images_std + std_eps)\\n test_images = (test_images - train_images_mean) / (train_images_std + std_eps)\\n\\n # Resize images\\n if res != 28:\\n train_images = np.array(list(map(lambda x: resize(x, (res, res)), train_images)))\\n test_images = np.array(list(map(lambda x: resize(x, (res, res)), test_images)))\\n\\n # Reshape and convert data\\n train_images = train_images.reshape(-1, res * res).astype('float32')\\n test_images = test_images.reshape(-1, res * res).astype('float32')\\n\\n return (train_images, train_labels), (test_images, test_labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a23bcd54999c5935ca4873f2c9c176e\",\n \"score\": \"0.5973087\",\n \"text\": \"def load_mnist_1D_large(filename = '../data/mnist.pkl.gz'):\\n \\n import keras.utils as ku\\n\\n # Load the data.\\n training_data, validation_data, test_data = get_data(filename)\\n\\n # Return the values.\\n return training_data[0], ku.to_categorical(training_data[1]), \\\\\\n validation_data[0], ku.to_categorical(validation_data[1]), \\\\\\n test_data[0], ku.to_categorical(test_data[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028418c6c4041a815da7b1c79aa73ced\",\n \"score\": \"0.59730726\",\n \"text\": \"def load_mnist_2D(filename = ''):\\n\\n # Get the data.\\n tr_d, tr_v, va_d, va_v, te_d, te_v = \\\\\\n load_mnist_1D_large(filename = filename)\\n\\n # Reshape the data.\\n training_inputs = np.array([x.reshape(28, 28, 1) for x in tr_d])\\n validation_inputs = np.array([x.reshape(28, 28, 1) for x in va_d])\\n test_inputs = np.array([x.reshape(28, 28, 1) for x in te_d])\\n\\n # Return the data.\\n return training_inputs, tr_v, validation_inputs, va_v, \\\\\\n test_inputs, te_v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ad00e777d4e3e0857a30601d4acf8d\",\n \"score\": \"0.59380287\",\n \"text\": \"def load_mnist_dataset():\\n # We first define a download function, supporting both Python 2 and 3.\\n if sys.version_info[0] == 2:\\n from urllib import urlretrieve\\n else:\\n from urllib.request import urlretrieve\\n\\n def download(foldername,filename, source='http://yann.lecun.com/exdb/mnist/'):\\n print(\\\"Downloading %s\\\" % filename)\\n urlretrieve(source + filename, foldername+filename)\\n\\n # We then define functions for loading MNIST images and labels.\\n # For convenience, they also download the requested files if needed.\\n\\n def load_mnist_images(foldername,filename):\\n if not os.path.exists(foldername+filename):\\n download(foldername,filename)\\n # Read the inputs in Yann LeCun's binary format.\\n with gzip.open(foldername+filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=16)\\n # The inputs are vectors now, we reshape them to monochrome 2D images,\\n # following the shape convention: (examples, channels, rows, columns)\\n data = data.reshape(-1, 1, 28, 28)\\n # The inputs come as bytes, we convert them to float32 in range [0,1].\\n # (Actually to range [0, 255/256], for compatibility to the version\\n # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)\\n return data / np.float32(256)\\n\\n def load_mnist_labels(foldername,filename):\\n if not os.path.exists(foldername+filename):\\n download(foldername,filename)\\n # Read the labels in Yann LeCun's binary format.\\n with gzip.open(foldername+filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=8)\\n # The labels are vectors of integers now, that's exactly what we want.\\n return data\\n\\n # We can now download and read the training and test set images and labels.\\n foldername = '../Data/MNIST/'\\n X_train = load_mnist_images(foldername,'train-images-idx3-ubyte.gz')\\n y_train = load_mnist_labels(foldername,'train-labels-idx1-ubyte.gz')\\n X_test = load_mnist_images(foldername,'t10k-images-idx3-ubyte.gz')\\n y_test = load_mnist_labels(foldername,'t10k-labels-idx1-ubyte.gz')\\n\\n # We reserve the last 10000 training examples for validation.\\n X_train, X_val = X_train[:-10000], X_train[-10000:]\\n y_train, y_val = y_train[:-10000], y_train[-10000:]\\n\\n # We just return all the arrays in order, as expected in main().\\n # (It doesn't matter how we do this as long as we can read them again.)\\n return X_train, y_train, X_val, y_val #, X_test, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"724f738281f98ab6ae3be9c40c2387f3\",\n \"score\": \"0.59180367\",\n \"text\": \"def load_mnist_1D_small(filename = '../data/mnist.pkl.gz'):\\n\\n # Load the data.\\n training_data, validation_data, test_data = get_data(filename)\\n\\n # Return the values.\\n return training_data[0][0:500, :], training_data[1][0:500], \\\\\\n validation_data[0][0:200, :], validation_data[1][0:200], \\\\\\n test_data[0][0:100, :], test_data[1][0:100]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c472ec63a2639bce0cda2954578cf0\",\n \"score\": \"0.5899108\",\n \"text\": \"def __init__(self, input_size = (1, 28, 28), num_classes = 10):\\n super(CNN_MNIST, self).__init__()\\n\\n self.layer1 = nn.Sequential(\\n nn.Conv2d(input_size[0], 32, kernel_size=5),\\n nn.ReLU(),\\n nn.MaxPool2d(kernel_size=2))\\n\\n self.layer2 = nn.Sequential(\\n nn.Conv2d(32, 64, kernel_size=5),\\n nn.ReLU(),\\n nn.MaxPool2d(kernel_size=2))\\n\\n self.fc1 = nn.Linear(4 * 4 * 64, num_classes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44b25eaf1606ad133fa08ffbc0a94c6\",\n \"score\": \"0.588867\",\n \"text\": \"def __init(self):\\n # self.size_of_test_items = 24\\n # self.size_of_training_items = 24\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe27c5856560bb794f78aa484eace31\",\n \"score\": \"0.58741045\",\n \"text\": \"def load_MNIST():\\n\\n with open('../data/X_train.pkl', 'rb') as f:\\n X_train = pickle.load(f)\\n X_train.reshape(-1, )\\n with open('../data/Y_train.pkl', 'rb') as f:\\n Y_train = pickle.load(f)\\n with open('../data/X_test.pkl', 'rb') as f:\\n X_test = pickle.load(f)\\n with open('../data/Y_test.pkl', 'rb') as f:\\n Y_test = pickle.load(f)\\n\\n num_classes = len(np.unique(Y_train))\\n input_dim, n_samples = X_train.shape\\n\\n print(f'num_classes = {num_classes}')\\n print(f'num_pixels = {input_dim}')\\n print(f'num_training_samples = {n_samples}')\\n\\n tr_y_multi_class = np.zeros((num_classes, n_samples))\\n for i in range(num_classes):\\n tr_y_multi_class[i, np.where(Y_train == i)] = 1\\n Y_train = np.asmatrix(tr_y_multi_class)\\n\\n input_dim, n_samples = X_test.shape\\n te_y_multi_class = np.zeros((num_classes, n_samples))\\n for i in range(num_classes):\\n te_y_multi_class[i, np.where(Y_test == i)] = 1\\n Y_test = np.asmatrix(te_y_multi_class)\\n\\n return X_train, Y_train, X_test, Y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63d0903c5254b5d821623914b81774b\",\n \"score\": \"0.5867644\",\n \"text\": \"def ld_mnist():\\n\\n def convert_types(image, label):\\n image = tf.cast(image, tf.float32)\\n image /= 255\\n return image, label\\n\\n dataset, info = tfds.load(\\n \\\"mnist\\\", with_info=True, as_supervised=True\\n )\\n\\n mnist_train, mnist_test = dataset[\\\"train\\\"], dataset[\\\"test\\\"]\\n mnist_train = mnist_train.map(convert_types).shuffle(10000).batch(128)\\n mnist_test = mnist_test.map(convert_types).batch(128)\\n return EasyDict(train=mnist_train, test=mnist_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"641dba05f815375836f7804b77778c47\",\n \"score\": \"0.5828651\",\n \"text\": \"def __init__(self, **kwargs):\\r\\n self.w = []\\r\\n self.epoch = 20000\\r\\n self.best_dimension = 0\\r\\n self.best_layers = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525c90419bb6b117f828d4fa0a33e70d\",\n \"score\": \"0.58113134\",\n \"text\": \"def init():\\n global width, height, map_width, map_height, epochs, batch_size, min_heat\\n width = 1024\\n height = 1024\\n\\n map_width = int(width / 2)\\n map_height = int(height / 2)\\n\\n epochs = 100\\n batch_size = 4\\n\\n # 200~255 to find heat point\\n min_heat = 150\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d467c34bf424771289762f22284049f5\",\n \"score\": \"0.57916975\",\n \"text\": \"def initializeMNISTData():\\n # Initialize the MNIST dataset\\n print()\\n print(\\\"Initializing MNIST dataset...\\\")\\n mnistData = MNISTData()\\n return mnistData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6212476ccc871b906045ccfc2b6a5b\",\n \"score\": \"0.5735987\",\n \"text\": \"def Load_MNIST ():\\n print(\\\"Collecting MNIST data .....\\\\n\\\")\\n (X_train,y_train),(X_test,y_test) = \\\\\\n keras.datasets.mnist.load_data()\\n X_test,y_test = X_test[:6000],y_test[:6000]\\n X_train,y_train = X_train[:10000],y_train[:10000]\\n return X_train,y_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0711743930f73b962820c5d4678e6c57\",\n \"score\": \"0.5718131\",\n \"text\": \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = cPickle.load(f)\\n f.close() \\n #sets up average stroke thickness=\\n '''for i in range(len(training_data[0])):\\n training_data[0][i] *= 255\\n image_list = []\\n tr_d = [np.reshape(x, (28, 28)) for x in training_data[0]]\\n for data in tr_d:\\n image = Image.fromarray(data)\\n image = image.convert('L')\\n image_list.append(image)\\n #image_list[0].show()\\n for i in range(10):\\n print stroke_thickness(image_list[i])\\n image_matrix = standardize_image(image_list[i])/255\\n print image_matrix\\n average_thick = average_thickness(image_list)\\n print average_thick'''\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c9cf5c5eea987a7ac52cef23aec3ac\",\n \"score\": \"0.5712747\",\n \"text\": \"def __init__(self, n_clusters, max_iter = 100):\\n self.n_clusters = n_clusters\\n self.max_iter = max_iter\\n self.centers = []\\n self.labels_ = []\\n self.input_shape = None\\n self.__trained = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a95b309e815b296fd71cc99b058ae2a\",\n \"score\": \"0.57126945\",\n \"text\": \"def __init__(self):\\n self.train_data = pd.read_csv('mnist_train.csv').values.tolist()\\n self.test_data = pd.read_csv('mnist_test.csv').values.tolist()\\n self.train_label = []\\n self.test_label = []\\n self.predict_test = []\\n self.test_error = {1: 0, 9: 0, 19: 0, 29: 0, 39: 0, 49: 0, 59: 0, 69: 0, 79: 0, 89: 0, 99: 0}\\n self.train_error = {1: 0, 9: 0, 19: 0, 29: 0, 39: 0, 49: 0, 59: 0, 69: 0, 79: 0, 89: 0, 99: 0}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd3ec5f8d228d1e73f22d8175681d6b\",\n \"score\": \"0.56758404\",\n \"text\": \"def __init__(\\n self, label_map: Dict[int, str], max_num_images: Optional[int] = None\\n ) -> None:\\n self.label_map = label_map\\n self.max_num_images = max_num_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac85bcd81b01171117b2caf3caaf5a1\",\n \"score\": \"0.56731814\",\n \"text\": \"def __init__(self, sizes, cost=CrossEntropyCost):\\n self.num_layers = len(sizes)\\n self.sizes = sizes\\n self.default_weight_initializer()\\n self.cost = cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd16648913966a98c66234952a5d425\",\n \"score\": \"0.5667201\",\n \"text\": \"def __init__(self):\\n\\n self.train_batch_size = 32\\n self.features = 26\\n self.test_batch_size = 100\\n self.training_set = None\\n self.target_set = None\\n self.dataset = None\\n self.data_index = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a63d5b2568aabcd8a082dc4c89b7f8\",\n \"score\": \"0.5654114\",\n \"text\": \"def test_keras_mnist():\\n data = fetch('mnist')\\n check(data, (60000, 28*28), (10000, 28*28))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d324e061cda743d9f59dcd6a469a651\",\n \"score\": \"0.5637487\",\n \"text\": \"def __init__(self, batch_size, classes_per_set=10, samples_per_class=1, seed=2591, queries_per_class=1):\\n np.random.seed(seed)\\n self.x = np.load(\\\"/data/omniglot.npy\\\")\\n self.x = np.reshape(self.x, newshape=(1622, 20, 28, 28, 1))\\n self.x_train, self.x_test, self.x_val = self.x[:1200], self.x[1200:1411], self.x[1411:]\\n self.normalization()\\n self.batch_size = batch_size\\n self.n_classes = self.x.shape[0]\\n self.classes_per_set = classes_per_set\\n self.samples_per_class = samples_per_class\\n self.queries_per_class = queries_per_class\\n\\n print(\\\"train_shape\\\", self.x_train.shape, \\\"test_shape\\\", self.x_test.shape, \\\"val_shape\\\", self.x_val.shape)\\n self.indexes = {\\\"train\\\": 0, \\\"val\\\": 0, \\\"test\\\": 0}\\n self.datasets = {\\\"train\\\": self.x_train, \\\"val\\\": self.x_val, \\\"test\\\": self.x_test} #original data cached\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16fd5da8472bba679c90c5d0692789ce\",\n \"score\": \"0.5636208\",\n \"text\": \"def build_data(self):\\n\\n # load original mnist dataset and expand each number with embedded \\\"0\\\"s\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n embedding_img = x_train[1]\\n x_train = np.array([self.expand_img(embedding_img, img) for img, label in zip(x_train, y_train)])\\n x_test = np.array([self.expand_img(embedding_img, img) for img, label in zip(x_test, y_test)])\\n\\n if K.image_data_format() == 'channels_first':\\n x_train = x_train.reshape(x_train.shape[0], 1, self.img_rows, self.img_cols)\\n x_test = x_test.reshape(x_test.shape[0], 1, self.img_rows, self.img_cols)\\n else:\\n x_train = x_train.reshape(x_train.shape[0], self.img_rows, self.img_cols, 1)\\n x_test = x_test.reshape(x_test.shape[0], self.img_rows, self.img_cols, 1)\\n\\n # normalize and cast\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n # convert class vectors to binary class matrices\\n y_train = keras.utils.to_categorical(y_train, self.num_classes)\\n y_test = keras.utils.to_categorical(y_test, self.num_classes)\\n\\n return x_train, x_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefeb54eb29ae672b9b18cb88136fc36\",\n \"score\": \"0.55747116\",\n \"text\": \"def __init__(self):\\r\\n self.edges = defaultdict(list)\\r\\n self.weights = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11480007ead7c784d88ba7315e0a0972\",\n \"score\": \"0.5571569\",\n \"text\": \"def __init__(self):\\n self.edges = defaultdict(list)\\n self.weights = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a2743d9741d5ed04fb7ef6e12c8f01\",\n \"score\": \"0.556744\",\n \"text\": \"def __init__(self,sizes):\\r\\n\\r\\n self.numberLayers = len(sizes)\\r\\n \\r\\n #Initialization of weights and biases\\r\\n self.biases = [np.random.randn(y,1) for y in sizes[1:]]\\r\\n self.weights = [np.random.randn(y,x) for x,y in zip(sizes[:-1], sizes[1:])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b0c7232881ec07d3fa0c824b4e8e8e\",\n \"score\": \"0.55670637\",\n \"text\": \"def __init__(self, filepath, batch_size=135):\\n # initialize the cursors to keep track where we are in the Dataset\\n self.train_cursor = 0\\n self.test_cursor = 0\\n self.train_batch_size = batch_size\\n self.test_batch_size = batch_size // 10\\n\\n # initialize the idx arrays\\n a_train_ = np.tile(np.arange(self.NUM_TRAIN_ITEMS_PER_CLASS), self.NUM_CLASSES).reshape(self.NUM_CLASSES, self.NUM_TRAIN_ITEMS_PER_CLASS)\\n b_train_ = np.tile(np.arange(self.NUM_CLASSES) * self.NUM_ITEMS_PER_CLASS, self.NUM_TRAIN_ITEMS_PER_CLASS).reshape(self.NUM_TRAIN_ITEMS_PER_CLASS, self.NUM_CLASSES).T\\n self.train_idxs = (a_train_ + b_train_).reshape(-1)\\n\\n a_test_ = np.tile(np.arange(self.NUM_TEST_ITEMS_PER_CLASS), self.NUM_CLASSES).reshape(self.NUM_CLASSES, self.NUM_TEST_ITEMS_PER_CLASS)\\n b_test_ = np.tile(np.arange(self.NUM_CLASSES) * self.NUM_ITEMS_PER_CLASS, self.NUM_TEST_ITEMS_PER_CLASS).reshape(self.NUM_TEST_ITEMS_PER_CLASS, self.NUM_CLASSES).T\\n self.test_idxs = (a_test_ + b_test_ + self.NUM_TRAIN_ITEMS_PER_CLASS).reshape(-1)\\n\\n # load the .mat file containing the dataset\\n print('Loading the dataset...')\\n data = h5py.File(filepath)\\n self.dataset_images = data['imdb']['images']['data']\\n self.dataset_labels = data['imdb']['images']['labels']\\n print('Dataset loaded!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2062f729e21bb114be8c1404f045432e\",\n \"score\": \"0.55592626\",\n \"text\": \"def load_mnist_keras():\\n train, test = tf.keras.datasets.mnist.load_data()\\n train_data, train_labels = train\\n test_data, test_labels = test\\n\\n train_data = np.array(train_data, dtype=np.float32) / 255\\n test_data = np.array(test_data, dtype=np.float32) / 255\\n\\n train_data = train_data.reshape(train_data.shape[0], 28, 28, 1)\\n test_data = test_data.reshape(test_data.shape[0], 28, 28, 1)\\n\\n train_labels = np.array(train_labels, dtype=np.int32)\\n test_labels = np.array(test_labels, dtype=np.int32)\\n\\n train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)\\n test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)\\n\\n assert train_data.min() == 0.\\n assert train_data.max() == 1.\\n assert test_data.min() == 0.\\n assert test_data.max() == 1.