3 else \\\"%b\\\"\\n\\n if ordering == 'dmy':\\n formatstring = dayformat+sep+monthformat+sep+yearformat\\n elif ordering == 'myd':\\n formatstring = monthformat+sep+yearformat+sep+dayformat\\n elif ordering == 'mdy':\\n formatstring = monthformat+sep+dayformat+sep+yearformat\\n elif ordering == 'ymd':\\n formatstring = yearformat+sep+monthformat+sep+dayformat\\n elif ordering == 'ydm':\\n formatstring = yearformat+sep+dayformat+sep+monthformat\\n else:\\n raise Exception(\\\"A date formating error occurred\\\")\\n\\n return formatstring\",\n \"def new_years_eve(year):\\n return (year, DEC, 31)\",\n \"def _time_regex_match(regex: str,\\n utterance: str,\\n char_offset_to_token_index: Dict[int, int],\\n map_match_to_query_value: Callable[[str], List[int]],\\n indices_of_approximate_words: Set[int]) -> Dict[str, List[int]]:\\n linking_scores_dict: Dict[str, List[int]] = defaultdict(list)\\n number_regex = re.compile(regex)\\n for match in number_regex.finditer(utterance):\\n query_values = map_match_to_query_value(match.group())\\n # If the time appears after a word like ``about`` then we also add\\n # the times that mark the start and end of the allowed range.\\n approximate_times = []\\n if char_offset_to_token_index.get(match.start(), 0) - 1 in indices_of_approximate_words:\\n approximate_times.extend(get_approximate_times(query_values))\\n query_values.extend(approximate_times)\\n if match.start() in char_offset_to_token_index:\\n for query_value in query_values:\\n linking_scores_dict[str(query_value)].extend([char_offset_to_token_index[match.start()],\\n char_offset_to_token_index[match.start()] + 1])\\n return linking_scores_dict\",\n \"def _read_sansculottide_date(match):\\n day_string = match.group(1)\\n d = None\\n\\n for n, candidate in enumerate(names.sans_culottides):\\n if candidate.sanitized == day_string:\\n d = n\\n break\\n else:\\n return\\n\\n y = roman_to_decimal(match.group(2))\\n\\n return (y, 13, d)\",\n \"def year_expand(s):\\n regex = r\\\"^((?:19|20)\\\\d{2})?(\\\\s*-\\\\s*)?((?:19|20)\\\\d{2})?$\\\"\\n try:\\n start, dash, end = re.match(regex, ustr(s)).groups()\\n start = start or 1900\\n end = end or 2099\\n except AttributeError:\\n return 1900, 2099\\n return (int(start), int(end)) if dash else (int(start), int(start))\",\n \"def add_date_ent(matcher, doc, i, matches):\\n match_id, start, end = matches[i]\\n match_str = doc[start:end].text\\n print(match_str, 'Suspect')\\n\\n if dateparser.parse(match_str):\\n entity = Span(doc, start, end, label=\\\"MYDATE\\\")\\n doc.ents += (entity,)\\n print( match_str, 'VALID')\\n else:\\n print( match_str, 'INVALID')\",\n \"def collect_year_and_holder(detected_copyright):\\n for copyr in detected_copyright:\\n if isinstance(copyr, nltk_tree_Tree):\\n # logger.debug('n: ' + str(copyr))\\n node_text = CopyrightDetector_as_str(copyr)\\n copyr_label = copyr.label()\\n if 'YR-RANGE' in copyr_label:\\n years_append(refine_date(node_text))\\n elif 'NAME' == copyr_label or 'COMPANY' in copyr_label:\\n # FIXME : this would wreck things like 23andme\\n # where a company name contains numbers\\n holders_append(refine_author(node_text))\\n # logger.debug('CopyrightDetector: node_text: ' + node_text)\\n collect_year_and_holder(copyr)\",\n \"def _parse_link_date_map(self, response):\\n link_date_map = defaultdict(list)\\n for link in response.css(\\n \\\".vc_col-sm-4.column_container:nth-child(1) .mk-text-block.indent16\\\"\\n )[:1].css(\\\"a\\\"):\\n link_str = link.xpath(\\\"./text()\\\").extract_first()\\n link_start = self._parse_start(link_str)\\n if link_start:\\n link_date_map[link_start.date()].append(\\n {\\n \\\"title\\\": re.sub(r\\\"\\\\s+\\\", \\\" \\\", link_str.split(\\\" – \\\")[-1]).strip(),\\n \\\"href\\\": link.attrib[\\\"href\\\"],\\n }\\n )\\n for section in response.css(\\n \\\".vc_col-sm-4.column_container:nth-child(1) .vc_tta-panel\\\"\\n ):\\n year_str = section.css(\\\".vc_tta-title-text::text\\\").extract_first().strip()\\n for section_link in section.css(\\\"p > a\\\"):\\n link_str = section_link.xpath(\\\"./text()\\\").extract_first()\\n link_dt = self._parse_start(link_str, year=year_str)\\n if link_dt:\\n link_date_map[link_dt.date()].append(\\n {\\n \\\"title\\\": re.sub(\\n r\\\"\\\\s+\\\", \\\" \\\", link_str.split(\\\" – \\\")[-1]\\n ).strip(),\\n \\\"href\\\": section_link.xpath(\\\"@href\\\").extract_first(),\\n }\\n )\\n return link_date_map\",\n \"def get_year(parameters_dictionary):\\n if \\\"start-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"start-year\\\"])\\n return str(year) + str(year + 1)\\n elif \\\"end-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"end-year\\\"])\\n return str(year - 1) + str(year)\\n else:\\n return str(THIS_YEAR - 1) + str(THIS_YEAR)\",\n \"def translate_years(val):\\n if val.find(\\\"-\\\") > 0:\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n one = int(tokens[0])\\n two = int(tokens[1])\\n one = (1900 + one) if one > 50 else (2000 + one)\\n two = (1900 + two) if two > 50 else (2000 + two)\\n return range(one, two + 1)\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n return [int(f\\\"{'19' if int(t) > 50 else '20'}{t}\\\") for t in tokens]\",\n \"def year_cost_rule(_m, y):\\r\\n\\r\\n return sum(m.RHO[y, s] * m.SCEN[y, s] for s in m.S)\",\n \"def parse_date_str(self, date_str, date_format=DATE_FORMAT):\\n try:\\n return self.adjust_year(datetime.strptime(date_str, date_format).date())\\n except ValueError:\\n return None\",\n \"def year(self):\\n\\n properties_file = open(self.scenario_path + \\\"/conf/sandag_abm.properties\\\", \\\"r\\\")\\n year = None\\n\\n for line in properties_file:\\n # strip all white space from the line\\n line = line.replace(\\\" \\\", \\\"\\\")\\n\\n # find line containing \\\"scenarioYear=\\\"\\n m = re.compile(\\\"scenarioYear=\\\").match(line)\\n if m:\\n # take the portion of the line after the matching string\\n # and return as the scenario year\\n year = int(line[m.end():])\\n break\\n\\n properties_file.close()\\n\\n return year\",\n \"def get_year_month_day_from_date(date: str) -> tuple:\\n match = re.findall(\\\"\\\\d{2,4}\\\", date[:len(DATE_FORMAT)])\\n if len(match) != 3:\\n raise ValueError(\\\"Invalid date input given at regex match. Got (%s)\\\" % date)\\n year, month, day = match\\n if len(year) != 4 or len(month) != 2 or len(day) != 2:\\n raise ValueError(\\\"Invalid date input given. Got (%s)\\\" % date)\\n return year, month, day\",\n \"def test_date_by_lt_yr(self):\\n spi_search = \\\"find date < 2002\\\"\\n inv_search = 'year:0->2002'\\n self._compare_searches(inv_search, spi_search)\",\n \"def year_absolute_price_difference_rule(_m, y):\\r\\n\\r\\n return m.z_p1[y] + m.z_p2[y]\",\n \"def year(self) -> int:\\n if self.is_old_style:\\n yy = int(self.split('/', 1)[1][0:2])\\n else:\\n yy = int(self[:2])\\n if yy > 90:\\n return 1900 + yy\\n return 2000 + yy\",\n \"def analyze(filename):\\n\\n date_dict = {\\\"2013\\\":0,\\\"2014\\\":0,\\\"2015\\\":0,\\\"2016\\\":0,\\\"2017\\\":0,\\\"2018\\\":0}\\n\\n start = datetime.datetime.now()\\n\\n with open(filename) as csvfile:\\n for line in csvfile:\\n lrow = line.split(',')\\n\\n if \\\"ao\\\" in lrow[6]:\\n found += 1\\n\\n # pylint: disable=C0122\\n # Less than should be the default comparison operation\\n if \\\"2012\\\" < lrow[5][6:] < \\\"2019\\\":\\n date_dict[lrow[5][6:]] += 1\\n\\n\\n print(f\\\"'ao' was found {found} times\\\")\\n print(\\n f\\\"2013:{date_dict['2013']}\\\\t\\\"\\n f\\\"2014:{date_dict['2014']}\\\\t\\\"\\n f\\\"2015:{date_dict['2015']}\\\\t\\\"\\n f\\\"2016:{date_dict['2016']}\\\\t\\\"\\n f\\\"2017:{date_dict['2017']}\\\\t\\\"\\n f\\\"2018:{date_dict['2018']}\\\\n\\\"\\n )\\n end = datetime.datetime.now()\\n return (\\n start,\\n end,\\n # {\\n # \\\"2013\\\": _2013,\\n # \\\"2014\\\": _2014,\\n # \\\"2015\\\": _2015,\\n # \\\"2016\\\": _2016,\\n # \\\"2017\\\": _2017,\\n # \\\"2018\\\": _2018,\\n # },\\n found,\\n )\",\n \"def year_emissions_rule(_m, y):\\r\\n\\r\\n return sum(m.SCENARIO_EMISSIONS[y, s] for s in m.S)\",\n \"def year_and_hour(self, text):\\n\\n # Find matches for \\\"yr\\\", \\\"yrs\\\", \\\"hr\\\", \\\"hrs\\\"\\n matches_year = re.findall(r'\\\\b\\\\d+\\\\s*yr\\\\b', text)\\n matches_years = re.findall(r'\\\\b\\\\d+\\\\s*yrs\\\\b', text)\\n matches_hour = re.findall(r'\\\\b\\\\d+\\\\s*hr\\\\b', text)\\n matches_hours = re.findall(r'\\\\b\\\\d+\\\\s*hrs\\\\b', text)\\n\\n # replace all matches accordingly\\n if len(matches_year) != 0:\\n text = re.sub(r'\\\\b\\\\d+\\\\s*yr\\\\b', \\\"year\\\", text)\\n if len(matches_years) != 0:\\n text = re.sub(r'\\\\b\\\\d+\\\\s*yrs\\\\b', \\\"year\\\", text)\\n if len(matches_hour) != 0:\\n text = re.sub(r'\\\\b\\\\d+\\\\s*hr\\\\b', \\\"hour\\\", text)\\n if len(matches_hours) != 0:\\n text = re.sub(r'\\\\b\\\\d+\\\\s*hrs\\\\b', \\\"hour\\\", text)\\n return text\",\n \"def _enumerate_years(self, preprocessed_data, disjoint):\\n pass\",\n \"def year_tracker(words):\\n new_words = []\\n for w in words:\\n new_word = re.sub(r\\\"^[1][789][0-9]{2}$\\\", \\\"jahreszahl\\\", w) # for 1700-1999\\n new_word = re.sub(r\\\"^[2][01][0-9]{2}$\\\", \\\"jahreszahl\\\", new_word) # for 2000-2199\\n new_words += [new_word]\\n return new_words\",\n \"def find_year(title):\\n # find all patterns that match the year pattern\\n matches = year_pattern.findall(title)\\n # if any matches\\n if matches:\\n # record for convienence\\n year = matches[-1]\\n too_short = len(title) < 8\\n # If the year is the title then return None\\n if year == title:\\n return None\\n # If we have enough room for 1 block of 4 digits and its at the start\\n elif too_short and title.startswith(year):\\n return None\\n else:\\n return year\",\n \"def __format_management(self, index, matched):\\n\\n for _, case_data in self.format_cases.items():\\n if int(index) in case_data[0]:\\n # The regex number is into the currently read case data.\\n\\n # We return a list with the formatted elements.\\n # 1. We convert the day to 2 digits.\\n # 2. We convert the month to the unified format.\\n # 3. We return the year.\\n return [\\n converter.Digit2Digits(matched[case_data[1][0]]).get_converted(),\\n converter.Month(matched[case_data[1][1]]).get_converted(),\\n str(matched[case_data[1][2]]),\\n ]\\n\\n return matched # pragma: no cover\",\n \"def get_year_with_links():\\n response = get_response(MAIN_PAGE)\\n if response.ok:\\n soup = BeautifulSoup(response.text, 'html.parser')\\n years_li = soup.find_all(\\n 'md-card-footer'\\n )\\n years_dict = {}\\n # Not including the last <a> tag because that is not relevant.\\n for years_html in years_li[:-1]:\\n year = [num for num in years_html.text.split() if num.isdigit()][0]\\n relative_link = years_html.select('a')[0].get('href')\\n full_link = HOME_PAGE + relative_link\\n years_dict[year] = full_link\\n return years_dict\\n else:\\n print('Something Went Wrong')\\n print(f'Status Code: {response.status_code}')\\n sys.exit(1)\",\n \"def getdate(regex, fname):\\n ex = re.compile(regex)\\n match = re.search(ex, fname)\\n date = match.group()\\n return date\",\n \"def getYearsOfService(person, chamber=\\\"\\\"):\\n\\tcongresses = []\\n\\n\\tif chamber and chamber not in [\\\"House\\\", \\\"Senate\\\"]:\\n\\t\\treturn []\\n\\n\\tif chamber:\\n\\t\\tif \\\"congresses_\\\" + chamber.lower() in person:\\n\\t\\t\\tcongresses = person[\\\"congresses_\\\" + chamber.lower()]\\n\\telse:\\n\\t\\tif \\\"congresses\\\" in person:\\n\\t\\t\\tcongresses = person[\\\"congresses\\\"]\\n\\n\\tif not congresses:\\n\\t\\treturn []\\n\\n\\tyearSet = [[congressToYear(congress[0], 0), congressToYear(congress[1], 1)] for congress in congresses]\\n\\n\\tif chamber and len(yearSet) and \\\"voting_dates\\\" in person and chamber in person[\\\"voting_dates\\\"] and int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\tif yearSet[-1][1] > int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\t\\tyearSet[-1][1] = int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0])\\n\\n\\treturn yearSet\",\n \"def get_number_year(text):\\n val = get_number(text)\\n if val is None or val < 1700 or val > (datetime.date.today().year + 1):\\n return None\\n return val\",\n \"def year(self):\\n return self._years\",\n \"def analyze(filename, search_term):\\n start = datetime.datetime.now()\\n with open(filename) as csvfile:\\n reader = csv.reader(csvfile, delimiter=',', quotechar='\\\"')\\n new_ones = []\\n year_count = {\\n \\\"2013\\\": 0,\\n \\\"2014\\\": 0,\\n \\\"2015\\\": 0,\\n \\\"2016\\\": 0,\\n \\\"2017\\\": 0,\\n \\\"2018\\\": 0\\n }\\n found = 0\\n for row in reader:\\n if row:\\n lrow = list(row)\\n if lrow[5] > '00/00/2012':\\n if search_term in row[6]:\\n found +=1\\n try:\\n year_count[lrow[5][-4:]] += 1 \\n except:\\n pass\\n print(year_count)\\n print(f\\\"'{search_term}' was found {found} times\\\")\\n end = datetime.datetime.now()\\n return (start, end, year_count, found)\",\n \"def year_lookup_bounds(self, value):\\n first = '%s-01-01 00:00:00'\\n # SQL Server doesn't support microseconds\\n last = '%s-12-31 23:59:59'\\n return [first % value, last % value]\",\n \"def year_price_target_difference_rule(_m, y):\\r\\n\\r\\n return m.z_t1[y] + m.z_t2[y]\",\n \"def parse_title_and_year(self, refstr):\\n\\n refstr = self.re_cleanup_unstructured.sub(', ', refstr, 1)\\n match = self.rec_field_unstructured.match(refstr)\\n if match:\\n year = match.group('year')\\n title = match.group('title')\\n return title,year\\n return None,None\",\n \"def get_first_year(data_id):\\n if data_id.startswith(\\\"global\\\"):\\n return 2011\\n if data_id.endswith(\\\"precip\\\"):\\n return 1948\\n if data_id.startswith(\\\"cfsv2\\\"):\\n return 2011\\n if data_id.startswith(\\\"nmme\\\"):\\n return 1982\\n if data_id.endswith(\\\"tmp2m\\\") or data_id.endswith(\\\"tmin\\\") or data_id.endswith(\\\"tmax\\\"):\\n return 1979\\n if \\\"sst\\\" in data_id or \\\"icec\\\" in data_id:\\n return 1981\\n if data_id.endswith(\\\"mei\\\"):\\n return 1950\\n if data_id.endswith(\\\"mjo\\\"):\\n return 1974\\n if data_id.endswith(\\\"sce\\\"):\\n return 1966\\n if \\\"wind\\\" in data_id:\\n return 1948\\n if (\\\"slp\\\" in data_id or \\\"pr_wtr.eatm\\\" in data_id or \\\"rhum.sig995\\\" in data_id or\\n \\\"pres.sfc.gauss\\\" in data_id or \\\"pevpr.sfc.gauss\\\" in data_id):\\n return 1948\\n raise ValueError(\\\"Unrecognized data_id \\\"+data_id)\",\n \"def _sanitize_year(self, datestr):\\n try:\\n year = str(datetime.datetime.strptime(datestr, '%Y').date().year)\\n except:\\n try:\\n year = str(datetime.datetime.strptime(datestr,\\n '%Y-%m-%d').date().year)\\n except:\\n year = None\\n return year\",\n \"def year_absolute_baseline_difference_rule(_m, y):\\r\\n\\r\\n return m.z_b1[y] + m.z_b2[y]\",\n \"def year_absolute_baseline_difference_rule(_m, y):\\r\\n\\r\\n return m.z_b1[y] + m.z_b2[y]\",\n \"def num2year(iyear):\\n iyear = np.asarray([int(y) for y in iyear])\\n fyear = lambda y, m, d: y + (m - 1)/12. + d/365.25\\n ymd = [int2ymd(iy) for iy in iyear]\\n return [fyear(y,m,d) for y,m,d in ymd]\",\n \"def convert_month(match):\\n day, month, year = tuple(match.group().split())\\n list_full_months = [\\\"janvier\\\", \\\"février\\\", \\\"mars\\\", \\\"avril\\\", \\\"mai\\\", \\\"juin\\\", \\\"juillet\\\", \\\"août\\\", \\\"septembre\\\", \\\"octobre\\\",\\n \\\"novembre\\\", \\\"décembre\\\"]\\n list_trunc_month = [\\\"janv.\\\", \\\"févr.\\\", \\\"mars\\\", \\\"avr.\\\", \\\"mai\\\", \\\"juin\\\", \\\"juill.\\\", \\\"août\\\", \\\"sept.\\\", \\\"oct.\\\",\\n \\\"nov.\\\", \\\"déc.\\\"]\\n if month in list_full_months:\\n month_number = list_full_months.index(month) + 1\\n elif month in list_trunc_month:\\n month_number = list_trunc_month.index(month) + 1\\n if day in [\\\"1°\\\", \\\"1er\\\"]:\\n day = 1\\n return \\\"%s%02d%02d\\\" % (year, month_number, int(day))\",\n \"def test_date_by_gt_yr(self):\\n spi_search = \\\"find date > 1980\\\"\\n inv_search = 'year:1980->9999'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_file_year(filepath):\\n filename = os.path.split(filepath)[-1]\\n date = filename.split('.')[-2]\\n year = int(date.split('-')[0])\\n month = int(date.split('-')[1])\\n return year, month\",\n \"def readdate(line):\\n splitted = line.split('::') \\n \\n # Convert the date\\n date = dt.datetime.strptime(splitted[1].strip(), '%a %b %d %H:%M:%S')\\n correctdate = date.replace(year=YEAR)\\n return correctdate\",\n \"def from_re_match(cls, match):\\n kwargs = match.groupdict()\\n location = kwargs['location'].split()\\n kwargs['location'] = (int(location[0]), int(location[1]),\\n int(location[2]))\\n return cls(**kwargs)\",\n \"def dict_by_year(incoming_dict):\\n by_year = {}\\n for key in incoming_dict.keys():\\n try:\\n year = incoming_dict[key]['year']\\n keywords = incoming_dict[key]['keywords']\\n for word in keywords:\\n if year in by_year.keys():\\n if word in by_year[year].keys():\\n by_year[year][word] += keywords[word]\\n else:\\n by_year[year][word] = keywords[word]\\n else:\\n by_year[year] = {word: keywords[word]}\\n except KeyError:\\n print \\\"KeyError in dict_by_year. Key = {0}\\\".format(key)\\n return by_year\",\n \"def agline2(line):\\n \\n vals = {}\\n y = line.strip('\\\\n').split(',')\\n y.extend(y[0].strip('.MTS').split('_'))\\n \\n #print(y)\\n \\n x = ['movie', 'moviecode', 'offset', 'well', 'agmin', 'agsec', 'agdur', \\n 'agtype', 'agcomm', 'escmin', 'escsec', 'escdur', 'esctype', 'escbeh', \\n 'esccomm', 'gen', 'date', 'assay', 'fps', 'flyid', 'side', 'moviepart']\\n \\n z = zip(x, y)\\n\\n for item in z:\\n vals[item[0]] = item[1]\\n\\n return(vals)\",\n \"def test_request_wmts_date_from_year_layer(self):\\n ref_hash = '7c7fcdfaea0faf91afdd690eb7fe4dea'\\n req_url = r'http://localhost/reproject/test/wmts/wmts.cgi?layer=test_weekly_jpg&tilematrixset=GoogleMapsCompatible_Level3&Service=WMTS&Request=GetTile&Version=1.0.0&Format=image%2Fjpeg&TileMatrix=0&TileCol=0&TileRow=0&time=2012-02-22'\\n if DEBUG:\\n print('\\\\nTesting: Request tile with date from \\\"year\\\" layer via WMTS')\\n print('URL: ' + req_url)\\n check_result = check_tile_request(req_url, ref_hash)\\n self.assertTrue(check_result, 'WMTS date request from \\\"year\\\" layer does not match what\\\\'s expected. URL: ' + req_url)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7254186","0.6608783","0.6011262","0.5957045","0.59214723","0.59050083","0.5880262","0.58387053","0.5810087","0.58070123","0.57679784","0.57420975","0.57067704","0.5700978","0.56853163","0.5677188","0.56761444","0.56604224","0.56588614","0.5644384","0.5627568","0.5617731","0.55672073","0.55098116","0.5491169","0.54310954","0.5423587","0.54230547","0.5416472","0.5383502","0.5379282","0.5338415","0.53015065","0.5296839","0.5293354","0.52878916","0.5280262","0.5277243","0.52609175","0.52601975","0.52322316","0.52254117","0.52219605","0.5183399","0.5153969","0.5149939","0.5145372","0.51407814","0.5132406","0.5119984","0.51107556","0.51080257","0.5085389","0.50772023","0.50665957","0.5063537","0.5059146","0.5045999","0.50302184","0.5006524","0.50050694","0.50036407","0.5002426","0.4990402","0.4983559","0.4969334","0.49647698","0.49540114","0.49493814","0.4938129","0.49002734","0.48877388","0.48848587","0.48831987","0.48813286","0.48487765","0.48403826","0.48339394","0.4830495","0.4818229","0.48147115","0.4814329","0.48138118","0.48077786","0.479941","0.4796586","0.47780848","0.4774372","0.47734866","0.47713605","0.47713605","0.4769724","0.47695795","0.47639874","0.47467452","0.4738076","0.47295696","0.4724944","0.47226396","0.47180152"],"string":"[\n \"0.7254186\",\n \"0.6608783\",\n \"0.6011262\",\n \"0.5957045\",\n \"0.59214723\",\n \"0.59050083\",\n \"0.5880262\",\n \"0.58387053\",\n \"0.5810087\",\n \"0.58070123\",\n \"0.57679784\",\n \"0.57420975\",\n \"0.57067704\",\n \"0.5700978\",\n \"0.56853163\",\n \"0.5677188\",\n \"0.56761444\",\n \"0.56604224\",\n \"0.56588614\",\n \"0.5644384\",\n \"0.5627568\",\n \"0.5617731\",\n \"0.55672073\",\n \"0.55098116\",\n \"0.5491169\",\n \"0.54310954\",\n \"0.5423587\",\n \"0.54230547\",\n \"0.5416472\",\n \"0.5383502\",\n \"0.5379282\",\n \"0.5338415\",\n \"0.53015065\",\n \"0.5296839\",\n \"0.5293354\",\n \"0.52878916\",\n \"0.5280262\",\n \"0.5277243\",\n \"0.52609175\",\n \"0.52601975\",\n \"0.52322316\",\n \"0.52254117\",\n \"0.52219605\",\n \"0.5183399\",\n \"0.5153969\",\n \"0.5149939\",\n \"0.5145372\",\n \"0.51407814\",\n \"0.5132406\",\n \"0.5119984\",\n \"0.51107556\",\n \"0.51080257\",\n \"0.5085389\",\n \"0.50772023\",\n \"0.50665957\",\n \"0.5063537\",\n \"0.5059146\",\n \"0.5045999\",\n \"0.50302184\",\n \"0.5006524\",\n \"0.50050694\",\n \"0.50036407\",\n \"0.5002426\",\n \"0.4990402\",\n \"0.4983559\",\n \"0.4969334\",\n \"0.49647698\",\n \"0.49540114\",\n \"0.49493814\",\n \"0.4938129\",\n \"0.49002734\",\n \"0.48877388\",\n \"0.48848587\",\n \"0.48831987\",\n \"0.48813286\",\n \"0.48487765\",\n \"0.48403826\",\n \"0.48339394\",\n \"0.4830495\",\n \"0.4818229\",\n \"0.48147115\",\n \"0.4814329\",\n \"0.48138118\",\n \"0.48077786\",\n \"0.479941\",\n \"0.4796586\",\n \"0.47780848\",\n \"0.4774372\",\n \"0.47734866\",\n \"0.47713605\",\n \"0.47713605\",\n \"0.4769724\",\n \"0.47695795\",\n \"0.47639874\",\n \"0.47467452\",\n \"0.4738076\",\n \"0.47295696\",\n \"0.4724944\",\n \"0.47226396\",\n \"0.47180152\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7606871"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80496,"cells":{"query":{"kind":"string","value":"Return a set of decades. If disjoint is false, returns a set of decades that spans the entire range. preprocessed_data a heterogeneous set of ints and tuples of ints"},"document":{"kind":"string","value":"def _enumerate_decades(self, preprocessed_data, disjoint):\n\n if disjoint:\n decades = set()\n for decade in preprocessed_data:\n if isinstance(decade, int):\n decades.add(decade // 10 * 10)\n elif isinstance(decade, tuple):\n decades = decades | set(range(decade[0] // 10 * 10, decade[1] + 1, 10))\n\n return decades"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def decades(self, data, disjoint=True):\n\n decade_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\n except (AssertionError, TypeError):\n # single value\n preprocessed_year_data = self._extract_year_data(data)\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\n\n return decade_set","def get_decade_list(start_decade, end_decade):\n decade_list = []\n for decade in range(start_decade, end_decade, 10):\n decade_list.append(decade)\n return decade_list","def years(self, data, disjoint=True):\n\n year_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\n\n except TypeError:\n # single value\n preprocessed_year_data = self._extract_year_data(data)\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\n\n return year_set","def dset_no_collection():\n _dset = dataset.Dataset(5)\n _dset.add_bool(\"idx\", val=[0, 1, 1, 0, 1])\n _dset.add_float(\"numbers\", val=[1, 2, 3, 4, 5])\n _dset.add_position(\"sat_pos\", val=np.ones((5, 3)), system=\"trs\")\n _dset.add_position(\"site_pos\", val=np.ones((5, 3)) * 2, system=\"trs\", other=_dset.sat_pos)\n _dset.add_position_delta(\"site_delta\", val=np.ones((5, 3)) * 0.5, system=\"trs\", ref_pos=_dset.site_pos)\n _dset.add_posvel(\"sat_posvel\", val=np.ones((5, 6)), system=\"trs\")\n _dset.add_posvel(\"site_posvel\", val=np.ones((5, 6)) * 2, system=\"trs\", other=_dset.sat_posvel)\n _dset.add_posvel_delta(\"site_posvel_delta\", val=np.ones((5, 6)) * 0.5, system=\"trs\", ref_pos=_dset.site_posvel)\n _dset.add_sigma(\"numbers2\", val=[3, 3, 3, 3, 3], sigma=[0.2, 0.2, 0.2, 0.2, 0.2])\n _dset.add_text(\"text\", val=[\"aaa\", \"aaa\", \"aaa\", \"aaa\", \"aaa\"])\n _dset.add_time(\"time\", val=[datetime(2015, 1, i) for i in range(5, 10)], scale=\"utc\", fmt=\"datetime\")\n _dset.add_time_delta(\"time_delta\", val=[timedelta(seconds=i) for i in range(20, 25)], scale=\"utc\", fmt=\"timedelta\")\n return _dset","def create_C1(data_set):\n C1 = set()\n for t in data_set:\n for item in t:\n item_set = frozenset([item])\n C1.add(item_set)\n return C1","def create_C1(data_set):\r\n C1 = set()\r\n for t in data_set:\r\n for item in t:\r\n item_set = frozenset([item])\r\n C1.add(item_set)\r\n return C1","def dset(self):\n\n a = 0.0\n b = 0.0\n sums = np.sum(self.descriptors, axis=0)\n for sum in sums:\n if sum > 0:\n if sum == self.d_length:\n b += 1.\n else:\n a += 1.\n return a / (a+b)","def __call__(self, uind: Set[Ind]) -> FrozenSet[Ind]:\n ks = len(uind)\n try:\n start_arity, G = self.generate_graph(uind)\n except StopIteration:\n return frozenset()\n\n _logger.info('Looking for hypercliques')\n H = find_hypercliques(G)\n _logger.info('Validating %d hypercliques', len(H))\n I = self._validate_all(H)\n\n result = set(filter(lambda i: len(i) == 1, I))\n for m in range(start_arity + 1, ks):\n _logger.info('Iteration %d (%d candidates)', m, len(I))\n _logger.info('Iteration %d (%d positives)', m, len(result))\n C = set()\n for c in I:\n if c.valid and len(c) >= m - 1:\n result.add(c)\n if not c.valid and len(c) >= m:\n C.add(c)\n k_ary = gen_k_ary_ind_from_cliques(m, C)\n _logger.info('%d %d-ary generated from %d', len(k_ary), m, len(C))\n Gm = Graph()\n Gm.E = self._validate_all(k_ary)\n Gm.E = set(filter(lambda e: e.valid, Gm.E))\n if Gm.empty() or True:\n return frozenset(map(Edge.to_ind, result))\n result.update(gen_sub_inds(m, Gm, result))\n Gm.V = frozenset(reduce(frozenset.union, map(lambda e: e.set, Gm.E), frozenset()))\n H = find_hypercliques(Gm)\n I = self._validate_all(H)\n\n # Convert candidates back to Ind\n return frozenset(map(Edge.to_ind, result))","def dominance_frontiers(G, start):\n idom = nx.immediate_dominators(G, start)\n\n df = {u: set() for u in idom}\n for u in idom:\n if len(G.pred[u]) >= 2:\n for v in G.pred[u]:\n if v in idom:\n while v != idom[u]:\n df[v].add(u)\n v = idom[v]\n return df","def split_by_year(config, dbsession, group):\n years = []\n decades = []\n centuries = []\n with_year = 0\n for item in group.items:\n if config[\"data\"][\"year_field\"] in item.attributes and item.attributes[config[\"data\"][\"year_field\"]]:\n years.append(item.attributes[config[\"data\"][\"year_field\"]])\n with_year = with_year + 1\n if with_year / len(group.items) > 0.95: # noqa: PLR2004\n common = [(int(v), c) for v, c in Counter(years).most_common()]\n start_year = min([c for c, _ in common])\n end_year = max([c for c, _ in common])\n if start_year != end_year:\n if (end_year - start_year) <= 100 and (end_year - start_year) > 10: # noqa: PLR2004\n start_decade = math.floor(start_year / 10)\n end_decade = math.floor(end_year / 10)\n decades = []\n for start_year in range(start_decade * 10, (end_decade + 1) * 10, 10):\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n start_year <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < start_year + 10\n ):\n if len(decades) == 0 or decades[-1][0][0] != start_year:\n decades.append([[start_year], 1])\n else:\n decades[-1][1] = decades[-1][1] + 1\n idx = 0\n while idx < len(decades) - 1:\n if decades[idx][1] + decades[idx + 1][1] < 100: # noqa: PLR2004\n decades[idx][0].extend(decades[idx + 1][0])\n decades[idx][1] = decades[idx][1] + decades[idx + 1][1]\n decades.pop(idx + 1)\n else:\n idx = idx + 1\n for years, _ in decades:\n new_group = None\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n years[0] <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < years[-1] + 10\n ):\n if new_group is None:\n if len(years) == 1:\n label = f\"{years[0]}s\"\n else:\n label = f\"{years[0]}s-{years[-1]}s\"\n new_group = Group(\n value=str(start_year),\n label=f\"{group.label} - {label}\",\n parent=group,\n split=\"time\",\n )\n dbsession.add(new_group)\n item.group = new_group\n if group.items:\n new_group = Group(value=group.label, label=group.label, parent=group, split=\"time\")\n dbsession.add(new_group)\n for item in list(group.items):\n item.group = new_group\n return True\n elif (end_year - start_year) > 100: # noqa: PLR2004\n start_century = math.floor(start_year / 100)\n end_century = math.floor(end_year / 100)\n centuries = []\n for start_year in range(start_century * 100, (end_century + 1) * 100, 100):\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n start_year <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < start_year + 100\n ):\n if len(centuries) == 0 or centuries[-1][0][0] != start_year:\n centuries.append([[start_year], 1])\n else:\n centuries[-1][1] = centuries[-1][1] + 1\n idx = 0\n while idx < len(centuries) - 1:\n if centuries[idx][1] + centuries[idx + 1][1] < 100: # noqa: PLR2004\n centuries[idx][0].extend(centuries[idx + 1][0])\n centuries[idx][1] = centuries[idx][1] + centuries[idx + 1][1]\n centuries.pop(idx + 1)\n else:\n idx = idx + 1\n for years, _ in centuries:\n new_group = None\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n years[0] <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < years[-1] + 100\n ):\n if new_group is None:\n if len(years) == 1:\n century = math.floor(years[0] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n label = f\"{century}rd\"\n else:\n label = f\"{century}th\"\n else:\n century = math.floor(years[0] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n start_label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n start_label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n start_label = f\"{century}rd\"\n else:\n start_label = f\"{century}th\"\n century = math.floor(years[-1] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n end_label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n end_label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n end_label = f\"{century}rd\"\n else:\n end_label = f\"{century}th\"\n label = f\"{start_label}-{end_label}\"\n new_group = Group(\n value=str(start_year),\n label=f\"{group.label} - {label} century\",\n parent=group,\n split=\"time\",\n )\n dbsession.add(new_group)\n item.group = new_group\n if group.items:\n new_group = Group(value=group.label, label=group.label, parent=group, split=\"time\")\n dbsession.add(new_group)\n for item in list(group.items):\n item.group = new_group\n return True\n return False","def ecdf(data):\n x = np.sort(data)\n cdf = np.linspace(0, 1, len(x))\n return cdf, x","def histogram_decades(our_data):\n decade_dict = {}\n for album in our_data:\n decade = int(album['year'])//10\n if decade in decade_dict:\n decade_dict[decade] += 1\n else:\n decade_dict[decade] = 1\n return decade_dict","def doffsets(self):\n self._flatten()\n for n in self.seq[1:len(self.seq)-1]:\n yield sorted([ d['idx'] - n['idx'] for d in n['edges'] ])\n return","def continuity_test(districts):\r\n for tag in range(1,6):\r\n if tag in districts:\r\n X = np.where((districts==tag)|(districts==0))\r\n contig = set([(X[0][0],X[1][0])])\r\n prev_len = 0\r\n while len(contig) > prev_len and prev_len<5:\r\n prev_len = len(contig)\r\n for r,c in contig.copy():\r\n if r>0 and districts[r-1,c] in [tag,0]:\r\n contig.add((r-1,c))\r\n if r<4 and districts[r+1,c] in [tag,0]:\r\n contig.add((r+1,c))\r\n if c>0 and districts[r,c-1] in [tag,0]:\r\n contig.add((r,c-1))\r\n if c<4 and districts[r,c+1] in [tag,0]:\r\n contig.add((r,c+1))\r\n if prev_len < 5:\r\n return False\r\n return True","def get_district_time_series(district,\n start_date,\n cumulative=False,\n num_days=-1):\n district_data = get_district_data(district)\n\n ret_list = []\n prev_date = dt.strptime(start_date, date_format)\n for obj in district_data:\n curr_date = dt.strptime(obj[0], date_format)\n # Condition to check if ret_list has been initiated.\n # Only then check if some days got missing in between.\n if ret_list:\n # Calculate number of days between prev_date and curr date\n days = (curr_date - prev_date).days\n if days > 1:\n # Append the last number again for those many days\n for _in in range(days - 1):\n ret_list.append(ret_list[-1])\n\n # Add data for the range given\n if curr_date >= dt.strptime(start_date, date_format):\n # Case: When the given start date is before the start of infections\n if not ret_list:\n initial_days = (curr_date - dt.strptime(start_date, date_format)).days\n for _in in range(initial_days):\n ret_list.append(0)\n ret_list.append(obj[1])\n\n prev_date = curr_date\n if len(ret_list) == num_days:\n break\n\n if not cumulative:\n convert_to_daily(ret_list)\n return ret_list","def _compute_semantic_cuts(self):\n # back-ward collect dominators as candidate merging points\n merging_points = [self._end_uid]\n cur_uid = self._end_uid\n while cur_uid != self._start_uid:\n cur_node = self._nodes[cur_uid]\n dominator_uid = cur_node.get_dominator()\n merging_points.append(dominator_uid)\n cur_uid = dominator_uid\n # create additional candidate couples\n semantic_cuts = []\n mp_len = len(merging_points)\n step = 2\n while step < mp_len:\n k = 0\n done = False\n while not done:\n tail_uid = merging_points[k]\n if k + step < mp_len:\n head_uid = merging_points[k + step]\n else:\n head_uid = merging_points[mp_len - 1]\n done = True\n if head_uid != tail_uid:\n semantic_cuts.append({\n \"head_uids\" : [head_uid],\n \"tail_uids\" : [tail_uid],\n })\n k += step\n step *= 2\n return semantic_cuts","def ecdf(data):\n x = np.sort(data)\n y = np.arange(1,len(x)+1)/len(x)\n\n return x,y","def dset_full():\n _dset = dataset.Dataset(5)\n _dset.add_bool(\"idx\", val=[0, 1, 1, 0, 1])\n _dset.add_float(\"numbers\", val=[1, 2, 3, 4, 5])\n _dset.add_position(\"sat_pos\", val=np.ones((5, 3)), system=\"trs\")\n _dset.add_position(\"site_pos\", val=np.ones((5, 3)) * 2, system=\"trs\", other=_dset.sat_pos)\n _dset.add_position_delta(\"site_delta\", val=np.ones((5, 3)) * 0.5, system=\"trs\", ref_pos=_dset.site_pos)\n _dset.add_posvel(\"sat_posvel\", val=np.ones((5, 6)), system=\"trs\")\n _dset.add_posvel(\"site_posvel\", val=np.ones((5, 6)) * 2, system=\"trs\", other=_dset.sat_posvel)\n _dset.add_posvel_delta(\"site_posvel_delta\", val=np.ones((5, 6)) * 0.5, system=\"trs\", ref_pos=_dset.site_posvel)\n _dset.add_sigma(\"numbers2\", val=[3, 3, 3, 3, 3], sigma=[0.2, 0.2, 0.2, 0.2, 0.2])\n _dset.add_text(\"text\", val=[\"aaa\", \"aaa\", \"aaa\", \"aaa\", \"aaa\"])\n _dset.add_time(\"time\", val=[datetime(2015, 1, i) for i in range(5, 10)], scale=\"utc\", fmt=\"datetime\")\n _dset.add_time_delta(\"time_delta\", val=[timedelta(seconds=i) for i in range(20, 25)], scale=\"utc\", fmt=\"timedelta\")\n\n # Collections\n _dset.add_bool(\"group.idx\", val=[0, 0, 0, 0, 0])\n _dset.add_float(\"group.numbers\", val=[6, 7, 8, 9, 10])\n _dset.add_position(\"group.sat_pos\", val=np.ones((5, 3)) * 7, system=\"trs\")\n _dset.add_position(\"group.site_pos\", val=np.ones((5, 3)) * 8, system=\"trs\", other=_dset.group.sat_pos)\n _dset.add_position_delta(\"group.site_delta\", val=np.ones((5, 3)) * 9.5, system=\"trs\", ref_pos=_dset.group.site_pos)\n _dset.add_posvel(\"group.sat_posvel\", val=np.ones((5, 6)) * 6, system=\"trs\")\n _dset.add_posvel(\"group.site_posvel\", val=np.ones((5, 6)) * 5, system=\"trs\", other=_dset.group.sat_posvel)\n _dset.add_posvel_delta(\n \"group.site_posvel_delta\", val=np.ones((5, 6)) * 1.5, system=\"trs\", ref_pos=_dset.group.site_posvel\n )\n _dset.add_sigma(\"group.numbers2\", val=[1.2, 1.2, 1.2, 1.2, 1.2], sigma=[3.2, 3.2, 3.2, 3.2, 3.2])\n _dset.add_text(\"group.text\", val=[\"bbb\", \"bbb\", \"bbb\", \"bbb\", \"bbb\"])\n _dset.add_time(\"group.time\", val=[datetime(2015, 1, i) for i in range(10, 15)], scale=\"utc\", fmt=\"datetime\")\n _dset.add_time_delta(\n \"group.time_delta\", val=[timedelta(seconds=i) for i in range(0, 5)], scale=\"utc\", fmt=\"timedelta\"\n )\n return _dset","def generate_scaffolds(self, dataset, cutoff=0.18):\n mols = []\n for ind, smiles in enumerate(dataset.ids):\n mols.append(Chem.MolFromSmiles(smiles))\n n_mols = len(mols)\n fps = [AllChem.GetMorganFingerprintAsBitVect(x, 2, 1024) for x in mols]\n\n scaffold_sets = ClusterFps(fps, cutoff=cutoff)\n scaffold_sets = sorted(scaffold_sets, key=lambda x: -len(x))\n scaffold_sets = [list(sfd) for sfd in scaffold_sets]\n return scaffold_sets","def dict_sets_intersection_test(self, data):\n\n data_info = self.get_data_info(data)\n finished = []\n\n for part in data:\n for union_part in data:\n if part != union_part and union_part not in finished:\n data[part].intersection(data[union_part])\n finished.append(part)\n\n return data_info","def points_in_distance(c, d):\n points = set()\n for i in range(0, d + 1):\n points = points | points_in_circle(c, i)\n return points","def compute_critical_values(P, Q, p, q, mdist, P_dist, Q_dist):\n origin = eucl_dist(P[0], Q[0])\n end = eucl_dist(P[-1], Q[-1])\n end_point = max(origin, end)\n cc = set([end_point])\n for i in range(p - 1):\n for j in range(q - 1):\n Lij = point_to_seg(Q[j], P[i], P[i + 1], mdist[i, j], mdist[i + 1, j], P_dist[i])\n if Lij > end_point:\n cc.add(Lij)\n Bij = point_to_seg(P[i], Q[j], Q[j + 1], mdist[i, j], mdist[i, j + 1], Q_dist[j])\n if Bij > end_point:\n cc.add(Bij)\n return sorted(list(cc))","def get_decs(self):\n return [i for i in self.decisions.keys()]","def cdf_discretize(self,variables=[]):\n #the errors in the code are due to the deleted files that require packages to be installed on the computer\n for i in variables:\n x=unique(self.data[:,i])\n m=max(x)-min(x)\n f=lambda x0,y0: array([m*(x0+y0)/(1+m**2), (x0*m+y0)/(1+m**2)])\n cdf=array([np.sum(self.data[:,i]<=t) for t in x])\n d=array([norm(array([x0,cdf[k]])-f(x0,cdf[k])) for k,x0 in\\\n enumerate(x)])","def test_kde_cumulative(limits):\n data = np.random.normal(0, 1, 1000)\n density = _kde(data, custom_lims=limits, cumulative=True)[1]\n np.testing.assert_almost_equal(round(density[-1], 3), 1)","def find_shitty_decoys(self):\n target_seqs = set()\n decoy_seqs = set()\n with open(self.csv_path, \"r\") as f:\n\n reader = csv.DictReader(f)\n\n sorted_reader = sorted(\n reader,\n reverse=self[\"bigger_scores_better\"],\n key=lambda d: float(d[self.col_for_sorting]),\n )\n\n for row in sorted_reader:\n self.observed_charges.add(int(row[\"Charge\"]))\n if row_is_decoy(row):\n decoy_seqs.add(unify_sequence(row[\"Sequence\"]))\n self.counter[\"decoy\"] += 1\n else:\n target_seqs.add(unify_sequence(row[\"Sequence\"]))\n self.counter[\"target\"] += 1\n\n self.shitty_decoy_seqs = target_seqs.intersection(decoy_seqs)\n if len(self.shitty_decoy_seqs) > 0:\n print(\n \"Warning! Found {0} sequences that are target AND decoy \"\n \"(immutable peptides?). These will not be used for training.\\n\".format(\n len(self.shitty_decoy_seqs)\n )\n )\n return","def get_sample_numbers(cons_df: pd.DataFrame) -> Set[int]:\n\n all_cols = list(cons_df.columns)\n sample_nbrs = set([int(col.split('_')[-1]) for col in all_cols[1:] if not col.endswith('cf')])\n return sample_nbrs","def cdf(self, data, survival=False, **kwargs):\n\n data = np.array(data)\n if not data.any():\n return array([np.nan]), array([np.nan])\n\n #data = trim_to_range(data, xmin=xmin, xmax=xmax)\n\n n = float(len(data))\n\n data = np.sort(data)\n all_unique = not( any( data[:-1]==data[1:] ) )\n\n if all_unique:\n CDF = np.arange(n)/n\n else:\n #This clever bit is a way of using searchsorted to rapidly calculate the\n #CDF of data with repeated values comes from Adam Ginsburg's plfit code,\n #specifically https://github.com/keflavich/plfit/commit/453edc36e4eb35f35a34b6c792a6d8c7e848d3b5#plfit/plfit.py\n CDF = np.searchsorted(data, data,side='left') / n\n unique_data, unique_indices = np.unique(data, return_index=True)\n data=unique_data\n CDF = CDF[unique_indices]\n\n if survival:\n CDF = 1-CDF\n return data, CDF","def parse_dset(clf, dset, idx):\n probs = []\n for s in range(int(np.ceil(len(idx)/100.0))):\n lower = s*100\n upper = min([(s+1)*100,len(idx)])\n X = np.array([dset.get(idx[k])[0] for k in range(lower,upper)])\n probs += [clf.fx_output(dset.reshape_batch(X))] \n \n return probs","def check_dose_range_sanity(dose_list):\n\n low_dose = 10000\n high_dose = 0\n constrained_list = []\n\n for dose in dose_list:\n if low_dose > dose:\n low_dose = dose\n if high_dose < dose:\n high_dose = dose\n\n if high_dose >= (low_dose * 10):\n # an order of magnitude or more range breaks the graphs\n for dose in dose_list:\n if dose > (low_dose * 10):\n constrained_list.append(low_dose * 10)\n # print(\"Chopping \" + str(dose) + \" to \" + str(low_dose * 10))\n else:\n constrained_list.append(dose)\n\n return constrained_list\n else:\n return dose_list","def _cont_calcs(srs: Srs, cfg: Config) -> Dict[str, List[Any]]:\n\n data: Dict[str, List[Any]] = {}\n\n # drop infinite values\n mask = srs.apply(\"isin\", {np.inf, -np.inf})\n srs = Srs(srs.getmask(mask, inverse=True), agg=True)\n min_max = srs.apply(\n \"map_partitions\", lambda x: pd.Series([x.max(), x.min()]), meta=pd.Series([], dtype=float)\n ).data\n min_max_comp = []\n if cfg.diff.density:\n for min_max_value in dask.compute(min_max)[0]:\n min_max_comp.append(math.isclose(min_max_value.min(), min_max_value.max()))\n min_max = dd.concat(min_max).repartition(npartitions=1)\n\n # histogram\n data[\"hist\"] = srs.self_map(\n da.histogram, bins=cfg.hist.bins, range=(min_max.min(), min_max.max())\n )\n\n # compute the density histogram\n if cfg.diff.density:\n data[\"dens\"] = srs.self_map(\n da.histogram,\n condition=min_max_comp,\n bins=cfg.kde.bins,\n range=(min_max.min(), min_max.max()),\n density=True,\n )\n # gaussian kernel density estimate\n data[\"kde\"] = []\n sample_data = dask.compute(\n srs.apply(\n \"map_partitions\",\n lambda x: x.sample(min(1000, x.shape[0])),\n meta=pd.Series([], dtype=float),\n ).data\n )\n for ind in range(len(sample_data[0])):\n data[\"kde\"].append(gaussian_kde(sample_data[0][ind]))\n\n return data","def get_intersecting_dissemination_ids(cross_section, dissemination_areas):\n assert 'DAUID' in cross_section.columns \n dissem_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].DAUID.unique()\n reg_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].CSDUID.unique()\n# code_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].CODEID.unique()\n\n\n return list(dissem_arr), list(reg_arr)","def get_dec_i_range(data_decs):\n data_dec_is = map_list(lambda x: (x - 1800)//10, data_decs)\n lower_range_i = data_dec_is[0]\n upper_range_i = data_dec_is[-1] + 1\n return (lower_range_i, upper_range_i)","def find_overlapping_cds_simple(v_start, v_stop, cds_begins, strand):\n # cds_start = cds_begin[0]\n if strand == '+':\n return list(filter(lambda x: x[0] >= v_start and x[0] < v_stop, cds_begins))\n else:\n return list(filter(lambda x: x[0] > v_start and x[0] <= v_stop, cds_begins))","def _get_cdf(self, data, threshold):\n index = data.sort_values()\n index.name = None\n series = pd.Series(np.linspace(0, 1, len(index)), index=index)\n series.name = data.name\n\n # Compute percentage of samples above/below the specified threshold\n below = float(max(series[:threshold]))\n above = 1 - below\n return series, above, below","def pruning(self):\n data = self.data.copy()\n for d in self.data:\n # cascade purning method. Inspired from \"Efficient Computation of Group Skyline Queries on MapReduce (FCU)\"\n if d in data:\n pastart = [self.drange[1] if i+self.radius>self.drange[1] else i+self.radius for i in d.getLocationMax()]\n pamax = [self.drange[1] for j in range(self.dim)]\n # prune data points that are obviously dominated by current data point\n pruned = (self.index.intersection(tuple(pastart+pamax),objects=True))\n for p in pruned:\n if p.object in data:\n data.remove(p.object)\n self.pruned = data","def generate_threat_kde_dataset(train_dataset):\n\n threat_grid_cells = train_dataset['X'][~train_dataset['Y']]\n kde_values = threat_grid_cells[['latitude_index', 'longitude_index', 'KDE']]\n\n threat_kde_df = kde_values.set_index(['latitude_index', 'longitude_index'])['KDE']\n threat_kde_df = threat_kde_df.sort_values(ascending=False)\n\n return list(threat_kde_df.index), threat_kde_df","def convexify(domain):\n\n if isinstance(domain, isl.BasicSet):\n return domain\n\n dom_bsets = domain.get_basic_sets()\n if len(dom_bsets) == 1:\n domain, = dom_bsets\n return domain\n\n hull_domain = domain.simple_hull()\n if isl.Set.from_basic_set(hull_domain) <= domain:\n return hull_domain\n\n domain = domain.coalesce()\n\n dom_bsets = domain.get_basic_sets()\n if len(domain.get_basic_sets()) == 1:\n domain, = dom_bsets\n return domain\n\n hull_domain = domain.simple_hull()\n if isl.Set.from_basic_set(hull_domain) <= domain:\n return hull_domain\n\n dom_bsets = domain.get_basic_sets()\n assert len(dom_bsets) > 1\n\n print(\"PIECES:\")\n for dbs in dom_bsets:\n print(\" %s\" % (isl.Set.from_basic_set(dbs).gist(domain)))\n raise NotImplementedError(\"Could not find convex representation of set\")","def detect_saccades(self, kernel_sigma=.003,\n start_thresh=(20, .005),\n end_thresh=(7, .005)):\n # Save a copy of the original eyelink saccades\n if not hasattr(self, \"eyelink_saccades\"):\n self.eyelink_saccades = self.saccades.copy()\n\n # Compute smoothing kernel size in sample bin units\n dt = 1 / self.sampling_rate\n kernel_width = kernel_sigma / dt\n\n # Extract gaze position\n xy = self.samples[[\"x\", \"y\"]]\n\n # Smooth the gaze position data\n xy_s = xy.apply(gaussian_filter1d, args=(kernel_width,))\n\n # Compute velocity\n v = (xy_s.diff()\n .apply(np.square)\n .sum(axis=1, skipna=False)\n .apply(np.sqrt)\n .divide(dt)\n .fillna(np.inf))\n\n # Identify saccade onsets\n start_window = int(start_thresh[1] / dt)\n sthr = (v > start_thresh[0]).rolling(start_window).min()\n starts = xy.where(sthr.diff() == 1).dropna()\n\n # Identify saccade offsets\n end_window = int(end_thresh[1] / dt)\n ethr = (v < end_thresh[0]).rolling(end_window).min()\n ends = xy.where(ethr.diff() == 1).dropna()\n\n # -- Parse each detected onset to identify the corresponding end\n saccades = []\n last_end_time = 0\n for start_time, start_pos in starts.iterrows():\n\n if start_time < last_end_time:\n # This is an acceration within a prior saccade that has not\n # yet ended; skip\n continue\n\n ends = ends.loc[start_time:]\n\n # Check if the dataset is ending in the middle of a saccade\n if ends.size:\n end = ends.iloc[0]\n else:\n break\n\n last_end_time = end.name\n\n saccades.append([start_time,\n start_pos[\"x\"],\n start_pos[\"y\"],\n end.name,\n end[\"x\"],\n end[\"y\"]])\n\n # Package the saccades into a dataframe\n columns = [\"start\", \"start_x\", \"start_y\", \"end\", \"end_x\", \"end_y\"]\n saccades = pd.DataFrame(saccades, columns=columns)\n\n # -- Compute duration, amplitude, velocity, and angle\n\n dx = saccades[\"end_x\"] - saccades[\"start_x\"]\n dy = saccades[\"end_y\"] - saccades[\"start_y\"]\n saccades[\"amplitude\"] = np.sqrt(np.square(dx) + np.square(dy))\n\n saccades[\"duration\"] = saccades[\"end\"] - saccades[\"start\"]\n saccades[\"velocity\"] = saccades[\"amplitude\"] / saccades[\"duration\"]\n\n saccades[\"angle\"] = np.rad2deg(np.arctan2(dy, dx)) % 360\n\n # Overwrite the saccade data structure with the new results\n self.saccades = saccades\n\n return self","def _computedivergentset(repo):\n divergent = set()\n obsstore = repo.obsstore\n newermap = {}\n for ctx in repo.set('(not public()) - obsolete()'):\n mark = obsstore.precursors.get(ctx.node(), ())\n toprocess = set(mark)\n while toprocess:\n prec = toprocess.pop()[0]\n if prec not in newermap:\n successorssets(repo, prec, newermap)\n newer = [n for n in newermap[prec] if n]\n if len(newer) > 1:\n divergent.add(ctx.rev())\n break\n toprocess.update(obsstore.precursors.get(prec, ()))\n return divergent","def get_span(self)->set:\n return set(range(self.get_start_offset(), self.get_end_offset()))","def find_crosstalk_peaks(spectrum_in, delta_crosstalk = 1.3):\n peaks = spectrum_in.get_peaks()\n precursor_mz = spectrum_in.get_precursor_mz()[0]\n upper_precursor_mz = precursor_mz + delta_crosstalk\n \n crosstalk_peaks = []\n\n for peak in peaks:\n \n mz = peak.get_mz()\n\n if mz > upper_precursor_mz:\n\n crosstalk_peaks.append(peak)\n \n return(crosstalk_peaks)","def dilation(hexs, diameter):\n new_hex_set = set(hexs)\n for a_hex in hexs:\n adjacent_hexs = h3.k_ring(a_hex, diameter)\n new_hex_set = new_hex_set.union(adjacent_hexs)\n new_hexs = list(new_hex_set)\n return new_hexs","def load_all_data():\r\n\r\n data = dict()\r\n for year in ['2010', '2011', '2014', '2016']:\r\n\r\n data[year] = load_data(int(year))\r\n\r\n # Calculate the dune widths\r\n data[year]['Dune Width'] = data[year]['x_heel'] - data[year]['x_toe']\r\n data[year]['Fenced Dune Width'] = data[year]['x_fence_heel'] - data[year]['x_fence_toe']\r\n data[year]['Fenced Dune System Width'] = data[year]['x_heel'] - data[year]['x_fence_toe']\r\n\r\n # For now, remove all negative widths and volumes, something went wrong with them\r\n width_condition = data[year]['Fenced Dune Width'] <= 0\r\n volume_condition = data[year]['Fenced Dune Volume'] <= 0\r\n\r\n data[year]['y_fence_crest'][width_condition] = np.nan\r\n data[year]['Fenced Dune Width'][width_condition] = np.nan\r\n data[year]['Fenced Dune Volume'][width_condition] = np.nan\r\n\r\n data[year]['y_fence_crest'][volume_condition] = np.nan\r\n data[year]['Fenced Dune Width'][volume_condition] = np.nan\r\n data[year]['Fenced Dune Volume'][volume_condition] = np.nan\r\n\r\n data[year]['Fenced Dune System Width'][data[year]['Fenced Dune System Width'] <= 0] = np.nan\r\n\r\n # Remove instances where the fenced and natural dune crest are not positioned correctly\r\n crest_condition_1 = data[year]['x_fence_crest'] >= data[year]['x_crest']\r\n crest_condition_2 = data[year]['y_fence_crest'] >= data[year]['y_crest']\r\n\r\n data[year]['y_fence_crest'][crest_condition_1] = np.nan\r\n data[year]['Fenced Dune Width'][crest_condition_1] = np.nan\r\n data[year]['Fenced Dune Volume'][crest_condition_1] = np.nan\r\n\r\n data[year]['y_fence_crest'][crest_condition_2] = np.nan\r\n data[year]['Fenced Dune Width'][crest_condition_2] = np.nan\r\n data[year]['Fenced Dune Volume'][crest_condition_2] = np.nan\r\n\r\n data['Fences'] = load_fence_locations(y=0)\r\n\r\n return data","def _recompute(self):\n current_date = self.start_date\n self.quarterly_date_list = []\n self.daily_date_list = []\n while current_date <= self.end_date:\n current_quarter = get_quarter(current_date)\n current_year = current_date.year\n next_year, next_quarter = add_quarter(current_year, current_quarter)\n next_start_quarter_date = date(next_year, get_month(next_quarter),\n 1)\n\n days_till_next_quarter = (next_start_quarter_date -\n current_date).days\n days_till_end = (self.end_date - current_date).days\n if days_till_next_quarter <= days_till_end:\n current_start_quarter_date = date(current_year,\n get_month(current_quarter), 1)\n if current_start_quarter_date == current_date:\n self.quarterly_date_list.append(\n (current_year, current_quarter, lambda x: True))\n current_date = next_start_quarter_date\n elif days_till_next_quarter > self.balancing_point:\n self.quarterly_date_list.append(\n (current_year, current_quarter,\n lambda x: date(x['date_filed']) >= self.start_date))\n current_date = next_start_quarter_date\n else:\n while current_date < next_start_quarter_date:\n self.daily_date_list.append(current_date)\n current_date += timedelta(days=1)\n else:\n if days_till_end > self.balancing_point:\n if days_till_next_quarter - 1 == days_till_end:\n self.quarterly_date_list.append(\n (current_year, current_quarter, lambda x: True))\n current_date = next_start_quarter_date\n else:\n self.quarterly_date_list.append(\n (current_year, current_quarter,\n lambda x: date(x['date_filed']) <= self.end_date))\n current_date = self.end_date\n else:\n while current_date <= self.end_date:\n self.daily_date_list.append(current_date)\n current_date += timedelta(days=1)","def generate_date_set(self, year, month, days):\n dates = set()\n for day in days:\n dates.add(date(year, month, day))\n return dates","def get_available_cops():\n allIncidents = Incident.get_all()\n cops = []\n \n for i in allIncidents:\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\n cops.append(i['operations_center']['id'])\n \n allReports = RelatoDeSituacao.get_all()\n \n for r in allReports:\n if (\n inicioAmostragem <= r.data_hora and \n r.data_hora <=terminoAmostragem and\n 'cop' in r.relator and # todos tem que ter o COP\n 'id' in r.relator['cop'] # todos tem que ter o id \n ):\n cops.append(r.relator['cop']['id'])\n \n return set(cops)","def _get_common_start_points(round_data):\n start_points = defaultdict(int)\n for rd in round_data:\n x = rd[\"x\"].iloc[0]\n y = rd[\"y\"].iloc[0]\n\n start_points[(x, y)] += 1\n\n common_start_points = []\n for key, val in start_points.items():\n # TODO: Tune, or percentage?\n if val > 5:\n common_start_points.append(key)\n return common_start_points","def all_cited_dois( corpus ) :\n cites = list(set(chain.from_iterable((cited_dois(x) for x in corpus))))\n return cites","def preprocess_data(data_path, last_day=None, schools_unis='two_separate', drop_features=None, min_confirmed=100,\n min_deaths=10, smoothing=1, mask_zero_deaths=False, mask_zero_cases=False):\n\n # load data from our csv\n df = pd.read_csv(data_path, parse_dates=[\"Date\"], infer_datetime_format=True).set_index(\n [\"Country Code\", \"Date\"])\n\n # handle custom last day of analysis\n if last_day is None:\n Ds = list(df.index.levels[1])\n else:\n Ds = list(df.index.levels[1])\n last_ts = pd.to_datetime(last_day, utc=True)\n Ds = Ds[:(1 + Ds.index(last_ts))]\n\n nDs = len(Ds)\n\n all_rs = list([r for r, _ in df.index])\n regions = list(df.index.levels[0])\n locations = [all_rs.index(r) for r in regions]\n sorted_regions = [r for l, r in sorted(zip(locations, regions))]\n nRs = len(sorted_regions)\n region_names = copy.deepcopy(sorted_regions)\n region_full_names = df.loc[region_names][\"Region Name\"]\n\n if drop_features is None:\n # note: these features are taken from the OxCGRT Dataset other than the mask wearing option\n drop_features = ['Mask Wearing', 'Travel Screen/Quarantine', 'Travel Bans', 'Public Transport Limited',\n 'Internal Movement Limited', 'Public Information Campaigns', 'Symptomatic Testing']\n\n for f in drop_features:\n print(f'Dropping NPI {f}')\n df = df.drop(f, axis=1)\n\n # pull data\n CMs = list(df.columns[4:])\n nCMs = len(CMs)\n\n ActiveCMs = np.zeros((nRs, nCMs, nDs))\n Confirmed = np.zeros((nRs, nDs))\n Deaths = np.zeros((nRs, nDs))\n Active = np.zeros((nRs, nDs))\n NewDeaths = np.zeros((nRs, nDs))\n NewCases = np.zeros((nRs, nDs))\n\n for r_i, r in enumerate(sorted_regions):\n region_names[r_i] = df.loc[(r, Ds[0])]['Region Name']\n for d_i, d in enumerate(Ds):\n Confirmed[r_i, d_i] = df.loc[(r, d)]['Confirmed']\n Deaths[r_i, d_i] = df.loc[(r, d)]['Deaths']\n Active[r_i, d_i] = df.loc[(r, d)]['Active']\n\n ActiveCMs[r_i, :, :] = df.loc[r].loc[Ds][CMs].values.T\n\n # compute new (daily) cases, after using thresholds\n Confirmed[Confirmed < min_confirmed] = np.nan\n Deaths[Deaths < min_deaths] = np.nan\n NewCases[:, 1:] = (Confirmed[:, 1:] - Confirmed[:, :-1])\n NewDeaths[:, 1:] = (Deaths[:, 1:] - Deaths[:, :-1])\n NewDeaths[NewDeaths < 0] = 0\n NewCases[NewCases < 0] = 0\n\n NewCases[np.isnan(NewCases)] = 0\n NewDeaths[np.isnan(NewDeaths)] = 0\n\n if smoothing != 1:\n print('Smoothing')\n\n # bulk smooth\n SmoothedNewCases = np.around(\n ss.convolve2d(NewCases, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary='symm',\n mode='same'))\n SmoothedNewDeaths = np.around(\n ss.convolve2d(NewDeaths, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary=\"symm\",\n mode='same'))\n\n # correct for specific regions\n for r in range(nRs):\n # if the country has too few deaths, ignore\n if Deaths[r, -1] < 50:\n print(f'Note: did not smooth deaths in {region_names[r]}')\n SmoothedNewDeaths[r, :] = NewDeaths[r, :]\n\n NewCases = SmoothedNewCases\n NewDeaths = SmoothedNewDeaths\n\n print('Masking invalid values')\n if mask_zero_deaths:\n NewDeaths[NewDeaths < 1] = np.nan\n else:\n NewDeaths[NewDeaths < 0] = np.nan\n\n if mask_zero_cases:\n NewCases[NewCases < 1] = np.nan\n else:\n NewCases[NewCases < 0] = np.nan\n\n Confirmed = np.ma.masked_invalid(Confirmed.astype(theano.config.floatX))\n Active = np.ma.masked_invalid(Active.astype(theano.config.floatX))\n Deaths = np.ma.masked_invalid(Deaths.astype(theano.config.floatX))\n NewDeaths = np.ma.masked_invalid(NewDeaths.astype(theano.config.floatX))\n NewCases = np.ma.masked_invalid(NewCases.astype(theano.config.floatX))\n\n # handle schools and universities\n if schools_unis == 'two_xor':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs_final[:, university_index, :] = np.logical_xor(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs = ActiveCMs_final\n CMs[school_index] = 'School and University Closure'\n CMs[university_index] = 'Schools xor University Closure'\n elif schools_unis == 'one_tiered':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :]) + 0.5 * np.logical_xor(\n ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\n CMs.remove('University Closure')\n elif schools_unis == 'two_separate':\n # don't need to do anything for this!\n pass\n elif schools_unis == 'one_and':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\n CMs[school_index] = 'School and University Closure'\n CMs.remove('University Closure')\n\n return PreprocessedData(Active,\n Confirmed,\n ActiveCMs,\n CMs,\n sorted_regions,\n Ds,\n Deaths,\n NewDeaths,\n NewCases,\n region_full_names)","def generate_Lk_by_Ck(data_set, Ck, min_support, support_data):\r\n Lk = set()\r\n item_count = {}\r\n for t in data_set:\r\n for item in Ck:\r\n if item.issubset(t):\r\n if item not in item_count:\r\n item_count[item] = 1\r\n else:\r\n item_count[item] += 1\r\n t_num = float(len(data_set))\r\n for item in item_count:\r\n if (item_count[item] ) >= min_support:\r\n Lk.add(item)\r\n support_data[item] = item_count[item] / t_num\r\n return Lk","def from_reduced(cls, data, random=False, signature=False, **umap_kwargs):\n new = Deduplicator([], random=random, signature=signature,\n **umap_kwargs)\n new.data_reduced = [data]\n new.data_kd = [cKDTree(new.data_reduced[0])]\n\n return new","def calcCascadeSizes(startles):\n ntimesteps = startles.shape[0]\n ncascades = 0\n cascade_sizes = []\n cascade_lengths = []\n single_startle = False\n ongoing_cascade = False\n current_cascade_members = []\n previous_startle_idc = []\n current_cascade_length = 0\n for t in np.arange(ntimesteps):\n startle_idc = np.where(startles[t, :])[0]\n # no startles at current time step:\n if startle_idc.size == 0:\n if ongoing_cascade:\n cascade_sizes.append(len(current_cascade_members))\n cascade_lengths.append(current_cascade_length)\n ncascades += 1\n ongoing_cascade = False\n current_cascade_members = []\n else:\n if single_startle:\n single_startle = False\n current_cascade_members = []\n current_cascade_length = 0\n # at least one fish startled:\n else:\n if ongoing_cascade:\n new_members = []\n for startle_idx in startle_idc:\n if startle_idx not in previous_startle_idc:\n new_members.append(startle_idx)\n if not len(new_members) == 0:\n current_cascade_members.extend(new_members)\n current_cascade_length += 1\n else:\n if single_startle:\n new_members = []\n for startle_idx in startle_idc:\n if startle_idx not in previous_startle_idc:\n new_members.append(startle_idx)\n if not len(new_members) == 0:\n single_startle = False\n ongoing_cascade = True\n current_cascade_members.extend(new_members)\n current_cascade_length += 1\n else:\n single_startle = True\n current_cascade_members.extend(startle_idc)\n current_cascade_length += 1\n previous_startle_idc = startle_idc\n return np.array(cascade_sizes), np.array(cascade_lengths)","def nondominated_sort(dataset):\n numPareto = 0\n\n # pairwise comparisons\n for n in range(len(dataset)):\n for m in range(len(dataset)):\n if dataset[m].dominates(dataset[n]):\n dataset[n].dominatedCount+=1\n dataset[m].addToDominatingSet(n)\n\n # find first pareto front\n front = []\n front2 = []\n tmpLevel = -10 # temporary value for Pareto level, will re-adjust later\n for n in range(len(dataset)):\n if dataset[n].dominatedCount == 0:\n dataset[n].paretoStatus = tmpLevel\n front.append(n)\n numPareto+=1\n\n # iteratively peel off pareto fronts\n while len(front) != 0:\n tmpLevel-=1\n for f in front:\n for s in dataset[f].dominatingSet:\n dataset[s].dominatedCount -= 1\n if dataset[s].dominatedCount == 0:\n front2.append(s)\n dataset[s].paretoStatus = tmpLevel\n front = front2 \n front2 = []\n\n # re-adjust pareto level\n for n in range(len(dataset)):\n oldLevel = dataset[n].paretoStatus\n if oldLevel != -1:\n dataset[n].paretoStatus = oldLevel-tmpLevel-1\n\n return numPareto","def create_cands(data):\n\n best = np.zeros(data.dim+1)\n best[0:data.dim] = data.xbest\n best[data.dim] = 1-np.sum(data.xbest)\n\n # Ncand times the best value\n cp_e = np.kron(np.ones((data.Ncand, 1)), np.asmatrix(best))\n # This generates random perturbations\n # need dim+1 to account for the \"missing\" value\n r = np.random.rand(data.Ncand, data.dim+1)\n a = r < data.pertP\n idx = np.where(np.sum(a, axis=1) == 0)\n for ii in range(len(idx[0])):\n f = np.random.permutation(data.dim+1)\n a[idx[0][ii], f[0]] = True\n randnums = np.random.randn(data.Ncand, data.dim+1)\n randnums[a == False] = 0\n pv = randnums*data.sigma_stdev\n # Create new points by adding random fluctucations to best point\n new_pts = cp_e+pv\n\n # Iterative, column wise procedure to force the randomly\n # sampled point to be in [0,1]\n for ii in range(data.dim+1):\n vec_ii = new_pts[:, ii]\n adj_l = np.where(vec_ii < data.xlow)\n vec_ii[adj_l[0]] = data.xlow + (data.xlow - vec_ii[adj_l[0]])\n adj_u = np.where(vec_ii > data.xup)\n vec_ii[adj_u[0]] = data.xup - (vec_ii[adj_u[0]]-data.xup)\n stillout_u = np.where(vec_ii > data.xup)\n vec_ii[stillout_u[0]] = data.xlow\n stillout_l = np.where(vec_ii < data.xlow)\n vec_ii[stillout_l[0]] = data.xup\n new_pts[:, ii] = copy.copy(vec_ii)\n\n new_pts = new_pts / np.sum(new_pts, axis=1)\n\n cp_e = copy.copy(new_pts)\n rand_pts = np.asmatrix(np.random.uniform(0, 1, [data.Ncand, data.dim + 1]))\n cp_r = rand_pts/np.sum(rand_pts, axis=1)\n\n CandPoint = np.concatenate((cp_e, cp_r), axis=0)\n # return only data.dim candidate points\n CandPoint_out = CandPoint[:, 0:data.dim]\n\n return CandPoint_out","def getPossibleTagSet(self, data, cutoff=None, add_common=False):\n full_tag_set_size = data.getTagSetSize()\n if cutoff is None:\n cutoff = ceil(full_tag_set_size * DEFAULT_CUTOFF_FRACT)\n elif cutoff >= full_tag_set_size:\n return data.getTagSet()\n word = self.getWord()\n tags_dict = data.getWordDict().get(word, False)\n if tags_dict is False:\n sorted_tags_list = data.getSortedTagsList()\n else:\n sorted_tags_list = sorted(tags_dict, key=tags_dict.get, reverse=True)\n if data.isNumberWord(word) and \"CD\" not in sorted_tags_list[:cutoff]:\n sorted_tags_list.insert(0, \"CD\")\n remainder = cutoff - len(sorted_tags_list)\n if remainder < 0:\n return tuple(sorted_tags_list[:cutoff])\n elif add_common is True and remainder > 0:\n top_candidate_tags = data.getSortedTagsList()\n sorted_tags_set = set(sorted_tags_list)\n candidate_set = set(top_candidate_tags) - sorted_tags_set\n while remainder > 0:\n tag_candidate = top_candidate_tags.pop(0)\n if tag_candidate in candidate_set:\n sorted_tags_list.append(tag_candidate)\n remainder -= 1\n return tuple(sorted_tags_list)","def _compute_soffsets(self):\n self.soffsets = [ [] for i in self.doffsets ]\n for idx,dofs in enumerate(self.doffsets):\n for o in dofs:\n self.soffsets[(idx + o) % self.p].append(-o)","def _count_parties(data_set): #DEMOCRATS, THEN REPUBLICANS\r\n reps = 0\r\n dems = 0\r\n for data_point in data_set:\r\n if data_point.dat_party == \"R\": reps+=1\r\n if data_point.dat_party == \"D\": dems+=1\r\n\r\n return (dems, reps)","def get_coref_set(self,\n step_idxs: List[int],\n use_entity: bool = False,\n ) -> Set[Tuple[int, str, int, str]]:\n if len(step_idxs) <= 1:\n return []\n\n coref_participants = defaultdict(set)\n for i, step_idx in enumerate(step_idxs):\n participants = self.steps[step_idx].participants\n for participant in participants:\n if use_entity:\n for value in participant.values:\n coref_participants[value.entity].add(\n (i, participant.role))\n else:\n if participant.refvar:\n coref_participants[participant.refvar].add(\n (i, participant.role))\n\n coref_set = set()\n for participants in coref_participants.values():\n for (i, role_1), (j, role_2) in combinations(participants, 2):\n if i == 0:\n continue\n if i > j:\n i, j, role_1, role_2 = j, i, role_2, role_1\n coref_set.add((i, role_1, j, role_2))\n\n return coref_set","def get_subsets(self):\n if self.begin_date is not None and self.end_date is not None:\n iv = get_timestamp_from_date(self.end_date) - get_timestamp_from_date(self.begin_date)\n fv = iv / 86400000 + 1\n self.n_days = fv\n X = self.data.copy().ix[:self.begin_date, :]\n X = X.fillna(method='bfill')\n y = self.data.copy().ix[:self.begin_date][self.symb]\n x_pred = X.ix[-(self.n_days):, :]\n init_val = self.original_data.ix[0][self.symb]\n print X.shape, y.shape, x_pred.shape\n return X, y, x_pred, init_val","def _as_set(self):\n included = set()\n excluded = set()\n # Add all points lying within each inclusion contour to S.\n for contour in self.contours:\n contour_matrix = contour.to_matrix()[:,:2]\n # Turn the contour closed if it's not.\n if (contour_matrix[0] != contour_matrix[-1]).all():\n contour_matrix = np.append(contour_matrix,\n contour_matrix[0].reshape(1,2),\n axis=0)\n\n # Create path object and test all pixels \n # within the contour's bounding box.\n path = mplpath.Path(contour_matrix, closed=True)\n mn = contour_matrix.min(axis=0)\n mx = contour_matrix.max(axis=0)\n x,y = np.mgrid[mn[0]:mx[0]+1, mn[1]:mx[1]+1]\n test_points = np.c_[x.flatten(), y.flatten()]\n points_in_contour = test_points[path.contains_points(test_points)]\n\n # Add the z coordinate.\n points_in_contour = np.c_[\\\n points_in_contour,\\\n np.ones(points_in_contour.shape[0])*contour.image_z_position\n ]\n\n # Now turn the numpy matrix into a list of tuples,\n # so we can add it to the corresponding set.\n points_in_contour = map(tuple, points_in_contour)\n\n # Update the corresponding set.\n if contour.inclusion:\n included.update(points_in_contour)\n else:\n excluded.update(points_in_contour)\n # Return the included points minus the excluded points.\n return included.difference( excluded )","def generate_Lk_by_Ck(fptree, data_set, Ck,dim, min_support, support_data):\n Lk = set()\n item_count = {}\n t_num = float(len(data_set))\n if dim == 1:\n for t in data_set:\n for item in Ck:\n if item.issubset(t):\n if item not in item_count:\n item_count[item] = 1\n else:\n item_count[item] += 1\n t_num = float(len(data_set))\n else:\n for item in Ck:\n array = list(item)\n subData, support = fptree.getSubData(array[len(array) - 1], dim)\n for i in range(0, len(subData)):\n if item.issubset(subData[i]):\n if item not in item_count:\n item_count[item] = support[i]\n else:\n item_count[item] += support[i]\n for item in item_count:\n if (item_count[item] / t_num) >= min_support:\n Lk.add(item)\n support_data[item] = item_count[item] / t_num\n\n return Lk","def delete(feature_set, population):\n features = [x for x in list(feature_set)]\n pop = [x for y in population for x in y]\n min = float(\"+inf\")\n rem = features[0]\n for i in range(0, len(features)):\n x = pop.count(features[i])\n if x < min:\n min = x\n rem = features[i]\n features.remove(rem)\n return set(features)","def cutSec(ppm, X, start, stop, featureMask):\n\tflip=0\n\tif ppm[0]>ppm[-1]:\n\t\tflip=1\n\t\tppm = ppm[::-1]\n\t\tX = X[:, ::-1]\n \n #find first entry in ppm with >='start' valu\n\tstart = (ppm>=start).nonzero()\n\tstart = start[0][0]#first entry\n\tstop = (ppm<=stop).nonzero()\n\tstop = stop[0][-1]#last entry\n\n#currently setting featureMask will get rid of peaks in start:stop region BUT it also marks as excluded so have removed as inaccurately marking for exclusion when all we want to do is remove from intensityData not mark as exluded\n\ttry:\n\t\tfeatureMask[0,start:stop]=False # this may only occur on unit test data, not sure need to check but either way was causing issue\n\texcept:\n\t\tfeatureMask[start:stop]=False\n\tif flip==1:\n\t\tppm = ppm[::-1]\n\t\tX = X[:, ::-1]\n\treturn ppm, X, featureMask\n\tpass","def _enumerate_years(self, preprocessed_data, disjoint):\n pass","def sub_poset(self, start, end):\n if start < 1 or start > end or end > self.size() + 1:\n raise ValueError(\"Invalid starting or ending value, accepted: 1 <= start <= end <= size+1\")\n if start == end:\n return TamariIntervalPoset(0, [])\n relations = [(i - start + 1, j - start + 1) for (i, j) in self.increasing_cover_relations() if i >= start and j < end]\n relations.extend([(j - start + 1, i - start + 1) for (j, i) in self.decreasing_cover_relations() if i >= start and j < end])\n return TamariIntervalPoset(end - start, relations)","def get_base_depos(self, start, end, currencies, tenor, cut=\"NYC\", source=\"bloomberg\",\n cache_algo=\"internet_load_return\"):\n\n market_data_generator = self.market_data_generator\n\n if isinstance(currencies, str): currencies = [currencies]\n if isinstance(tenor, str): tenor = [tenor]\n\n tickers = []\n\n for cr in currencies:\n for tn in tenor:\n tickers.append(cr + tn)\n\n market_data_request = MarketDataRequest(\n start_date=start, finish_date=end,\n data_source=source,\n category='base-depos',\n freq='daily',\n cut=cut,\n tickers=tickers,\n fields=['close'],\n cache_algo=cache_algo,\n environment='backtest'\n )\n\n data_frame = market_data_generator.fetch_market_data(market_data_request)\n data_frame.index.name = 'Date'\n\n return data_frame","def test_cover():\n # D corresponds to the items and the transactions in which they appear, it is the standard code table\n D = {\n \"B\": Bitmap([0, 1, 2]),\n \"A\": Bitmap([0, 1]),\n \"C\": Bitmap([0, 2]),\n \"D\": Bitmap([2])\n }\n # ct corresponds to the itemsets on which we want to calculate the cover\n ct = [\n frozenset(\"ABC\"),\n frozenset(\"AB\"),\n frozenset(\"BC\"),\n frozenset(\"A\"),\n frozenset(\"B\"),\n frozenset(\"C\"),\n frozenset(\"D\")\n ]\n CTc = cover(D, ct)\n\n assert CTc[frozenset(\"ABC\")] == Bitmap([0])\n assert CTc[frozenset(\"AB\")] == Bitmap([1]) # AB appears only in tid_1 for usage because ABC is placed before in ct\n # so the AB of the first transaction has been covered by ABC\n assert CTc[frozenset(\"BC\")] == Bitmap([2])\n assert CTc[frozenset(\"A\")] == Bitmap()\n assert CTc[frozenset(\"B\")] == Bitmap()\n assert CTc[frozenset(\"C\")] == Bitmap()\n assert CTc[frozenset(\"D\")] == Bitmap([2])","def count_sites_under_condition_vcf_to_set(vcf_file,chrom,start,end,mincov=0,maxcov=10000,inds=\"all\",bgzip=True,nb_ind_with_min_cov=\"all\",nalleles=[1,2],snps=False):\n\tset_ok_sites = set()\n\tinput_vcf=vcf.Reader(fsock=None, filename=vcf_file, compressed=bgzip, prepend_chr=\"False\", strict_whitespace=False)#open the vcf parser\n\tnsites_OK=0\n\tnsites_total=0\n\t#print \"in count_sites_under_condition_vcf nb_ind_with_min_cov :\",nb_ind_with_min_cov, \" inds\", ind\n\tif chrom!=\"all\":\n\t\t\t#print chrom,start,end\n\t\t\tcheck=len(sh.tabix(vcf_file,str(chrom)+\":\"+str(start)+\"-\"+str(end)))\n\t\t\t#print check\n\t\t\t#print \"check;' \",check,\"'\"\n\t\t\tif check==0: \n\t\t\t\treturn [0,0]\n\t\t\tfor record in input_vcf.fetch(chrom,start,end):# for every site\n\t\t\t\tcond=checkSnp_Cov(input_vcf,record,mincov,maxcov,inds=inds,nalleles=nalleles,nb_ind_with_min_cov=nb_ind_with_min_cov,snps=snps)# check if the site respect our condition\n\t\t\t\tnsites_total+=1\n\t\t\t\tif cond:# if it does\n\t\t\t\t\t#if any([int(sample['DP'])<5 for sample in record.samples]): print [int(sample['DP']) for sample in record.samples] # to check this argument nb_ind_with_min_cov\n\t\t\t\t\tset_ok_sites.add(str(record.CHROM)+\"_\"+str(record.POS))\n\treturn set_ok_sites","def disease_descriptors(civic_did8):\n return [civic_did8]","def _init_continuations(self):\n\n # if the continuations dset already exists we reinitialize the\n # data\n if CONTINUATIONS in self.settings_grp:\n cont_dset = self.settings_grp[CONTINUATIONS]\n cont_dset.resize( (0,2) )\n\n # otherwise we just create the data\n else:\n cont_dset = self.settings_grp.create_dataset(CONTINUATIONS, shape=(0,2), dtype=int,\n maxshape=(None, 2))\n\n return cont_dset","def CumulativeDistribution(data, nbins, range=None, normed=True, centerbins=False):\n\n # 1) COMPUTE THE DISTRIBUTION OF THE DATA\n ydata, xdata = np.histogram(data, nbins, range, normed)\n\n # 1.1) Compute the cumulative sum of the probability\n ydata = ydata.cumsum()\n\n # 2) RETURN THE RESULTS\n if centerbins:\n dif = 0.5 * (xdata[-1] - xdata[0]) / nbins\n xdata += dif\n\n if normed:\n norm = 1.0 / ydata[-1]\n ydata *= norm\n\n return xdata[:-1], ydata\n\n else:\n return xdata[:-1], ydata","def get_coarseness(my_core, meshset, entity_ranges, geom_dim):\n\n coarseness = []\n\n for surface in entity_ranges:\n surf_area = get_area_triangle(my_core, surface, geom_dim)\n coarseness.append(len(surf_area)/sum(surf_area))\n\n return coarseness","def discrete_dataset(data):\n dataset = [0] * 10\n for value in data:\n dataset[value] += 1\n return dataset","def discretize(v, cutoff):\n v = np.array(v)\n ix = np.zeros(v.shape, dtype=np.long)\n \n cutoff = list(cutoff)\n cutoff.append(np.inf)\n n = len(cutoff)\n \n for ii in range(1,n):\n ix[(v >= cutoff[ii - 1]) & (v < cutoff[ii])] = ii\n \n ix[v >= cutoff[-1]] = n - 1 \n return ix","def create_intersection_report(self, data=None):\n\n if not data:\n data = self.build_dict_of_sets(self.data_dict)\n\n self.intersection_report_data = []\n finished = []\n\n for part in data:\n for union_part in data:\n if part != union_part and union_part not in finished:\n union_set = data[part].intersection(data[union_part])\n self.intersection_report_data.append([part, union_part, union_set])\n finished.append(part)","def test_range():\n begin_date = datetime.datetime(2000, 1, 1)\n end_date = datetime.datetime.today()\n\n if os.path.isfile(\"spy_price_cache_\" + str(datetime.date.today()) + \".csv\"):\n dates_available = pickle.load(open(\"spy_price_cache_\" + str(datetime.date.today()) + \".csv\", \"r\"))\n else:\n prices_available = yahoo.webload_symbol_price(\"SPY\", begin_date, end_date)\n dates_available = set(timestamp.to_pydatetime() for timestamp in prices_available.index.tolist())\n pickle.dump(dates_available, open(\"spy_price_cache_\" + str(datetime.date.today()) + \".csv\", \"w\"))\n\n dates_expected = set([day for day in itertools.takewhile(\n lambda d: d <= end_date,\n CALENDAR.every_nth_between(begin_date, end_date, 1)\n )])\n\n dates_misaligned = dates_available.symmetric_difference(dates_expected)\n\n assert len(dates_misaligned) == 0","def get_support(self, complement=False):\n if self.__fitOK:\n\n keepList = []\n dropList = []\n\n for col in self.__Ddrifts:\n\n if (self.__Ddrifts[col] < self.threshold):\n keepList.append(col)\n else:\n dropList.append(col)\n\n if complement:\n return dropList\n else:\n return keepList\n else:\n raise ValueError('Call the fit function before !')","def peptideSetCoverage(peptides1,peptides2,mmTolerance=1):\n\n oh1 = objhist(peptides1)\n coveredPeps = {k:[] for k in set(oh1.keys())}\n coveredPeps.update({'tot':[]})\n cache = {}\n for pep2 in peptides2:\n anyCover = False\n for pep1 in set(peptides1):\n try:\n dist = cache[(pep1, pep2)]\n except KeyError:\n dist = seq_distance(pep1, pep2, subst=binarySubst, normed=False)\n cache.update({(pep1, pep2):dist,(pep2, pep1):dist})\n if dist <= mmTolerance:\n coveredPeps[pep1].append(pep2)\n anyCover = True\n if anyCover:\n coveredPeps['tot'].append(pep2)\n coverage={k:len(v)/len(peptides2) for k, v in list(coveredPeps.items())}\n return coverage","def get_decks(self, include_cards=True):\n deck_previews = self.data_source.get_decks(self.user_id,\n not include_cards)\n\n return deck_previews","def fast_non_dominated_sort(self, population):\n current_front = []\n\n for solution in population:\n solution.dominates_list = []\n solution.dominated_by = 0\n\n for candidate in population:\n if solution.dominates(candidate):\n solution.dominates_list.append(candidate)\n elif candidate.dominates(solution):\n solution.dominated_by += 1\n\n if solution.dominated_by == 0:\n current_front.append(solution)\n solution.rank = 0\n\n fronts = []\n front_index = 0\n\n while current_front:\n self.crowding_distance_assignment(current_front)\n fronts.append(current_front)\n next_front = []\n\n for solution in current_front:\n for dominated in solution.dominates_list:\n dominated.dominated_by -= 1\n\n if dominated.dominated_by == 0:\n next_front.append(dominated)\n dominated.rank = front_index + 1\n\n current_front = next_front\n front_index += 1\n\n return fronts","def is_dominating_set(G, nbunch):\n testset = set(n for n in nbunch if n in G)\n nbrs = set(chain.from_iterable(G[n] for n in testset))\n return len(set(G) - testset - nbrs) == 0","def get_comments_between(self, start_date, end_date):\n ret = []\n ids = self.get_post_ids(start_date, end_date)\n\n for id in ids:\n comments = self.reddit.submission(id).comments\n ret.append(self.get_nested_comments(comments))\n return ret","def fitDAC(fineThresholds, coarseThresholds, bias, adcs0, adcs1, limLow, limHigh, fitLim):\n \n # array to hold fit parameters\n \n mevs = numarray.zeros((calConstant.NUM_TEM, calConstant.NUM_ROW, calConstant.NUM_END,\n calConstant.NUM_FE, 2), numarray.Float32)\n \n # array to hold range info \n \n ranges = numarray.zeros((calConstant.NUM_TEM, calConstant.NUM_ROW, calConstant.NUM_END,\n calConstant.NUM_FE), numarray.Int8) \n \n adcs0 = adcs0 - bias\n adcs1 = adcs1 - bias\n adcs0 = adcs0[...,numarray.NewAxis]\n adcs1 = adcs1[...,numarray.NewAxis]\n\n # compare upper and lower limits against FINE range thresholds\n q = (fineThresholds > adcs0) & (fineThresholds < adcs1)\n \n for tem in range(calConstant.NUM_TEM):\n for row in range(calConstant.NUM_ROW):\n for end in range(calConstant.NUM_END):\n for fe in range(calConstant.NUM_FE):\n \n qx = q[tem,row,end,fe,:]\n rng = calConstant.CDAC_FINE\n \n # if there are not enough points in the FINE range or high point is out of range, \n # try the COARSE range\n\n if len(numarray.nonzero(qx)[0]) < 3 or qx[-1]:\n \n qx[...] = \\\n (coarseThresholds[tem,row,end,fe,:] > adcs0[tem,row,end,fe,:]) & \\\n (coarseThresholds[tem,row,end,fe,:] < adcs1[tem,row,end,fe,:])\n \n tholds = coarseThresholds[tem,row,end,fe,:]\n rng = calConstant.CDAC_COARSE\n \n else:\n \n tholds = fineThresholds[tem,row,end,fe,:] \n \n tholds = tholds + bias[tem,row,end,fe,numarray.NewAxis]\n d = numarray.compress(qx, D0)\n a = numarray.compress(qx, tholds)\n # try to fit the preferred range data\n \n fail = False\n \n try:\n import ROOTFit\n import ROOT\n (fitParms, fitErrs, chisq) = ROOTFit.ROOTFit(ROOT.TF1(\"p1\",\"pol1\"),\n d,\n a,\n P0)\n\n dnorm = (chisq / len(d))\n if dnorm > fitLim:\n log.warning(\"fit error > %0.2f on T%d,%s%s,%d\", dnorm, tem, \n calConstant.CROW[row], calConstant.CPM[end], fe)\n fail = True \n except ValueError, e:\n log.error(\"fit excep on T%d,%s%s,%d: %s,%s,%s\", tem, calConstant.CROW[row],\n calConstant.CPM[end], fe, e, d, a)\n fail = True\n \n # check slope parameter value for reasonableness\n \n if not fail and rng == calConstant.CDAC_FINE:\n m = fitParms[1]\n if m < limLow[tem,row,end,fe] or m > limHigh[tem,row,end,fe]:\n log.warning(\"bad slope %0.3f on T%d,%s%s,%d\", m, tem, \n calConstant.CROW[row], calConstant.CPM[end], fe)\n fail = True\n \n # if the range is FINE, and the fit fails, or the resulting slope is\n # out of limits, try to get fit parameters for the COARSE range\n \n if fail:\n \n log.warning(\"trying COARSE range for T%d,%s%s,%d\", tem, calConstant.CROW[row],\n calConstant.CPM[end], fe)\n \n qx[...] = \\\n (coarseThresholds[tem,row,end,fe,:] > adcs0[tem,row,end,fe,:]) & \\\n (coarseThresholds[tem,row,end,fe,:] < adcs1[tem,row,end,fe,:])\n \n tholds = coarseThresholds[tem,row,end,fe,:] \n rng = calConstant.CDAC_COARSE\n \n tholds = tholds + bias[tem,row,end,fe,numarray.NewAxis]\n d = numarray.compress(qx, D0)\n a = numarray.compress(qx, tholds)\n \n try:\n import ROOTFit\n import ROOT\n (fitParms, fitErrs, chisq) = ROOTFit.ROOTFit(ROOT.TF1(\"p1\",\"pol1\"),\n d,\n a,\n P0)\n \n fail = False\n except ValueError, e:\n log.error(\"fit excep on T%d,%s%s,%d: %s,%s,%s\", tem, calConstant.CROW[row],\n calConstant.CPM[end], fe, e, d, a)\n \n # save fit parameters (or substitute)\n mevs[tem,row,end,fe,0] = fitParms[1]\n mevs[tem,row,end,fe,1] = fitParms[0] \n ranges[tem,row,end,fe] = rng\n \n return (mevs, ranges)","def test_general_subset_dset():\n pass","def compute_nodeset(data):\n xset = NodeSet()\n for nodeset in data.split():\n xset.update(nodeset)\n return xset","def evaluate_continuation(original, generated, last_onset_prime,\n onset_increment, evaluate_from_onset,\n evaluate_until_onset):\n scores = {'Onset': [], 'Precision': [], 'Recall': [], 'F1': []}\n nr_steps = int((evaluate_until_onset - evaluate_from_onset)\n / onset_increment)\n max_onset = evaluate_until_onset + last_onset_prime\n for step in range(nr_steps + 1):\n onset = step * onset_increment + evaluate_from_onset\n cutoff = last_onset_prime + onset\n if cutoff <= max_onset:\n scores['Onset'].append(onset)\n # Select all rows with onset times less than or equal to cutoff\n original_events = original[original['onset'] <= cutoff]\n generated_events = generated[generated['onset'] <= cutoff]\n if (len(original_events) <= 1 or len(generated_events) <= 1):\n scores['Precision'].append(None)\n scores['Recall'].append(None)\n scores['F1'].append(None)\n continue\n else:\n output = evaluate_cs(original_events, generated_events)\n scores['Precision'].append(output['prec'])\n scores['Recall'].append(output['rec'])\n scores['F1'].append(output['F1'])\n return pd.DataFrame(scores)","def non_mcnugget():\n nugget = [0, 6, 9, 20]\n mcnugget = set([6, 9, 20])\n\n while True:\n mcnugget = set([m+n for m in mcnugget for n in nugget])\n\n for m in mcnugget:\n found = all([m+j in mcnugget for j in range(6)])\n if found:\n return [k for k in range(1, m) if k not in mcnugget]","def get_demographic_set(code_dict, year, state_code, county_code, bg=False):\n key = Names.get_census_key()\n census = Census(key)\n dataset = 'acs/acs5' if int(year) >= 2016 else 'acs5'\n data = census.get(code_dict, state_code, county_code, year, dataset, bg)\n\n return data","def build_constellation(edges, start, visited):\n num_points = edges.shape[0]\n constellation = set()\n nodes = [start]\n while nodes:\n point = nodes.pop()\n visited.add(point)\n\n for other in range(num_points):\n if edges[point, other] and other not in constellation:\n constellation.add(other)\n nodes.append(other)\n\n return constellation","def linspace(self, start: float, end: float, samples: int, flexible: bool = False, bip: bool = True):\n\n if not isinstance(samples, (int)):\n raise ValueError('samples: must be an integer')\n if not isinstance(start, (int, float)):\n raise ValueError('start: must be a number')\n if not isinstance(end, (int, float)):\n raise ValueError('end: must be a number')\n if samples < 2:\n raise ValueError('points: needs to be 2 or more')\n\n use_reversed = end < start\n if use_reversed:\n start,end = end,start\n half = 0.5 if bip else 0.0 # half bit difference between bip and neg,pos\n byte_start = int(math.ceil(half + start/self.dac_quata))\n byte_end = int(math.floor(half + end/self.dac_quata))\n delta_bytes = abs(byte_end - byte_start)-1\n spacing = max(int(math.floor(delta_bytes / (samples-1))),2)\n l = [(el+half)*self.dac_quata\n for el in range(byte_start, byte_end,spacing)]\n # Adjust the points until the length is correct\n if not flexible:\n if len(l) > samples:\n if (len(l) - samples)%2==1:\n l = l[1:]\n s = int((len(l) - samples) / 2)\n if s > 0:\n l = l[s:-s]\n if len(l) < samples:\n msg = ( 'Insufficient resolution for '+ str(samples)\n + ' samples in the range '\n + str(start)+' to ' + str(end) )\n msg += '. Maximum :' + str(len(l))\n raise ValueError(msg)\n if len(l) == 0:\n msg = ('No DAC values exist in the range ' +\n str(start) + ' : ' + str(end)\n )\n raise ValueError(msg)\n\n if use_reversed:\n l = list(reversed(l))\n return l","def load_fence_locations(y, begin=None, end=None):\r\n\r\n # Load the data. Can be from any year 2010-2016, results\r\n # won't change for this application\r\n data = load_data(2010)\r\n\r\n # Create a fence category column and initialize everything to\r\n # a value of 0\r\n data['Category'] = 'darkred'\r\n data['Number'] = 0\r\n\r\n # Set the rest of the categories\r\n fort_macon = 2229 # Profiles in from the end where Fort Macon starts\r\n data['Category'].iloc[:-fort_macon].loc[np.isnan(data['x_fence'])] = 'dimgray'\r\n data['Category'].iloc[:-fort_macon].loc[~np.isnan(data['x_fence'])] = 'cornflowerblue'\r\n\r\n data['Number'].iloc[:-fort_macon].loc[np.isnan(data['x_fence'])] = 1\r\n data['Number'].iloc[:-fort_macon].loc[~np.isnan(data['x_fence'])] = 2\r\n\r\n # Add a y-value column\r\n data['Y'] = y\r\n\r\n # Sort out fences using chunks\r\n for n in chunks(data[:fort_macon], 5):\r\n if np.any(data['x_fence'].iloc[n.index] > 0):\r\n data['Number'].iloc[n.index] = 2\r\n else:\r\n data['Number'].iloc[n.index] = 1\r\n\r\n # Just take the needed data\r\n data = data[['Profile No.', 'Category', 'Number', 'Y']]\r\n\r\n if end is not None:\r\n data = data.loc[(data['Profile No.'] >= begin) & (data['Profile No.'] <= end)]\r\n\r\n # Return the dataframe\r\n return data","def _pareto_front(teams_population, novelty):\r\n for team in teams_population:\r\n team.dom_by_ = 0\r\n team.dom_of_ = 0\r\n\r\n front = []\r\n dominateds = []\r\n for teamA in teams_population:\r\n for teamB in teams_population:\r\n # check if there are teams that have a better or equal [fitness, novelty] and that are better in at least \r\n # one of the dimensions. If yes, then teamA is dominated by these teams.\r\n if ParetoDominanceForTeams._is_dominated(teamA, teamB, novelty):\r\n teamA.dom_by_ += 1\r\n teamB.dom_of_ += 1\r\n if teamA not in dominateds:\r\n dominateds.append(teamA)\r\n if teamA.dom_by_ == 0:\r\n front.append(teamA)\r\n\r\n # use this score to balance the teams between remove and keep\r\n for team in teams_population:\r\n team.submission_score_ = team.dom_by_/float(len(teams_population)) # use it to add teams to the front (the lower, the better)\r\n team.dominance_score_ = team.dom_of_/float(len(teams_population)) # use it to remove teams from the front (the higher, the better)\r\n return front, dominateds","def infer(\n self, data: xr.Dataset, num_samples: int, seed: int, **infer_args\n ) -> xr.Dataset:\n raise NotImplementedError","def bddf_polyset(reference, order):\n dim = reference.tdim\n pset = []\n assert reference.name == \"hexahedron\"\n pset = polynomial_set(dim, dim, order)\n pset.append(curl((0, 0, x[0] ** (order + 1) * x[1])))\n pset.append(curl((0, x[0] * x[2] ** (order + 1), 0)))\n pset.append(curl((x[1] ** (order + 1) * x[2], 0, 0)))\n for i in range(1, order + 1):\n pset.append(curl((0, 0, x[0] * x[1] ** (i + 1) * x[2] ** (order - i))))\n pset.append(curl((0, x[0] ** (i + 1) * x[1] ** (order - i) * x[2], 0)))\n pset.append(curl((x[0] ** (order - i) * x[1] * x[2] ** (i + 1), 0, 0)))\n\n return pset","def downset(self,\n _sortkey=operator.attrgetter('dindex'),\n _next_concepts=operator.attrgetter('lower_neighbors')):\n return algorithms.iterunion([self], _sortkey, _next_concepts)","def get_dates(self, candidates=None, start=None, end=None):\n if candidates is not None:\n return [date for date in candidates if date in self.data]\n if start is None:\n start = self.first_date\n if end is None:\n end = self.last_date\n return [date for date in self.data if start <= date <= end]","def clueDedupe(clue_mapped, clue_export, preference):\n cm_dates = [c['day'] for c in clue_mapped]\n for ce in clue_export['data']:\n if ce['day'] in cm_dates:\n for idx, cm in enumerate(clue_mapped):\n #find matching dates\n if ce['day'] == cm['day']:\n #combine both date's data points if 'combine' is chosen, but preference clue export\n #on conflict\n if preference.lower() == 'combined':\n for k, v in clue_mapped[idx].items(): \n if k in ce:\n clue_mapped[idx][k].extend(ce[k])\n clue_mapped[idx][k] = list(set(clue_mapped[idx][k]))\n else:\n clue_mapped[idx][k] = ce[k]\n else:\n clue_mapped.append(ce)\n return clue_mapped","def get_all_districts():\n with open(district_data_dir + 'district-data.json') as f:\n district_dict = json.load(f)\n districts = set([])\n\n for date, data in district_dict.items():\n if date == '03/02/2020':\n continue\n districts.update(data.keys())\n\n # Remove unnecessary points\n districts.remove('total-infected')\n districts.remove('max-legend-value')\n districts.remove('splitPoints')\n return districts","def create_subsets(self, start_ids):\n subsets = list()\n df = self.all_df.copy()\n for sid in start_ids:\n df2 = df.loc[sid:, :]\n subsets.append(df.drop(df2.index, axis=0))\n df = df2.copy()\n subsets.append(df)\n return subsets"],"string":"[\n \"def decades(self, data, disjoint=True):\\n\\n decade_set = set()\\n try:\\n # multi valued\\n assert isinstance(data, set)\\n for datum in data:\\n preprocessed_year_data = self._extract_year_data(datum)\\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\\n except (AssertionError, TypeError):\\n # single value\\n preprocessed_year_data = self._extract_year_data(data)\\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\\n\\n return decade_set\",\n \"def get_decade_list(start_decade, end_decade):\\n decade_list = []\\n for decade in range(start_decade, end_decade, 10):\\n decade_list.append(decade)\\n return decade_list\",\n \"def years(self, data, disjoint=True):\\n\\n year_set = set()\\n try:\\n # multi valued\\n assert isinstance(data, set)\\n for datum in data:\\n preprocessed_year_data = self._extract_year_data(datum)\\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n except TypeError:\\n # single value\\n preprocessed_year_data = self._extract_year_data(data)\\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n return year_set\",\n \"def dset_no_collection():\\n _dset = dataset.Dataset(5)\\n _dset.add_bool(\\\"idx\\\", val=[0, 1, 1, 0, 1])\\n _dset.add_float(\\\"numbers\\\", val=[1, 2, 3, 4, 5])\\n _dset.add_position(\\\"sat_pos\\\", val=np.ones((5, 3)), system=\\\"trs\\\")\\n _dset.add_position(\\\"site_pos\\\", val=np.ones((5, 3)) * 2, system=\\\"trs\\\", other=_dset.sat_pos)\\n _dset.add_position_delta(\\\"site_delta\\\", val=np.ones((5, 3)) * 0.5, system=\\\"trs\\\", ref_pos=_dset.site_pos)\\n _dset.add_posvel(\\\"sat_posvel\\\", val=np.ones((5, 6)), system=\\\"trs\\\")\\n _dset.add_posvel(\\\"site_posvel\\\", val=np.ones((5, 6)) * 2, system=\\\"trs\\\", other=_dset.sat_posvel)\\n _dset.add_posvel_delta(\\\"site_posvel_delta\\\", val=np.ones((5, 6)) * 0.5, system=\\\"trs\\\", ref_pos=_dset.site_posvel)\\n _dset.add_sigma(\\\"numbers2\\\", val=[3, 3, 3, 3, 3], sigma=[0.2, 0.2, 0.2, 0.2, 0.2])\\n _dset.add_text(\\\"text\\\", val=[\\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\"])\\n _dset.add_time(\\\"time\\\", val=[datetime(2015, 1, i) for i in range(5, 10)], scale=\\\"utc\\\", fmt=\\\"datetime\\\")\\n _dset.add_time_delta(\\\"time_delta\\\", val=[timedelta(seconds=i) for i in range(20, 25)], scale=\\\"utc\\\", fmt=\\\"timedelta\\\")\\n return _dset\",\n \"def create_C1(data_set):\\n C1 = set()\\n for t in data_set:\\n for item in t:\\n item_set = frozenset([item])\\n C1.add(item_set)\\n return C1\",\n \"def create_C1(data_set):\\r\\n C1 = set()\\r\\n for t in data_set:\\r\\n for item in t:\\r\\n item_set = frozenset([item])\\r\\n C1.add(item_set)\\r\\n return C1\",\n \"def dset(self):\\n\\n a = 0.0\\n b = 0.0\\n sums = np.sum(self.descriptors, axis=0)\\n for sum in sums:\\n if sum > 0:\\n if sum == self.d_length:\\n b += 1.\\n else:\\n a += 1.\\n return a / (a+b)\",\n \"def __call__(self, uind: Set[Ind]) -> FrozenSet[Ind]:\\n ks = len(uind)\\n try:\\n start_arity, G = self.generate_graph(uind)\\n except StopIteration:\\n return frozenset()\\n\\n _logger.info('Looking for hypercliques')\\n H = find_hypercliques(G)\\n _logger.info('Validating %d hypercliques', len(H))\\n I = self._validate_all(H)\\n\\n result = set(filter(lambda i: len(i) == 1, I))\\n for m in range(start_arity + 1, ks):\\n _logger.info('Iteration %d (%d candidates)', m, len(I))\\n _logger.info('Iteration %d (%d positives)', m, len(result))\\n C = set()\\n for c in I:\\n if c.valid and len(c) >= m - 1:\\n result.add(c)\\n if not c.valid and len(c) >= m:\\n C.add(c)\\n k_ary = gen_k_ary_ind_from_cliques(m, C)\\n _logger.info('%d %d-ary generated from %d', len(k_ary), m, len(C))\\n Gm = Graph()\\n Gm.E = self._validate_all(k_ary)\\n Gm.E = set(filter(lambda e: e.valid, Gm.E))\\n if Gm.empty() or True:\\n return frozenset(map(Edge.to_ind, result))\\n result.update(gen_sub_inds(m, Gm, result))\\n Gm.V = frozenset(reduce(frozenset.union, map(lambda e: e.set, Gm.E), frozenset()))\\n H = find_hypercliques(Gm)\\n I = self._validate_all(H)\\n\\n # Convert candidates back to Ind\\n return frozenset(map(Edge.to_ind, result))\",\n \"def dominance_frontiers(G, start):\\n idom = nx.immediate_dominators(G, start)\\n\\n df = {u: set() for u in idom}\\n for u in idom:\\n if len(G.pred[u]) >= 2:\\n for v in G.pred[u]:\\n if v in idom:\\n while v != idom[u]:\\n df[v].add(u)\\n v = idom[v]\\n return df\",\n \"def split_by_year(config, dbsession, group):\\n years = []\\n decades = []\\n centuries = []\\n with_year = 0\\n for item in group.items:\\n if config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]:\\n years.append(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n with_year = with_year + 1\\n if with_year / len(group.items) > 0.95: # noqa: PLR2004\\n common = [(int(v), c) for v, c in Counter(years).most_common()]\\n start_year = min([c for c, _ in common])\\n end_year = max([c for c, _ in common])\\n if start_year != end_year:\\n if (end_year - start_year) <= 100 and (end_year - start_year) > 10: # noqa: PLR2004\\n start_decade = math.floor(start_year / 10)\\n end_decade = math.floor(end_year / 10)\\n decades = []\\n for start_year in range(start_decade * 10, (end_decade + 1) * 10, 10):\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n start_year <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < start_year + 10\\n ):\\n if len(decades) == 0 or decades[-1][0][0] != start_year:\\n decades.append([[start_year], 1])\\n else:\\n decades[-1][1] = decades[-1][1] + 1\\n idx = 0\\n while idx < len(decades) - 1:\\n if decades[idx][1] + decades[idx + 1][1] < 100: # noqa: PLR2004\\n decades[idx][0].extend(decades[idx + 1][0])\\n decades[idx][1] = decades[idx][1] + decades[idx + 1][1]\\n decades.pop(idx + 1)\\n else:\\n idx = idx + 1\\n for years, _ in decades:\\n new_group = None\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n years[0] <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < years[-1] + 10\\n ):\\n if new_group is None:\\n if len(years) == 1:\\n label = f\\\"{years[0]}s\\\"\\n else:\\n label = f\\\"{years[0]}s-{years[-1]}s\\\"\\n new_group = Group(\\n value=str(start_year),\\n label=f\\\"{group.label} - {label}\\\",\\n parent=group,\\n split=\\\"time\\\",\\n )\\n dbsession.add(new_group)\\n item.group = new_group\\n if group.items:\\n new_group = Group(value=group.label, label=group.label, parent=group, split=\\\"time\\\")\\n dbsession.add(new_group)\\n for item in list(group.items):\\n item.group = new_group\\n return True\\n elif (end_year - start_year) > 100: # noqa: PLR2004\\n start_century = math.floor(start_year / 100)\\n end_century = math.floor(end_year / 100)\\n centuries = []\\n for start_year in range(start_century * 100, (end_century + 1) * 100, 100):\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n start_year <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < start_year + 100\\n ):\\n if len(centuries) == 0 or centuries[-1][0][0] != start_year:\\n centuries.append([[start_year], 1])\\n else:\\n centuries[-1][1] = centuries[-1][1] + 1\\n idx = 0\\n while idx < len(centuries) - 1:\\n if centuries[idx][1] + centuries[idx + 1][1] < 100: # noqa: PLR2004\\n centuries[idx][0].extend(centuries[idx + 1][0])\\n centuries[idx][1] = centuries[idx][1] + centuries[idx + 1][1]\\n centuries.pop(idx + 1)\\n else:\\n idx = idx + 1\\n for years, _ in centuries:\\n new_group = None\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n years[0] <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < years[-1] + 100\\n ):\\n if new_group is None:\\n if len(years) == 1:\\n century = math.floor(years[0] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n label = f\\\"{century}rd\\\"\\n else:\\n label = f\\\"{century}th\\\"\\n else:\\n century = math.floor(years[0] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n start_label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n start_label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n start_label = f\\\"{century}rd\\\"\\n else:\\n start_label = f\\\"{century}th\\\"\\n century = math.floor(years[-1] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n end_label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n end_label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n end_label = f\\\"{century}rd\\\"\\n else:\\n end_label = f\\\"{century}th\\\"\\n label = f\\\"{start_label}-{end_label}\\\"\\n new_group = Group(\\n value=str(start_year),\\n label=f\\\"{group.label} - {label} century\\\",\\n parent=group,\\n split=\\\"time\\\",\\n )\\n dbsession.add(new_group)\\n item.group = new_group\\n if group.items:\\n new_group = Group(value=group.label, label=group.label, parent=group, split=\\\"time\\\")\\n dbsession.add(new_group)\\n for item in list(group.items):\\n item.group = new_group\\n return True\\n return False\",\n \"def ecdf(data):\\n x = np.sort(data)\\n cdf = np.linspace(0, 1, len(x))\\n return cdf, x\",\n \"def histogram_decades(our_data):\\n decade_dict = {}\\n for album in our_data:\\n decade = int(album['year'])//10\\n if decade in decade_dict:\\n decade_dict[decade] += 1\\n else:\\n decade_dict[decade] = 1\\n return decade_dict\",\n \"def doffsets(self):\\n self._flatten()\\n for n in self.seq[1:len(self.seq)-1]:\\n yield sorted([ d['idx'] - n['idx'] for d in n['edges'] ])\\n return\",\n \"def continuity_test(districts):\\r\\n for tag in range(1,6):\\r\\n if tag in districts:\\r\\n X = np.where((districts==tag)|(districts==0))\\r\\n contig = set([(X[0][0],X[1][0])])\\r\\n prev_len = 0\\r\\n while len(contig) > prev_len and prev_len<5:\\r\\n prev_len = len(contig)\\r\\n for r,c in contig.copy():\\r\\n if r>0 and districts[r-1,c] in [tag,0]:\\r\\n contig.add((r-1,c))\\r\\n if r<4 and districts[r+1,c] in [tag,0]:\\r\\n contig.add((r+1,c))\\r\\n if c>0 and districts[r,c-1] in [tag,0]:\\r\\n contig.add((r,c-1))\\r\\n if c<4 and districts[r,c+1] in [tag,0]:\\r\\n contig.add((r,c+1))\\r\\n if prev_len < 5:\\r\\n return False\\r\\n return True\",\n \"def get_district_time_series(district,\\n start_date,\\n cumulative=False,\\n num_days=-1):\\n district_data = get_district_data(district)\\n\\n ret_list = []\\n prev_date = dt.strptime(start_date, date_format)\\n for obj in district_data:\\n curr_date = dt.strptime(obj[0], date_format)\\n # Condition to check if ret_list has been initiated.\\n # Only then check if some days got missing in between.\\n if ret_list:\\n # Calculate number of days between prev_date and curr date\\n days = (curr_date - prev_date).days\\n if days > 1:\\n # Append the last number again for those many days\\n for _in in range(days - 1):\\n ret_list.append(ret_list[-1])\\n\\n # Add data for the range given\\n if curr_date >= dt.strptime(start_date, date_format):\\n # Case: When the given start date is before the start of infections\\n if not ret_list:\\n initial_days = (curr_date - dt.strptime(start_date, date_format)).days\\n for _in in range(initial_days):\\n ret_list.append(0)\\n ret_list.append(obj[1])\\n\\n prev_date = curr_date\\n if len(ret_list) == num_days:\\n break\\n\\n if not cumulative:\\n convert_to_daily(ret_list)\\n return ret_list\",\n \"def _compute_semantic_cuts(self):\\n # back-ward collect dominators as candidate merging points\\n merging_points = [self._end_uid]\\n cur_uid = self._end_uid\\n while cur_uid != self._start_uid:\\n cur_node = self._nodes[cur_uid]\\n dominator_uid = cur_node.get_dominator()\\n merging_points.append(dominator_uid)\\n cur_uid = dominator_uid\\n # create additional candidate couples\\n semantic_cuts = []\\n mp_len = len(merging_points)\\n step = 2\\n while step < mp_len:\\n k = 0\\n done = False\\n while not done:\\n tail_uid = merging_points[k]\\n if k + step < mp_len:\\n head_uid = merging_points[k + step]\\n else:\\n head_uid = merging_points[mp_len - 1]\\n done = True\\n if head_uid != tail_uid:\\n semantic_cuts.append({\\n \\\"head_uids\\\" : [head_uid],\\n \\\"tail_uids\\\" : [tail_uid],\\n })\\n k += step\\n step *= 2\\n return semantic_cuts\",\n \"def ecdf(data):\\n x = np.sort(data)\\n y = np.arange(1,len(x)+1)/len(x)\\n\\n return x,y\",\n \"def dset_full():\\n _dset = dataset.Dataset(5)\\n _dset.add_bool(\\\"idx\\\", val=[0, 1, 1, 0, 1])\\n _dset.add_float(\\\"numbers\\\", val=[1, 2, 3, 4, 5])\\n _dset.add_position(\\\"sat_pos\\\", val=np.ones((5, 3)), system=\\\"trs\\\")\\n _dset.add_position(\\\"site_pos\\\", val=np.ones((5, 3)) * 2, system=\\\"trs\\\", other=_dset.sat_pos)\\n _dset.add_position_delta(\\\"site_delta\\\", val=np.ones((5, 3)) * 0.5, system=\\\"trs\\\", ref_pos=_dset.site_pos)\\n _dset.add_posvel(\\\"sat_posvel\\\", val=np.ones((5, 6)), system=\\\"trs\\\")\\n _dset.add_posvel(\\\"site_posvel\\\", val=np.ones((5, 6)) * 2, system=\\\"trs\\\", other=_dset.sat_posvel)\\n _dset.add_posvel_delta(\\\"site_posvel_delta\\\", val=np.ones((5, 6)) * 0.5, system=\\\"trs\\\", ref_pos=_dset.site_posvel)\\n _dset.add_sigma(\\\"numbers2\\\", val=[3, 3, 3, 3, 3], sigma=[0.2, 0.2, 0.2, 0.2, 0.2])\\n _dset.add_text(\\\"text\\\", val=[\\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\", \\\"aaa\\\"])\\n _dset.add_time(\\\"time\\\", val=[datetime(2015, 1, i) for i in range(5, 10)], scale=\\\"utc\\\", fmt=\\\"datetime\\\")\\n _dset.add_time_delta(\\\"time_delta\\\", val=[timedelta(seconds=i) for i in range(20, 25)], scale=\\\"utc\\\", fmt=\\\"timedelta\\\")\\n\\n # Collections\\n _dset.add_bool(\\\"group.idx\\\", val=[0, 0, 0, 0, 0])\\n _dset.add_float(\\\"group.numbers\\\", val=[6, 7, 8, 9, 10])\\n _dset.add_position(\\\"group.sat_pos\\\", val=np.ones((5, 3)) * 7, system=\\\"trs\\\")\\n _dset.add_position(\\\"group.site_pos\\\", val=np.ones((5, 3)) * 8, system=\\\"trs\\\", other=_dset.group.sat_pos)\\n _dset.add_position_delta(\\\"group.site_delta\\\", val=np.ones((5, 3)) * 9.5, system=\\\"trs\\\", ref_pos=_dset.group.site_pos)\\n _dset.add_posvel(\\\"group.sat_posvel\\\", val=np.ones((5, 6)) * 6, system=\\\"trs\\\")\\n _dset.add_posvel(\\\"group.site_posvel\\\", val=np.ones((5, 6)) * 5, system=\\\"trs\\\", other=_dset.group.sat_posvel)\\n _dset.add_posvel_delta(\\n \\\"group.site_posvel_delta\\\", val=np.ones((5, 6)) * 1.5, system=\\\"trs\\\", ref_pos=_dset.group.site_posvel\\n )\\n _dset.add_sigma(\\\"group.numbers2\\\", val=[1.2, 1.2, 1.2, 1.2, 1.2], sigma=[3.2, 3.2, 3.2, 3.2, 3.2])\\n _dset.add_text(\\\"group.text\\\", val=[\\\"bbb\\\", \\\"bbb\\\", \\\"bbb\\\", \\\"bbb\\\", \\\"bbb\\\"])\\n _dset.add_time(\\\"group.time\\\", val=[datetime(2015, 1, i) for i in range(10, 15)], scale=\\\"utc\\\", fmt=\\\"datetime\\\")\\n _dset.add_time_delta(\\n \\\"group.time_delta\\\", val=[timedelta(seconds=i) for i in range(0, 5)], scale=\\\"utc\\\", fmt=\\\"timedelta\\\"\\n )\\n return _dset\",\n \"def generate_scaffolds(self, dataset, cutoff=0.18):\\n mols = []\\n for ind, smiles in enumerate(dataset.ids):\\n mols.append(Chem.MolFromSmiles(smiles))\\n n_mols = len(mols)\\n fps = [AllChem.GetMorganFingerprintAsBitVect(x, 2, 1024) for x in mols]\\n\\n scaffold_sets = ClusterFps(fps, cutoff=cutoff)\\n scaffold_sets = sorted(scaffold_sets, key=lambda x: -len(x))\\n scaffold_sets = [list(sfd) for sfd in scaffold_sets]\\n return scaffold_sets\",\n \"def dict_sets_intersection_test(self, data):\\n\\n data_info = self.get_data_info(data)\\n finished = []\\n\\n for part in data:\\n for union_part in data:\\n if part != union_part and union_part not in finished:\\n data[part].intersection(data[union_part])\\n finished.append(part)\\n\\n return data_info\",\n \"def points_in_distance(c, d):\\n points = set()\\n for i in range(0, d + 1):\\n points = points | points_in_circle(c, i)\\n return points\",\n \"def compute_critical_values(P, Q, p, q, mdist, P_dist, Q_dist):\\n origin = eucl_dist(P[0], Q[0])\\n end = eucl_dist(P[-1], Q[-1])\\n end_point = max(origin, end)\\n cc = set([end_point])\\n for i in range(p - 1):\\n for j in range(q - 1):\\n Lij = point_to_seg(Q[j], P[i], P[i + 1], mdist[i, j], mdist[i + 1, j], P_dist[i])\\n if Lij > end_point:\\n cc.add(Lij)\\n Bij = point_to_seg(P[i], Q[j], Q[j + 1], mdist[i, j], mdist[i, j + 1], Q_dist[j])\\n if Bij > end_point:\\n cc.add(Bij)\\n return sorted(list(cc))\",\n \"def get_decs(self):\\n return [i for i in self.decisions.keys()]\",\n \"def cdf_discretize(self,variables=[]):\\n #the errors in the code are due to the deleted files that require packages to be installed on the computer\\n for i in variables:\\n x=unique(self.data[:,i])\\n m=max(x)-min(x)\\n f=lambda x0,y0: array([m*(x0+y0)/(1+m**2), (x0*m+y0)/(1+m**2)])\\n cdf=array([np.sum(self.data[:,i]<=t) for t in x])\\n d=array([norm(array([x0,cdf[k]])-f(x0,cdf[k])) for k,x0 in\\\\\\n enumerate(x)])\",\n \"def test_kde_cumulative(limits):\\n data = np.random.normal(0, 1, 1000)\\n density = _kde(data, custom_lims=limits, cumulative=True)[1]\\n np.testing.assert_almost_equal(round(density[-1], 3), 1)\",\n \"def find_shitty_decoys(self):\\n target_seqs = set()\\n decoy_seqs = set()\\n with open(self.csv_path, \\\"r\\\") as f:\\n\\n reader = csv.DictReader(f)\\n\\n sorted_reader = sorted(\\n reader,\\n reverse=self[\\\"bigger_scores_better\\\"],\\n key=lambda d: float(d[self.col_for_sorting]),\\n )\\n\\n for row in sorted_reader:\\n self.observed_charges.add(int(row[\\\"Charge\\\"]))\\n if row_is_decoy(row):\\n decoy_seqs.add(unify_sequence(row[\\\"Sequence\\\"]))\\n self.counter[\\\"decoy\\\"] += 1\\n else:\\n target_seqs.add(unify_sequence(row[\\\"Sequence\\\"]))\\n self.counter[\\\"target\\\"] += 1\\n\\n self.shitty_decoy_seqs = target_seqs.intersection(decoy_seqs)\\n if len(self.shitty_decoy_seqs) > 0:\\n print(\\n \\\"Warning! Found {0} sequences that are target AND decoy \\\"\\n \\\"(immutable peptides?). These will not be used for training.