\\n \\n return train_data, train_labels, test_data, test_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd315c56a952e15095d7914445d6520\",\n \"score\": \"0.555627\",\n \"text\": \"def __init__(self, sizes, input, bits=None):\\r\\n\\t\\t\\r\\n\\t\\tif bits is not None:\\r\\n\\t\\t\\tself.bits = bits\\r\\n\\t\\telse: \\r\\n\\t\\t\\tself.bits = 8\\r\\n\\t\\t\\r\\n\\t\\tself.num_layers = len(sizes)\\r\\n\\t\\tself.sizes = sizes\\r\\n\\t\\tself.cost = Cost\\r\\n\\t\\tself.a_L = Criterion.a_r(sizes[-1])\\r\\n\\t\\tself.weight_initializer()\\r\\n\\t\\t\\r\\n\\t\\tself.x0 = b10_to_b2(input[0],self.bits)\\r\\n\\t\\tself.y0 = b10_to_b2(input[1],self.bits)\\r\\n\\t\\t\\r\\n\\t\\tself.last_input = np.append(self.x0,self.y0)\\r\\n\\t\\tself.new_input = np.zeros(2*self.bits)\\r\\n\\t\\tself.kappa = 0 # The network standard will start out being random\\r\\n\\t\\tself.kappas = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcec21a38eedebcafc2f1aff04329793\",\n \"score\": \"0.5555787\",\n \"text\": \"def __init__(self, max_depth=1000, size_allowed=1, n_features=None, n_split=None):\\n\\n self.root = 1\\n self.max_depth = max_depth\\n self.size_allowed = size_allowed\\n self.n_features = n_features\\n self.n_split = n_split\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f3416c5d6ff5a28b9e781e248fa17b\",\n \"score\": \"0.5555058\",\n \"text\": \"def __init__(self, intrinsics_path, max_instances = 256, num_classes = 41):\\n self.no_instance = 999\\n self.max_instances = max_instances\\n self.num_classes = num_classes\\n self.critical_instance_count = False\\n self.instance_matrix = np.zeros((num_classes, max_instances, 3)) + self.no_instance\\n self.intrinisc_inv = np.linalg.inv(np.loadtxt(intrinsics_path))\\n self.class_inst_to_number_map = None\\n self.class_inst_cnt = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cab01e8c050457e700fa79ee4c394b0\",\n \"score\": \"0.5554475\",\n \"text\": \"def __init__(self, path):\\n self.path = path\\n self.train_set = {}\\n self.test_set = {}\\n self.classes = []\\n self.classes_counts = []\\n self.myarray = []\\n self.imageList = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a0720c22c8aebb454e7c6b1991f4c1\",\n \"score\": \"0.5553256\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n# self.save_hyperparameters()\\n\\n self.dense = torch.nn.Linear(HIDDEN_SIZE*2, NUM_GAMES)\\n self.ndcg = NDCGMetric(k=20)\\n self.map = MAPMetric()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b04558fd859bb3eb3f42b8673c458e4\",\n \"score\": \"0.5539841\",\n \"text\": \"def __init__(self):\\n\\n prototxt = 'face_detector/deploy.prototxt'\\n caffemodel = 'face_detector/res10_300x300_ssd_iter_140000.caffemodel'\\n self.inWidth = 300\\n self.inHeight = 300\\n self.net = dnn.readNetFromCaffe(prototxt, caffemodel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4e9c9835e8e0c32b91fd1ca3689f18f\",\n \"score\": \"0.55364\",\n \"text\": \"def load_mnist():\\r\\n f = open(\\\"C:\\\\Users\\\\Michael\\\\Desktop\\\\Research\\\\Data\\\\mnist\\\\mnist_noval.pkl\\\", 'rb')\\r\\n train_data, test_data = cPickle.load(f)\\r\\n f.close()\\r\\n \\r\\n train_images, train_labels = train_data\\r\\n test_images, test_labels = test_data\\r\\n \\r\\n return train_images, train_labels, test_images, test_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91700da5ef3464ebdb43c7a37bf1589\",\n \"score\": \"0.55361855\",\n \"text\": \"def __init__(self, learning_rate, batch_size):\\n # Parameters\\n self.learning_rate = learning_rate\\n self.mnist = mnist\\n self.batch_size = batch_size\\n self.num_epochs = 50\\n self.num_classes = 10\\n self.input_size = 784\\n self.input_weight, self.input_height = 28, 28\\n self.batch_per_epoch = int(self.mnist.train.num_examples/self.batch_size)\\n self.display_step = 1\\n\\n # Placeholders\\n self.X = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\\n self.Y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899a55462958d4f6dd514b87239b3537\",\n \"score\": \"0.5519446\",\n \"text\": \"def build_image_embeddings(self):\\n\\n #用densenet生成一个imageembedding\\n densenet_out = image_embedding.densenet_161(\\n self.images,\\n trainable=self.train_inception,\\n is_training=self.is_training())\\n self.densenet_variable = tf.get_collection(\\n tf.GraphKeys.GLOBAL_VARIABLES, scope=\\\"densenet161\\\")\\n\\n\\n\\n # Save the embedding size in the graph.\\n self.embedding_size=384\\n tf.constant(self.embedding_size, name=\\\"embedding_size\\\")\\n\\n self.features = densenet_out\\n self.L = 196\\n self.D = 384\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b139d273e169514f393d7167b5c88331\",\n \"score\": \"0.55185604\",\n \"text\": \"def load_data():\\n X, Y, testX, testY = mnist.load_data(one_hot=True)\\n X = X.reshape([-1, 28, 28, 1])\\n testX = testX.reshape([-1, 28, 28, 1])\\n\\n return X, Y, testX, testY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d856e4cb34281300fc055622a1f71ec4\",\n \"score\": \"0.5513635\",\n \"text\": \"def load_mnist():\\n train, test = tf.keras.datasets.mnist.load_data()\\n train_data, train_labels = train\\n test_data, test_labels = test\\n\\n train_data = np.array(train_data, dtype=np.float32) / 255\\n test_data = np.array(test_data, dtype=np.float32) / 255\\n\\n train_labels = np.array(train_labels, dtype=np.int32)\\n test_labels = np.array(test_labels, dtype=np.int32)\\n\\n assert train_data.min() == 0.\\n assert train_data.max() == 1.\\n assert test_data.min() == 0.\\n assert test_data.max() == 1.\\n assert train_labels.ndim == 1\\n assert test_labels.ndim == 1\\n\\n return train_data, train_labels, test_data, test_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd6d27bc522e0a741ca176164d845b3\",\n \"score\": \"0.55117446\",\n \"text\": \"def __init__(self, input_dimensions=2,number_of_nodes=4):\\r\\n if input_dimensions == 0 or number_of_nodes == 0:\\r\\n print()\\r\\n return\\r\\n else:\\r\\n self.input_dimension = input_dimensions + 1\\r\\n self.weights = np.ones((number_of_nodes,self.input_dimension))\\r\\n self.initialize_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c940f2709486d5dfa5499ca01a7ca55\",\n \"score\": \"0.5506797\",\n \"text\": \"def MNIST(one_hot=True):\\n return input_data.read_data_sets('MNIST_data/', one_hot=one_hot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd244f960a7df6d1aa3942755ae4afb\",\n \"score\": \"0.55056965\",\n \"text\": \"def __init__(self, batch_norm, n=2):\\n super(AlexNetSmall, self).__init__(batch_norm)\\n while n > 0:\\n name = self.features[-1]._get_name()\\n if name.__contains__(\\\"MaxPool\\\") or name.__contains__(\\\"Conv2d\\\"):\\n n -= 1\\n self.features.__delitem__(len(self.features) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d38d9329cdbcbdfeab2077e8e29346cf\",\n \"score\": \"0.55013484\",\n \"text\": \"def setup():\\r\\n # import MNIST dataset from scikit_learn\\r\\n mnist = sklearn.datasets.load_digits()\\r\\n X, y = mnist[\\\"data\\\"], mnist[\\\"target\\\"]\\r\\n \\r\\n # normalize the image data\\r\\n X = X / 255\\r\\n \\r\\n # shuffle the data-set randomly\\r\\n shuffle_index = np.random.permutation(y.shape[0])\\r\\n X_new = X[shuffle_index, :] \\r\\n y_new = y[shuffle_index]\\r\\n \\r\\n # train and test sets of length 1499 and 297 respectively \\r\\n # hard-coded this but can easily just split it up based on percentiles\\r\\n X_train = X_new[1:1500, :]\\r\\n y_train = y_new[1:1500]\\r\\n X_test = X_new[1500:, :]\\r\\n y_test = y_new[1500:]\\r\\n y_test_temp = y_test\\r\\n \\r\\n # one-hot encode the labels for the training and test sets\\r\\n y_test_new = np.zeros((y_test.size, y_test.max()+1))\\r\\n y_test_new[np.arange(y_test.size), y_test] = 1\\r\\n y_test = y_test_new\\r\\n y_train_new = np.zeros((y_train.size, y_train.max()+1))\\r\\n y_train_new[np.arange(y_train.size), y_train] = 1\\r\\n y_train = y_train_new\\r\\n \\r\\n # set up the parameters for the model\\r\\n params = {}\\r\\n params[\\\"i_size\\\"], params[\\\"h_size\\\"], params[\\\"o_size\\\"] = X.shape[0], 35, 10\\r\\n params[\\\"act\\\"], params[\\\"loss\\\"] = [\\\"tanh\\\", \\\"softmax\\\"], \\\"ce\\\"\\r\\n params[\\\"eps\\\"], params[\\\"num_epochs\\\"], params[\\\"lambda\\\"] = 0.001, 2000, 0.1\\r\\n \\r\\n # set up a ffnn with sigmoid activation, cross-entropy loss to recognize MNIST digits\\r\\n mnist_classifier = nn_classifier(X_train, y_train, params)\\r\\n\\r\\n # train the network\\r\\n mnist_classifier.train() \\r\\n\\r\\n # make predictions on the test-set\\r\\n # then, generate a confusion matrix using seaborn's heatmap()\\r\\n results = mnist_classifier.predict(X_test)\\r\\n \\r\\n cm = confusion_matrix(y_test_temp, results)\\r\\n ax = plt.axes()\\r\\n sb.heatmap(cm, ax = ax)\\r\\n ax.set_title('MNIST Classification: Three-Layer FFNN with Tanh + Softmax + Cross-enropy')\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9950151bdcb7a5c27844ce372e92b094\",\n \"score\": \"0.5497196\",\n \"text\": \"def __init__(self, input_size, output_size, activation):\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84cf8caf42887620a27e23964172b25b\",\n \"score\": \"0.54940647\",\n \"text\": \"def __init__(self, path='../data/train', size=128, channel=3, normalize=0):\\n self._size = size\\n self._channel = channel\\n self._imgs = []\\n self._ids = []\\n self._masks = []\\n self._n = 0\\n \\n print(\\\"Extracting training image info ...\\\")\\n start_time = time.time()\\n for img_id in os.listdir(path):\\n self._ids.append(img_id)\\n img_file_list = os.listdir('{0}/{1}/images'.format(path, img_id))\\n assert len(img_file_list) == 1, \\\"Multiple images found in one images id folder.\\\"\\n assert img_file_list[0] == img_id + '.png', \\\"Image id and image name do not match.\\\"\\n img = imread('{0}/{1}/images/{1}.png'.format(path, img_id, img_id))[:, :, :channel]\\n ## Do normalization\\n if normalize==1:\\n img = (img.astype(np.float32) - img.mean())/max(1., img.std())\\n elif normalize > 1:\\n img = img.astype(np.float32)/normalize\\n \\n mask = np.zeros(img.shape[:2])\\n for m in os.listdir('{0}/{1}/masks'.format(path, img_id)):\\n mask_ = imread('{0}/{1}/masks/{2}'.format(path, img_id, m))\\n assert mask_.shape == img.shape[:2], \\\"Image shape and mask shape do not match.\\\"\\n mask = np.maximum(mask, mask_)\\n mask = mask.astype(np.bool)\\n self._imgs.append(img)\\n self._masks.append(mask.astype(np.float32))\\n self._n = len(self._imgs)\\n \\n print(\\\"Time Usage: {0} sec\\\".format(str(time.time() - start_time)))\\n print len(self._ids), len(self._imgs), len(self._masks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d5185a0666e611dbe359493ceb869ab\",\n \"score\": \"0.5493848\",\n \"text\": \"def __init__(self, train_size, loss_function=nn.MSELoss()):\\n super(ELBO, self).__init__()\\n self.train_size = train_size\\n self.loss_function = loss_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23bbb778c79d3b2fd8cd15edfea805be\",\n \"score\": \"0.5484616\",\n \"text\": \"def load_binarized_mnist():\\n\\n (train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()\\n\\n train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\\n test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype('float32')\\n\\n # Normalizing the images to the range of [0., 1.]\\n train_images /= 255.\\n test_images /= 255.\\n\\n # Binarization\\n train_images[train_images >= .5] = 1.\\n train_images[train_images < .5] = 0.\\n test_images[test_images >= .5] = 1.\\n test_images[test_images < .5] = 0.\\n\\n return train_images, test_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b43ef1d0b3e4a7599e2fbdc877bc00c\",\n \"score\": \"0.5482675\",\n \"text\": \"def bmnist(root='../data/', batch_size=128, num_workers=4, download=True):\\n data_transforms = transforms.Compose([transforms.ToTensor(),\\n Binarize(threshold=0.5)])\\n\\n dataset = torchvision.datasets.MNIST(\\n root, train=True, transform=data_transforms, download=download)\\n test_set = torchvision.datasets.MNIST(\\n root, train=False, transform=data_transforms, download=download)\\n\\n train_dataset = data.dataset.Subset(dataset, np.arange(45000))\\n val_dataset = data.dataset.Subset(dataset, np.arange(45000, 50000))\\n\\n train_loader = data.DataLoader(\\n train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, \\n pin_memory=True)\\n val_loader = data.DataLoader(\\n val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers,\\n drop_last=False)\\n test_loader = data.DataLoader(\\n test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers,\\n drop_last=False)\\n\\n return train_loader, val_loader, test_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8562c697e7374a8443c23ac5ed4406c\",\n \"score\": \"0.54824346\",\n \"text\": \"def __init__(self, nx, layers):\\n if type(nx) is not int:\\n raise TypeError(\\\"nx must be an integer\\\")\\n if nx < 1:\\n raise ValueError(\\\"nx must be a positive integer\\\")\\n if type(layers) is not list:\\n raise TypeError(\\\"layers must be a list of positive integers\\\")\\n if len(layers) is 0:\\n raise TypeError(\\\"layers must be a list of positive integers\\\")\\n\\n self.nx = nx\\n self.layers = layers\\n self.__L = len(layers)\\n self.__cache = {}\\n self.__weights = {}\\n for l in range(self.__L):\\n if layers[l] <= 0 or type(layers[l]) is not int:\\n raise TypeError(\\\"layers must be a list of positive integers\\\")\\n if l is 0:\\n he_init = np.random.randn(layers[l], nx)*np.sqrt(2/nx)\\n self.__weights['W' + str(l+1)] = he_init\\n if l > 0:\\n he_init1 = np.random.randn(layers[l], layers[l-1])\\n he_init2 = np.sqrt(2/layers[l-1])\\n self.__weights['W' + str(l+1)] = he_init1 * he_init2\\n self.__weights['b' + str(l + 1)] = np.zeros((layers[l], 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265aad832adc385eb6683c520662de7e\",\n \"score\": \"0.5479937\",\n \"text\": \"def __init__(self, size):\\r\\n self._storage = []\\r\\n self._eps_dict = {}\\r\\n self._maxsize = size\\r\\n self._next_idx = 0\\r\\n self._hit_count = np.zeros(int(size))\\r\\n self._num_added = 0\\r\\n self._num_sampled = 0\\r\\n self._episodes_registry = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e110640c0a25cdbbbe2879a0cf18ce\",\n \"score\": \"0.5469335\",\n \"text\": \"def __init__(self, layers):\\n self.weights = {}\\n self.biases = {}\\n self.layers = layers # length L\\n self.L = len(layers)\\n for i in range(2, len(layers) + 1):\\n self.weights[i] = np.random.randn(layers[i - 1], layers[i - 2])\\n self.biases[i] = np.random.randn(layers[i - 1], 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96541f5ebe28bd1125d00623c0e2d35c\",\n \"score\": \"0.5462261\",\n \"text\": \"def loading():\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n assert x_train.shape == (60000, 28, 28)\\n assert x_test.shape == (10000, 28, 28)\\n assert y_train.shape == (60000,)\\n assert y_test.shape == (10000,) \\n\\n temp = []\\n for i in range(len(y_train)):\\n temp.append(to_categorical(y_train[i], num_classes=10))\\n y_train = np.array(temp)\\n temp = []\\n for i in range(len(y_test)): \\n temp.append(to_categorical(y_test[i], num_classes=10))\\n y_test = np.array(temp)\\n \\n return x_train, x_test, y_train, y_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d02cdb28352ca67d8f57a99e758887\",\n \"score\": \"0.54538333\",\n \"text\": \"def __init__(self, n_features: int = 5):\\n self.n_features = int(n_features)\\n self.n_samples = int(1e15)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6e0a34faab80de451ed1e33f655823\",\n \"score\": \"0.5447168\",\n \"text\": \"def load_MNIST_clusters(self, digits_used=[2, 8], image_set='training',\\n images_base_dir='/home/escalafl/Databases/MNIST'):\\n from . import mnist\\n\\n images, labels = mnist.read(digits_used, image_set, images_base_dir)\\n labels = labels.reshape((-1,))\\n\\n num_images = len(labels)\\n all_indices = numpy.arange(num_images)\\n\\n clusters = {}\\n for i, digit in enumerate(digits_used):\\n # print \\\"cluster \\\", i, \\\" for digit \\\", digit\\n cluster_indices = all_indices[(labels == digit)]\\n clusters[digit] = cluster_indices\\n numpy.random.shuffle(clusters[digit])\\n print(\\\"cluster %d for digit %d has %d images\\\" % (i, digit, len(cluster_indices)))\\n return clusters, images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0be626feccaf432dee79a6c4455cc3\",\n \"score\": \"0.5442395\",\n \"text\": \"def load_deep_lesion(self, dataset_dir, subset): #I don't think we need dataset directory.\\n # Add classes. We have only one class to add.\\n self.add_class(\\\"Lesion\\\", 1, \\\"Bone\\\") # \\\"Bone\\\"\\n self.add_class(\\\"Lesion\\\", 2, \\\"Abdomen_notLiver_notKidney\\\") # \\\"Abdomen_notLiver_notKidney\\\"\\n self.add_class(\\\"Lesion\\\", 3, \\\"Mediastinum\\\") # \\\"Mediastinum\\\"\\n self.add_class(\\\"Lesion\\\", 4, \\\"Liver\\\") # \\\"Liver\\\"\\n self.add_class(\\\"Lesion\\\", 5, \\\"Lung\\\") #\\\"Lung\\\"\\n self.add_class(\\\"Lesion\\\", 6, \\\"Kidney\\\") #\\\"Kidney\\\"\\n self.add_class(\\\"Lesion\\\", 7, \\\"Soft_tissue\\\") #Soft tissue: miscellaneous lesions in the body wall, muscle, skin, fat, limbs, head, and neck\\n self.add_class(\\\"Lesion\\\", 8, \\\"Pelvis\\\") #\\\"Pelvis\\\"\\n \\n \\n \\n \\n \\n ##################### UNKNOWN CASES, WE WILL LEAVE THESE OUT. \\n #self.add_class(\\\"-1\\\", 9, \\\"-1\\\") #Can i have a negative number here? This is straight from Deep Lesion format. \\n \\n#########################\\n # Train or validation dataset?