\\\\n\\\".format(\\n len(self.shitty_decoy_seqs)\\n )\\n )\\n return\",\n \"def get_sample_numbers(cons_df: pd.DataFrame) -> Set[int]:\\n\\n all_cols = list(cons_df.columns)\\n sample_nbrs = set([int(col.split('_')[-1]) for col in all_cols[1:] if not col.endswith('cf')])\\n return sample_nbrs\",\n \"def cdf(self, data, survival=False, **kwargs):\\n\\n data = np.array(data)\\n if not data.any():\\n return array([np.nan]), array([np.nan])\\n\\n #data = trim_to_range(data, xmin=xmin, xmax=xmax)\\n\\n n = float(len(data))\\n\\n data = np.sort(data)\\n all_unique = not( any( data[:-1]==data[1:] ) )\\n\\n if all_unique:\\n CDF = np.arange(n)/n\\n else:\\n #This clever bit is a way of using searchsorted to rapidly calculate the\\n #CDF of data with repeated values comes from Adam Ginsburg's plfit code,\\n #specifically https://github.com/keflavich/plfit/commit/453edc36e4eb35f35a34b6c792a6d8c7e848d3b5#plfit/plfit.py\\n CDF = np.searchsorted(data, data,side='left') / n\\n unique_data, unique_indices = np.unique(data, return_index=True)\\n data=unique_data\\n CDF = CDF[unique_indices]\\n\\n if survival:\\n CDF = 1-CDF\\n return data, CDF\",\n \"def parse_dset(clf, dset, idx):\\n probs = []\\n for s in range(int(np.ceil(len(idx)/100.0))):\\n lower = s*100\\n upper = min([(s+1)*100,len(idx)])\\n X = np.array([dset.get(idx[k])[0] for k in range(lower,upper)])\\n probs += [clf.fx_output(dset.reshape_batch(X))] \\n \\n return probs\",\n \"def check_dose_range_sanity(dose_list):\\n\\n low_dose = 10000\\n high_dose = 0\\n constrained_list = []\\n\\n for dose in dose_list:\\n if low_dose > dose:\\n low_dose = dose\\n if high_dose < dose:\\n high_dose = dose\\n\\n if high_dose >= (low_dose * 10):\\n # an order of magnitude or more range breaks the graphs\\n for dose in dose_list:\\n if dose > (low_dose * 10):\\n constrained_list.append(low_dose * 10)\\n # print(\\\"Chopping \\\" + str(dose) + \\\" to \\\" + str(low_dose * 10))\\n else:\\n constrained_list.append(dose)\\n\\n return constrained_list\\n else:\\n return dose_list\",\n \"def _cont_calcs(srs: Srs, cfg: Config) -> Dict[str, List[Any]]:\\n\\n data: Dict[str, List[Any]] = {}\\n\\n # drop infinite values\\n mask = srs.apply(\\\"isin\\\", {np.inf, -np.inf})\\n srs = Srs(srs.getmask(mask, inverse=True), agg=True)\\n min_max = srs.apply(\\n \\\"map_partitions\\\", lambda x: pd.Series([x.max(), x.min()]), meta=pd.Series([], dtype=float)\\n ).data\\n min_max_comp = []\\n if cfg.diff.density:\\n for min_max_value in dask.compute(min_max)[0]:\\n min_max_comp.append(math.isclose(min_max_value.min(), min_max_value.max()))\\n min_max = dd.concat(min_max).repartition(npartitions=1)\\n\\n # histogram\\n data[\\\"hist\\\"] = srs.self_map(\\n da.histogram, bins=cfg.hist.bins, range=(min_max.min(), min_max.max())\\n )\\n\\n # compute the density histogram\\n if cfg.diff.density:\\n data[\\\"dens\\\"] = srs.self_map(\\n da.histogram,\\n condition=min_max_comp,\\n bins=cfg.kde.bins,\\n range=(min_max.min(), min_max.max()),\\n density=True,\\n )\\n # gaussian kernel density estimate\\n data[\\\"kde\\\"] = []\\n sample_data = dask.compute(\\n srs.apply(\\n \\\"map_partitions\\\",\\n lambda x: x.sample(min(1000, x.shape[0])),\\n meta=pd.Series([], dtype=float),\\n ).data\\n )\\n for ind in range(len(sample_data[0])):\\n data[\\\"kde\\\"].append(gaussian_kde(sample_data[0][ind]))\\n\\n return data\",\n \"def get_intersecting_dissemination_ids(cross_section, dissemination_areas):\\n assert 'DAUID' in cross_section.columns \\n dissem_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].DAUID.unique()\\n reg_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].CSDUID.unique()\\n# code_arr = dissemination_areas.loc[dissemination_areas['DAUID'].isin(np.unique(cross_section['DAUID'].values))].CODEID.unique()\\n\\n\\n return list(dissem_arr), list(reg_arr)\",\n \"def get_dec_i_range(data_decs):\\n data_dec_is = map_list(lambda x: (x - 1800)//10, data_decs)\\n lower_range_i = data_dec_is[0]\\n upper_range_i = data_dec_is[-1] + 1\\n return (lower_range_i, upper_range_i)\",\n \"def find_overlapping_cds_simple(v_start, v_stop, cds_begins, strand):\\n # cds_start = cds_begin[0]\\n if strand == '+':\\n return list(filter(lambda x: x[0] >= v_start and x[0] < v_stop, cds_begins))\\n else:\\n return list(filter(lambda x: x[0] > v_start and x[0] <= v_stop, cds_begins))\",\n \"def _get_cdf(self, data, threshold):\\n index = data.sort_values()\\n index.name = None\\n series = pd.Series(np.linspace(0, 1, len(index)), index=index)\\n series.name = data.name\\n\\n # Compute percentage of samples above/below the specified threshold\\n below = float(max(series[:threshold]))\\n above = 1 - below\\n return series, above, below\",\n \"def pruning(self):\\n data = self.data.copy()\\n for d in self.data:\\n # cascade purning method. Inspired from \\\"Efficient Computation of Group Skyline Queries on MapReduce (FCU)\\\"\\n if d in data:\\n pastart = [self.drange[1] if i+self.radius>self.drange[1] else i+self.radius for i in d.getLocationMax()]\\n pamax = [self.drange[1] for j in range(self.dim)]\\n # prune data points that are obviously dominated by current data point\\n pruned = (self.index.intersection(tuple(pastart+pamax),objects=True))\\n for p in pruned:\\n if p.object in data:\\n data.remove(p.object)\\n self.pruned = data\",\n \"def generate_threat_kde_dataset(train_dataset):\\n\\n threat_grid_cells = train_dataset['X'][~train_dataset['Y']]\\n kde_values = threat_grid_cells[['latitude_index', 'longitude_index', 'KDE']]\\n\\n threat_kde_df = kde_values.set_index(['latitude_index', 'longitude_index'])['KDE']\\n threat_kde_df = threat_kde_df.sort_values(ascending=False)\\n\\n return list(threat_kde_df.index), threat_kde_df\",\n \"def convexify(domain):\\n\\n if isinstance(domain, isl.BasicSet):\\n return domain\\n\\n dom_bsets = domain.get_basic_sets()\\n if len(dom_bsets) == 1:\\n domain, = dom_bsets\\n return domain\\n\\n hull_domain = domain.simple_hull()\\n if isl.Set.from_basic_set(hull_domain) <= domain:\\n return hull_domain\\n\\n domain = domain.coalesce()\\n\\n dom_bsets = domain.get_basic_sets()\\n if len(domain.get_basic_sets()) == 1:\\n domain, = dom_bsets\\n return domain\\n\\n hull_domain = domain.simple_hull()\\n if isl.Set.from_basic_set(hull_domain) <= domain:\\n return hull_domain\\n\\n dom_bsets = domain.get_basic_sets()\\n assert len(dom_bsets) > 1\\n\\n print(\\\"PIECES:\\\")\\n for dbs in dom_bsets:\\n print(\\\" %s\\\" % (isl.Set.from_basic_set(dbs).gist(domain)))\\n raise NotImplementedError(\\\"Could not find convex representation of set\\\")\",\n \"def detect_saccades(self, kernel_sigma=.003,\\n start_thresh=(20, .005),\\n end_thresh=(7, .005)):\\n # Save a copy of the original eyelink saccades\\n if not hasattr(self, \\\"eyelink_saccades\\\"):\\n self.eyelink_saccades = self.saccades.copy()\\n\\n # Compute smoothing kernel size in sample bin units\\n dt = 1 / self.sampling_rate\\n kernel_width = kernel_sigma / dt\\n\\n # Extract gaze position\\n xy = self.samples[[\\\"x\\\", \\\"y\\\"]]\\n\\n # Smooth the gaze position data\\n xy_s = xy.apply(gaussian_filter1d, args=(kernel_width,))\\n\\n # Compute velocity\\n v = (xy_s.diff()\\n .apply(np.square)\\n .sum(axis=1, skipna=False)\\n .apply(np.sqrt)\\n .divide(dt)\\n .fillna(np.inf))\\n\\n # Identify saccade onsets\\n start_window = int(start_thresh[1] / dt)\\n sthr = (v > start_thresh[0]).rolling(start_window).min()\\n starts = xy.where(sthr.diff() == 1).dropna()\\n\\n # Identify saccade offsets\\n end_window = int(end_thresh[1] / dt)\\n ethr = (v < end_thresh[0]).rolling(end_window).min()\\n ends = xy.where(ethr.diff() == 1).dropna()\\n\\n # -- Parse each detected onset to identify the corresponding end\\n saccades = []\\n last_end_time = 0\\n for start_time, start_pos in starts.iterrows():\\n\\n if start_time < last_end_time:\\n # This is an acceration within a prior saccade that has not\\n # yet ended; skip\\n continue\\n\\n ends = ends.loc[start_time:]\\n\\n # Check if the dataset is ending in the middle of a saccade\\n if ends.size:\\n end = ends.iloc[0]\\n else:\\n break\\n\\n last_end_time = end.name\\n\\n saccades.append([start_time,\\n start_pos[\\\"x\\\"],\\n start_pos[\\\"y\\\"],\\n end.name,\\n end[\\\"x\\\"],\\n end[\\\"y\\\"]])\\n\\n # Package the saccades into a dataframe\\n columns = [\\\"start\\\", \\\"start_x\\\", \\\"start_y\\\", \\\"end\\\", \\\"end_x\\\", \\\"end_y\\\"]\\n saccades = pd.DataFrame(saccades, columns=columns)\\n\\n # -- Compute duration, amplitude, velocity, and angle\\n\\n dx = saccades[\\\"end_x\\\"] - saccades[\\\"start_x\\\"]\\n dy = saccades[\\\"end_y\\\"] - saccades[\\\"start_y\\\"]\\n saccades[\\\"amplitude\\\"] = np.sqrt(np.square(dx) + np.square(dy))\\n\\n saccades[\\\"duration\\\"] = saccades[\\\"end\\\"] - saccades[\\\"start\\\"]\\n saccades[\\\"velocity\\\"] = saccades[\\\"amplitude\\\"] / saccades[\\\"duration\\\"]\\n\\n saccades[\\\"angle\\\"] = np.rad2deg(np.arctan2(dy, dx)) % 360\\n\\n # Overwrite the saccade data structure with the new results\\n self.saccades = saccades\\n\\n return self\",\n \"def _computedivergentset(repo):\\n divergent = set()\\n obsstore = repo.obsstore\\n newermap = {}\\n for ctx in repo.set('(not public()) - obsolete()'):\\n mark = obsstore.precursors.get(ctx.node(), ())\\n toprocess = set(mark)\\n while toprocess:\\n prec = toprocess.pop()[0]\\n if prec not in newermap:\\n successorssets(repo, prec, newermap)\\n newer = [n for n in newermap[prec] if n]\\n if len(newer) > 1:\\n divergent.add(ctx.rev())\\n break\\n toprocess.update(obsstore.precursors.get(prec, ()))\\n return divergent\",\n \"def get_span(self)->set:\\n return set(range(self.get_start_offset(), self.get_end_offset()))\",\n \"def find_crosstalk_peaks(spectrum_in, delta_crosstalk = 1.3):\\n peaks = spectrum_in.get_peaks()\\n precursor_mz = spectrum_in.get_precursor_mz()[0]\\n upper_precursor_mz = precursor_mz + delta_crosstalk\\n \\n crosstalk_peaks = []\\n\\n for peak in peaks:\\n \\n mz = peak.get_mz()\\n\\n if mz > upper_precursor_mz:\\n\\n crosstalk_peaks.append(peak)\\n \\n return(crosstalk_peaks)\",\n \"def dilation(hexs, diameter):\\n new_hex_set = set(hexs)\\n for a_hex in hexs:\\n adjacent_hexs = h3.k_ring(a_hex, diameter)\\n new_hex_set = new_hex_set.union(adjacent_hexs)\\n new_hexs = list(new_hex_set)\\n return new_hexs\",\n \"def load_all_data():\\r\\n\\r\\n data = dict()\\r\\n for year in ['2010', '2011', '2014', '2016']:\\r\\n\\r\\n data[year] = load_data(int(year))\\r\\n\\r\\n # Calculate the dune widths\\r\\n data[year]['Dune Width'] = data[year]['x_heel'] - data[year]['x_toe']\\r\\n data[year]['Fenced Dune Width'] = data[year]['x_fence_heel'] - data[year]['x_fence_toe']\\r\\n data[year]['Fenced Dune System Width'] = data[year]['x_heel'] - data[year]['x_fence_toe']\\r\\n\\r\\n # For now, remove all negative widths and volumes, something went wrong with them\\r\\n width_condition = data[year]['Fenced Dune Width'] <= 0\\r\\n volume_condition = data[year]['Fenced Dune Volume'] <= 0\\r\\n\\r\\n data[year]['y_fence_crest'][width_condition] = np.nan\\r\\n data[year]['Fenced Dune Width'][width_condition] = np.nan\\r\\n data[year]['Fenced Dune Volume'][width_condition] = np.nan\\r\\n\\r\\n data[year]['y_fence_crest'][volume_condition] = np.nan\\r\\n data[year]['Fenced Dune Width'][volume_condition] = np.nan\\r\\n data[year]['Fenced Dune Volume'][volume_condition] = np.nan\\r\\n\\r\\n data[year]['Fenced Dune System Width'][data[year]['Fenced Dune System Width'] <= 0] = np.nan\\r\\n\\r\\n # Remove instances where the fenced and natural dune crest are not positioned correctly\\r\\n crest_condition_1 = data[year]['x_fence_crest'] >= data[year]['x_crest']\\r\\n crest_condition_2 = data[year]['y_fence_crest'] >= data[year]['y_crest']\\r\\n\\r\\n data[year]['y_fence_crest'][crest_condition_1] = np.nan\\r\\n data[year]['Fenced Dune Width'][crest_condition_1] = np.nan\\r\\n data[year]['Fenced Dune Volume'][crest_condition_1] = np.nan\\r\\n\\r\\n data[year]['y_fence_crest'][crest_condition_2] = np.nan\\r\\n data[year]['Fenced Dune Width'][crest_condition_2] = np.nan\\r\\n data[year]['Fenced Dune Volume'][crest_condition_2] = np.nan\\r\\n\\r\\n data['Fences'] = load_fence_locations(y=0)\\r\\n\\r\\n return data\",\n \"def _recompute(self):\\n current_date = self.start_date\\n self.quarterly_date_list = []\\n self.daily_date_list = []\\n while current_date <= self.end_date:\\n current_quarter = get_quarter(current_date)\\n current_year = current_date.year\\n next_year, next_quarter = add_quarter(current_year, current_quarter)\\n next_start_quarter_date = date(next_year, get_month(next_quarter),\\n 1)\\n\\n days_till_next_quarter = (next_start_quarter_date -\\n current_date).days\\n days_till_end = (self.end_date - current_date).days\\n if days_till_next_quarter <= days_till_end:\\n current_start_quarter_date = date(current_year,\\n get_month(current_quarter), 1)\\n if current_start_quarter_date == current_date:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter, lambda x: True))\\n current_date = next_start_quarter_date\\n elif days_till_next_quarter > self.balancing_point:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter,\\n lambda x: date(x['date_filed']) >= self.start_date))\\n current_date = next_start_quarter_date\\n else:\\n while current_date < next_start_quarter_date:\\n self.daily_date_list.append(current_date)\\n current_date += timedelta(days=1)\\n else:\\n if days_till_end > self.balancing_point:\\n if days_till_next_quarter - 1 == days_till_end:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter, lambda x: True))\\n current_date = next_start_quarter_date\\n else:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter,\\n lambda x: date(x['date_filed']) <= self.end_date))\\n current_date = self.end_date\\n else:\\n while current_date <= self.end_date:\\n self.daily_date_list.append(current_date)\\n current_date += timedelta(days=1)\",\n \"def generate_date_set(self, year, month, days):\\n dates = set()\\n for day in days:\\n dates.add(date(year, month, day))\\n return dates\",\n \"def get_available_cops():\\n allIncidents = Incident.get_all()\\n cops = []\\n \\n for i in allIncidents:\\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\\n cops.append(i['operations_center']['id'])\\n \\n allReports = RelatoDeSituacao.get_all()\\n \\n for r in allReports:\\n if (\\n inicioAmostragem <= r.data_hora and \\n r.data_hora <=terminoAmostragem and\\n 'cop' in r.relator and # todos tem que ter o COP\\n 'id' in r.relator['cop'] # todos tem que ter o id \\n ):\\n cops.append(r.relator['cop']['id'])\\n \\n return set(cops)\",\n \"def _get_common_start_points(round_data):\\n start_points = defaultdict(int)\\n for rd in round_data:\\n x = rd[\\\"x\\\"].iloc[0]\\n y = rd[\\\"y\\\"].iloc[0]\\n\\n start_points[(x, y)] += 1\\n\\n common_start_points = []\\n for key, val in start_points.items():\\n # TODO: Tune, or percentage?\\n if val > 5:\\n common_start_points.append(key)\\n return common_start_points\",\n \"def all_cited_dois( corpus ) :\\n cites = list(set(chain.from_iterable((cited_dois(x) for x in corpus))))\\n return cites\",\n \"def preprocess_data(data_path, last_day=None, schools_unis='two_separate', drop_features=None, min_confirmed=100,\\n min_deaths=10, smoothing=1, mask_zero_deaths=False, mask_zero_cases=False):\\n\\n # load data from our csv\\n df = pd.read_csv(data_path, parse_dates=[\\\"Date\\\"], infer_datetime_format=True).set_index(\\n [\\\"Country Code\\\", \\\"Date\\\"])\\n\\n # handle custom last day of analysis\\n if last_day is None:\\n Ds = list(df.index.levels[1])\\n else:\\n Ds = list(df.index.levels[1])\\n last_ts = pd.to_datetime(last_day, utc=True)\\n Ds = Ds[:(1 + Ds.index(last_ts))]\\n\\n nDs = len(Ds)\\n\\n all_rs = list([r for r, _ in df.index])\\n regions = list(df.index.levels[0])\\n locations = [all_rs.index(r) for r in regions]\\n sorted_regions = [r for l, r in sorted(zip(locations, regions))]\\n nRs = len(sorted_regions)\\n region_names = copy.deepcopy(sorted_regions)\\n region_full_names = df.loc[region_names][\\\"Region Name\\\"]\\n\\n if drop_features is None:\\n # note: these features are taken from the OxCGRT Dataset other than the mask wearing option\\n drop_features = ['Mask Wearing', 'Travel Screen/Quarantine', 'Travel Bans', 'Public Transport Limited',\\n 'Internal Movement Limited', 'Public Information Campaigns', 'Symptomatic Testing']\\n\\n for f in drop_features:\\n print(f'Dropping NPI {f}')\\n df = df.drop(f, axis=1)\\n\\n # pull data\\n CMs = list(df.columns[4:])\\n nCMs = len(CMs)\\n\\n ActiveCMs = np.zeros((nRs, nCMs, nDs))\\n Confirmed = np.zeros((nRs, nDs))\\n Deaths = np.zeros((nRs, nDs))\\n Active = np.zeros((nRs, nDs))\\n NewDeaths = np.zeros((nRs, nDs))\\n NewCases = np.zeros((nRs, nDs))\\n\\n for r_i, r in enumerate(sorted_regions):\\n region_names[r_i] = df.loc[(r, Ds[0])]['Region Name']\\n for d_i, d in enumerate(Ds):\\n Confirmed[r_i, d_i] = df.loc[(r, d)]['Confirmed']\\n Deaths[r_i, d_i] = df.loc[(r, d)]['Deaths']\\n Active[r_i, d_i] = df.loc[(r, d)]['Active']\\n\\n ActiveCMs[r_i, :, :] = df.loc[r].loc[Ds][CMs].values.T\\n\\n # compute new (daily) cases, after using thresholds\\n Confirmed[Confirmed < min_confirmed] = np.nan\\n Deaths[Deaths < min_deaths] = np.nan\\n NewCases[:, 1:] = (Confirmed[:, 1:] - Confirmed[:, :-1])\\n NewDeaths[:, 1:] = (Deaths[:, 1:] - Deaths[:, :-1])\\n NewDeaths[NewDeaths < 0] = 0\\n NewCases[NewCases < 0] = 0\\n\\n NewCases[np.isnan(NewCases)] = 0\\n NewDeaths[np.isnan(NewDeaths)] = 0\\n\\n if smoothing != 1:\\n print('Smoothing')\\n\\n # bulk smooth\\n SmoothedNewCases = np.around(\\n ss.convolve2d(NewCases, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary='symm',\\n mode='same'))\\n SmoothedNewDeaths = np.around(\\n ss.convolve2d(NewDeaths, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary=\\\"symm\\\",\\n mode='same'))\\n\\n # correct for specific regions\\n for r in range(nRs):\\n # if the country has too few deaths, ignore\\n if Deaths[r, -1] < 50:\\n print(f'Note: did not smooth deaths in {region_names[r]}')\\n SmoothedNewDeaths[r, :] = NewDeaths[r, :]\\n\\n NewCases = SmoothedNewCases\\n NewDeaths = SmoothedNewDeaths\\n\\n print('Masking invalid values')\\n if mask_zero_deaths:\\n NewDeaths[NewDeaths < 1] = np.nan\\n else:\\n NewDeaths[NewDeaths < 0] = np.nan\\n\\n if mask_zero_cases:\\n NewCases[NewCases < 1] = np.nan\\n else:\\n NewCases[NewCases < 0] = np.nan\\n\\n Confirmed = np.ma.masked_invalid(Confirmed.astype(theano.config.floatX))\\n Active = np.ma.masked_invalid(Active.astype(theano.config.floatX))\\n Deaths = np.ma.masked_invalid(Deaths.astype(theano.config.floatX))\\n NewDeaths = np.ma.masked_invalid(NewDeaths.astype(theano.config.floatX))\\n NewCases = np.ma.masked_invalid(NewCases.astype(theano.config.floatX))\\n\\n # handle schools and universities\\n if schools_unis == 'two_xor':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs_final[:, university_index, :] = np.logical_xor(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs = ActiveCMs_final\\n CMs[school_index] = 'School and University Closure'\\n CMs[university_index] = 'Schools xor University Closure'\\n elif schools_unis == 'one_tiered':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :]) + 0.5 * np.logical_xor(\\n ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n\\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\\n CMs.remove('University Closure')\\n elif schools_unis == 'two_separate':\\n # don't need to do anything for this!\\n pass\\n elif schools_unis == 'one_and':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\\n CMs[school_index] = 'School and University Closure'\\n CMs.remove('University Closure')\\n\\n return PreprocessedData(Active,\\n Confirmed,\\n ActiveCMs,\\n CMs,\\n sorted_regions,\\n Ds,\\n Deaths,\\n NewDeaths,\\n NewCases,\\n region_full_names)\",\n \"def generate_Lk_by_Ck(data_set, Ck, min_support, support_data):\\r\\n Lk = set()\\r\\n item_count = {}\\r\\n for t in data_set:\\r\\n for item in Ck:\\r\\n if item.issubset(t):\\r\\n if item not in item_count:\\r\\n item_count[item] = 1\\r\\n else:\\r\\n item_count[item] += 1\\r\\n t_num = float(len(data_set))\\r\\n for item in item_count:\\r\\n if (item_count[item] ) >= min_support:\\r\\n Lk.add(item)\\r\\n support_data[item] = item_count[item] / t_num\\r\\n return Lk\",\n \"def from_reduced(cls, data, random=False, signature=False, **umap_kwargs):\\n new = Deduplicator([], random=random, signature=signature,\\n **umap_kwargs)\\n new.data_reduced = [data]\\n new.data_kd = [cKDTree(new.data_reduced[0])]\\n\\n return new\",\n \"def calcCascadeSizes(startles):\\n ntimesteps = startles.shape[0]\\n ncascades = 0\\n cascade_sizes = []\\n cascade_lengths = []\\n single_startle = False\\n ongoing_cascade = False\\n current_cascade_members = []\\n previous_startle_idc = []\\n current_cascade_length = 0\\n for t in np.arange(ntimesteps):\\n startle_idc = np.where(startles[t, :])[0]\\n # no startles at current time step:\\n if startle_idc.size == 0:\\n if ongoing_cascade:\\n cascade_sizes.append(len(current_cascade_members))\\n cascade_lengths.append(current_cascade_length)\\n ncascades += 1\\n ongoing_cascade = False\\n current_cascade_members = []\\n else:\\n if single_startle:\\n single_startle = False\\n current_cascade_members = []\\n current_cascade_length = 0\\n # at least one fish startled:\\n else:\\n if ongoing_cascade:\\n new_members = []\\n for startle_idx in startle_idc:\\n if startle_idx not in previous_startle_idc:\\n new_members.append(startle_idx)\\n if not len(new_members) == 0:\\n current_cascade_members.extend(new_members)\\n current_cascade_length += 1\\n else:\\n if single_startle:\\n new_members = []\\n for startle_idx in startle_idc:\\n if startle_idx not in previous_startle_idc:\\n new_members.append(startle_idx)\\n if not len(new_members) == 0:\\n single_startle = False\\n ongoing_cascade = True\\n current_cascade_members.extend(new_members)\\n current_cascade_length += 1\\n else:\\n single_startle = True\\n current_cascade_members.extend(startle_idc)\\n current_cascade_length += 1\\n previous_startle_idc = startle_idc\\n return np.array(cascade_sizes), np.array(cascade_lengths)\",\n \"def nondominated_sort(dataset):\\n numPareto = 0\\n\\n # pairwise comparisons\\n for n in range(len(dataset)):\\n for m in range(len(dataset)):\\n if dataset[m].dominates(dataset[n]):\\n dataset[n].dominatedCount+=1\\n dataset[m].addToDominatingSet(n)\\n\\n # find first pareto front\\n front = []\\n front2 = []\\n tmpLevel = -10 # temporary value for Pareto level, will re-adjust later\\n for n in range(len(dataset)):\\n if dataset[n].dominatedCount == 0:\\n dataset[n].paretoStatus = tmpLevel\\n front.append(n)\\n numPareto+=1\\n\\n # iteratively peel off pareto fronts\\n while len(front) != 0:\\n tmpLevel-=1\\n for f in front:\\n for s in dataset[f].dominatingSet:\\n dataset[s].dominatedCount -= 1\\n if dataset[s].dominatedCount == 0:\\n front2.append(s)\\n dataset[s].paretoStatus = tmpLevel\\n front = front2 \\n front2 = []\\n\\n # re-adjust pareto level\\n for n in range(len(dataset)):\\n oldLevel = dataset[n].paretoStatus\\n if oldLevel != -1:\\n dataset[n].paretoStatus = oldLevel-tmpLevel-1\\n\\n return numPareto\",\n \"def create_cands(data):\\n\\n best = np.zeros(data.dim+1)\\n best[0:data.dim] = data.xbest\\n best[data.dim] = 1-np.sum(data.xbest)\\n\\n # Ncand times the best value\\n cp_e = np.kron(np.ones((data.Ncand, 1)), np.asmatrix(best))\\n # This generates random perturbations\\n # need dim+1 to account for the \\\"missing\\\" value\\n r = np.random.rand(data.Ncand, data.dim+1)\\n a = r < data.pertP\\n idx = np.where(np.sum(a, axis=1) == 0)\\n for ii in range(len(idx[0])):\\n f = np.random.permutation(data.dim+1)\\n a[idx[0][ii], f[0]] = True\\n randnums = np.random.randn(data.Ncand, data.dim+1)\\n randnums[a == False] = 0\\n pv = randnums*data.sigma_stdev\\n # Create new points by adding random fluctucations to best point\\n new_pts = cp_e+pv\\n\\n # Iterative, column wise procedure to force the randomly\\n # sampled point to be in [0,1]\\n for ii in range(data.dim+1):\\n vec_ii = new_pts[:, ii]\\n adj_l = np.where(vec_ii < data.xlow)\\n vec_ii[adj_l[0]] = data.xlow + (data.xlow - vec_ii[adj_l[0]])\\n adj_u = np.where(vec_ii > data.xup)\\n vec_ii[adj_u[0]] = data.xup - (vec_ii[adj_u[0]]-data.xup)\\n stillout_u = np.where(vec_ii > data.xup)\\n vec_ii[stillout_u[0]] = data.xlow\\n stillout_l = np.where(vec_ii < data.xlow)\\n vec_ii[stillout_l[0]] = data.xup\\n new_pts[:, ii] = copy.copy(vec_ii)\\n\\n new_pts = new_pts / np.sum(new_pts, axis=1)\\n\\n cp_e = copy.copy(new_pts)\\n rand_pts = np.asmatrix(np.random.uniform(0, 1, [data.Ncand, data.dim + 1]))\\n cp_r = rand_pts/np.sum(rand_pts, axis=1)\\n\\n CandPoint = np.concatenate((cp_e, cp_r), axis=0)\\n # return only data.dim candidate points\\n CandPoint_out = CandPoint[:, 0:data.dim]\\n\\n return CandPoint_out\",\n \"def getPossibleTagSet(self, data, cutoff=None, add_common=False):\\n full_tag_set_size = data.getTagSetSize()\\n if cutoff is None:\\n cutoff = ceil(full_tag_set_size * DEFAULT_CUTOFF_FRACT)\\n elif cutoff >= full_tag_set_size:\\n return data.getTagSet()\\n word = self.getWord()\\n tags_dict = data.getWordDict().get(word, False)\\n if tags_dict is False:\\n sorted_tags_list = data.getSortedTagsList()\\n else:\\n sorted_tags_list = sorted(tags_dict, key=tags_dict.get, reverse=True)\\n if data.isNumberWord(word) and \\\"CD\\\" not in sorted_tags_list[:cutoff]:\\n sorted_tags_list.insert(0, \\\"CD\\\")\\n remainder = cutoff - len(sorted_tags_list)\\n if remainder < 0:\\n return tuple(sorted_tags_list[:cutoff])\\n elif add_common is True and remainder > 0:\\n top_candidate_tags = data.getSortedTagsList()\\n sorted_tags_set = set(sorted_tags_list)\\n candidate_set = set(top_candidate_tags) - sorted_tags_set\\n while remainder > 0:\\n tag_candidate = top_candidate_tags.pop(0)\\n if tag_candidate in candidate_set:\\n sorted_tags_list.append(tag_candidate)\\n remainder -= 1\\n return tuple(sorted_tags_list)\",\n \"def _compute_soffsets(self):\\n self.soffsets = [ [] for i in self.doffsets ]\\n for idx,dofs in enumerate(self.doffsets):\\n for o in dofs:\\n self.soffsets[(idx + o) % self.p].append(-o)\",\n \"def _count_parties(data_set): #DEMOCRATS, THEN REPUBLICANS\\r\\n reps = 0\\r\\n dems = 0\\r\\n for data_point in data_set:\\r\\n if data_point.dat_party == \\\"R\\\": reps+=1\\r\\n if data_point.dat_party == \\\"D\\\": dems+=1\\r\\n\\r\\n return (dems, reps)\",\n \"def get_coref_set(self,\\n step_idxs: List[int],\\n use_entity: bool = False,\\n ) -> Set[Tuple[int, str, int, str]]:\\n if len(step_idxs) <= 1:\\n return []\\n\\n coref_participants = defaultdict(set)\\n for i, step_idx in enumerate(step_idxs):\\n participants = self.steps[step_idx].participants\\n for participant in participants:\\n if use_entity:\\n for value in participant.values:\\n coref_participants[value.entity].add(\\n (i, participant.role))\\n else:\\n if participant.refvar:\\n coref_participants[participant.refvar].add(\\n (i, participant.role))\\n\\n coref_set = set()\\n for participants in coref_participants.values():\\n for (i, role_1), (j, role_2) in combinations(participants, 2):\\n if i == 0:\\n continue\\n if i > j:\\n i, j, role_1, role_2 = j, i, role_2, role_1\\n coref_set.add((i, role_1, j, role_2))\\n\\n return coref_set\",\n \"def get_subsets(self):\\n if self.begin_date is not None and self.end_date is not None:\\n iv = get_timestamp_from_date(self.end_date) - get_timestamp_from_date(self.begin_date)\\n fv = iv / 86400000 + 1\\n self.n_days = fv\\n X = self.data.copy().ix[:self.begin_date, :]\\n X = X.fillna(method='bfill')\\n y = self.data.copy().ix[:self.begin_date][self.symb]\\n x_pred = X.ix[-(self.n_days):, :]\\n init_val = self.original_data.ix[0][self.symb]\\n print X.shape, y.shape, x_pred.shape\\n return X, y, x_pred, init_val\",\n \"def _as_set(self):\\n included = set()\\n excluded = set()\\n # Add all points lying within each inclusion contour to S.\\n for contour in self.contours:\\n contour_matrix = contour.to_matrix()[:,:2]\\n # Turn the contour closed if it's not.\\n if (contour_matrix[0] != contour_matrix[-1]).all():\\n contour_matrix = np.append(contour_matrix,\\n contour_matrix[0].reshape(1,2),\\n axis=0)\\n\\n # Create path object and test all pixels \\n # within the contour's bounding box.\\n path = mplpath.Path(contour_matrix, closed=True)\\n mn = contour_matrix.min(axis=0)\\n mx = contour_matrix.max(axis=0)\\n x,y = np.mgrid[mn[0]:mx[0]+1, mn[1]:mx[1]+1]\\n test_points = np.c_[x.flatten(), y.flatten()]\\n points_in_contour = test_points[path.contains_points(test_points)]\\n\\n # Add the z coordinate.\\n points_in_contour = np.c_[\\\\\\n points_in_contour,\\\\\\n np.ones(points_in_contour.shape[0])*contour.image_z_position\\n ]\\n\\n # Now turn the numpy matrix into a list of tuples,\\n # so we can add it to the corresponding set.\\n points_in_contour = map(tuple, points_in_contour)\\n\\n # Update the corresponding set.\\n if contour.inclusion:\\n included.update(points_in_contour)\\n else:\\n excluded.update(points_in_contour)\\n # Return the included points minus the excluded points.\\n return included.difference( excluded )\",\n \"def generate_Lk_by_Ck(fptree, data_set, Ck,dim, min_support, support_data):\\n Lk = set()\\n item_count = {}\\n t_num = float(len(data_set))\\n if dim == 1:\\n for t in data_set:\\n for item in Ck:\\n if item.issubset(t):\\n if item not in item_count:\\n item_count[item] = 1\\n else:\\n item_count[item] += 1\\n t_num = float(len(data_set))\\n else:\\n for item in Ck:\\n array = list(item)\\n subData, support = fptree.getSubData(array[len(array) - 1], dim)\\n for i in range(0, len(subData)):\\n if item.issubset(subData[i]):\\n if item not in item_count:\\n item_count[item] = support[i]\\n else:\\n item_count[item] += support[i]\\n for item in item_count:\\n if (item_count[item] / t_num) >= min_support:\\n Lk.add(item)\\n support_data[item] = item_count[item] / t_num\\n\\n return Lk\",\n \"def delete(feature_set, population):\\n features = [x for x in list(feature_set)]\\n pop = [x for y in population for x in y]\\n min = float(\\\"+inf\\\")\\n rem = features[0]\\n for i in range(0, len(features)):\\n x = pop.count(features[i])\\n if x < min:\\n min = x\\n rem = features[i]\\n features.remove(rem)\\n return set(features)\",\n \"def cutSec(ppm, X, start, stop, featureMask):\\n\\tflip=0\\n\\tif ppm[0]>ppm[-1]:\\n\\t\\tflip=1\\n\\t\\tppm = ppm[::-1]\\n\\t\\tX = X[:, ::-1]\\n \\n #find first entry in ppm with >='start' valu\\n\\tstart = (ppm>=start).nonzero()\\n\\tstart = start[0][0]#first entry\\n\\tstop = (ppm<=stop).nonzero()\\n\\tstop = stop[0][-1]#last entry\\n\\n#currently setting featureMask will get rid of peaks in start:stop region BUT it also marks as excluded so have removed as inaccurately marking for exclusion when all we want to do is remove from intensityData not mark as exluded\\n\\ttry:\\n\\t\\tfeatureMask[0,start:stop]=False # this may only occur on unit test data, not sure need to check but either way was causing issue\\n\\texcept:\\n\\t\\tfeatureMask[start:stop]=False\\n\\tif flip==1:\\n\\t\\tppm = ppm[::-1]\\n\\t\\tX = X[:, ::-1]\\n\\treturn ppm, X, featureMask\\n\\tpass\",\n \"def _enumerate_years(self, preprocessed_data, disjoint):\\n pass\",\n \"def sub_poset(self, start, end):\\n if start < 1 or start > end or end > self.size() + 1:\\n raise ValueError(\\\"Invalid starting or ending value, accepted: 1 <= start <= end <= size+1\\\")\\n if start == end:\\n return TamariIntervalPoset(0, [])\\n relations = [(i - start + 1, j - start + 1) for (i, j) in self.increasing_cover_relations() if i >= start and j < end]\\n relations.extend([(j - start + 1, i - start + 1) for (j, i) in self.decreasing_cover_relations() if i >= start and j < end])\\n return TamariIntervalPoset(end - start, relations)\",\n \"def get_base_depos(self, start, end, currencies, tenor, cut=\\\"NYC\\\", source=\\\"bloomberg\\\",\\n cache_algo=\\\"internet_load_return\\\"):\\n\\n market_data_generator = self.market_data_generator\\n\\n if isinstance(currencies, str): currencies = [currencies]\\n if isinstance(tenor, str): tenor = [tenor]\\n\\n tickers = []\\n\\n for cr in currencies:\\n for tn in tenor:\\n tickers.append(cr + tn)\\n\\n market_data_request = MarketDataRequest(\\n start_date=start, finish_date=end,\\n data_source=source,\\n category='base-depos',\\n freq='daily',\\n cut=cut,\\n tickers=tickers,\\n fields=['close'],\\n cache_algo=cache_algo,\\n environment='backtest'\\n )\\n\\n data_frame = market_data_generator.fetch_market_data(market_data_request)\\n data_frame.index.name = 'Date'\\n\\n return data_frame\",\n \"def test_cover():\\n # D corresponds to the items and the transactions in which they appear, it is the standard code table\\n D = {\\n \\\"B\\\": Bitmap([0, 1, 2]),\\n \\\"A\\\": Bitmap([0, 1]),\\n \\\"C\\\": Bitmap([0, 2]),\\n \\\"D\\\": Bitmap([2])\\n }\\n # ct corresponds to the itemsets on which we want to calculate the cover\\n ct = [\\n frozenset(\\\"ABC\\\"),\\n frozenset(\\\"AB\\\"),\\n frozenset(\\\"BC\\\"),\\n frozenset(\\\"A\\\"),\\n frozenset(\\\"B\\\"),\\n frozenset(\\\"C\\\"),\\n frozenset(\\\"D\\\")\\n ]\\n CTc = cover(D, ct)\\n\\n assert CTc[frozenset(\\\"ABC\\\")] == Bitmap([0])\\n assert CTc[frozenset(\\\"AB\\\")] == Bitmap([1]) # AB appears only in tid_1 for usage because ABC is placed before in ct\\n # so the AB of the first transaction has been covered by ABC\\n assert CTc[frozenset(\\\"BC\\\")] == Bitmap([2])\\n assert CTc[frozenset(\\\"A\\\")] == Bitmap()\\n assert CTc[frozenset(\\\"B\\\")] == Bitmap()\\n assert CTc[frozenset(\\\"C\\\")] == Bitmap()\\n assert CTc[frozenset(\\\"D\\\")] == Bitmap([2])\",\n \"def count_sites_under_condition_vcf_to_set(vcf_file,chrom,start,end,mincov=0,maxcov=10000,inds=\\\"all\\\",bgzip=True,nb_ind_with_min_cov=\\\"all\\\",nalleles=[1,2],snps=False):\\n\\tset_ok_sites = set()\\n\\tinput_vcf=vcf.Reader(fsock=None, filename=vcf_file, compressed=bgzip, prepend_chr=\\\"False\\\", strict_whitespace=False)#open the vcf parser\\n\\tnsites_OK=0\\n\\tnsites_total=0\\n\\t#print \\\"in count_sites_under_condition_vcf nb_ind_with_min_cov :\\\",nb_ind_with_min_cov, \\\" inds\\\", ind\\n\\tif chrom!=\\\"all\\\":\\n\\t\\t\\t#print chrom,start,end\\n\\t\\t\\tcheck=len(sh.tabix(vcf_file,str(chrom)+\\\":\\\"+str(start)+\\\"-\\\"+str(end)))\\n\\t\\t\\t#print check\\n\\t\\t\\t#print \\\"check;' \\\",check,\\\"'\\\"\\n\\t\\t\\tif check==0: \\n\\t\\t\\t\\treturn [0,0]\\n\\t\\t\\tfor record in input_vcf.fetch(chrom,start,end):# for every site\\n\\t\\t\\t\\tcond=checkSnp_Cov(input_vcf,record,mincov,maxcov,inds=inds,nalleles=nalleles,nb_ind_with_min_cov=nb_ind_with_min_cov,snps=snps)# check if the site respect our condition\\n\\t\\t\\t\\tnsites_total+=1\\n\\t\\t\\t\\tif cond:# if it does\\n\\t\\t\\t\\t\\t#if any([int(sample['DP'])<5 for sample in record.samples]): print [int(sample['DP']) for sample in record.samples] # to check this argument nb_ind_with_min_cov\\n\\t\\t\\t\\t\\tset_ok_sites.add(str(record.CHROM)+\\\"_\\\"+str(record.POS))\\n\\treturn set_ok_sites\",\n \"def disease_descriptors(civic_did8):\\n return [civic_did8]\",\n \"def _init_continuations(self):\\n\\n # if the continuations dset already exists we reinitialize the\\n # data\\n if CONTINUATIONS in self.settings_grp:\\n cont_dset = self.settings_grp[CONTINUATIONS]\\n cont_dset.resize( (0,2) )\\n\\n # otherwise we just create the data\\n else:\\n cont_dset = self.settings_grp.create_dataset(CONTINUATIONS, shape=(0,2), dtype=int,\\n maxshape=(None, 2))\\n\\n return cont_dset\",\n \"def CumulativeDistribution(data, nbins, range=None, normed=True, centerbins=False):\\n\\n # 1) COMPUTE THE DISTRIBUTION OF THE DATA\\n ydata, xdata = np.histogram(data, nbins, range, normed)\\n\\n # 1.1) Compute the cumulative sum of the probability\\n ydata = ydata.cumsum()\\n\\n # 2) RETURN THE RESULTS\\n if centerbins:\\n dif = 0.5 * (xdata[-1] - xdata[0]) / nbins\\n xdata += dif\\n\\n if normed:\\n norm = 1.0 / ydata[-1]\\n ydata *= norm\\n\\n return xdata[:-1], ydata\\n\\n else:\\n return xdata[:-1], ydata\",\n \"def get_coarseness(my_core, meshset, entity_ranges, geom_dim):\\n\\n coarseness = []\\n\\n for surface in entity_ranges:\\n surf_area = get_area_triangle(my_core, surface, geom_dim)\\n coarseness.append(len(surf_area)/sum(surf_area))\\n\\n return coarseness\",\n \"def discrete_dataset(data):\\n dataset = [0] * 10\\n for value in data:\\n dataset[value] += 1\\n return dataset\",\n \"def discretize(v, cutoff):\\n v = np.array(v)\\n ix = np.zeros(v.shape, dtype=np.long)\\n \\n cutoff = list(cutoff)\\n cutoff.append(np.inf)\\n n = len(cutoff)\\n \\n for ii in range(1,n):\\n ix[(v >= cutoff[ii - 1]) & (v < cutoff[ii])] = ii\\n \\n ix[v >= cutoff[-1]] = n - 1 \\n return ix\",\n \"def create_intersection_report(self, data=None):\\n\\n if not data:\\n data = self.build_dict_of_sets(self.data_dict)\\n\\n self.intersection_report_data = []\\n finished = []\\n\\n for part in data:\\n for union_part in data:\\n if part != union_part and union_part not in finished:\\n union_set = data[part].intersection(data[union_part])\\n self.intersection_report_data.append([part, union_part, union_set])\\n finished.append(part)\",\n \"def test_range():\\n begin_date = datetime.datetime(2000, 1, 1)\\n end_date = datetime.datetime.today()\\n\\n if os.path.isfile(\\\"spy_price_cache_\\\" + str(datetime.date.today()) + \\\".csv\\\"):\\n dates_available = pickle.load(open(\\\"spy_price_cache_\\\" + str(datetime.date.today()) + \\\".csv\\\", \\\"r\\\"))\\n else:\\n prices_available = yahoo.webload_symbol_price(\\\"SPY\\\", begin_date, end_date)\\n dates_available = set(timestamp.to_pydatetime() for timestamp in prices_available.index.tolist())\\n pickle.dump(dates_available, open(\\\"spy_price_cache_\\\" + str(datetime.date.today()) + \\\".csv\\\", \\\"w\\\"))\\n\\n dates_expected = set([day for day in itertools.takewhile(\\n lambda d: d <= end_date,\\n CALENDAR.every_nth_between(begin_date, end_date, 1)\\n )])\\n\\n dates_misaligned = dates_available.symmetric_difference(dates_expected)\\n\\n assert len(dates_misaligned) == 0\",\n \"def get_support(self, complement=False):\\n if self.__fitOK:\\n\\n keepList = []\\n dropList = []\\n\\n for col in self.__Ddrifts:\\n\\n if (self.__Ddrifts[col] < self.threshold):\\n keepList.append(col)\\n else:\\n dropList.append(col)\\n\\n if complement:\\n return dropList\\n else:\\n return keepList\\n else:\\n raise ValueError('Call the fit function before !')\",\n \"def peptideSetCoverage(peptides1,peptides2,mmTolerance=1):\\n\\n oh1 = objhist(peptides1)\\n coveredPeps = {k:[] for k in set(oh1.keys())}\\n coveredPeps.update({'tot':[]})\\n cache = {}\\n for pep2 in peptides2:\\n anyCover = False\\n for pep1 in set(peptides1):\\n try:\\n dist = cache[(pep1, pep2)]\\n except KeyError:\\n dist = seq_distance(pep1, pep2, subst=binarySubst, normed=False)\\n cache.update({(pep1, pep2):dist,(pep2, pep1):dist})\\n if dist <= mmTolerance:\\n coveredPeps[pep1].append(pep2)\\n anyCover = True\\n if anyCover:\\n coveredPeps['tot'].append(pep2)\\n coverage={k:len(v)/len(peptides2) for k, v in list(coveredPeps.items())}\\n return coverage\",\n \"def get_decks(self, include_cards=True):\\n deck_previews = self.data_source.get_decks(self.user_id,\\n not include_cards)\\n\\n return deck_previews\",\n \"def fast_non_dominated_sort(self, population):\\n current_front = []\\n\\n for solution in population:\\n solution.dominates_list = []\\n solution.dominated_by = 0\\n\\n for candidate in population:\\n if solution.dominates(candidate):\\n solution.dominates_list.append(candidate)\\n elif candidate.dominates(solution):\\n solution.dominated_by += 1\\n\\n if solution.dominated_by == 0:\\n current_front.append(solution)\\n solution.rank = 0\\n\\n fronts = []\\n front_index = 0\\n\\n while current_front:\\n self.crowding_distance_assignment(current_front)\\n fronts.append(current_front)\\n next_front = []\\n\\n for solution in current_front:\\n for dominated in solution.dominates_list:\\n dominated.dominated_by -= 1\\n\\n if dominated.dominated_by == 0:\\n next_front.append(dominated)\\n dominated.rank = front_index + 1\\n\\n current_front = next_front\\n front_index += 1\\n\\n return fronts\",\n \"def is_dominating_set(G, nbunch):\\n testset = set(n for n in nbunch if n in G)\\n nbrs = set(chain.from_iterable(G[n] for n in testset))\\n return len(set(G) - testset - nbrs) == 0\",\n \"def get_comments_between(self, start_date, end_date):\\n ret = []\\n ids = self.get_post_ids(start_date, end_date)\\n\\n for id in ids:\\n comments = self.reddit.submission(id).comments\\n ret.append(self.get_nested_comments(comments))\\n return ret\",\n \"def fitDAC(fineThresholds, coarseThresholds, bias, adcs0, adcs1, limLow, limHigh, fitLim):\\n \\n # array to hold fit parameters\\n \\n mevs = numarray.zeros((calConstant.NUM_TEM, calConstant.NUM_ROW, calConstant.NUM_END,\\n calConstant.NUM_FE, 2), numarray.Float32)\\n \\n # array to hold range info \\n \\n ranges = numarray.zeros((calConstant.NUM_TEM, calConstant.NUM_ROW, calConstant.NUM_END,\\n calConstant.NUM_FE), numarray.Int8) \\n \\n adcs0 = adcs0 - bias\\n adcs1 = adcs1 - bias\\n adcs0 = adcs0[...,numarray.NewAxis]\\n adcs1 = adcs1[...,numarray.NewAxis]\\n\\n # compare upper and lower limits against FINE range thresholds\\n q = (fineThresholds > adcs0) & (fineThresholds < adcs1)\\n \\n for tem in range(calConstant.NUM_TEM):\\n for row in range(calConstant.NUM_ROW):\\n for end in range(calConstant.NUM_END):\\n for fe in range(calConstant.NUM_FE):\\n \\n qx = q[tem,row,end,fe,:]\\n rng = calConstant.CDAC_FINE\\n \\n # if there are not enough points in the FINE range or high point is out of range, \\n # try the COARSE range\\n\\n if len(numarray.nonzero(qx)[0]) < 3 or qx[-1]:\\n \\n qx[...] = \\\\\\n (coarseThresholds[tem,row,end,fe,:] > adcs0[tem,row,end,fe,:]) & \\\\\\n (coarseThresholds[tem,row,end,fe,:] < adcs1[tem,row,end,fe,:])\\n \\n tholds = coarseThresholds[tem,row,end,fe,:]\\n rng = calConstant.CDAC_COARSE\\n \\n else:\\n \\n tholds = fineThresholds[tem,row,end,fe,:] \\n \\n tholds = tholds + bias[tem,row,end,fe,numarray.NewAxis]\\n d = numarray.compress(qx, D0)\\n a = numarray.compress(qx, tholds)\\n # try to fit the preferred range data\\n \\n fail = False\\n \\n try:\\n import ROOTFit\\n import ROOT\\n (fitParms, fitErrs, chisq) = ROOTFit.ROOTFit(ROOT.TF1(\\\"p1\\\",\\\"pol1\\\"),\\n d,\\n a,\\n P0)\\n\\n dnorm = (chisq / len(d))\\n if dnorm > fitLim:\\n log.warning(\\\"fit error > %0.2f on T%d,%s%s,%d\\\", dnorm, tem, \\n calConstant.CROW[row], calConstant.CPM[end], fe)\\n fail = True \\n except ValueError, e:\\n log.error(\\\"fit excep on T%d,%s%s,%d: %s,%s,%s\\\", tem, calConstant.CROW[row],\\n calConstant.CPM[end], fe, e, d, a)\\n fail = True\\n \\n # check slope parameter value for reasonableness\\n \\n if not fail and rng == calConstant.CDAC_FINE:\\n m = fitParms[1]\\n if m < limLow[tem,row,end,fe] or m > limHigh[tem,row,end,fe]:\\n log.warning(\\\"bad slope %0.3f on T%d,%s%s,%d\\\", m, tem, \\n calConstant.CROW[row], calConstant.CPM[end], fe)\\n fail = True\\n \\n # if the range is FINE, and the fit fails, or the resulting slope is\\n # out of limits, try to get fit parameters for the COARSE range\\n \\n if fail:\\n \\n log.warning(\\\"trying COARSE range for T%d,%s%s,%d\\\", tem, calConstant.CROW[row],\\n calConstant.CPM[end], fe)\\n \\n qx[...] = \\\\\\n (coarseThresholds[tem,row,end,fe,:] > adcs0[tem,row,end,fe,:]) & \\\\\\n (coarseThresholds[tem,row,end,fe,:] < adcs1[tem,row,end,fe,:])\\n \\n tholds = coarseThresholds[tem,row,end,fe,:] \\n rng = calConstant.CDAC_COARSE\\n \\n tholds = tholds + bias[tem,row,end,fe,numarray.NewAxis]\\n d = numarray.compress(qx, D0)\\n a = numarray.compress(qx, tholds)\\n \\n try:\\n import ROOTFit\\n import ROOT\\n (fitParms, fitErrs, chisq) = ROOTFit.ROOTFit(ROOT.TF1(\\\"p1\\\",\\\"pol1\\\"),\\n d,\\n a,\\n P0)\\n \\n fail = False\\n except ValueError, e:\\n log.error(\\\"fit excep on T%d,%s%s,%d: %s,%s,%s\\\", tem, calConstant.CROW[row],\\n calConstant.CPM[end], fe, e, d, a)\\n \\n # save fit parameters (or substitute)\\n mevs[tem,row,end,fe,0] = fitParms[1]\\n mevs[tem,row,end,fe,1] = fitParms[0] \\n ranges[tem,row,end,fe] = rng\\n \\n return (mevs, ranges)\",\n \"def test_general_subset_dset():\\n pass\",\n \"def compute_nodeset(data):\\n xset = NodeSet()\\n for nodeset in data.split():\\n xset.update(nodeset)\\n return xset\",\n \"def evaluate_continuation(original, generated, last_onset_prime,\\n onset_increment, evaluate_from_onset,\\n evaluate_until_onset):\\n scores = {'Onset': [], 'Precision': [], 'Recall': [], 'F1': []}\\n nr_steps = int((evaluate_until_onset - evaluate_from_onset)\\n / onset_increment)\\n max_onset = evaluate_until_onset + last_onset_prime\\n for step in range(nr_steps + 1):\\n onset = step * onset_increment + evaluate_from_onset\\n cutoff = last_onset_prime + onset\\n if cutoff <= max_onset:\\n scores['Onset'].append(onset)\\n # Select all rows with onset times less than or equal to cutoff\\n original_events = original[original['onset'] <= cutoff]\\n generated_events = generated[generated['onset'] <= cutoff]\\n if (len(original_events) <= 1 or len(generated_events) <= 1):\\n scores['Precision'].append(None)\\n scores['Recall'].append(None)\\n scores['F1'].append(None)\\n continue\\n else:\\n output = evaluate_cs(original_events, generated_events)\\n scores['Precision'].append(output['prec'])\\n scores['Recall'].append(output['rec'])\\n scores['F1'].append(output['F1'])\\n return pd.DataFrame(scores)\",\n \"def non_mcnugget():\\n nugget = [0, 6, 9, 20]\\n mcnugget = set([6, 9, 20])\\n\\n while True:\\n mcnugget = set([m+n for m in mcnugget for n in nugget])\\n\\n for m in mcnugget:\\n found = all([m+j in mcnugget for j in range(6)])\\n if found:\\n return [k for k in range(1, m) if k not in mcnugget]\",\n \"def get_demographic_set(code_dict, year, state_code, county_code, bg=False):\\n key = Names.get_census_key()\\n census = Census(key)\\n dataset = 'acs/acs5' if int(year) >= 2016 else 'acs5'\\n data = census.get(code_dict, state_code, county_code, year, dataset, bg)\\n\\n return data\",\n \"def build_constellation(edges, start, visited):\\n num_points = edges.shape[0]\\n constellation = set()\\n nodes = [start]\\n while nodes:\\n point = nodes.pop()\\n visited.add(point)\\n\\n for other in range(num_points):\\n if edges[point, other] and other not in constellation:\\n constellation.add(other)\\n nodes.append(other)\\n\\n return constellation\",\n \"def linspace(self, start: float, end: float, samples: int, flexible: bool = False, bip: bool = True):\\n\\n if not isinstance(samples, (int)):\\n raise ValueError('samples: must be an integer')\\n if not isinstance(start, (int, float)):\\n raise ValueError('start: must be a number')\\n if not isinstance(end, (int, float)):\\n raise ValueError('end: must be a number')\\n if samples < 2:\\n raise ValueError('points: needs to be 2 or more')\\n\\n use_reversed = end < start\\n if use_reversed:\\n start,end = end,start\\n half = 0.5 if bip else 0.0 # half bit difference between bip and neg,pos\\n byte_start = int(math.ceil(half + start/self.dac_quata))\\n byte_end = int(math.floor(half + end/self.dac_quata))\\n delta_bytes = abs(byte_end - byte_start)-1\\n spacing = max(int(math.floor(delta_bytes / (samples-1))),2)\\n l = [(el+half)*self.dac_quata\\n for el in range(byte_start, byte_end,spacing)]\\n # Adjust the points until the length is correct\\n if not flexible:\\n if len(l) > samples:\\n if (len(l) - samples)%2==1:\\n l = l[1:]\\n s = int((len(l) - samples) / 2)\\n if s > 0:\\n l = l[s:-s]\\n if len(l) < samples:\\n msg = ( 'Insufficient resolution for '+ str(samples)\\n + ' samples in the range '\\n + str(start)+' to ' + str(end) )\\n msg += '. Maximum :' + str(len(l))\\n raise ValueError(msg)\\n if len(l) == 0:\\n msg = ('No DAC values exist in the range ' +\\n str(start) + ' : ' + str(end)\\n )\\n raise ValueError(msg)\\n\\n if use_reversed:\\n l = list(reversed(l))\\n return l\",\n \"def load_fence_locations(y, begin=None, end=None):\\r\\n\\r\\n # Load the data. Can be from any year 2010-2016, results\\r\\n # won't change for this application\\r\\n data = load_data(2010)\\r\\n\\r\\n # Create a fence category column and initialize everything to\\r\\n # a value of 0\\r\\n data['Category'] = 'darkred'\\r\\n data['Number'] = 0\\r\\n\\r\\n # Set the rest of the categories\\r\\n fort_macon = 2229 # Profiles in from the end where Fort Macon starts\\r\\n data['Category'].iloc[:-fort_macon].loc[np.isnan(data['x_fence'])] = 'dimgray'\\r\\n data['Category'].iloc[:-fort_macon].loc[~np.isnan(data['x_fence'])] = 'cornflowerblue'\\r\\n\\r\\n data['Number'].iloc[:-fort_macon].loc[np.isnan(data['x_fence'])] = 1\\r\\n data['Number'].iloc[:-fort_macon].loc[~np.isnan(data['x_fence'])] = 2\\r\\n\\r\\n # Add a y-value column\\r\\n data['Y'] = y\\r\\n\\r\\n # Sort out fences using chunks\\r\\n for n in chunks(data[:fort_macon], 5):\\r\\n if np.any(data['x_fence'].iloc[n.index] > 0):\\r\\n data['Number'].iloc[n.index] = 2\\r\\n else:\\r\\n data['Number'].iloc[n.index] = 1\\r\\n\\r\\n # Just take the needed data\\r\\n data = data[['Profile No.', 'Category', 'Number', 'Y']]\\r\\n\\r\\n if end is not None:\\r\\n data = data.loc[(data['Profile No.'] >= begin) & (data['Profile No.'] <= end)]\\r\\n\\r\\n # Return the dataframe\\r\\n return data\",\n \"def _pareto_front(teams_population, novelty):\\r\\n for team in teams_population:\\r\\n team.dom_by_ = 0\\r\\n team.dom_of_ = 0\\r\\n\\r\\n front = []\\r\\n dominateds = []\\r\\n for teamA in teams_population:\\r\\n for teamB in teams_population:\\r\\n # check if there are teams that have a better or equal [fitness, novelty] and that are better in at least \\r\\n # one of the dimensions. If yes, then teamA is dominated by these teams.\\r\\n if ParetoDominanceForTeams._is_dominated(teamA, teamB, novelty):\\r\\n teamA.dom_by_ += 1\\r\\n teamB.dom_of_ += 1\\r\\n if teamA not in dominateds:\\r\\n dominateds.append(teamA)\\r\\n if teamA.dom_by_ == 0:\\r\\n front.append(teamA)\\r\\n\\r\\n # use this score to balance the teams between remove and keep\\r\\n for team in teams_population:\\r\\n team.submission_score_ = team.dom_by_/float(len(teams_population)) # use it to add teams to the front (the lower, the better)\\r\\n team.dominance_score_ = team.dom_of_/float(len(teams_population)) # use it to remove teams from the front (the higher, the better)\\r\\n return front, dominateds\",\n \"def infer(\\n self, data: xr.Dataset, num_samples: int, seed: int, **infer_args\\n ) -> xr.Dataset:\\n raise NotImplementedError\",\n \"def bddf_polyset(reference, order):\\n dim = reference.tdim\\n pset = []\\n assert reference.name == \\\"hexahedron\\\"\\n pset = polynomial_set(dim, dim, order)\\n pset.append(curl((0, 0, x[0] ** (order + 1) * x[1])))\\n pset.append(curl((0, x[0] * x[2] ** (order + 1), 0)))\\n pset.append(curl((x[1] ** (order + 1) * x[2], 0, 0)))\\n for i in range(1, order + 1):\\n pset.append(curl((0, 0, x[0] * x[1] ** (i + 1) * x[2] ** (order - i))))\\n pset.append(curl((0, x[0] ** (i + 1) * x[1] ** (order - i) * x[2], 0)))\\n pset.append(curl((x[0] ** (order - i) * x[1] * x[2] ** (i + 1), 0, 0)))\\n\\n return pset\",\n \"def downset(self,\\n _sortkey=operator.attrgetter('dindex'),\\n _next_concepts=operator.attrgetter('lower_neighbors')):\\n return algorithms.iterunion([self], _sortkey, _next_concepts)\",\n \"def get_dates(self, candidates=None, start=None, end=None):\\n if candidates is not None:\\n return [date for date in candidates if date in self.data]\\n if start is None:\\n start = self.first_date\\n if end is None:\\n end = self.last_date\\n return [date for date in self.data if start <= date <= end]\",\n \"def clueDedupe(clue_mapped, clue_export, preference):\\n cm_dates = [c['day'] for c in clue_mapped]\\n for ce in clue_export['data']:\\n if ce['day'] in cm_dates:\\n for idx, cm in enumerate(clue_mapped):\\n #find matching dates\\n if ce['day'] == cm['day']:\\n #combine both date's data points if 'combine' is chosen, but preference clue export\\n #on conflict\\n if preference.lower() == 'combined':\\n for k, v in clue_mapped[idx].items(): \\n if k in ce:\\n clue_mapped[idx][k].extend(ce[k])\\n clue_mapped[idx][k] = list(set(clue_mapped[idx][k]))\\n else:\\n clue_mapped[idx][k] = ce[k]\\n else:\\n clue_mapped.append(ce)\\n return clue_mapped\",\n \"def get_all_districts():\\n with open(district_data_dir + 'district-data.json') as f:\\n district_dict = json.load(f)\\n districts = set([])\\n\\n for date, data in district_dict.items():\\n if date == '03/02/2020':\\n continue\\n districts.update(data.keys())\\n\\n # Remove unnecessary points\\n districts.remove('total-infected')\\n districts.remove('max-legend-value')\\n districts.remove('splitPoints')\\n return districts\",\n \"def create_subsets(self, start_ids):\\n subsets = list()\\n df = self.all_df.copy()\\n for sid in start_ids:\\n df2 = df.loc[sid:, :]\\n subsets.append(df.drop(df2.index, axis=0))\\n df = df2.copy()\\n subsets.append(df)\\n return subsets\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7483984","0.51474196","0.5103328","0.5055087","0.50201726","0.50139266","0.49108776","0.48341992","0.4811175","0.4799761","0.47267008","0.46881026","0.46321952","0.4605094","0.45930317","0.45531568","0.4522701","0.45008975","0.44996762","0.44854528","0.44708714","0.44677788","0.44587684","0.44310614","0.4405215","0.43907028","0.43856835","0.43838257","0.43797532","0.4377579","0.43733075","0.43694133","0.43637925","0.43634984","0.43608606","0.4359826","0.43465826","0.43440694","0.43438634","0.43383428","0.43333572","0.43317282","0.43260008","0.4319764","0.43071494","0.43067235","0.42886508","0.42847562","0.42819715","0.42813057","0.42744672","0.4270577","0.42694664","0.42638272","0.4259366","0.42537495","0.42520082","0.42492434","0.42460024","0.42433763","0.42387134","0.4235087","0.42300934","0.42211682","0.42128584","0.42117223","0.42099717","0.42051315","0.4186494","0.41821253","0.41776317","0.4173737","0.41689906","0.41663313","0.41613236","0.4160859","0.41517508","0.41471365","0.41457802","0.41418225","0.41376472","0.41374367","0.41324306","0.4131595","0.412936","0.41291434","0.41204453","0.41203052","0.41123754","0.41107854","0.41013846","0.4100478","0.40976727","0.40928566","0.4089618","0.40891486","0.408676","0.40867013","0.40798515","0.40728804"],"string":"[\n \"0.7483984\",\n \"0.51474196\",\n \"0.5103328\",\n \"0.5055087\",\n \"0.50201726\",\n \"0.50139266\",\n \"0.49108776\",\n \"0.48341992\",\n \"0.4811175\",\n \"0.4799761\",\n \"0.47267008\",\n \"0.46881026\",\n \"0.46321952\",\n \"0.4605094\",\n \"0.45930317\",\n \"0.45531568\",\n \"0.4522701\",\n \"0.45008975\",\n \"0.44996762\",\n \"0.44854528\",\n \"0.44708714\",\n \"0.44677788\",\n \"0.44587684\",\n \"0.44310614\",\n \"0.4405215\",\n \"0.43907028\",\n \"0.43856835\",\n \"0.43838257\",\n \"0.43797532\",\n \"0.4377579\",\n \"0.43733075\",\n \"0.43694133\",\n \"0.43637925\",\n \"0.43634984\",\n \"0.43608606\",\n \"0.4359826\",\n \"0.43465826\",\n \"0.43440694\",\n \"0.43438634\",\n \"0.43383428\",\n \"0.43333572\",\n \"0.43317282\",\n \"0.43260008\",\n \"0.4319764\",\n \"0.43071494\",\n \"0.43067235\",\n \"0.42886508\",\n \"0.42847562\",\n \"0.42819715\",\n \"0.42813057\",\n \"0.42744672\",\n \"0.4270577\",\n \"0.42694664\",\n \"0.42638272\",\n \"0.4259366\",\n \"0.42537495\",\n \"0.42520082\",\n \"0.42492434\",\n \"0.42460024\",\n \"0.42433763\",\n \"0.42387134\",\n \"0.4235087\",\n \"0.42300934\",\n \"0.42211682\",\n \"0.42128584\",\n \"0.42117223\",\n \"0.42099717\",\n \"0.42051315\",\n \"0.4186494\",\n \"0.41821253\",\n \"0.41776317\",\n \"0.4173737\",\n \"0.41689906\",\n \"0.41663313\",\n \"0.41613236\",\n \"0.4160859\",\n \"0.41517508\",\n \"0.41471365\",\n \"0.41457802\",\n \"0.41418225\",\n \"0.41376472\",\n \"0.41374367\",\n \"0.41324306\",\n \"0.4131595\",\n \"0.412936\",\n \"0.41291434\",\n \"0.41204453\",\n \"0.41203052\",\n \"0.41123754\",\n \"0.41107854\",\n \"0.41013846\",\n \"0.4100478\",\n \"0.40976727\",\n \"0.40928566\",\n \"0.4089618\",\n \"0.40891486\",\n \"0.408676\",\n \"0.40867013\",\n \"0.40798515\",\n \"0.40728804\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7381708"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":80497,"cells":{"query":{"kind":"string","value":"Return a set of years. If disjoint is false, returns a set of years that spans the entire range. preprocessed_data a heterogeneous set of ints and tuples of ints"},"document":{"kind":"string","value":"def _enumerate_years(self, preprocessed_data, disjoint):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def years(self, data, disjoint=True):\n\n year_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\n\n except TypeError:\n # single value\n preprocessed_year_data = self._extract_year_data(data)\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\n\n return year_set","def multiple_years(our_data, start, end):\n count = start\n album_list = []\n while count <= end:\n album_list.append(find_by_year(our_data,count))\n count += 1","def t_range_years(t_range):\r\n start_year = int(t_range[0].split(\"-\")[0])\r\n end_year = int(t_range[1].split(\"-\")[0])\r\n end_month = int(t_range[1].split(\"-\")[1])\r\n end_day = int(t_range[1].split(\"-\")[2])\r\n if end_month == 1 and end_day == 1:\r\n year_range_list = np.arange(start_year, end_year)\r\n else:\r\n year_range_list = np.arange(start_year, end_year + 1)\r\n return year_range_list","def years(self):\n return sorted(set(self._years))","def decades(self, data, disjoint=True):\n\n decade_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\n except (AssertionError, TypeError):\n # single value\n preprocessed_year_data = self._extract_year_data(data)\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\n\n return decade_set","def year_cv_split(X, year_range):\n return [\n ((X[\"year\"] < year).to_numpy(), (X[\"year\"] == year).to_numpy())\n for year in range(*year_range)\n ]","def drop_years(dataframe, start, end):\n tmp = dataframe\n tmp = tmp[(start <= tmp['year'].astype(int)) & (tmp['year'].astype(int) <= end)]\n\n return tmp","def dbf_years(self):\n return [year for year in self.years if year <= 2020]","def get_yearly_data(name, startyr=None, endyr=None, interpolated=False):\n varinfo = get_varinfo(name)\n \n if varinfo[\"type\"] == \"yearly\":\n data = get_data(varinfo[\"id\"], startyr=startyr, endyr=endyr)\n giddict = dict()\n sorteddata = sorted(data[\"cells\"], key=lambda vd: vd[\"gid\"])\n for gid,valuedicts in itertools.groupby(sorteddata, key=lambda vd: vd[\"gid\"]):\n yrdict = dict([(valuedict[\"year\"],valuedict[\"value\"])\n for valuedict in valuedicts\n ])\n info = {\"data\": yrdict}\n giddict[gid] = info\n\n if interpolated:\n def pairwise(iterable):\n a, b = itertools.tee(iterable)\n next(b, None)\n return zip(a, b)\n \n def lerp(factor, fromval, toval):\n valrange = toval - fromval\n return fromval + valrange * factor\n \n for gid,info in giddict.items():\n yrdict = info[\"data\"]\n if len(yrdict) > 1:\n for (fromyr,fromval),(toyr,toval) in pairwise(sorted(yrdict.items(),key=lambda i: i[0])):\n curyr = fromyr + 1\n interpneeded = fromval != toval\n \n while curyr != toyr:\n if interpneeded:\n factor = (curyr - fromyr) / float(toyr - fromyr)\n yrdict[curyr] = lerp(factor, fromval, toval)\n else:\n yrdict[curyr] = fromval\n curyr += 1\n\n return giddict\n\n else:\n raise Exception(\"Could not find a yearly variable with that name\")","def getYearlyPrism(all_basin_geoms, fromYear, toYear):\n \n yearly_Prism = {}\n for yy in range(fromYear, toYear):\n print(\"Processing year\", yy)\n for mm in range(1, 3):\n print(\"Processing month\", mm) \n if(mm<10):\n mmyy = '0'+str(mm)+'-'+str(yy)\n else:\n mmyy = str(mm)+'-'+str(yy)\n print(mmyy)\n yearly_Prism[mmyy] = get_intersected_basins_ppt_data(all_basin_geoms, month=mm, year=yy)\n \n return yearly_Prism","def _year_range(m):\n return (m.group(1), m.group(2))","def xbrl_years(self):\n return [year for year in self.years if year >= 2021]","def get_years():\n if request.method == 'OPTIONS':\n logging.info(\"Handle options\")\n return create_response({}, 200, '*', 'content-type, token')\n\n logging.info(\"Getting recruitment years\")\n\n role, response = handle_request_token(request)\n\n if role is None:\n logging.warning(\"Role is None!\")\n return response\n\n years = set()\n for rec in Recruitment.query.all():\n if rec.end_date.year not in years:\n years.add(rec.end_date.year)\n\n years = list(years)\n years.sort(reverse=True)\n\n return create_response(years, 200, '*')","def grab_by_year(dataframe, year_col, value_col, year_start=None,\n year_end=None):\n\n if year_start is None and year_end is None:\n print(\"year_start and year_end are not defined. Insert a value \\\n for at least one year variable.\")\n\n if year_end is None:\n grab_years = dataframe[(dataframe[year_col] >= year_start)\n ][value_col]\n if year_start is None:\n grab_years = dataframe[(dataframe[year_col] <= year_end)\n ][value_col]\n\n if year_start is not None and year_end is not None:\n grab_years = dataframe[(dataframe[year_col] >= year_start) &\n (dataframe[year_col] <= year_end)\n ][value_col]\n\n return grab_years","def year_data(self,year):\n idx = [i for i in range(self.dates.shape[0]) if self.dates[i].year == year]\n year_dates = self.dates[idx]\n year_dc = self.dc[idx]\n return year_dates, year_dc","def split_by_year(config, dbsession, group):\n years = []\n decades = []\n centuries = []\n with_year = 0\n for item in group.items:\n if config[\"data\"][\"year_field\"] in item.attributes and item.attributes[config[\"data\"][\"year_field\"]]:\n years.append(item.attributes[config[\"data\"][\"year_field\"]])\n with_year = with_year + 1\n if with_year / len(group.items) > 0.95: # noqa: PLR2004\n common = [(int(v), c) for v, c in Counter(years).most_common()]\n start_year = min([c for c, _ in common])\n end_year = max([c for c, _ in common])\n if start_year != end_year:\n if (end_year - start_year) <= 100 and (end_year - start_year) > 10: # noqa: PLR2004\n start_decade = math.floor(start_year / 10)\n end_decade = math.floor(end_year / 10)\n decades = []\n for start_year in range(start_decade * 10, (end_decade + 1) * 10, 10):\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n start_year <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < start_year + 10\n ):\n if len(decades) == 0 or decades[-1][0][0] != start_year:\n decades.append([[start_year], 1])\n else:\n decades[-1][1] = decades[-1][1] + 1\n idx = 0\n while idx < len(decades) - 1:\n if decades[idx][1] + decades[idx + 1][1] < 100: # noqa: PLR2004\n decades[idx][0].extend(decades[idx + 1][0])\n decades[idx][1] = decades[idx][1] + decades[idx + 1][1]\n decades.pop(idx + 1)\n else:\n idx = idx + 1\n for years, _ in decades:\n new_group = None\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n years[0] <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < years[-1] + 10\n ):\n if new_group is None:\n if len(years) == 1:\n label = f\"{years[0]}s\"\n else:\n label = f\"{years[0]}s-{years[-1]}s\"\n new_group = Group(\n value=str(start_year),\n label=f\"{group.label} - {label}\",\n parent=group,\n split=\"time\",\n )\n dbsession.add(new_group)\n item.group = new_group\n if group.items:\n new_group = Group(value=group.label, label=group.label, parent=group, split=\"time\")\n dbsession.add(new_group)\n for item in list(group.items):\n item.group = new_group\n return True\n elif (end_year - start_year) > 100: # noqa: PLR2004\n start_century = math.floor(start_year / 100)\n end_century = math.floor(end_year / 100)\n centuries = []\n for start_year in range(start_century * 100, (end_century + 1) * 100, 100):\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n start_year <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < start_year + 100\n ):\n if len(centuries) == 0 or centuries[-1][0][0] != start_year:\n centuries.append([[start_year], 1])\n else:\n centuries[-1][1] = centuries[-1][1] + 1\n idx = 0\n while idx < len(centuries) - 1:\n if centuries[idx][1] + centuries[idx + 1][1] < 100: # noqa: PLR2004\n centuries[idx][0].extend(centuries[idx + 1][0])\n centuries[idx][1] = centuries[idx][1] + centuries[idx + 1][1]\n centuries.pop(idx + 1)\n else:\n idx = idx + 1\n for years, _ in centuries:\n new_group = None\n for item in list(group.items):\n if (\n config[\"data\"][\"year_field\"] in item.attributes\n and item.attributes[config[\"data\"][\"year_field\"]]\n ):\n if (\n years[0] <= int(item.attributes[config[\"data\"][\"year_field\"]])\n and int(item.attributes[config[\"data\"][\"year_field\"]]) < years[-1] + 100\n ):\n if new_group is None:\n if len(years) == 1:\n century = math.floor(years[0] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n label = f\"{century}rd\"\n else:\n label = f\"{century}th\"\n else:\n century = math.floor(years[0] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n start_label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n start_label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n start_label = f\"{century}rd\"\n else:\n start_label = f\"{century}th\"\n century = math.floor(years[-1] / 100) + 1\n if century % 10 == 1 and century != 11: # noqa: PLR2004\n end_label = f\"{century}st\"\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\n end_label = f\"{century}nd\"\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\n end_label = f\"{century}rd\"\n else:\n end_label = f\"{century}th\"\n label = f\"{start_label}-{end_label}\"\n new_group = Group(\n value=str(start_year),\n label=f\"{group.label} - {label} century\",\n parent=group,\n split=\"time\",\n )\n dbsession.add(new_group)\n item.group = new_group\n if group.items:\n new_group = Group(value=group.label, label=group.label, parent=group, split=\"time\")\n dbsession.add(new_group)\n for item in list(group.items):\n item.group = new_group\n return True\n return False","def get_subset(mlist,year):\n newlist = []\n for entry in mlist:\n if int(entry[0][:4]) > int(year):\n continue\n newvec = entry[:8]\n citations = entry[8]['citations']\n citations = filter(lambda a: int(a[:4]) <= int(year), citations)\n newvec[2] = len(citations)\n newlist.append(newvec)\n return newlist","def get_start_end_years(df: pd.DataFrame) -> Tuple[int, int]:\n return df.iloc[0].year, df.iloc[-1].year","def _date_match_to_int_or_tuple(self, m):\n\n years = set()\n try:\n if m[0] != '':\n # yyyy-dd-mm\n years = int(m[0][0:4])\n elif m[1] != '':\n # year-range derived from a century\n century = int(re.match(re.compile('\\\\d+'), m[1]).group(0))\n\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\n suffix = match.group(2)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = (100 * -century, 100 * -century + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n elif m[2] != '':\n # explicit year-range\n\n # FIXME: spaghetti code, but it works!\n range_of_stuff = []\n i = 0\n first_none = None\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\1>|<\\2', m[2]).split('>|<'):\n # get rid of whitespace\n y = y.strip()\n match = re.compile(self.regexes['capture']['year']).match(y)\n\n # if there is a suffix, one of these will not be None\n suffix = match.group(2) or match.group(4)\n if suffix:\n if i == 0:\n first_none = False\n\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\n else:\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n else:\n if i == 0:\n first_none = True\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n\n i += 1\n if first_none:\n if range_of_stuff[1] <= 0:\n range_of_stuff[0] = -1 * range_of_stuff[0]\n\n years = (range_of_stuff[0], range_of_stuff[1])\n elif m[3] != '':\n # extract single year\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\1', m[3]).strip()\n years = int(prep)\n elif m[4] != '':\n # year with unknown ones\n y = m[4].strip()\n match = re.compile(r'[1-9]\\d{3}').match(y)\n if match is None:\n years = int(self._resolve_unknown_ones(y))\n\n else:\n years = int(match.group(0))\n\n elif m[5] != '':\n # plain old year\n match = re.compile(self.regexes['capture']['year']).match(m[5])\n suffix = match.group(2) or match.group(4)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = -1 * int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n raise Error\n\n except ValueError as e:\n #logger.error('An error occurred while trying to match \"{}\": {}'.format(m, e))\n pass\n\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\n return years","def _extract_year_data(self, date_string):\n\n try:\n # first see if dateutil can parse the date string\n # simplest case, a single year\n return {parse(date_string).year}\n\n except ValueError:\n try:\n # strip alphabetical chars and spaces from the left side and try again\n alpha = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n return {parse(date_string.lstrip(alpha + ' ')).year}\n\n except ValueError:\n # find as many substrings that look like dates as possible\n matches = re.findall(re.compile(self.regexes['match']['date']), date_string)\n #logger.debug('{} date string matches found in \"{}\"'.format(len(matches), dateString))\n if len(matches) > 0:\n return {self._date_match_to_int_or_tuple(m) for m in matches}\n else:\n return set()","def get_years_before_and_after_failures(end_year, der_list):\n rerun_opt_on = []\n for der_instance in der_list:\n last_operation_year = None\n if der_instance.tag == 'Battery' and der_instance.incl_cycle_degrade:\n # ignore battery's failure years as defined by user if user wants to include degradation in their analysis\n # instead set it to be the project's last year+1\n last_operation_year = end_year.year\n yrs_failed = der_instance.set_failure_years(end_year, last_operation_year)\n if not der_instance.replaceable:\n # if the DER is not replaceable then add the following year to the set of analysis years\n rerun_opt_on += yrs_failed\n # filter out any years beyond end_year\n rerun_opt_on = [year for year in rerun_opt_on if year < end_year.year]\n # add years that the operational DER mix will change (year after last year of operation)\n rerun_opt_on += [year+1 for year in rerun_opt_on if year < end_year.year]\n return list(set(rerun_opt_on)) # get rid of any duplicates","def filter_years(text):\n months = ['january', 'february', 'march', 'april', 'may', 'june',\n 'july', 'august', 'september', 'october', 'november', 'december']\n prepositions = ['around', 'after', 'at', 'as',\n 'approximately', 'before', 'between', 'by',\n 'during', 'from', 'in', 'near', 'past',\n 'since', 'until', 'within'] # removed: about, on\n conjugations = ['and']\n articles = ['the']\n times = ['early', 'mid', 'late']\n patterns = months + prepositions + conjugations + articles + times\n re_string = r'\\b(' + '|'.join(patterns) + r')\\b(\\s|-)\\b([0-9]{3,4})s?\\b(?i)(?!\\sMYA)\\s?(BCE|BC)?'\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\s?(BCE|BC)?'\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n years += centuries\n years = [y for y in years if y<Dump.MAX_YEAR]\n return sorted(years + centuries)","def _enumerate_decades(self, preprocessed_data, disjoint):\n\n if disjoint:\n decades = set()\n for decade in preprocessed_data:\n if isinstance(decade, int):\n decades.add(decade // 10 * 10)\n elif isinstance(decade, tuple):\n decades = decades | set(range(decade[0] // 10 * 10, decade[1] + 1, 10))\n\n return decades","def estimate_year_data(self, years, frequency):\n data_year = self.price.index.year.unique()\n no_data_year = {pd.Period(year) for year in years} - {pd.Period(year) for year in data_year} # which years do we not have data for\n\n if len(no_data_year) > 0:\n for yr in no_data_year:\n source_year = pd.Period(max(data_year))\n\n source_data = self.price[self.price.index.year == source_year.year] # use source year data\n new_data = Lib.apply_growth(source_data, self.energy_growth, source_year, yr, frequency)\n self.price = pd.concat([self.price, new_data], sort=True) # add to existing\n\n source_data = self.p_regu[self.p_regu.index.year == source_year.year] # use source year data\n new_data = Lib.apply_growth(source_data, self.growth, source_year, yr, frequency)\n self.p_regu = pd.concat([self.p_regu, new_data], sort=True) # add to existing\n\n source_data = self.p_regd[self.p_regd.index.year == source_year.year] # use source year data\n new_data = Lib.apply_growth(source_data, self.growth, source_year, yr, frequency)\n self.p_regd = pd.concat([self.p_regd, new_data], sort=True) # add to existing","def yearly_date_range(date1: datetime, date2: datetime):\n ctr = date1\n list = [ctr]\n\n while ctr < date2:\n ctr += timedelta(days=366)\n list.append(datetime(ctr.year, date1.month, 1))\n ctr = datetime(ctr.year, date1.month, 1)\n return list","def delete_years_lover_than(self, years, w_years, m_years, threashold_year):\n years = [year for year in years if year >= threashold_year]\n w_years = [year for year in w_years if year >= threashold_year]\n m_years = [year for year in m_years if year >= threashold_year]\n return years, w_years, m_years","def check_year_sets(job_sets, file_list, sim_start_year, sim_end_year, debug, add_jobs):\n incomplete_job_sets = [s for s in job_sets\n if s.status != SetStatus.COMPLETED\n and s.status != SetStatus.RUNNING\n and s.status != SetStatus.FAILED]\n for job_set in incomplete_job_sets:\n\n start_year = job_set.set_start_year\n end_year = job_set.set_end_year\n\n non_zero_data = False\n data_ready = True\n for i in range(start_year, end_year + 1):\n for j in range(1, 13):\n file_key = '{0}-{1}'.format(i, j)\n status = file_list['ATM'][file_key]\n\n if status in [SetStatus.NO_DATA, SetStatus.IN_TRANSIT, SetStatus.PARTIAL_DATA]:\n data_ready = False\n elif status == SetStatus.DATA_READY:\n non_zero_data = True\n\n if data_ready:\n job_set.status = SetStatus.DATA_READY\n job_set = add_jobs(job_set)\n continue\n if not data_ready and non_zero_data:\n job_set.status = SetStatus.PARTIAL_DATA\n continue\n if not data_ready and not non_zero_data:\n job_set.status = SetStatus.NO_DATA\n\n # if debug:\n # for job_set in job_sets:\n # start_year = job_set.set_start_year\n # end_year = job_set.set_end_year\n # print_message('year_set: {0}: {1}'.format(job_set.set_number, job_set.status), 'ok')\n # for i in range(start_year, end_year + 1):\n # for j in range(1, 13):\n # file_key = '{0}-{1}'.format(i, j)\n # status = file_list[file_key]\n # print_message(' {key}: {value}'.format(key=file_key, value=status), 'ok')","def year_range(df):\n\n if not isinstance(df, pd.DataFrame):\n print(\"year_range was not passed a pandas DataFrame.\")\n return\n\n df['year_start'] = df['year'].min()\n df['year_end'] = df['year'].max()\n df.drop('year' , axis = 1, inplace = True)\n return df","def load_all_data():\r\n\r\n data = dict()\r\n for year in ['2010', '2011', '2014', '2016']:\r\n\r\n data[year] = load_data(int(year))\r\n\r\n # Calculate the dune widths\r\n data[year]['Dune Width'] = data[year]['x_heel'] - data[year]['x_toe']\r\n data[year]['Fenced Dune Width'] = data[year]['x_fence_heel'] - data[year]['x_fence_toe']\r\n data[year]['Fenced Dune System Width'] = data[year]['x_heel'] - data[year]['x_fence_toe']\r\n\r\n # For now, remove all negative widths and volumes, something went wrong with them\r\n width_condition = data[year]['Fenced Dune Width'] <= 0\r\n volume_condition = data[year]['Fenced Dune Volume'] <= 0\r\n\r\n data[year]['y_fence_crest'][width_condition] = np.nan\r\n data[year]['Fenced Dune Width'][width_condition] = np.nan\r\n data[year]['Fenced Dune Volume'][width_condition] = np.nan\r\n\r\n data[year]['y_fence_crest'][volume_condition] = np.nan\r\n data[year]['Fenced Dune Width'][volume_condition] = np.nan\r\n data[year]['Fenced Dune Volume'][volume_condition] = np.nan\r\n\r\n data[year]['Fenced Dune System Width'][data[year]['Fenced Dune System Width'] <= 0] = np.nan\r\n\r\n # Remove instances where the fenced and natural dune crest are not positioned correctly\r\n crest_condition_1 = data[year]['x_fence_crest'] >= data[year]['x_crest']\r\n crest_condition_2 = data[year]['y_fence_crest'] >= data[year]['y_crest']\r\n\r\n data[year]['y_fence_crest'][crest_condition_1] = np.nan\r\n data[year]['Fenced Dune Width'][crest_condition_1] = np.nan\r\n data[year]['Fenced Dune Volume'][crest_condition_1] = np.nan\r\n\r\n data[year]['y_fence_crest'][crest_condition_2] = np.nan\r\n data[year]['Fenced Dune Width'][crest_condition_2] = np.nan\r\n data[year]['Fenced Dune Volume'][crest_condition_2] = np.nan\r\n\r\n data['Fences'] = load_fence_locations(y=0)\r\n\r\n return data","def get_years(met_fname, nyear_spinup):\n pre_indust = 1850\n\n ds = xr.open_dataset(met_fname)\n\n st_yr = pd.to_datetime(ds.time[0].values).year\n\n # PALS met files final year tag only has a single 30 min, so need to\n # end at the previous year, which is the real file end\n en_yr = pd.to_datetime(ds.time[-1].values).year - 1\n\n # length of met record\n nrec = en_yr - st_yr + 1\n\n # number of times met data is recycled during transient simulation\n nloop_transient = np.ceil((st_yr - 1 - pre_indust) / nrec) - 1\n\n # number of times met data is recycled with a spinup run of nyear_spinup\n nloop_spin = np.ceil(nyear_spinup / nrec)\n\n st_yr_transient = st_yr - 1 - nloop_transient * nrec + 1\n en_yr_transient = st_yr_transient + nloop_transient * nrec - 1\n\n en_yr_spin = st_yr_transient - 1\n st_yr_spin = en_yr_spin - nloop_spin * nrec + 1\n\n return (st_yr, en_yr, st_yr_transient, en_yr_transient,\n st_yr_spin, en_yr_spin)","def get_first_year(data_id):\n if data_id.startswith(\"global\"):\n return 2011\n if data_id.endswith(\"precip\"):\n return 1948\n if data_id.startswith(\"cfsv2\"):\n return 2011\n if data_id.startswith(\"nmme\"):\n return 1982\n if data_id.endswith(\"tmp2m\") or data_id.endswith(\"tmin\") or data_id.endswith(\"tmax\"):\n return 1979\n if \"sst\" in data_id or \"icec\" in data_id:\n return 1981\n if data_id.endswith(\"mei\"):\n return 1950\n if data_id.endswith(\"mjo\"):\n return 1974\n if data_id.endswith(\"sce\"):\n return 1966\n if \"wind\" in data_id:\n return 1948\n if (\"slp\" in data_id or \"pr_wtr.eatm\" in data_id or \"rhum.sig995\" in data_id or\n \"pres.sfc.gauss\" in data_id or \"pevpr.sfc.gauss\" in data_id):\n return 1948\n raise ValueError(\"Unrecognized data_id \"+data_id)","def get_school_year(data, date_col, data_path):\n\n # generate empty df with all dates between start and end\n start = data[date_col].min()\n end = data[date_col].max()\n df = pd.date_range(start, end, freq=\"D\").to_frame(index=False, name=\"date\")\n\n # read external holidays csv\n def _parser(date):\n return pd.to_datetime(date)\n\n holidays = pd.read_csv(f'{data_path}',\n parse_dates=['date_debut', 'date_fin'],\n date_parser=_parser)\n holidays = holidays[[\"annee_scolaire\", \"date_debut\", \"date_fin\"]]\n holidays = holidays.drop_duplicates()\n\n # simulate an interval based left join using pandas\n # perform a cross join on temp_key\n up_bound = \"date_fin\"\n low_bound = \"date_debut\"\n df['temp_key'] = 1\n holidays['temp_key'] = 1\n crossjoindf = pd.merge(df, holidays, on=['temp_key'])\n\n df.drop(columns=['temp_key'], inplace=True)\n crossjoindf.drop(columns=['temp_key'], inplace=True)\n \n # filter with lower_bound & upper_bound\n conditionnal_df = crossjoindf[\n (crossjoindf[\"date\"] >= crossjoindf[low_bound]) & (crossjoindf[\"date\"] <= crossjoindf[up_bound])]\n\n # merge on the main df with all cols as keys to simulate left join\n df_col = df.columns.values.tolist()\n conditionnal_df.set_index(df_col, inplace=True)\n df = df.merge(conditionnal_df, left_on=df_col, right_index=True, how='left')\n\n df.set_index('date', inplace=True) \n data = pd.merge(data, df['annee_scolaire'], on='date')\n \n return data","def split_data(df: pd.DataFrame, cutoff_year: int) -> Tuple[pd.DataFrame, pd.DataFrame]:\n return df[df.year >= cutoff_year], df[df.year < cutoff_year]","def extract_years(timestamps):\n return np.asarray([dt.year for dt in timestamps.astype(datetime)])","def get_counts_for_year(data, year):\r\n \r\n accidents_counts = list(map(list, zip(*list((filter(lambda x: x[0] == year, data))))))[2]\r\n return accidents_counts","def load_evictions_data(data_path, date_col, min_year, max_year, create_geoid = True):\n ev_raw = (\n pd.read_csv(\n data_path,\n parse_dates=[date_col],\n infer_datetime_format=True,\n )\n .dropna(how=\"all\")\n .drop_duplicates()\n )\n ev_raw[\"year\"] = ev_raw[date_col].dt.year\n ev_df = ev_raw[(ev_raw.year >= min_year) & (ev_raw.year <= max_year)]\n ev_df[\"year\"] = ev_df[\"year\"].astype(int)\n ev_df[\"month\"] = get_month_as_str_col(ev_df, date_col)\n \n if create_geoid == True:\n # Convert to strings\n ev_df[\"state_code\"] = ev_df[\"state_code\"].astype(str).replace(r'\\.0', '', regex = True) \n ev_df[\"county_code\"] = ev_df[\"county_code\"].astype(str).replace(r'\\.0', '', regex = True) \n ev_df[\"tract_code\"] = ev_df[\"tract_code\"].astype(str).replace(r'\\.0', '', regex = True)\n # Add zeroes as necessary\n ev_df[\"state_code\"] = ev_df[\"state_code\"].apply(lambda x: (((2-len(x)) * \"0\") + x) if len(x) < 2 else x)\n ev_df[\"county_code\"] = ev_df[\"county_code\"].apply(lambda x: (((3-len(x)) * \"0\") + x) if len(x) < 3 else x)\n ev_df[\"tract_code\"] = ev_df[\"tract_code\"].apply(lambda x: (((6-len(x)) * \"0\") + x) if len(x) < 6 else x)\n # Finally, concat everything\n ev_df[\"GEOID\"] = (\n ev_df[\"state_code\"] + ev_df[\"county_code\"] + ev_df[\"tract_code\"]\n )\n \n return ev_df","def generate_admission_year_points(future=False):\n if future:\n start_year = datetime.now().year\n target_year = start_year + 2\n points = [year for year in range(start_year, target_year)]\n else:\n start_year = 2006\n current_year = datetime.now().year\n points = [year for year in range(start_year, current_year)]\n return points","def gap_years_aggregated(mongo_client):\n db = mongo_client[\"nobel\"]\n\n original_categories = sorted(set(db.prizes.distinct(\"category\", {\"year\": \"1901\"})))\n\n pipeline = [\n {\"$match\": {\"category\": {\"$in\": original_categories}}},\n {\"$project\": {\"category\": 1, \"year\": 1}},\n\n # Collect the set of category values for each prize year.\n {\"$group\": {\"_id\": \"$year\", \"categories\": {\"$addToSet\": \"$category\"}}},\n\n # Project categories *not* awarded (i.e., that are missing this year).\n {\"$project\": {\"missing\": {\"$setDifference\": [original_categories, \"$categories\"]}}},\n\n # Only include years with at least one missing category\n {\"$match\": {\"missing.0\": {\"$exists\": True}}},\n\n # Sort in reverse chronological order. Note that \"_id\" is a distinct year at this stage.\n {\"$sort\": OrderedDict([(\"_id\", -1)])},\n ]\n\n for doc in db.prizes.aggregate(pipeline):\n print(\"{year}: {missing}\".format(year=doc[\"_id\"], missing=\", \".join(sorted(doc[\"missing\"]))))","def test_load_points_by_year_length():\n years = ['2014','2015','2016','2017','2018']\n for year in years:\n df = leiap.get_points_by_year([year])\n assert df.shape[0] > 0","def year_dropdown_populator():\n start_year_unique = funding_data['start_year'].unique()\n\n year_list = []\n for i in start_year_unique:\n if i == -1:\n # print({'label': i, 'value': i})\n # NA values has been changes to -1\n year_list.append({'label': 'NA', 'value': -1})\n else:\n x = int(i)\n # print({'label': x, 'value': i})\n year_list.append({'label': i, 'value': i})\n return year_list","def years():\r\n # Use Pandas to perform the sql query\r\n results = db.session.query(Worldmapdata.year.distinct().label(\"year\"))\r\n year = [row.year for row in results.all()]\r\n # Return a list of the column names (sample names)\r\n return jsonify(list(year))","def year_lookup_bounds(self, value):\n first = '%s-01-01 00:00:00'\n # SQL Server doesn't support microseconds\n last = '%s-12-31 23:59:59'\n return [first % value, last % value]","def get_years(self):\n\n year = cast(extract('year', ArchivedResult.date), Integer)\n query = self.session.query\n query = query(distinct(year))\n query = query.order_by(desc(year))\n\n return list(r[0] for r in query.all())","def clean_data(df, start = 1995, stop = 2018):\n country_iso3_code = pd.read_html('https://unstats.un.org/unsd/methodology/m49/')\n country_iso3_code = country_iso3_code[0]['ISO-alpha3 code']\n df = df.loc[df.country_iso3_code.isin(country_iso3_code)]\n df = df.set_index(['indicator', 'country_iso3_code', 'country', 'year']).unstack(level = 0)\n df.columns = df.columns.get_level_values(1)\n df = df.rename(columns = {'NY.GDP.PCAP.KD.ZG': 'pc_GDP_growth',\n 'NY.GDP.PCAP.PP.CD': 'pc_GDP_PPP'})\n df = df.reset_index()\n df = df.loc[(df.year >= (start - 1)) & (df.year <= stop)]\n df = df.dropna()\n return df","def guess_date_interval(data):\n interval = \"yearly\"\n for serie in data:\n dates = [to_date(x[0]) for x in serie]\n years = [x.year for x in dates]\n months = [x.month for x in dates]\n yearmonths = [x.strftime(\"%Y-%m\") for x in dates]\n weeks = [str(x.year) + str(x.isocalendar()[1]) for x in dates]\n if len(years) > len(set(years)):\n # there are years with more than one point\n unique_months = sorted(list(set(months)))\n if len(unique_months) == 4 \\\n and unique_months[0] + 3 == unique_months[1] \\\n and unique_months[1] + 3 == unique_months[2] \\\n and unique_months[2] + 3 == unique_months[3]:\n # all in all four months, and they are non-conscutive\n interval = \"quarterly\"\n else:\n interval = \"monthly\"\n if len(yearmonths) > len(set(yearmonths)):\n interval = \"weekly\"\n if len(weeks) > len(set(weeks)):\n interval = \"daily\"\n return interval","def arrayofyears(month1,day1,year1,month2,day2,year2):\n return (arrayofdaysmonthsyears(month1,day1,year1,month2,day2,year2))[2]","def get_yearly_energies_prices(\n energies,\n years_range,\n is_collective=False,\n ref_year=2002,\n scenario=\"normal\",\n price_variation_func=calculate_price_variation,\n):\n energies_data = Energy.objects.filter(identifier__in=energies)\n yearly_datum = list(\n YearlyEnergyPrice.objects.filter(\n energy__in=energies_data, year__range=years_range\n ).order_by(\"year\")\n )\n\n series = []\n for energy_data in energies_data:\n ratio = energy_data.price_multi_unit_discount if is_collective else 1\n yearly_energy_data = [x for x in yearly_datum if x.energy_id == energy_data.pk]\n series.append(\n {\n \"identifier\": energy_data.identifier,\n \"priceVariation\": price_variation_func(\n energy_data, years_range, ref_year, yearly_energy_data\n ),\n \"yearlyPrices\": [multiply(x.price, ratio) for x in yearly_energy_data],\n }\n )\n\n return series","def getYearsOfService(person, chamber=\"\"):\n\tcongresses = []\n\n\tif chamber and chamber not in [\"House\", \"Senate\"]:\n\t\treturn []\n\n\tif chamber:\n\t\tif \"congresses_\" + chamber.lower() in person:\n\t\t\tcongresses = person[\"congresses_\" + chamber.lower()]\n\telse:\n\t\tif \"congresses\" in person:\n\t\t\tcongresses = person[\"congresses\"]\n\n\tif not congresses:\n\t\treturn []\n\n\tyearSet = [[congressToYear(congress[0], 0), congressToYear(congress[1], 1)] for congress in congresses]\n\n\tif chamber and len(yearSet) and \"voting_dates\" in person and chamber in person[\"voting_dates\"] and int(person[\"voting_dates\"][chamber][1].split(\"-\")[0]):\n\t\tif yearSet[-1][1] > int(person[\"voting_dates\"][chamber][1].split(\"-\")[0]):\n\t\t\tyearSet[-1][1] = int(person[\"voting_dates\"][chamber][1].split(\"-\")[0])\n\n\treturn yearSet","def test_found_all_years(self):\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n self.assertEqual(ar.years, [2008,2009])","def parse_counts(data):\r\n \r\n results = {}\r\n region_year = np.stack([data[0], data[4].astype('datetime64[Y]').astype(int) + 1970], axis=0)\r\n region_years, counts = np.unique(region_year, return_counts=True, axis=1)\r\n region_years_counts = list(zip(region_years[1], region_years[0], counts))\r\n results['years'] = np.unique(region_year[1])\r\n results['regions'] = np.unique(region_year[0])\r\n for year in results['years']:\r\n results[year] = np.array(get_counts_for_year(region_years_counts, year))\r\n return results","def test_iter_all_years(self):\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n self.assertEqual([ary.year for ary in ar], [2008,2009])","def periods_stat_yearly(self, x, start_time, end_time, func = np.sum, start_record = None, end_record = None):\n start_time, end_time = to_datetime(start_time, end_time)\n tmin, tmax = self._extrema_time_dim(x)\n # TODO more stringent input arg checks.\n if start_record is None:\n start_record = tmin\n if end_record is None:\n end_record = tmax\n max_years_shift = max_shifting_years(start_record, end_record, start_time, end_time) + 1\n td = x[self.time_dimname].values\n start_record = td[0]\n end_record = td[-1]\n d, e = get_first_period(start_record, end_record, start_time, end_time) \n cumulated = [self._apply_timeslice(x, d + relativedelta(years=year), e + relativedelta(years=year), func) for year in range(max_years_shift)]\n y = xr.concat(cumulated, dim=self.time_dimname)\n # maybe an optional arg for the resulting time dimension (start or end of periods)\n y[self.time_dimname] = np.array([pd.to_datetime(e + relativedelta(years=year)) for year in range(max_years_shift)])\n return y","def merge_ranges():","def bins_per_year(self):\n # Load the vector version #\n df = self.grouped_bins.reset_index()\n # Add year and remove TimeStep #\n df['year'] = self.country.timestep_to_year(df['time_step'])\n df = df.drop('time_step', axis=1)\n # Only if we are in the calibration scenario #\n if self.parent.parent.scenario.short_name == 'calibration':\n # Patch the harvest data frame to stop at the simulation year #\n selector = df['year'] <= self.parent.parent.country.base_year\n df = df.loc[selector].copy()\n # Return #\n return df","def getXy_all_all(year):\r\n print 'getXy_all_all(year=%d)' % year\r\n\r\n patient_keys, patient_dict = get_patient_dict()\r\n keys, counts_dict = patient_keys[1:], patient_dict\r\n #print 'patient_dict = %d' % len(counts_dict)\r\n\r\n drug_keys, drug_dict = get_drugcount_dict(year-1)\r\n keys, counts_dict = common.combine_dicts(keys, counts_dict, drug_keys[1:], drug_dict, use_dict1 = True)\r\n #print '+drug_dict = %d' % len(counts_dict)\r\n\r\n lab_keys, lab_dict = get_labcount_dict(year-1)\r\n keys, counts_dict = common.combine_dicts(keys, counts_dict, lab_keys[1:], lab_dict, use_dict1 = True)\r\n #print '+lab_dict = %d' % len(counts_dict)\r\n\r\n if False:\r\n pcg_keys, pcg_dict = get_pcg_counts_dict(year-1)\r\n keys, counts_dict = common.combine_dicts(keys, counts_dict, pcg_keys[1:], pcg_dict)\r\n print '+pcg_dict = %d' % len(counts_dict) \r\n\r\n for prefix in COUNTS_PREFIXES:\r\n pre_keys, pre_dict = get_counts_dict(prefix, year-1)\r\n pre_keys = ['%s=%s' % (prefix, k) for k in pre_keys]\r\n keys, counts_dict = common.combine_dicts(keys, counts_dict, pre_keys[1:], pre_dict)\r\n #print '+%s_dict = %d' % (prefix, len(counts_dict)) \r\n \r\n X,y = getXy_for_dict(year, keys, counts_dict)\r\n return X,y,keys","def update_dropdowns_years_options(df_in, aux):\n\n df = u.uos.b64_to_df(df_in)\n return lay.get_options(df[c.cols.YEAR].unique().tolist())","def get_look_a_like_conso_per_year(self):\n return {\n land.name: land.get_conso_per_year(\n self.analyse_start_date,\n self.analyse_end_date,\n )\n for land in self.get_look_a_like()\n }","def calcrange(a4lim,data):\r\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\r\n return a4range","def get_film_locations(input_year):\n film_set = set()\n with open('data/locations.csv', 'r', encoding=\"utf-8\", errors='ignore') as file:\n line = file.readline()\n while line:\n if line.split(',')[1] == input_year and line.split(',')[1] != 'NO DATA':\n film_set.add(tuple([line.split(',')[0].strip(), line.split(',')[-1].strip()]))\n line = file.readline()\n return film_set","def start_year(self) -> float:\n\n start_year = np.inf\n for td_table in list(self.tdve.values()) + self.transfers + self.interpops:\n if len(td_table.tvec) and np.amin(td_table.tvec) < start_year:\n start_year = np.amin(td_table.tvec)\n return start_year","def _get_data_pre2007(date):\n #the data is obtained from one file for each year.\n\n url = '{}/Environmental_Data_{}/'.format(BASE_URL, date.year)\n print('Fetching online data for {} (full year)'.format(date.year))\n\n try:\n year_data = request.urlopen(url).read().decode(encoding='utf_8').split('\\n')\n except:\n raise ValueError(date)\n else:\n year_data.pop(0)\n\n for line in year_data:\n elements = line.split()\n yield dict(Date = elements[0],\n Time = elements[1],\n Status = 'COMPLETE',\n Air_Temp = elements[5],\n Barometric_Press = elements[7],\n Wind_Speed = elements[2])","def retrieve_all_years(database_connection: mysql.connector.connect) -> List[int]:\n cursor = database_connection.cursor()\n query = (\"SELECT DISTINCT YEAR(s.showdate) FROM ww_shows s \"\n \"ORDER BY YEAR(s.showdate) ASC\")\n cursor.execute(query)\n result = cursor.fetchall()\n cursor.close()\n\n if not result:\n return None\n\n years = []\n for row in result:\n years.append(row[0])\n\n return years","def reduce_dataset(X, year):\n\n drop_list = [i for i in range(config.DB_YEAR_MIN, config.DB_YEAR_MAX + 1)]\n drop_list.remove(year - 1)\n red_X = X.drop(drop_list, axis=0)\n return red_X","def low_income_countries():\r\n low_countries_data = []\r\n years = []\r\n medians = []\r\n lst = []\r\n for idx in range(1960, 2016):\r\n years.append(idx)\r\n for idx in low_countries:\r\n low_countries_data.append(life_expectancy_graph(idx))\r\n y_idx = 0\r\n for idx in low_countries_data:\r\n if idx != {}:\r\n if (list(idx.keys())[y_idx]) == years[y_idx]:\r\n lst.append((list(idx.values())[y_idx]))\r\n lst = sorted(lst)\r\n medians.append(median(lst))\r\n return medians","def range_(headers, data):\n\tcolumn_matrix=data.get_data(headers).getT() # get columns as rows, as this makes analysis much easier by just perfoming operations on column list directly\n\tif column_matrix==[]:\n\t\tprint \"wrong headers, not present in data Object\"\n\t\treturn []\n\tcolumn_max=column_matrix.max(1)\n\tcolumn_min=column_matrix.min(1)\n\tfinal=np.concatenate((column_min, column_max), axis=1)\n\t\n\trng=final.tolist()\n\treturn rng","def generate_year_data(self, data_type: str, max_int: int = 100, n: int = 30\r\n ) -> List[Tuple[datetime, int]]:\r\n self._set_seed()\r\n if data_type == \"ones\":\r\n upper = 1\r\n data = [(self._random_date(), random.randint(1, upper)) for i in range(0, n)]\r\n elif data_type == \"integers\":\r\n upper = max_int\r\n data = [(self._random_date(), random.randint(1, upper)) for i in range(0, n)]\r\n elif data_type == \"amount\":\r\n data = [(self._random_date(),\r\n np.round(np.random.exponential(scale=self.exp_dist_mean), 2)\r\n ) for i in range(0, n)]\r\n else:\r\n raise NoProperOptionInIf\r\n\r\n return data","def years(self):\n password_list = []\n year_start = int(input(\"Years range start at: \"))\n year_stop = int(input(\"Years range stop at: \"))\n for generated in JugglerPassGen.generate(self.word):\n for year in range(year_start, year_stop + 1):\n modified_record = generated + str(year)\n password_list.append(modified_record)\n year = year % 100\n modified_record = generated + str('%02d' % year)\n password_list.append(modified_record)\n return password_list","def year(cls, year: PositiveInteger) -> \"DateRange\":\n return cls(Date(year, 1, 1), Date(year, 12, 31))","def year(self):\n return self._years","def crawl_all_by_years(years, verbose):\n for year in years:\n crawl_all_by_year(year, verbose)","def printedWords(words):\r\n wordsPerYear={}\r\n for yearData in words.values():\r\n for year in yearData:\r\n if not (year.year in wordsPerYear):\r\n wordsPerYear[year.year]=wordData.YearCount(year.year,year.count)\r\n else:\r\n wordsPerYear[year.year].count=wordsPerYear[year.year].count+year.count\r\n yearlist=[]\r\n year=1900\r\n while year<=2008:\r\n if (year in wordsPerYear):\r\n yearlist=yearlist+[wordsPerYear[year]]\r\n year=year+1\r\n else:\r\n year=year+1\r\n return yearlist","def string_to_years(s):\n pattern = r'\\d\\d\\d\\d'\n r = re.compile(pattern)\n min_year = 1960\n max_year = datetime.now().year + 1\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))","def year_text_range_filter(self, queryset, name, value):\n if value:\n if value[0] and value[1]:\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\n .filter(**{name+'_int__range': (value[0], value[1])})\n else:\n if value[0]:\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\n .filter(**{name+'_int__gte': value[0]})\n if value[1]:\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\n .filter(**{name+'_int__lte': value[1]})\n\n return queryset","def find_by_year(our_data,year):\n return [album for album in our_data if album['number'] == str(year)]","def load_data(year, begin=0, end=None):\r\n\r\n fname = os.path.join('..', 'Data', 'Morphometrics for Bogue ' + str(year) + '.csv')\r\n data = pd.read_csv(fname, header=0, delimiter=',')\r\n\r\n data = data.loc[data['Profile No.'] >= begin]\r\n\r\n if end is not None:\r\n data = data.loc[data['Profile No.'] <= end]\r\n\r\n return data","def FS2Years(inputFolderPath = './FormattedFilesWithoutMissingToNextYear', outputFolderPath = './FormattedFilesWithoutMissingToNextYear'):\n\tfileList = []\n\tfor root, dirs, files in os.walk(inputFolderPath): \n\t for afile in files:\n\t \tfileList.append(afile)\n\n\ttargetList = [2704,2707,2713,2716,2718,808,811,1954]\n\t# targetList = [1994,1997,2003,2006,2008,807,810,1953]\n\tyearList = [(1998,2015),(2005,2015),(2005,2015),(2005,2015),(2005,2015),(1960,2014),(1961,2014),(2002,2012)]\n\n\n\tfor i in range(len(targetList)):\n\t\t# i = 0\n\t\trows = []\n\t\tfor year in range(yearList[i][0],yearList[i][1]+1):\n\t\t\t# print str(year) + '-' + str(targetList[i]) \n\t\t\tregex = re.compile(\"(\"+ str(year) +\").*\")\n\t\t\tfiles = [m.group(0) for l in fileList for m in [regex.search(l)] if m and len(l) == 28]\n\t\t\t\n\n\t\t\t# load the CSV file as a numpy matrix\n\t\t\twith open(inputFolderPath+'/'+files[0],'rb') as f:\n\t\t\t reader = csv.reader(f)\n\t\t\t header = next(reader)\n\t\t\t num_cols = len(header)\n\t\t\t # print header\n\t\t\t print i\n\t\t\t target_idx = [idx for idx, item in enumerate(header) if item.startswith(str(targetList[i]).zfill(4)+'N')]\n\t\t\t regex = re.compile(\"....N:.*\")\n\t\t\t nextYearIDs = [idx for idx, item in enumerate(header) if regex.search(item)]\n\t\t\t nextYearCount = len(nextYearIDs)\n\t\t\t if len(target_idx) > 0:\n\t\t\t \ttarget = target_idx[0]-1\n\t\t\t \tprint ('OK',year, targetList[i], inputFolderPath+'/'+files[0])\n\t\t\t else:\n\t\t\t \tprint (year, targetList[i], inputFolderPath+'/'+files[0])\n\t\t\t \tbreak\n\t\t\t f.close()\n\t\t\tdataset = np.genfromtxt(inputFolderPath+'/'+files[0], delimiter=\",\", skip_header=1, autostrip=True, missing_values=np.nan, usecols=tuple(range(1,num_cols)))\n\t\t\t# print (dataset.shape)\n\t\t\t# X = np.concatenate((dataset[:,0:target],dataset[:,target+1:dataset.shape[1]]),axis=1)\n\t\t\tX = dataset[:,nextYearCount:dataset.shape[1]]\n\t\t\t# X = np.concatenate((dataset[:,0:2],dataset[:,3:dataset.shape[1]),axis=1)\n\t\t\ty = dataset[:,target]\n\t\t\t\n\t\t\timp = Imputer(missing_values='NaN', strategy='median', axis=0)\n\t\t\timputedX = imp.fit_transform(X,y)\n\t\t\timputedX = np.array([imputedX[j] for j in range(imputedX.shape[0]) if not np.isnan(y[j])])\n\t\t\tdeleteMissingY = np.array([x1 for x1 in y if not np.isnan(x1)])\n\n\t\t\tk = 40\n\t\t\tselection = SelectKBest(f_regression, k=k)\n\t\t\timputedX_new = selection.fit_transform(imputedX, deleteMissingY)\n\t\t\t\n\t\t\tselectedFeatures = [[item, selection.scores_[idx], selection.pvalues_[idx]] for idx, item in enumerate(header[nextYearCount+1:]) if selection.get_support()[idx]]\n\t\t\tselectedFeatures.sort(key=lambda x: x[1], reverse=True)\n\t\t\t\n\t\t\trows.append([year, 'score', 'p-value'])\n\t\t\trows.extend(selectedFeatures)\n\t\t\trows.append(['', '', ''])\n\t\t\tprint 'Hey'\n\n\t\tfilename = outputFolderPath+'/'+('FeatureSelectionIndicator%d - k%d - %s.csv' % (targetList[i], k, 'f_regression'))\n\t\twith open(filename,'wb') as w:\n\t\t\ta = csv.writer(w, delimiter = ',')\n\t\t\ta.writerows(rows)\n\t\tw.close()","def calculate_daily_climatology(\n pctile,\n windowHalfWidth,\n lenClimYear,\n smoothPercentile,\n smoothPercentileWidth,\n thresh_climYear, # empty array\n seas_climYear, # empty array\n clim, # empty dict\n feb29,\n doyClim,\n clim_start,\n clim_end,\n tempClim,\n temp,\n):\n # Loop over all day-of-year values, and calculate threshold and seasonal climatology across years\n for d in range(1, lenClimYear + 1):\n # Special case for Feb 29\n if d == feb29:\n continue\n # find all indices for each day of the year +/- windowHalfWidth and from them calculate the threshold\n tt0 = np.where(doyClim[clim_start : clim_end + 1] == d)[\n 0\n ] # the index for that day each year\n # If this doy value does not exist (i.e. in 360-day calendars) then skip it\n if len(tt0) == 0:\n continue\n tt = np.array([])\n for w in range(-windowHalfWidth, windowHalfWidth + 1): # -5 : 5 default\n tt = np.append(\n tt, clim_start + tt0 + w\n ) # append the daily values 5days before and 5days after\n tt = tt[tt >= 0] # Reject indices \"before\" the first element\n tt = tt[tt < TClim] # Reject indices \"after\" the last element\n thresh_climYear[d - 1] = np.percentile(nonans(tempClim[tt.astype(int)]), pctile)\n seas_climYear[d - 1] = np.mean(nonans(tempClim[tt.astype(int)]))\n\n # Special case for Feb 29 (LEAP YEAR)\n thresh_climYear[feb29 - 1] = (\n 0.5 * thresh_climYear[feb29 - 2] + 0.5 * thresh_climYear[feb29]\n )\n seas_climYear[feb29 - 1] = (\n 0.5 * seas_climYear[feb29 - 2] + 0.5 * seas_climYear[feb29]\n )\n\n if smoothPercentile:\n thresh_climYear, seas_climYear = smooth_climatologies(\n thresh_climYear, seas_climYear, smoothPercentileWidth\n )\n\n # Generate threshold for full time series\n clim[\"thresh\"] = thresh_climYear[doy.astype(int) - 1]\n clim[\"seas\"] = seas_climYear[doy.astype(int) - 1]\n # Save vector indicating which points in temp are missing values\n clim[\"missing\"] = np.isnan(temp)\n\n return clim","def get_all_cfb_teams_for_year_range(start_year, end_year):\n \n full_team_list = []\n \n for year in range(start_year, end_year + 1):\n print(year)\n \n team_list = get_all_cfb_teams(year)\n full_team_list += team_list\n \n wait_time = random.randint(3, 15)\n time.sleep(wait_time)\n \n return full_team_list","def check_date_range(data, date_column, sep_type=\"\", skip_rows=0, date_format=\"jd\"):\n\n if sep_type==\"\":\n info=pd.read_csv(data, skiprows=skip_rows, header=None, delim_whitespace=True) \n else:\n info=pd.read_csv(data, skiprows=skip_rows, header=None, sep=sep_type)\n\n dates=Time(info[int(date_column)].values, format=date_format)\n years=dates.byear\n\n print(\"Earliest year record: %f\" %(years.min()))\n print(\"Latest year record: %f\" %(years.max()))","def austral_year_daily(x, y):\n if isinstance(x, xr.DataArray):\n x = x.values\n \n jfmamj = x < 182.\n jasond = x >= 182.\n \n x_jasond = []\n y_jasond = []\n if any(jasond):\n x_jasond = x[jasond] - 181\n y_jasond = y[jasond]\n\n x_jfmamj = []\n y_jfmamj = []\n if any(jfmamj):\n x_jfmamj = x[jfmamj] + 184\n y_jfmamj = y[jfmamj]\n\n xout = np.concatenate([xi for xi in [x_jasond, x_jfmamj] if len(xi)])\n yout = np.concatenate([yi for yi in [y_jasond, y_jfmamj] if len(yi)])\n \n return xout, yout","def get_value_year(year, user):\n year_list_income = Money.objects.values_list('valor', flat=True).filter(\n add_or_remove=True, user=user, entry_date__year=year)\n year_income = sum(year_list_income)\n year_list_outcome = Money.objects.values_list('valor', flat=True).filter(\n add_or_remove=False, entry_date__year=year, user=user)\n year_outcome = sum(year_list_outcome)\n queryset_year = list(Money.objects.values('category', 'valor').filter(\n add_or_remove=False, entry_date__year=year, user=user))\n cat_list = ['Outro', 'Trabalho', 'Lazer',\n 'Aluguel', 'Gastos Pessoais', 'Investimentos']\n val_list = []\n total_outro = 00.00\n total_categoria = Decimal(total_outro)\n for l in range(len(cat_list)):\n tes = 0\n categoria = cat_list[l]\n for i in range(len(queryset_year)):\n if queryset_year[i]['category'] == categoria:\n tes += queryset_year[i]['valor']\n val_list.append(tes)\n data1 = list(zip(cat_list, val_list))\n return year_income, year_outcome, data1","def get_gbd_estimation_years(gbd_round_id: int) -> List[int]:\n from db_queries import get_demographics\n warnings.filterwarnings(\"default\", module=\"db_queries\")\n\n return get_demographics(gbd_constants.CONN_DEFS.EPI, gbd_round_id=gbd_round_id)['year_id']","def get_models_between(start_year, end_year):\n\n results = db.session.query(Model).filter(Model.year >= start_year, Model.year < end_year).all()\n\n return results","def r_style_interval(from_tuple, end_tuple, frequency):\n from_year, from_seg = from_tuple\n end_year, end_seg = end_tuple\n n = (end_year - from_year + 1) * frequency\n full_range = np.linspace(from_year, end_year + 1, num=n, endpoint=False)\n real_range = full_range[(from_seg - 1):n - (frequency - end_seg)]\n return real_range","def substitute_year(self, ref_years, qa3_answer):\n question = self\n for match in re.finditer('[0-9]{4}', question):\n year = match.group(0)\n if 1899 < int(year) < 2099:\n i = qa3_answer.index_by_chunk(year)\n if i is None or qa3_answer.result[i].get_type() != 'refYear':\n if year not in ref_years:\n ref_years.append(year)\n question = re.sub(year, '<YEAR' + string.ascii_uppercase[ref_years.index(year)] + '>', question, 1)\n return Qa3Question(question)","def generate_years(nyears, year=2022, month=1, day=1):\n return np.asarray([\n f'{year+i:04d}-{month:02d}-{day:02d}T00:00:00.000000'\n for i in range(nyears)\n ], dtype='datetime64[us]')","def award_id_year(award_wikidata) -> 'award_id_year':\n assert re.match(r\"^\\d{4}$\", award_wikidata['value']['year'])\n return {'value': (award_wikidata['value']['award_id'], award_wikidata['value']['year'])}","def test_leap_years_1899to1904_and_1999to2004():\n\tyears = [1899,1900,1901,1902,1903,1904,1999,2000,2001,2002,2003,2004]\n\tleaps = date_functions.is_leap( years )\n\tassert not leaps[0] # 1899 was not a leap year\n\tassert not leaps[1] # 1900 was not a leap year\n\tassert not leaps[2] # 1901 was not a leap year\n\tassert not leaps[3] # 1902 was not a leap year\n\tassert not leaps[4] # 1903 was not a leap year\n\tassert leaps[5] # 1904 was a leap year\n\tassert not leaps[6] # 1999 was not a leap year\n\tassert leaps[7] # 2000 was a leap year\n\tassert not leaps[8] # 2001 was not a leap year\n\tassert not leaps[9] # 2002 was not a leap year\n\tassert not leaps[10] # 2003 was not a leap year\n\tassert leaps[11] # 2004 was a leap year","def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\n dict, dict, dict, dict,\n dict, dict]:\n name_table = create_name_table(citelist)\n unique_names = list()\n nameset = set()\n # total_binomial_year_cnts = {}\n total_binomial_year_cnts = collections.Counter()\n for c in citelist:\n if c.name != \".\":\n clean = clean_name(c.name)\n if not clean.lower() in nameset:\n nameset |= {clean.lower()}\n unique_names.append(clean)\n y = refdict[c.cite_key].year()\n if y is not None:\n total_binomial_year_cnts.update([y])\n # if y in total_binomial_year_cnts:\n # total_binomial_year_cnts[y] += 1\n # else:\n # total_binomial_year_cnts[y] = 1\n unique_names.sort(key=lambda s: s.lower())\n\n # identify genera used per paper\n genera_per_paper = {}\n for c in citelist:\n if c.name != \".\":\n if c.cite_key not in genera_per_paper:\n genera_per_paper[c.cite_key] = set()\n genera_set = genera_per_paper[c.cite_key]\n genera_set |= {extract_genus(clean_name(c.name))}\n genus_cnts = {}\n for c in genera_per_paper:\n y = refdict[c].year()\n if y is not None:\n if init_data().start_year <= y <= init_data().current_year:\n genera_set = genera_per_paper[c]\n for genus in genera_set:\n genus = clean_genus(genus)\n if genus != \"\":\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\n init_data().current_year + 1)})[y] += 1\n # if genus not in genus_cnts:\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\n # init_data().current_year + 1)}\n # gcnts = genus_cnts[genus]\n # gcnts[y] += 1\n\n binomial_usage_cnts_by_year = {}\n binomial_location_applications = {}\n binomial_usage_cnts = {}\n for name in unique_names:\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\n if tmptotal > 0:\n binomial_usage_cnts[name] = tmptotal\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\n\n # specific_year_cnts = {}\n specific_year_cnts = collections.Counter()\n specific_usage_cnts_by_year = {}\n specific_location_applications = {}\n specific_usage_cnts = {}\n for name in specific_names:\n (specific_usage_cnts_by_year[name.name],\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\n if tmptotal > 0:\n specific_usage_cnts[name.name] = tmptotal\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\n binomial_location_applications)\n tmpkey = name.priority_source\n if tmpkey != \".\":\n y = refdict[tmpkey].year()\n if y is not None:\n specific_year_cnts.update([y])\n # if y in specific_year_cnts:\n # specific_year_cnts[y] += 1\n # else:\n # specific_year_cnts[y] = 1\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\n binomial_usage_cnts, specific_usage_cnts)","def get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule):\n\n occ = []\n el = []\n dhw = []\n pro = []\n\n if dhw_schedules[0].sum() != 0:\n dhw_weekday_max = dhw_schedules[0].sum() ** -1\n else: dhw_weekday_max = 0\n\n if dhw_schedules[1].sum() != 0:\n dhw_sat_max = dhw_schedules[1].sum() ** -1\n else: dhw_sat_max = 0\n\n if dhw_schedules[2].sum() != 0:\n dhw_sun_max = dhw_schedules[2].sum() ** -1\n else: dhw_sun_max = 0\n\n for date in dates:\n month_year = month_schedule[date.month - 1]\n hour_day = date.hour\n dayofweek = date.dayofweek\n if 0 <= dayofweek < 5: # weekday\n occ.append(occ_schedules[0][hour_day] * month_year)\n el.append(el_schedules[0][hour_day] * month_year)\n dhw.append(dhw_schedules[0][hour_day] * month_year * dhw_weekday_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[0][hour_day] * month_year)\n elif dayofweek is 5: # saturday\n occ.append(occ_schedules[1][hour_day] * month_year)\n el.append(el_schedules[1][hour_day] * month_year)\n dhw.append(dhw_schedules[1][hour_day] * month_year * dhw_sat_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[1][hour_day] * month_year)\n else: # sunday\n occ.append(occ_schedules[2][hour_day] * month_year)\n el.append(el_schedules[2][hour_day] * month_year)\n dhw.append(dhw_schedules[2][hour_day] * month_year * dhw_sun_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[2][hour_day] * month_year)\n\n return occ, el, dhw, pro","def filter_timespans(self, minTime=2.0):\n igList = [ig.Rsc['PAIR'] for ig in self.Set if abs(float(ig.Rsc['TIME_SPAN_YEAR'])) < minTime]\n self.Set.omit(IG=igList)","def test_get_teams_in_year_len():\n assert len(gtiy(2008)) == 8\n assert len(gtiy(2009)) == 8\n assert len(gtiy(2010)) == 8\n assert len(gtiy(2011)) == 10\n assert len(gtiy(2012)) == 9\n assert len(gtiy(2013)) == 9\n assert len(gtiy(2014)) == 8\n assert len(gtiy(2015)) == 8\n assert len(gtiy(2016)) == 8\n assert len(gtiy(2017)) == 8","def compute_leap_years(start, end):\n # Generate the leap years between 1971 and 2020 inclusive\n leap_years = tuple(1972 + 4*x for x in range(13))\n\n # Looks for the closest leap year greater than or equal to the start year\n min_leap_year = 0\n for leap_year in leap_years:\n if leap_year >= start.year:\n min_leap_year = leap_year\n break\n\n # Looks for the closest leap year less than or equal to the end year\n max_leap_year = 0\n for leap_year in reversed(leap_years):\n if leap_year <= end.year:\n max_leap_year = leap_year\n break\n\n # Gets the number of leap years between the start and end year\n # Note that if the leap year in between is just the same year it will zero out, thus the +1\n leap_days_between = ((max_leap_year - min_leap_year) // 4) + 1\n\n # If the start date occurs after Feb 29th of that year, we don't consider\n if (start - datetime.date(min_leap_year, 2, 29)).days > 0:\n leap_days_between -= 1\n\n # If the end date occurs before Feb 29th of that year, we don't consider\n if (datetime.date(max_leap_year, 2, 29) - end).days > 0:\n leap_days_between -= 1\n\n return leap_days_between","def fix_years(self, row):\n raise NotImplementedError","def _construct_yearly_climate(self, cl, now, end):\n if self.simulation:\n years = range(now.year, end.year + 1)\n if len(years)>1:\n # the ordinals of days within the year\n lastord = float(date(now.year, 12, 31).timetuple()[7])\n noword = float(now.timetuple()[7])\n firstyearweight = (lastord - noword) / lastord\n lastord = float(date(end.year, 12, 31).timetuple()[7])\n endord = float(end.timetuple()[7])\n lastyearweight = endord / lastord\n else:\n # for steady state computation year 0 or 1 used\n years = [0]\n firstyearweight = 1.0\n lastyearweight = 1.0\n self.curr_yr_ind = 0\n rain = 0.0\n temp = 0.0\n ampl = 0.0\n addyear = True\n if len(years)==1:\n firstyearweight = 1.0\n if now.year==end.year and not(end.month==12 and end.day==31):\n addyear = False\n maxind = len(self.md.yearly_climate) - 1\n for ind in range(len(years)):\n if self.curr_yr_ind > maxind:\n self.curr_yr_ind = 0\n cy = self.md.yearly_climate[self.curr_yr_ind]\n if self.simulation and cy.timestep==0:\n # timestep 0 is used only for steady state calculation\n self.curr_yr_ind += 1\n if self.curr_yr_ind <= maxind:\n cy = self.md.yearly_climate[self.curr_yr_ind]\n if ind == 0:\n weight = firstyearweight\n passedzero = False\n elif ind == len(years) - 1:\n weight = lastyearweight\n else:\n weight = 1.0\n temp += weight * cy.mean_temperature\n rain += weight * cy.annual_rainfall\n ampl += weight * cy.variation_amplitude\n if addyear:\n self.curr_yr_ind += 1\n # backs one year back, if the last weight was less than 1\n if weight < 1.0 and addyear:\n self.curr_yr_ind -= 1\n if self.curr_yr_ind < 0:\n self.curr_yr_ind = len(self.md.yearly_climate) - 1\n cl['rain'] = rain / len(years)\n cl['temp'] = temp / len(years)\n cl['amplitude'] = ampl / len(years)\n return cl","def get_all_valid_deer_load_shapes(database_year):\n query = \"\"\"\n SELECT *\n FROM deer_load_shapes\n limit 1\n \"\"\"\n valid_deer_load_shapes = execute_query(database_year, query)\n all_columns_w_utilities = list(\n valid_deer_load_shapes.drop(\"hour_of_year\", axis=1).columns\n )\n # TODO (ssuffian): Reshape db so it is a query by utility column\n return list(\n set(\n [\n c.replace(\"PGE_\", \"\")\n .replace(\"SDGE_\", \"\")\n .replace(\"SCG_\", \"\")\n .replace(\"SCE_\", \"\")\n for c in all_columns_w_utilities\n ]\n )\n )","def wordsForYear(year, yearlist):\r\n for el in yearlist:\r\n if el.year==year:\r\n return el.count\r\n return 0","def get_models_between(start_year, end_year):\n\n models = Model.query.filter(Model.year >= start_year,\n Model.year < end_year).all()\n return models","def collect_data_by_year(year):\n \n # Format page request\n url = 'http://nflcombineresults.com/nflcombinedata_expanded.php'\n headers = {'user-agent': UserAgent().random}\n params = {'year': year}\n \n soup = get_soup(url, headers=headers, params=params)\n \n # Check if the database contains the requested year\n possible_set = set([])\n possible_years = soup.find('select', attrs={'id': 'year'}).find_all('option')\n \n for y in possible_years:\n possible_set.add(y.get_text())\n \n if str(year) not in possible_set:\n return {} # Return empty dict if the year is incompatible\n \n rows = soup.find_all('tr', attrs={'class': 'tablefont'})\n full_dict = {}\n counter = 1\n\n for row in rows:\n tds = row.find_all('td')\n row_dict = {}\n \n for i, td in enumerate(tds):\n is_hidden = td.find('div', attrs={'style': 'visibility:hidden;'})\n \n # Check if the player has any data for each cell\n if is_hidden:\n text = ''\n else:\n text = td.get_text()\n \n # Add data to a preliminary dict\n row_dict[i] = text\n \n # Assign relevant keys to data\n row_dict['year'] = row_dict.pop(0)\n row_dict['name'] = row_dict.pop(1)\n row_dict['school'] = row_dict.pop(2)\n row_dict['POS'] = row_dict.pop(3)\n row_dict['height'] = row_dict.pop(4)\n row_dict['weight'] = row_dict.pop(5)\n row_dict['handSize'] = row_dict.pop(6)\n row_dict['armLength'] = row_dict.pop(7)\n row_dict['wonderlic'] = row_dict.pop(8)\n row_dict['forty'] = row_dict.pop(9)\n row_dict['bench'] = row_dict.pop(10)\n row_dict['vert'] = row_dict.pop(11)\n row_dict['broad'] = row_dict.pop(12)\n row_dict['shuttle'] = row_dict.pop(13)\n row_dict['3cone'] = row_dict.pop(14)\n row_dict['60shuttle'] = row_dict.pop(15)\n \n # Create unique player code\n name = row_dict['name']\n name_code = name.replace(\"'\", '').replace('.', '').replace(' ', '-').lower()\n key = '{}-{}-{}'.format(name_code, counter, year)\n counter += 1\n \n # Create dict of dicts containing all data\n full_dict[key] = row_dict\n \n return full_dict","def test_contains_year_true(self):\n ary = self.ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n self.assertTrue(2008 in ary)"],"string":"[\n \"def years(self, data, disjoint=True):\\n\\n year_set = set()\\n try:\\n # multi valued\\n assert isinstance(data, set)\\n for datum in data:\\n preprocessed_year_data = self._extract_year_data(datum)\\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n except TypeError:\\n # single value\\n preprocessed_year_data = self._extract_year_data(data)\\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n return year_set\",\n \"def multiple_years(our_data, start, end):\\n count = start\\n album_list = []\\n while count <= end:\\n album_list.append(find_by_year(our_data,count))\\n count += 1\",\n \"def t_range_years(t_range):\\r\\n start_year = int(t_range[0].split(\\\"-\\\")[0])\\r\\n end_year = int(t_range[1].split(\\\"-\\\")[0])\\r\\n end_month = int(t_range[1].split(\\\"-\\\")[1])\\r\\n end_day = int(t_range[1].split(\\\"-\\\")[2])\\r\\n if end_month == 1 and end_day == 1:\\r\\n year_range_list = np.arange(start_year, end_year)\\r\\n else:\\r\\n year_range_list = np.arange(start_year, end_year + 1)\\r\\n return year_range_list\",\n \"def years(self):\\n return sorted(set(self._years))\",\n \"def decades(self, data, disjoint=True):\\n\\n decade_set = set()\\n try:\\n # multi valued\\n assert isinstance(data, set)\\n for datum in data:\\n preprocessed_year_data = self._extract_year_data(datum)\\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\\n except (AssertionError, TypeError):\\n # single value\\n preprocessed_year_data = self._extract_year_data(data)\\n decade_set = decade_set | self._enumerate_decades(preprocessed_year_data, disjoint)\\n\\n return decade_set\",\n \"def year_cv_split(X, year_range):\\n return [\\n ((X[\\\"year\\\"] < year).to_numpy(), (X[\\\"year\\\"] == year).to_numpy())\\n for year in range(*year_range)\\n ]\",\n \"def drop_years(dataframe, start, end):\\n tmp = dataframe\\n tmp = tmp[(start <= tmp['year'].astype(int)) & (tmp['year'].astype(int) <= end)]\\n\\n return tmp\",\n \"def dbf_years(self):\\n return [year for year in self.years if year <= 2020]\",\n \"def get_yearly_data(name, startyr=None, endyr=None, interpolated=False):\\n varinfo = get_varinfo(name)\\n \\n if varinfo[\\\"type\\\"] == \\\"yearly\\\":\\n data = get_data(varinfo[\\\"id\\\"], startyr=startyr, endyr=endyr)\\n giddict = dict()\\n sorteddata = sorted(data[\\\"cells\\\"], key=lambda vd: vd[\\\"gid\\\"])\\n for gid,valuedicts in itertools.groupby(sorteddata, key=lambda vd: vd[\\\"gid\\\"]):\\n yrdict = dict([(valuedict[\\\"year\\\"],valuedict[\\\"value\\\"])\\n for valuedict in valuedicts\\n ])\\n info = {\\\"data\\\": yrdict}\\n giddict[gid] = info\\n\\n if interpolated:\\n def pairwise(iterable):\\n a, b = itertools.tee(iterable)\\n next(b, None)\\n return zip(a, b)\\n \\n def lerp(factor, fromval, toval):\\n valrange = toval - fromval\\n return fromval + valrange * factor\\n \\n for gid,info in giddict.items():\\n yrdict = info[\\\"data\\\"]\\n if len(yrdict) > 1:\\n for (fromyr,fromval),(toyr,toval) in pairwise(sorted(yrdict.items(),key=lambda i: i[0])):\\n curyr = fromyr + 1\\n interpneeded = fromval != toval\\n \\n while curyr != toyr:\\n if interpneeded:\\n factor = (curyr - fromyr) / float(toyr - fromyr)\\n yrdict[curyr] = lerp(factor, fromval, toval)\\n else:\\n yrdict[curyr] = fromval\\n curyr += 1\\n\\n return giddict\\n\\n else:\\n raise Exception(\\\"Could not find a yearly variable with that name\\\")\",\n \"def getYearlyPrism(all_basin_geoms, fromYear, toYear):\\n \\n yearly_Prism = {}\\n for yy in range(fromYear, toYear):\\n print(\\\"Processing year\\\", yy)\\n for mm in range(1, 3):\\n print(\\\"Processing month\\\", mm) \\n if(mm<10):\\n mmyy = '0'+str(mm)+'-'+str(yy)\\n else:\\n mmyy = str(mm)+'-'+str(yy)\\n print(mmyy)\\n yearly_Prism[mmyy] = get_intersected_basins_ppt_data(all_basin_geoms, month=mm, year=yy)\\n \\n return yearly_Prism\",\n \"def _year_range(m):\\n return (m.group(1), m.group(2))\",\n \"def xbrl_years(self):\\n return [year for year in self.years if year >= 2021]\",\n \"def get_years():\\n if request.method == 'OPTIONS':\\n logging.info(\\\"Handle options\\\")\\n return create_response({}, 200, '*', 'content-type, token')\\n\\n logging.info(\\\"Getting recruitment years\\\")\\n\\n role, response = handle_request_token(request)\\n\\n if role is None:\\n logging.warning(\\\"Role is None!