\\n \\n #assert subset in [\\\"train\\\", \\\"val\\\"]\\n #dataset_dir = os.path.join(dataset_dir, subset)\\n\\n ###### ---> SAVED under \\\"Image\\\" in data.json, there is variable called \\\"train_val_test\\\" \\n #which has a int value (0-2 or 1-3) indicating it is training, test or validation. \\n # NEED TO FIGURE OUT HOW TO USE THIS TO ASSIGN TRAIN, VALIDATION, TEST. \\n##########################\\n\\n\\n # Load annotations\\n # VGG Image Annotator (up to version 1.6) saves each image in the form:\\n # { 'filename': '28503151_5b5b7ec140_b.jpg',\\n # 'regions': {\\n # '0': {\\n # 'region_attributes': {},\\n # 'shape_attributes': {\\n # 'all_points_x': [...],\\n # 'all_points_y': [...],\\n # 'name': 'polygon'}},\\n # ... more regions ...\\n # },\\n # 'size': 100202\\n # }\\n # We mostly care about the x and y coordinates of each region\\n # Note: In VIA 2.0, regions was changed from a dict to a list.\\n annotations = json.load(open(os.path.join(DEXTR_DIR, \\\"data.json\\\")))\\n #annotations_seg = list(annotations.values()) # don't need the dict keys\\n\\n annotations_seg = annotations['annotations']\\n \\n #annotations_seg = segmentation[2]\\n\\n # The VIA tool saves images in the JSON even if they don't have any\\n # annotations. Skip unannotated images.\\n #annotations_seg = [a for a in annotations_seg if a['segmentation']]\\n\\n\\n \\n b=0\\n for a in annotations_seg:\\n image_info = annotations['images'][b]\\n win = annotations_seg[b]['Windowing']\\n image_id = annotations['images'][b]['id']\\n image_cat = annotations['categories'][b]['category_id']\\n\\n \\n \\n \\n ############\\n ############\\n ########## Copy Food.py\\n polygons=[]\\n objects=[]\\n #for r in a['regions'].values():\\n for r in a['regions']:\\n polygons.append(r['shape_attributes'])\\n # print(\\\"polygons=\\\", polygons)\\n objects.append(r['region_attributes'])\\n \\n class_ids = [int(n['Lesion']) for n in objects]\\n \\n \\n \\n train_valid_test = annotations['images'][b]['Train_Val_Test']\\n #### Must use index 'b' before here, because after this point it\\n # will point to next image/index/\\n b=b+1\\n \\n \\n # Get the x, y coordinaets of points of the polygons that make up\\n # the outline of each object instance. These are stores in the\\n # shape_attributes (see json format above)\\n # The if condition is needed to support VIA versions 1.x and 2.x.\\n ######## polygons \\n # needs to be a list of dictionaries for each lesion\\n # so if there is one lesion.\\n # polygons = list of size 1\\n # dict of size 3. \\n # all_point_x is list of variable size(depends on number of points)\\n # same for y\\n # name is str 'polygon'\\n\\n \\n \\n # load_mask() needs the image size to convert polygons to masks.\\n # Unfortunately, VIA doesn't include it in JSON, so we must read\\n # the image. This is only managable since the dataset is tiny.\\n \\n ###### LETS STORE PATH TO IMAGE INTO JSON FILE. \\n \\n ## --- RERUN. added to images under annotations. current_file_path\\n #image_path = image_info['File_path']\\n #image_path = os.path.join(dataset_dir, a['filename'])\\n image_path = os.path.join(DEXTR_DIR,image_info['File_path'])\\n \\n \\n #***************************************************************\\n ########################################################### use this to import all png files in this directory and load them as blanks. NaN for segmentation. \\n #############################################################\\n #********************************************************************\\n \\n \\n # use files_bg to add in non-segmentated images to the dataset. \\n ##\\n #\\n #\\n # polygons = [] ? or polygons = NaN?\\n # Need to figure out what format will work so it will train on these background images and not throw an error. \\n #\\n #\\n ##\\n \\n \\n\\n \\n \\n \\n ################### image format should be: unit8 rgb \\n #image = skimage.io.imread(image_path)\\n ############################### WORKS! LOAD WITH DEFAULT WINDOWING.\\n #image = util.load_im_with_default_windowing(win,image_path)\\n \\n ############################### LOAD WITH default 16bit format?\\n image = cv2.imread(image_path, -1)\\n\\n \\n ###############\\n \\n height, width = image.shape[:2]\\n\\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\\n #### SEE IMAGE_INFO, INFOb = randint(0,len(annotations_seg)) BELOW: I think it gets this from here\\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\\n #### SEE IMAGE_INFO, INFO BELOW: I think it gets this from here\\n \\n print(subset)\\n print(int(image_cat))\\n print(train_valid_test)\\n #### PROBLEM: Most of the \\\"1\\\" training images are \\\"unknown\\\" and therefore worthless. There is 4831 in 3 and 4793 or so in 2. ZERO LABELS IMAGES IN 1. How lame is that?\\n if subset == 'train' and train_valid_test==3 and int(image_cat) >= 0: #Last checks that there are no unknowns.\\n print(\\\"Image Added for training\\\")\\n print(\\\"Image Added for training\\\")\\n print(\\\"Image Added for training\\\")\\n print(\\\"Image Added for training\\\")\\n print(\\\"Image Added for training\\\")\\n \\n\\n self.add_image(\\n ############ Replace balloon with CLASSES ABOVE, take from category. \\n \\\"Lesion\\\",\\n image_id=image_id, # id is set above. \\n path=image_path,\\n width=width, height=height,\\n polygons=polygons,\\n win=win,\\n class_ids=class_ids) \\n\\n elif subset == 'val' and train_valid_test==2 and int(image_cat) >= 0: #Last checks that there are no unknowns.\\n print(\\\"Image added for validation\\\")\\n\\n self.add_image(\\n ############ Replace balloon with CLASSES ABOVE, take from category. \\n \\\"Lesion\\\",\\n image_id=image_id, # id is set above. \\n path=image_path,\\n width=width, height=height,\\n polygons=polygons,\\n win=win,\\n class_ids=class_ids)\\n \\n else:\\n print(\\\"No image added...\\\")\\n print(\\\"Unknown, should say -1 below\\\")\\n print(int(image_cat)) # 3 is saved for validation since we are using both training and validation (1&2) for training.\\n print(\\\"train_valid_test\\\")\\n print(train_valid_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cdcb51f8e1cf48c6a9e59ec9f785cd\",\n \"score\": \"0.54401577\",\n \"text\": \"def __init__(self, size):\\n self.cache = None\\n self.size = size\\n self.layer_type = 'connected'\\n self.weights = None\\n self.biases = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85196aa1934f41cab68bcc24a80439f\",\n \"score\": \"0.5435952\",\n \"text\": \"def get_and_process_MNIST_data(self):\\n\\n #mndata = MNIST() \\n #self.train_images, self.train_labels = mndata.load_training() \\n self.train_images, self.train_labels = np.reshape(mndata.train_images(),(60000,784)), mndata.train_labels()\\n self.train_images, self.train_labels = self.train_images[:500], self.train_labels[:500] \\n print(np.shape(self.train_images)) \\n print(np.shape(self.train_labels)) \\n ## labeling the pixels back \\n self.train_images, self.train_labels = np.array([[1 if p > 0.5 else -1 for p in i] for i in self.train_images]), np.array(self.train_labels)\\n \\n ### i need to change the below code so it iterate through the matrix properly \\n #self.train_images, self.train_labels = np.array([[1 if p > 0.5 else -1 for p in i] for i in self.train_images), np.array(self.train_labels)\\n side_length = int(np.sqrt(self.train_images.shape[1]))\\n self.orig_train_images = copy.deepcopy(self.train_images.reshape((self.train_images.shape[0], side_length, side_length)))\\n self.noisy_train_images = np.zeros((self.train_images.shape[0], side_length, side_length))\\n for im in np.arange(self.train_images.shape[0]):\\n random_inds = random.sample(range(1, self.train_images.shape[1]), int(0.02 * self.train_images.shape[1]))\\n self.train_images[im, random_inds] = np.where(self.train_images[im, random_inds] == -1, 1, -1)\\n self.noisy_train_images[im, :, :] = self.train_images[im, :].reshape(side_length, side_length)\\n self.side_length = side_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8839341faf50b58a58b84f4dd3341ed9\",\n \"score\": \"0.54333675\",\n \"text\": \"def __init__(self, hidden_layer_sizes=(100,), max_iter=200, verbose=False):\\n super().__init__(\\n hidden_layer_sizes=hidden_layer_sizes,\\n max_iter=max_iter,\\n verbose=verbose,\\n )\\n self._num_classes = 0\\n self.epochs = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163ea3ae9e52c21a4b1cb0837a3836b3\",\n \"score\": \"0.5431743\",\n \"text\": \"def __init__(self, path, **kwargs):\\n super().__init__(path, **kwargs)\\n\\n self._largeImagePath = self._getLargeImagePath()\\n # Read the root dzi file and check that the expected image files exist\\n try:\\n with builtins.open(self._largeImagePath) as fptr:\\n if fptr.read(1024).strip()[:5] != '<?xml':\\n msg = 'File cannot be opened via deepzoom reader.'\\n raise TileSourceError(msg)\\n fptr.seek(0)\\n xml = ElementTree.parse(self._largeImagePath).getroot()\\n self._info = etreeToDict(xml)['Image']\\n except (ElementTree.ParseError, KeyError, UnicodeDecodeError):\\n msg = 'File cannot be opened via deepzoom reader.'\\n raise TileSourceError(msg)\\n except FileNotFoundError:\\n if not os.path.isfile(self._largeImagePath):\\n raise TileSourceFileNotFoundError(self._largeImagePath) from None\\n raise\\n # We should now have a dictionary like\\n # {'Format': 'png', # or 'jpeg'\\n # 'Overlap': '1',\\n # 'Size': {'Height': '41784', 'Width': '44998'},\\n # 'TileSize': '254'}\\n # and a file structure like\\n # <rootname>_files/<level>/<x>_<y>.<format>\\n # images will be TileSize+Overlap square; final images will be\\n # truncated. Base level is either 0 or probably 8 (level 0 is a 1x1\\n # pixel tile)\\n self.sizeX = int(self._info['Size']['Width'])\\n self.sizeY = int(self._info['Size']['Height'])\\n self.tileWidth = self.tileHeight = int(self._info['TileSize'])\\n maxXY = max(self.sizeX, self.sizeY)\\n self.levels = int(math.ceil(\\n math.log(maxXY / self.tileWidth) / math.log(2))) + 1\\n tiledirName = os.path.splitext(os.path.basename(self._largeImagePath))[0] + '_files'\\n rootdir = os.path.dirname(self._largeImagePath)\\n self._tiledir = os.path.join(rootdir, tiledirName)\\n if not os.path.isdir(self._tiledir):\\n rootdir = os.path.dirname(rootdir)\\n self._tiledir = os.path.join(rootdir, tiledirName)\\n zeroname = '0_0.%s' % self._info['Format']\\n self._nested = os.path.isdir(os.path.join(self._tiledir, '0', zeroname))\\n zeroimg = PIL.Image.open(\\n os.path.join(self._tiledir, '0', zeroname) if not self._nested else\\n os.path.join(self._tiledir, '0', zeroname, zeroname))\\n if zeroimg.size == (1, 1):\\n self._baselevel = int(\\n math.ceil(math.log(maxXY) / math.log(2)) -\\n math.ceil(math.log(maxXY / self.tileWidth) / math.log(2)))\\n else:\\n self._baselevel = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ce2ce3a4a5e6425ace05cec2bd6675\",\n \"score\": \"0.5430628\",\n \"text\": \"def __init__(self, size):\\n self.size = size\\n self.bias = npy.random.uniform(0, 1)\\n self.weights = []\\n for _ in range(0, self.size):\\n self.weights.append(npy.random.uniform(0, 1))\\n self.inputs = [] # storing inputs for later\\n self.output = 0\\n self.delta = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beec6e6905611c730ece2313d52ff785\",\n \"score\": \"0.5412695\",\n \"text\": \"def __init__(self, nx, layers):\\n if type(nx) != int:\\n raise TypeError(\\\"nx must be an integer\\\")\\n if nx < 1:\\n raise ValueError(\\\"nx must be a positive integer\\\")\\n\\n if type(layers) != list or len(layers) == 0:\\n raise TypeError(\\\"layers must be a list of positive integers\\\")\\n\\n self.L = len(layers)\\n self.cache = {}\\n self.weights = {}\\n for i in range(0, self.L):\\n if layers[i] < 0:\\n raise TypeError(\\\"layers must be a list of positive integers\\\")\\n if i == 0:\\n self.weights[\\\"W\\\" + str(i + 1)] = np.random.randn(\\n layers[i], nx)*np.sqrt(2/(nx))\\n self.weights[\\\"b\\\" + str(i + 1)] = np.zeros((layers[i], 1))\\n else:\\n self.weights[\\\"W\\\" + str(i + 1)] = np.random.randn(\\n layers[i], layers[i-1]) * np.sqrt(2/(layers[i-1]))\\n self.weights[\\\"b\\\" + str(i + 1)] = np.zeros((layers[i], 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60b49cc3a81e8f67ea5d0c4724b419a\",\n \"score\": \"0.5407968\",\n \"text\": \"def __init__(self, pool_size):\\n self.pool_size = pool_size\\n if self.pool_size > 0: # create an empty pool\\n self.num_imgs = 0\\n self.images = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1bdffdc896f875c5020d12597efd265\",\n \"score\": \"0.54054046\",\n \"text\": \"def make_DBN():\\n dataset = datasets.fetch_mldata(\\\"MNIST Original\\\") # mnist original, uci 20070111 letter, datasets uci letter\\n\\n # Split our easy dataset so we can use it to train the DBN\\n (trainX, testX, trainY, testY) = train_test_split(dataset.data/float(dataset.data.max()), # DBM only understands 0.0-1.0\\n dataset.target.astype(\\\"int0\\\"),\\n test_size=0.33)\\n\\n # Make Deep Belief Network\\n dbn = DBN(\\n # [nodes in input layer (pixel size of vectorized dataset),\\n # nodes in hidden layer(s),\\n # nodes in output layer (size of output dataset)]\\n [trainX.shape[1], 1000, 300, 10], # out = 10,\\n learn_rates=0.3,\\n learn_rate_decays=0.9,\\n epochs=10,\\n verbose=1\\n )\\n # Teach DBM to recognize digits\\n dbn.fit(trainX, trainY)\\n\\n # Predict using our test set\\n preds = dbn.predict(testX)\\n# print classification_report(testY, preds)\\n\\n\\n # Examine random selection from our test set\\n for i in np.random.choice(np.arange(0, len(testY)), size=(100,)):\\n # Classify actual digit\\n pred = dbn.predict(np.atleast_2d(testX[i]))\\n\\n # Reshape to 28x28\\n image = (testX[i] * 255).reshape((28,28)).astype('uint8')\\n\\n # Show image and prediction\\n seen = pred[0]\\n actual = testY[i]\\n if seen != actual:\\n print \\\"Actual digit is {0}, saw {1}\\\".format(actual, seen)\\n # plt.imshow(image)\\n # plt.show()\\n\\n with open('dbn.pickle', 'wb') as f:\\n pickle.dump(dbn, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1e9ed9271e677b28f7cded66ee1d38\",\n \"score\": \"0.54044276\",\n \"text\": \"def load_data(path, ):\\n\\n # Read all EMNIST test and train data\\n mndata = MNIST(path)\\n\\n X_train, y_train = mndata.load(path + '/emnist-byclass-train-images-idx3-ubyte',\\n path + '/emnist-byclass-train-labels-idx1-ubyte')\\n X_test, y_test = mndata.load(path + '/emnist-byclass-test-images-idx3-ubyte',\\n path + '/emnist-byclass-test-labels-idx1-ubyte')\\n\\n # Read mapping of the labels and convert ASCII values to chars\\n mapping = []\\n\\n with open(path + '/emnist-byclass-mapping.txt') as f:\\n for line in f:\\n mapping.append(chr(int(line.split()[1])))\\n\\n X_train = np.array(X_train)\\n y_train = np.array(y_train)\\n X_test = np.array(X_test)\\n y_test = np.array(y_test)\\n\\n X_train = normalize(X_train)\\n X_test = normalize(X_test)\\n\\n X_train = reshape_for_cnn(X_train)\\n X_test = reshape_for_cnn(X_test)\\n\\n y_train = preprocess_labels(y_train, len(mapping))\\n y_test = preprocess_labels(y_test, len(mapping))\\n\\n return X_train, y_train, X_test, y_test, mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470bd542ba3e9473555070b6c85c7079\",\n \"score\": \"0.5402779\",\n \"text\": \"def __init__(self, numelem=100, cycles=1, maxweight=100, filename=\\\"data\\\", printonce=False, verbose=False):\\n\\n self.printonce = printonce\\n self.verbose = verbose\\n self.executiontime = 0\\n self.__numelem = numelem\\n self.weights = numpy.random.random_integers(1, maxweight, size=self.__numelem).astype(numpy.uint32)\\n self.values = numpy.random.random_integers(1, maxweight*2, size=self.__numelem).astype(numpy.uint32)\\n self.sumofweights = numpy.uint32(self.weights.sum())\\n self.capacity = numpy.uint32(self.sumofweights/2)\\n self.f0 = numpy.zeros(self.capacity+1).astype(numpy.uint32)\\n self.f1 = numpy.zeros_like(self.f0)\\n self.m_d = numpy.zeros_like(self.f0)\\n self.cycles = cycles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa300c5743e388c73a00b979898ff5b4\",\n \"score\": \"0.53982687\",\n \"text\": \"def __init__(self, embed_size, model_path=None):\\n super(EncoderCNN, self).__init__()\\n resnet = models.resnet152(pretrained=True)\\n modules = list(resnet.children())[:-1] # delete the last fc layer.