\\\")\\n return response\\n\\n years = set()\\n for rec in Recruitment.query.all():\\n if rec.end_date.year not in years:\\n years.add(rec.end_date.year)\\n\\n years = list(years)\\n years.sort(reverse=True)\\n\\n return create_response(years, 200, '*')\",\n \"def grab_by_year(dataframe, year_col, value_col, year_start=None,\\n year_end=None):\\n\\n if year_start is None and year_end is None:\\n print(\\\"year_start and year_end are not defined. Insert a value \\\\\\n for at least one year variable.\\\")\\n\\n if year_end is None:\\n grab_years = dataframe[(dataframe[year_col] >= year_start)\\n ][value_col]\\n if year_start is None:\\n grab_years = dataframe[(dataframe[year_col] <= year_end)\\n ][value_col]\\n\\n if year_start is not None and year_end is not None:\\n grab_years = dataframe[(dataframe[year_col] >= year_start) &\\n (dataframe[year_col] <= year_end)\\n ][value_col]\\n\\n return grab_years\",\n \"def year_data(self,year):\\n idx = [i for i in range(self.dates.shape[0]) if self.dates[i].year == year]\\n year_dates = self.dates[idx]\\n year_dc = self.dc[idx]\\n return year_dates, year_dc\",\n \"def split_by_year(config, dbsession, group):\\n years = []\\n decades = []\\n centuries = []\\n with_year = 0\\n for item in group.items:\\n if config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]:\\n years.append(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n with_year = with_year + 1\\n if with_year / len(group.items) > 0.95: # noqa: PLR2004\\n common = [(int(v), c) for v, c in Counter(years).most_common()]\\n start_year = min([c for c, _ in common])\\n end_year = max([c for c, _ in common])\\n if start_year != end_year:\\n if (end_year - start_year) <= 100 and (end_year - start_year) > 10: # noqa: PLR2004\\n start_decade = math.floor(start_year / 10)\\n end_decade = math.floor(end_year / 10)\\n decades = []\\n for start_year in range(start_decade * 10, (end_decade + 1) * 10, 10):\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n start_year <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < start_year + 10\\n ):\\n if len(decades) == 0 or decades[-1][0][0] != start_year:\\n decades.append([[start_year], 1])\\n else:\\n decades[-1][1] = decades[-1][1] + 1\\n idx = 0\\n while idx < len(decades) - 1:\\n if decades[idx][1] + decades[idx + 1][1] < 100: # noqa: PLR2004\\n decades[idx][0].extend(decades[idx + 1][0])\\n decades[idx][1] = decades[idx][1] + decades[idx + 1][1]\\n decades.pop(idx + 1)\\n else:\\n idx = idx + 1\\n for years, _ in decades:\\n new_group = None\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n years[0] <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < years[-1] + 10\\n ):\\n if new_group is None:\\n if len(years) == 1:\\n label = f\\\"{years[0]}s\\\"\\n else:\\n label = f\\\"{years[0]}s-{years[-1]}s\\\"\\n new_group = Group(\\n value=str(start_year),\\n label=f\\\"{group.label} - {label}\\\",\\n parent=group,\\n split=\\\"time\\\",\\n )\\n dbsession.add(new_group)\\n item.group = new_group\\n if group.items:\\n new_group = Group(value=group.label, label=group.label, parent=group, split=\\\"time\\\")\\n dbsession.add(new_group)\\n for item in list(group.items):\\n item.group = new_group\\n return True\\n elif (end_year - start_year) > 100: # noqa: PLR2004\\n start_century = math.floor(start_year / 100)\\n end_century = math.floor(end_year / 100)\\n centuries = []\\n for start_year in range(start_century * 100, (end_century + 1) * 100, 100):\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n start_year <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < start_year + 100\\n ):\\n if len(centuries) == 0 or centuries[-1][0][0] != start_year:\\n centuries.append([[start_year], 1])\\n else:\\n centuries[-1][1] = centuries[-1][1] + 1\\n idx = 0\\n while idx < len(centuries) - 1:\\n if centuries[idx][1] + centuries[idx + 1][1] < 100: # noqa: PLR2004\\n centuries[idx][0].extend(centuries[idx + 1][0])\\n centuries[idx][1] = centuries[idx][1] + centuries[idx + 1][1]\\n centuries.pop(idx + 1)\\n else:\\n idx = idx + 1\\n for years, _ in centuries:\\n new_group = None\\n for item in list(group.items):\\n if (\\n config[\\\"data\\\"][\\\"year_field\\\"] in item.attributes\\n and item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]\\n ):\\n if (\\n years[0] <= int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]])\\n and int(item.attributes[config[\\\"data\\\"][\\\"year_field\\\"]]) < years[-1] + 100\\n ):\\n if new_group is None:\\n if len(years) == 1:\\n century = math.floor(years[0] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n label = f\\\"{century}rd\\\"\\n else:\\n label = f\\\"{century}th\\\"\\n else:\\n century = math.floor(years[0] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n start_label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n start_label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n start_label = f\\\"{century}rd\\\"\\n else:\\n start_label = f\\\"{century}th\\\"\\n century = math.floor(years[-1] / 100) + 1\\n if century % 10 == 1 and century != 11: # noqa: PLR2004\\n end_label = f\\\"{century}st\\\"\\n elif century % 10 == 2 and century != 12: # noqa: PLR2004\\n end_label = f\\\"{century}nd\\\"\\n elif century % 10 == 3 and century != 13: # noqa: PLR2004\\n end_label = f\\\"{century}rd\\\"\\n else:\\n end_label = f\\\"{century}th\\\"\\n label = f\\\"{start_label}-{end_label}\\\"\\n new_group = Group(\\n value=str(start_year),\\n label=f\\\"{group.label} - {label} century\\\",\\n parent=group,\\n split=\\\"time\\\",\\n )\\n dbsession.add(new_group)\\n item.group = new_group\\n if group.items:\\n new_group = Group(value=group.label, label=group.label, parent=group, split=\\\"time\\\")\\n dbsession.add(new_group)\\n for item in list(group.items):\\n item.group = new_group\\n return True\\n return False\",\n \"def get_subset(mlist,year):\\n newlist = []\\n for entry in mlist:\\n if int(entry[0][:4]) > int(year):\\n continue\\n newvec = entry[:8]\\n citations = entry[8]['citations']\\n citations = filter(lambda a: int(a[:4]) <= int(year), citations)\\n newvec[2] = len(citations)\\n newlist.append(newvec)\\n return newlist\",\n \"def get_start_end_years(df: pd.DataFrame) -> Tuple[int, int]:\\n return df.iloc[0].year, df.iloc[-1].year\",\n \"def _date_match_to_int_or_tuple(self, m):\\n\\n years = set()\\n try:\\n if m[0] != '':\\n # yyyy-dd-mm\\n years = int(m[0][0:4])\\n elif m[1] != '':\\n # year-range derived from a century\\n century = int(re.match(re.compile('\\\\\\\\d+'), m[1]).group(0))\\n\\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\\n suffix = match.group(2)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = (100 * -century, 100 * -century + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n elif m[2] != '':\\n # explicit year-range\\n\\n # FIXME: spaghetti code, but it works!\\n range_of_stuff = []\\n i = 0\\n first_none = None\\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\\\1>|<\\\\2', m[2]).split('>|<'):\\n # get rid of whitespace\\n y = y.strip()\\n match = re.compile(self.regexes['capture']['year']).match(y)\\n\\n # if there is a suffix, one of these will not be None\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if i == 0:\\n first_none = False\\n\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\\n else:\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n else:\\n if i == 0:\\n first_none = True\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n\\n i += 1\\n if first_none:\\n if range_of_stuff[1] <= 0:\\n range_of_stuff[0] = -1 * range_of_stuff[0]\\n\\n years = (range_of_stuff[0], range_of_stuff[1])\\n elif m[3] != '':\\n # extract single year\\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\\\1', m[3]).strip()\\n years = int(prep)\\n elif m[4] != '':\\n # year with unknown ones\\n y = m[4].strip()\\n match = re.compile(r'[1-9]\\\\d{3}').match(y)\\n if match is None:\\n years = int(self._resolve_unknown_ones(y))\\n\\n else:\\n years = int(match.group(0))\\n\\n elif m[5] != '':\\n # plain old year\\n match = re.compile(self.regexes['capture']['year']).match(m[5])\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = -1 * int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n raise Error\\n\\n except ValueError as e:\\n #logger.error('An error occurred while trying to match \\\"{}\\\": {}'.format(m, e))\\n pass\\n\\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\\n return years\",\n \"def _extract_year_data(self, date_string):\\n\\n try:\\n # first see if dateutil can parse the date string\\n # simplest case, a single year\\n return {parse(date_string).year}\\n\\n except ValueError:\\n try:\\n # strip alphabetical chars and spaces from the left side and try again\\n alpha = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\\n return {parse(date_string.lstrip(alpha + ' ')).year}\\n\\n except ValueError:\\n # find as many substrings that look like dates as possible\\n matches = re.findall(re.compile(self.regexes['match']['date']), date_string)\\n #logger.debug('{} date string matches found in \\\"{}\\\"'.format(len(matches), dateString))\\n if len(matches) > 0:\\n return {self._date_match_to_int_or_tuple(m) for m in matches}\\n else:\\n return set()\",\n \"def get_years_before_and_after_failures(end_year, der_list):\\n rerun_opt_on = []\\n for der_instance in der_list:\\n last_operation_year = None\\n if der_instance.tag == 'Battery' and der_instance.incl_cycle_degrade:\\n # ignore battery's failure years as defined by user if user wants to include degradation in their analysis\\n # instead set it to be the project's last year+1\\n last_operation_year = end_year.year\\n yrs_failed = der_instance.set_failure_years(end_year, last_operation_year)\\n if not der_instance.replaceable:\\n # if the DER is not replaceable then add the following year to the set of analysis years\\n rerun_opt_on += yrs_failed\\n # filter out any years beyond end_year\\n rerun_opt_on = [year for year in rerun_opt_on if year < end_year.year]\\n # add years that the operational DER mix will change (year after last year of operation)\\n rerun_opt_on += [year+1 for year in rerun_opt_on if year < end_year.year]\\n return list(set(rerun_opt_on)) # get rid of any duplicates\",\n \"def filter_years(text):\\n months = ['january', 'february', 'march', 'april', 'may', 'june',\\n 'july', 'august', 'september', 'october', 'november', 'december']\\n prepositions = ['around', 'after', 'at', 'as',\\n 'approximately', 'before', 'between', 'by',\\n 'during', 'from', 'in', 'near', 'past',\\n 'since', 'until', 'within'] # removed: about, on\\n conjugations = ['and']\\n articles = ['the']\\n times = ['early', 'mid', 'late']\\n patterns = months + prepositions + conjugations + articles + times\\n re_string = r'\\\\b(' + '|'.join(patterns) + r')\\\\b(\\\\s|-)\\\\b([0-9]{3,4})s?\\\\b(?i)(?!\\\\sMYA)\\\\s?(BCE|BC)?'\\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\\\s?(BCE|BC)?'\\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n years += centuries\\n years = [y for y in years if y<Dump.MAX_YEAR]\\n return sorted(years + centuries)\",\n \"def _enumerate_decades(self, preprocessed_data, disjoint):\\n\\n if disjoint:\\n decades = set()\\n for decade in preprocessed_data:\\n if isinstance(decade, int):\\n decades.add(decade // 10 * 10)\\n elif isinstance(decade, tuple):\\n decades = decades | set(range(decade[0] // 10 * 10, decade[1] + 1, 10))\\n\\n return decades\",\n \"def estimate_year_data(self, years, frequency):\\n data_year = self.price.index.year.unique()\\n no_data_year = {pd.Period(year) for year in years} - {pd.Period(year) for year in data_year} # which years do we not have data for\\n\\n if len(no_data_year) > 0:\\n for yr in no_data_year:\\n source_year = pd.Period(max(data_year))\\n\\n source_data = self.price[self.price.index.year == source_year.year] # use source year data\\n new_data = Lib.apply_growth(source_data, self.energy_growth, source_year, yr, frequency)\\n self.price = pd.concat([self.price, new_data], sort=True) # add to existing\\n\\n source_data = self.p_regu[self.p_regu.index.year == source_year.year] # use source year data\\n new_data = Lib.apply_growth(source_data, self.growth, source_year, yr, frequency)\\n self.p_regu = pd.concat([self.p_regu, new_data], sort=True) # add to existing\\n\\n source_data = self.p_regd[self.p_regd.index.year == source_year.year] # use source year data\\n new_data = Lib.apply_growth(source_data, self.growth, source_year, yr, frequency)\\n self.p_regd = pd.concat([self.p_regd, new_data], sort=True) # add to existing\",\n \"def yearly_date_range(date1: datetime, date2: datetime):\\n ctr = date1\\n list = [ctr]\\n\\n while ctr < date2:\\n ctr += timedelta(days=366)\\n list.append(datetime(ctr.year, date1.month, 1))\\n ctr = datetime(ctr.year, date1.month, 1)\\n return list\",\n \"def delete_years_lover_than(self, years, w_years, m_years, threashold_year):\\n years = [year for year in years if year >= threashold_year]\\n w_years = [year for year in w_years if year >= threashold_year]\\n m_years = [year for year in m_years if year >= threashold_year]\\n return years, w_years, m_years\",\n \"def check_year_sets(job_sets, file_list, sim_start_year, sim_end_year, debug, add_jobs):\\n incomplete_job_sets = [s for s in job_sets\\n if s.status != SetStatus.COMPLETED\\n and s.status != SetStatus.RUNNING\\n and s.status != SetStatus.FAILED]\\n for job_set in incomplete_job_sets:\\n\\n start_year = job_set.set_start_year\\n end_year = job_set.set_end_year\\n\\n non_zero_data = False\\n data_ready = True\\n for i in range(start_year, end_year + 1):\\n for j in range(1, 13):\\n file_key = '{0}-{1}'.format(i, j)\\n status = file_list['ATM'][file_key]\\n\\n if status in [SetStatus.NO_DATA, SetStatus.IN_TRANSIT, SetStatus.PARTIAL_DATA]:\\n data_ready = False\\n elif status == SetStatus.DATA_READY:\\n non_zero_data = True\\n\\n if data_ready:\\n job_set.status = SetStatus.DATA_READY\\n job_set = add_jobs(job_set)\\n continue\\n if not data_ready and non_zero_data:\\n job_set.status = SetStatus.PARTIAL_DATA\\n continue\\n if not data_ready and not non_zero_data:\\n job_set.status = SetStatus.NO_DATA\\n\\n # if debug:\\n # for job_set in job_sets:\\n # start_year = job_set.set_start_year\\n # end_year = job_set.set_end_year\\n # print_message('year_set: {0}: {1}'.format(job_set.set_number, job_set.status), 'ok')\\n # for i in range(start_year, end_year + 1):\\n # for j in range(1, 13):\\n # file_key = '{0}-{1}'.format(i, j)\\n # status = file_list[file_key]\\n # print_message(' {key}: {value}'.format(key=file_key, value=status), 'ok')\",\n \"def year_range(df):\\n\\n if not isinstance(df, pd.DataFrame):\\n print(\\\"year_range was not passed a pandas DataFrame.\\\")\\n return\\n\\n df['year_start'] = df['year'].min()\\n df['year_end'] = df['year'].max()\\n df.drop('year' , axis = 1, inplace = True)\\n return df\",\n \"def load_all_data():\\r\\n\\r\\n data = dict()\\r\\n for year in ['2010', '2011', '2014', '2016']:\\r\\n\\r\\n data[year] = load_data(int(year))\\r\\n\\r\\n # Calculate the dune widths\\r\\n data[year]['Dune Width'] = data[year]['x_heel'] - data[year]['x_toe']\\r\\n data[year]['Fenced Dune Width'] = data[year]['x_fence_heel'] - data[year]['x_fence_toe']\\r\\n data[year]['Fenced Dune System Width'] = data[year]['x_heel'] - data[year]['x_fence_toe']\\r\\n\\r\\n # For now, remove all negative widths and volumes, something went wrong with them\\r\\n width_condition = data[year]['Fenced Dune Width'] <= 0\\r\\n volume_condition = data[year]['Fenced Dune Volume'] <= 0\\r\\n\\r\\n data[year]['y_fence_crest'][width_condition] = np.nan\\r\\n data[year]['Fenced Dune Width'][width_condition] = np.nan\\r\\n data[year]['Fenced Dune Volume'][width_condition] = np.nan\\r\\n\\r\\n data[year]['y_fence_crest'][volume_condition] = np.nan\\r\\n data[year]['Fenced Dune Width'][volume_condition] = np.nan\\r\\n data[year]['Fenced Dune Volume'][volume_condition] = np.nan\\r\\n\\r\\n data[year]['Fenced Dune System Width'][data[year]['Fenced Dune System Width'] <= 0] = np.nan\\r\\n\\r\\n # Remove instances where the fenced and natural dune crest are not positioned correctly\\r\\n crest_condition_1 = data[year]['x_fence_crest'] >= data[year]['x_crest']\\r\\n crest_condition_2 = data[year]['y_fence_crest'] >= data[year]['y_crest']\\r\\n\\r\\n data[year]['y_fence_crest'][crest_condition_1] = np.nan\\r\\n data[year]['Fenced Dune Width'][crest_condition_1] = np.nan\\r\\n data[year]['Fenced Dune Volume'][crest_condition_1] = np.nan\\r\\n\\r\\n data[year]['y_fence_crest'][crest_condition_2] = np.nan\\r\\n data[year]['Fenced Dune Width'][crest_condition_2] = np.nan\\r\\n data[year]['Fenced Dune Volume'][crest_condition_2] = np.nan\\r\\n\\r\\n data['Fences'] = load_fence_locations(y=0)\\r\\n\\r\\n return data\",\n \"def get_years(met_fname, nyear_spinup):\\n pre_indust = 1850\\n\\n ds = xr.open_dataset(met_fname)\\n\\n st_yr = pd.to_datetime(ds.time[0].values).year\\n\\n # PALS met files final year tag only has a single 30 min, so need to\\n # end at the previous year, which is the real file end\\n en_yr = pd.to_datetime(ds.time[-1].values).year - 1\\n\\n # length of met record\\n nrec = en_yr - st_yr + 1\\n\\n # number of times met data is recycled during transient simulation\\n nloop_transient = np.ceil((st_yr - 1 - pre_indust) / nrec) - 1\\n\\n # number of times met data is recycled with a spinup run of nyear_spinup\\n nloop_spin = np.ceil(nyear_spinup / nrec)\\n\\n st_yr_transient = st_yr - 1 - nloop_transient * nrec + 1\\n en_yr_transient = st_yr_transient + nloop_transient * nrec - 1\\n\\n en_yr_spin = st_yr_transient - 1\\n st_yr_spin = en_yr_spin - nloop_spin * nrec + 1\\n\\n return (st_yr, en_yr, st_yr_transient, en_yr_transient,\\n st_yr_spin, en_yr_spin)\",\n \"def get_first_year(data_id):\\n if data_id.startswith(\\\"global\\\"):\\n return 2011\\n if data_id.endswith(\\\"precip\\\"):\\n return 1948\\n if data_id.startswith(\\\"cfsv2\\\"):\\n return 2011\\n if data_id.startswith(\\\"nmme\\\"):\\n return 1982\\n if data_id.endswith(\\\"tmp2m\\\") or data_id.endswith(\\\"tmin\\\") or data_id.endswith(\\\"tmax\\\"):\\n return 1979\\n if \\\"sst\\\" in data_id or \\\"icec\\\" in data_id:\\n return 1981\\n if data_id.endswith(\\\"mei\\\"):\\n return 1950\\n if data_id.endswith(\\\"mjo\\\"):\\n return 1974\\n if data_id.endswith(\\\"sce\\\"):\\n return 1966\\n if \\\"wind\\\" in data_id:\\n return 1948\\n if (\\\"slp\\\" in data_id or \\\"pr_wtr.eatm\\\" in data_id or \\\"rhum.sig995\\\" in data_id or\\n \\\"pres.sfc.gauss\\\" in data_id or \\\"pevpr.sfc.gauss\\\" in data_id):\\n return 1948\\n raise ValueError(\\\"Unrecognized data_id \\\"+data_id)\",\n \"def get_school_year(data, date_col, data_path):\\n\\n # generate empty df with all dates between start and end\\n start = data[date_col].min()\\n end = data[date_col].max()\\n df = pd.date_range(start, end, freq=\\\"D\\\").to_frame(index=False, name=\\\"date\\\")\\n\\n # read external holidays csv\\n def _parser(date):\\n return pd.to_datetime(date)\\n\\n holidays = pd.read_csv(f'{data_path}',\\n parse_dates=['date_debut', 'date_fin'],\\n date_parser=_parser)\\n holidays = holidays[[\\\"annee_scolaire\\\", \\\"date_debut\\\", \\\"date_fin\\\"]]\\n holidays = holidays.drop_duplicates()\\n\\n # simulate an interval based left join using pandas\\n # perform a cross join on temp_key\\n up_bound = \\\"date_fin\\\"\\n low_bound = \\\"date_debut\\\"\\n df['temp_key'] = 1\\n holidays['temp_key'] = 1\\n crossjoindf = pd.merge(df, holidays, on=['temp_key'])\\n\\n df.drop(columns=['temp_key'], inplace=True)\\n crossjoindf.drop(columns=['temp_key'], inplace=True)\\n \\n # filter with lower_bound & upper_bound\\n conditionnal_df = crossjoindf[\\n (crossjoindf[\\\"date\\\"] >= crossjoindf[low_bound]) & (crossjoindf[\\\"date\\\"] <= crossjoindf[up_bound])]\\n\\n # merge on the main df with all cols as keys to simulate left join\\n df_col = df.columns.values.tolist()\\n conditionnal_df.set_index(df_col, inplace=True)\\n df = df.merge(conditionnal_df, left_on=df_col, right_index=True, how='left')\\n\\n df.set_index('date', inplace=True) \\n data = pd.merge(data, df['annee_scolaire'], on='date')\\n \\n return data\",\n \"def split_data(df: pd.DataFrame, cutoff_year: int) -> Tuple[pd.DataFrame, pd.DataFrame]:\\n return df[df.year >= cutoff_year], df[df.year < cutoff_year]\",\n \"def extract_years(timestamps):\\n return np.asarray([dt.year for dt in timestamps.astype(datetime)])\",\n \"def get_counts_for_year(data, year):\\r\\n \\r\\n accidents_counts = list(map(list, zip(*list((filter(lambda x: x[0] == year, data))))))[2]\\r\\n return accidents_counts\",\n \"def load_evictions_data(data_path, date_col, min_year, max_year, create_geoid = True):\\n ev_raw = (\\n pd.read_csv(\\n data_path,\\n parse_dates=[date_col],\\n infer_datetime_format=True,\\n )\\n .dropna(how=\\\"all\\\")\\n .drop_duplicates()\\n )\\n ev_raw[\\\"year\\\"] = ev_raw[date_col].dt.year\\n ev_df = ev_raw[(ev_raw.year >= min_year) & (ev_raw.year <= max_year)]\\n ev_df[\\\"year\\\"] = ev_df[\\\"year\\\"].astype(int)\\n ev_df[\\\"month\\\"] = get_month_as_str_col(ev_df, date_col)\\n \\n if create_geoid == True:\\n # Convert to strings\\n ev_df[\\\"state_code\\\"] = ev_df[\\\"state_code\\\"].astype(str).replace(r'\\\\.0', '', regex = True) \\n ev_df[\\\"county_code\\\"] = ev_df[\\\"county_code\\\"].astype(str).replace(r'\\\\.0', '', regex = True) \\n ev_df[\\\"tract_code\\\"] = ev_df[\\\"tract_code\\\"].astype(str).replace(r'\\\\.0', '', regex = True)\\n # Add zeroes as necessary\\n ev_df[\\\"state_code\\\"] = ev_df[\\\"state_code\\\"].apply(lambda x: (((2-len(x)) * \\\"0\\\") + x) if len(x) < 2 else x)\\n ev_df[\\\"county_code\\\"] = ev_df[\\\"county_code\\\"].apply(lambda x: (((3-len(x)) * \\\"0\\\") + x) if len(x) < 3 else x)\\n ev_df[\\\"tract_code\\\"] = ev_df[\\\"tract_code\\\"].apply(lambda x: (((6-len(x)) * \\\"0\\\") + x) if len(x) < 6 else x)\\n # Finally, concat everything\\n ev_df[\\\"GEOID\\\"] = (\\n ev_df[\\\"state_code\\\"] + ev_df[\\\"county_code\\\"] + ev_df[\\\"tract_code\\\"]\\n )\\n \\n return ev_df\",\n \"def generate_admission_year_points(future=False):\\n if future:\\n start_year = datetime.now().year\\n target_year = start_year + 2\\n points = [year for year in range(start_year, target_year)]\\n else:\\n start_year = 2006\\n current_year = datetime.now().year\\n points = [year for year in range(start_year, current_year)]\\n return points\",\n \"def gap_years_aggregated(mongo_client):\\n db = mongo_client[\\\"nobel\\\"]\\n\\n original_categories = sorted(set(db.prizes.distinct(\\\"category\\\", {\\\"year\\\": \\\"1901\\\"})))\\n\\n pipeline = [\\n {\\\"$match\\\": {\\\"category\\\": {\\\"$in\\\": original_categories}}},\\n {\\\"$project\\\": {\\\"category\\\": 1, \\\"year\\\": 1}},\\n\\n # Collect the set of category values for each prize year.\\n {\\\"$group\\\": {\\\"_id\\\": \\\"$year\\\", \\\"categories\\\": {\\\"$addToSet\\\": \\\"$category\\\"}}},\\n\\n # Project categories *not* awarded (i.e., that are missing this year).\\n {\\\"$project\\\": {\\\"missing\\\": {\\\"$setDifference\\\": [original_categories, \\\"$categories\\\"]}}},\\n\\n # Only include years with at least one missing category\\n {\\\"$match\\\": {\\\"missing.0\\\": {\\\"$exists\\\": True}}},\\n\\n # Sort in reverse chronological order. Note that \\\"_id\\\" is a distinct year at this stage.\\n {\\\"$sort\\\": OrderedDict([(\\\"_id\\\", -1)])},\\n ]\\n\\n for doc in db.prizes.aggregate(pipeline):\\n print(\\\"{year}: {missing}\\\".format(year=doc[\\\"_id\\\"], missing=\\\", \\\".join(sorted(doc[\\\"missing\\\"]))))\",\n \"def test_load_points_by_year_length():\\n years = ['2014','2015','2016','2017','2018']\\n for year in years:\\n df = leiap.get_points_by_year([year])\\n assert df.shape[0] > 0\",\n \"def year_dropdown_populator():\\n start_year_unique = funding_data['start_year'].unique()\\n\\n year_list = []\\n for i in start_year_unique:\\n if i == -1:\\n # print({'label': i, 'value': i})\\n # NA values has been changes to -1\\n year_list.append({'label': 'NA', 'value': -1})\\n else:\\n x = int(i)\\n # print({'label': x, 'value': i})\\n year_list.append({'label': i, 'value': i})\\n return year_list\",\n \"def years():\\r\\n # Use Pandas to perform the sql query\\r\\n results = db.session.query(Worldmapdata.year.distinct().label(\\\"year\\\"))\\r\\n year = [row.year for row in results.all()]\\r\\n # Return a list of the column names (sample names)\\r\\n return jsonify(list(year))\",\n \"def year_lookup_bounds(self, value):\\n first = '%s-01-01 00:00:00'\\n # SQL Server doesn't support microseconds\\n last = '%s-12-31 23:59:59'\\n return [first % value, last % value]\",\n \"def get_years(self):\\n\\n year = cast(extract('year', ArchivedResult.date), Integer)\\n query = self.session.query\\n query = query(distinct(year))\\n query = query.order_by(desc(year))\\n\\n return list(r[0] for r in query.all())\",\n \"def clean_data(df, start = 1995, stop = 2018):\\n country_iso3_code = pd.read_html('https://unstats.un.org/unsd/methodology/m49/')\\n country_iso3_code = country_iso3_code[0]['ISO-alpha3 code']\\n df = df.loc[df.country_iso3_code.isin(country_iso3_code)]\\n df = df.set_index(['indicator', 'country_iso3_code', 'country', 'year']).unstack(level = 0)\\n df.columns = df.columns.get_level_values(1)\\n df = df.rename(columns = {'NY.GDP.PCAP.KD.ZG': 'pc_GDP_growth',\\n 'NY.GDP.PCAP.PP.CD': 'pc_GDP_PPP'})\\n df = df.reset_index()\\n df = df.loc[(df.year >= (start - 1)) & (df.year <= stop)]\\n df = df.dropna()\\n return df\",\n \"def guess_date_interval(data):\\n interval = \\\"yearly\\\"\\n for serie in data:\\n dates = [to_date(x[0]) for x in serie]\\n years = [x.year for x in dates]\\n months = [x.month for x in dates]\\n yearmonths = [x.strftime(\\\"%Y-%m\\\") for x in dates]\\n weeks = [str(x.year) + str(x.isocalendar()[1]) for x in dates]\\n if len(years) > len(set(years)):\\n # there are years with more than one point\\n unique_months = sorted(list(set(months)))\\n if len(unique_months) == 4 \\\\\\n and unique_months[0] + 3 == unique_months[1] \\\\\\n and unique_months[1] + 3 == unique_months[2] \\\\\\n and unique_months[2] + 3 == unique_months[3]:\\n # all in all four months, and they are non-conscutive\\n interval = \\\"quarterly\\\"\\n else:\\n interval = \\\"monthly\\\"\\n if len(yearmonths) > len(set(yearmonths)):\\n interval = \\\"weekly\\\"\\n if len(weeks) > len(set(weeks)):\\n interval = \\\"daily\\\"\\n return interval\",\n \"def arrayofyears(month1,day1,year1,month2,day2,year2):\\n return (arrayofdaysmonthsyears(month1,day1,year1,month2,day2,year2))[2]\",\n \"def get_yearly_energies_prices(\\n energies,\\n years_range,\\n is_collective=False,\\n ref_year=2002,\\n scenario=\\\"normal\\\",\\n price_variation_func=calculate_price_variation,\\n):\\n energies_data = Energy.objects.filter(identifier__in=energies)\\n yearly_datum = list(\\n YearlyEnergyPrice.objects.filter(\\n energy__in=energies_data, year__range=years_range\\n ).order_by(\\\"year\\\")\\n )\\n\\n series = []\\n for energy_data in energies_data:\\n ratio = energy_data.price_multi_unit_discount if is_collective else 1\\n yearly_energy_data = [x for x in yearly_datum if x.energy_id == energy_data.pk]\\n series.append(\\n {\\n \\\"identifier\\\": energy_data.identifier,\\n \\\"priceVariation\\\": price_variation_func(\\n energy_data, years_range, ref_year, yearly_energy_data\\n ),\\n \\\"yearlyPrices\\\": [multiply(x.price, ratio) for x in yearly_energy_data],\\n }\\n )\\n\\n return series\",\n \"def getYearsOfService(person, chamber=\\\"\\\"):\\n\\tcongresses = []\\n\\n\\tif chamber and chamber not in [\\\"House\\\", \\\"Senate\\\"]:\\n\\t\\treturn []\\n\\n\\tif chamber:\\n\\t\\tif \\\"congresses_\\\" + chamber.lower() in person:\\n\\t\\t\\tcongresses = person[\\\"congresses_\\\" + chamber.lower()]\\n\\telse:\\n\\t\\tif \\\"congresses\\\" in person:\\n\\t\\t\\tcongresses = person[\\\"congresses\\\"]\\n\\n\\tif not congresses:\\n\\t\\treturn []\\n\\n\\tyearSet = [[congressToYear(congress[0], 0), congressToYear(congress[1], 1)] for congress in congresses]\\n\\n\\tif chamber and len(yearSet) and \\\"voting_dates\\\" in person and chamber in person[\\\"voting_dates\\\"] and int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\tif yearSet[-1][1] > int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\t\\tyearSet[-1][1] = int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0])\\n\\n\\treturn yearSet\",\n \"def test_found_all_years(self):\\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n self.assertEqual(ar.years, [2008,2009])\",\n \"def parse_counts(data):\\r\\n \\r\\n results = {}\\r\\n region_year = np.stack([data[0], data[4].astype('datetime64[Y]').astype(int) + 1970], axis=0)\\r\\n region_years, counts = np.unique(region_year, return_counts=True, axis=1)\\r\\n region_years_counts = list(zip(region_years[1], region_years[0], counts))\\r\\n results['years'] = np.unique(region_year[1])\\r\\n results['regions'] = np.unique(region_year[0])\\r\\n for year in results['years']:\\r\\n results[year] = np.array(get_counts_for_year(region_years_counts, year))\\r\\n return results\",\n \"def test_iter_all_years(self):\\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n self.assertEqual([ary.year for ary in ar], [2008,2009])\",\n \"def periods_stat_yearly(self, x, start_time, end_time, func = np.sum, start_record = None, end_record = None):\\n start_time, end_time = to_datetime(start_time, end_time)\\n tmin, tmax = self._extrema_time_dim(x)\\n # TODO more stringent input arg checks.\\n if start_record is None:\\n start_record = tmin\\n if end_record is None:\\n end_record = tmax\\n max_years_shift = max_shifting_years(start_record, end_record, start_time, end_time) + 1\\n td = x[self.time_dimname].values\\n start_record = td[0]\\n end_record = td[-1]\\n d, e = get_first_period(start_record, end_record, start_time, end_time) \\n cumulated = [self._apply_timeslice(x, d + relativedelta(years=year), e + relativedelta(years=year), func) for year in range(max_years_shift)]\\n y = xr.concat(cumulated, dim=self.time_dimname)\\n # maybe an optional arg for the resulting time dimension (start or end of periods)\\n y[self.time_dimname] = np.array([pd.to_datetime(e + relativedelta(years=year)) for year in range(max_years_shift)])\\n return y\",\n \"def merge_ranges():\",\n \"def bins_per_year(self):\\n # Load the vector version #\\n df = self.grouped_bins.reset_index()\\n # Add year and remove TimeStep #\\n df['year'] = self.country.timestep_to_year(df['time_step'])\\n df = df.drop('time_step', axis=1)\\n # Only if we are in the calibration scenario #\\n if self.parent.parent.scenario.short_name == 'calibration':\\n # Patch the harvest data frame to stop at the simulation year #\\n selector = df['year'] <= self.parent.parent.country.base_year\\n df = df.loc[selector].copy()\\n # Return #\\n return df\",\n \"def getXy_all_all(year):\\r\\n print 'getXy_all_all(year=%d)' % year\\r\\n\\r\\n patient_keys, patient_dict = get_patient_dict()\\r\\n keys, counts_dict = patient_keys[1:], patient_dict\\r\\n #print 'patient_dict = %d' % len(counts_dict)\\r\\n\\r\\n drug_keys, drug_dict = get_drugcount_dict(year-1)\\r\\n keys, counts_dict = common.combine_dicts(keys, counts_dict, drug_keys[1:], drug_dict, use_dict1 = True)\\r\\n #print '+drug_dict = %d' % len(counts_dict)\\r\\n\\r\\n lab_keys, lab_dict = get_labcount_dict(year-1)\\r\\n keys, counts_dict = common.combine_dicts(keys, counts_dict, lab_keys[1:], lab_dict, use_dict1 = True)\\r\\n #print '+lab_dict = %d' % len(counts_dict)\\r\\n\\r\\n if False:\\r\\n pcg_keys, pcg_dict = get_pcg_counts_dict(year-1)\\r\\n keys, counts_dict = common.combine_dicts(keys, counts_dict, pcg_keys[1:], pcg_dict)\\r\\n print '+pcg_dict = %d' % len(counts_dict) \\r\\n\\r\\n for prefix in COUNTS_PREFIXES:\\r\\n pre_keys, pre_dict = get_counts_dict(prefix, year-1)\\r\\n pre_keys = ['%s=%s' % (prefix, k) for k in pre_keys]\\r\\n keys, counts_dict = common.combine_dicts(keys, counts_dict, pre_keys[1:], pre_dict)\\r\\n #print '+%s_dict = %d' % (prefix, len(counts_dict)) \\r\\n \\r\\n X,y = getXy_for_dict(year, keys, counts_dict)\\r\\n return X,y,keys\",\n \"def update_dropdowns_years_options(df_in, aux):\\n\\n df = u.uos.b64_to_df(df_in)\\n return lay.get_options(df[c.cols.YEAR].unique().tolist())\",\n \"def get_look_a_like_conso_per_year(self):\\n return {\\n land.name: land.get_conso_per_year(\\n self.analyse_start_date,\\n self.analyse_end_date,\\n )\\n for land in self.get_look_a_like()\\n }\",\n \"def calcrange(a4lim,data):\\r\\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\\r\\n return a4range\",\n \"def get_film_locations(input_year):\\n film_set = set()\\n with open('data/locations.csv', 'r', encoding=\\\"utf-8\\\", errors='ignore') as file:\\n line = file.readline()\\n while line:\\n if line.split(',')[1] == input_year and line.split(',')[1] != 'NO DATA':\\n film_set.add(tuple([line.split(',')[0].strip(), line.split(',')[-1].strip()]))\\n line = file.readline()\\n return film_set\",\n \"def start_year(self) -> float:\\n\\n start_year = np.inf\\n for td_table in list(self.tdve.values()) + self.transfers + self.interpops:\\n if len(td_table.tvec) and np.amin(td_table.tvec) < start_year:\\n start_year = np.amin(td_table.tvec)\\n return start_year\",\n \"def _get_data_pre2007(date):\\n #the data is obtained from one file for each year.\\n\\n url = '{}/Environmental_Data_{}/'.format(BASE_URL, date.year)\\n print('Fetching online data for {} (full year)'.format(date.year))\\n\\n try:\\n year_data = request.urlopen(url).read().decode(encoding='utf_8').split('\\\\n')\\n except:\\n raise ValueError(date)\\n else:\\n year_data.pop(0)\\n\\n for line in year_data:\\n elements = line.split()\\n yield dict(Date = elements[0],\\n Time = elements[1],\\n Status = 'COMPLETE',\\n Air_Temp = elements[5],\\n Barometric_Press = elements[7],\\n Wind_Speed = elements[2])\",\n \"def retrieve_all_years(database_connection: mysql.connector.connect) -> List[int]:\\n cursor = database_connection.cursor()\\n query = (\\\"SELECT DISTINCT YEAR(s.showdate) FROM ww_shows s \\\"\\n \\\"ORDER BY YEAR(s.showdate) ASC\\\")\\n cursor.execute(query)\\n result = cursor.fetchall()\\n cursor.close()\\n\\n if not result:\\n return None\\n\\n years = []\\n for row in result:\\n years.append(row[0])\\n\\n return years\",\n \"def reduce_dataset(X, year):\\n\\n drop_list = [i for i in range(config.DB_YEAR_MIN, config.DB_YEAR_MAX + 1)]\\n drop_list.remove(year - 1)\\n red_X = X.drop(drop_list, axis=0)\\n return red_X\",\n \"def low_income_countries():\\r\\n low_countries_data = []\\r\\n years = []\\r\\n medians = []\\r\\n lst = []\\r\\n for idx in range(1960, 2016):\\r\\n years.append(idx)\\r\\n for idx in low_countries:\\r\\n low_countries_data.append(life_expectancy_graph(idx))\\r\\n y_idx = 0\\r\\n for idx in low_countries_data:\\r\\n if idx != {}:\\r\\n if (list(idx.keys())[y_idx]) == years[y_idx]:\\r\\n lst.append((list(idx.values())[y_idx]))\\r\\n lst = sorted(lst)\\r\\n medians.append(median(lst))\\r\\n return medians\",\n \"def range_(headers, data):\\n\\tcolumn_matrix=data.get_data(headers).getT() # get columns as rows, as this makes analysis much easier by just perfoming operations on column list directly\\n\\tif column_matrix==[]:\\n\\t\\tprint \\\"wrong headers, not present in data Object\\\"\\n\\t\\treturn []\\n\\tcolumn_max=column_matrix.max(1)\\n\\tcolumn_min=column_matrix.min(1)\\n\\tfinal=np.concatenate((column_min, column_max), axis=1)\\n\\t\\n\\trng=final.tolist()\\n\\treturn rng\",\n \"def generate_year_data(self, data_type: str, max_int: int = 100, n: int = 30\\r\\n ) -> List[Tuple[datetime, int]]:\\r\\n self._set_seed()\\r\\n if data_type == \\\"ones\\\":\\r\\n upper = 1\\r\\n data = [(self._random_date(), random.randint(1, upper)) for i in range(0, n)]\\r\\n elif data_type == \\\"integers\\\":\\r\\n upper = max_int\\r\\n data = [(self._random_date(), random.randint(1, upper)) for i in range(0, n)]\\r\\n elif data_type == \\\"amount\\\":\\r\\n data = [(self._random_date(),\\r\\n np.round(np.random.exponential(scale=self.exp_dist_mean), 2)\\r\\n ) for i in range(0, n)]\\r\\n else:\\r\\n raise NoProperOptionInIf\\r\\n\\r\\n return data\",\n \"def years(self):\\n password_list = []\\n year_start = int(input(\\\"Years range start at: \\\"))\\n year_stop = int(input(\\\"Years range stop at: \\\"))\\n for generated in JugglerPassGen.generate(self.word):\\n for year in range(year_start, year_stop + 1):\\n modified_record = generated + str(year)\\n password_list.append(modified_record)\\n year = year % 100\\n modified_record = generated + str('%02d' % year)\\n password_list.append(modified_record)\\n return password_list\",\n \"def year(cls, year: PositiveInteger) -> \\\"DateRange\\\":\\n return cls(Date(year, 1, 1), Date(year, 12, 31))\",\n \"def year(self):\\n return self._years\",\n \"def crawl_all_by_years(years, verbose):\\n for year in years:\\n crawl_all_by_year(year, verbose)\",\n \"def printedWords(words):\\r\\n wordsPerYear={}\\r\\n for yearData in words.values():\\r\\n for year in yearData:\\r\\n if not (year.year in wordsPerYear):\\r\\n wordsPerYear[year.year]=wordData.YearCount(year.year,year.count)\\r\\n else:\\r\\n wordsPerYear[year.year].count=wordsPerYear[year.year].count+year.count\\r\\n yearlist=[]\\r\\n year=1900\\r\\n while year<=2008:\\r\\n if (year in wordsPerYear):\\r\\n yearlist=yearlist+[wordsPerYear[year]]\\r\\n year=year+1\\r\\n else:\\r\\n year=year+1\\r\\n return yearlist\",\n \"def string_to_years(s):\\n pattern = r'\\\\d\\\\d\\\\d\\\\d'\\n r = re.compile(pattern)\\n min_year = 1960\\n max_year = datetime.now().year + 1\\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))\",\n \"def year_text_range_filter(self, queryset, name, value):\\n if value:\\n if value[0] and value[1]:\\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\\\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\\\\n .filter(**{name+'_int__range': (value[0], value[1])})\\n else:\\n if value[0]:\\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\\\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\\\\n .filter(**{name+'_int__gte': value[0]})\\n if value[1]:\\n queryset = queryset.filter(**{name+'__regex': r'^[0-9]{3,4}$'}) \\\\\\n .annotate(**{name+'_int': Cast(name, IntegerField())})\\\\\\n .filter(**{name+'_int__lte': value[1]})\\n\\n return queryset\",\n \"def find_by_year(our_data,year):\\n return [album for album in our_data if album['number'] == str(year)]\",\n \"def load_data(year, begin=0, end=None):\\r\\n\\r\\n fname = os.path.join('..', 'Data', 'Morphometrics for Bogue ' + str(year) + '.csv')\\r\\n data = pd.read_csv(fname, header=0, delimiter=',')\\r\\n\\r\\n data = data.loc[data['Profile No.'] >= begin]\\r\\n\\r\\n if end is not None:\\r\\n data = data.loc[data['Profile No.'] <= end]\\r\\n\\r\\n return data\",\n \"def FS2Years(inputFolderPath = './FormattedFilesWithoutMissingToNextYear', outputFolderPath = './FormattedFilesWithoutMissingToNextYear'):\\n\\tfileList = []\\n\\tfor root, dirs, files in os.walk(inputFolderPath): \\n\\t for afile in files:\\n\\t \\tfileList.append(afile)\\n\\n\\ttargetList = [2704,2707,2713,2716,2718,808,811,1954]\\n\\t# targetList = [1994,1997,2003,2006,2008,807,810,1953]\\n\\tyearList = [(1998,2015),(2005,2015),(2005,2015),(2005,2015),(2005,2015),(1960,2014),(1961,2014),(2002,2012)]\\n\\n\\n\\tfor i in range(len(targetList)):\\n\\t\\t# i = 0\\n\\t\\trows = []\\n\\t\\tfor year in range(yearList[i][0],yearList[i][1]+1):\\n\\t\\t\\t# print str(year) + '-' + str(targetList[i]) \\n\\t\\t\\tregex = re.compile(\\\"(\\\"+ str(year) +\\\").*\\\")\\n\\t\\t\\tfiles = [m.group(0) for l in fileList for m in [regex.search(l)] if m and len(l) == 28]\\n\\t\\t\\t\\n\\n\\t\\t\\t# load the CSV file as a numpy matrix\\n\\t\\t\\twith open(inputFolderPath+'/'+files[0],'rb') as f:\\n\\t\\t\\t reader = csv.reader(f)\\n\\t\\t\\t header = next(reader)\\n\\t\\t\\t num_cols = len(header)\\n\\t\\t\\t # print header\\n\\t\\t\\t print i\\n\\t\\t\\t target_idx = [idx for idx, item in enumerate(header) if item.startswith(str(targetList[i]).zfill(4)+'N')]\\n\\t\\t\\t regex = re.compile(\\\"....N:.*\\\")\\n\\t\\t\\t nextYearIDs = [idx for idx, item in enumerate(header) if regex.search(item)]\\n\\t\\t\\t nextYearCount = len(nextYearIDs)\\n\\t\\t\\t if len(target_idx) > 0:\\n\\t\\t\\t \\ttarget = target_idx[0]-1\\n\\t\\t\\t \\tprint ('OK',year, targetList[i], inputFolderPath+'/'+files[0])\\n\\t\\t\\t else:\\n\\t\\t\\t \\tprint (year, targetList[i], inputFolderPath+'/'+files[0])\\n\\t\\t\\t \\tbreak\\n\\t\\t\\t f.close()\\n\\t\\t\\tdataset = np.genfromtxt(inputFolderPath+'/'+files[0], delimiter=\\\",\\\", skip_header=1, autostrip=True, missing_values=np.nan, usecols=tuple(range(1,num_cols)))\\n\\t\\t\\t# print (dataset.shape)\\n\\t\\t\\t# X = np.concatenate((dataset[:,0:target],dataset[:,target+1:dataset.shape[1]]),axis=1)\\n\\t\\t\\tX = dataset[:,nextYearCount:dataset.shape[1]]\\n\\t\\t\\t# X = np.concatenate((dataset[:,0:2],dataset[:,3:dataset.shape[1]),axis=1)\\n\\t\\t\\ty = dataset[:,target]\\n\\t\\t\\t\\n\\t\\t\\timp = Imputer(missing_values='NaN', strategy='median', axis=0)\\n\\t\\t\\timputedX = imp.fit_transform(X,y)\\n\\t\\t\\timputedX = np.array([imputedX[j] for j in range(imputedX.shape[0]) if not np.isnan(y[j])])\\n\\t\\t\\tdeleteMissingY = np.array([x1 for x1 in y if not np.isnan(x1)])\\n\\n\\t\\t\\tk = 40\\n\\t\\t\\tselection = SelectKBest(f_regression, k=k)\\n\\t\\t\\timputedX_new = selection.fit_transform(imputedX, deleteMissingY)\\n\\t\\t\\t\\n\\t\\t\\tselectedFeatures = [[item, selection.scores_[idx], selection.pvalues_[idx]] for idx, item in enumerate(header[nextYearCount+1:]) if selection.get_support()[idx]]\\n\\t\\t\\tselectedFeatures.sort(key=lambda x: x[1], reverse=True)\\n\\t\\t\\t\\n\\t\\t\\trows.append([year, 'score', 'p-value'])\\n\\t\\t\\trows.extend(selectedFeatures)\\n\\t\\t\\trows.append(['', '', ''])\\n\\t\\t\\tprint 'Hey'\\n\\n\\t\\tfilename = outputFolderPath+'/'+('FeatureSelectionIndicator%d - k%d - %s.csv' % (targetList[i], k, 'f_regression'))\\n\\t\\twith open(filename,'wb') as w:\\n\\t\\t\\ta = csv.writer(w, delimiter = ',')\\n\\t\\t\\ta.writerows(rows)\\n\\t\\tw.close()\",\n \"def calculate_daily_climatology(\\n pctile,\\n windowHalfWidth,\\n lenClimYear,\\n smoothPercentile,\\n smoothPercentileWidth,\\n thresh_climYear, # empty array\\n seas_climYear, # empty array\\n clim, # empty dict\\n feb29,\\n doyClim,\\n clim_start,\\n clim_end,\\n tempClim,\\n temp,\\n):\\n # Loop over all day-of-year values, and calculate threshold and seasonal climatology across years\\n for d in range(1, lenClimYear + 1):\\n # Special case for Feb 29\\n if d == feb29:\\n continue\\n # find all indices for each day of the year +/- windowHalfWidth and from them calculate the threshold\\n tt0 = np.where(doyClim[clim_start : clim_end + 1] == d)[\\n 0\\n ] # the index for that day each year\\n # If this doy value does not exist (i.e. in 360-day calendars) then skip it\\n if len(tt0) == 0:\\n continue\\n tt = np.array([])\\n for w in range(-windowHalfWidth, windowHalfWidth + 1): # -5 : 5 default\\n tt = np.append(\\n tt, clim_start + tt0 + w\\n ) # append the daily values 5days before and 5days after\\n tt = tt[tt >= 0] # Reject indices \\\"before\\\" the first element\\n tt = tt[tt < TClim] # Reject indices \\\"after\\\" the last element\\n thresh_climYear[d - 1] = np.percentile(nonans(tempClim[tt.astype(int)]), pctile)\\n seas_climYear[d - 1] = np.mean(nonans(tempClim[tt.astype(int)]))\\n\\n # Special case for Feb 29 (LEAP YEAR)\\n thresh_climYear[feb29 - 1] = (\\n 0.5 * thresh_climYear[feb29 - 2] + 0.5 * thresh_climYear[feb29]\\n )\\n seas_climYear[feb29 - 1] = (\\n 0.5 * seas_climYear[feb29 - 2] + 0.5 * seas_climYear[feb29]\\n )\\n\\n if smoothPercentile:\\n thresh_climYear, seas_climYear = smooth_climatologies(\\n thresh_climYear, seas_climYear, smoothPercentileWidth\\n )\\n\\n # Generate threshold for full time series\\n clim[\\\"thresh\\\"] = thresh_climYear[doy.astype(int) - 1]\\n clim[\\\"seas\\\"] = seas_climYear[doy.astype(int) - 1]\\n # Save vector indicating which points in temp are missing values\\n clim[\\\"missing\\\"] = np.isnan(temp)\\n\\n return clim\",\n \"def get_all_cfb_teams_for_year_range(start_year, end_year):\\n \\n full_team_list = []\\n \\n for year in range(start_year, end_year + 1):\\n print(year)\\n \\n team_list = get_all_cfb_teams(year)\\n full_team_list += team_list\\n \\n wait_time = random.randint(3, 15)\\n time.sleep(wait_time)\\n \\n return full_team_list\",\n \"def check_date_range(data, date_column, sep_type=\\\"\\\", skip_rows=0, date_format=\\\"jd\\\"):\\n\\n if sep_type==\\\"\\\":\\n info=pd.read_csv(data, skiprows=skip_rows, header=None, delim_whitespace=True) \\n else:\\n info=pd.read_csv(data, skiprows=skip_rows, header=None, sep=sep_type)\\n\\n dates=Time(info[int(date_column)].values, format=date_format)\\n years=dates.byear\\n\\n print(\\\"Earliest year record: %f\\\" %(years.min()))\\n print(\\\"Latest year record: %f\\\" %(years.max()))\",\n \"def austral_year_daily(x, y):\\n if isinstance(x, xr.DataArray):\\n x = x.values\\n \\n jfmamj = x < 182.\\n jasond = x >= 182.\\n \\n x_jasond = []\\n y_jasond = []\\n if any(jasond):\\n x_jasond = x[jasond] - 181\\n y_jasond = y[jasond]\\n\\n x_jfmamj = []\\n y_jfmamj = []\\n if any(jfmamj):\\n x_jfmamj = x[jfmamj] + 184\\n y_jfmamj = y[jfmamj]\\n\\n xout = np.concatenate([xi for xi in [x_jasond, x_jfmamj] if len(xi)])\\n yout = np.concatenate([yi for yi in [y_jasond, y_jfmamj] if len(yi)])\\n \\n return xout, yout\",\n \"def get_value_year(year, user):\\n year_list_income = Money.objects.values_list('valor', flat=True).filter(\\n add_or_remove=True, user=user, entry_date__year=year)\\n year_income = sum(year_list_income)\\n year_list_outcome = Money.objects.values_list('valor', flat=True).filter(\\n add_or_remove=False, entry_date__year=year, user=user)\\n year_outcome = sum(year_list_outcome)\\n queryset_year = list(Money.objects.values('category', 'valor').filter(\\n add_or_remove=False, entry_date__year=year, user=user))\\n cat_list = ['Outro', 'Trabalho', 'Lazer',\\n 'Aluguel', 'Gastos Pessoais', 'Investimentos']\\n val_list = []\\n total_outro = 00.00\\n total_categoria = Decimal(total_outro)\\n for l in range(len(cat_list)):\\n tes = 0\\n categoria = cat_list[l]\\n for i in range(len(queryset_year)):\\n if queryset_year[i]['category'] == categoria:\\n tes += queryset_year[i]['valor']\\n val_list.append(tes)\\n data1 = list(zip(cat_list, val_list))\\n return year_income, year_outcome, data1\",\n \"def get_gbd_estimation_years(gbd_round_id: int) -> List[int]:\\n from db_queries import get_demographics\\n warnings.filterwarnings(\\\"default\\\", module=\\\"db_queries\\\")\\n\\n return get_demographics(gbd_constants.CONN_DEFS.EPI, gbd_round_id=gbd_round_id)['year_id']\",\n \"def get_models_between(start_year, end_year):\\n\\n results = db.session.query(Model).filter(Model.year >= start_year, Model.year < end_year).all()\\n\\n return results\",\n \"def r_style_interval(from_tuple, end_tuple, frequency):\\n from_year, from_seg = from_tuple\\n end_year, end_seg = end_tuple\\n n = (end_year - from_year + 1) * frequency\\n full_range = np.linspace(from_year, end_year + 1, num=n, endpoint=False)\\n real_range = full_range[(from_seg - 1):n - (frequency - end_seg)]\\n return real_range\",\n \"def substitute_year(self, ref_years, qa3_answer):\\n question = self\\n for match in re.finditer('[0-9]{4}', question):\\n year = match.group(0)\\n if 1899 < int(year) < 2099:\\n i = qa3_answer.index_by_chunk(year)\\n if i is None or qa3_answer.result[i].get_type() != 'refYear':\\n if year not in ref_years:\\n ref_years.append(year)\\n question = re.sub(year, '<YEAR' + string.ascii_uppercase[ref_years.index(year)] + '>', question, 1)\\n return Qa3Question(question)\",\n \"def generate_years(nyears, year=2022, month=1, day=1):\\n return np.asarray([\\n f'{year+i:04d}-{month:02d}-{day:02d}T00:00:00.000000'\\n for i in range(nyears)\\n ], dtype='datetime64[us]')\",\n \"def award_id_year(award_wikidata) -> 'award_id_year':\\n assert re.match(r\\\"^\\\\d{4}$\\\", award_wikidata['value']['year'])\\n return {'value': (award_wikidata['value']['award_id'], award_wikidata['value']['year'])}\",\n \"def test_leap_years_1899to1904_and_1999to2004():\\n\\tyears = [1899,1900,1901,1902,1903,1904,1999,2000,2001,2002,2003,2004]\\n\\tleaps = date_functions.is_leap( years )\\n\\tassert not leaps[0] # 1899 was not a leap year\\n\\tassert not leaps[1] # 1900 was not a leap year\\n\\tassert not leaps[2] # 1901 was not a leap year\\n\\tassert not leaps[3] # 1902 was not a leap year\\n\\tassert not leaps[4] # 1903 was not a leap year\\n\\tassert leaps[5] # 1904 was a leap year\\n\\tassert not leaps[6] # 1999 was not a leap year\\n\\tassert leaps[7] # 2000 was a leap year\\n\\tassert not leaps[8] # 2001 was not a leap year\\n\\tassert not leaps[9] # 2002 was not a leap year\\n\\tassert not leaps[10] # 2003 was not a leap year\\n\\tassert leaps[11] # 2004 was a leap year\",\n \"def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\\n dict, dict, dict, dict,\\n dict, dict]:\\n name_table = create_name_table(citelist)\\n unique_names = list()\\n nameset = set()\\n # total_binomial_year_cnts = {}\\n total_binomial_year_cnts = collections.Counter()\\n for c in citelist:\\n if c.name != \\\".\\\":\\n clean = clean_name(c.name)\\n if not clean.lower() in nameset:\\n nameset |= {clean.lower()}\\n unique_names.append(clean)\\n y = refdict[c.cite_key].year()\\n if y is not None:\\n total_binomial_year_cnts.update([y])\\n # if y in total_binomial_year_cnts:\\n # total_binomial_year_cnts[y] += 1\\n # else:\\n # total_binomial_year_cnts[y] = 1\\n unique_names.sort(key=lambda s: s.lower())\\n\\n # identify genera used per paper\\n genera_per_paper = {}\\n for c in citelist:\\n if c.name != \\\".\\\":\\n if c.cite_key not in genera_per_paper:\\n genera_per_paper[c.cite_key] = set()\\n genera_set = genera_per_paper[c.cite_key]\\n genera_set |= {extract_genus(clean_name(c.name))}\\n genus_cnts = {}\\n for c in genera_per_paper:\\n y = refdict[c].year()\\n if y is not None:\\n if init_data().start_year <= y <= init_data().current_year:\\n genera_set = genera_per_paper[c]\\n for genus in genera_set:\\n genus = clean_genus(genus)\\n if genus != \\\"\\\":\\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\\n init_data().current_year + 1)})[y] += 1\\n # if genus not in genus_cnts:\\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\\n # init_data().current_year + 1)}\\n # gcnts = genus_cnts[genus]\\n # gcnts[y] += 1\\n\\n binomial_usage_cnts_by_year = {}\\n binomial_location_applications = {}\\n binomial_usage_cnts = {}\\n for name in unique_names:\\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\\n if tmptotal > 0:\\n binomial_usage_cnts[name] = tmptotal\\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\\n\\n # specific_year_cnts = {}\\n specific_year_cnts = collections.Counter()\\n specific_usage_cnts_by_year = {}\\n specific_location_applications = {}\\n specific_usage_cnts = {}\\n for name in specific_names:\\n (specific_usage_cnts_by_year[name.name],\\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\\n if tmptotal > 0:\\n specific_usage_cnts[name.name] = tmptotal\\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\\n binomial_location_applications)\\n tmpkey = name.priority_source\\n if tmpkey != \\\".\\\":\\n y = refdict[tmpkey].year()\\n if y is not None:\\n specific_year_cnts.update([y])\\n # if y in specific_year_cnts:\\n # specific_year_cnts[y] += 1\\n # else:\\n # specific_year_cnts[y] = 1\\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\\n binomial_usage_cnts, specific_usage_cnts)\",\n \"def get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule):\\n\\n occ = []\\n el = []\\n dhw = []\\n pro = []\\n\\n if dhw_schedules[0].sum() != 0:\\n dhw_weekday_max = dhw_schedules[0].sum() ** -1\\n else: dhw_weekday_max = 0\\n\\n if dhw_schedules[1].sum() != 0:\\n dhw_sat_max = dhw_schedules[1].sum() ** -1\\n else: dhw_sat_max = 0\\n\\n if dhw_schedules[2].sum() != 0:\\n dhw_sun_max = dhw_schedules[2].sum() ** -1\\n else: dhw_sun_max = 0\\n\\n for date in dates:\\n month_year = month_schedule[date.month - 1]\\n hour_day = date.hour\\n dayofweek = date.dayofweek\\n if 0 <= dayofweek < 5: # weekday\\n occ.append(occ_schedules[0][hour_day] * month_year)\\n el.append(el_schedules[0][hour_day] * month_year)\\n dhw.append(dhw_schedules[0][hour_day] * month_year * dhw_weekday_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[0][hour_day] * month_year)\\n elif dayofweek is 5: # saturday\\n occ.append(occ_schedules[1][hour_day] * month_year)\\n el.append(el_schedules[1][hour_day] * month_year)\\n dhw.append(dhw_schedules[1][hour_day] * month_year * dhw_sat_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[1][hour_day] * month_year)\\n else: # sunday\\n occ.append(occ_schedules[2][hour_day] * month_year)\\n el.append(el_schedules[2][hour_day] * month_year)\\n dhw.append(dhw_schedules[2][hour_day] * month_year * dhw_sun_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[2][hour_day] * month_year)\\n\\n return occ, el, dhw, pro\",\n \"def filter_timespans(self, minTime=2.0):\\n igList = [ig.Rsc['PAIR'] for ig in self.Set if abs(float(ig.Rsc['TIME_SPAN_YEAR'])) < minTime]\\n self.Set.omit(IG=igList)\",\n \"def test_get_teams_in_year_len():\\n assert len(gtiy(2008)) == 8\\n assert len(gtiy(2009)) == 8\\n assert len(gtiy(2010)) == 8\\n assert len(gtiy(2011)) == 10\\n assert len(gtiy(2012)) == 9\\n assert len(gtiy(2013)) == 9\\n assert len(gtiy(2014)) == 8\\n assert len(gtiy(2015)) == 8\\n assert len(gtiy(2016)) == 8\\n assert len(gtiy(2017)) == 8\",\n \"def compute_leap_years(start, end):\\n # Generate the leap years between 1971 and 2020 inclusive\\n leap_years = tuple(1972 + 4*x for x in range(13))\\n\\n # Looks for the closest leap year greater than or equal to the start year\\n min_leap_year = 0\\n for leap_year in leap_years:\\n if leap_year >= start.year:\\n min_leap_year = leap_year\\n break\\n\\n # Looks for the closest leap year less than or equal to the end year\\n max_leap_year = 0\\n for leap_year in reversed(leap_years):\\n if leap_year <= end.year:\\n max_leap_year = leap_year\\n break\\n\\n # Gets the number of leap years between the start and end year\\n # Note that if the leap year in between is just the same year it will zero out, thus the +1\\n leap_days_between = ((max_leap_year - min_leap_year) // 4) + 1\\n\\n # If the start date occurs after Feb 29th of that year, we don't consider\\n if (start - datetime.date(min_leap_year, 2, 29)).days > 0:\\n leap_days_between -= 1\\n\\n # If the end date occurs before Feb 29th of that year, we don't consider\\n if (datetime.date(max_leap_year, 2, 29) - end).days > 0:\\n leap_days_between -= 1\\n\\n return leap_days_between\",\n \"def fix_years(self, row):\\n raise NotImplementedError\",\n \"def _construct_yearly_climate(self, cl, now, end):\\n if self.simulation:\\n years = range(now.year, end.year + 1)\\n if len(years)>1:\\n # the ordinals of days within the year\\n lastord = float(date(now.year, 12, 31).timetuple()[7])\\n noword = float(now.timetuple()[7])\\n firstyearweight = (lastord - noword) / lastord\\n lastord = float(date(end.year, 12, 31).timetuple()[7])\\n endord = float(end.timetuple()[7])\\n lastyearweight = endord / lastord\\n else:\\n # for steady state computation year 0 or 1 used\\n years = [0]\\n firstyearweight = 1.0\\n lastyearweight = 1.0\\n self.curr_yr_ind = 0\\n rain = 0.0\\n temp = 0.0\\n ampl = 0.0\\n addyear = True\\n if len(years)==1:\\n firstyearweight = 1.0\\n if now.year==end.year and not(end.month==12 and end.day==31):\\n addyear = False\\n maxind = len(self.md.yearly_climate) - 1\\n for ind in range(len(years)):\\n if self.curr_yr_ind > maxind:\\n self.curr_yr_ind = 0\\n cy = self.md.yearly_climate[self.curr_yr_ind]\\n if self.simulation and cy.timestep==0:\\n # timestep 0 is used only for steady state calculation\\n self.curr_yr_ind += 1\\n if self.curr_yr_ind <= maxind:\\n cy = self.md.yearly_climate[self.curr_yr_ind]\\n if ind == 0:\\n weight = firstyearweight\\n passedzero = False\\n elif ind == len(years) - 1:\\n weight = lastyearweight\\n else:\\n weight = 1.0\\n temp += weight * cy.mean_temperature\\n rain += weight * cy.annual_rainfall\\n ampl += weight * cy.variation_amplitude\\n if addyear:\\n self.curr_yr_ind += 1\\n # backs one year back, if the last weight was less than 1\\n if weight < 1.0 and addyear:\\n self.curr_yr_ind -= 1\\n if self.curr_yr_ind < 0:\\n self.curr_yr_ind = len(self.md.yearly_climate) - 1\\n cl['rain'] = rain / len(years)\\n cl['temp'] = temp / len(years)\\n cl['amplitude'] = ampl / len(years)\\n return cl\",\n \"def get_all_valid_deer_load_shapes(database_year):\\n query = \\\"\\\"\\\"\\n SELECT *\\n FROM deer_load_shapes\\n limit 1\\n \\\"\\\"\\\"\\n valid_deer_load_shapes = execute_query(database_year, query)\\n all_columns_w_utilities = list(\\n valid_deer_load_shapes.drop(\\\"hour_of_year\\\", axis=1).columns\\n )\\n # TODO (ssuffian): Reshape db so it is a query by utility column\\n return list(\\n set(\\n [\\n c.replace(\\\"PGE_\\\", \\\"\\\")\\n .replace(\\\"SDGE_\\\", \\\"\\\")\\n .replace(\\\"SCG_\\\", \\\"\\\")\\n .replace(\\\"SCE_\\\", \\\"\\\")\\n for c in all_columns_w_utilities\\n ]\\n )\\n )\",\n \"def wordsForYear(year, yearlist):\\r\\n for el in yearlist:\\r\\n if el.year==year:\\r\\n return el.count\\r\\n return 0\",\n \"def get_models_between(start_year, end_year):\\n\\n models = Model.query.filter(Model.year >= start_year,\\n Model.year < end_year).all()\\n return models\",\n \"def collect_data_by_year(year):\\n \\n # Format page request\\n url = 'http://nflcombineresults.com/nflcombinedata_expanded.php'\\n headers = {'user-agent': UserAgent().random}\\n params = {'year': year}\\n \\n soup = get_soup(url, headers=headers, params=params)\\n \\n # Check if the database contains the requested year\\n possible_set = set([])\\n possible_years = soup.find('select', attrs={'id': 'year'}).find_all('option')\\n \\n for y in possible_years:\\n possible_set.add(y.get_text())\\n \\n if str(year) not in possible_set:\\n return {} # Return empty dict if the year is incompatible\\n \\n rows = soup.find_all('tr', attrs={'class': 'tablefont'})\\n full_dict = {}\\n counter = 1\\n\\n for row in rows:\\n tds = row.find_all('td')\\n row_dict = {}\\n \\n for i, td in enumerate(tds):\\n is_hidden = td.find('div', attrs={'style': 'visibility:hidden;'})\\n \\n # Check if the player has any data for each cell\\n if is_hidden:\\n text = ''\\n else:\\n text = td.get_text()\\n \\n # Add data to a preliminary dict\\n row_dict[i] = text\\n \\n # Assign relevant keys to data\\n row_dict['year'] = row_dict.pop(0)\\n row_dict['name'] = row_dict.pop(1)\\n row_dict['school'] = row_dict.pop(2)\\n row_dict['POS'] = row_dict.pop(3)\\n row_dict['height'] = row_dict.pop(4)\\n row_dict['weight'] = row_dict.pop(5)\\n row_dict['handSize'] = row_dict.pop(6)\\n row_dict['armLength'] = row_dict.pop(7)\\n row_dict['wonderlic'] = row_dict.pop(8)\\n row_dict['forty'] = row_dict.pop(9)\\n row_dict['bench'] = row_dict.pop(10)\\n row_dict['vert'] = row_dict.pop(11)\\n row_dict['broad'] = row_dict.pop(12)\\n row_dict['shuttle'] = row_dict.pop(13)\\n row_dict['3cone'] = row_dict.pop(14)\\n row_dict['60shuttle'] = row_dict.pop(15)\\n \\n # Create unique player code\\n name = row_dict['name']\\n name_code = name.replace(\\\"'\\\", '').replace('.', '').replace(' ', '-').lower()\\n key = '{}-{}-{}'.format(name_code, counter, year)\\n counter += 1\\n \\n # Create dict of dicts containing all data\\n full_dict[key] = row_dict\\n \\n return full_dict\",\n \"def test_contains_year_true(self):\\n ary = self.ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n self.assertTrue(2008 in ary)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80802757","0.6195092","0.6097575","0.6085509","0.5956418","0.57805914","0.5775583","0.57030433","0.56905186","0.55768806","0.5571027","0.55363435","0.5514044","0.54679793","0.54667073","0.54420847","0.5440692","0.54319644","0.5420999","0.5370453","0.531315","0.53074336","0.530405","0.5300675","0.5295927","0.52754384","0.5266602","0.5253164","0.5250741","0.5214515","0.5197049","0.5192297","0.5185421","0.51811606","0.5159917","0.5154587","0.512815","0.5120999","0.51003015","0.50978154","0.5071925","0.506241","0.50570935","0.50539094","0.5034075","0.5018358","0.49909282","0.4963682","0.49595258","0.49226624","0.48952472","0.4893977","0.48859745","0.48782337","0.4873939","0.48718137","0.48568377","0.4855698","0.48485702","0.4847801","0.48370525","0.48291677","0.48237962","0.4819086","0.479688","0.47744727","0.47645843","0.47536528","0.47362357","0.471056","0.47102803","0.47081137","0.47039407","0.46930084","0.4689364","0.46888542","0.4687905","0.4680457","0.46796393","0.46697804","0.46658537","0.46617836","0.46573386","0.46478832","0.46465617","0.46437743","0.4636701","0.46353978","0.4633682","0.4631581","0.46260798","0.46223867","0.4622311","0.46221974","0.46168622","0.4615988","0.46082628","0.46046677","0.4599628","0.45972598"],"string":"[\n \"0.80802757\",\n \"0.6195092\",\n \"0.6097575\",\n \"0.6085509\",\n \"0.5956418\",\n \"0.57805914\",\n \"0.5775583\",\n \"0.57030433\",\n \"0.56905186\",\n \"0.55768806\",\n \"0.5571027\",\n \"0.55363435\",\n \"0.5514044\",\n \"0.54679793\",\n \"0.54667073\",\n \"0.54420847\",\n \"0.5440692\",\n \"0.54319644\",\n \"0.5420999\",\n \"0.5370453\",\n \"0.531315\",\n \"0.53074336\",\n \"0.530405\",\n \"0.5300675\",\n \"0.5295927\",\n \"0.52754384\",\n \"0.5266602\",\n \"0.5253164\",\n \"0.5250741\",\n \"0.5214515\",\n \"0.5197049\",\n \"0.5192297\",\n \"0.5185421\",\n \"0.51811606\",\n \"0.5159917\",\n \"0.5154587\",\n \"0.512815\",\n \"0.5120999\",\n \"0.51003015\",\n \"0.50978154\",\n \"0.5071925\",\n \"0.506241\",\n \"0.50570935\",\n \"0.50539094\",\n \"0.5034075\",\n \"0.5018358\",\n \"0.49909282\",\n \"0.4963682\",\n \"0.49595258\",\n \"0.49226624\",\n \"0.48952472\",\n \"0.4893977\",\n \"0.48859745\",\n \"0.48782337\",\n \"0.4873939\",\n \"0.48718137\",\n \"0.48568377\",\n \"0.4855698\",\n \"0.48485702\",\n \"0.4847801\",\n \"0.48370525\",\n \"0.48291677\",\n \"0.48237962\",\n \"0.4819086\",\n \"0.479688\",\n \"0.47744727\",\n \"0.47645843\",\n \"0.47536528\",\n \"0.47362357\",\n \"0.471056\",\n \"0.47102803\",\n \"0.47081137\",\n \"0.47039407\",\n \"0.46930084\",\n \"0.4689364\",\n \"0.46888542\",\n \"0.4687905\",\n \"0.4680457\",\n \"0.46796393\",\n \"0.46697804\",\n \"0.46658537\",\n \"0.46617836\",\n \"0.46573386\",\n \"0.46478832\",\n \"0.46465617\",\n \"0.46437743\",\n \"0.4636701\",\n \"0.46353978\",\n \"0.4633682\",\n \"0.4631581\",\n \"0.46260798\",\n \"0.46223867\",\n \"0.4622311\",\n \"0.46221974\",\n \"0.46168622\",\n \"0.4615988\",\n \"0.46082628\",\n \"0.46046677\",\n \"0.4599628\",\n \"0.45972598\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7213386"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":80498,"cells":{"query":{"kind":"string","value":"Extracts the year(s) and/or year range(s) embedded in the date_string, and return a set of ints (year) and/or tuples of ints (year range, start/end). date_string the string containing the dirty date"},"document":{"kind":"string","value":"def _extract_year_data(self, date_string):\n\n try:\n # first see if dateutil can parse the date string\n # simplest case, a single year\n return {parse(date_string).year}\n\n except ValueError:\n try:\n # strip alphabetical chars and spaces from the left side and try again\n alpha = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n return {parse(date_string.lstrip(alpha + ' ')).year}\n\n except ValueError:\n # find as many substrings that look like dates as possible\n matches = re.findall(re.compile(self.regexes['match']['date']), date_string)\n #logger.debug('{} date string matches found in \"{}\"'.format(len(matches), dateString))\n if len(matches) > 0:\n return {self._date_match_to_int_or_tuple(m) for m in matches}\n else:\n return set()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def string_to_years(s):\n pattern = r'\\d\\d\\d\\d'\n r = re.compile(pattern)\n min_year = 1960\n max_year = datetime.now().year + 1\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))","def _year_range(m):\n return (m.group(1), m.group(2))","def parse_year(date_string):\n if len(date_string) == 0:\n return None\n\n date_string = date_string.lower()\n for cleaner_re in CLEANER_RES:\n m = cleaner_re.match(date_string)\n if m:\n return parse_year(m.group(1))\n\n for pattern, parser in PARSERS:\n m = pattern.match(date_string)\n if m:\n return parser(m)\n\n result = use_date_util_parser(date_string)\n if result:\n return result\n else:\n # If the whitelist of patterns fails, look for four digit numbers as a last resort.\n return _find_loose_date(date_string)","def _date_match_to_int_or_tuple(self, m):\n\n years = set()\n try:\n if m[0] != '':\n # yyyy-dd-mm\n years = int(m[0][0:4])\n elif m[1] != '':\n # year-range derived from a century\n century = int(re.match(re.compile('\\\\d+'), m[1]).group(0))\n\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\n suffix = match.group(2)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = (100 * -century, 100 * -century + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n elif m[2] != '':\n # explicit year-range\n\n # FIXME: spaghetti code, but it works!\n range_of_stuff = []\n i = 0\n first_none = None\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\1>|<\\2', m[2]).split('>|<'):\n # get rid of whitespace\n y = y.strip()\n match = re.compile(self.regexes['capture']['year']).match(y)\n\n # if there is a suffix, one of these will not be None\n suffix = match.group(2) or match.group(4)\n if suffix:\n if i == 0:\n first_none = False\n\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\n else:\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n else:\n if i == 0:\n first_none = True\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n\n i += 1\n if first_none:\n if range_of_stuff[1] <= 0:\n range_of_stuff[0] = -1 * range_of_stuff[0]\n\n years = (range_of_stuff[0], range_of_stuff[1])\n elif m[3] != '':\n # extract single year\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\1', m[3]).strip()\n years = int(prep)\n elif m[4] != '':\n # year with unknown ones\n y = m[4].strip()\n match = re.compile(r'[1-9]\\d{3}').match(y)\n if match is None:\n years = int(self._resolve_unknown_ones(y))\n\n else:\n years = int(match.group(0))\n\n elif m[5] != '':\n # plain old year\n match = re.compile(self.regexes['capture']['year']).match(m[5])\n suffix = match.group(2) or match.group(4)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = -1 * int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n raise Error\n\n except ValueError as e:\n #logger.error('An error occurred while trying to match \"{}\": {}'.format(m, e))\n pass\n\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\n return years","def get_year(string): \n return int(string[11:15])","def filter_years(text):\n months = ['january', 'february', 'march', 'april', 'may', 'june',\n 'july', 'august', 'september', 'october', 'november', 'december']\n prepositions = ['around', 'after', 'at', 'as',\n 'approximately', 'before', 'between', 'by',\n 'during', 'from', 'in', 'near', 'past',\n 'since', 'until', 'within'] # removed: about, on\n conjugations = ['and']\n articles = ['the']\n times = ['early', 'mid', 'late']\n patterns = months + prepositions + conjugations + articles + times\n re_string = r'\\b(' + '|'.join(patterns) + r')\\b(\\s|-)\\b([0-9]{3,4})s?\\b(?i)(?!\\sMYA)\\s?(BCE|BC)?'\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\s?(BCE|BC)?'\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n years += centuries\n years = [y for y in years if y<Dump.MAX_YEAR]\n return sorted(years + centuries)","def find_year(self,datelimits): \t \t \n year = \"\"\n\n\t \tmatch = re.search(r\"[I1][\\dG]{3}\",self.string)\n\n\t \tif match: \t \t \t \n\t \t\tif re.search(r\"(\\d{4})\",match.group()):\n\t \t\t\tyear = match.group()\n elif re.search(r\"I\\d{3}\",match.group()):\n\t \t\t\tmatch = re.sub(r\"I(\\d{3})\",r\"1\\1\",match.group())\n\t \t\t\tyear = match\n\t \t\telif re.search(r\"(\\d[G\\d]{3})\",match.group()):\n\t \t\t\tmatch = re.sub(r\"G\",r\"6\",match.group())\n\t \t\t\tyear = match\n\n \n if year == \"\" or int(year) < datelimits[0] or int(year) > datelimits[1]:\n year = \"\"\n \n\n\t \treturn year","def _find_loose_date(date_string):\n if re.search(r'digit', date_string):\n # Might be something like \"digitized 2010\", which we want to avoid.\n return None\n # find all the (unique) four digit numbers in the date_string.\n matches = set(re.findall(r'\\b\\d{4}\\b', date_string))\n if len(matches) != 1:\n return None\n year = list(matches)[0]\n if is_valid_year(int(year)):\n LOG.debug('Parsed %s from \"%s\" as a loose date.' % (year, date_string))\n return (year, year)","def year_expand(s):\n regex = r\"^((?:19|20)\\d{2})?(\\s*-\\s*)?((?:19|20)\\d{2})?$\"\n try:\n start, dash, end = re.match(regex, ustr(s)).groups()\n start = start or 1900\n end = end or 2099\n except AttributeError:\n return 1900, 2099\n return (int(start), int(end)) if dash else (int(start), int(start))","def parse_range_set(range_string):\n # TODO: add UTs for this.\n\n # Parse a range string as specified by format_range_set() below\n # Be generous dealing with duplicate entries in the specification.\n if not range_string:\n return []\n ranges = [\n (lambda sublist: range(sublist[0], sublist[-1] + 1))\n (list(map(int, subrange.split('-')))) for subrange in range_string.split(',')]\n return list(set([y for x in ranges for y in x]))","def parse_year(txt):\n\n txt = txt.strip()\n if \"-\" in txt:\n res = re.sub('[^0-9]', '', txt)\n return [res[0:4], res[4:8]]\n else:\n return [txt, txt]","def year_parse(s):\n regex = r\"((?:19|20)\\d{2})(?:$|[-/]\\d{2}[-/]\\d{2})\"\n try:\n year = int(re.findall(regex, ustr(s))[0])\n except IndexError:\n year = None\n return year","def search_year(self,strz):\t\n\t\tyr_pattern = compile(\"(19[56789]\\d|20[01]\\d)\")\n\t\tyr = yr_pattern.search(strz)\t\t\n\t\tif yr is None:\n\t\t\treturn strz\t#not find\n\t\telse:\n\t\t\tyr= yr.group(1)\n\t\t\tself.release_year=yr\n\t\t\treturn strz.replace(yr,\"\")","def _sanitize_year(self, datestr):\n try:\n year = str(datetime.datetime.strptime(datestr, '%Y').date().year)\n except:\n try:\n year = str(datetime.datetime.strptime(datestr,\n '%Y-%m-%d').date().year)\n except:\n year = None\n return year","def dateParser(str):\n\tyear = ''\n\tfor c in str:\n\t\tif c.isspace():\n\t\t\tyear = ''\n\t\telif c.isdigit():\n\t\t\tyear = year + c\n\t\t\tif len(year) == 4:\n\t\t\t\tbreak\n\t\telse:\n\t\t\tyear = ''\n\tif len(year) < 4:\n\t\treturn None\n\treturn int(year)","def findYear(str):\n return int(re.search(\"(?<=yr=)\\d*\", str)[0])","def t_range_years(t_range):\r\n start_year = int(t_range[0].split(\"-\")[0])\r\n end_year = int(t_range[1].split(\"-\")[0])\r\n end_month = int(t_range[1].split(\"-\")[1])\r\n end_day = int(t_range[1].split(\"-\")[2])\r\n if end_month == 1 and end_day == 1:\r\n year_range_list = np.arange(start_year, end_year)\r\n else:\r\n year_range_list = np.arange(start_year, end_year + 1)\r\n return year_range_list","def analyze(filename):\n\n start = datetime.datetime.now()\n found = 0\n new_ones = []\n\n # read file into a generator\n lines_generator = (line for line in open(filename, encoding=\"ISO-8859-1\"))\n\n # read generator into a list comprehension\n lists_generator = (l.split(\",\") for l in lines_generator)\n\n for line in lists_generator:\n if 'ao' in line[6]:\n found += 1\n lrow: List[str] = list(line)\n if lrow[5] > '00/00/2012':\n new_ones.append((lrow[5], lrow[0]))\n print(f\"'ao' was found {found}, times\")\n end = datetime.datetime.now()\n year_count = {\n \"2013\": 0,\n \"2014\": 0,\n \"2015\": 0,\n \"2016\": 0,\n \"2017\": 0,\n \"2018\": 0\n }\n # create yyyy from tuple, start at char(6) and grab to end of string\n # for each yyyy, add 1 yyyy if yyyy 2013-2017\n for new in new_ones:\n if new[0][6:] == '2013':\n year_count[\"2013\"] += 1\n if new[0][6:] == '2014':\n year_count[\"2014\"] += 1\n if new[0][6:] == '2015':\n year_count[\"2015\"] += 1\n if new[0][6:] == '2016':\n year_count[\"2016\"] += 1\n if new[0][6:] == '2017':\n year_count[\"2017\"] += 1\n if new[0][6:] == '2018':\n year_count[\"2017\"] += 1\n print(year_count)\n return start, end, year_count, found","def get_date(date_str):\n date_list = date_str.split(\"-\")\n start_date, end_date = map(lambda x: x.strip(), date_list)\n return (start_date, end_date)","def split_fr_date(date_str):\n day=date_str[:2]\n month=date_str[3:5]\n try:\n #date coded on 4 digits\n year=date_str[6:]\n except:\n #date coded on 2 digits\n year=date_str[4:]\n\n return year, month, day","def test_date_by_yr(self):\n spi_search = \"find date 2002\"\n inv_search = \"year:2002\"\n self._compare_searches(inv_search, spi_search)","def parse_date(date_str, date_re):\n date_str_tidy = date_str.replace('-', '')\n date_str_tidy = date_str_tidy.replace(' ', '')\n match = re.search(date_re, date_str_tidy)\n if match:\n year = match.group(3)\n if len(year) == 2:\n year = '19' + year\n try:\n date_utc = datetime.datetime(\n int(year), int(match.group(1)), int(match.group(2)),\n 0, 0, tzinfo=datetime.timezone.utc).isoformat()\n except:\n return (date_str, True)\n else:\n return (date_utc, False)\n else:\n return (date_str, True)","def test_date_by_lt_yr(self):\n spi_search = \"find date < 2002\"\n inv_search = 'year:0->2002'\n self._compare_searches(inv_search, spi_search)","def parse_date_range(\n date_range: str,\n) -> Tuple[Optional[datetime.date], Optional[datetime.date]]:\n date_range = date_range.lstrip(\"\\\\\")\n split = date_range.split(\"-\")\n if len(split) > 2:\n raise ValueError(\n \"Invalid date range, should only specify one dash in date range string.\"\n )\n if len(split) == 1:\n date = split[0]\n return parse_date(date), parse_date(date, fill_strategy=FillStrategy.Max)\n if not split[0]:\n start = None\n else:\n start = parse_date(split[0])\n if not split[1]:\n end = None\n else:\n end = parse_date(split[1], fill_strategy=FillStrategy.Max)\n if start is not None and end is not None:\n if start > end:\n raise ValueError(\"Start date must be less than or equal to end date.\")\n return (start, end)","def _parse_date(self, date_string):\n\n date_string = date_string.split(\"-\")\n date_start_list = [int(float(x)) for x in date_string[0].split(\".\")]\n date_start = datetime(date_start_list[2],date_start_list[1],date_start_list[0])\n if len(date_string) > 1:\n date_end_list = [int(float(x)) for x in date_string[1].split(\".\")]\n date_end = datetime(date_end_list[2],date_end_list[1],date_end_list[0])\n else:\n date_end = None\n\n return {\n \"start\": date_start,\n \"end\": date_end\n }","def parse_date_str(self, date_str, date_format=DATE_FORMAT):\n try:\n return self.adjust_year(datetime.strptime(date_str, date_format).date())\n except ValueError:\n return None","def years(self, data, disjoint=True):\n\n year_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\n\n except TypeError:\n # single value\n preprocessed_year_data = self._extract_year_data(data)\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\n\n return year_set","def word2daterange(datestr):\n today = date.today()\n\n if datestr == 'today':\n return today, today\n elif datestr == 'yesterday':\n yesterday = today - timedelta(days=1)\n return yesterday, yesterday\n elif datestr == 'last-week':\n first_day = today - timedelta(days=today.weekday() + 7)\n last_day = first_day + timedelta(days=7)\n return first_day, last_day\n elif datestr == 'last-month':\n # getting the first day of previous month\n current_month = today.month\n if current_month == 1:\n month = 12\n year = today.year - 1\n else:\n month = current_month - 1\n year = today.year\n\n last_day_of_month = monthrange(year, month)[1]\n return date(year, month, 1), date(year, month, last_day_of_month)\n elif datestr == 'last-year':\n year = today.year - 1\n last_day_of_month = monthrange(year, 12)[1]\n return date(year, 1, 1), date(year, 12, last_day_of_month)\n\n raise NotImplementedError(datestr)","def year_lookup_bounds(self, value):\n first = '%s-01-01 00:00:00'\n # SQL Server doesn't support microseconds\n last = '%s-12-31 23:59:59'\n return [first % value, last % value]","def parser(date):\n\n return pd.datetime.strptime(date, '%Y')","def get_start_end_years(df: pd.DataFrame) -> Tuple[int, int]:\n return df.iloc[0].year, df.iloc[-1].year","def date_range(s):\n dates = s.split(':')\n if len(dates) != 2:\n raise ValueError(\"Date range not in format '<date1>:<date2>'\")\n result = []\n for date in dates:\n try:\n epoch_sec = time.mktime(time.strptime(date, '%Y-%m-%d'))\n except ValueError:\n raise ValueError(\"Date '{}' not in format 'YYYY-MM-DD'\".format(date))\n result.append(epoch_sec)\n return tuple(result)","def _parse_start(self, start_str, year=None):\n # Allow for overriding year where not provided\n date_re = r\"\\w+\\s+\\d{1,2}\" if year else r\"\\w+\\s+\\d{1,2},\\s+\\d{4}\"\n date_match = re.search(date_re, start_str)\n if not date_match:\n return\n date_str = date_match.group().replace(\",\", \"\")\n if year:\n date_str += \" {}\".format(year)\n\n time_match = re.search(r\"\\d{1,2}:\\d{2}\\s+[APM\\.]{2,4}\", start_str)\n # Override for defaulting 2019 meetings to 8am, otherwise default to midnight\n if not year or year == \"2019\":\n time_str = \"8:00 AM\"\n else:\n time_str = \"12:00 AM\"\n if time_match:\n time_str = time_match.group().replace(\".\", \"\").strip()\n\n return datetime.strptime(\n \"{} {}\".format(date_str, time_str), \"%B %d %Y %I:%M %p\"\n )","def split_date_string(date_string: str):\n try:\n units,_,epoch = date_string.split(None, 2)\n except ValueError:\n raise ValueError(f'Invalid format: {date_string}')\n else:\n return (units.lower(), parse(epoch))","def split_date_string(date_string: str):\n try:\n units,_,epoch = date_string.split(None, 2)\n except ValueError:\n raise ValueError(f'Invalid format: {date_string}')\n else:\n return (units.lower(), parse(epoch))","def yearly_sql_dates(begin_date, end_date, mtable, date_col,\n df=\"%d-%m-%Y\"):\n # Parse the dates as they're probably strings\n begin_date = parse_date(begin_date)\n end_date = parse_date(end_date)\n\n # Set up dummy strings\n jan1, dec31 = \"01-01-%s\", \"31-01-%s\"\n query_string = \"\"\"SELECT * FROM %s_%s WHERE %s BETWEEN '%s' AND '%s'\"\"\"\n\n if begin_date.year == end_date.year:\n fd, ld = begin_date.strftime(df), end_date.strftime(df)\n yield query_string % (mtable, begin_date.year, date_col, fd, ld)\n\n else:\n\n # Return the first string\n # From the beginning date to the 31st of December\n fd, ld = begin_date.strftime(df), dec31 % begin_date.year\n yield query_string % (mtable, begin_date.year, date_col, fd, ld)\n\n # Yield the intermediate dates if any\n # For example, if we are \"12/12/2013\" and \"02/04/2015\" we would\n # still want all of the dates in 2014 to be returned.\n years = range(begin_date.year + 1, end_date.year)\n if len(years) > 0:\n for year in years:\n fd, ld = jan1 % year, dec31 % year\n yield query_string % (mtable, year, date_col, fd, ld)\n\n # Yield the last date\n # This is from the 1st of January of that year to the ending date\n if end_date.year != begin_date.year:\n fd, ld = jan1 % end_date.year, end_date.strftime(df)\n yield query_string % (mtable, end_date.year, date_col, fd, ld)","def parse_date(line):\n match = YEAR_PAT.search(line)\n if match:\n return int(match.group())\n return 9999","def parse_date(input):\n input = input.strip()\n if input == '':\n return None, None\n\n # Parse the start\n mo = yyyymmdd_re.match(input)\n if not mo:\n mo = yyyymmdd_hyphen_re.match(input)\n if not mo:\n mo = ddmmyyyy_re.match(input)\n if not mo:\n mo = ddmmyyyy_hyphen_re.match(input)\n if mo:\n start = Date(*map(lambda x: x and int(x), (mo.group('year'), mo.group('month'), mo.group('day'))))\n else:\n return None, 'N'\n\n\n # Check if we're at the end of the input\n pos = mo.end()\n if pos == len(input):\n return DateRange(start, start), None\n\n # Check for a range specifier\n mo = range_re.match(input, pos)\n if mo:\n pos = mo.end()\n else:\n return DateRange(start, start), 'T'\n\n # Parse the end date\n mo = yyyymmdd_re.match(input, pos)\n if not mo:\n mo = yyyymmdd_hyphen_re.match(input, pos)\n if not mo:\n mo = ddmmyyyy_re.match(input, pos)\n if not mo:\n mo = ddmmyyyy_hyphen_re.match(input, pos)\n if mo:\n end = Date(*map(lambda x: x and int(x), (mo.group('year'), mo.group('month'), mo.group('day'))))\n else:\n return DateRange(start, start), 'T'\n\n pos = mo.end()\n if pos == len(input):\n return DateRange(start, end), None\n return DateRange(start, end), 'T'","def test_date_by_gt_yr(self):\n spi_search = \"find date > 1980\"\n inv_search = 'year:1980->9999'\n self._compare_searches(inv_search, spi_search)","def format_birthday2(textstring):\r\n # Remove space and tab from the left and right part of the string\r\n stripped_text = textstring.strip(\"\\n\\t\")\r\n # Split and get the date\r\n split_text = stripped_text.split(\"\\n\")[6:]\r\n split_text = [s.split(\"/\") for s in split_text]\r\n split_text2 = [s[:2] for s in split_text] \r\n split_text3 = [s[2].split(\"\\t\") for s in split_text]\r\n # Merge the date and count\r\n merge2 = [] \r\n for i in xrange(365):\r\n merge2.append(map(int, split_text2[i] + split_text3[i]))\r\n return merge2","def convert_covered(datestring):\n start_string, end_string = datestring.split(\" to \")\n start_date = convert_date_run(start_string)\n end_date = convert_date_run(end_string)\n\n return start_date, end_date","def clean_date(date_str):\n date_str = date_str.split(\"-\")\n return [int(x) for x in date_str]","def get_user_input():\n # Gets user input in M\\nD\\nYYYY format for the start date\n start_instrings = [\"Enter start month: \",\n \"Enter start day: \", \"Enter start year: \"]\n raw_start_date = tuple(input(s) for s in start_instrings)\n # Gets user input in M\\nD\\nYYYY format for the end date\n end_instrings = [\"Enter end month: \",\n \"Enter end day: \", \"Enter end year: \"]\n raw_end_date = tuple(input(s) for s in end_instrings)\n\n # Uses map to convert string input to integers and stores the values in a tuple\n start_date = tuple(map(int, raw_start_date))\n end_date = tuple(map(int, raw_end_date))\n\n # Checks if each year is within the date limit\n if not(1971 <= start_date[2] <= 2020 and 1971 <= end_date[2] <= 2020):\n raise Exception(\"Input date/s outside date limit.\")\n\n # Cyclic rotation of elements (because I really really **really** want to unpack)\n # Source: https://www.geeksforgeeks.org/python-shift-last-element-to-first-position-in-list/\n start_date, end_date = start_date[-1:] + \\\n start_date[:-1], end_date[-1:] + end_date[:-1]\n\n # As you can see unpacking makes the line smaller and more readable\n # return DateRange(datetime.date(start_date[2], start_date[0], start_date[1]), datetime.date(end_date[2], end_date[0], end_date[1]))\n return DateRange(datetime.date(*start_date), datetime.date(*end_date))","def extract_year(text):\n # type: (str) -> int\n data = re.search(r\"\\d{4}\", text)\n return int(data.group()) if data else 0","def parse_year(year):\n\n return datetime.strptime(year, '%Y')","def parse_date_str(self, date_str, date_format=None):\n if not date_format:\n date_format = self.parsing_date_format\n result = datetime.datetime.strptime(date_str, date_format).date()\n if result.year < 1900:\n # we have a typo in the house. Just use 2000 + last-two-digits\n year = (result.year % 100) + 2000\n result = result.replace(year=year)\n return result","def test_date_by_yr_mo(self):\n spi_search = \"find date 1976-04\"\n inv_search = 'year:1976-04'\n self._compare_searches(inv_search, spi_search)","def parse_range(seq: str) -> list[int]:\n seq = seq.split(\",\")\n acc = []\n for i in seq:\n m = re.match(r\" *(?P<start>\\d+) *(- *(?P<end>\\d+))? *\", i)\n\n if not m:\n continue\n\n a = [m.group(\"start\"), m.group(\"end\")]\n a = [int(x) for x in a if x]\n\n if len(a) > 1:\n a = range(int(a[0]), int(a[1] + 1))\n\n acc.append(a)\n\n return list(\n set([x for x in list(itertools.chain.from_iterable(acc)) if x])\n )","def bornDied(str):\n\n\tbornDied = []\n\n\tyear = ''\n\tfor c in str:\n\t\tif c == 's' and len(year) == 4:\n\t\t\treturn (int(year), int(year) + 10)\n\t\tif c.isspace():\n\t\t\tyear = ''\n\t\telif c.isdigit():\n\t\t\tyear = year + c\n\t\t\tif len(year) == 4:\n\t\t\t\tbornDied.append(year)\n\t\telse:\n\t\t\tyear = ''\n\tif len(bornDied) <= 1:\n\t\tprint str\n\t\treturn (None, None)\n\treturn (int(bornDied[0]), int(bornDied[1]))","def parse_date_string(date_string: str):\n # try parsing the original date string as a date\n try:\n epoch = parse(date_string)\n except ValueError:\n pass\n else:\n # return the epoch (as list)\n return (datetime_to_list(epoch), 0.0)\n # split the date string into units and epoch\n units,epoch = split_date_string(date_string)\n if units not in _to_sec.keys():\n raise ValueError(f'Invalid units: {units}')\n # return the epoch (as list) and the time unit conversion factors\n return (datetime_to_list(epoch), _to_sec[units])","def parse_date_string(date_string: str):\n # try parsing the original date string as a date\n try:\n epoch = parse(date_string)\n except ValueError:\n pass\n else:\n # return the epoch (as list)\n return (datetime_to_list(epoch), 0.0)\n # split the date string into units and epoch\n units, epoch = split_date_string(date_string)\n if units not in _to_sec.keys():\n raise ValueError(f'Invalid units: {units}')\n # return the epoch (as list) and the time unit conversion factors\n return (datetime_to_list(epoch), _to_sec[units])","def get_parsed_date(row):\n input_years = row.get('date', '').strip()\n return parse_year(input_years)","def parse_date(datetext):\n datetext = datetext.split(' ')[0]\n if datetext.count('-') > 0:\n return [int(date) for date in datetext.split('-')]\n return int(datetext),","def test_convert_date_to_year(self):\n # TODO there might be a more robust way to write this with try except statements.","def dateHandler(aDateString):\n day, month, year = ddmmyyyy_pattern.search(aDateString).groups()\n return (int(year), int(month), int(day), 0, 0, 0, 0, 0, 0)","def get_year_from_date(date_pattern=None, **kwargs):\n if kwargs and not date_pattern:\n # Use `NAMCSFieldEnum.SOURCE_FILE_ID` to calculate year\n # Example: 2011_NAMCS so year: 2011\n source_file_id = kwargs.get(NAMCSFieldEnum.SOURCE_FILE_ID.value)\n return int(source_file_id.split(\"_\")[0])\n\n date = None\n for pattern in NAMCS_DATASET_YEAR_PATTERNS:\n try:\n date = datetime.strptime(date_pattern, pattern)\n if date:\n break\n except ValueError:\n continue\n\n # Numeric format of year\n year = date.strftime(\"%Y\")\n return int(year)","def get_years():\n if request.method == 'OPTIONS':\n logging.info(\"Handle options\")\n return create_response({}, 200, '*', 'content-type, token')\n\n logging.info(\"Getting recruitment years\")\n\n role, response = handle_request_token(request)\n\n if role is None:\n logging.warning(\"Role is None!\")\n return response\n\n years = set()\n for rec in Recruitment.query.all():\n if rec.end_date.year not in years:\n years.add(rec.end_date.year)\n\n years = list(years)\n years.sort(reverse=True)\n\n return create_response(years, 200, '*')","def yearly_date_range(date1: datetime, date2: datetime):\n ctr = date1\n list = [ctr]\n\n while ctr < date2:\n ctr += timedelta(days=366)\n list.append(datetime(ctr.year, date1.month, 1))\n ctr = datetime(ctr.year, date1.month, 1)\n return list","def date_parser(dt_string):\n\n if not dt_string:\n raise WrongInput(\"Input cannot be blank\")\n if not isinstance(dt_string, str):\n raise WrongInput(\"Invalid date format\")\n\n dt_string = re.sub(r'\\b0', '', dt_string)\n date_pattern = re.compile(r'''\n \\s* # optional whitespace\n ([1-9]|[1,2][0-9]|3[0,1]) # day (1-31)\n [-/.] # separator\n ([1-9]|1[0-2]) # month (1-12)\n [-/.] # separator\n (\\d{4}) # year (YYYY)\n \\s* # optional whitespace\n ''', re.VERBOSE)\n if not re.search(date_pattern, dt_string):\n raise WrongInput(\"Invalid date format.\")\n date_obj = date_pattern.search(dt_string)\n day, month, year = date_obj.groups()\n return int(year), int(month), int(day)","def test_date_by_lt_yr_mo(self):\n spi_search = \"find date < 1978-10-21\"\n inv_search = 'year:0->1978-10-21'\n self._compare_searches(inv_search, spi_search)","def date_year(date):\n return date.year","def parse_string_datetime(date):\n date_string_parse = date.split('/')\n year = int(date_string_parse[0])\n month = int(date_string_parse[1])\n day = int(date_string_parse[2])\n return year, month, day","def get_ymd(date):\n date = date.split()[0]\n return [int(x) for x in date.split('-')]","def _parseDate(self, dateString):\n if self.isInterval:\n offset = numpy.int64(0)\n else:\n offset = numpy.int64(1)\n\n if '_' in dateString:\n ymd, hms = dateString.split('_')\n else:\n if '-' in dateString:\n ymd = dateString\n # error can result if dateString = '1990-01'\n # assume this means '1990-01-01'\n if len(ymd.split('-')) == 2:\n ymd += '-01'\n hms = '00:00:00'\n else:\n if self.isInterval:\n ymd = '0000-00-00'\n else:\n ymd = '0000-01-01'\n hms = dateString\n\n if '.' in hms:\n hms = hms.replace('.', ':')\n\n if '-' in ymd:\n (self.years, self.months, self.days) \\\n = [numpy.int64(sub) for sub in ymd.split('-')]\n self.months -= offset\n self.days -= offset\n else:\n self.days = numpy.int64(ymd) - offset\n self.years = numpy.int64(0)\n self.months = numpy.int64(0)\n\n if ':' in hms:\n (self.hours, self.minutes, self.seconds) \\\n = [numpy.int64(sub) for sub in hms.split(':')]\n else:\n self.seconds = numpy.int64(hms)\n self.minutes = numpy.int64(0)\n self.hours = numpy.int64(0)\n self._setTotalSeconds()","def get_year(parameters_dictionary):\n if \"start-year\" in parameters_dictionary.keys():\n year = int(parameters_dictionary[\"start-year\"])\n return str(year) + str(year + 1)\n elif \"end-year\" in parameters_dictionary.keys():\n year = int(parameters_dictionary[\"end-year\"])\n return str(year - 1) + str(year)\n else:\n return str(THIS_YEAR - 1) + str(THIS_YEAR)","def validate_input(date_string):\n #I decided to make sure the input was valid by checking each individual piece. I did this by splitting the input string by the dashes.\n #I checked first that the month value was between 1 and 12. I then checked depending on the month if the day value was valid.\n #I also made sure to check that the year was greater than 1000.\n #For February, I made a specific check for if it was a leap year or not. If the year inputted is not a leap year and the user entered\n #29 as the day value, it throws an error. Finally, once all values are checked and are valid, they are put into a tuple.\n splitdate = date_string.split(\"-\")\n if splitdate[0] != '' and splitdate[1] != '' and splitdate[2] != '':\n if int(splitdate[0]) >= 1 and int(splitdate[0]) <= 12:\n if int(splitdate[0]) == 1 or int(splitdate[0]) == 3 or int(splitdate[0]) == 5 or int(splitdate[0]) == 7 or int(splitdate[0]) == 8 or int(splitdate[0]) == 10 or int(splitdate[0]) == 12:\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 31:\n if int(splitdate[2]) >= 1000:\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\n return date\n elif int(splitdate[0]) == 4 or int(splitdate[0]) == 6 or int(splitdate[0]) == 9 or int(splitdate[0]) == 11:\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 30:\n if int(splitdate[2]) >= 1000:\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\n return date\n elif int(splitdate[0]) == 2:\n if int(splitdate[2]) % 4 == 0 or int(splitdate[2]) % 1000 == 0:\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 29:\n if int(splitdate[2]) >= 1000:\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\n return date\n elif int(splitdate[1]) >= 1 and int(splitdate[1]) <= 28:\n if int(splitdate[2]) >= 1000:\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\n return date\n return None","def get_year(line):\n year = line.split(')')[0][-4:]\n return year","def parse_year_movie(date, pageurl):\n\t# remove dashes if 01-05-2015 format\n\tif '-' in date:\n\t\tdate = date.strip()[1:-1]\n\t\tyear = date.split('-')[0]\n\t# remove commas if format is 01,05,2015\n\telse:\n\t\tif ',' in date:\n\t\t\tyear = date.split(',')[1].strip()\n\t\telif ' ' in date.strip():\n\t\t\tif len(date.split(' ')) < 3:\n\t\t\t\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\n\t\t\t\treturn None\n\t\t\tyear = date.split(' ')[2]\n\t\telse:\n\t\t\tyear = date\n\t# strip whitespace\n\tyear = year.strip()\n\t# make sure we received valid numerical value to cast\n\tif not year.isdigit():\n\t\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\n\t\treturn None\n\t# cast to int and return\n\treturn int(year)","def get_date_from_string(datestring):\n try:\n month,day,year = [int(date_num) for date_num in datestring.split('/')]\n except:\n Exception(f\"Date string ({datestring}) must be in MM/DD/YY format.\")\n return None\n\n # If a 2-digit year is provided, assume it's from 1900s if it's greater\n # than current year - 2000. Otherwise, assume it's from the 2000s. \n if year < 100: \n # got a 2-digit year in the datestring. Make it 4digits. \n if year > date.today().year - 2000 + 5:\n year += 1900\n else:\n year += 2000\n\n #sanity check for years\n if year < 1900 or year > 2023: \n raise Exception(f\"Year must be > 1900 and < 2023: {datestring}\")\n\n if month < 1 or month > 12:\n raise Exception(f\"Month in date is out of range: {datestring}\")\n\n if day < 1 or day > 31:\n raise Exception(f\"Month in date is out of range: {datestring}\")\n\n #sanity check for day values given a particular month\n # To do handle year 2000 (no leap year)\n if ((month in [4, 6, 9, 11] and day > 30) or\n (month == 2 and \n ((day > 28 and (year%4 != 0 or year == 2000)) or\n (day > 29 and (year%4 == 0 and year!= 2000))))):\n raise Exception(f\"Day in date is out of range: {datestring}\")\n\n return date(year, month, day)","def YEAR(date):\n return _make_datetime(date).year","def test_date_by_gt_yr_mo(self):\n spi_search = \"find date > 1978-10-21\"\n inv_search = 'year:1978-10-21->9999'\n self._compare_searches(inv_search, spi_search)","def get_year_month_day_from_date(date: str) -> tuple:\n match = re.findall(\"\\d{2,4}\", date[:len(DATE_FORMAT)])\n if len(match) != 3:\n raise ValueError(\"Invalid date input given at regex match. Got (%s)\" % date)\n year, month, day = match\n if len(year) != 4 or len(month) != 2 or len(day) != 2:\n raise ValueError(\"Invalid date input given. Got (%s)\" % date)\n return year, month, day","def parse_inception(date):\n start = 0\n while date[start] != \"(\":\n start += 1\n end = start\n while date[end] != \")\":\n end += 1\n date = date[start+1:end].encode('utf-8')\n date = date.split(\"-\")\n return {\"day\":date[2], \"year\":date[0], \"month\":date[1]}","def _enumerate_years(self, preprocessed_data, disjoint):\n pass","def getYearsOfService(person, chamber=\"\"):\n\tcongresses = []\n\n\tif chamber and chamber not in [\"House\", \"Senate\"]:\n\t\treturn []\n\n\tif chamber:\n\t\tif \"congresses_\" + chamber.lower() in person:\n\t\t\tcongresses = person[\"congresses_\" + chamber.lower()]\n\telse:\n\t\tif \"congresses\" in person:\n\t\t\tcongresses = person[\"congresses\"]\n\n\tif not congresses:\n\t\treturn []\n\n\tyearSet = [[congressToYear(congress[0], 0), congressToYear(congress[1], 1)] for congress in congresses]\n\n\tif chamber and len(yearSet) and \"voting_dates\" in person and chamber in person[\"voting_dates\"] and int(person[\"voting_dates\"][chamber][1].split(\"-\")[0]):\n\t\tif yearSet[-1][1] > int(person[\"voting_dates\"][chamber][1].split(\"-\")[0]):\n\t\t\tyearSet[-1][1] = int(person[\"voting_dates\"][chamber][1].split(\"-\")[0])\n\n\treturn yearSet","def get_year(url):\n year = re.search(\"\\d{4}\", url).group(0)\n return int(year)","def parse_academic_year(year):\n return int(year.split(\"/\")[0])","def test_evaluate_year_expression(self):\n for f, r in (\n (\"year\", 2013),\n (\"month\", 9),\n (\"day\", 1),\n (\"hour\", 10),\n (\"minute\", 56),\n (\"second\", 0)):\n value = self.evaluate_common(\"%s(datetime'2013-09-01T10:56')\" % f)\n self.assertTrue(\n value.type_code == edm.SimpleType.Int32, \"Expected Int32\")\n self.assertTrue(value.value == r)\n try:\n value = self.evaluate_common(\n \"%s(datetimeoffset'2013-09-01T10:56:12-05:00')\" % f)\n self.fail(\"datetimeoffset %s\" % f)\n except odata.EvaluationError:\n pass\n try:\n value = self.evaluate_common(\n \"%s(datetime'2013-09-01T10:56',\"\n \"datetime'2013-09-01T10:57')\" % f)\n self.fail(\"2 parameters\")\n except odata.EvaluationError:\n pass","def parseDate(date):\r\n\tyearNode = date.getElementsByTagName('year').item(0)\r\n\tmonthNode = date.getElementsByTagName('month').item(0)\r\n\tdayNode = date.getElementsByTagName('day').item(0)\r\n\r\n\tyear = yearNode.firstChild.data\r\n\tmonth = monthNode.firstChild.data\r\n\tday = dayNode.firstChild.data\r\n\r\n\treturn year, month, day","def parse_selection(selection_str: str) -> List[int]:\n indices = []\n for group in selection_str.split(','):\n if not re.match(r'^(?:-?\\d+)|(?:\\d+(?:-\\d+))$', group):\n print(\"Invalid selection\", group)\n sys.exit()\n spl = group.split('-')\n if len(spl) == 1:\n indices.append(int(spl[0]))\n elif len(spl) == 2:\n begin = int(spl[0]) if spl[0] else 0\n end = int(spl[1])\n indices.extend(range(begin, end + 1))\n return indices","def extract_years(timestamps):\n return np.asarray([dt.year for dt in timestamps.astype(datetime)])","def years_between(date1, date2):\n if date1 > date2:\n temp = date2\n date2 = date1\n date1 = temp\n try:\n date1_this_year = date(date2.year, date1.month, date1.day)\n except ValueError:\n # Encountered a leap year\n date1_this_year = date(date2.year, 3, 1)\n return date2.year - date1.year - (date1_this_year > date2)","def translate_years(val):\n if val.find(\"-\") > 0:\n tokens = re.findall(\"[0-9]+\", val)\n one = int(tokens[0])\n two = int(tokens[1])\n one = (1900 + one) if one > 50 else (2000 + one)\n two = (1900 + two) if two > 50 else (2000 + two)\n return range(one, two + 1)\n tokens = re.findall(\"[0-9]+\", val)\n return [int(f\"{'19' if int(t) > 50 else '20'}{t}\") for t in tokens]","def extract_dates(data):\n dates = []\n \n for line in data.splitlines():\n if line[6:8] == \"20\":\n dates.append(datetime.strptime(line[6:16], '%Y-%m-%d').date())\n \n return list(set(dates))\n pass","def get_yearweek(yearweekstr: str) -> tuple:\n return tuple(map(int, yearweekstr.split('-W')))","def year(self):\n\n properties_file = open(self.scenario_path + \"/conf/sandag_abm.properties\", \"r\")\n year = None\n\n for line in properties_file:\n # strip all white space from the line\n line = line.replace(\" \", \"\")\n\n # find line containing \"scenarioYear=\"\n m = re.compile(\"scenarioYear=\").match(line)\n if m:\n # take the portion of the line after the matching string\n # and return as the scenario year\n year = int(line[m.end():])\n break\n\n properties_file.close()\n\n return year","def get_year(x):\n return x[\"SALE DATE\"].year","def test_date_accept_date_minus_days_with_year_wrap(self):\n spi_search = \"find date 2011-01-01 - 1\"\n inv_search = \"year:2010-12-31\"\n self._compare_searches(inv_search, spi_search)","def _read_range(range: str) -> Tuple[str, List[Tuple[Union[int, None], Union[int, None]]]]:\n format, split_on_pairs = range.split('=', 1)\n split_on_pairs = split_on_pairs.split(',')\n pairs = []\n for pair_str in split_on_pairs:\n split_on_range = pair_str.split('-', 1)\n start = int(split_on_range[0]) if len(split_on_range[0]) > 0 else None\n stop = int(split_on_range[1]) if len(split_on_range[1]) > 0 else None\n pairs.append((start, stop))\n return format, pairs","def test_short_format_contains_year(self):\n locale = {\n 'timeformat': '%H:%M',\n 'dateformat': '%Y-%m-%d',\n 'longdateformat': '%Y-%m-%d',\n 'datetimeformat': '%Y-%m-%d %H:%M',\n 'longdatetimeformat': '%Y-%m-%d %H:%M',\n }\n assert (dt.datetime(2017, 1, 1), dt.datetime(2017, 1, 2), True) == \\\n guessrangefstr('2017-1-1 2017-1-1', locale=locale)","def year(self) -> int:\n if self.is_old_style:\n yy = int(self.split('/', 1)[1][0:2])\n else:\n yy = int(self[:2])\n if yy > 90:\n return 1900 + yy\n return 2000 + yy","def get_first_year(data_id):\n if data_id.startswith(\"global\"):\n return 2011\n if data_id.endswith(\"precip\"):\n return 1948\n if data_id.startswith(\"cfsv2\"):\n return 2011\n if data_id.startswith(\"nmme\"):\n return 1982\n if data_id.endswith(\"tmp2m\") or data_id.endswith(\"tmin\") or data_id.endswith(\"tmax\"):\n return 1979\n if \"sst\" in data_id or \"icec\" in data_id:\n return 1981\n if data_id.endswith(\"mei\"):\n return 1950\n if data_id.endswith(\"mjo\"):\n return 1974\n if data_id.endswith(\"sce\"):\n return 1966\n if \"wind\" in data_id:\n return 1948\n if (\"slp\" in data_id or \"pr_wtr.eatm\" in data_id or \"rhum.sig995\" in data_id or\n \"pres.sfc.gauss\" in data_id or \"pevpr.sfc.gauss\" in data_id):\n return 1948\n raise ValueError(\"Unrecognized data_id \"+data_id)","def month_year_range(enter_date):\n year_actual = enter_date.year\n YEARS = range(year_actual - 1, year_actual + 1)\n YEARS.reverse()\n m_list = []\n for n in YEARS:\n if year_actual == n:\n month_no = enter_date.month + 1\n else:\n month_no = 13\n months_list = range(1, month_no)\n months_list.reverse()\n for m in months_list:\n name = datetime(n, m, 1).strftime(\"%B\")\n str_year = datetime(n, m, 1).strftime(\"%Y\")\n str_month = datetime(n, m, 1).strftime(\"%m\")\n sample_str = str_year + \"-\" + str_month\n sample_name_str = name + \"-\" + str_year\n m_list.append((sample_str, sample_name_str))\n return m_list","def test_found_all_years(self):\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n self.assertEqual(ar.years, [2008,2009])","def year_range(df):\n\n if not isinstance(df, pd.DataFrame):\n print(\"year_range was not passed a pandas DataFrame.\")\n return\n\n df['year_start'] = df['year'].min()\n df['year_end'] = df['year'].max()\n df.drop('year' , axis = 1, inplace = True)\n return df","def get_years(met_fname, nyear_spinup):\n pre_indust = 1850\n\n ds = xr.open_dataset(met_fname)\n\n st_yr = pd.to_datetime(ds.time[0].values).year\n\n # PALS met files final year tag only has a single 30 min, so need to\n # end at the previous year, which is the real file end\n en_yr = pd.to_datetime(ds.time[-1].values).year - 1\n\n # length of met record\n nrec = en_yr - st_yr + 1\n\n # number of times met data is recycled during transient simulation\n nloop_transient = np.ceil((st_yr - 1 - pre_indust) / nrec) - 1\n\n # number of times met data is recycled with a spinup run of nyear_spinup\n nloop_spin = np.ceil(nyear_spinup / nrec)\n\n st_yr_transient = st_yr - 1 - nloop_transient * nrec + 1\n en_yr_transient = st_yr_transient + nloop_transient * nrec - 1\n\n en_yr_spin = st_yr_transient - 1\n st_yr_spin = en_yr_spin - nloop_spin * nrec + 1\n\n return (st_yr, en_yr, st_yr_transient, en_yr_transient,\n st_yr_spin, en_yr_spin)","def getYears():\n url = \"http://www.boxofficemojo.com/weekend/\"\n src = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(src, 'html.parser')\n year_header = soup.find_all(name = \"b\")[1]\n year_elems = year_header.find_all([\"a\", \"font\"])\n years = [int(year.get_text()) for year in year_elems]\n return years","def get_year(self, grab):\n return int(\n grab.doc.select(\n '//time[@itemprop=\"releaseDate\"]'\n ).attr('datetime')\n )","def __init__(self, start_date_str: str, end_date_str: str):\r\n start_date, end_date = create_date_from_string(start_date_str, end_date_str)\r\n if is_date_valid(start_date, end_date):\r\n self.days_range_array = create_days_range(start_date, end_date)\r\n self.months_range_array = create_months_range(self.days_range_array)\r\n else:\r\n raise Exception","def get_date_year_month(date):\n cut_date = date.split('-')\n date_month_year = cut_date[0] + '-' + cut_date[1]\n return date_month_year"],"string":"[\n \"def string_to_years(s):\\n pattern = r'\\\\d\\\\d\\\\d\\\\d'\\n r = re.compile(pattern)\\n min_year = 1960\\n max_year = datetime.now().year + 1\\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))\",\n \"def _year_range(m):\\n return (m.group(1), m.group(2))\",\n \"def parse_year(date_string):\\n if len(date_string) == 0:\\n return None\\n\\n date_string = date_string.lower()\\n for cleaner_re in CLEANER_RES:\\n m = cleaner_re.match(date_string)\\n if m:\\n return parse_year(m.group(1))\\n\\n for pattern, parser in PARSERS:\\n m = pattern.match(date_string)\\n if m:\\n return parser(m)\\n\\n result = use_date_util_parser(date_string)\\n if result:\\n return result\\n else:\\n # If the whitelist of patterns fails, look for four digit numbers as a last resort.\\n return _find_loose_date(date_string)\",\n \"def _date_match_to_int_or_tuple(self, m):\\n\\n years = set()\\n try:\\n if m[0] != '':\\n # yyyy-dd-mm\\n years = int(m[0][0:4])\\n elif m[1] != '':\\n # year-range derived from a century\\n century = int(re.match(re.compile('\\\\\\\\d+'), m[1]).group(0))\\n\\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\\n suffix = match.group(2)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = (100 * -century, 100 * -century + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n elif m[2] != '':\\n # explicit year-range\\n\\n # FIXME: spaghetti code, but it works!\\n range_of_stuff = []\\n i = 0\\n first_none = None\\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\\\1>|<\\\\2', m[2]).split('>|<'):\\n # get rid of whitespace\\n y = y.strip()\\n match = re.compile(self.regexes['capture']['year']).match(y)\\n\\n # if there is a suffix, one of these will not be None\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if i == 0:\\n first_none = False\\n\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\\n else:\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n else:\\n if i == 0:\\n first_none = True\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n\\n i += 1\\n if first_none:\\n if range_of_stuff[1] <= 0:\\n range_of_stuff[0] = -1 * range_of_stuff[0]\\n\\n years = (range_of_stuff[0], range_of_stuff[1])\\n elif m[3] != '':\\n # extract single year\\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\\\1', m[3]).strip()\\n years = int(prep)\\n elif m[4] != '':\\n # year with unknown ones\\n y = m[4].strip()\\n match = re.compile(r'[1-9]\\\\d{3}').match(y)\\n if match is None:\\n years = int(self._resolve_unknown_ones(y))\\n\\n else:\\n years = int(match.group(0))\\n\\n elif m[5] != '':\\n # plain old year\\n match = re.compile(self.regexes['capture']['year']).match(m[5])\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = -1 * int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n raise Error\\n\\n except ValueError as e:\\n #logger.error('An error occurred while trying to match \\\"{}\\\": {}'.format(m, e))\\n pass\\n\\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\\n return years\",\n \"def get_year(string): \\n return int(string[11:15])\",\n \"def filter_years(text):\\n months = ['january', 'february', 'march', 'april', 'may', 'june',\\n 'july', 'august', 'september', 'october', 'november', 'december']\\n prepositions = ['around', 'after', 'at', 'as',\\n 'approximately', 'before', 'between', 'by',\\n 'during', 'from', 'in', 'near', 'past',\\n 'since', 'until', 'within'] # removed: about, on\\n conjugations = ['and']\\n articles = ['the']\\n times = ['early', 'mid', 'late']\\n patterns = months + prepositions + conjugations + articles + times\\n re_string = r'\\\\b(' + '|'.join(patterns) + r')\\\\b(\\\\s|-)\\\\b([0-9]{3,4})s?\\\\b(?i)(?!\\\\sMYA)\\\\s?(BCE|BC)?'\\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\\\s?(BCE|BC)?'\\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n years += centuries\\n years = [y for y in years if y<Dump.MAX_YEAR]\\n return sorted(years + centuries)\",\n \"def find_year(self,datelimits): \\t \\t \\n year = \\\"\\\"\\n\\n\\t \\tmatch = re.search(r\\\"[I1][\\\\dG]{3}\\\",self.string)\\n\\n\\t \\tif match: \\t \\t \\t \\n\\t \\t\\tif re.search(r\\\"(\\\\d{4})\\\",match.group()):\\n\\t \\t\\t\\tyear = match.group()\\n elif re.search(r\\\"I\\\\d{3}\\\",match.group()):\\n\\t \\t\\t\\tmatch = re.sub(r\\\"I(\\\\d{3})\\\",r\\\"1\\\\1\\\",match.group())\\n\\t \\t\\t\\tyear = match\\n\\t \\t\\telif re.search(r\\\"(\\\\d[G\\\\d]{3})\\\",match.group()):\\n\\t \\t\\t\\tmatch = re.sub(r\\\"G\\\",r\\\"6\\\",match.group())\\n\\t \\t\\t\\tyear = match\\n\\n \\n if year == \\\"\\\" or int(year) < datelimits[0] or int(year) > datelimits[1]:\\n year = \\\"\\\"\\n \\n\\n\\t \\treturn year\",\n \"def _find_loose_date(date_string):\\n if re.search(r'digit', date_string):\\n # Might be something like \\\"digitized 2010\\\", which we want to avoid.\\n return None\\n # find all the (unique) four digit numbers in the date_string.\\n matches = set(re.findall(r'\\\\b\\\\d{4}\\\\b', date_string))\\n if len(matches) != 1:\\n return None\\n year = list(matches)[0]\\n if is_valid_year(int(year)):\\n LOG.debug('Parsed %s from \\\"%s\\\" as a loose date.' % (year, date_string))\\n return (year, year)\",\n \"def year_expand(s):\\n regex = r\\\"^((?:19|20)\\\\d{2})?(\\\\s*-\\\\s*)?((?:19|20)\\\\d{2})?$\\\"\\n try:\\n start, dash, end = re.match(regex, ustr(s)).groups()\\n start = start or 1900\\n end = end or 2099\\n except AttributeError:\\n return 1900, 2099\\n return (int(start), int(end)) if dash else (int(start), int(start))\",\n \"def parse_range_set(range_string):\\n # TODO: add UTs for this.\\n\\n # Parse a range string as specified by format_range_set() below\\n # Be generous dealing with duplicate entries in the specification.\\n if not range_string:\\n return []\\n ranges = [\\n (lambda sublist: range(sublist[0], sublist[-1] + 1))\\n (list(map(int, subrange.split('-')))) for subrange in range_string.split(',')]\\n return list(set([y for x in ranges for y in x]))\",\n \"def parse_year(txt):\\n\\n txt = txt.strip()\\n if \\\"-\\\" in txt:\\n res = re.sub('[^0-9]', '', txt)\\n return [res[0:4], res[4:8]]\\n else:\\n return [txt, txt]\",\n \"def year_parse(s):\\n regex = r\\\"((?:19|20)\\\\d{2})(?:$|[-/]\\\\d{2}[-/]\\\\d{2})\\\"\\n try:\\n year = int(re.findall(regex, ustr(s))[0])\\n except IndexError:\\n year = None\\n return year\",\n \"def search_year(self,strz):\\t\\n\\t\\tyr_pattern = compile(\\\"(19[56789]\\\\d|20[01]\\\\d)\\\")\\n\\t\\tyr = yr_pattern.search(strz)\\t\\t\\n\\t\\tif yr is None:\\n\\t\\t\\treturn strz\\t#not find\\n\\t\\telse:\\n\\t\\t\\tyr= yr.group(1)\\n\\t\\t\\tself.release_year=yr\\n\\t\\t\\treturn strz.replace(yr,\\\"\\\")\",\n \"def _sanitize_year(self, datestr):\\n try:\\n year = str(datetime.datetime.strptime(datestr, '%Y').date().year)\\n except:\\n try:\\n year = str(datetime.datetime.strptime(datestr,\\n '%Y-%m-%d').date().year)\\n except:\\n year = None\\n return year\",\n \"def dateParser(str):\\n\\tyear = ''\\n\\tfor c in str:\\n\\t\\tif c.isspace():\\n\\t\\t\\tyear = ''\\n\\t\\telif c.isdigit():\\n\\t\\t\\tyear = year + c\\n\\t\\t\\tif len(year) == 4:\\n\\t\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tyear = ''\\n\\tif len(year) < 4:\\n\\t\\treturn None\\n\\treturn int(year)\",\n \"def findYear(str):\\n return int(re.search(\\\"(?<=yr=)\\\\d*\\\", str)[0])\",\n \"def t_range_years(t_range):\\r\\n start_year = int(t_range[0].split(\\\"-\\\")[0])\\r\\n end_year = int(t_range[1].split(\\\"-\\\")[0])\\r\\n end_month = int(t_range[1].split(\\\"-\\\")[1])\\r\\n end_day = int(t_range[1].split(\\\"-\\\")[2])\\r\\n if end_month == 1 and end_day == 1:\\r\\n year_range_list = np.arange(start_year, end_year)\\r\\n else:\\r\\n year_range_list = np.arange(start_year, end_year + 1)\\r\\n return year_range_list\",\n \"def analyze(filename):\\n\\n start = datetime.datetime.now()\\n found = 0\\n new_ones = []\\n\\n # read file into a generator\\n lines_generator = (line for line in open(filename, encoding=\\\"ISO-8859-1\\\"))\\n\\n # read generator into a list comprehension\\n lists_generator = (l.split(\\\",\\\") for l in lines_generator)\\n\\n for line in lists_generator:\\n if 'ao' in line[6]:\\n found += 1\\n lrow: List[str] = list(line)\\n if lrow[5] > '00/00/2012':\\n new_ones.append((lrow[5], lrow[0]))\\n print(f\\\"'ao' was found {found}, times\\\")\\n end = datetime.datetime.now()\\n year_count = {\\n \\\"2013\\\": 0,\\n \\\"2014\\\": 0,\\n \\\"2015\\\": 0,\\n \\\"2016\\\": 0,\\n \\\"2017\\\": 0,\\n \\\"2018\\\": 0\\n }\\n # create yyyy from tuple, start at char(6) and grab to end of string\\n # for each yyyy, add 1 yyyy if yyyy 2013-2017\\n for new in new_ones:\\n if new[0][6:] == '2013':\\n year_count[\\\"2013\\\"] += 1\\n if new[0][6:] == '2014':\\n year_count[\\\"2014\\\"] += 1\\n if new[0][6:] == '2015':\\n year_count[\\\"2015\\\"] += 1\\n if new[0][6:] == '2016':\\n year_count[\\\"2016\\\"] += 1\\n if new[0][6:] == '2017':\\n year_count[\\\"2017\\\"] += 1\\n if new[0][6:] == '2018':\\n year_count[\\\"2017\\\"] += 1\\n print(year_count)\\n return start, end, year_count, found\",\n \"def get_date(date_str):\\n date_list = date_str.split(\\\"-\\\")\\n start_date, end_date = map(lambda x: x.strip(), date_list)\\n return (start_date, end_date)\",\n \"def split_fr_date(date_str):\\n day=date_str[:2]\\n month=date_str[3:5]\\n try:\\n #date coded on 4 digits\\n year=date_str[6:]\\n except:\\n #date coded on 2 digits\\n year=date_str[4:]\\n\\n return year, month, day\",\n \"def test_date_by_yr(self):\\n spi_search = \\\"find date 2002\\\"\\n inv_search = \\\"year:2002\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def parse_date(date_str, date_re):\\n date_str_tidy = date_str.replace('-', '')\\n date_str_tidy = date_str_tidy.replace(' ', '')\\n match = re.search(date_re, date_str_tidy)\\n if match:\\n year = match.group(3)\\n if len(year) == 2:\\n year = '19' + year\\n try:\\n date_utc = datetime.datetime(\\n int(year), int(match.group(1)), int(match.group(2)),\\n 0, 0, tzinfo=datetime.timezone.utc).isoformat()\\n except:\\n return (date_str, True)\\n else:\\n return (date_utc, False)\\n else:\\n return (date_str, True)\",\n \"def test_date_by_lt_yr(self):\\n spi_search = \\\"find date < 2002\\\"\\n inv_search = 'year:0->2002'\\n self._compare_searches(inv_search, spi_search)\",\n \"def parse_date_range(\\n date_range: str,\\n) -> Tuple[Optional[datetime.date], Optional[datetime.date]]:\\n date_range = date_range.lstrip(\\\"\\\\\\\\\\\")\\n split = date_range.split(\\\"-\\\")\\n if len(split) > 2:\\n raise ValueError(\\n \\\"Invalid date range, should only specify one dash in date range string.