\\n self.resnet = nn.Sequential(*modules)\\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\\n self.init_weights(model_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8950db980c8e5032def8fa6982637c\",\n \"score\": \"0.5389867\",\n \"text\": \"def __init__(self, layers, optimizer, minibatch_size=100, learning_rate=0.001):\\n self._layers = layers\\n self._optimizer = optimizer\\n self._learning_rate = learning_rate\\n self._minibatch_size = minibatch_size # size of minibatch samples\\n # add widen and deepen functionality\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3e8c261a3147ecbf4c8933353e312e\",\n \"score\": \"0.53868204\",\n \"text\": \"def mnist_noniid_class(dataset, num_users, class_per_user):\\n # 60,000 training imgs --> 300 imgs/shard X 200 shards\\n # num_shards, num_imgs = 200, 300\\n # idx_shard = [i for i in range(num_shards)]\\n dict_users = {i: np.array([]) for i in range(num_users)}\\n dict_class = {}\\n\\n idxs = np.arange(200*300)\\n labels = dataset.train_labels.numpy()\\n labels_his=Counter(labels)\\n\\n # sort labels\\n idxs_labels = np.vstack((idxs, labels))\\n idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()]\\n\\n total_class=len(np.unique(labels))\\n print('total_class',total_class)\\n per_class_avg_users=int(num_users/total_class*class_per_user)\\n print('per_class_avg_users',per_class_avg_users)\\n remain_users=num_users*class_per_user-per_class_avg_users*total_class\\n print('remain_users',remain_users)\\n last_class_users=per_class_avg_users+remain_users\\n print('last_class_users',last_class_users)\\n\\n idx_accumulate=0\\n shard=0\\n idx_shard={}\\n for i in range(total_class):\\n if i==total_class-1:\\n per_class_avg_users=last_class_users\\n idxs_per_class = idxs_labels[0, idx_accumulate:idx_accumulate+labels_his[i]]\\n # print('idxs_per_class',idxs_per_class)\\n num_imgs_per_user=int(len(idxs_per_class)/per_class_avg_users) \\n dict_class[i]=shard+np.arange(per_class_avg_users)\\n #idx of per shard \\n for k in range(per_class_avg_users):\\n idx_shard[shard+k]= idxs_per_class[k*num_imgs_per_user:(k+1)*num_imgs_per_user]\\n\\n shard+=per_class_avg_users\\n print('shard',shard)\\n idx_accumulate+=labels_his[i] \\n num_shard= [k for k in range(shard)]\\n\\n print(len(idx_shard)) \\n print('dict_class',dict_class)\\n # divide and assign class_per_user shards/client\\n # ensure each user has class_per_user\\n for i in range(num_users):\\n # print('user',i)\\n flag=1\\n while flag:\\n if len(num_shard)>class_per_user:\\n rand_set = np.random.choice(num_shard, class_per_user, replace=False)\\n # print('rand_set',rand_set)\\n dict_keys=[]\\n for j in rand_set:\\n dict_keys.append([key for idx, key in enumerate(dict_class) if j in dict_class[key]])\\n if len(np.unique(dict_keys))==class_per_user:\\n flag=0\\n for rand in rand_set:\\n dict_users[i] = np.concatenate((dict_users[i], idx_shard[rand]), axis=0).astype(int)\\n num_shard = list(set(num_shard) - set(rand_set))\\n else:\\n rand_set = num_shard\\n flag=0\\n for rand in rand_set:\\n dict_users[i] = np.concatenate((dict_users[i], idx_shard[rand]), axis=0).astype(int)\\n num_shard = list(set(num_shard) - set(rand_set))\\n print('num_shard',num_shard)\\n return dict_users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0483f18a170f8f89b313e6216a457e1\",\n \"score\": \"0.53723264\",\n \"text\": \"def load_mnist(images_path, labels_path):\\n with open(labels_path, 'rb') as lbpath:\\n struct.unpack('>II', lbpath.read(8))\\n labels = np.fromfile(lbpath, dtype=np.uint8)\\n with open(images_path, 'rb') as imgpath:\\n struct.unpack('>IIII', imgpath.read(16))\\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 28, 28, 1)\\n return images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369613a70b6bfe8f8d879b35c70f1343\",\n \"score\": \"0.53714097\",\n \"text\": \"def __init__(self, sizes):\\n self.num_layers = len(sizes)\\n self.sizes = sizes\\n # play with biases to start from i/p layer, i.e., ... fory in sizes[:-1]\\n self.biases = [np.random.randn(y, 1) for y in sizes[1:]] \\n self.weights = [np.random.randn(y, x)\\n for x, y in zip(sizes[:-1], sizes[1:])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb8e4f5779759228f9c5a07b00fa6920\",\n \"score\": \"0.53709626\",\n \"text\": \"def load_mnist_data():\\n\\n file_name = 'dataset/mnist.pkl.gz'\\n\\n # pickle.load return ((train_x, train_y), (valid_x, valid_y), (test_x, test_y))\\n with gzip.open(file_name, 'rb') as f:\\n train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\\n\\n train_x, train_y = train_set\\n valid_x, valid_y = valid_set\\n\\n # concatenation valid set to train set\\n train_x = np.vstack((train_x, valid_x))\\n train_y = np.append(train_y, valid_y)\\n\\n test_x, test_y = test_set\\n\\n return (train_x, train_y, test_x, test_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298a3ab7f183b172c7d1583b52e3318f\",\n \"score\": \"0.53653\",\n \"text\": \"def __init__(self, embed_size):\\n super(EncoderCNN, self).__init__()\\n resnet = models.resnet152(pretrained=True)\\n modules = list(resnet.children())[:-1] # delete the last fc layer.\\n self.resnet = nn.Sequential(*modules)\\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\\n self.init_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f873d1046774486e26d2bd3671deee3\",\n \"score\": \"0.5363259\",\n \"text\": \"def __init__(self, hidden_layer_sizes=(100,), max_iter=200, verbose=False):\\n super().__init__(\\n hidden_layer_sizes=hidden_layer_sizes,\\n max_iter=max_iter,\\n verbose=verbose,\\n )\\n self.epochs = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51980cef0426fc5da83574956d4cd948\",\n \"score\": \"0.5359663\",\n \"text\": \"def load_MiniImageNet():\\n\\n base_path = '/home/USER/Documents'\\n if (not (os.path.exists(base_path))):\\n base_path = '/home/ubuntu/Projects'\\n if (not (os.path.exists(base_path))):\\n base_path = '/home/USER/Projects'\\n if (not (os.path.exists(base_path))):\\n base_path = '/home/USER/Projects'\\n \\n data_path = base_path + '/MAML/input_data/miniImageNet/'\\n train_tasks_file = open(data_path + 'miniImageNet_train_tasks.txt', 'rb')\\n train_tasks = pickle.load(train_tasks_file)\\n\\n val_tasks_file = open(data_path + 'miniImageNet_val_tasks.txt', 'rb')\\n val_tasks = pickle.load(val_tasks_file)\\n\\n test_tasks_file = open(data_path + 'miniImageNet_test_tasks.txt', 'rb')\\n test_tasks = pickle.load(test_tasks_file)\\n\\n mtl_train_tasks = {}\\n mtl_train_tasks['X'] = [np.concatenate((train_task['X_inner'], train_task['X_outer']), axis=0) for train_task in train_tasks]\\n mtl_train_tasks['Y'] = [np.concatenate((train_task['Y_inner'], train_task['Y_outer']), axis=0) for train_task in train_tasks]\\n\\n return mtl_train_tasks, val_tasks, test_tasks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7555885361549d9dd0b4334c765e7e1e\",\n \"score\": \"0.5358751\",\n \"text\": \"def __init__(self,test_size):\\n self.letters = {0:'A',1:'B',2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M',13:'N',14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X', 24:'Y',25:'Z'}\\n self.data_path = \\\"A_Z Handwritten Data.csv\\\"\\n self.test_size = test_size\\n self.X_train = None\\n self.X_test = None\\n self.y_train = None\\n self.y_test = None\\n self.model = None\\n self.y_train_cat = None\\n self.y_test_cat = None\\n self.history = None\\n self.cm = None\\n self.y_pred = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"975313f00f937f602974a2a3b83ba336\",\n \"score\": \"0.5351087\",\n \"text\": \"def parse_image():\\r\\n LABELS = ['t10k-images.idx3-ubyte',\\r\\n 't10k-labels.idx1-ubyte',\\r\\n 'train-images.idx3-ubyte',\\r\\n 'train-labels.idx1-ubyte']\\r\\n\\r\\n TRAIN_IMAGE = LABELS[2]\\r\\n TRAIN_LABELS = LABELS[3]\\r\\n TEST_IMAGE = LABELS[0]\\r\\n TEST_LABELS = LABELS[1]\\r\\n\\r\\n f_image = open('mnist/' + TRAIN_IMAGE, 'rb')\\r\\n f_label = open('mnist/' + TRAIN_LABELS, 'rb')\\r\\n\\r\\n i_magic, n_images, n_row, n_col = struct.unpack(\\\">4I\\\", f_image.read(16))\\r\\n l_magic, n_labels = struct.unpack(\\\">2I\\\", f_label.read(8))\\r\\n\\r\\n assert n_labels == n_images\\r\\n\\r\\n training = [tuple([np.array([[i/255.0]\\r\\n for _ in range(n_row)\\r\\n for i in struct.unpack(\\\">%dB\\\" % (n_col), f_image.read(n_col))]),\\r\\n vectorized_result(struct.unpack(\\\">B\\\", f_label.read(1))[0])])\\r\\n for _ in range(n_images-10000)]\\r\\n\\r\\n validation = [tuple([np.array([[i/255.0]\\r\\n for _ in range(n_row)\\r\\n for i in struct.unpack(\\\">%dB\\\" % (n_col), f_image.read(n_col))]),\\r\\n vectorized_result(struct.unpack(\\\">B\\\", f_label.read(1))[0])])\\r\\n for _ in range(n_images-10000, n_images)]\\r\\n\\r\\n f_image.close()\\r\\n f_label.close()\\r\\n\\r\\n f_image = open('mnist/' + TEST_IMAGE, 'rb')\\r\\n f_label = open('mnist/' + TEST_LABELS, 'rb')\\r\\n\\r\\n i_magic, n_images, n_row, n_col = struct.unpack(\\\">4I\\\", f_image.read(16))\\r\\n l_magic, n_labels = struct.unpack(\\\">2I\\\", f_label.read(8))\\r\\n\\r\\n testing = [tuple([np.array([i/255.0\\r\\n for _ in range(n_row)\\r\\n for i in struct.unpack(\\\">%dB\\\" % (n_col), f_image.read(n_col))]),\\r\\n vectorized_result(struct.unpack(\\\">B\\\", f_label.read(1))[0])])\\r\\n for _ in range(n_images)]\\r\\n\\r\\n with open('mnist_data', 'wb') as f:\\r\\n pickle.dump((training, validation, testing), f)\\r\\n f.close()\\r\\n\\r\\n return training, validation, testing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9457a09a51dfb9ffd8a3d4b88704925\",\n \"score\": \"0.5348022\",\n \"text\": \"def load_mnist_realval(path=data_path, one_hot=True, dequantify=False):\\n if not os.path.isfile(path):\\n data_dir = os.path.dirname(path)\\n if not os.path.exists(os.path.dirname(path)):\\n os.makedirs(data_dir)\\n # download_dataset('http://www.iro.umontreal.ca/~lisa/deep/data/mnist'\\n # '/mnist.pkl.gz', path)\\n download_dataset('https://oneflow-public.oss-cn-beijing.aliyuncs.com/datasets/zhusuan_oneflow/mnist.pkl.gz',\\n path)\\n \\n\\n f = gzip.open(path, 'rb')\\n if six.PY2:\\n train_set, valid_set, test_set = pickle.load(f)\\n else:\\n train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\\n f.close()\\n x_train, t_train = train_set[0], train_set[1]\\n x_valid, t_valid = valid_set[0], valid_set[1]\\n x_test, t_test = test_set[0], test_set[1]\\n # x_train, t_train = train_set[0][:64*500], train_set[1][:64*500]\\n # x_valid, t_valid = valid_set[0][:64*50], valid_set[1][:64*50]\\n # x_test, t_test = test_set[0][:64*10], test_set[1][:64*10]\\n if dequantify:\\n x_train += np.random.uniform(0, 1. / 256,\\n size=x_train.shape).astype('float32')\\n x_valid += np.random.uniform(0, 1. / 256,\\n size=x_valid.shape).astype('float32')\\n x_test += np.random.uniform(0, 1. / 256,\\n size=x_test.shape).astype('float32')\\n n_y = t_train.max() + 1\\n t_transform = (lambda x: to_one_hot(x, n_y)) if one_hot else (lambda x: x)\\n return x_train, t_transform(t_train), x_valid, t_transform(t_valid), \\\\\\n x_test, t_transform(t_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a214882a9e05c68f2d56950e031e18b\",\n \"score\": \"0.5346792\",\n \"text\": \"def mnist_noniid(args, dataset, num_users, n_list, k_list):\\n\\n # 60,000 training imgs --> 200 imgs/shard X 300 shards\\n num_shards, num_imgs = 10, 6000\\n idx_shard = [i for i in range(num_shards)]\\n dict_users = {}\\n idxs = np.arange(num_shards*num_imgs)\\n labels = dataset.train_labels.numpy()\\n # sort labels\\n idxs_labels = np.vstack((idxs, labels))\\n idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()]\\n idxs = idxs_labels[0, :]\\n label_begin = {}\\n cnt=0\\n for i in idxs_labels[1,:]:\\n if i not in label_begin:\\n label_begin[i] = cnt\\n cnt+=1\\n\\n classes_list = []\\n for i in range(num_users):\\n n = n_list[i]\\n k = k_list[i]\\n k_len = args.train_shots_max\\n classes = random.sample(range(0,args.num_classes), n)\\n classes = np.sort(classes)\\n print(\\\"user {:d}: {:d}-way {:d}-shot\\\".format(i + 1, n, k))\\n print(\\\"classes:\\\", classes)\\n user_data = np.array([])\\n for each_class in classes:\\n # begin = i*10 + label_begin[each_class.item()]\\n begin = i * k_len + label_begin[each_class.item()]\\n user_data = np.concatenate((user_data, idxs[begin : begin+k]),axis=0)\\n dict_users[i] = user_data\\n classes_list.append(classes)\\n\\n return dict_users, classes_list\\n #\\n #\\n #\\n #\\n #\\n # # divide and assign 2 shards/client\\n # for i in range(num_users):\\n # rand_set = set(np.random.choice(idx_shard, n_list[i], replace=False))\\n # idx_shard = list(set(idx_shard) - rand_set)\\n # for rand in rand_set:\\n # dict_users[i] = np.concatenate(\\n # (dict_users[i], idxs[rand*num_imgs:(rand+1)*num_imgs]), axis=0)\\n # return dict_users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77550f7146ed3484d918c9895ced1ff5\",\n \"score\": \"0.5345506\",\n \"text\": \"def __init__(self, input_dict):\\n self.instance = tik.Tik(tik.Dprofile())\\n self.dtype = input_dict.get(\\\"x\\\").get(\\\"dtype\\\").lower()\\n self.dsize = common_util.get_data_size(self.dtype)\\n total_size = tbe_platform.cce_conf.get_soc_spec(tbe_platform.cce_conf.UB_SIZE)\\n ub_size = (total_size - RESERVE_SIZE) // (2 * self.dsize)\\n burnest_len = constant.BLOCK_SIZE // self.dsize\\n ub_size = ((ub_size + burnest_len - 1) // burnest_len) * burnest_len\\n self.one_max_size = ub_size\\n x_len = get_shape_total_number(input_dict.get(\\\"x\\\").get(\\\"shape\\\"))\\n x_len = ((x_len + burnest_len - 1) // burnest_len) * burnest_len\\n hw = input_dict.get(\\\"y\\\").get(\\\"shape\\\")[2] * \\\\\\n input_dict.get(\\\"y\\\").get(\\\"shape\\\")[3]\\n mod = hw % burnest_len\\n if mod != 0:\\n x_len = x_len + burnest_len\\n self.x_gm = self.instance.Tensor(self.dtype, (x_len,), name=\\\"x_gm\\\",\\n scope=tik.scope_gm)\\n self.y_gm = self.instance.Tensor(self.dtype, (x_len,), name=\\\"y_gm\\\",\\n scope=tik.scope_gm)\\n self.input_dict = input_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d71e32a8e187a45ca323630c79aaf6c\",\n \"score\": \"0.5339088\",\n \"text\": \"def get_train(self, even=None, min_blobs=1, max_blobs=1): # MT\\n self.images, self.labels, self.areas = create_data_natural.generate_data(even, min_blobs, max_blobs, scalar_output=True)\\n self.length = len(self.images)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e9dd8f1c7bf87977d7b32c5d7eb3ae\",\n \"score\": \"0.5338825\",\n \"text\": \"def __init__(self, inputSize, layers=[], costFunction=None):\\n self.inputSize = inputSize\\n self.layers = layers\\n self.costFunction = costFunction\\n self._weights = []\\n self.d_weights = []\\n self.layerCache=[]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7257ad3fdda6bd45ad2a3a9b6d81cf7\",\n \"score\": \"0.533793\",\n \"text\": \"def __init__(self, data, train_labels, train_indices, train_size):\\n self.data = data\\n self.train_labels = train_labels\\n self.train_indices = train_indices\\n self.train_size = train_size\\n if len(self.classes) > train_size:\\n raise ValueError(\\\"The train size can not be smaller than the number of classes.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"347c4319a0c62185d73a5416e8e5a6ff\",\n \"score\": \"0.5336137\",\n \"text\": \"def __init__(self, ext_mem: Dict, mem_size: int, adaptive_size=True,\\n total_num_classes=-1):\\n self.ext_mem = ext_mem\\n self.mem_size = mem_size\\n self.adaptive_size = adaptive_size\\n self.total_num_classes = total_num_classes\\n self.seen_classes = set()\\n\\n if not self.adaptive_size:\\n assert self.total_num_classes > 0, \\\\\\n \\\"\\\"\\\"When fixed exp mem size, total_num_classes should be > 0.\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e6b5257df783629c930a5bc66ea7a1\",\n \"score\": \"0.53353137\",\n \"text\": \"def load_train(self):\\n images, labels = self.load(os.path.join('mnist', 'train', 'images'),\\n os.path.join('mnist', 'train', 'labels'))\\n self.train_data = list(zip(images, labels))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9dc526202ac9c72af509fb05945b74e\",\n \"score\": \"0.53287077\",\n \"text\": \"def __init__(self):\\n #self.lambda_mixture = 0.86752\\n self.lambda_mixture = 0.86\\n self.label_dict = {}\\n self.num_class = 14\\n self.prior = []\\n self.vocab = 0\\n self.class_vocab = []\\n self.vocab_bigram = 0\\n self.class_vocab_bigram = []\\n self.label_dict_bigram = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e86efff4639867ee68d811d92584df\",\n \"score\": \"0.5319188\",\n \"text\": \"def __init__(self, images, labels, type_=\\\"mnist\\\"):\\n images = self._normalize(images, type_)\\n labels = (labels).astype(np.int64)\\n self.images = torch.from_numpy(images)\\n self.labels = torch.from_numpy(labels)\\n self.num_samples = images.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fbf6ca7b20aa2e66eb256130336d76\",\n \"score\": \"0.5316506\",\n \"text\": \"def mnist(batch_size=64, size=28, path_to_data='../../mnist_data'):\\n all_transforms = transforms.Compose([\\n transforms.Resize(size),\\n transforms.ToTensor()\\n ])\\n\\n train_data = datasets.MNIST(path_to_data, train=True, download=True,\\n transform=all_transforms)\\n test_data = datasets.MNIST(path_to_data, train=False,\\n transform=all_transforms)\\n\\n train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\\n test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\\n\\n return train_loader, test_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3940b6b86239bd177b2ca011aa0ff5b5\",\n \"score\": \"0.5307942\",\n \"text\": \"def __init__(self):\\n self.vert_dict = {}\\n self.num_verticies = 0\\n self.num_edges = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85a7296f3c5e7fe74171f0b4fed755d0\",\n \"score\": \"0.5305711\",\n \"text\": \"def __init__(self, h5_path, input_size=None):\\n self.input_size = input_size\\n self.h5_path = h5_path\\n self.x, self.y = self.load_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95775e637e5125854d21d4c60cb1cfc7\",\n \"score\": \"0.530351\",\n \"text\": \"def __init__(self, batch_size, shuffle=False):\\n train, _ = tf.keras.datasets.mnist.load_data()\\n X, y = train\\n X = X.astype(np.float32) / 255\\n X = X.reshape((X.shape[0], -1))\\n self.X, self.y = X, y\\n self.batch_size, self.shuffle = batch_size, shuffle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332ab19572c3da0b9e202cbb4ea76fe3\",\n \"score\": \"0.53026295\",\n \"text\": \"def __init__(self, hidden_width, depth, input_size, output_size):\\n self.width = hidden_width\\n self.depth = depth\\n self.network = []\\n\\n # activations are in columns\\n # minimum one hidden layer\\n # depth = 0 => perceptron\\n if depth == 0:\\n self.network.append((np.random.normal(0, 1, (input_size + 1, output_size))))\\n return\\n\\n # deep NN with normally distributed initial weights\\n self.network.append(np.random.normal(0, 1, (input_size + 1, hidden_width)))\\n for i in range(depth - 1):\\n self.network.append(np.random.normal(0, 1, (self.network[-1].shape[1] + 1, hidden_width)))\\n self.network.append(np.random.normal(0, 1, (self.network[-1].shape[1] + 1, output_size)))\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10f49cf4117d2060d9a12d7d58dc00b\",\n \"score\": \"0.5298141\",\n \"text\": \"def __init__( self\\n , hidden_size\\n , input_size):\\n super(MLPEncodingCell, self).__init__()\\n self._MLP = tf.keras.layers.Dense(hidden_size, activation='relu')\\n self.hidden_size = hidden_size\\n self.input_size = input_size\\n self.state_size = tf.TensorShape([self.hidden_size])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf5a700e50e78ef46639c1208ec4c3f\",\n \"score\": \"0.52919877\",\n \"text\": \"def load_mnist():\\r\\n\\r\\n print('Loading train data...')\\r\\n train_data = torch.utils.data.DataLoader(\\r\\n torchvision.datasets.MNIST('mnist/', \\r\\n train=True, \\r\\n download=True,\\r\\n transform=torchvision.transforms.Compose([\\r\\n torchvision.transforms.ToTensor()\\r\\n ])),\\r\\n shuffle=True,)\\r\\n\\r\\n train_input = []\\r\\n train_label = []\\r\\n \\r\\n cnt = 0\\r\\n for batch, label in tqdm(train_data):\\r\\n train_input.append(batch.squeeze().numpy().reshape(784,))\\r\\n train_label.append(label.numpy())\\r\\n cnt += 1\\r\\n if cnt == 1300: break\\r\\n\\r\\n print('Loading test data...')\\r\\n test_data = torch.utils.data.DataLoader(\\r\\n torchvision.datasets.MNIST('mnist/', \\r\\n train=False, \\r\\n download=True,\\r\\n transform=torchvision.transforms.Compose([\\r\\n torchvision.transforms.ToTensor()\\r\\n ])),\\r\\n shuffle=True,)\\r\\n\\r\\n test_input = []\\r\\n test_label = []\\r\\n \\r\\n for batch, label in tqdm(test_data):\\r\\n test_input.append(batch.squeeze().numpy().reshape(784,))\\r\\n test_label.append(label.numpy())\\r\\n\\r\\n return np.array(train_input), np.array(train_label), np.array(test_input), np.array(test_label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878d7d61a85338a7814220a6687af10d\",\n \"score\": \"0.52839684\",\n \"text\": \"def __init__(self, embed_size):\\n # pdb.set_trace()\\n super(EncoderCNN, self).__init__()\\n resnet = models.resnet152(pretrained=True)\\n modules = list(resnet.children())[:-1] # delete the last fc layer.\\n self.resnet = nn.Sequential(*modules)\\n self.linear = nn.Linear(resnet.fc.in_features, embed_size)\\n self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6dfd6112fe6e2dc61fb6222112a8dcc\",\n \"score\": \"0.52795196\",\n \"text\": \"def load_train(self):\\n images, labels = self.load(os.path.join('mnist', 'train', 'images'),\\n os.path.join('mnist', 'train', 'labels'))\\n self.train_data = zip(images, labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307f7eefcfe692b04bf8051839437a14\",\n \"score\": \"0.5279294\",\n \"text\": \"def __init__(self, input_size, output_size, hidden_sizes=(64, 64)):\\n super(Decoder, self).__init__()\\n self.net = make_nn(input_size, output_size, hidden_sizes)\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9942,"numItemsPerPage":100,"numTotalItems":994482,"offset":994200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODU5NjgxNSwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc3ODYwMDQxNSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.H0Vw4TmFTMTwviR8kSg6zd7TTeR2_Am8EguACfcHU_t1ZM3TJ2Zf_1GXU3y0QHG1D9uzMmFQLEvE4Uy-MWwUBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
e3bcf07f29dd0a2ef68aa2ee5d2f3ad4 | Test that matchers get registered on an object instance, not just on the class | [
{
"docid": "4a39c5a1523715880d07644b7dfcff65",
"score": "0.7619825",
"text": "def test_matcher_on_instance(self):\n\n skill = _TestSkill(None, None)\n self.assertTrue(hasattr(skill.hello_skill, \"matchers\"))",
"title": ""
}
] | [
{
"docid": "3d7a2ce6ee5ae6ba010c1db150b1f754",
"score": "0.65179825",
"text": "def test_constructors(self, name, obj):\n assert getattr(forge, name) == obj",
"title": ""
},
{
"docid": "3d7a2ce6ee5ae6ba010c1db150b1f754",
"score": "0.65179825",
"text": "def test_constructors(sel... |
09455bd871518c4a53af2f9f48a37509 | Create a dataset from a set of Pandas dataframes. | [
{
"docid": "5f505662b44bbcd1d99bc426c0522263",
"score": "0.7133669",
"text": "def from_pandas(dfs: List[ObjectRef[\"pandas.DataFrame\"]],\n parallelism: int = 200) -> Dataset[ArrowRow]:\n import pyarrow as pa\n\n @ray.remote(num_returns=2)\n def df_to_block(df: \"pandas.DataFrame... | [
{
"docid": "e40768fbaba2d023630cbcdf0a2fbf9e",
"score": "0.6521647",
"text": "def create_datasets():\n df1 = apy.find_datasets(\"ENSEMBL_MART_ENSEMBL\")\n df1.to_pickle(os.path.join(DATADIR, \"datasets_ensembl.pkl\"))\n df2 = apy.find_datasets(\"ENSEMBL_MART_MOUSE\")\n df2.to_pickle(os.path.... |
f92705a33ea09aa3a95aff8186a07a47 | Test that valid arguments does not result in crash. Only validates that there are no crashes, does not validate any other behavior! | [
{
"docid": "8b03cc9fbef3905149ad3e71edf82a0f",
"score": "0.63943064",
"text": "def test_no_crash_on_valid_args(\n self, parsed_args_all_subparsers, dummyapi_instance, command_mock\n ):\n _repobee.cli.dispatch.dispatch_command(\n parsed_args_all_subparsers, dummyapi_instance, ... | [
{
"docid": "9f45e9ad983cfb134ea46c64981b0821",
"score": "0.72151494",
"text": "def test_CleanArgs_err():\n pass",
"title": ""
},
{
"docid": "7d80397c5c922d3fab33ecabb0b21c0c",
"score": "0.72042733",
"text": "def validate_arguments(args):\n pass",
"title": ""
},
{
"d... |
0d2a15b708c95ece5c8488c626ddefba | Use scipy.optimize.fmin_cg() to find the maximum likelihood estimate for the data in logregression.txt. | [
{
"docid": "591e0ae89011601957b664f98385a58a",
"score": "0.734154",
"text": "def prob4(filename=\"logregression.txt\"):\n def objective(b):\n return (np.log(1+np.exp(x.dot(b)))-y*(x.dot(b))).sum()\n \n data=np.loadtxt(filename)\n y=data[:,0]\n x=np.column_stack((np.ones_like(y), da... | [
{
"docid": "d2644a818c39fe93faf5848ca557d225",
"score": "0.5914348",
"text": "def __call__(self, X, y):\n X = np.hstack((np.ones((len(X),1)), X))\n\n # optimizacija\n theta = fmin_l_bfgs_b(\n cost,\n x0=np.zeros(X.shape[1]),\n args=(X, y, self.lambda... |
9ec554404ab602d2d537e9d7fe2ae34f | set internal self.params from a Parameters object or a list/tuple of Parameters | [
{
"docid": "94a70df51a827fbfd24ce757ea6ce9ca",
"score": "0.85300606",
"text": "def __set_params(self, params):\n if params is None or isinstance(params, Parameters):\n self.params = params\n elif isinstance(params, (list, tuple)):\n _params = Parameters()\n ... | [
{
"docid": "2af869554d507f9e39fd156e99656bad",
"score": "0.8027624",
"text": "def set_parameters(self, **params):\n with self.parametersLock:\n for param in params:\n setattr(self, param, params[param])",
"title": ""
},
{
"docid": "bc4ea99fdf77f79589f8ebc56bb... |
3242bfb4350542e64fc09c95bb72fb58 | Sets the fontsize of the legend. | [
{
"docid": "574f2950504e126970ecef1b51abdb6e",
"score": "0.8113846",
"text": "def set_legend_fontsize(command):\n try:\n plt.rc('legend', fontsize=int(command))\n except ValueError:\n print \"couldn't convert {} to int.\".format(command)",
"title": ""
}
] | [
{
"docid": "61a6071a42ba742408afd723536ffc16",
"score": "0.7425878",
"text": "def setcafontsize(fontsize=20):\n ax = pl.gca()\n for tick in ax.xaxis.get_major_ticks():\n tick.label1.set_fontsize(20)\n for tick in ax.yaxis.get_major_ticks():\n tick.label1.set_fontsize(20)",
"ti... |
3f97a90acbb37dfb912487589401e8d8 | Serialize a dict of cloudinit templates. It will serialize the names of cloudinit templates, thus allowing nailgun to request particular version for every template to be rendered during provisioning. | [
{
"docid": "2fcca3281cf15d1f30a48e690ffb7a6e",
"score": "0.8214958",
"text": "def serialize_cloud_init_templates(cls, release):\n cloud_init_templates = {}\n for k in (consts.CLOUD_INIT_TEMPLATES.boothook,\n consts.CLOUD_INIT_TEMPLATES.cloud_config,\n cons... | [
{
"docid": "0d781a4bebda31651e06ba83460827b4",
"score": "0.58914036",
"text": "def templates(self):\r\n params = {\"f\" : \"json\"}\r\n exportURL = self._url + \"/templates\"\r\n return self._con.get(path=exportURL,\r\n params=params, token=self._toke... |
f9406bcd7c389dd26528a55309644db0 | This is the customizable parrt of derived classes, used to set parameters for a particular type of implicit solvation | [
{
"docid": "f9438647cfc9d1e5c4ede606537bddb7",
"score": "0.0",
"text": "def setup(self):\n pass",
"title": ""
}
] | [
{
"docid": "9d312f1507d143e7fd675c7906961b22",
"score": "0.6681285",
"text": "def define_param(self, *pargs): ###\r\n self._subclass_paramdefs = getattr(self, \"_subclass_paramdefs\", [])\r\n self._subclass_paramdefs += list(pargs)",
"title": ""
},
{
"docid": "f92f0b2b9b13eb00... |
18772f8e3ebd9b6ba811cbfd85ece254 | Ensures that Renderman is loaded, and channels are expected values. | [
{
"docid": "ee5f1d66f0bd1cc64a73ff437c03c358",
"score": "0.5228379",
"text": "def validate(self):\n valid = self.valid_channels()\n filtered_list = []\n \n if not self.channels: raise PassMakerError, 'must select at least one channel'\n \n for chan in self.chann... | [
{
"docid": "8f7b5096222aaf3f14153ade9dc5b7b0",
"score": "0.5716942",
"text": "def _checkInitialization(self):\n if not(self.general and self.runtime and self.groups):\n raise RuntimeError(\"Settings are not completely initialized\")",
"title": ""
},
{
"docid": "1a08789e4e30... |
772ff500c953b77f98c57c3de2a8c6fb | Removes detections with lower score than 'score_thres' and performs NonMaximum Suppression to further filter detections. | [
{
"docid": "9409c3f76fb8eaac6f934602edc839a7",
"score": "0.70985776",
"text": "def non_max_suppression(prediction,\n num_classes,\n score_thres=0.5,\n nms_thres=0.4):\n\n # From (center x, center y, width, height) to (x1, y1, x2, y2... | [
{
"docid": "7217d95319326f924337efc1a6a0a023",
"score": "0.68844444",
"text": "def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):\n\n # From (center x, center y, width, height) to (x1, y1, x2, y2)\n prediction[..., :4] = xywh2xyxy(prediction[..., :4])\n output = [None for _ in ... |
f49af8f74c165029b6ecd7c9bd35a5ef | Compute the limit of the expression at the infinity. Examples ======== >>> limitinf(exp(x)(exp(1/x exp(x)) exp(1/x)), x) 1 | [
{
"docid": "2140363f8cc9d011cf824d6cdb5a0520",
"score": "0.70066345",
"text": "def limitinf(e, x):\n # Rewrite e in terms of tractable functions only:\n e = e.rewrite('tractable', wrt=x)\n\n if not e.has(x):\n # This is a bit of a heuristic for nice results. We always rewrite\n #... | [
{
"docid": "5f7b5f373758238dd0dc4b1e16ef8479",
"score": "0.6337069",
"text": "def inf(self):\n return self._inf",
"title": ""
},
{
"docid": "b3c056338e95bc7f89d42d99169dc947",
"score": "0.62408656",
"text": "def inf(self):\r\n\t\treturn float('inf')",
"title": ""
},
{
... |
5e7513caf8e5285320509fdb64e9dc85 | Run Dragonfly Bayesian optimization to minimize function f. | [
{
"docid": "aebc2f1871e7dadc85272c82d92c67c9",
"score": "0.64840174",
"text": "def minimize_function(self, f, n_iter=10, verbose=True, seed=None):\n if seed is not None:\n np.random.seed(seed)\n\n domain = self._get_domain()\n opt_method = 'bo'\n parsed_config = se... | [
{
"docid": "2e2b69b3ef096ecfff7938346f50cb3d",
"score": "0.6806004",
"text": "def minimize(f,df, x0):\n\n return scipy.optimize.minimize(\n f, x0, method='BFGS', jac=df, options={'maxiter':100})",
"title": ""
},
{
"docid": "e3bfd706e2203a2c42a9b09bac6e85c1",
"score": "0.6345694... |
03837da3826d0bff0b22a4d8a399f93e | r"""Time clip the input (if time interval is TSeries clip between start and stop). | [
{
"docid": "963146cf0cd2edfe32c5da92dfc5a943",
"score": "0.65635616",
"text": "def time_clip(inp, tint):\n\n if isinstance(inp, xr.Dataset):\n coords_data = [inp[k] for k in filter(lambda x: x != \"time\", inp.dims)]\n coords_data = [time_clip(inp.time, tint), *coords_data]\n out... | [
{
"docid": "c6e85b5f14ed32323e7239a36a9143d9",
"score": "0.6463437",
"text": "def crop(self, tmin: Optional[float] = None, tmax: Optional[float] = None) -> None:\n logger.debug(\"Cropping the signal between %s and %s.\", tmin, tmax)\n self._tmin, self._tmax = Sound._check_tmin_tmax(\n ... |
cd64915f0781bb045fb400e7aa888318 | SQL insert 1. insert help 2. insert query | [
{
"docid": "e62ef99df5246c1e629392599cafe13c",
"score": "0.64105207",
"text": "def insert(self, arg):\n if arg == '' or arg.lower() == 'help':\n return dbhelp(self, 'insert')\n if not db_ready():\n return\n DB.execute(\"insert \"+arg.replace(\";\", \"\"), db_alias(), list_results ... | [
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.72062004",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
... |
3f38572930214b31e362d80d752a5bef | Loops the queue. Invoke this command again to unloop the queue. | [
{
"docid": "85398434c04e430f30d630ab4c452835",
"score": "0.7258371",
"text": "async def _loop(self, ctx: commands.Context):\n\n await ctx.message.delete(delay=5)\n if not ctx.voice_state.is_playing:\n return await ctx.send('Nothing being played at the moment.', delete_after=5)\n... | [
{
"docid": "89b15d25c448917b330f9c88c7c4275a",
"score": "0.7013208",
"text": "def loop(self, queue, name):\n try:\n while self.running:\n try:\n gcode = queue.get(block=True, timeout=1)\n except Queue.Empty:\n continue... |
e0530609128272644ed30911328ec071 | Segments the input frame using a trained SVM. This function requires that the SVM classifier is already available. Use the `ml.classifier.trainClassifiers()` function to create it. | [
{
"docid": "7b5213ce699472c14495cebf02ccc3f6",
"score": "0.5688241",
"text": "def locateBricksSvm(self, frame, depth, hsv=False, scale=0.25):\n\n # check if the SVM classifier is available\n if self.svm_clf is None:\n self.logger.error('The SVM classifier is None; cannot do segm... | [
{
"docid": "3e67222fc58ff68145d7ff5d86d4d6bc",
"score": "0.6459603",
"text": "def train_svm():\n\n X, y = load_training_set()\n linear_svm.fit(X, y.flatten())",
"title": ""
},
{
"docid": "5921c2cdf30cfcbc2db0faa0a513742d",
"score": "0.634443",
"text": "def train_svm(X, y):\n ... |
d2944facf207d685092d2fdb00226d73 | This API has never heard of any patron. | [
{
"docid": "8214a506e3ee07e5063cf26ec01e8faf",
"score": "0.6529164",
"text": "def patron_remote_identifier_lookup(self, patron):\n return None",
"title": ""
}
] | [
{
"docid": "fde88ffd4ca41b2f91383e063f307889",
"score": "0.56945896",
"text": "def Pattern(self) -> str:",
"title": ""
},
{
"docid": "fde88ffd4ca41b2f91383e063f307889",
"score": "0.56945896",
"text": "def Pattern(self) -> str:",
"title": ""
},
{
"docid": "d5763184627af065... |
50cb75941cf794fc96b9dbd7f9cf2e89 | Returns Number of bikes currently at the station. | [
{
"docid": "2a2be470d151c40a4873acb78b349c31",
"score": "0.739501",
"text": "def bikes(self):\n bikes = [1 if dock.bike else 0 for dock in self.docks]\n return sum(bikes)",
"title": ""
}
] | [
{
"docid": "816080d3a0ea44c12fb05843b733ad49",
"score": "0.7562642",
"text": "def _get_bikes_available(sta):\n # 'num_ebikes_available\" is not part of the GBFS spec, but it appears\n # in the Divvy API response\n return sta['num_bikes_available'] + sta.get('num_ebikes_available', 0)",
"tit... |
e8a33131373fbef4487cf5835a7feadb | Update any single hyper parameter or group of parameters ``params`` with ``values``. | [
{
"docid": "d05d2b6d5ed40fdac465c9f72cbc14e3",
"score": "0.77267337",
"text": "def update_hyper(self, params, values):\n self.hyper_parameters.update(params, values)",
"title": ""
}
] | [
{
"docid": "e4a668246ffbf52c77360299cfa41a1f",
"score": "0.7678789",
"text": "def update_params(self, params, grads):\n updates = self._get_updates(grads)\n for param, update in zip((p for p in params), updates):\n param += update",
"title": ""
},
{
"docid": "140209d... |
2885d4cb316da36680a9f9014f740ba0 | Given a mapping, return an instance of this wff. | [
{
"docid": "5499a8b6d0b49c5ca79eebea683f22a4",
"score": "0.51883656",
"text": "def makeInstance( self, aMapping ):\n assert isinstance( aMapping, dict )\n\n assert isinstance( self._premiseFormSet, FormSet )\n assert isinstance( self._conclusionFormSet, FormSet )\n\n ... | [
{
"docid": "f09b2d07dcb0f3bb276b3001b9dc68a9",
"score": "0.65558213",
"text": "def makeInstance( self, aMapping ):\n assert isinstance( aMapping, dict )\n\n assert isinstance( self._operator, str )\n assert isinstance( self._operands, list ) and (len(self._operands) in (1,2))\... |
7125395a73ffefee23e546967d285002 | Defines whether manager email notifications are disabled | [
{
"docid": "5c31e01435d151dd270a8668fb24a20e",
"score": "0.84846765",
"text": "def disable_manager_email_notification(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"disable_manager_email_notification\")",
"title": ""
}
] | [
{
"docid": "61f5b4c8dcc3ca35df9d2c861dee6770",
"score": "0.7824334",