\\\"\\n )\\n if len(split) == 1:\\n date = split[0]\\n return parse_date(date), parse_date(date, fill_strategy=FillStrategy.Max)\\n if not split[0]:\\n start = None\\n else:\\n start = parse_date(split[0])\\n if not split[1]:\\n end = None\\n else:\\n end = parse_date(split[1], fill_strategy=FillStrategy.Max)\\n if start is not None and end is not None:\\n if start > end:\\n raise ValueError(\\\"Start date must be less than or equal to end date.\\\")\\n return (start, end)\",\n \"def _parse_date(self, date_string):\\n\\n date_string = date_string.split(\\\"-\\\")\\n date_start_list = [int(float(x)) for x in date_string[0].split(\\\".\\\")]\\n date_start = datetime(date_start_list[2],date_start_list[1],date_start_list[0])\\n if len(date_string) > 1:\\n date_end_list = [int(float(x)) for x in date_string[1].split(\\\".\\\")]\\n date_end = datetime(date_end_list[2],date_end_list[1],date_end_list[0])\\n else:\\n date_end = None\\n\\n return {\\n \\\"start\\\": date_start,\\n \\\"end\\\": date_end\\n }\",\n \"def parse_date_str(self, date_str, date_format=DATE_FORMAT):\\n try:\\n return self.adjust_year(datetime.strptime(date_str, date_format).date())\\n except ValueError:\\n return None\",\n \"def years(self, data, disjoint=True):\\n\\n year_set = set()\\n try:\\n # multi valued\\n assert isinstance(data, set)\\n for datum in data:\\n preprocessed_year_data = self._extract_year_data(datum)\\n year_set = year_set | self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n except TypeError:\\n # single value\\n preprocessed_year_data = self._extract_year_data(data)\\n year_set = self._enumerate_years(preprocessed_year_data, disjoint)\\n\\n return year_set\",\n \"def word2daterange(datestr):\\n today = date.today()\\n\\n if datestr == 'today':\\n return today, today\\n elif datestr == 'yesterday':\\n yesterday = today - timedelta(days=1)\\n return yesterday, yesterday\\n elif datestr == 'last-week':\\n first_day = today - timedelta(days=today.weekday() + 7)\\n last_day = first_day + timedelta(days=7)\\n return first_day, last_day\\n elif datestr == 'last-month':\\n # getting the first day of previous month\\n current_month = today.month\\n if current_month == 1:\\n month = 12\\n year = today.year - 1\\n else:\\n month = current_month - 1\\n year = today.year\\n\\n last_day_of_month = monthrange(year, month)[1]\\n return date(year, month, 1), date(year, month, last_day_of_month)\\n elif datestr == 'last-year':\\n year = today.year - 1\\n last_day_of_month = monthrange(year, 12)[1]\\n return date(year, 1, 1), date(year, 12, last_day_of_month)\\n\\n raise NotImplementedError(datestr)\",\n \"def year_lookup_bounds(self, value):\\n first = '%s-01-01 00:00:00'\\n # SQL Server doesn't support microseconds\\n last = '%s-12-31 23:59:59'\\n return [first % value, last % value]\",\n \"def parser(date):\\n\\n return pd.datetime.strptime(date, '%Y')\",\n \"def get_start_end_years(df: pd.DataFrame) -> Tuple[int, int]:\\n return df.iloc[0].year, df.iloc[-1].year\",\n \"def date_range(s):\\n dates = s.split(':')\\n if len(dates) != 2:\\n raise ValueError(\\\"Date range not in format '<date1>:<date2>'\\\")\\n result = []\\n for date in dates:\\n try:\\n epoch_sec = time.mktime(time.strptime(date, '%Y-%m-%d'))\\n except ValueError:\\n raise ValueError(\\\"Date '{}' not in format 'YYYY-MM-DD'\\\".format(date))\\n result.append(epoch_sec)\\n return tuple(result)\",\n \"def _parse_start(self, start_str, year=None):\\n # Allow for overriding year where not provided\\n date_re = r\\\"\\\\w+\\\\s+\\\\d{1,2}\\\" if year else r\\\"\\\\w+\\\\s+\\\\d{1,2},\\\\s+\\\\d{4}\\\"\\n date_match = re.search(date_re, start_str)\\n if not date_match:\\n return\\n date_str = date_match.group().replace(\\\",\\\", \\\"\\\")\\n if year:\\n date_str += \\\" {}\\\".format(year)\\n\\n time_match = re.search(r\\\"\\\\d{1,2}:\\\\d{2}\\\\s+[APM\\\\.]{2,4}\\\", start_str)\\n # Override for defaulting 2019 meetings to 8am, otherwise default to midnight\\n if not year or year == \\\"2019\\\":\\n time_str = \\\"8:00 AM\\\"\\n else:\\n time_str = \\\"12:00 AM\\\"\\n if time_match:\\n time_str = time_match.group().replace(\\\".\\\", \\\"\\\").strip()\\n\\n return datetime.strptime(\\n \\\"{} {}\\\".format(date_str, time_str), \\\"%B %d %Y %I:%M %p\\\"\\n )\",\n \"def split_date_string(date_string: str):\\n try:\\n units,_,epoch = date_string.split(None, 2)\\n except ValueError:\\n raise ValueError(f'Invalid format: {date_string}')\\n else:\\n return (units.lower(), parse(epoch))\",\n \"def split_date_string(date_string: str):\\n try:\\n units,_,epoch = date_string.split(None, 2)\\n except ValueError:\\n raise ValueError(f'Invalid format: {date_string}')\\n else:\\n return (units.lower(), parse(epoch))\",\n \"def yearly_sql_dates(begin_date, end_date, mtable, date_col,\\n df=\\\"%d-%m-%Y\\\"):\\n # Parse the dates as they're probably strings\\n begin_date = parse_date(begin_date)\\n end_date = parse_date(end_date)\\n\\n # Set up dummy strings\\n jan1, dec31 = \\\"01-01-%s\\\", \\\"31-01-%s\\\"\\n query_string = \\\"\\\"\\\"SELECT * FROM %s_%s WHERE %s BETWEEN '%s' AND '%s'\\\"\\\"\\\"\\n\\n if begin_date.year == end_date.year:\\n fd, ld = begin_date.strftime(df), end_date.strftime(df)\\n yield query_string % (mtable, begin_date.year, date_col, fd, ld)\\n\\n else:\\n\\n # Return the first string\\n # From the beginning date to the 31st of December\\n fd, ld = begin_date.strftime(df), dec31 % begin_date.year\\n yield query_string % (mtable, begin_date.year, date_col, fd, ld)\\n\\n # Yield the intermediate dates if any\\n # For example, if we are \\\"12/12/2013\\\" and \\\"02/04/2015\\\" we would\\n # still want all of the dates in 2014 to be returned.\\n years = range(begin_date.year + 1, end_date.year)\\n if len(years) > 0:\\n for year in years:\\n fd, ld = jan1 % year, dec31 % year\\n yield query_string % (mtable, year, date_col, fd, ld)\\n\\n # Yield the last date\\n # This is from the 1st of January of that year to the ending date\\n if end_date.year != begin_date.year:\\n fd, ld = jan1 % end_date.year, end_date.strftime(df)\\n yield query_string % (mtable, end_date.year, date_col, fd, ld)\",\n \"def parse_date(line):\\n match = YEAR_PAT.search(line)\\n if match:\\n return int(match.group())\\n return 9999\",\n \"def parse_date(input):\\n input = input.strip()\\n if input == '':\\n return None, None\\n\\n # Parse the start\\n mo = yyyymmdd_re.match(input)\\n if not mo:\\n mo = yyyymmdd_hyphen_re.match(input)\\n if not mo:\\n mo = ddmmyyyy_re.match(input)\\n if not mo:\\n mo = ddmmyyyy_hyphen_re.match(input)\\n if mo:\\n start = Date(*map(lambda x: x and int(x), (mo.group('year'), mo.group('month'), mo.group('day'))))\\n else:\\n return None, 'N'\\n\\n\\n # Check if we're at the end of the input\\n pos = mo.end()\\n if pos == len(input):\\n return DateRange(start, start), None\\n\\n # Check for a range specifier\\n mo = range_re.match(input, pos)\\n if mo:\\n pos = mo.end()\\n else:\\n return DateRange(start, start), 'T'\\n\\n # Parse the end date\\n mo = yyyymmdd_re.match(input, pos)\\n if not mo:\\n mo = yyyymmdd_hyphen_re.match(input, pos)\\n if not mo:\\n mo = ddmmyyyy_re.match(input, pos)\\n if not mo:\\n mo = ddmmyyyy_hyphen_re.match(input, pos)\\n if mo:\\n end = Date(*map(lambda x: x and int(x), (mo.group('year'), mo.group('month'), mo.group('day'))))\\n else:\\n return DateRange(start, start), 'T'\\n\\n pos = mo.end()\\n if pos == len(input):\\n return DateRange(start, end), None\\n return DateRange(start, end), 'T'\",\n \"def test_date_by_gt_yr(self):\\n spi_search = \\\"find date > 1980\\\"\\n inv_search = 'year:1980->9999'\\n self._compare_searches(inv_search, spi_search)\",\n \"def format_birthday2(textstring):\\r\\n # Remove space and tab from the left and right part of the string\\r\\n stripped_text = textstring.strip(\\\"\\\\n\\\\t\\\")\\r\\n # Split and get the date\\r\\n split_text = stripped_text.split(\\\"\\\\n\\\")[6:]\\r\\n split_text = [s.split(\\\"/\\\") for s in split_text]\\r\\n split_text2 = [s[:2] for s in split_text] \\r\\n split_text3 = [s[2].split(\\\"\\\\t\\\") for s in split_text]\\r\\n # Merge the date and count\\r\\n merge2 = [] \\r\\n for i in xrange(365):\\r\\n merge2.append(map(int, split_text2[i] + split_text3[i]))\\r\\n return merge2\",\n \"def convert_covered(datestring):\\n start_string, end_string = datestring.split(\\\" to \\\")\\n start_date = convert_date_run(start_string)\\n end_date = convert_date_run(end_string)\\n\\n return start_date, end_date\",\n \"def clean_date(date_str):\\n date_str = date_str.split(\\\"-\\\")\\n return [int(x) for x in date_str]\",\n \"def get_user_input():\\n # Gets user input in M\\\\nD\\\\nYYYY format for the start date\\n start_instrings = [\\\"Enter start month: \\\",\\n \\\"Enter start day: \\\", \\\"Enter start year: \\\"]\\n raw_start_date = tuple(input(s) for s in start_instrings)\\n # Gets user input in M\\\\nD\\\\nYYYY format for the end date\\n end_instrings = [\\\"Enter end month: \\\",\\n \\\"Enter end day: \\\", \\\"Enter end year: \\\"]\\n raw_end_date = tuple(input(s) for s in end_instrings)\\n\\n # Uses map to convert string input to integers and stores the values in a tuple\\n start_date = tuple(map(int, raw_start_date))\\n end_date = tuple(map(int, raw_end_date))\\n\\n # Checks if each year is within the date limit\\n if not(1971 <= start_date[2] <= 2020 and 1971 <= end_date[2] <= 2020):\\n raise Exception(\\\"Input date/s outside date limit.\\\")\\n\\n # Cyclic rotation of elements (because I really really **really** want to unpack)\\n # Source: https://www.geeksforgeeks.org/python-shift-last-element-to-first-position-in-list/\\n start_date, end_date = start_date[-1:] + \\\\\\n start_date[:-1], end_date[-1:] + end_date[:-1]\\n\\n # As you can see unpacking makes the line smaller and more readable\\n # return DateRange(datetime.date(start_date[2], start_date[0], start_date[1]), datetime.date(end_date[2], end_date[0], end_date[1]))\\n return DateRange(datetime.date(*start_date), datetime.date(*end_date))\",\n \"def extract_year(text):\\n # type: (str) -> int\\n data = re.search(r\\\"\\\\d{4}\\\", text)\\n return int(data.group()) if data else 0\",\n \"def parse_year(year):\\n\\n return datetime.strptime(year, '%Y')\",\n \"def parse_date_str(self, date_str, date_format=None):\\n if not date_format:\\n date_format = self.parsing_date_format\\n result = datetime.datetime.strptime(date_str, date_format).date()\\n if result.year < 1900:\\n # we have a typo in the house. Just use 2000 + last-two-digits\\n year = (result.year % 100) + 2000\\n result = result.replace(year=year)\\n return result\",\n \"def test_date_by_yr_mo(self):\\n spi_search = \\\"find date 1976-04\\\"\\n inv_search = 'year:1976-04'\\n self._compare_searches(inv_search, spi_search)\",\n \"def parse_range(seq: str) -> list[int]:\\n seq = seq.split(\\\",\\\")\\n acc = []\\n for i in seq:\\n m = re.match(r\\\" *(?P<start>\\\\d+) *(- *(?P<end>\\\\d+))? *\\\", i)\\n\\n if not m:\\n continue\\n\\n a = [m.group(\\\"start\\\"), m.group(\\\"end\\\")]\\n a = [int(x) for x in a if x]\\n\\n if len(a) > 1:\\n a = range(int(a[0]), int(a[1] + 1))\\n\\n acc.append(a)\\n\\n return list(\\n set([x for x in list(itertools.chain.from_iterable(acc)) if x])\\n )\",\n \"def bornDied(str):\\n\\n\\tbornDied = []\\n\\n\\tyear = ''\\n\\tfor c in str:\\n\\t\\tif c == 's' and len(year) == 4:\\n\\t\\t\\treturn (int(year), int(year) + 10)\\n\\t\\tif c.isspace():\\n\\t\\t\\tyear = ''\\n\\t\\telif c.isdigit():\\n\\t\\t\\tyear = year + c\\n\\t\\t\\tif len(year) == 4:\\n\\t\\t\\t\\tbornDied.append(year)\\n\\t\\telse:\\n\\t\\t\\tyear = ''\\n\\tif len(bornDied) <= 1:\\n\\t\\tprint str\\n\\t\\treturn (None, None)\\n\\treturn (int(bornDied[0]), int(bornDied[1]))\",\n \"def parse_date_string(date_string: str):\\n # try parsing the original date string as a date\\n try:\\n epoch = parse(date_string)\\n except ValueError:\\n pass\\n else:\\n # return the epoch (as list)\\n return (datetime_to_list(epoch), 0.0)\\n # split the date string into units and epoch\\n units,epoch = split_date_string(date_string)\\n if units not in _to_sec.keys():\\n raise ValueError(f'Invalid units: {units}')\\n # return the epoch (as list) and the time unit conversion factors\\n return (datetime_to_list(epoch), _to_sec[units])\",\n \"def parse_date_string(date_string: str):\\n # try parsing the original date string as a date\\n try:\\n epoch = parse(date_string)\\n except ValueError:\\n pass\\n else:\\n # return the epoch (as list)\\n return (datetime_to_list(epoch), 0.0)\\n # split the date string into units and epoch\\n units, epoch = split_date_string(date_string)\\n if units not in _to_sec.keys():\\n raise ValueError(f'Invalid units: {units}')\\n # return the epoch (as list) and the time unit conversion factors\\n return (datetime_to_list(epoch), _to_sec[units])\",\n \"def get_parsed_date(row):\\n input_years = row.get('date', '').strip()\\n return parse_year(input_years)\",\n \"def parse_date(datetext):\\n datetext = datetext.split(' ')[0]\\n if datetext.count('-') > 0:\\n return [int(date) for date in datetext.split('-')]\\n return int(datetext),\",\n \"def test_convert_date_to_year(self):\\n # TODO there might be a more robust way to write this with try except statements.\",\n \"def dateHandler(aDateString):\\n day, month, year = ddmmyyyy_pattern.search(aDateString).groups()\\n return (int(year), int(month), int(day), 0, 0, 0, 0, 0, 0)\",\n \"def get_year_from_date(date_pattern=None, **kwargs):\\n if kwargs and not date_pattern:\\n # Use `NAMCSFieldEnum.SOURCE_FILE_ID` to calculate year\\n # Example: 2011_NAMCS so year: 2011\\n source_file_id = kwargs.get(NAMCSFieldEnum.SOURCE_FILE_ID.value)\\n return int(source_file_id.split(\\\"_\\\")[0])\\n\\n date = None\\n for pattern in NAMCS_DATASET_YEAR_PATTERNS:\\n try:\\n date = datetime.strptime(date_pattern, pattern)\\n if date:\\n break\\n except ValueError:\\n continue\\n\\n # Numeric format of year\\n year = date.strftime(\\\"%Y\\\")\\n return int(year)\",\n \"def get_years():\\n if request.method == 'OPTIONS':\\n logging.info(\\\"Handle options\\\")\\n return create_response({}, 200, '*', 'content-type, token')\\n\\n logging.info(\\\"Getting recruitment years\\\")\\n\\n role, response = handle_request_token(request)\\n\\n if role is None:\\n logging.warning(\\\"Role is None!\\\")\\n return response\\n\\n years = set()\\n for rec in Recruitment.query.all():\\n if rec.end_date.year not in years:\\n years.add(rec.end_date.year)\\n\\n years = list(years)\\n years.sort(reverse=True)\\n\\n return create_response(years, 200, '*')\",\n \"def yearly_date_range(date1: datetime, date2: datetime):\\n ctr = date1\\n list = [ctr]\\n\\n while ctr < date2:\\n ctr += timedelta(days=366)\\n list.append(datetime(ctr.year, date1.month, 1))\\n ctr = datetime(ctr.year, date1.month, 1)\\n return list\",\n \"def date_parser(dt_string):\\n\\n if not dt_string:\\n raise WrongInput(\\\"Input cannot be blank\\\")\\n if not isinstance(dt_string, str):\\n raise WrongInput(\\\"Invalid date format\\\")\\n\\n dt_string = re.sub(r'\\\\b0', '', dt_string)\\n date_pattern = re.compile(r'''\\n \\\\s* # optional whitespace\\n ([1-9]|[1,2][0-9]|3[0,1]) # day (1-31)\\n [-/.] # separator\\n ([1-9]|1[0-2]) # month (1-12)\\n [-/.] # separator\\n (\\\\d{4}) # year (YYYY)\\n \\\\s* # optional whitespace\\n ''', re.VERBOSE)\\n if not re.search(date_pattern, dt_string):\\n raise WrongInput(\\\"Invalid date format.\\\")\\n date_obj = date_pattern.search(dt_string)\\n day, month, year = date_obj.groups()\\n return int(year), int(month), int(day)\",\n \"def test_date_by_lt_yr_mo(self):\\n spi_search = \\\"find date < 1978-10-21\\\"\\n inv_search = 'year:0->1978-10-21'\\n self._compare_searches(inv_search, spi_search)\",\n \"def date_year(date):\\n return date.year\",\n \"def parse_string_datetime(date):\\n date_string_parse = date.split('/')\\n year = int(date_string_parse[0])\\n month = int(date_string_parse[1])\\n day = int(date_string_parse[2])\\n return year, month, day\",\n \"def get_ymd(date):\\n date = date.split()[0]\\n return [int(x) for x in date.split('-')]\",\n \"def _parseDate(self, dateString):\\n if self.isInterval:\\n offset = numpy.int64(0)\\n else:\\n offset = numpy.int64(1)\\n\\n if '_' in dateString:\\n ymd, hms = dateString.split('_')\\n else:\\n if '-' in dateString:\\n ymd = dateString\\n # error can result if dateString = '1990-01'\\n # assume this means '1990-01-01'\\n if len(ymd.split('-')) == 2:\\n ymd += '-01'\\n hms = '00:00:00'\\n else:\\n if self.isInterval:\\n ymd = '0000-00-00'\\n else:\\n ymd = '0000-01-01'\\n hms = dateString\\n\\n if '.' in hms:\\n hms = hms.replace('.', ':')\\n\\n if '-' in ymd:\\n (self.years, self.months, self.days) \\\\\\n = [numpy.int64(sub) for sub in ymd.split('-')]\\n self.months -= offset\\n self.days -= offset\\n else:\\n self.days = numpy.int64(ymd) - offset\\n self.years = numpy.int64(0)\\n self.months = numpy.int64(0)\\n\\n if ':' in hms:\\n (self.hours, self.minutes, self.seconds) \\\\\\n = [numpy.int64(sub) for sub in hms.split(':')]\\n else:\\n self.seconds = numpy.int64(hms)\\n self.minutes = numpy.int64(0)\\n self.hours = numpy.int64(0)\\n self._setTotalSeconds()\",\n \"def get_year(parameters_dictionary):\\n if \\\"start-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"start-year\\\"])\\n return str(year) + str(year + 1)\\n elif \\\"end-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"end-year\\\"])\\n return str(year - 1) + str(year)\\n else:\\n return str(THIS_YEAR - 1) + str(THIS_YEAR)\",\n \"def validate_input(date_string):\\n #I decided to make sure the input was valid by checking each individual piece. I did this by splitting the input string by the dashes.\\n #I checked first that the month value was between 1 and 12. I then checked depending on the month if the day value was valid.\\n #I also made sure to check that the year was greater than 1000.\\n #For February, I made a specific check for if it was a leap year or not. If the year inputted is not a leap year and the user entered\\n #29 as the day value, it throws an error. Finally, once all values are checked and are valid, they are put into a tuple.\\n splitdate = date_string.split(\\\"-\\\")\\n if splitdate[0] != '' and splitdate[1] != '' and splitdate[2] != '':\\n if int(splitdate[0]) >= 1 and int(splitdate[0]) <= 12:\\n if int(splitdate[0]) == 1 or int(splitdate[0]) == 3 or int(splitdate[0]) == 5 or int(splitdate[0]) == 7 or int(splitdate[0]) == 8 or int(splitdate[0]) == 10 or int(splitdate[0]) == 12:\\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 31:\\n if int(splitdate[2]) >= 1000:\\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\\n return date\\n elif int(splitdate[0]) == 4 or int(splitdate[0]) == 6 or int(splitdate[0]) == 9 or int(splitdate[0]) == 11:\\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 30:\\n if int(splitdate[2]) >= 1000:\\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\\n return date\\n elif int(splitdate[0]) == 2:\\n if int(splitdate[2]) % 4 == 0 or int(splitdate[2]) % 1000 == 0:\\n if int(splitdate[1]) >= 1 and int(splitdate[1]) <= 29:\\n if int(splitdate[2]) >= 1000:\\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\\n return date\\n elif int(splitdate[1]) >= 1 and int(splitdate[1]) <= 28:\\n if int(splitdate[2]) >= 1000:\\n date = (int(splitdate[0]), int(splitdate[1]), int(splitdate[2]))\\n return date\\n return None\",\n \"def get_year(line):\\n year = line.split(')')[0][-4:]\\n return year\",\n \"def parse_year_movie(date, pageurl):\\n\\t# remove dashes if 01-05-2015 format\\n\\tif '-' in date:\\n\\t\\tdate = date.strip()[1:-1]\\n\\t\\tyear = date.split('-')[0]\\n\\t# remove commas if format is 01,05,2015\\n\\telse:\\n\\t\\tif ',' in date:\\n\\t\\t\\tyear = date.split(',')[1].strip()\\n\\t\\telif ' ' in date.strip():\\n\\t\\t\\tif len(date.split(' ')) < 3:\\n\\t\\t\\t\\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\\n\\t\\t\\t\\treturn None\\n\\t\\t\\tyear = date.split(' ')[2]\\n\\t\\telse:\\n\\t\\t\\tyear = date\\n\\t# strip whitespace\\n\\tyear = year.strip()\\n\\t# make sure we received valid numerical value to cast\\n\\tif not year.isdigit():\\n\\t\\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\\n\\t\\treturn None\\n\\t# cast to int and return\\n\\treturn int(year)\",\n \"def get_date_from_string(datestring):\\n try:\\n month,day,year = [int(date_num) for date_num in datestring.split('/')]\\n except:\\n Exception(f\\\"Date string ({datestring}) must be in MM/DD/YY format.\\\")\\n return None\\n\\n # If a 2-digit year is provided, assume it's from 1900s if it's greater\\n # than current year - 2000. Otherwise, assume it's from the 2000s. \\n if year < 100: \\n # got a 2-digit year in the datestring. Make it 4digits. \\n if year > date.today().year - 2000 + 5:\\n year += 1900\\n else:\\n year += 2000\\n\\n #sanity check for years\\n if year < 1900 or year > 2023: \\n raise Exception(f\\\"Year must be > 1900 and < 2023: {datestring}\\\")\\n\\n if month < 1 or month > 12:\\n raise Exception(f\\\"Month in date is out of range: {datestring}\\\")\\n\\n if day < 1 or day > 31:\\n raise Exception(f\\\"Month in date is out of range: {datestring}\\\")\\n\\n #sanity check for day values given a particular month\\n # To do handle year 2000 (no leap year)\\n if ((month in [4, 6, 9, 11] and day > 30) or\\n (month == 2 and \\n ((day > 28 and (year%4 != 0 or year == 2000)) or\\n (day > 29 and (year%4 == 0 and year!= 2000))))):\\n raise Exception(f\\\"Day in date is out of range: {datestring}\\\")\\n\\n return date(year, month, day)\",\n \"def YEAR(date):\\n return _make_datetime(date).year\",\n \"def test_date_by_gt_yr_mo(self):\\n spi_search = \\\"find date > 1978-10-21\\\"\\n inv_search = 'year:1978-10-21->9999'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_year_month_day_from_date(date: str) -> tuple:\\n match = re.findall(\\\"\\\\d{2,4}\\\", date[:len(DATE_FORMAT)])\\n if len(match) != 3:\\n raise ValueError(\\\"Invalid date input given at regex match. Got (%s)\\\" % date)\\n year, month, day = match\\n if len(year) != 4 or len(month) != 2 or len(day) != 2:\\n raise ValueError(\\\"Invalid date input given. Got (%s)\\\" % date)\\n return year, month, day\",\n \"def parse_inception(date):\\n start = 0\\n while date[start] != \\\"(\\\":\\n start += 1\\n end = start\\n while date[end] != \\\")\\\":\\n end += 1\\n date = date[start+1:end].encode('utf-8')\\n date = date.split(\\\"-\\\")\\n return {\\\"day\\\":date[2], \\\"year\\\":date[0], \\\"month\\\":date[1]}\",\n \"def _enumerate_years(self, preprocessed_data, disjoint):\\n pass\",\n \"def getYearsOfService(person, chamber=\\\"\\\"):\\n\\tcongresses = []\\n\\n\\tif chamber and chamber not in [\\\"House\\\", \\\"Senate\\\"]:\\n\\t\\treturn []\\n\\n\\tif chamber:\\n\\t\\tif \\\"congresses_\\\" + chamber.lower() in person:\\n\\t\\t\\tcongresses = person[\\\"congresses_\\\" + chamber.lower()]\\n\\telse:\\n\\t\\tif \\\"congresses\\\" in person:\\n\\t\\t\\tcongresses = person[\\\"congresses\\\"]\\n\\n\\tif not congresses:\\n\\t\\treturn []\\n\\n\\tyearSet = [[congressToYear(congress[0], 0), congressToYear(congress[1], 1)] for congress in congresses]\\n\\n\\tif chamber and len(yearSet) and \\\"voting_dates\\\" in person and chamber in person[\\\"voting_dates\\\"] and int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\tif yearSet[-1][1] > int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0]):\\n\\t\\t\\tyearSet[-1][1] = int(person[\\\"voting_dates\\\"][chamber][1].split(\\\"-\\\")[0])\\n\\n\\treturn yearSet\",\n \"def get_year(url):\\n year = re.search(\\\"\\\\d{4}\\\", url).group(0)\\n return int(year)\",\n \"def parse_academic_year(year):\\n return int(year.split(\\\"/\\\")[0])\",\n \"def test_evaluate_year_expression(self):\\n for f, r in (\\n (\\\"year\\\", 2013),\\n (\\\"month\\\", 9),\\n (\\\"day\\\", 1),\\n (\\\"hour\\\", 10),\\n (\\\"minute\\\", 56),\\n (\\\"second\\\", 0)):\\n value = self.evaluate_common(\\\"%s(datetime'2013-09-01T10:56')\\\" % f)\\n self.assertTrue(\\n value.type_code == edm.SimpleType.Int32, \\\"Expected Int32\\\")\\n self.assertTrue(value.value == r)\\n try:\\n value = self.evaluate_common(\\n \\\"%s(datetimeoffset'2013-09-01T10:56:12-05:00')\\\" % f)\\n self.fail(\\\"datetimeoffset %s\\\" % f)\\n except odata.EvaluationError:\\n pass\\n try:\\n value = self.evaluate_common(\\n \\\"%s(datetime'2013-09-01T10:56',\\\"\\n \\\"datetime'2013-09-01T10:57')\\\" % f)\\n self.fail(\\\"2 parameters\\\")\\n except odata.EvaluationError:\\n pass\",\n \"def parseDate(date):\\r\\n\\tyearNode = date.getElementsByTagName('year').item(0)\\r\\n\\tmonthNode = date.getElementsByTagName('month').item(0)\\r\\n\\tdayNode = date.getElementsByTagName('day').item(0)\\r\\n\\r\\n\\tyear = yearNode.firstChild.data\\r\\n\\tmonth = monthNode.firstChild.data\\r\\n\\tday = dayNode.firstChild.data\\r\\n\\r\\n\\treturn year, month, day\",\n \"def parse_selection(selection_str: str) -> List[int]:\\n indices = []\\n for group in selection_str.split(','):\\n if not re.match(r'^(?:-?\\\\d+)|(?:\\\\d+(?:-\\\\d+))$', group):\\n print(\\\"Invalid selection\\\", group)\\n sys.exit()\\n spl = group.split('-')\\n if len(spl) == 1:\\n indices.append(int(spl[0]))\\n elif len(spl) == 2:\\n begin = int(spl[0]) if spl[0] else 0\\n end = int(spl[1])\\n indices.extend(range(begin, end + 1))\\n return indices\",\n \"def extract_years(timestamps):\\n return np.asarray([dt.year for dt in timestamps.astype(datetime)])\",\n \"def years_between(date1, date2):\\n if date1 > date2:\\n temp = date2\\n date2 = date1\\n date1 = temp\\n try:\\n date1_this_year = date(date2.year, date1.month, date1.day)\\n except ValueError:\\n # Encountered a leap year\\n date1_this_year = date(date2.year, 3, 1)\\n return date2.year - date1.year - (date1_this_year > date2)\",\n \"def translate_years(val):\\n if val.find(\\\"-\\\") > 0:\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n one = int(tokens[0])\\n two = int(tokens[1])\\n one = (1900 + one) if one > 50 else (2000 + one)\\n two = (1900 + two) if two > 50 else (2000 + two)\\n return range(one, two + 1)\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n return [int(f\\\"{'19' if int(t) > 50 else '20'}{t}\\\") for t in tokens]\",\n \"def extract_dates(data):\\n dates = []\\n \\n for line in data.splitlines():\\n if line[6:8] == \\\"20\\\":\\n dates.append(datetime.strptime(line[6:16], '%Y-%m-%d').date())\\n \\n return list(set(dates))\\n pass\",\n \"def get_yearweek(yearweekstr: str) -> tuple:\\n return tuple(map(int, yearweekstr.split('-W')))\",\n \"def year(self):\\n\\n properties_file = open(self.scenario_path + \\\"/conf/sandag_abm.properties\\\", \\\"r\\\")\\n year = None\\n\\n for line in properties_file:\\n # strip all white space from the line\\n line = line.replace(\\\" \\\", \\\"\\\")\\n\\n # find line containing \\\"scenarioYear=\\\"\\n m = re.compile(\\\"scenarioYear=\\\").match(line)\\n if m:\\n # take the portion of the line after the matching string\\n # and return as the scenario year\\n year = int(line[m.end():])\\n break\\n\\n properties_file.close()\\n\\n return year\",\n \"def get_year(x):\\n return x[\\\"SALE DATE\\\"].year\",\n \"def test_date_accept_date_minus_days_with_year_wrap(self):\\n spi_search = \\\"find date 2011-01-01 - 1\\\"\\n inv_search = \\\"year:2010-12-31\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def _read_range(range: str) -> Tuple[str, List[Tuple[Union[int, None], Union[int, None]]]]:\\n format, split_on_pairs = range.split('=', 1)\\n split_on_pairs = split_on_pairs.split(',')\\n pairs = []\\n for pair_str in split_on_pairs:\\n split_on_range = pair_str.split('-', 1)\\n start = int(split_on_range[0]) if len(split_on_range[0]) > 0 else None\\n stop = int(split_on_range[1]) if len(split_on_range[1]) > 0 else None\\n pairs.append((start, stop))\\n return format, pairs\",\n \"def test_short_format_contains_year(self):\\n locale = {\\n 'timeformat': '%H:%M',\\n 'dateformat': '%Y-%m-%d',\\n 'longdateformat': '%Y-%m-%d',\\n 'datetimeformat': '%Y-%m-%d %H:%M',\\n 'longdatetimeformat': '%Y-%m-%d %H:%M',\\n }\\n assert (dt.datetime(2017, 1, 1), dt.datetime(2017, 1, 2), True) == \\\\\\n guessrangefstr('2017-1-1 2017-1-1', locale=locale)\",\n \"def year(self) -> int:\\n if self.is_old_style:\\n yy = int(self.split('/', 1)[1][0:2])\\n else:\\n yy = int(self[:2])\\n if yy > 90:\\n return 1900 + yy\\n return 2000 + yy\",\n \"def get_first_year(data_id):\\n if data_id.startswith(\\\"global\\\"):\\n return 2011\\n if data_id.endswith(\\\"precip\\\"):\\n return 1948\\n if data_id.startswith(\\\"cfsv2\\\"):\\n return 2011\\n if data_id.startswith(\\\"nmme\\\"):\\n return 1982\\n if data_id.endswith(\\\"tmp2m\\\") or data_id.endswith(\\\"tmin\\\") or data_id.endswith(\\\"tmax\\\"):\\n return 1979\\n if \\\"sst\\\" in data_id or \\\"icec\\\" in data_id:\\n return 1981\\n if data_id.endswith(\\\"mei\\\"):\\n return 1950\\n if data_id.endswith(\\\"mjo\\\"):\\n return 1974\\n if data_id.endswith(\\\"sce\\\"):\\n return 1966\\n if \\\"wind\\\" in data_id:\\n return 1948\\n if (\\\"slp\\\" in data_id or \\\"pr_wtr.eatm\\\" in data_id or \\\"rhum.sig995\\\" in data_id or\\n \\\"pres.sfc.gauss\\\" in data_id or \\\"pevpr.sfc.gauss\\\" in data_id):\\n return 1948\\n raise ValueError(\\\"Unrecognized data_id \\\"+data_id)\",\n \"def month_year_range(enter_date):\\n year_actual = enter_date.year\\n YEARS = range(year_actual - 1, year_actual + 1)\\n YEARS.reverse()\\n m_list = []\\n for n in YEARS:\\n if year_actual == n:\\n month_no = enter_date.month + 1\\n else:\\n month_no = 13\\n months_list = range(1, month_no)\\n months_list.reverse()\\n for m in months_list:\\n name = datetime(n, m, 1).strftime(\\\"%B\\\")\\n str_year = datetime(n, m, 1).strftime(\\\"%Y\\\")\\n str_month = datetime(n, m, 1).strftime(\\\"%m\\\")\\n sample_str = str_year + \\\"-\\\" + str_month\\n sample_name_str = name + \\\"-\\\" + str_year\\n m_list.append((sample_str, sample_name_str))\\n return m_list\",\n \"def test_found_all_years(self):\\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n self.assertEqual(ar.years, [2008,2009])\",\n \"def year_range(df):\\n\\n if not isinstance(df, pd.DataFrame):\\n print(\\\"year_range was not passed a pandas DataFrame.\\\")\\n return\\n\\n df['year_start'] = df['year'].min()\\n df['year_end'] = df['year'].max()\\n df.drop('year' , axis = 1, inplace = True)\\n return df\",\n \"def get_years(met_fname, nyear_spinup):\\n pre_indust = 1850\\n\\n ds = xr.open_dataset(met_fname)\\n\\n st_yr = pd.to_datetime(ds.time[0].values).year\\n\\n # PALS met files final year tag only has a single 30 min, so need to\\n # end at the previous year, which is the real file end\\n en_yr = pd.to_datetime(ds.time[-1].values).year - 1\\n\\n # length of met record\\n nrec = en_yr - st_yr + 1\\n\\n # number of times met data is recycled during transient simulation\\n nloop_transient = np.ceil((st_yr - 1 - pre_indust) / nrec) - 1\\n\\n # number of times met data is recycled with a spinup run of nyear_spinup\\n nloop_spin = np.ceil(nyear_spinup / nrec)\\n\\n st_yr_transient = st_yr - 1 - nloop_transient * nrec + 1\\n en_yr_transient = st_yr_transient + nloop_transient * nrec - 1\\n\\n en_yr_spin = st_yr_transient - 1\\n st_yr_spin = en_yr_spin - nloop_spin * nrec + 1\\n\\n return (st_yr, en_yr, st_yr_transient, en_yr_transient,\\n st_yr_spin, en_yr_spin)\",\n \"def getYears():\\n url = \\\"http://www.boxofficemojo.com/weekend/\\\"\\n src = urllib.request.urlopen(url).read()\\n soup = BeautifulSoup(src, 'html.parser')\\n year_header = soup.find_all(name = \\\"b\\\")[1]\\n year_elems = year_header.find_all([\\\"a\\\", \\\"font\\\"])\\n years = [int(year.get_text()) for year in year_elems]\\n return years\",\n \"def get_year(self, grab):\\n return int(\\n grab.doc.select(\\n '//time[@itemprop=\\\"releaseDate\\\"]'\\n ).attr('datetime')\\n )\",\n \"def __init__(self, start_date_str: str, end_date_str: str):\\r\\n start_date, end_date = create_date_from_string(start_date_str, end_date_str)\\r\\n if is_date_valid(start_date, end_date):\\r\\n self.days_range_array = create_days_range(start_date, end_date)\\r\\n self.months_range_array = create_months_range(self.days_range_array)\\r\\n else:\\r\\n raise Exception\",\n \"def get_date_year_month(date):\\n cut_date = date.split('-')\\n date_month_year = cut_date[0] + '-' + cut_date[1]\\n return date_month_year\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71493185","0.7043156","0.68464625","0.67900616","0.63941294","0.62800896","0.6208942","0.6202308","0.6173974","0.60687256","0.6009146","0.5986021","0.5980821","0.59670657","0.59482944","0.5917469","0.58205444","0.57989943","0.574035","0.5707159","0.569944","0.5677446","0.56465167","0.5634224","0.56335783","0.562071","0.5586594","0.5585654","0.5559417","0.5545898","0.55075264","0.5471564","0.54679453","0.5463177","0.5463177","0.5451394","0.5449817","0.5444677","0.54407406","0.5429196","0.54245424","0.54219013","0.541769","0.54064435","0.53971255","0.53891444","0.53680134","0.53465825","0.5342407","0.5334799","0.53091794","0.5300882","0.52853906","0.527878","0.52765346","0.5276461","0.5273692","0.52334875","0.5230519","0.5228798","0.52155995","0.5215453","0.51973903","0.51918036","0.5186324","0.51776844","0.5174429","0.51693743","0.515653","0.5148729","0.5140929","0.5124061","0.50913876","0.50663847","0.50564075","0.50418633","0.50392294","0.5035755","0.5024356","0.50231314","0.5002186","0.49984375","0.49886397","0.4979719","0.49674353","0.49667203","0.49552298","0.4953899","0.49538854","0.49515185","0.49427235","0.49409813","0.4940503","0.49366277","0.49296707","0.49232844","0.49226603","0.49154854","0.49127325","0.49123913"],"string":"[\n \"0.71493185\",\n \"0.7043156\",\n \"0.68464625\",\n \"0.67900616\",\n \"0.63941294\",\n \"0.62800896\",\n \"0.6208942\",\n \"0.6202308\",\n \"0.6173974\",\n \"0.60687256\",\n \"0.6009146\",\n \"0.5986021\",\n \"0.5980821\",\n \"0.59670657\",\n \"0.59482944\",\n \"0.5917469\",\n \"0.58205444\",\n \"0.57989943\",\n \"0.574035\",\n \"0.5707159\",\n \"0.569944\",\n \"0.5677446\",\n \"0.56465167\",\n \"0.5634224\",\n \"0.56335783\",\n \"0.562071\",\n \"0.5586594\",\n \"0.5585654\",\n \"0.5559417\",\n \"0.5545898\",\n \"0.55075264\",\n \"0.5471564\",\n \"0.54679453\",\n \"0.5463177\",\n \"0.5463177\",\n \"0.5451394\",\n \"0.5449817\",\n \"0.5444677\",\n \"0.54407406\",\n \"0.5429196\",\n \"0.54245424\",\n \"0.54219013\",\n \"0.541769\",\n \"0.54064435\",\n \"0.53971255\",\n \"0.53891444\",\n \"0.53680134\",\n \"0.53465825\",\n \"0.5342407\",\n \"0.5334799\",\n \"0.53091794\",\n \"0.5300882\",\n \"0.52853906\",\n \"0.527878\",\n \"0.52765346\",\n \"0.5276461\",\n \"0.5273692\",\n \"0.52334875\",\n \"0.5230519\",\n \"0.5228798\",\n \"0.52155995\",\n \"0.5215453\",\n \"0.51973903\",\n \"0.51918036\",\n \"0.5186324\",\n \"0.51776844\",\n \"0.5174429\",\n \"0.51693743\",\n \"0.515653\",\n \"0.5148729\",\n \"0.5140929\",\n \"0.5124061\",\n \"0.50913876\",\n \"0.50663847\",\n \"0.50564075\",\n \"0.50418633\",\n \"0.50392294\",\n \"0.5035755\",\n \"0.5024356\",\n \"0.50231314\",\n \"0.5002186\",\n \"0.49984375\",\n \"0.49886397\",\n \"0.4979719\",\n \"0.49674353\",\n \"0.49667203\",\n \"0.49552298\",\n \"0.4953899\",\n \"0.49538854\",\n \"0.49515185\",\n \"0.49427235\",\n \"0.49409813\",\n \"0.4940503\",\n \"0.49366277\",\n \"0.49296707\",\n \"0.49232844\",\n \"0.49226603\",\n \"0.49154854\",\n \"0.49127325\",\n \"0.49123913\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8044839"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":80499,"cells":{"query":{"kind":"string","value":"i is a string that represents a year with a possibly missing ones value, like \"199?\" or \"199?\". Round down to the nearest decade."},"document":{"kind":"string","value":"def _resolve_unknown_ones(self, i):\n\n m = re.compile('(^\\\\d{4}$)').match(i)\n if m is not None:\n return int(m.group(1))\n else:\n m = re.compile('(^\\\\d{1,3})[-*?]$').match(i)\n return i if m is None else int(m.group(1) + '0')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def decade(year):\r\n # get the first 3 digits of the year\r\n partial = (year[0]//10).item()\r\n # add a 0 to the end, return as decade\r\n return partial * 10","def translate_years(val):\n if val.find(\"-\") > 0:\n tokens = re.findall(\"[0-9]+\", val)\n one = int(tokens[0])\n two = int(tokens[1])\n one = (1900 + one) if one > 50 else (2000 + one)\n two = (1900 + two) if two > 50 else (2000 + two)\n return range(one, two + 1)\n tokens = re.findall(\"[0-9]+\", val)\n return [int(f\"{'19' if int(t) > 50 else '20'}{t}\") for t in tokens]","def get_year(string): \n return int(string[11:15])","def get_number_year(text):\n val = get_number(text)\n if val is None or val < 1700 or val > (datetime.date.today().year + 1):\n return None\n return val","def normalise_two_digit_year(y):\r\n if y[0] == \"'\":\r\n y = y[1:]\r\n if int(y) < 39:\r\n return '%04d' % (int(y) + 2000)\r\n elif int(y) < 100:\r\n return '%04d' % (int(y) + 1900)\r\n else:\r\n return '%04d' % int(y[:4])","def num2year(iyear):\n iyear = np.asarray([int(y) for y in iyear])\n fyear = lambda y, m, d: y + (m - 1)/12. + d/365.25\n ymd = [int2ymd(iy) for iy in iyear]\n return [fyear(y,m,d) for y,m,d in ymd]","def find_year(self,datelimits): \t \t \n year = \"\"\n\n\t \tmatch = re.search(r\"[I1][\\dG]{3}\",self.string)\n\n\t \tif match: \t \t \t \n\t \t\tif re.search(r\"(\\d{4})\",match.group()):\n\t \t\t\tyear = match.group()\n elif re.search(r\"I\\d{3}\",match.group()):\n\t \t\t\tmatch = re.sub(r\"I(\\d{3})\",r\"1\\1\",match.group())\n\t \t\t\tyear = match\n\t \t\telif re.search(r\"(\\d[G\\d]{3})\",match.group()):\n\t \t\t\tmatch = re.sub(r\"G\",r\"6\",match.group())\n\t \t\t\tyear = match\n\n \n if year == \"\" or int(year) < datelimits[0] or int(year) > datelimits[1]:\n year = \"\"\n \n\n\t \treturn year","def yearlyDepreciation():\n return .10","def year(self) -> int:\n if self.is_old_style:\n yy = int(self.split('/', 1)[1][0:2])\n else:\n yy = int(self[:2])\n if yy > 90:\n return 1900 + yy\n return 2000 + yy","def findYear(str):\n return int(re.search(\"(?<=yr=)\\d*\", str)[0])","def year_parse(s):\n regex = r\"((?:19|20)\\d{2})(?:$|[-/]\\d{2}[-/]\\d{2})\"\n try:\n year = int(re.findall(regex, ustr(s))[0])\n except IndexError:\n year = None\n return year","def decade(palabra):\n try:\n return palabra[:-1] + '0'\n except:\n return palabra","def getagefromyear(year=None):\n if year is None:\n print(\"Please enter the year to assign class to them\")\n try:\n t = datetime.datetime.today()\n b = datetime.datetime.strptime(str(year), '%Y')\n a = (t - b).days / 365\n a = int(a)\n if (a < 10) or (a > 80):\n a = None\n except:\n a = None\n return a","def closeyear(year):\n\n # Return the specific year\n return int(year % 4)","def string_to_years(s):\n pattern = r'\\d\\d\\d\\d'\n r = re.compile(pattern)\n min_year = 1960\n max_year = datetime.now().year + 1\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))","def clean_year(palabra):\n palabra = str(palabra)\n palabra = palabra.replace('.', '')\n if palabra < '1000' or palabra > '2021':\n return None\n else:\n return palabra","def year_sure(palabra):\n \n palabra = str(palabra)\n palabra = palabra.strip()\n respuesta = str(palabra)\n \n while len(respuesta) > 0:\n #print (respuesta)\n if respuesta[-1].isnumeric():\n return respuesta\n else:\n respuesta = respuesta[:-1]","def extract_year(text):\n # type: (str) -> int\n data = re.search(r\"\\d{4}\", text)\n return int(data.group()) if data else 0","def clean_antiquity(x):\n\tnow = datetime.datetime.now().year\n\tm = re.match(r'^A estrenar$', x)\n\tif m:\n\t\treturn now\n\n\tn = int(extract_only_number(x))\n\tif n > 0:\n\t\treturn now - n\n\treturn -1","def test_20th_century(self):\r\n season = \"1989-90\"\r\n res = get_end_year(season)\r\n assert res == 1990","def _days_before_year(year):\n y = year - 1\n return y * 365 + y // 4 - y // 100 + y // 400","def year_expand(s):\n regex = r\"^((?:19|20)\\d{2})?(\\s*-\\s*)?((?:19|20)\\d{2})?$\"\n try:\n start, dash, end = re.match(regex, ustr(s)).groups()\n start = start or 1900\n end = end or 2099\n except AttributeError:\n return 1900, 2099\n return (int(start), int(end)) if dash else (int(start), int(start))","def parse_academic_year(year):\n return int(year.split(\"/\")[0])","def _ord2ymd(n):\n n -= 1\n n400, n = divmod(n, _DI400Y)\n year = n400 * 400 + 1 # ..., -399, 1, 401, ...\n n100, n = divmod(n, _DI100Y)\n n4, n = divmod(n, _DI4Y)\n n1, n = divmod(n, 365)\n year += n100 * 100 + n4 * 4 + n1\n if n1 == 4 or n100 == 4:\n assert n == 0\n return year - 1, 12, 31\n leapyear = n1 == 3 and (n4 != 24 or n100 == 3)\n assert leapyear == _is_leap(year)\n month = (n + 50) >> 5\n preceding = _DAYS_BEFORE_MONTH[month] + (month > 2 and leapyear)\n if preceding > n: # estimate is too large\n month -= 1\n preceding -= _DAYS_IN_MONTH[month] + (month == 2 and leapyear)\n n -= preceding\n assert 0 <= n < _days_in_month(year, month)\n return year, month, n + 1","def get_year(url):\n year = re.search(\"\\d{4}\", url).group(0)\n return int(year)","def _four_digit_year(t):\n dt = safe_fromtimestamp(t)\n year = dt.year\n if dt.month >= 7:\n year += 1\n return str(year)","def test_convert_date_to_year(self):\n # TODO there might be a more robust way to write this with try except statements.","def find_yearly_playlist(data):\n\n if data['year'] and str(data['year']).isdigit():\n median = round(int(data['year']), -1)\n return 'years-{}-{}.m3u'.format((int(median) - 5), (int(median) + 4))\n\n return '00-without-year.m3u'","def test_year_2000(self):\r\n season = \"1999-00\"\r\n res = get_end_year(season)\r\n assert res == 2000","def century(year):\r\n century = 0\r\n last_digit = year % 10\r\n if year >= 1 and last_digit == 0:\r\n century = year // 100 \r\n else:\r\n century = year // 100 + 1\r\n return century","def y2k_coding(n):\n if n < 60:\n return 2000 + n\n else:\n return 1900 + n","def dateParser(str):\n\tyear = ''\n\tfor c in str:\n\t\tif c.isspace():\n\t\t\tyear = ''\n\t\telif c.isdigit():\n\t\t\tyear = year + c\n\t\t\tif len(year) == 4:\n\t\t\t\tbreak\n\t\telse:\n\t\t\tyear = ''\n\tif len(year) < 4:\n\t\treturn None\n\treturn int(year)","def clean_year_suciedad(palabra):\n\n try:\n if len(palabra) < 4:\n return None\n else:\n return palabra\n except:\n pass","def filter_years(text):\n months = ['january', 'february', 'march', 'april', 'may', 'june',\n 'july', 'august', 'september', 'october', 'november', 'december']\n prepositions = ['around', 'after', 'at', 'as',\n 'approximately', 'before', 'between', 'by',\n 'during', 'from', 'in', 'near', 'past',\n 'since', 'until', 'within'] # removed: about, on\n conjugations = ['and']\n articles = ['the']\n times = ['early', 'mid', 'late']\n patterns = months + prepositions + conjugations + articles + times\n re_string = r'\\b(' + '|'.join(patterns) + r')\\b(\\s|-)\\b([0-9]{3,4})s?\\b(?i)(?!\\sMYA)\\s?(BCE|BC)?'\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\s?(BCE|BC)?'\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n years += centuries\n years = [y for y in years if y<Dump.MAX_YEAR]\n return sorted(years + centuries)","def search_year(self,strz):\t\n\t\tyr_pattern = compile(\"(19[56789]\\d|20[01]\\d)\")\n\t\tyr = yr_pattern.search(strz)\t\t\n\t\tif yr is None:\n\t\t\treturn strz\t#not find\n\t\telse:\n\t\t\tyr= yr.group(1)\n\t\t\tself.release_year=yr\n\t\t\treturn strz.replace(yr,\"\")","def get_first_year(data_id):\n if data_id.startswith(\"global\"):\n return 2011\n if data_id.endswith(\"precip\"):\n return 1948\n if data_id.startswith(\"cfsv2\"):\n return 2011\n if data_id.startswith(\"nmme\"):\n return 1982\n if data_id.endswith(\"tmp2m\") or data_id.endswith(\"tmin\") or data_id.endswith(\"tmax\"):\n return 1979\n if \"sst\" in data_id or \"icec\" in data_id:\n return 1981\n if data_id.endswith(\"mei\"):\n return 1950\n if data_id.endswith(\"mjo\"):\n return 1974\n if data_id.endswith(\"sce\"):\n return 1966\n if \"wind\" in data_id:\n return 1948\n if (\"slp\" in data_id or \"pr_wtr.eatm\" in data_id or \"rhum.sig995\" in data_id or\n \"pres.sfc.gauss\" in data_id or \"pevpr.sfc.gauss\" in data_id):\n return 1948\n raise ValueError(\"Unrecognized data_id \"+data_id)","def get_nuts_spec(year):\n for t in [2016, 2013, 2010, 2006, 2003]:\n if year >= t:\n return t","def _two_digit_year(t):\n dt = safe_fromtimestamp(t)\n year = dt.year\n if dt.month >= 7:\n year += 1\n return \"'%02d\" % (year % 100)","def conv_i_to_dec(i):\n return 1800 + i*10","def is_leap_year(n):\n if n % 400 == 0:\n return True\n elif n % 100 == 0:\n return False\n elif n % 4 == 0:\n return True\n else:\n return False","def test_spider_gets_specific_year(self):\n spider = Eia923Spider()\n resp = factories.TestResponseFactory(eia923=True)\n\n result = spider.form_for_year(resp, 2007)\n\n assert result is not None\n assert result.url == \"https://www.eia.gov/electricity/data/eia923/\" \\\n \"archive/xls/f906920_2007.zip\"\n assert result.meta[\"year\"] == 2007\n\n for year in range(2001, 2019):\n result = spider.form_for_year(resp, year)\n assert result is not None","def decadeDecoder(year):\r\n \r\n decade = int(str(year)[2])\r\n decoder = {\r\n 8:'1980\\'',\r\n 9:'1990\\'',\r\n 0:'2000\\'',\r\n 1:'2010\\''\r\n }\r\n \r\n return decoder[decade]","def _date_match_to_int_or_tuple(self, m):\n\n years = set()\n try:\n if m[0] != '':\n # yyyy-dd-mm\n years = int(m[0][0:4])\n elif m[1] != '':\n # year-range derived from a century\n century = int(re.match(re.compile('\\\\d+'), m[1]).group(0))\n\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\n suffix = match.group(2)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = (100 * -century, 100 * -century + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n else:\n years = (100 * (century - 1), 100 * (century - 1) + 99)\n elif m[2] != '':\n # explicit year-range\n\n # FIXME: spaghetti code, but it works!\n range_of_stuff = []\n i = 0\n first_none = None\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\1>|<\\2', m[2]).split('>|<'):\n # get rid of whitespace\n y = y.strip()\n match = re.compile(self.regexes['capture']['year']).match(y)\n\n # if there is a suffix, one of these will not be None\n suffix = match.group(2) or match.group(4)\n if suffix:\n if i == 0:\n first_none = False\n\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\n else:\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n else:\n if i == 0:\n first_none = True\n range_of_stuff.append(int(match.group(1) or match.group(3)))\n\n i += 1\n if first_none:\n if range_of_stuff[1] <= 0:\n range_of_stuff[0] = -1 * range_of_stuff[0]\n\n years = (range_of_stuff[0], range_of_stuff[1])\n elif m[3] != '':\n # extract single year\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\1', m[3]).strip()\n years = int(prep)\n elif m[4] != '':\n # year with unknown ones\n y = m[4].strip()\n match = re.compile(r'[1-9]\\d{3}').match(y)\n if match is None:\n years = int(self._resolve_unknown_ones(y))\n\n else:\n years = int(match.group(0))\n\n elif m[5] != '':\n # plain old year\n match = re.compile(self.regexes['capture']['year']).match(m[5])\n suffix = match.group(2) or match.group(4)\n if suffix:\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\n years = -1 * int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n years = int(match.group(1) or match.group(3))\n else:\n raise Error\n\n except ValueError as e:\n #logger.error('An error occurred while trying to match \"{}\": {}'.format(m, e))\n pass\n\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\n return years","def make_year(res):\n return str(res['issued']['date-parts'][0][0])","def bornDied(str):\n\n\tbornDied = []\n\n\tyear = ''\n\tfor c in str:\n\t\tif c == 's' and len(year) == 4:\n\t\t\treturn (int(year), int(year) + 10)\n\t\tif c.isspace():\n\t\t\tyear = ''\n\t\telif c.isdigit():\n\t\t\tyear = year + c\n\t\t\tif len(year) == 4:\n\t\t\t\tbornDied.append(year)\n\t\telse:\n\t\t\tyear = ''\n\tif len(bornDied) <= 1:\n\t\tprint str\n\t\treturn (None, None)\n\treturn (int(bornDied[0]), int(bornDied[1]))","def parse_date(line):\n match = YEAR_PAT.search(line)\n if match:\n return int(match.group())\n return 9999","def getIssnYear(artMeta):\n if artMeta == None:\n return\n else:\n issn = getIssn(artMeta)\n if issn == '':\n issn = artMeta['journal']\n if issn == '':\n return ('noJournal', artMeta['year'])\n issnYear = (issn, artMeta['year'])\n return issnYear","def test_21st_century(self):\r\n season = \"2019-20\"\r\n res = get_end_year(season)\r\n assert res == 2020","def days_to_years(datum):\n return datum/DAYS_PER_YEAR","def award_id_year(award_wikidata) -> 'award_id_year':\n assert re.match(r\"^\\d{4}$\", award_wikidata['value']['year'])\n return {'value': (award_wikidata['value']['award_id'], award_wikidata['value']['year'])}","def get_year(parameters_dictionary):\n if \"start-year\" in parameters_dictionary.keys():\n year = int(parameters_dictionary[\"start-year\"])\n return str(year) + str(year + 1)\n elif \"end-year\" in parameters_dictionary.keys():\n year = int(parameters_dictionary[\"end-year\"])\n return str(year - 1) + str(year)\n else:\n return str(THIS_YEAR - 1) + str(THIS_YEAR)","def year_lookup_bounds(self, value):\n first = '%s-01-01 00:00:00'\n # SQL Server doesn't support microseconds\n last = '%s-12-31 23:59:59'\n return [first % value, last % value]","def get_year():\n try:\n year = input(\"Enter Year: \")\n year = int(year)\n if year > 2021 or year < 2000:\n os.system('cls')\n print(\"Accepted Values: 2000-2021\")\n return get_year()\n else:\n os.system('cls')\n return year\n except ValueError:\n os.system('cls')\n print(\"Accepted Values: 2000-2021\")\n return get_year()","def yy(self):\n return str(self._year)[-2:]","def _sanitize_year(self, datestr):\n try:\n year = str(datetime.datetime.strptime(datestr, '%Y').date().year)\n except:\n try:\n year = str(datetime.datetime.strptime(datestr,\n '%Y-%m-%d').date().year)\n except:\n year = None\n return year","def get_int_year(target_year:str, base_year:int, logger:lg.Logger = None) -> int:\n if logger:\n logger.debug(F\"year = {target_year}; base year = {base_year}\")\n\n if not( target_year.isnumeric() and len(target_year) == 4 ):\n msg = \"Input MUST be the String representation of a RECENT year, e.g. '2013'!\"\n if logger:\n c_frame = inspect.currentframe().f_back\n logger.error(msg, c_frame)\n raise Exception(msg)\n\n int_year = int( float(target_year) )\n if int_year > now_dt.year or int_year < base_year:\n msg = F\"Input MUST be a Year between {base_year} and {now_dt.year}!\"\n if logger:\n c_frame = inspect.currentframe().f_back\n logger.error(msg, c_frame)\n raise Exception(msg)\n\n return int_year","def yearfrac(self) -> float:\n return (self.last_idx - self.first_idx).days / 365.25","def validate_issue_year(passport: map) -> bool:\n if passport.get('iyr'):\n if int(passport['iyr']) >= 2010 and int(passport['iyr']) <= 2020:\n return True\n\n return False","def test_date_by_yr(self):\n spi_search = \"find date 2002\"\n inv_search = \"year:2002\"\n self._compare_searches(inv_search, spi_search)","def test_invalid_beginning_of_year(self):\n year, month, day, error = clean_year_month_day(2014, 12, 31, 1)\n self.assertEqual(year, 2015)\n self.assertEqual(month, 1)\n self.assertEqual(day, 1)\n self.assertEqual(error, False)","def time2num(year):\n # time1 = [1948, 1952, 1956, 1960, 1964]\n # time2 = [1968, 1972, 1976, 1980, 1984, ]\n # time3 = [1988, 1992, 1996, 2000, 2004]\n # time4 = [2008, 2012, 2016]\n time1 = [1948, 1952, 1956] # Eisenhower and truman: mixed\n time2 = [1960, 1964, 1968] # kennedy and johnson: democratic\n time3 = [1972, 1976, 1980] # carter and ford, nixon: mixed\n time4 = [1984, 1988, 1992] # reagan and bush: republican\n time5 = [1996, 2000, 2004] # clinton and bush: mixed\n time6 = [2008, 2012, 2016] # obama democratic\n if year in time1:\n return 1\n elif year in time2:\n return 2\n elif year in time3:\n return 3\n elif year in time4:\n return 4\n elif year in time5:\n return 5\n elif year in time6:\n return 6","def year(cls, year: typing.Union[int, str])->str:\n yearstr: str\n if isinstance(year, int):\n yearstr = str(year)\n else:\n yearstr = year\n return cls.DATE_AND_TIMES_SIGIL + yearstr + \"-01-01T00:00:00/9\"","def sectoFracYear(stime):\n\n ltime = convertCtimeToYdate(stime)\n atemp = re.split(':', ltime)\n year= int(atemp[0])\n ydate = int(atemp[1])\n hours = int(atemp[2])\n minutes = int(atemp[3])\n seconds = int(atemp[4])\n \n chk = 4.0 * int(0.25 * year)\n if chk == year:\n base = 366\n else:\n base = 365\n \n day = ydate + hours / 24.0 + minutes / 1440.0 + seconds / 86400.0\n \n return year + day / base","def getage(birth_year):\n today = datetime.date.today()\n print(\"Today is: \", today)\n year = today.year\n\n age = year - int(birth_year)\n # print(type(age)) you return int value by taking str\n return age","def number_times_per_year(f, f_unit):\n return round(f * converter['y'] / converter[f_unit])","def question_7():\n dob_string = input(\"DOB: \")\n birth_year = int(dob_string[-4:]) # Extract the last 4 characters\n age = REFERENCE_YEAR - birth_year\n print(f\"You were born in {birth_year}\")\n print(f\"You turn/ed {age} in {REFERENCE_YEAR}\")","def _year_of_graduation(self):\n return str((12 - int(self._grade)) + self._this_year)[2:]","def get_full_year(gender_number: int, year_number: int) -> int:\n if gender_number in range(1, 3):\n full_year = \"18\" + \"{:02d}\".format(year_number)\n return int(full_year)\n elif gender_number in range(3, 5):\n full_year = \"19\" + \"{:02d}\".format(year_number)\n return int(full_year)\n elif gender_number in range(5, 7):\n full_year = \"20\" + \"{:02d}\".format(year_number)\n return int(full_year)","def year(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"year\")","def test_get_cve_id_year_format_with_digits(reg_user_headers):\n res = requests.get(\n f'{env.AWG_BASE_URL}{CVE_ID_URL}',\n headers=reg_user_headers,\n params={\n 'cve_id_year': '20111',\n }\n )\n assert res.status_code == 400\n response_contains_json(res, 'error', 'BAD_INPUT')","def get_age(self):\n return int(CURRENT_YEAR[:4]) - self.year # String-slice only the year","def test_date_by_yr_mo(self):\n spi_search = \"find date 1976-04\"\n inv_search = 'year:1976-04'\n self._compare_searches(inv_search, spi_search)","def validate_issue_year(issue_year: str) -> None:\n if not 2010 <= int(issue_year) <= 2020:\n raise ValueError(\"Issue year is outside permissible range\")","def test_valid_year(self):\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n obj = ar[2009]\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsYear))","def parse_year_movie(date, pageurl):\n\t# remove dashes if 01-05-2015 format\n\tif '-' in date:\n\t\tdate = date.strip()[1:-1]\n\t\tyear = date.split('-')[0]\n\t# remove commas if format is 01,05,2015\n\telse:\n\t\tif ',' in date:\n\t\t\tyear = date.split(',')[1].strip()\n\t\telif ' ' in date.strip():\n\t\t\tif len(date.split(' ')) < 3:\n\t\t\t\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\n\t\t\t\treturn None\n\t\t\tyear = date.split(' ')[2]\n\t\telse:\n\t\t\tyear = date\n\t# strip whitespace\n\tyear = year.strip()\n\t# make sure we received valid numerical value to cast\n\tif not year.isdigit():\n\t\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\n\t\treturn None\n\t# cast to int and return\n\treturn int(year)","def _unit_yr(self):\n return ((self.time_base * 60.0) * 24.0) * 365.0","def columna_year(palabra):\n if len(palabra) >= 4:\n return palabra[-4:]","def test_fix_year_month_next(self):\n # 13 - 10 = next query of 3\n year, month, error = clean_year_month(2014, 13, 10)\n self.assertEqual(year, 2015)\n self.assertEqual(month, 1)\n self.assertEqual(error, False)","def test_change_of_year(self):\n\n input_ = [\n self.indicator_record(date=datetime.date(2006, 11, 1), value=0.31),\n self.indicator_record(date=datetime.date(2006, 12, 1), value=0.48),\n ]\n output = self.expander._ipca_from_15_expander(input_)\n expected = self.indicator_record(date=datetime.date(2007, 1, 1), value=0.35)\n actual = output[-1]\n\n self.assertEqual(expected, actual)","def test_interval_to_seconds_with_years(self):\n self.assert_interval_to_seconds(0, \"0y\", \"0year\", \"0years\")\n self.assert_interval_to_seconds(31536000, \"1y\", \"1year\", \"1years\")\n self.assert_interval_to_seconds(5 * 31536000, \"5y\", \"5year\", \"5years\")\n self.assert_interval_to_seconds(\n 123 * 31536000, \"123y\", \"123year\", \"123years\")\n self.assert_interval_to_seconds(\n 2 * 31536000, \"02y\", \"02year\", \"02years\")","def substitute_year(self, ref_years, qa3_answer):\n question = self\n for match in re.finditer('[0-9]{4}', question):\n year = match.group(0)\n if 1899 < int(year) < 2099:\n i = qa3_answer.index_by_chunk(year)\n if i is None or qa3_answer.result[i].get_type() != 'refYear':\n if year not in ref_years:\n ref_years.append(year)\n question = re.sub(year, '<YEAR' + string.ascii_uppercase[ref_years.index(year)] + '>', question, 1)\n return Qa3Question(question)","def get_year(self):\n\n # First we get the first 8 bits stored in the yqr register\n year_bcd = self.