"text": "def disable_email_notification(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"disable_email_notification\")",
"title": ""
},
{
"docid": "61f5b4c8dcc3ca35df9d2c861dee6770",
"score": "0... |
752a38333d4fb5079420442a28da054b | Export the image shapes from the exported images in the target folder | [
{
"docid": "17b50c9e141e3a6d954a0a85f1430b1e",
"score": "0.692356",
"text": "def export_shapes(self, *args):\n try:\n # default answer\n ans = list()\n # working variables\n coln = args[0]\n tfext = args[1]\n tsufix = args[2]\n\n ... | [
{
"docid": "1edd76a4bc85079ce0cea5930497783b",
"score": "0.6876715",
"text": "def export(self, img_files, targets, output_folder, filename_prefix=\"dataset\"):\n assert isinstance(img_files, (list, tuple)), \"Arguments `img_files` should be lists or tuples\"\n if targets is not None:\n ... |
ac0f065a765912fa47ff960bcb4606e6 | r""" Converts a list of connections into a Scipy sparse adjacency matrix | [
{
"docid": "0cc0fe08a43fbbda265fbb121d33cbd4",
"score": "0.67700714",
"text": "def conns_to_am(conns, shape=None, force_triu=True, drop_diag=True,\n drop_dupes=True, drop_negs=True):\n if force_triu: # Sort connections to [low, high]\n conns = np.sort(conns, axis=1)\n if dro... | [
{
"docid": "22712fc23bf9be8e59110fc71423b200",
"score": "0.6723433",
"text": "def retrieve_adjacency_matrix(cgpms, v_to_c):\n adjacency_list = retrieve_adjacency_list(cgpms, v_to_c)\n adjacency_matrix = np.zeros((len(adjacency_list), len(adjacency_list)))\n for i in adjacency_list:\n adj... |
a569a968b10802e67698e98d11f0fc52 | If needed to initialize within CPU sampler (e.g. vector epsilongreedy, see EpsilonGreedyAgent for details). | [
{
"docid": "7fe7fdffb15fc98bad9fff2213283bd3",
"score": "0.6219568",
"text": "def collector_initialize(self, global_B=1, env_ranks=None):\n pass",
"title": ""
}
] | [
{
"docid": "469d5d7feab90369d1d6c0f4d9d4f23d",
"score": "0.70388573",
"text": "def initialize(self):\n self.actor_optimizer.sync()\n self.critic_optimizer.sync()\n self.sess.run(self.target_init_updates)",
"title": ""
},
{
"docid": "658e8b56c9e2945fd00f727f4cad78f7",
... |
bbcc36d5579f24bc1c2dc5198e458a9d | Adds the feasibility pumprelated configurations. | [
{
"docid": "e2e70289d6e3debbad8212b243eff57a",
"score": "0.6846095",
"text": "def _add_fp_configs(CONFIG):\n CONFIG.declare(\n 'fp_cutoffdecr',\n ConfigValue(\n default=1e-1,\n domain=PositiveFloat,\n description='Additional relative decrement of cutoff ... | [
{
"docid": "342b722c53e5f59010effacf09af25a0",
"score": "0.55445564",
"text": "def pibooth_configure(cfg):",
"title": ""
},
{
"docid": "0ddc05c18d735661f13caa0f82e6d32e",
"score": "0.5481886",
"text": "def _fp_setup2(self):\n # TODO: right now it's hard to implement this requi... |
4e2db4273aef647e4d55e183e06eefd6 | Return all company ids listed in organisations. | [
{
"docid": "81954b5d12648e52d6fa2fc00a550007",
"score": "0.79137444",
"text": "def company_ids(self) -> Generator[Optional[CompanyIDType], None, None]:\n for organisation in self:\n yield organisation.company_id",
"title": ""
}
] | [
{
"docid": "0b7a3b6dbcd6a308d05b331ee622257a",
"score": "0.75206494",
"text": "def listCompanyIds( self ):\n return self._data[ COMPANIES ].keys()",
"title": ""
},
{
"docid": "04e10cbead0a53dd80e56af38a329a04",
"score": "0.71382254",
"text": "def active_company_ids(self) -> Ge... |
26eaf61bb7f4a99fb0e89f50c19e4bf2 | __init__(self) > TIntFltKd __init__(self, TIntFltKd KeyDat) > TIntFltKd | [
{
"docid": "c593c371c475387a5595c875a3d9bcda",
"score": "0.78430516",
"text": "def __init__(self, *args):\n _snap.TIntFltKd_swiginit(self,_snap.new_TIntFltKd(*args))",
"title": ""
}
] | [
{
"docid": "3debcee7b0b5e1e909aa548561aac8e6",
"score": "0.7764347",
"text": "def __init__(self, *args):\n _snap.TIntFltKdV_swiginit(self,_snap.new_TIntFltKdV(*args))",
"title": ""
},
{
"docid": "c3a74e11c3091ae23a0f522af746fbb0",
"score": "0.76881486",
"text": "def __init__(s... |
30cf92c27cfc5ed339a3444c06c710b5 | To set up customized header | [
{
"docid": "cbf1c5d4831d97c3deaa3c656596ed74",
"score": "0.0",
"text": "def init_setup(self, header_list):\n assert(isinstance(header_list, list))\n assert(len(header_list) == self._myNumCols)\n\n # Set up header\n for i_col in range(self._myNumCols):\n header = he... | [
{
"docid": "2a3fba76992b3059f9c767bed3bb2505",
"score": "0.7375767",
"text": "def create_header(self, controller, header):\n label = tkinter.Label(self, text=header, font=controller.header_font)\n label.pack(side='top', fill='x', pady=20)",
"title": ""
},
{
"docid": "6b411a021c... |
209c4283cd4724a4d2adf5555c56efe6 | A simple fully connected network with regression output | [
{
"docid": "1545ffa21720990180abeb0b06f3acde",
"score": "0.0",
"text": "def SimpleFn(x, x_additional, architecture = [100, 1, 32, 32, 1], context = 'buy_'):\n with tf.name_scope(context + 'fc1'):\n W_fc1 = weight_variable([architecture[0], architecture[2]])\n b_fc1 = bias_variable([architecture[2... | [
{
"docid": "06e365561eea5dfa4c7ad3164413baed",
"score": "0.6496318",
"text": "def _simple_network():\n input = paddle.static.data(\n name=\"input\", shape=[None, 2, 2], dtype=\"float32\"\n )\n weight = paddle.create_parameter(\n shape=[2, 3],\n dtype=\"float32\",\n a... |
c937572e1392a3d6b5d31b56b7b8c048 | Checks if the packet is of a certain type | [
{
"docid": "4e73f806677fd2173749ceb58404fe15",
"score": "0.8320609",
"text": "def check_packet_type(packet, target_packet):\n\n # Grabs the first section of the Packet\n packet_string = str(packet)\n split = packet_string.split(' ')\n\n # Checks for the target packet type\n if target_pack... | [
{
"docid": "28a3b0fb6b49327dc364d8b14122cbd8",
"score": "0.76044613",
"text": "def check_type(cls, packet_type=None, data=None):\n if data is None:\n raise ValueError('Not data supplied. Data cannot be None')\n elif packet_type is None:\n raise ValueError('Not packet_... |
840baef06fe1889c2a7a3f59e70fdb1c | Starts a scan and returns a waitable for when the scan completes | [
{
"docid": "d8337579a571ba249ad925553b7e5870",
"score": "0.65212065",
"text": "def start_scan(self, scan_parameters: ScanParameters = None, clear_scan_reports=True) -> scan_waitable.ScanFinishedWaitable:\r\n self.stop()\r\n # Cache the device's address on scan start\r\n self._own_ad... | [
{
"docid": "f992fe331afed5a1f4fcc791d974e11d",
"score": "0.7121392",
"text": "def start(self):\n\n print(\"Starting scan task...\")\n if self.t_scan is None:\n self.t_scan = Timer(-1)\n self.t_scan.init(\n period=self.scan_period,\n mode=... |
007d503ca4fa5e3e5cacc0a036098e48 | Opens list column to rows Attributes | [
{
"docid": "b212ced1fba0651b841b10e760e0a8b0",
"score": "0.0",
"text": "def listToRows(self, column:str, id:str, column_final:str):\n import pandas as pd\n import numpy as np\n # Creates new column converting the current list into a string\n self.data[column+'_str'] = self.data[column].apply... | [
{
"docid": "68dfd020f3fa8e0141a597e0e7a6ceae",
"score": "0.642196",
"text": "def column2list():",
"title": ""
},
{
"docid": "bbf1d89fde156270f86018b1828732dc",
"score": "0.59959775",
"text": "def __init__(self, row, kw):\n super().__init__([], **kw)\n\n self._columns = ... |
6a5f4d250ef0388ef9618f6955fc436c | _insertStream_ Insert a stream into the stream table in T0AST. This will work even if the stream already exists inside the table. | [
{
"docid": "98f7b4eae4e6b60dca02f4125dc85195",
"score": "0.7189676",
"text": "def insertStream(dbConn, streamName):\n sqlQuery = \"\"\"INSERT INTO stream (ID, NAME) SELECT stream_SEQ.nextval, :p_1\n FROM DUAL WHERE NOT EXISTS\n (SELECT NAME FROM stream WHERE NAME = :... | [
{
"docid": "918e1ed716c3d91d297b2e53f580057e",
"score": "0.64942634",
"text": "def insert_datastream(self, stream):\n for flight in stream:\n # ArrDelay and DepDelay and TailNum can be 'NA' when cancelled is true\n if (flight.year != 'NA'\n and flight.month !=... |
d30191c262bc57e94f5ce39f6d349885 | smartctl d scsi a | [
{
"docid": "3c72ac6085e8b7a17cc81f23544665b8",
"score": "0.64197683",
"text": "def intel_scsi_cmd_all():\n stderr = \"\"\n stdout = \"\"\"\nsmartctl 6.2 2013-07-26 r3841 [x86_64-linux-3.10.0-327.3.1.el7.x86_64] (local build)\nCopyright (C) 2002-13, Bruce Allen, Christian Franke, www.smartmontools.... | [
{
"docid": "149a9828f502d9dd58b679576e1b7ba9",
"score": "0.67724967",
"text": "def intel_scsi_cmd_sasphy():\n stderr = \"\"\n stdout = \"\"\"\nsmartctl 6.2 2013-07-26 r3841 [x86_64-linux-3.10.0-327.3.1.el7.x86_64] (local build)\nCopyright (C) 2002-13, Bruce Allen, Christian Franke, www.smartmontoo... |
d9e0c850fb5ac39f1ac1485d0d02f903 | Delete the given entity and save if specified. | [
{
"docid": "77927a1bebad2d96ec15cffe291ba3a0",
"score": "0.61194575",
"text": "def delete(self, commit=True):\n ufo.db.session.delete(self)\n return commit and ufo.db.session.commit()",
"title": ""
}
] | [
{
"docid": "895f750707f81a5f6c61c536bfe65074",
"score": "0.78031045",
"text": "def delete(self, entity):\n\n entity.delete()\n # entity has been deleted call _onDelete\n self._onDelete(entity)",
"title": ""
},
{
"docid": "dc5642c37ec78fb25ad36ba7f63fa2a4",
"score": "0.7014826",
... |
a310024ff88502d4a0f995bd83129ca2 | Gets the ac of this ResultUniprotEntries. | [
{
"docid": "da54b5d5690d45cfaab31f193e2a7fb2",
"score": "0.6681408",
"text": "def ac(self) -> str:\n return self._ac",
"title": ""
}
] | [
{
"docid": "51c9ab7c299c1a8bb422cc0ec0b90cc8",
"score": "0.61584806",
"text": "def getAcr(self):\n pass",
"title": ""
},
{
"docid": "22f9792a7026c3202de799bbe14f5e70",
"score": "0.5821461",
"text": "def ac(self):\n return np.array(self['ac'], dtype=np.float32) / 1000",
... |
c094f9d2a50f484f869a9927fcb66753 | Return a postorder list of all items in this binary search tree. | [
{
"docid": "bd0a7e7e008793639ed841a8839d03af",
"score": "0.84094524",
"text": "def items_post_order(self):\n items = []\n if not self.is_empty():\n # Traverse tree post-order from root, appending each node's item\n self._traverse_post_order_iterative(self.root, items.... | [
{
"docid": "811485f014d979a705648958971be696",
"score": "0.7364006",
"text": "def post_order(self):\n output = []\n def walk(node):\n if not node:\n return\n walk(node.left)\n walk(node.right)\n output.append(node.value)\n w... |
9da9283cd11ad789fa2024aae0cc15dd | verifies that the density sums to number of electrons | [
{
"docid": "c040f739c7369e7c4ff7b97cb58f507f",
"score": "0.81512326",
"text": "def _check_density(self,density, num_electrons):\n\n FLOAT_PRECISION = 0.01\n #integrate the density over the spherical space\n #s = float(np.sum(density))\n #s = 4*np.pi * float(np.sum(density * s... | [
{
"docid": "a2143906e5d0c2ae91b15a37fbb28e96",
"score": "0.6757781",
"text": "def _calc_density(self, EigenVecs, num_electrons): \n density = 0\n\n for i in range (0, len(self.occupation_list)):\n #print(\"orbital number - {0} adding occupation: {1}\".format(i, self.occupatio... |
f66314df9dbb5323c0a87f20ba09e248 | Test add as numarrayarray for basic function with array limit Array code b. | [
{
"docid": "ca7e711de6ccf497bd1fac675d6d8c8a",
"score": "0.0",
"text": "def test_add_basic_num_array_array_d3(self):\n\t\tfor testvalx, testdatay in self.groupeddatay:\n\t\t\twith self.subTest(msg='Failed with parameter', testval = (testvalx, testdatay)):\n\n\t\t\t\tdata1 = array.array('b', testdatay)\n... | [
{
"docid": "5d9a0aca89a47d67865d0746fcaa1754",
"score": "0.773321",
"text": "def test_add_num_array_array_b4(self):\n\t\t# This version is expected to pass.\n\t\tarrayfunc.add(self.MinLimit, self.zero1array, self.dataout)\n\n\t\t# This is the actual test.\n\t\twith self.assertRaises(OverflowError):\n\t\... |
4c37e6bc30849391acf8f7382aa80bde | Test precedence of rc over env and whitelisting. | [
{
"docid": "3f163da996d456259d8bcfa00adc4413",
"score": "0.0",
"text": "def test_set_settings(self):\n in_toto.user_settings.set_settings()\n\n # From envvar IN_TOTO_ARTIFACT_BASE_PATH\n self.assertEquals(in_toto.settings.ARTIFACT_BASE_PATH, \"e/n/v\")\n\n # From RCfile setting (has preceden... | [
{
"docid": "fb408c71fae228d4c62e72491b1bfd25",
"score": "0.6072304",
"text": "def check_environment_presets():\n presets = [x for x in os.environ.copy().keys()\n if x.startswith('IRONIC') or x.startswith('OS_')]\n if presets:\n print(\"_\" * 80)\n print(\"*WARNING* Foun... |
52e98db146334c52dcf6cc896b73dc5f | Identify stream handlers writing to the given streams(s). | [
{
"docid": "08b2c4b0c538d95191f54db5270cadb1",
"score": "0.6958262",
"text": "def match_stream_handler(handler, streams=[]):\n return (isinstance(handler, logging.StreamHandler) and\n getattr(handler, 'stream') in (streams or (sys.stdout, sys.stderr)))",
"title": ""
}
] | [
{
"docid": "d3ad778637d048076f6d24a6fcd020c0",
"score": "0.59115714",
"text": "def _file_descriptors_work(*streams):\n # test whether we can get fds for out and error\n try:\n for stream in streams:\n stream.fileno()\n return True\n except BaseException:\n return... |
9b484ac9123e68d269b5f623c2c64969 | Surface2Right { get; } > SurfaceSurfaceIntersectorConfiguration | [
{
"docid": "d277b3aca70753fa1664609bf69d1cf1",
"score": "0.9119296",
"text": "def Surface2Right(self) -> SurfaceSurfaceIntersectorConfiguration:",
"title": ""
}
] | [
{
"docid": "a0061321502479ed8b10ba1ceaec5cb3",
"score": "0.9002965",
"text": "def Surface1Right(self) -> SurfaceSurfaceIntersectorConfiguration:",
"title": ""
},
{
"docid": "fa008281c2a24443fcc5dd185b9a55d1",
"score": "0.7710068",
"text": "def Surface2Left(self) -> SurfaceSurfaceInte... |
45c5f13c77ebdc051ef9f5aef0a88630 | takes the initiate_mining_package dictionary and the qMachines of each machine to calculate emissions | [
{
"docid": "9dc3685d84a3954bced6dda399dcfe18",
"score": "0.6304393",
"text": "def package_emissions(self):\n\n longwallShearer_emissions = longwallSheerer.qMachine(self.longwallShearer.power,\n self.required_output,\n ... | [
{
"docid": "115568d12c9f6c0dc12ac73118cf65c6",
"score": "0.5337584",
"text": "def post_solve(m, outdir=None):\n\n if outdir is None:\n outdir = m.options.outputs_dir\n\n zone_fuel_cost = get_zone_fuel_cost(m)\n has_subsidies = hasattr(m, 'gen_investment_subsidy_fraction')\n\n gen_data... |
a5c96308d71b0c760e1e640269275490 | Gzip a given string content. | [
{
"docid": "ee9d130fab1feae0dc17187cfa91f5f9",
"score": "0.7249553",
"text": "def _compress_content(self, content):\n zbuf = StringIO()\n zfile = GzipFile(mode='wb', compresslevel=6, fileobj=zbuf)\n try:\n zfile.write(content.read())\n finally:\n zfile.c... | [
{
"docid": "c92d8854cb03b0dee3bae0841a817b12",
"score": "0.75606656",
"text": "def compress_string(self, s):\n import cStringIO, gzip\n zbuf = cStringIO.StringIO()\n zfile = gzip.GzipFile(mode='wb', compresslevel=6, fileobj=zbuf)\n zfile.write(s)\n zfile.close()\n ... |
6200d473f1718524d9d2c9c3a1298ca0 | For the most part, reformatting of | [
{
"docid": "53b89fad17638ca30b49dff33075ec2b",
"score": "0.0",
"text": "def plot_county_errors(model, svg_file=Path(\"data/counties.svg\"), save_colorbar=True):\n\n model_sd = torch.load(model, map_location=\"cpu\")\n\n model_dir = model.parents[0]\n\n real_values = model_sd[\"test_real\"]\n ... | [
{
"docid": "c71ddc9cfaee61c31bb33d4b6057df39",
"score": "0.6501458",
"text": "def Format():",
"title": ""
},
{
"docid": "6a147248e93e4251e8b209992430c030",
"score": "0.61220425",
"text": "def format(self):\n ...",
"title": ""
},
{
"docid": "df179818f68aaad3ea353809... |
35ad6880de89a0177445728f2773a0ba | Starts a new subroutine scope (i.e. erases all names in the previous subroutine's scope.) | [
{
"docid": "ad7c5468c895132ec22ad7756b240832",
"score": "0.6358833",
"text": "def start_subroutine(self):\n\n self._subroutine_table.clear()\n self._kind_counter['argument'] = 0\n self._kind_counter['var'] = 0",
"title": ""
}
] | [
{
"docid": "ce7b3e090f92b78d8e305cac7c33755e",
"score": "0.6175013",
"text": "def startRoutine(self):\n self.symbolTable[\"subroutine\"] = {}\n self.counts['ARG'] = 0\n self.counts['VAR'] = 0",
"title": ""
},
{
"docid": "0b40c38b32a688e503b0eb805ae43e21",
"score": "0... |
0c45ad1ca37f394b9dd2683d932f02b0 | Generates list of cal curve, qc, and blank samples to append in sample_queue() | [
{
"docid": "3d72f771a08a44bbfed35e1b09d25845",
"score": "0.499762",
"text": "def cal_curve(state: int, number: int, platform: str, wash_no: int, pool_no: int):\n cal_list = list()\n std_plate_name = \"SP\" + str(ceil(number/7))\n\n row = list('ABCDEFG')[(number-1) % 7]\n if state < 2:\n ... | [
{
"docid": "9f2cb6765d7c16b8e03dd0016209394a",
"score": "0.60471046",
"text": "def fill_batch_queue(self):\r\n while True:\r\n\r\n # print 'hi'\r\n if self._hps.mode != 'decode' and self._hps.mode != 'calc_features':\r\n # Get bucketing_cache_size-many batches... |
ce4d3a818559285dd4ad0d0459e39396 | 'send2all' sends a message to all users | [
{
"docid": "070aca796895cb047b8bcf76b8687a59",
"score": "0.7041352",
"text": "def send2all(update, context):\n\n # read all users from StatBot.log\n user = []\n with open('./StatBot.log') as fid:\n for line in fid:\n ele = line.split(' - ')\n user.append(int(ele[4].... | [
{
"docid": "eb5132fe50e1452e1b243d7e243512af",
"score": "0.6550152",
"text": "def send_to_all(self, message):\n [self.send(message, addr) for addr in self.clients]",
"title": ""
},
{
"docid": "af6f69d694fb5a50a67dcaf705a9dc1a",
"score": "0.6523553",
"text": "def send_to_all(cl... |
b10c12870f2d2ab26405055591433408 | A copy activity source for SAP Cloud for Customer source. | [
{
"docid": "fd8c2c3d8b508202dd8f01837b771e6b",
"score": "0.55718714",
"text": "def __init__(__self__, *,\n type: pulumi.Input[str],\n query: Optional[Any] = None,\n source_retry_count: Optional[Any] = None,\n source_retry_wait: Optional[Any... | [
{
"docid": "6a291caf413f569ac7787a8509365b3c",
"score": "0.56941664",
"text": "def on_copy_source(self):\n source = self.data.get(\"source\", None)\n if not source:\n return\n\n project_name = self.dbcon.Session[\"AVALON_PROJECT\"]\n root = RegisteredRoots.register... |
d22a81c545031a3b6b5decaa0d244ede | List all UUIDs in the archive Returns | [
{
"docid": "d9d04b7c847b2b6a431bfeace34e5ab2",
"score": "0.6825332",
"text": "def list_uuids():\n # return common_utils.file_list('ephys/')\n if braingeneers.get_default_endpoint().startswith('http'):\n return [s.split('/')[-2] for s in s3wrangler.list_directories('s3://braingeneers' + '/ep... | [
{
"docid": "11ec5dae8e00fc06f688c12de46acdcd",
"score": "0.68951184",
"text": "def uuids(self, count=None) -> Iterable[str]:\n response = self.session.get(\n urljoin(self.url, '_uuids'), \n params = {'count': count} if count is not None else None\n )\n if not r... |
e0d4f32a4abc50f1cee0808579b0aa39 | Computes and returns a file's hash digest. | [
{
"docid": "ff27398279d04e808916db9a1727ec9e",
"score": "0.75774205",
"text": "def get_file_hash(filename):\n\t# Use SHA-2 256-bit\n\thash_function = hashlib.sha256()\n\t# Use the storage's default buffer\n\tbuffer_size = io.DEFAULT_BUFFER_SIZE\n\twith open(filename, \"rb\") as handler:\n\t\tbuffer = ha... | [
{
"docid": "d79ccda8a7f00e31c016ed25ae02799b",
"score": "0.809099",
"text": "def _digest_file(file):\n BUF_SIZE = 65536\n\n m = hashlib.sha256()\n with open(file, 'rb') as f:\n while True:\n buf = f.read(BUF_SIZE)\n if not buf:\n break\n m.... |
bf03a23c0273b35a0d761f0e33ab7b44 | DataBall constructor, connect to the go_fish database, define a cursor, and get the start time of the game. Initialize several other fields used in the database. 8 | [
{
"docid": "50c0f8aaed5ffd2608573129f5b91381",
"score": "0.626582",
"text": "def __init__(self,difficulty=0):\n self.conn = sqlite3.connect('go_fish.db') #Connect to database\n self.curs = self.conn.cursor()\n \n # start time of game\n self.ts = datetime.datetime.now(... | [
{
"docid": "caceb3c401ac325d0e26409cca25db8d",
"score": "0.62242234",
"text": "def __init__(self):\n self.score = 0\n self.assassin_kills = 0\n self.mage_kills = 0\n self.ogre_kills = 0\n self.db = Database()",
"title": ""
},
{
"docid": "cd3453ce1bf460fd52d... |
cf83537c115725c409330215d6b5a422 | Represents a list of spdx.document.ExternalDocumentRef as a Python list of dictionaries | [
{
"docid": "3aef02b25c3f372a6dd9dc469f800c58",
"score": "0.69072384",
"text": "def ext_document_references_to_list(cls, ext_doc_refs):\n ext_doc_refs_list = []\n\n for ext_doc_ref in ext_doc_refs:\n ext_doc_ref_dict = OrderedDict([\n ('externalDocumentId', ext_doc... | [
{
"docid": "02f0c1fdf2e1ca3705bb50309444c0c7",
"score": "0.6509022",
"text": "def to_dict(ret, deref_list=[]):\n retdict = ret.to_mongo().to_dict()\n for ref in deref_list:\n if isinstance(ret._data[ref], list):\n retdict[ref] = [x.to_mongo().to_dict() for x in ret._data[ref]]\n ... |
5be360da2c57ecc95fded4d0ffa094a2 | Filter your queryset as you want, then return it. | [
{
"docid": "034fef724d5ff4b2b0c05a72b18f3891",
"score": "0.0",
"text": "def get_receivers_queryset(self, receiver_ids):\n return receiver_ids",
"title": ""
}
] | [
{
"docid": "2eab7744c15e7982917bedecf46b5c80",
"score": "0.76472706",
"text": "def filter(self, qs):\n return qs.all()",
"title": ""
},
{
"docid": "0d55fa6bdebd3337e64ce5e6580d617b",
"score": "0.7603844",
"text": "def filter_queryset(self, qs, filter_param):\n return qs... |
eea86eec1e8b1516c6844724dff753de | Checks if a user is a staff in the academic office group. academic office group only has the permissions to view and respond to transcript requests except the student who made them | [
{
"docid": "37eb986ac632d23448faf8d27388334b",
"score": "0.7403345",
"text": "def academic_office_staff_only(function):\n\n def wrap(request, *args, **kwargs):\n staff = Staff.objects.filter(user=request.user).first()\n group = get_object_or_404(Group, name='Academic Office')\n\n ... | [
{
"docid": "9ed70dacee95781f7afb5723dc3fb0b1",
"score": "0.7718486",
"text": "def is_staff(self):\n return self.role == 'AD' or self.role == 'SA'",
"title": ""
},
{
"docid": "ede8d602b0890c5162be4ebab03a37b5",
"score": "0.75189376",
"text": "def is_staff(self):\n return... |
d089ca98a7f206ce1b664456d5053423 | Write grasp metrics to database | [
{
"docid": "b4e254f3886d18043c38f274672b02c7",
"score": "0.61728704",
"text": "def write_grasp_metrics(grasp_metric_dict, data, force_overwrite=False):\n for grasp_id, metric_dict in grasp_metric_dict.iteritems():\n grasp_key = GRASP_KEY + '_' + str(grasp_id)\n if grasp_key ... | [
{
"docid": "163855fe95f27b3266f7cf87530e0800",
"score": "0.65698516",
"text": "def write(self, metrics):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ff5e1267431f6473fa5a4050fcf11be3",
"score": "0.64918494",
"text": "def put_genomicsMetrics(db, metrics):\n retur... |
ffef628e950fce7d8be4f34b8515a83f | If no cars created, an appropriate message is displayed | [
{
"docid": "ec556e3ffac45418701b4b8e014dfa8c",
"score": "0.48073006",
"text": "def test_context_data(self):\n response = self.client.get(reverse('popular'))\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'No cars added')\n self.assertQuerysetEqu... | [
{
"docid": "bdd0de10bfc411623eb167a11b334115",
"score": "0.6560931",
"text": "def display_no_car(form):\n return render_template(\"/customer/car_view.html\", cars=[], form=form, start_date=\"\", end_date=\"\")",
"title": ""
},
{
"docid": "501ba74b54ecd9bb6f4aa11bed9e743a",
"score": "0... |
4d4b550de13792535a2bbaa8a9d4149b | Populate the indexing item with a Property, for both lists and dictionaries. | [
{
"docid": "7f99c19011917125cda8bfca1c99d0fd",
"score": "0.5757633",
"text": "def __getitem__(self, item: Union[int, str]):\n if item not in self._items.keys():\n shape = Properties._shapes_map.get(self.service_name, {}).get(self.shape_name)\n member = shape[\"value\"][\"sha... | [
{
"docid": "006a309296cbac0e8bfcae5d3ad05ceb",
"score": "0.6605211",
"text": "def __index_data_for_property(self, data, property_getter):\n self._get_logger().debug(\"Creating index on getter '{}' for #{} entries\".format(property_getter, len(data)))\n return {property_getter(data_item): d... |
7546c3e37e518b99914041ff80f2a2e3 | Return the field value in the job data identified by jobid. If jobid does not identify a job, the default value is returned. If the named field does not exist, an AttributeError exception is raised. | [
{
"docid": "256ec9577e03e7f7ece7d395387949d5",
"score": "0.8610517",
"text": "def getfield(self, jobid, name, default=None):\n if name not in Fields:\n raise AttributeError(\"Job record has no attribute '%s'\" % name)\n key = _key(jobid)\n if not self.__client.exists(key)... | [
{
"docid": "69bf28909f7acc163c363d958c69d13c",
"score": "0.6560788",
"text": "def _get_field_data_value(self, field_id, key_name):\n d = self._get_field_data_dict(field_id)\n return d.get(key_name, None)",
"title": ""
},
{
"docid": "9ab5e206528d04f8a1e7e2c834bb5f4d",
"score... |
56a33b94c67f641b5f4000ff1f60be92 | Display favorites from Favorite table | [
{
"docid": "7c0370360c0dcd5614b530927e1608e2",
"score": "0.7621096",
"text": "def display_favorites(self):\n self.methods_for_db.get_favorites_products()\n sentence_for_favorites = \"your favorite products!\"\n if self.methods_for_db.favorites == []:\n print(self.messages... | [
{
"docid": "ba80cdffd086ef672938b89a19b59f09",
"score": "0.75344425",
"text": "def show_favorites(self, client_id):\n favorites = self.favorite_m.retrieve_favorites(client_id)\n print(\"\\n****** FAVORIS ******\\n\")\n for i, dictionary in enumerate(favorites):\n code = d... |
76688fe53cd914782ded9a48e64176b9 | Return a discrete colormap from the continuous colormap cmap. | [
{
"docid": "abf7018deae5cecaf093263ba6dfd96b",
"score": "0.62132716",
"text": "def cmap_discretize(self, cmap, N):\n\t\tif type(cmap) == str:\n\t\t\tcmap = cm.get_cmap(cmap)\n\t\t\tcolors_i = np.concatenate((np.linspace(0, 1., N), (0., 0., 0., 0.)))\n\t\t\tcolors_rgba = cmap(colors_i)\n\t\t\tindices = n... | [
{
"docid": "64530397dc432e3fd67edf428f43658b",
"score": "0.7468833",
"text": "def _continuous_colormap(colormap, values, vmin=None, vmax=None):\n assert values is not None\n assert colormap.shape[1] == 3\n n = colormap.shape[0]\n vmin = vmin if vmin is not None else values.min()\n vmax = ... |
db0af1a186c2fbf834cf81400266dddd | Wrapper around submit for python api. | [
{
"docid": "1ed696ceed191d790787d19b8ed39d8d",
"score": "0.6731706",
"text": "def submit_api(config: dict, **kwargs):\n cfg = Config.from_dict(config)\n return submit(cfg=cfg, **kwargs)",
"title": ""
}
] | [
{
"docid": "b3160e79d99c6bf5469e3b1f4a9dcb99",
"score": "0.78354293",
"text": "def submit(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "685d566c2b16f9ade7eb9ad70c3d3e39",
"score": "0.76533663",
"text": "def submit(self):\n pass",
"title": ""
},
... |
439fbb9be6d578e0ff2961c46bc9a7d4 | Saves the coordinates of all zone arrow and aligning vertex groups | [
{
"docid": "c0af716381c56c14ccb6fc72e5d9de22",
"score": "0.6373062",
"text": "def save_vertex_groups(mesh):\n print('saving Zones vertex groups')\n current_object = bpy.context.object\n current_mode = bpy.context.mode\n mesh.update_from_editmode()\n vgroup_names = {vgroup.index: vgroup.na... | [
{
"docid": "1b3ae2c9b3a777eb8d116287449da2ec",
"score": "0.5913553",
"text": "def savepoint():",
"title": ""
},
{
"docid": "61721e6bc0615cdba21eb803df749aa2",
"score": "0.53940016",
"text": "def save_as_ijk(markupnodes, dir_path, prefix):\n for i, node in enumerate(markupnodes):\n... |
80f239ecd1d7f5cd5b2419f1cea49b12 | Recibe un mensaje con un color y lo pone en la pantalla en una posicion indicada | [
{
"docid": "e7aa712035f605d02fcca551bc774d29",
"score": "0.6340553",
"text": "def message_to_screen(msg,color,x,y):\n screen_text = font.render(msg, True, color)\n display.blit(screen_text, (x,y))",
"title": ""
}
] | [
{
"docid": "b79ea9df88e1c50474bb5318da2ead30",
"score": "0.7239434",
"text": "def eyes_color(self, message):\n # TODO use opencv2 to edit frames\n r = message.data.get(\"r\")\n if r > 255/2:\n self.current_frame = self.draw_custom(self.alarm)\n g = message.data.get... |
998ac34f3ca67be64fc68b36be75906c | It allows to use set literals. | [
{
"docid": "f3ce205a5cc66bbc4d76cef40f77b209",
"score": "0.6530405",
"text": "def test_Set():\n assert restricted_eval('{1, 2, 3}') == set([1, 2, 3])",
"title": ""
}
] | [
{
"docid": "e70be52f1c10ed4811277ad0453827fc",
"score": "0.7011735",
"text": "def sets(*args, **kwargs):\n\n pass",
"title": ""
},
{
"docid": "2f47632f0dd26d1d75200dd3a942aa2d",
"score": "0.691617",
"text": "def set(self, s):\r\n self.handle(set, s)",
"title": ""
},
... |
eb6bfd65ff86d411b9e914fa5d93df5a | Get movie showtimes for cinema. Given the cinema name as a lowercase string (e.g. arena, delft, rembrandt) gets all the current movie showtimes for this cinema. Returns a list of twovalue tuples representing showtime date and time as `datetime.datetime` object and showtime technology. | [
{
"docid": "6d3e5a53c3c54afbc0c3db2b8065824f",
"score": "0.7254321",
"text": "def get_movie_showtimes_for_cinema(movie_page_doc, cinema):\n cinema_movie_showtimes = []\n showtime_days = movie_page_doc.xpath(SHOWTIME_DAYS.format(cinema))\n for showtime_day in showtime_days:\n day_name = s... | [
{
"docid": "274542cf0da3b9dc0d4ffa7f6c2d9c6d",
"score": "0.63900685",
"text": "def getShowList(self):\n showXML = self.sendRequestAndReadData(\"core/getShows\")\n showNames = [] \n if showXML:\n root = ET.fromstring(showXML)\n shows = root.findall('Show'... |
a594995c748a30819fd0e6d94b30a20b | We discard the first 0.5 seconds of each trial as it does not contain any information. | [
{
"docid": "ee29ece24dd63cfd2b7b24d1d030d35e",
"score": "0.51472926",
"text": "def discard_irrelevant_measurements(train_x, sfreq):\n tmin = 0\n tmax = 1\n tmin_original = -0.5\n\n beginning = np.round((tmin - tmin_original) * sfreq).astype(np.int)\n end = np.round((tmax - tmin_original) ... | [
{
"docid": "510154af90407dcb2af1c70437e247e3",
"score": "0.5970883",
"text": "def trimOut(self, time):\n ...",
"title": ""
},
{
"docid": "00b793f9cf69953663cf316d57150db6",
"score": "0.58393484",
"text": "def _timeskip(self):\n tm = time.time()\n bundy.bundy.comp... |
981994ac7d1b428ccae299d8115d26e2 | Create an empty list, append an empty list for every row (height) and append a 0 to every sublist for each column (width). For any element cells[i][j], i and j represent x and y coordinates respectively on the grid. | [
{
"docid": "17e5bce32d632431826aa3ff78ad80a0",
"score": "0.8121759",
"text": "def init_empty_cells(width, height):\n cells = []\n if width <= 0 or height <= 0:\n return(cells)\n else:\n for i in range(height):\n cells.append([])\n for i in cells:\n for... | [
{
"docid": "e96b05476685bbbae7d04a7e8291668d",
"score": "0.7523356",
"text": "def create_empty_grid(width: int, height: int, value=tile_empty) -> List:\n\n grid = []\n for row in range(height):\n grid.append([])\n for column in range(width):\n grid[row].append(value)\n ... |
a24db6b35b2e15cbd0ffa24ee0106685 | Convert a list of ranges to a set of numbers | [
{
"docid": "07869e22c41238d7506ad869c60bc49a",
"score": "0.78628695",
"text": "def range_to_set(ranges):\n return set().union(*[set(range(l, h + 1)) for l, h in ranges])",
"title": ""
}
] | [
{
"docid": "e034e9ef47574a444309c76e9a1af6e0",
"score": "0.7111665",
"text": "def parse_ranges(ccdlist):\n s = set()\n for r1 in ccdlist.split(','):\n r2 = r1.split('-')\n if len(r2) == 1:\n s.add(int(r2[0]))\n elif len(r2) == 2:\n for j in range(int(r2[0... |
a6f2094b23fd016f411dd70f2df2b7ef | Using three different increments, find how much tilt, roll, and twist of the birdbath yields maximum waterholding ability. | [
{
"docid": "6001527e7637b1559b11b1ce1fe85531",
"score": "0.60584825",
"text": "def helper(func):\n curr_best_water = -1\n curr_best_roll = -1\n curr_best_twist = -1\n curr_best_tilt = -1\n mini = -45\n maxi = 45\n delta = [12, 6, 1]\n for d in delta: # use thr... | [
{
"docid": "aeef00b8dc0602f922a09d4c7ef36bf5",
"score": "0.65780234",
"text": "def findbest(roll, tilt, twist, min, max, delta, func):\n best_water = -1\n best_roll = -1\n best_tilt = -1\n best_twist = -1\n while roll < max: # until roll, tilt, twist values hit m... |
28ac727e4fdb7ae12cfd62748788dfbe | Test to verify that a configuration error is thrown when supplying the heading_line_length value with a string that is not an integer. | [
{
"docid": "4b5961f878003329402a5978b49890bb",
"score": "0.76512426",
"text": "def test_md013_bad_configuration_heading_line_length():\n\n # Arrange\n scanner = MarkdownScanner()\n source_path = os.path.join(\n \"test\", \"resources\", \"rules\", \"md013\", \"good_small_line.md\"\n )\... | [
{
"docid": "62254daa2a5181a2d6752378cd4df595",
"score": "0.730549",
"text": "def test_md013_bad_configuration_line_length():\n\n # Arrange\n scanner = MarkdownScanner()\n source_path = os.path.join(\n \"test\", \"resources\", \"rules\", \"md013\", \"good_small_line.md\"\n )\n suppl... |
ce2fce563527b8e094866bde874c343f | Check Data Objects for Points. | [
{
"docid": "e2ebabb16e6e7c6eb007db609116141c",
"score": "0.0",
"text": "def checkData(file_list):\n for f in file_list:\n f_sp = f[\"syntenic_points\"]\n sp1 = f_sp.keys()[0]\n sp2 = f_sp[sp1].keys()[0]\n data = f_sp[sp1][sp2]\n if len(data) < 1:\n return... | [
{
"docid": "08399fe9669baae6573686b86cd56247",
"score": "0.6803532",
"text": "def check(self, points):\n pts=[tuple(p) for p in points]\n if sorted(list(set(pts))) != sorted(pts):\n raise Exception(\"No point must be upon another.\")",
"title": ""
},
{
"docid": "cb5b... |
0bf1acc1c0ed809c58b52cf75dd8fd46 | Create a socket for an existing file descriptor Duplicate the file descriptor and return a `Socket` for it. The blocking mode can be set via `blocking`. If `close_fd` is True (the default) `fd` will be closed. | [
{
"docid": "25560e410d51cd9abd4df3482b2e9811",
"score": "0.7590436",
"text": "def new_from_fd(cls, fd: int, *, blocking=True, close_fd=True):\n sock = socket.fromfd(fd, socket.AF_UNIX, socket.SOCK_SEQPACKET)\n sock.setblocking(blocking)\n if close_fd:\n os.close(fd)\n ... | [
{
"docid": "846525ea98faa4e7fbc363b4d62f546b",
"score": "0.7232971",
"text": "def fromfd(fd, family, type, proto=0):\n nfd = dup(fd)\n return socket(family, type, proto, nfd)",
"title": ""
},
{
"docid": "54022c648a189d4cfefa93374116dc19",
"score": "0.65049374",
"text": "def dup... |
948eb2200aadd4573b7870328e1c4953 | Create gene fixture for . | [
{
"docid": "b987e859690a938cf96237cad8db8225",
"score": "0.0",
"text": "def loc106783576():\n params = {\n \"match_type\": MatchType.NO_MATCH,\n \"label\": \"nonconserved acetylation island sequence 68 enhancer\",\n \"concept_id\": \"ncbigene:106783576\",\n \"symbol\": \"L... | [
{
"docid": "275881221669d5e4e9ea5e76b98e58c3",
"score": "0.69135976",
"text": "def setUp(self):\n \n self.gene = self.construct_gene()",
"title": ""
},
{
"docid": "91eb98ca9e3d5b66f096f43a6c9dcbd5",
"score": "0.6581844",
"text": "def create_fixtures(self):",
"title"... |
6b417230d88e9e07ef0045cbddfe6ec8 | Uses the user's chosen automatic allocation method, or if none, assigns all to STR. | [
{
"docid": "1ff12aa63eb4f1aca8f2e6e12021aa91",
"score": "0.0",
"text": "def allocateAllAttributePoints(self):\n\t\turl = \"https://habitica.com/api/v3/user/allocate-now\"\n\t\treturn(postUrl(url, self.credentials))",
"title": ""
}
] | [
{
"docid": "905e351024885095ad50ec9017ad8686",
"score": "0.52877396",
"text": "def autostring(min_length=3):\n\n assert min_length >= 2\n addr = memorymanager.BinaryAddr(0)\n while addr < len(memory_binary):\n i = 0\n while (addr + i) < len(memory_binary) and memory_binary[addr + ... |
7ccb997b2a94b9bc16a7af1440063646 | Registers the existence of an image with `name`, and that the image used displayable d. `name` A tuple of strings. | [
{
"docid": "aced93de0282fe4d81942a6afc252a95",
"score": "0.72605515",
"text": "def register_image(name, d):\r\n\r\n tag = name[0]\r\n rest = name[1:]\r\n\r\n images[name] = d\r\n image_attributes[tag].append(rest)",
"title": ""
}
] | [
{
"docid": "63112d4bac877160bcf20cebe0265b09",
"score": "0.66050875",
"text": "def add_image(self, name, camera_instance):\n lbl = ClientLabel(self.canvas, bg=\"white\", bd=self.canvas.label_border_size).define(name, camera_instance)\n self.canvas.images[name] = lbl\n # self.canvas.... |
288646a7a29a1398ae5c2c55e1c5a00e | Override of handle function | [
{
"docid": "984cb6beaa8290b18073f668e7e95949",
"score": "0.0",
"text": "def handle(self):\n server_interface = Authorization(self.server.database, self._set_auth_user)\n self.transport.start_server(server=server_interface)\n channel = self.transport.accept(self.TIMEOUT)\n log... | [
{
"docid": "b207aca386aaf79999970d1b3215a694",
"score": "0.82228774",
"text": "def _handle(self, *args, **options):\n return super()._handle(*args, **options)",
"title": ""
},
{
"docid": "2d78b26b871ee02791e22a63f67b33c3",
"score": "0.81764233",
"text": "def handle(self):",
... |
8dd9313f810d85d87377c4c83da5271c | Returns list comprising names of Stan parameters. Note that the length of this list will not generally equal ``n_parameters`` since each name may correspond to a vector. | [
{
"docid": "72a6fbab24b7172766423478b29438e6",
"score": "0.0",
"text": "def names(self):\n return self._names",
"title": ""
}
] | [
{
"docid": "0704c257661eb8a6786f81ceb412dd13",
"score": "0.85193115",
"text": "def get_parameters_names(self) -> List:\n return [variable[\"name\"] for variable in self.parameters]",
"title": ""
},
{
"docid": "afd09fdd4979c219478623e76aec6235",
"score": "0.7589632",
"text": "d... |
3bcad392966a6fcd6b42d6cc935c47f2 | Get `SENTENCE BREAK` property. | [
{
"docid": "6a5a4a570ee367fd4599aecc9f3924bc",
"score": "0.68026906",
"text": "def get_sentence_break_property(value, binary=False):\n\n obj = unidata.ascii_sentence_break if binary else unidata.unicode_sentence_break\n\n if value.startswith('^'):\n negated = value[1:]\n value = '^' ... | [
{
"docid": "ca993ec8155e0498f61f8c1284fbd91e",
"score": "0.5473725",
"text": "def _get_sent(self):\n return self.__sent",
"title": ""
},
{
"docid": "ca993ec8155e0498f61f8c1284fbd91e",
"score": "0.5473725",
"text": "def _get_sent(self):\n return self.__sent",
"title": ""
}... |
bc12ac2bd81b6cd878534a3d4f323653 | Provides the user with a list of options to search the csv file and calls the appropriate search functions | [
{
"docid": "4d768fac5d7955f410d296ac111496db",
"score": "0.587037",
"text": "def search_entry():\n clear_screen()\n print(\"Search for an Existing Entry:\")\n print('''\n [A] Search by Date\n [B] Amount of Time\n [C] Exact Search\n [D] Regex Pattern\n [E] Return to Main Menu\n ... | [
{
"docid": "0f8e592987aa93a7d33fabc7963d8d61",
"score": "0.64782333",
"text": "def search(bot, update, args):\n csv_file = read_csv(file)\n user_string = update.message.text.split()\n result = str()\n if len(user_string) == 2:\n for i in range(len(csv_file)):\n csv_file[i] ... |
d1a63422da033794916a714b5fd0b99a | Given the following, generate a list of dict results split by fiscal years/quarters/months | [
{
"docid": "68bc9b80b22032ac74bf4c74d2f56c9a",
"score": "0.49230993",
"text": "def bolster_missing_time_periods(filter_time_periods, queryset, date_range_type, columns):\n min_date, max_date = min_and_max_from_date_ranges(filter_time_periods)\n results = create_full_time_periods(min_date, max_date... | [
{
"docid": "d277920cd02ddd135c01e630b1d38afa",
"score": "0.6372415",
"text": "def get_fiscalyear_revenue_timeseris_data(client_id:str)->List[Dict]:\n df = load_process_data(client_id)\n fiscalyears = df['date'].dt.to_period('A-MAR').astype(str).astype(int).unique().tolist()\n fiscalyears = so... |
3d6d60c4d0a2893d1edeb7e1161b1596 | Pull ungoogledchromium repositories, run scripts and apply patches. | [
{
"docid": "706f0331272d80f78e074f013adb38fa",
"score": "0.6706228",
"text": "def prepare(config):\n # Checkout ungoogled-chromium\n uc_git_origin = 'https://github.com/Eloston/ungoogled-chromium.git'\\\n if ungoogled_chromium_origin is None else ungoogled_chromium_origin\n git_maybe_che... | [
{
"docid": "ecac29780f2dec0c3d136abb6f000c6c",
"score": "0.57570237",
"text": "def exec_main():\n\n try_get_lock()\n\n # Allow user to force a SHA1 from env, mostly for testing purpose without\n # putting pressure on Github API (anon is rate-limited to 60 reqs/hr from the\n # same IP address... |
a5654440ae5c404efcd3140429dcd2df | Excutes 'query' in textarea in respose to the 'submit' button. Either the 'status' div or the cell's output region are updated according to whether the output mode is set to 'html' or 'text'. An explict COMMIT is required to modify a database file opened in either 'r' or 'w' mode. The pseudo command ".schema" dumps the... | [
{
"docid": "1aebb93a9022179b9fdb512f14d20a1d",
"score": "0.64445883",
"text": "def executeSQL(self, button):\n query = self.query.value\n self.history.append(query)\n self.histIdx = -1\n if (query.strip().lower().startswith('.schema')):\n table = query[7:].strip()\... | [
{
"docid": "e21bbedcfe57e4dde89c501f78053c1d",
"score": "0.5836899",
"text": "def commit(message):\n settings = _load_settings()\n status = _load_settings(\".TDB\", check=[])\n settings.update(status)\n database = settings[\"database\"]\n client, msg = _connect(settings)\n click.echo(m... |
cab3e85aee2ae1056716a2a86c357d5f | Prefect task to finalize the downscaling run. | [
{
"docid": "b68008b92ad4c8e52eef51cd0120f6e8",
"score": "0.0",
"text": "def finalize(run_parameters: RunParameters = None, **paths):\n _finalize(run_parameters, kind='runs', **paths)",
"title": ""
}
] | [
{
"docid": "f1f9202e19312af090558d9300f3c976",
"score": "0.6260884",
"text": "def teardown(self, task, job_config):",
"title": ""
},
{
"docid": "1a1c2f4fd22c23c94eab7333d3e99aee",
"score": "0.616505",
"text": "def finalize(self):\n self.logger.info(\"Please wait while finalizi... |
8c0e015fc5660b31466a762e418555be | Updates the total amount of posts made | [
{
"docid": "45fe69c7411c9f44665fd644334b8242",
"score": "0.0",
"text": "def updatepost(id):\n file_name = 'posts.json'\n posts = json.loads(openfile(file_name))\n posts['ids'].append(id)\n writefile(file_name, posts)",
"title": ""
}
] | [
{
"docid": "c75a06323bbbf01792c1d8e98464103f",
"score": "0.6925383",
"text": "def total_posts():\n return Post.published.count()",
"title": ""
},
{
"docid": "777b5802ad52cea96062f0865321a171",
"score": "0.6705593",
"text": "def update_totals(self):\r\n result = self.votes.a... |
c513c30ba32a62d232eab81145ddc9f0 | Moves the aliens to the right. | [
{
"docid": "f2ac8bb5224bf272097534b659d3890c",
"score": "0.72090906",
"text": "def alien_right(self):\n for alien_col in self._aliens:\n for alien in alien_col:\n if alien != None:\n alien.x = alien.x + ALIEN_H_WALK",
"title": ""
}
] | [
{
"docid": "323e0c73ed9e7793c55a6f67e0c32085",
"score": "0.80261123",
"text": "def move_aliens_right(self):\n self.x += ALIEN_H_WALK",
"title": ""
},
{
"docid": "5ea375b75597fc52cec7d2c55d54c02d",
"score": "0.75233734",
"text": "def _moveRight(self,list):\n for row in l... |
71f970be8e50de1a97900e71ccd59979 | Specifies the clear text password used to encrypt traffic. This field will not be displayed. | [
{
"docid": "08db6c2f6f5e31900a83b8ab61939029",
"score": "0.6029469",
"text": "def privacy_password(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"privacy_password\")",
"title": ""
}
] | [
{
"docid": "8175e7a43331357572d476380ddec002",
"score": "0.7123115",
"text": "def passwordInClear(self):",
"title": ""
},
{
"docid": "0dacc1dd60134b989efcff3f93d26faa",
"score": "0.69737387",
"text": "def password(self):\n raise AttributeError('password: write-only field')",
... |
ffd0299e38b55062b10879e459715b0a | genereates two qubit identity equal circuit with given length | [
{
"docid": "12e2084757eca5a7228e3ce921786841",
"score": "0.7121084",
"text": "def two_qubit_circuit(length: int, qubit_one: int, qubit_two: int):\n\n p = Program()\n\n for j in range(int(length/2)):\n theta = 2 * np.pi * random.random()\n gate_list = [RZ(theta, qubit_one), RX(np.pi /... | [
{
"docid": "3bbdc1b872fee8bfda6527b96be9eb4e",
"score": "0.6130477",
"text": "def test_two_qubit_gates_with_symbols(gate: cirq.Gate, expected_length: int):\n q0 = cirq.GridQubit(5, 3)\n q1 = cirq.GridQubit(5, 4)\n original_circuit = cirq.Circuit(gate(q0, q1))\n converted_circuit = original_c... |
6f7832c72923e4e0dee8f9a49ea9635e | Removes a flag from a video. | [
{
"docid": "f517e7999eef48df8f719e8d1e866d98",
"score": "0.63815784",
"text": "def allow_video(self, video_id):\r\n videos = dict([[video.video_id, video] for video in self._video_library.get_all_videos()])\r\n \r\n if video_id in videos:\r\n video = videos[video_id]\r\n ... | [
{
"docid": "c42b7812b127cfa046024ccae96f9efc",
"score": "0.7078656",
"text": "def allow_video(self, video_id):\n video_to_unflag = self._video_library.get_video(video_id)\n\n if video_to_unflag == None:\n print(\"Cannot remove flag from video: Video does not exist\")\n ... |
41be8483af4a03a53aafa33f33ad536c | pc_work_time(pll_freqdet_cf_sptr self) > float | [
{
"docid": "64d794bfd74730c9df565151b581a2c3",
"score": "0.7803105",
"text": "def pc_work_time(self):\n return _analog_swig.pll_freqdet_cf_sptr_pc_work_time(self)",
"title": ""
}
] | [
{
"docid": "d7ad19f22cde099f35faba2f23a68439",
"score": "0.7412064",
"text": "def pc_work_time(self):\n return _analog_swig.frequency_modulator_fc_sptr_pc_work_time(self)",
"title": ""
},
{
"docid": "29328812c152e88a0a6f211c963781ad",
"score": "0.74057776",
"text": "def pc_wor... |
38a4e4a6142363076e0e5fd4ce2c8300 | Set the end time. | [
{
"docid": "2d79efedc2e0fe4bf7e27917603fc06a",
"score": "0.8860281",
"text": "def set_end_time(self, t):\n self.end = spss_time(t)",
"title": ""
}
] | [
{
"docid": "ea848c0b6f5e014782c1d4d2645a38c7",
"score": "0.89383185",
"text": "def end_time(self, end_time):\n\n self._end_time = end_time",
"title": ""
},
{
"docid": "ea848c0b6f5e014782c1d4d2645a38c7",
"score": "0.89383185",
"text": "def end_time(self, end_time):\n\n s... |
db9f171c1c3e83d889bd8b0d52a853ae | Main function, starts the program, setups the paths and submits the bidnumber to process | [
{
"docid": "9f9cdb5c08ad1252520c46caea6628bc",
"score": "0.59576374",
"text": "def main(bid_number, config, logger, test=False,\n test_bids=5, timeout=30, validations=False):\n pd.options.mode.chained_assignment = None\n\n # Load paths from config\n home = os.environ[config[\"PATHS\"][\... | [
{
"docid": "5eb65670ae87521a2fc47c3a73258747",
"score": "0.67149067",
"text": "def main(argv):\n\n\tlogger.setLevel(logging.INFO)\n\thandleOptions(argv)\n\t# Load the config file\n\tconfigfile = 'config.json'\n\twith open(configfile, 'r') as f:\n\t\tconfig = json.load(f)\n\n\tcreateOutputFolder()\n\n\tl... |
22d35a6550c7797bc852d7e1e578c7a7 | Test we get the expected triggers from a button. | [
{
"docid": "f8100c7694ef9f6d37756857cd0f71c9",
"score": "0.62324995",
"text": "async def test_get_triggers(\n hass: HomeAssistant,\n device_reg: device_registry.DeviceRegistry,\n entity_reg: EntityRegistry,\n) -> None:\n config_entry = MockConfigEntry(domain=\"test\", data={})\n config_en... | [
{
"docid": "6923c51cf107f5f2eea29735f0a6cc08",
"score": "0.70568377",
"text": "def test_get_triggers(self):\n pass",
"title": ""
},
{
"docid": "74a38034de88a9c3953c027771e7e558",
"score": "0.7040851",
"text": "def getTriggerPressed(self) -> bool:\n ...",
"title": ""... |
9535fe6c46cb906bf64698332e3cd3aa | Blueprint for a simple model assigning stellar mass and quiescent/active designation to a subhalo catalog. | [
{
"docid": "7013fc7bb776bc7cea36b4c0a616cfd0",
"score": "0.0",
"text": "def SmHmBinarySFR_blueprint(\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \n smhm_model=smhm_components.Moster13SmHm, \n scatter_level = 0.2, \n redshift = sim_defaults.default_redshift, \n sfr_abcissa ... | [
{
"docid": "3193e2ab42a83b12b66740baeb711288",
"score": "0.6471101",
"text": "def Campbell15_blueprint(\n prim_haloprop_key = model_defaults.default_smhm_haloprop, \n sec_galprop_key = 'ssfr', sec_haloprop_key = 'vpeak', \n smhm_model=smhm_components.Moster13SmHm, \n scatter_level = 0.2, \n ... |
e3c73bcf5a8acca28636ed972e9037de | Set up the game and initialize the variables. | [
{
"docid": "5763ce967360b1ff9e7daaca59da4982",
"score": "0.66307664",
"text": "def setup(self):\n\n # Sprite lists\n self.player_list = arcade.SpriteList()\n self.wall_list = arcade.SpriteList(use_spatial_hash=True,\n spatial_hash_cell_size=... | [
{
"docid": "3956c41832a0e4cb1cf11a8add517eef",
"score": "0.8164389",
"text": "def setup(self):\n # Create your sprites and sprite lists here\n self.game: Game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, TILE_SIZE, 1, grid_layers = 4)\n self.game.game_message = \"Lead the Rabbit home\"\n\n ... |
eff4806a2046b7b3e0818da3f6b39388 | create instantiate of a DeploymentRequest This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. | [
{
"docid": "1f6251f81e8736df9255b2bec99d496b",
"score": "0.5890666",
"text": "def create_namespaced_deployment_request_instantiate_with_http_info(self, body, name, namespace, **kwargs):\n\n all_params = ['body', 'name', 'namespace', 'pretty']\n all_params.append('callback')\n all_pa... | [
{
"docid": "c8faee09c9ac08cfa320b38d31c94c48",
"score": "0.74152315",
"text": "def create_namespaced_deployment_request_instantiate(self, body, name, namespace, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.create_namespaced_depl... |
c708c456f86c5430638d2bf062dea899 | r"""Updates a zone resource. | [
{
"docid": "dd6d9c86894eb6ab1a1e288ee79a75af",
"score": "0.0",
"text": "def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)",
"title": ""
}
] | [
{
"docid": "b908bbf5043f2fb197e9223c0fc925f6",
"score": "0.66193604",
"text": "def _update(self) -> None:\n import regenmaschine.exceptions as exceptions\n\n try:\n self._entity_json = self._client.zones.get(self.rainmachine_id)\n except exceptions.HTTPError as exc_info:\... |
6e7c3d3e2a05a9577615a6066acf2de1 | This method is deprecated. Please switch to AddIsGameOver. | [
{
"docid": "c420d43d69cc43b11061c0e151a99f80",
"score": "0.5853067",
"text": "def DeepSingleAgentEnvWithDiscreteActionsStateDataAddIsGameOver(builder, isGameOver):\n return AddIsGameOver(builder, isGameOver)",
"title": ""
}
] | [
{
"docid": "0741ff7b41ba00efd002b94655689183",
"score": "0.67969733",
"text": "def game_over(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "1110ad2a849ae1de5830ea21087ddd3e",
"score": "0.63831997",
"text": "def game_over(self):\n # The game is over! D... |
797776956240c714c4b1dc4f47bbfbd4 | Obtain the value of node_vm_size. | [
{
"docid": "25cfcaaaf05a56b0ced7a9b70d3dd850",
"score": "0.91161466",
"text": "def get_node_vm_size(self) -> str:\n return self._get_node_vm_size()",
"title": ""
}
] | [
{
"docid": "0581d2bf606539b195ce91b0880ccd28",
"score": "0.8251246",
"text": "def vm_size(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"vm_size\")",
"title": ""
},
{
"docid": "9f2c5a1a0b8fbc3b59c1289a3fdbb736",
"score": "0.80528116",
"text": "def vm_size(self) -> Op... |
708e08f481bab3715f95ae75d509388e | Constructor, load edgeless MNIST dictionary. Also calculate each digit's max size. | [
{
"docid": "fbd16a5b9c97236697cc4ed2f6b87374",
"score": "0.8259669",
"text": "def __init__(self):\n self.dictionary = load_white_edgeless_mnist_dictionary()\n\n self.digit_to_max_size = {d: 0 for d in range(0, 10)}\n\n for k in self.dictionary:\n for arr in self.dictionar... | [
{
"docid": "6b75515e27977d064177dbb284d2c0b7",
"score": "0.6917801",
"text": "def __init__(self, batch_size=50, exluded_digits=(1, 5)):\n mnist = input_data.read_data_sets('MNIST_data', one_hot=True, reshape=False, validation_size=0)\n self.train_data = mnist.train.images # Returns np.arr... |