__read_register(_REGISTER_YEAR)\n\n # Then we extract the digits and the tens\n tens = (year_bcd & 0xF0) >> 4 # 0xF0 = 0b11110000\n digit = (year_bcd & 0x0F) # 0x0F = 0b00001111\n\n # We return year value shifted in range [1970..2129]\n return (10 * (tens) + digit) + 1970","def is_valid_issue_year(issue_year: int) -> bool:\n return issue_year.isnumeric() and 2010 <= int(issue_year) <= 2020","def test_date_by_gt_yr(self):\n spi_search = \"find date > 1980\"\n inv_search = 'year:1980->9999'\n self._compare_searches(inv_search, spi_search)","def get_last_year(data_id):\n if data_id.startswith(\"cfsv2\"):\n return 2017\n return 2018","def dia_independencia(year):\n return year, SEP, 16","def get_full_year(gender_number: int, year_number: int) -> int:\n year = \"\"\n year_digit = len(str(year_number))\n if year_digit == 1:\n if gender_number == 1 or gender_number == 2:\n year = \"18\"\n elif gender_number == 3 or gender_number == 4:\n year = \"19\"\n elif gender_number == 5 or gender_number == 6:\n year = \"20\"\n full_year = int(str(year) + \"0\" + str(year_number))\n else:\n if gender_number == 1 or gender_number == 2:\n year = \"18\"\n elif gender_number == 3 or gender_number == 4:\n year = \"19\"\n elif gender_number == 5 or gender_number == 6:\n year = \"20\"\n full_year = int(str(year) + str(year_number))\n return full_year","def get_year(self) -> Optional[int]:\n return self.year","def day_of_year(time_string):\n SECONDS_IN_DAY = 60 * 60 * 24.0\n time = parse_time(time_string)\n time_diff = time - datetime(time.year, 1, 1, 0, 0, 0)\n return time_diff.days + time_diff.seconds / SECONDS_IN_DAY + 1","def is_leap_year(year):\n try:\n pattern = re.compile('([\\d]{4})')\n if pattern.match(str(year)):\n if not year % 4 and year % 100 or not year % 400:\n return True\n else:\n return False\n except ValueError:\n print('Four-digit integer not supplied.')","def parse_date(td):\n\tresYear = float(td.days)/364.0 # get the number of years including the the numbers after the dot\n\tresMonth = int((resYear - int(resYear))*364/30) # get the number of months, by multiply the number after the dot by 364 and divide by 30.\n\tresYear = int(resYear)\n\treturn str(resYear) + \"y\" + str(resMonth) + \"m\"","def parse_year(year):\n\n return datetime.strptime(year, '%Y')","def birth_date_or_min_year(individual):\n year = fuzzy_date_year(individual.birth_date)\n if year:\n return year\n return 0","def get_year(x):\n return x[\"SALE DATE\"].year","def year_tracker(words):\n new_words = []\n for w in words:\n new_word = re.sub(r\"^[1][789][0-9]{2}$\", \"jahreszahl\", w) # for 1700-1999\n new_word = re.sub(r\"^[2][01][0-9]{2}$\", \"jahreszahl\", new_word) # for 2000-2199\n new_words += [new_word]\n return new_words","def find_year(title):\n # find all patterns that match the year pattern\n matches = year_pattern.findall(title)\n # if any matches\n if matches:\n # record for convienence\n year = matches[-1]\n too_short = len(title) < 8\n # If the year is the title then return None\n if year == title:\n return None\n # If we have enough room for 1 block of 4 digits and its at the start\n elif too_short and title.startswith(year):\n return None\n else:\n return year","def findingLeapYear (year):\n leapYear = False\n if year % 4 != 0:\n leapYear = False\n elif year % 100 != 0:\n leapYear = True\n elif year % 400 != 0:\n leapYear = False\n else:\n leapYear = True\n return leapYear","def getCurrentYear(self):\n return math.ceil(self.wcount / 48)","def calculate_year_fraction(self, t):\n try:\n return t / 365.\n except (TypeError, AttributeError):\n return self.day_counter.year_fraction(self.curve_date, t)","def read_year():\n try:\n year = int(input(\"Enter year: \"))\n return year\n except:\n print(\"Invalid input! Enter year!\")\n exit(-1)"],"string":"[\n \"def decade(year):\\r\\n # get the first 3 digits of the year\\r\\n partial = (year[0]//10).item()\\r\\n # add a 0 to the end, return as decade\\r\\n return partial * 10\",\n \"def translate_years(val):\\n if val.find(\\\"-\\\") > 0:\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n one = int(tokens[0])\\n two = int(tokens[1])\\n one = (1900 + one) if one > 50 else (2000 + one)\\n two = (1900 + two) if two > 50 else (2000 + two)\\n return range(one, two + 1)\\n tokens = re.findall(\\\"[0-9]+\\\", val)\\n return [int(f\\\"{'19' if int(t) > 50 else '20'}{t}\\\") for t in tokens]\",\n \"def get_year(string): \\n return int(string[11:15])\",\n \"def get_number_year(text):\\n val = get_number(text)\\n if val is None or val < 1700 or val > (datetime.date.today().year + 1):\\n return None\\n return val\",\n \"def normalise_two_digit_year(y):\\r\\n if y[0] == \\\"'\\\":\\r\\n y = y[1:]\\r\\n if int(y) < 39:\\r\\n return '%04d' % (int(y) + 2000)\\r\\n elif int(y) < 100:\\r\\n return '%04d' % (int(y) + 1900)\\r\\n else:\\r\\n return '%04d' % int(y[:4])\",\n \"def num2year(iyear):\\n iyear = np.asarray([int(y) for y in iyear])\\n fyear = lambda y, m, d: y + (m - 1)/12. + d/365.25\\n ymd = [int2ymd(iy) for iy in iyear]\\n return [fyear(y,m,d) for y,m,d in ymd]\",\n \"def find_year(self,datelimits): \\t \\t \\n year = \\\"\\\"\\n\\n\\t \\tmatch = re.search(r\\\"[I1][\\\\dG]{3}\\\",self.string)\\n\\n\\t \\tif match: \\t \\t \\t \\n\\t \\t\\tif re.search(r\\\"(\\\\d{4})\\\",match.group()):\\n\\t \\t\\t\\tyear = match.group()\\n elif re.search(r\\\"I\\\\d{3}\\\",match.group()):\\n\\t \\t\\t\\tmatch = re.sub(r\\\"I(\\\\d{3})\\\",r\\\"1\\\\1\\\",match.group())\\n\\t \\t\\t\\tyear = match\\n\\t \\t\\telif re.search(r\\\"(\\\\d[G\\\\d]{3})\\\",match.group()):\\n\\t \\t\\t\\tmatch = re.sub(r\\\"G\\\",r\\\"6\\\",match.group())\\n\\t \\t\\t\\tyear = match\\n\\n \\n if year == \\\"\\\" or int(year) < datelimits[0] or int(year) > datelimits[1]:\\n year = \\\"\\\"\\n \\n\\n\\t \\treturn year\",\n \"def yearlyDepreciation():\\n return .10\",\n \"def year(self) -> int:\\n if self.is_old_style:\\n yy = int(self.split('/', 1)[1][0:2])\\n else:\\n yy = int(self[:2])\\n if yy > 90:\\n return 1900 + yy\\n return 2000 + yy\",\n \"def findYear(str):\\n return int(re.search(\\\"(?<=yr=)\\\\d*\\\", str)[0])\",\n \"def year_parse(s):\\n regex = r\\\"((?:19|20)\\\\d{2})(?:$|[-/]\\\\d{2}[-/]\\\\d{2})\\\"\\n try:\\n year = int(re.findall(regex, ustr(s))[0])\\n except IndexError:\\n year = None\\n return year\",\n \"def decade(palabra):\\n try:\\n return palabra[:-1] + '0'\\n except:\\n return palabra\",\n \"def getagefromyear(year=None):\\n if year is None:\\n print(\\\"Please enter the year to assign class to them\\\")\\n try:\\n t = datetime.datetime.today()\\n b = datetime.datetime.strptime(str(year), '%Y')\\n a = (t - b).days / 365\\n a = int(a)\\n if (a < 10) or (a > 80):\\n a = None\\n except:\\n a = None\\n return a\",\n \"def closeyear(year):\\n\\n # Return the specific year\\n return int(year % 4)\",\n \"def string_to_years(s):\\n pattern = r'\\\\d\\\\d\\\\d\\\\d'\\n r = re.compile(pattern)\\n min_year = 1960\\n max_year = datetime.now().year + 1\\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))\",\n \"def clean_year(palabra):\\n palabra = str(palabra)\\n palabra = palabra.replace('.', '')\\n if palabra < '1000' or palabra > '2021':\\n return None\\n else:\\n return palabra\",\n \"def year_sure(palabra):\\n \\n palabra = str(palabra)\\n palabra = palabra.strip()\\n respuesta = str(palabra)\\n \\n while len(respuesta) > 0:\\n #print (respuesta)\\n if respuesta[-1].isnumeric():\\n return respuesta\\n else:\\n respuesta = respuesta[:-1]\",\n \"def extract_year(text):\\n # type: (str) -> int\\n data = re.search(r\\\"\\\\d{4}\\\", text)\\n return int(data.group()) if data else 0\",\n \"def clean_antiquity(x):\\n\\tnow = datetime.datetime.now().year\\n\\tm = re.match(r'^A estrenar$', x)\\n\\tif m:\\n\\t\\treturn now\\n\\n\\tn = int(extract_only_number(x))\\n\\tif n > 0:\\n\\t\\treturn now - n\\n\\treturn -1\",\n \"def test_20th_century(self):\\r\\n season = \\\"1989-90\\\"\\r\\n res = get_end_year(season)\\r\\n assert res == 1990\",\n \"def _days_before_year(year):\\n y = year - 1\\n return y * 365 + y // 4 - y // 100 + y // 400\",\n \"def year_expand(s):\\n regex = r\\\"^((?:19|20)\\\\d{2})?(\\\\s*-\\\\s*)?((?:19|20)\\\\d{2})?$\\\"\\n try:\\n start, dash, end = re.match(regex, ustr(s)).groups()\\n start = start or 1900\\n end = end or 2099\\n except AttributeError:\\n return 1900, 2099\\n return (int(start), int(end)) if dash else (int(start), int(start))\",\n \"def parse_academic_year(year):\\n return int(year.split(\\\"/\\\")[0])\",\n \"def _ord2ymd(n):\\n n -= 1\\n n400, n = divmod(n, _DI400Y)\\n year = n400 * 400 + 1 # ..., -399, 1, 401, ...\\n n100, n = divmod(n, _DI100Y)\\n n4, n = divmod(n, _DI4Y)\\n n1, n = divmod(n, 365)\\n year += n100 * 100 + n4 * 4 + n1\\n if n1 == 4 or n100 == 4:\\n assert n == 0\\n return year - 1, 12, 31\\n leapyear = n1 == 3 and (n4 != 24 or n100 == 3)\\n assert leapyear == _is_leap(year)\\n month = (n + 50) >> 5\\n preceding = _DAYS_BEFORE_MONTH[month] + (month > 2 and leapyear)\\n if preceding > n: # estimate is too large\\n month -= 1\\n preceding -= _DAYS_IN_MONTH[month] + (month == 2 and leapyear)\\n n -= preceding\\n assert 0 <= n < _days_in_month(year, month)\\n return year, month, n + 1\",\n \"def get_year(url):\\n year = re.search(\\\"\\\\d{4}\\\", url).group(0)\\n return int(year)\",\n \"def _four_digit_year(t):\\n dt = safe_fromtimestamp(t)\\n year = dt.year\\n if dt.month >= 7:\\n year += 1\\n return str(year)\",\n \"def test_convert_date_to_year(self):\\n # TODO there might be a more robust way to write this with try except statements.\",\n \"def find_yearly_playlist(data):\\n\\n if data['year'] and str(data['year']).isdigit():\\n median = round(int(data['year']), -1)\\n return 'years-{}-{}.m3u'.format((int(median) - 5), (int(median) + 4))\\n\\n return '00-without-year.m3u'\",\n \"def test_year_2000(self):\\r\\n season = \\\"1999-00\\\"\\r\\n res = get_end_year(season)\\r\\n assert res == 2000\",\n \"def century(year):\\r\\n century = 0\\r\\n last_digit = year % 10\\r\\n if year >= 1 and last_digit == 0:\\r\\n century = year // 100 \\r\\n else:\\r\\n century = year // 100 + 1\\r\\n return century\",\n \"def y2k_coding(n):\\n if n < 60:\\n return 2000 + n\\n else:\\n return 1900 + n\",\n \"def dateParser(str):\\n\\tyear = ''\\n\\tfor c in str:\\n\\t\\tif c.isspace():\\n\\t\\t\\tyear = ''\\n\\t\\telif c.isdigit():\\n\\t\\t\\tyear = year + c\\n\\t\\t\\tif len(year) == 4:\\n\\t\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tyear = ''\\n\\tif len(year) < 4:\\n\\t\\treturn None\\n\\treturn int(year)\",\n \"def clean_year_suciedad(palabra):\\n\\n try:\\n if len(palabra) < 4:\\n return None\\n else:\\n return palabra\\n except:\\n pass\",\n \"def filter_years(text):\\n months = ['january', 'february', 'march', 'april', 'may', 'june',\\n 'july', 'august', 'september', 'october', 'november', 'december']\\n prepositions = ['around', 'after', 'at', 'as',\\n 'approximately', 'before', 'between', 'by',\\n 'during', 'from', 'in', 'near', 'past',\\n 'since', 'until', 'within'] # removed: about, on\\n conjugations = ['and']\\n articles = ['the']\\n times = ['early', 'mid', 'late']\\n patterns = months + prepositions + conjugations + articles + times\\n re_string = r'\\\\b(' + '|'.join(patterns) + r')\\\\b(\\\\s|-)\\\\b([0-9]{3,4})s?\\\\b(?i)(?!\\\\sMYA)\\\\s?(BCE|BC)?'\\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\\\s?(BCE|BC)?'\\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n years += centuries\\n years = [y for y in years if y<Dump.MAX_YEAR]\\n return sorted(years + centuries)\",\n \"def search_year(self,strz):\\t\\n\\t\\tyr_pattern = compile(\\\"(19[56789]\\\\d|20[01]\\\\d)\\\")\\n\\t\\tyr = yr_pattern.search(strz)\\t\\t\\n\\t\\tif yr is None:\\n\\t\\t\\treturn strz\\t#not find\\n\\t\\telse:\\n\\t\\t\\tyr= yr.group(1)\\n\\t\\t\\tself.release_year=yr\\n\\t\\t\\treturn strz.replace(yr,\\\"\\\")\",\n \"def get_first_year(data_id):\\n if data_id.startswith(\\\"global\\\"):\\n return 2011\\n if data_id.endswith(\\\"precip\\\"):\\n return 1948\\n if data_id.startswith(\\\"cfsv2\\\"):\\n return 2011\\n if data_id.startswith(\\\"nmme\\\"):\\n return 1982\\n if data_id.endswith(\\\"tmp2m\\\") or data_id.endswith(\\\"tmin\\\") or data_id.endswith(\\\"tmax\\\"):\\n return 1979\\n if \\\"sst\\\" in data_id or \\\"icec\\\" in data_id:\\n return 1981\\n if data_id.endswith(\\\"mei\\\"):\\n return 1950\\n if data_id.endswith(\\\"mjo\\\"):\\n return 1974\\n if data_id.endswith(\\\"sce\\\"):\\n return 1966\\n if \\\"wind\\\" in data_id:\\n return 1948\\n if (\\\"slp\\\" in data_id or \\\"pr_wtr.eatm\\\" in data_id or \\\"rhum.sig995\\\" in data_id or\\n \\\"pres.sfc.gauss\\\" in data_id or \\\"pevpr.sfc.gauss\\\" in data_id):\\n return 1948\\n raise ValueError(\\\"Unrecognized data_id \\\"+data_id)\",\n \"def get_nuts_spec(year):\\n for t in [2016, 2013, 2010, 2006, 2003]:\\n if year >= t:\\n return t\",\n \"def _two_digit_year(t):\\n dt = safe_fromtimestamp(t)\\n year = dt.year\\n if dt.month >= 7:\\n year += 1\\n return \\\"'%02d\\\" % (year % 100)\",\n \"def conv_i_to_dec(i):\\n return 1800 + i*10\",\n \"def is_leap_year(n):\\n if n % 400 == 0:\\n return True\\n elif n % 100 == 0:\\n return False\\n elif n % 4 == 0:\\n return True\\n else:\\n return False\",\n \"def test_spider_gets_specific_year(self):\\n spider = Eia923Spider()\\n resp = factories.TestResponseFactory(eia923=True)\\n\\n result = spider.form_for_year(resp, 2007)\\n\\n assert result is not None\\n assert result.url == \\\"https://www.eia.gov/electricity/data/eia923/\\\" \\\\\\n \\\"archive/xls/f906920_2007.zip\\\"\\n assert result.meta[\\\"year\\\"] == 2007\\n\\n for year in range(2001, 2019):\\n result = spider.form_for_year(resp, year)\\n assert result is not None\",\n \"def decadeDecoder(year):\\r\\n \\r\\n decade = int(str(year)[2])\\r\\n decoder = {\\r\\n 8:'1980\\\\'',\\r\\n 9:'1990\\\\'',\\r\\n 0:'2000\\\\'',\\r\\n 1:'2010\\\\''\\r\\n }\\r\\n \\r\\n return decoder[decade]\",\n \"def _date_match_to_int_or_tuple(self, m):\\n\\n years = set()\\n try:\\n if m[0] != '':\\n # yyyy-dd-mm\\n years = int(m[0][0:4])\\n elif m[1] != '':\\n # year-range derived from a century\\n century = int(re.match(re.compile('\\\\\\\\d+'), m[1]).group(0))\\n\\n match = re.compile(self.regexes['capture']['century-plus-suffix']).match(m[1])\\n suffix = match.group(2)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = (100 * -century, 100 * -century + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n else:\\n years = (100 * (century - 1), 100 * (century - 1) + 99)\\n elif m[2] != '':\\n # explicit year-range\\n\\n # FIXME: spaghetti code, but it works!\\n range_of_stuff = []\\n i = 0\\n first_none = None\\n for y in re.sub(self.regexes['substitution']['year-year-splitter'], r'\\\\1>|<\\\\2', m[2]).split('>|<'):\\n # get rid of whitespace\\n y = y.strip()\\n match = re.compile(self.regexes['capture']['year']).match(y)\\n\\n # if there is a suffix, one of these will not be None\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if i == 0:\\n first_none = False\\n\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n range_of_stuff.append(-1 * int(match.group(1) or match.group(3)))\\n else:\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n else:\\n if i == 0:\\n first_none = True\\n range_of_stuff.append(int(match.group(1) or match.group(3)))\\n\\n i += 1\\n if first_none:\\n if range_of_stuff[1] <= 0:\\n range_of_stuff[0] = -1 * range_of_stuff[0]\\n\\n years = (range_of_stuff[0], range_of_stuff[1])\\n elif m[3] != '':\\n # extract single year\\n prep = re.sub(self.regexes['substitution']['dd-mon-year-time'], r'\\\\1', m[3]).strip()\\n years = int(prep)\\n elif m[4] != '':\\n # year with unknown ones\\n y = m[4].strip()\\n match = re.compile(r'[1-9]\\\\d{3}').match(y)\\n if match is None:\\n years = int(self._resolve_unknown_ones(y))\\n\\n else:\\n years = int(match.group(0))\\n\\n elif m[5] != '':\\n # plain old year\\n match = re.compile(self.regexes['capture']['year']).match(m[5])\\n suffix = match.group(2) or match.group(4)\\n if suffix:\\n if re.compile(self.regexes['match']['suffix-bce']).match(suffix) is not None:\\n years = -1 * int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n years = int(match.group(1) or match.group(3))\\n else:\\n raise Error\\n\\n except ValueError as e:\\n #logger.error('An error occurred while trying to match \\\"{}\\\": {}'.format(m, e))\\n pass\\n\\n #logger.debug('Mapping match to years: {} -> {}'.format(m, years))\\n return years\",\n \"def make_year(res):\\n return str(res['issued']['date-parts'][0][0])\",\n \"def bornDied(str):\\n\\n\\tbornDied = []\\n\\n\\tyear = ''\\n\\tfor c in str:\\n\\t\\tif c == 's' and len(year) == 4:\\n\\t\\t\\treturn (int(year), int(year) + 10)\\n\\t\\tif c.isspace():\\n\\t\\t\\tyear = ''\\n\\t\\telif c.isdigit():\\n\\t\\t\\tyear = year + c\\n\\t\\t\\tif len(year) == 4:\\n\\t\\t\\t\\tbornDied.append(year)\\n\\t\\telse:\\n\\t\\t\\tyear = ''\\n\\tif len(bornDied) <= 1:\\n\\t\\tprint str\\n\\t\\treturn (None, None)\\n\\treturn (int(bornDied[0]), int(bornDied[1]))\",\n \"def parse_date(line):\\n match = YEAR_PAT.search(line)\\n if match:\\n return int(match.group())\\n return 9999\",\n \"def getIssnYear(artMeta):\\n if artMeta == None:\\n return\\n else:\\n issn = getIssn(artMeta)\\n if issn == '':\\n issn = artMeta['journal']\\n if issn == '':\\n return ('noJournal', artMeta['year'])\\n issnYear = (issn, artMeta['year'])\\n return issnYear\",\n \"def test_21st_century(self):\\r\\n season = \\\"2019-20\\\"\\r\\n res = get_end_year(season)\\r\\n assert res == 2020\",\n \"def days_to_years(datum):\\n return datum/DAYS_PER_YEAR\",\n \"def award_id_year(award_wikidata) -> 'award_id_year':\\n assert re.match(r\\\"^\\\\d{4}$\\\", award_wikidata['value']['year'])\\n return {'value': (award_wikidata['value']['award_id'], award_wikidata['value']['year'])}\",\n \"def get_year(parameters_dictionary):\\n if \\\"start-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"start-year\\\"])\\n return str(year) + str(year + 1)\\n elif \\\"end-year\\\" in parameters_dictionary.keys():\\n year = int(parameters_dictionary[\\\"end-year\\\"])\\n return str(year - 1) + str(year)\\n else:\\n return str(THIS_YEAR - 1) + str(THIS_YEAR)\",\n \"def year_lookup_bounds(self, value):\\n first = '%s-01-01 00:00:00'\\n # SQL Server doesn't support microseconds\\n last = '%s-12-31 23:59:59'\\n return [first % value, last % value]\",\n \"def get_year():\\n try:\\n year = input(\\\"Enter Year: \\\")\\n year = int(year)\\n if year > 2021 or year < 2000:\\n os.system('cls')\\n print(\\\"Accepted Values: 2000-2021\\\")\\n return get_year()\\n else:\\n os.system('cls')\\n return year\\n except ValueError:\\n os.system('cls')\\n print(\\\"Accepted Values: 2000-2021\\\")\\n return get_year()\",\n \"def yy(self):\\n return str(self._year)[-2:]\",\n \"def _sanitize_year(self, datestr):\\n try:\\n year = str(datetime.datetime.strptime(datestr, '%Y').date().year)\\n except:\\n try:\\n year = str(datetime.datetime.strptime(datestr,\\n '%Y-%m-%d').date().year)\\n except:\\n year = None\\n return year\",\n \"def get_int_year(target_year:str, base_year:int, logger:lg.Logger = None) -> int:\\n if logger:\\n logger.debug(F\\\"year = {target_year}; base year = {base_year}\\\")\\n\\n if not( target_year.isnumeric() and len(target_year) == 4 ):\\n msg = \\\"Input MUST be the String representation of a RECENT year, e.g. '2013'!\\\"\\n if logger:\\n c_frame = inspect.currentframe().f_back\\n logger.error(msg, c_frame)\\n raise Exception(msg)\\n\\n int_year = int( float(target_year) )\\n if int_year > now_dt.year or int_year < base_year:\\n msg = F\\\"Input MUST be a Year between {base_year} and {now_dt.year}!\\\"\\n if logger:\\n c_frame = inspect.currentframe().f_back\\n logger.error(msg, c_frame)\\n raise Exception(msg)\\n\\n return int_year\",\n \"def yearfrac(self) -> float:\\n return (self.last_idx - self.first_idx).days / 365.25\",\n \"def validate_issue_year(passport: map) -> bool:\\n if passport.get('iyr'):\\n if int(passport['iyr']) >= 2010 and int(passport['iyr']) <= 2020:\\n return True\\n\\n return False\",\n \"def test_date_by_yr(self):\\n spi_search = \\\"find date 2002\\\"\\n inv_search = \\\"year:2002\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def test_invalid_beginning_of_year(self):\\n year, month, day, error = clean_year_month_day(2014, 12, 31, 1)\\n self.assertEqual(year, 2015)\\n self.assertEqual(month, 1)\\n self.assertEqual(day, 1)\\n self.assertEqual(error, False)\",\n \"def time2num(year):\\n # time1 = [1948, 1952, 1956, 1960, 1964]\\n # time2 = [1968, 1972, 1976, 1980, 1984, ]\\n # time3 = [1988, 1992, 1996, 2000, 2004]\\n # time4 = [2008, 2012, 2016]\\n time1 = [1948, 1952, 1956] # Eisenhower and truman: mixed\\n time2 = [1960, 1964, 1968] # kennedy and johnson: democratic\\n time3 = [1972, 1976, 1980] # carter and ford, nixon: mixed\\n time4 = [1984, 1988, 1992] # reagan and bush: republican\\n time5 = [1996, 2000, 2004] # clinton and bush: mixed\\n time6 = [2008, 2012, 2016] # obama democratic\\n if year in time1:\\n return 1\\n elif year in time2:\\n return 2\\n elif year in time3:\\n return 3\\n elif year in time4:\\n return 4\\n elif year in time5:\\n return 5\\n elif year in time6:\\n return 6\",\n \"def year(cls, year: typing.Union[int, str])->str:\\n yearstr: str\\n if isinstance(year, int):\\n yearstr = str(year)\\n else:\\n yearstr = year\\n return cls.DATE_AND_TIMES_SIGIL + yearstr + \\\"-01-01T00:00:00/9\\\"\",\n \"def sectoFracYear(stime):\\n\\n ltime = convertCtimeToYdate(stime)\\n atemp = re.split(':', ltime)\\n year= int(atemp[0])\\n ydate = int(atemp[1])\\n hours = int(atemp[2])\\n minutes = int(atemp[3])\\n seconds = int(atemp[4])\\n \\n chk = 4.0 * int(0.25 * year)\\n if chk == year:\\n base = 366\\n else:\\n base = 365\\n \\n day = ydate + hours / 24.0 + minutes / 1440.0 + seconds / 86400.0\\n \\n return year + day / base\",\n \"def getage(birth_year):\\n today = datetime.date.today()\\n print(\\\"Today is: \\\", today)\\n year = today.year\\n\\n age = year - int(birth_year)\\n # print(type(age)) you return int value by taking str\\n return age\",\n \"def number_times_per_year(f, f_unit):\\n return round(f * converter['y'] / converter[f_unit])\",\n \"def question_7():\\n dob_string = input(\\\"DOB: \\\")\\n birth_year = int(dob_string[-4:]) # Extract the last 4 characters\\n age = REFERENCE_YEAR - birth_year\\n print(f\\\"You were born in {birth_year}\\\")\\n print(f\\\"You turn/ed {age} in {REFERENCE_YEAR}\\\")\",\n \"def _year_of_graduation(self):\\n return str((12 - int(self._grade)) + self._this_year)[2:]\",\n \"def get_full_year(gender_number: int, year_number: int) -> int:\\n if gender_number in range(1, 3):\\n full_year = \\\"18\\\" + \\\"{:02d}\\\".format(year_number)\\n return int(full_year)\\n elif gender_number in range(3, 5):\\n full_year = \\\"19\\\" + \\\"{:02d}\\\".format(year_number)\\n return int(full_year)\\n elif gender_number in range(5, 7):\\n full_year = \\\"20\\\" + \\\"{:02d}\\\".format(year_number)\\n return int(full_year)\",\n \"def year(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"year\\\")\",\n \"def test_get_cve_id_year_format_with_digits(reg_user_headers):\\n res = requests.get(\\n f'{env.AWG_BASE_URL}{CVE_ID_URL}',\\n headers=reg_user_headers,\\n params={\\n 'cve_id_year': '20111',\\n }\\n )\\n assert res.status_code == 400\\n response_contains_json(res, 'error', 'BAD_INPUT')\",\n \"def get_age(self):\\n return int(CURRENT_YEAR[:4]) - self.year # String-slice only the year\",\n \"def test_date_by_yr_mo(self):\\n spi_search = \\\"find date 1976-04\\\"\\n inv_search = 'year:1976-04'\\n self._compare_searches(inv_search, spi_search)\",\n \"def validate_issue_year(issue_year: str) -> None:\\n if not 2010 <= int(issue_year) <= 2020:\\n raise ValueError(\\\"Issue year is outside permissible range\\\")\",\n \"def test_valid_year(self):\\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n obj = ar[2009]\\n self.assertTrue(isinstance(obj, awstats_reader.AwstatsYear))\",\n \"def parse_year_movie(date, pageurl):\\n\\t# remove dashes if 01-05-2015 format\\n\\tif '-' in date:\\n\\t\\tdate = date.strip()[1:-1]\\n\\t\\tyear = date.split('-')[0]\\n\\t# remove commas if format is 01,05,2015\\n\\telse:\\n\\t\\tif ',' in date:\\n\\t\\t\\tyear = date.split(',')[1].strip()\\n\\t\\telif ' ' in date.strip():\\n\\t\\t\\tif len(date.split(' ')) < 3:\\n\\t\\t\\t\\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\\n\\t\\t\\t\\treturn None\\n\\t\\t\\tyear = date.split(' ')[2]\\n\\t\\telse:\\n\\t\\t\\tyear = date\\n\\t# strip whitespace\\n\\tyear = year.strip()\\n\\t# make sure we received valid numerical value to cast\\n\\tif not year.isdigit():\\n\\t\\tlogging.warning('' + pageurl + ' has a year with invalid format, parsing will terminate')\\n\\t\\treturn None\\n\\t# cast to int and return\\n\\treturn int(year)\",\n \"def _unit_yr(self):\\n return ((self.time_base * 60.0) * 24.0) * 365.0\",\n \"def columna_year(palabra):\\n if len(palabra) >= 4:\\n return palabra[-4:]\",\n \"def test_fix_year_month_next(self):\\n # 13 - 10 = next query of 3\\n year, month, error = clean_year_month(2014, 13, 10)\\n self.assertEqual(year, 2015)\\n self.assertEqual(month, 1)\\n self.assertEqual(error, False)\",\n \"def test_change_of_year(self):\\n\\n input_ = [\\n self.indicator_record(date=datetime.date(2006, 11, 1), value=0.31),\\n self.indicator_record(date=datetime.date(2006, 12, 1), value=0.48),\\n ]\\n output = self.expander._ipca_from_15_expander(input_)\\n expected = self.indicator_record(date=datetime.date(2007, 1, 1), value=0.35)\\n actual = output[-1]\\n\\n self.assertEqual(expected, actual)\",\n \"def test_interval_to_seconds_with_years(self):\\n self.assert_interval_to_seconds(0, \\\"0y\\\", \\\"0year\\\", \\\"0years\\\")\\n self.assert_interval_to_seconds(31536000, \\\"1y\\\", \\\"1year\\\", \\\"1years\\\")\\n self.assert_interval_to_seconds(5 * 31536000, \\\"5y\\\", \\\"5year\\\", \\\"5years\\\")\\n self.assert_interval_to_seconds(\\n 123 * 31536000, \\\"123y\\\", \\\"123year\\\", \\\"123years\\\")\\n self.assert_interval_to_seconds(\\n 2 * 31536000, \\\"02y\\\", \\\"02year\\\", \\\"02years\\\")\",\n \"def substitute_year(self, ref_years, qa3_answer):\\n question = self\\n for match in re.finditer('[0-9]{4}', question):\\n year = match.group(0)\\n if 1899 < int(year) < 2099:\\n i = qa3_answer.index_by_chunk(year)\\n if i is None or qa3_answer.result[i].get_type() != 'refYear':\\n if year not in ref_years:\\n ref_years.append(year)\\n question = re.sub(year, '<YEAR' + string.ascii_uppercase[ref_years.index(year)] + '>', question, 1)\\n return Qa3Question(question)\",\n \"def get_year(self):\\n\\n # First we get the first 8 bits stored in the yqr register\\n year_bcd = self.__read_register(_REGISTER_YEAR)\\n\\n # Then we extract the digits and the tens\\n tens = (year_bcd & 0xF0) >> 4 # 0xF0 = 0b11110000\\n digit = (year_bcd & 0x0F) # 0x0F = 0b00001111\\n\\n # We return year value shifted in range [1970..2129]\\n return (10 * (tens) + digit) + 1970\",\n \"def is_valid_issue_year(issue_year: int) -> bool:\\n return issue_year.isnumeric() and 2010 <= int(issue_year) <= 2020\",\n \"def test_date_by_gt_yr(self):\\n spi_search = \\\"find date > 1980\\\"\\n inv_search = 'year:1980->9999'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_last_year(data_id):\\n if data_id.startswith(\\\"cfsv2\\\"):\\n return 2017\\n return 2018\",\n \"def dia_independencia(year):\\n return year, SEP, 16\",\n \"def get_full_year(gender_number: int, year_number: int) -> int:\\n year = \\\"\\\"\\n year_digit = len(str(year_number))\\n if year_digit == 1:\\n if gender_number == 1 or gender_number == 2:\\n year = \\\"18\\\"\\n elif gender_number == 3 or gender_number == 4:\\n year = \\\"19\\\"\\n elif gender_number == 5 or gender_number == 6:\\n year = \\\"20\\\"\\n full_year = int(str(year) + \\\"0\\\" + str(year_number))\\n else:\\n if gender_number == 1 or gender_number == 2:\\n year = \\\"18\\\"\\n elif gender_number == 3 or gender_number == 4:\\n year = \\\"19\\\"\\n elif gender_number == 5 or gender_number == 6:\\n year = \\\"20\\\"\\n full_year = int(str(year) + str(year_number))\\n return full_year\",\n \"def get_year(self) -> Optional[int]:\\n return self.year\",\n \"def day_of_year(time_string):\\n SECONDS_IN_DAY = 60 * 60 * 24.0\\n time = parse_time(time_string)\\n time_diff = time - datetime(time.year, 1, 1, 0, 0, 0)\\n return time_diff.days + time_diff.seconds / SECONDS_IN_DAY + 1\",\n \"def is_leap_year(year):\\n try:\\n pattern = re.compile('([\\\\d]{4})')\\n if pattern.match(str(year)):\\n if not year % 4 and year % 100 or not year % 400:\\n return True\\n else:\\n return False\\n except ValueError:\\n print('Four-digit integer not supplied.')\",\n \"def parse_date(td):\\n\\tresYear = float(td.days)/364.0 # get the number of years including the the numbers after the dot\\n\\tresMonth = int((resYear - int(resYear))*364/30) # get the number of months, by multiply the number after the dot by 364 and divide by 30.\\n\\tresYear = int(resYear)\\n\\treturn str(resYear) + \\\"y\\\" + str(resMonth) + \\\"m\\\"\",\n \"def parse_year(year):\\n\\n return datetime.strptime(year, '%Y')\",\n \"def birth_date_or_min_year(individual):\\n year = fuzzy_date_year(individual.birth_date)\\n if year:\\n return year\\n return 0\",\n \"def get_year(x):\\n return x[\\\"SALE DATE\\\"].year\",\n \"def year_tracker(words):\\n new_words = []\\n for w in words:\\n new_word = re.sub(r\\\"^[1][789][0-9]{2}$\\\", \\\"jahreszahl\\\", w) # for 1700-1999\\n new_word = re.sub(r\\\"^[2][01][0-9]{2}$\\\", \\\"jahreszahl\\\", new_word) # for 2000-2199\\n new_words += [new_word]\\n return new_words\",\n \"def find_year(title):\\n # find all patterns that match the year pattern\\n matches = year_pattern.findall(title)\\n # if any matches\\n if matches:\\n # record for convienence\\n year = matches[-1]\\n too_short = len(title) < 8\\n # If the year is the title then return None\\n if year == title:\\n return None\\n # If we have enough room for 1 block of 4 digits and its at the start\\n elif too_short and title.startswith(year):\\n return None\\n else:\\n return year\",\n \"def findingLeapYear (year):\\n leapYear = False\\n if year % 4 != 0:\\n leapYear = False\\n elif year % 100 != 0:\\n leapYear = True\\n elif year % 400 != 0:\\n leapYear = False\\n else:\\n leapYear = True\\n return leapYear\",\n \"def getCurrentYear(self):\\n return math.ceil(self.wcount / 48)\",\n \"def calculate_year_fraction(self, t):\\n try:\\n return t / 365.\\n except (TypeError, AttributeError):\\n return self.day_counter.year_fraction(self.curve_date, t)\",\n \"def read_year():\\n try:\\n year = int(input(\\\"Enter year: \\\"))\\n return year\\n except:\\n print(\\\"Invalid input! Enter year!\\\")\\n exit(-1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6792026","0.65144795","0.64973646","0.64911294","0.63963395","0.6301948","0.6170404","0.61197406","0.6097923","0.6078981","0.6054153","0.598611","0.5985679","0.5947528","0.5931075","0.5913055","0.5898607","0.5834762","0.5772921","0.5737773","0.57252026","0.57127255","0.5683384","0.5667694","0.5659907","0.5653415","0.56388396","0.56335443","0.5608183","0.55998176","0.55972785","0.5589549","0.5573224","0.55649984","0.5563879","0.5559318","0.55437225","0.5536844","0.5512505","0.54618233","0.54529274","0.5447986","0.5432676","0.5376404","0.53744876","0.53661376","0.5359423","0.53570366","0.53444535","0.5343244","0.53318053","0.5322798","0.53226423","0.53187895","0.5316374","0.5304725","0.52673787","0.5250515","0.52493465","0.5219658","0.52180266","0.5206579","0.5205881","0.51940256","0.5173827","0.5168922","0.5160755","0.5158483","0.5147272","0.5146886","0.5140863","0.51370615","0.5132332","0.5123592","0.5123586","0.51201427","0.5113951","0.5111474","0.51018375","0.509629","0.50961757","0.5075239","0.50711995","0.5057692","0.5056318","0.50536394","0.5038723","0.50383174","0.50320154","0.5022032","0.5011876","0.5010254","0.50086874","0.50056624","0.5005347","0.500419","0.49970108","0.49968022","0.49966112","0.49964076"],"string":"[\n \"0.6792026\",\n \"0.65144795\",\n \"0.64973646\",\n \"0.64911294\",\n \"0.63963395\",\n \"0.6301948\",\n \"0.6170404\",\n \"0.61197406\",\n \"0.6097923\",\n \"0.6078981\",\n \"0.6054153\",\n \"0.598611\",\n \"0.5985679\",\n \"0.5947528\",\n \"0.5931075\",\n \"0.5913055\",\n \"0.5898607\",\n \"0.5834762\",\n \"0.5772921\",\n \"0.5737773\",\n \"0.57252026\",\n \"0.57127255\",\n \"0.5683384\",\n \"0.5667694\",\n \"0.5659907\",\n \"0.5653415\",\n \"0.56388396\",\n \"0.56335443\",\n \"0.5608183\",\n \"0.55998176\",\n \"0.55972785\",\n \"0.5589549\",\n \"0.5573224\",\n \"0.55649984\",\n \"0.5563879\",\n \"0.5559318\",\n \"0.55437225\",\n \"0.5536844\",\n \"0.5512505\",\n \"0.54618233\",\n \"0.54529274\",\n \"0.5447986\",\n \"0.5432676\",\n \"0.5376404\",\n \"0.53744876\",\n \"0.53661376\",\n \"0.5359423\",\n \"0.53570366\",\n \"0.53444535\",\n \"0.5343244\",\n \"0.53318053\",\n \"0.5322798\",\n \"0.53226423\",\n \"0.53187895\",\n \"0.5316374\",\n \"0.5304725\",\n \"0.52673787\",\n \"0.5250515\",\n \"0.52493465\",\n \"0.5219658\",\n \"0.52180266\",\n \"0.5206579\",\n \"0.5205881\",\n \"0.51940256\",\n \"0.5173827\",\n \"0.5168922\",\n \"0.5160755\",\n \"0.5158483\",\n \"0.5147272\",\n \"0.5146886\",\n \"0.5140863\",\n \"0.51370615\",\n \"0.5132332\",\n \"0.5123592\",\n \"0.5123586\",\n \"0.51201427\",\n \"0.5113951\",\n \"0.5111474\",\n \"0.51018375\",\n \"0.509629\",\n \"0.50961757\",\n \"0.5075239\",\n \"0.50711995\",\n \"0.5057692\",\n \"0.5056318\",\n \"0.50536394\",\n \"0.5038723\",\n \"0.50383174\",\n \"0.50320154\",\n \"0.5022032\",\n \"0.5011876\",\n \"0.5010254\",\n \"0.50086874\",\n \"0.50056624\",\n \"0.5005347\",\n \"0.500419\",\n \"0.49970108\",\n \"0.49968022\",\n \"0.49966112\",\n \"0.49964076\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5021449"},"document_rank":{"kind":"string","value":"90"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":804,"numItemsPerPage":100,"numTotalItems":95772,"offset":80400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIyMjM5NSwianRpIjoiOTQyZTcxYmEtYjE5YS00ZmNjLTgyZDYtZjQ4ZGQ4YTc0OWFhIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMjU5OTUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.EwqS8f2X0_b4kgjTr_kiJyZpEAt5TinNuIrSLMqvHt_bD8hz3_MI9PY0X_EtxIGwdPewfLS5vIkMGjE4-E1iAA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
First test to see if the ObjectRegister works | def test_object_register():
dummy = "Stub"
value = " "
o_reg = ObjectRegister()
o_reg[dummy] = value
assert dummy in o_reg # is dummy in register?
assert o_reg.Stub == value # is value correct?
o_reg.Stub = 1 # can I change the value?
assert o_reg.Stub == 1
# does the setter... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_object(self, obj):\n self.modules.append(obj)",
"def register(self):\n raise NotImplementedError",
"def register(self):\n raise NotImplementedError",
"def test_hook_register(self):\n self.assertEqual(list(self.registry), [])\n\n item = self.DummyItem(123)\n ... | [
"0.6522508",
"0.63567525",
"0.63567525",
"0.63141686",
"0.6301099",
"0.6301099",
"0.62986296",
"0.62878317",
"0.62532073",
"0.62528384",
"0.6219237",
"0.6157695",
"0.61491543",
"0.61461836",
"0.61439496",
"0.6071152",
"0.60204035",
"0.60055697",
"0.59368014",
"0.593578",
"0.5... | 0.72219026 | 0 |
Next lets see if the register_instance works | def test_decorator():
@register_instance
class Manequin:
"""Our test object"""
def __init__(self):
self.value = 1
global Register
korper = Manequin()
assert Register.Manequin # is object in Register?
assert Register.Manequin.value == 1 # is it's value correct?
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_instance(self, instance):\n self.instance = instance",
"def registration_started(self):\n pass",
"def _get_instance(self):",
"def _get_instance(self):",
"def test_registration(self):\n models = [BlogEntry, BlogRoll]\n pubsub.register(models)\n self.assertTrue... | [
"0.7148825",
"0.6755327",
"0.6734898",
"0.6734898",
"0.66697145",
"0.66041887",
"0.6599104",
"0.6571648",
"0.6525776",
"0.65197366",
"0.649908",
"0.649908",
"0.646479",
"0.646479",
"0.64578915",
"0.6387077",
"0.62822306",
"0.627266",
"0.62263185",
"0.6148355",
"0.61444074",
... | 0.6127357 | 23 |
Allocate various types of arrays for the tests | def allocate_arrays(self):
self.prng_state = np.random.get_state()
# Generate arrays of random shape
self.shape1 = np.random.randint(20, high=512, size=(2,))
self.shape2 = np.random.randint(20, high=512, size=(2,))
self.array1 = np.random.rand(*self.shape1).astype(np.float32)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_array( n ):",
"def _init_info_arrays(self, dtype: type) -> Tuple[np.ndarray, np.ndarray]:\n if dtype in [int, float, bool] or issubclass(dtype, np.number):\n array = np.zeros(self.num_envs, dtype=dtype)\n else:\n array = np.zeros(self.num_envs, dtype=object)\n ... | [
"0.6825754",
"0.6444625",
"0.63543457",
"0.63317055",
"0.63061434",
"0.6246702",
"0.6245863",
"0.618402",
"0.61740255",
"0.61383665",
"0.6134396",
"0.61152494",
"0.61099625",
"0.60825396",
"0.60662067",
"0.6044954",
"0.6013208",
"0.6001334",
"0.59249824",
"0.5896931",
"0.5895... | 0.5630084 | 36 |
Test rectangular transfer of self.d_array1 to self.d_array2 | def test_cpy2d(self):
# Reference
o1 = self.offset1
o2 = self.offset2
T = self.transfer_shape
logger.info("""Testing D->D rectangular copy with (N1_y, N1_x) = %s,
(N2_y, N2_x) = %s:
array2[%d:%d, %d:%d] = array1[%d:%d, %d:%d]""" %
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allocate_arrays(self):\n self.prng_state = np.random.get_state()\n # Generate arrays of random shape\n self.shape1 = np.random.randint(20, high=512, size=(2,))\n self.shape2 = np.random.randint(20, high=512, size=(2,))\n self.array1 = np.random.rand(*self.shape1).astype(np.fl... | [
"0.6161169",
"0.59514517",
"0.5920784",
"0.58555543",
"0.584373",
"0.5841961",
"0.5769012",
"0.57632625",
"0.57464427",
"0.5729769",
"0.5712302",
"0.5710319",
"0.5658859",
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"0.56449014",
"0.5638125",
"0.5599991",
"0.55829954",
"0.5496433",
"0.54961497",
"0.5490821... | 0.80503255 | 0 |
PutPlaceOfDeliveryRequest a model defined in OpenAPI | def __init__(self, name=None, address=None, near_address=None, coordinates=None, description=None, is_active=None, is_public=None, preferred_certificates=None, open_times=None, contact_person=None, waybill_authorizations=None, user_custom_data=None, local_vars_configuration=None): # noqa: E501 # noqa: E501
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def put(self, request, *args, **kwargs):\n\n payload = request.data\n\n instance = self.get_object()\n\n # validate request data body length is zero raise error message\n if len(payload) == 0:\n return APIResponse({'message': NOT_FOUND_JSON_DATA}, HTTP_400_BAD_REQUEST)\n\n ... | [
"0.5903997",
"0.5893658",
"0.5852392",
"0.5724939",
"0.55767655",
"0.544517",
"0.53316027",
"0.5291103",
"0.52791566",
"0.52769244",
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"0.5190872",
"0.5157879",
"0.5118747",
"0.5111841",
"0.50886256",
"0.50723624",
"0.5069011"... | 0.0 | -1 |
Sets the name of this PutPlaceOfDeliveryRequest. | def name(self, name):
if self.local_vars_configuration.client_side_validation and name is None: # noqa: E501
raise ValueError("Invalid value for `name`, must not be `None`") # noqa: E501
if (self.local_vars_configuration.client_side_validation and
name is not None and len(n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self, name):\n\n self._set_field(\"name\", name)",
"def name(self, name):\n\n self._set_field(\"name\", name.get_json())",
"def setName(self, name):\n self._name = name",
"def setName(self, name):\n self._name = name",
"def setName(self, name): \n\n self._... | [
"0.6076372",
"0.6032923",
"0.6018987",
"0.6018987",
"0.60096157",
"0.5992969",
"0.5963822",
"0.5963822",
"0.59596694",
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"0.5938504",
"0.59374225",
"0.5912693",
"0.5912693",
"0.5912693",
"0.5912693",
... | 0.0 | -1 |
Sets the address of this PutPlaceOfDeliveryRequest. | def address(self, address):
if self.local_vars_configuration.client_side_validation and address is None: # noqa: E501
raise ValueError("Invalid value for `address`, must not be `None`") # noqa: E501
self._address = address | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_address(self, address):\n pass",
"def address(self, address: object):\n\n self._address = address",
"def address(self, address):\n if address is None:\n raise ValueError(\"Invalid value for `address`, must not be `None`\")\n\n self._address = address",
"def addr... | [
"0.68177485",
"0.6768427",
"0.6641226",
"0.6530036",
"0.6525921",
"0.6525921",
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"0.6444913",
"0.6377512",
"0.6147737",
"0.6036644",
"0.6008579",
"0.60085624",... | 0.6639484 | 3 |
Sets the coordinates of this PutPlaceOfDeliveryRequest. | def coordinates(self, coordinates):
if self.local_vars_configuration.client_side_validation and coordinates is None: # noqa: E501
raise ValueError("Invalid value for `coordinates`, must not be `None`") # noqa: E501
self._coordinates = coordinates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_coordinates(self, coordinates):\n self.coordinates = coordinates",
"def coordinates(self, coordinates):\n\n self._coordinates = coordinates",
"def setCoords(self, coords):\n\n self.coords = coords",
"def set_coordinates(self):\n client = Client(api_key=settings.YANDEX_GEOC... | [
"0.6460083",
"0.62213266",
"0.58959496",
"0.5604809",
"0.5557838",
"0.54878265",
"0.53129894",
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"0.5284653",
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"0.5209307",
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"0.5072016",
"0.5042672",
"0.5032309",
"0.49954793",
"0.49842325",
"0.49693778",
"0.4945690... | 0.6003503 | 2 |
Sets the is_active of this PutPlaceOfDeliveryRequest. | def is_active(self, is_active):
if self.local_vars_configuration.client_side_validation and is_active is None: # noqa: E501
raise ValueError("Invalid value for `is_active`, must not be `None`") # noqa: E501
self._is_active = is_active | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_active(self, is_active: bool):\n if is_active is None:\n raise ValueError(\"Invalid value for `is_active`, must not be `None`\")\n\n self._is_active = is_active",
"def is_active(self, is_active):\n \n self._is_active = is_active",
"def is_active(self, is_active):\n... | [
"0.6953809",
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"0.6882505",
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"0.6220443",
"0.6220443",
"0.6220443",
"0.61376953",
"0.6026736",
"0.5875359... | 0.66459626 | 6 |
Sets the preferred_certificates of this PutPlaceOfDeliveryRequest. | def preferred_certificates(self, preferred_certificates):
self._preferred_certificates = preferred_certificates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _populate_placements(self, preferred):\n # local copy of placement energies\n self._placements = deepcopy(placements)\n \n # top right is the Imhof-approved default\n if preferred == 'top right' or not preferred:\n return\n \n # bump up the cost of ev... | [
"0.518077",
"0.48272905",
"0.47806227",
"0.47716486",
"0.47564572",
"0.44579473",
"0.42863858",
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"0.3873847",
"0.3873847",
"0.3866805",
"0.3838608",
"0.38366878",
"0.3... | 0.7998088 | 0 |
Sets the open_times of this PutPlaceOfDeliveryRequest. | def open_times(self, open_times):
self._open_times = open_times | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open(self, open: float):\n if open is None:\n raise ValueError(\"Invalid value for `open`, must not be `None`\") # noqa: E501\n\n self._open = open",
"def opening_hours(self, opening_hours):\n if self.local_vars_configuration.client_side_validation and opening_hours is None: ... | [
"0.5362166",
"0.53160775",
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"0.43738458",
"0.43536964",
"0.4325754",
"0.4316397",
"0.42769918",
"0.4... | 0.80050266 | 0 |
Sets the contact_person of this PutPlaceOfDeliveryRequest. | def contact_person(self, contact_person):
self._contact_person = contact_person | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def contact(self, contact):\n\n self.logger.debug(\"In 'contact' setter.\")\n\n self._contact = contact",
"def contact(self, contact):\n\n self._contact = contact",
"def contact(self, contact):\n\n self._contact = contact",
"def contact_point(self, contact_point: object):\n\n ... | [
"0.6630224",
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"0.5246978",
"0.5200048",
"0.5163523",
"0.51238424",
"0.50968397",
"0.5069963... | 0.75833505 | 1 |
Sets the user_custom_data of this PutPlaceOfDeliveryRequest. | def user_custom_data(self, user_custom_data):
self._user_custom_data = user_custom_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def custom_data(self, custom_data):\n\n self._custom_data = custom_data",
"def user_defined_data(self, user_defined_data: UserDefinedDatadef):\n\n self._user_defined_data = user_defined_data",
"def replace_user_data(self, new_data):\n self._user_data = new_data",
"def custom_user_id(self... | [
"0.6408397",
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"0.4876837",
"0.48690352",
"0.4847708",
"0.48377663",
"0.483... | 0.7539687 | 0 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751457",
"0.7751457",
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"0.72970754",
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"0.7044403",
"0.70337415",
"0.7023439",
"0.6964421",
"0.69585776",
"0.6... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.8585799",
"0.7814791",
"0.77903897",
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"0.7487798",
"0.7487798",
"0.7470098",
"0.74518627",
"0.7446157... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.75569",
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"0.66583663",
"0.66097337",
"0.66073257",
"0.66028357",
"0.6562171... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, PutPlaceOfDeliveryRequest):
return False
return self.to_dict() == other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
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"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
if not isinstance(other, PutPlaceOfDeliveryRequest):
return True
return self.to_dict() != other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.8457167",
"0.8393036",
"0.81455255",
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"0.80435175",
"0.80435175",
"0.80435175",
"0.80435175",
"0.80435175",
"0.8043... | 0.0 | -1 |
Rearragne data matrix X of size (n_stylesdim_x, n_contents) to (n_styles, n_contents, dim_x) | def to_3dim(X: torch.Tensor, target_size: Tuple[int, int, int], dtype=torch.float32) -> torch.Tensor:
assert X.ndim == 2
n_styles, n_contents, dim_x = target_size
assert X.shape[0] == n_styles * dim_x
assert X.shape[1] == n_contents
target = torch.zeros(target_size, dtype=X.dtype)
for s in ran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reshape_mat(self, data):\n if len(data['x'][0]) < self.num_ofdim:\n x = data['x']\n x_plus = np.zeros([len(x), self.num_ofdim - len(x[0])])\n data['x'] = np.append(x, x_plus, axis=1)\n return data",
"def resh(x):\r\n a = x.shape[0]\r\n b = x.shape[1]\r\n ... | [
"0.58718014",
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"0.5412315",
"0.53561217",
"0.5170308",
"0.51700735",
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"0.48594663",
"0.48462933",
"0.48333767",
"0.48239657",
"0.48218593",
"0... | 0.5035376 | 8 |
Training logic for an epoch | def _train_epoch(self, epoch):
self.model.train()
self.train_metrics.reset()
for batch_idx, (data, target_seg, target_class) in enumerate(self.data_loader):
data, target_seg, target_class = data.to(self.device), target_seg.to(self.device), target_class.to(self.device)
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _train_epoch(self, epoch):\n raise NotImplementedError",
"def _train_epoch(self, epoch):\n raise NotImplementedError",
"def _train_epoch(self, epoch):\n raise NotImplementedError",
"def _train_epoch(self, epoch):\n raise NotImplementedError",
"def train_one_epoch(self):\n ... | [
"0.84083605",
"0.84083605",
"0.84083605",
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"0.81284416",
"0.8044275",
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"0.7620618",
"0.7620618",
"0.7620618",
"0.7620618",
"0.7620618",
"0.7610982... | 0.7407834 | 49 |
Validate after training an epoch | def _valid_epoch(self, epoch):
self.model.eval()
self.valid_metrics.reset()
with torch.no_grad():
for batch_idx, (data, target_seg, target_class) in enumerate(self.valid_data_loader):
data, target_seg, target_class = data.to(self.device), target_seg.to(self.device), t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validation_epoch(self):\n self.model.eval()\n\n # Compute for training set\n train_loss, train_acc = compute_loss_and_accuracy(\n self.dataloader_train, self.model, self.loss_criterion\n )\n self.TRAIN_ACC.append(train_acc)\n self.TRAIN_LOSS.append(train_los... | [
"0.79534644",
"0.78457445",
"0.7790192",
"0.76798767",
"0.7650407",
"0.75477976",
"0.75045013",
"0.7459676",
"0.74196595",
"0.73464423",
"0.729629",
"0.72881",
"0.7285671",
"0.7259691",
"0.7237066",
"0.72170967",
"0.717824",
"0.7166362",
"0.7154711",
"0.71380985",
"0.71259",
... | 0.7779426 | 3 |
format and display input data on tensorboard | def _visualize_input(self, input):
self.writer.add_image('input', make_grid(input[0, 0, :, :], nrow=8, normalize=True)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format():\n train_data.info() # retreiving general info about train data\n print('Number of rows and columns of train set:', train_data.shape)\n print('dtype: ', train_data.dtypes) # analyse data type of each column\n print('First 3 rows of the train data:', train_data.head(3))",
"def showInput... | [
"0.69057506",
"0.6352291",
"0.62028694",
"0.5871135",
"0.5687523",
"0.5672438",
"0.56449336",
"0.56428796",
"0.5618645",
"0.5578692",
"0.55682015",
"0.55551356",
"0.55546844",
"0.5553714",
"0.5549513",
"0.55420536",
"0.5539764",
"0.55274934",
"0.55232847",
"0.5508486",
"0.548... | 0.6598129 | 1 |
format and display output and target data on tensorboard | def _visualize_prediction(self, input, output, target):
out_b1 = binary(output)
out_b1 = impose_labels_on_image(input[0, 0, :, :], target[0, :, :], out_b1[0, 1, :, :])
self.writer.add_image('output', make_grid(out_b1, nrow=8, normalize=False)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def args_to_tensorboard(writer, args):\n\n txt = \"\"\n for arg in vars(args):\n txt += arg + \": \" + str(getattr(args, arg)) + \"<br/>\"\n\n writer.add_text('command_line_parameters', txt, 0)",
"def to_tensorboard(self, **kwargs) -> 'PlotLosses':\n self.outputs.append(outputs.Tensorboard... | [
"0.6666576",
"0.66039413",
"0.65872866",
"0.65118855",
"0.64563",
"0.6371887",
"0.6223408",
"0.6148363",
"0.61152786",
"0.60947365",
"0.60759556",
"0.60550654",
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"0.5932281",
"0.59251475",
"0.590935",
"0.5873628",
"0.5835232",
"0.5829114",
"0.5814904... | 0.5765577 | 26 |
Do not return anything, modify s inplace instead. | def reverseString(self, s) -> None:
# 방법 1
s.reverse()
# 방법 2
# half_len = int(len(s) / 2)
# for i in range(half_len):
# temp = s[i]
# s[i] = s[len(s) - 1 - i]
# s[len(s) - 1 - i] = temp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def r(self, s):\n\t\tif self.m() > 0:\n\t\t\tself.setto(s)",
"def copy(self):\r\n return self.replace()",
"def test_string_inplace_update(self):\r\n vm = String.value_manager(None, None, 'str')\r\n assert not vm.changed\r\n vm.value += 's'\r\n assert vm.changed",
"def setto... | [
"0.627839",
"0.5948614",
"0.59482366",
"0.59214234",
"0.58858985",
"0.5761662",
"0.57322884",
"0.5647384",
"0.5646522",
"0.55934894",
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"0.5444118",
"0.5401756",
"0.5359094",
"0.5344482",
"0.5342857",
"0.53393996... | 0.49923488 | 48 |
Replaces the crypto key in the keys/ folder. This key has to be sync'ed across all cloud instances until we use proper key management. This function intentionally has no command line option due to its rarity. | def write_key():
key = fernet.Fernet.generate_key()
keyfile = open(KEY_PATH,'wb')
keyfile.write(key)
keyfile.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replace_key(newKey, oldKey, filename, ssh):\n ssh.exec_command(\"sed -i 's;%s;%s;g' %s\" % (oldKey, newKey, filename))",
"def test_rekey_defaults(self, settings):\n old_key = b'0' * 32\n new_key = b'1' * 32\n\n settings.CHITON_ENCRYPTION_KEY = new_key\n settings.CHITON_PREVIOUS... | [
"0.63903826",
"0.5915563",
"0.57853013",
"0.57296944",
"0.5701325",
"0.5640512",
"0.5525865",
"0.5520725",
"0.5461842",
"0.54392695",
"0.5428072",
"0.5419951",
"0.540152",
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"0.53822595",
"0.5379103",
"0.5364567",
"0.536304",
"0.5328512",
"0.53189105",
"0.5316111"... | 0.51862335 | 28 |
Returns the encrypted version of the given raw string. | def encrypt_string(self, raw_string):
return self.fernet_instance.encrypt(raw_string.encode('utf-8')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encrypted(data: str) -> str:\n return b64encode(data.encode('ascii')).decode('ascii')",
"def encrypt(self, string):\n return self.__Cipher(self.OP_ENCRYPT).update(string)",
"def __decrypt(string: str) -> str:\n key = 171\n result = \"\"\n i: int\n for i in string:\... | [
"0.68234164",
"0.6811647",
"0.674292",
"0.6731018",
"0.65214574",
"0.6518302",
"0.64426047",
"0.6384672",
"0.6362766",
"0.6348285",
"0.6306222",
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"0.62036544",
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"0.61147386",
"0.60869455",
"0.6019685",
"0.6001533",
"0.5989513",
"0.59647626",
"0.595060... | 0.81583804 | 0 |
Returns the raw version of the given encrypted string. | def decrypt_string(self, encrypted_string):
return self.fernet_instance.decrypt(encrypted_string.encode('utf-8')).decode('utf-8') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encrypt_string(self, raw_string):\n return self.fernet_instance.encrypt(raw_string.encode('utf-8'))",
"def encrypted(data: str) -> str:\n return b64encode(data.encode('ascii')).decode('ascii')",
"def raw_password_to_string(raw_string):\n return hashlib.sha256(str(raw_string).encode('utf-8')).h... | [
"0.71437955",
"0.65881705",
"0.6477882",
"0.64496297",
"0.63538414",
"0.62263876",
"0.60818547",
"0.60766107",
"0.6060412",
"0.6047937",
"0.60411847",
"0.59877646",
"0.59608394",
"0.587944",
"0.58562565",
"0.5847599",
"0.5836144",
"0.5808924",
"0.5767597",
"0.5747277",
"0.573... | 0.6516151 | 2 |
Token builder helper class to create digest tokens that can be used with the Phenix platform. | def __init__(self):
self.application_id = None
self.secret = None
self.token = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(self):\n token = DigestTokens()\n\n if not self.application_id:\n raise ValueError('application_id must be set using the \\\n \"with_application_id\" method before calling \"build\"')\n\n if not self.secret:\n raise ValueError('secret... | [
"0.7009748",
"0.6283412",
"0.61528003",
"0.59743154",
"0.59382457",
"0.5930618",
"0.59070903",
"0.5873708",
"0.5859789",
"0.58429897",
"0.58213156",
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"0.5655496",
"0.56375253",
"0.5617134",
"0.55772907",
"0.5519892",
"0.5487191",
"0.54808... | 0.0 | -1 |
The application ID used to sign the token. (required) | def with_application_id(self, application_id):
if not isinstance(application_id, str):
raise TypeError('Application Id must be a string')
self.application_id = application_id
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ApplicationId(self) -> _n_0_t_0:",
"def app_id(self) -> str:\n return self._app_id",
"def app_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"app_id\")",
"def app_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"app_id\")",
"def app_id(self):\n return s... | [
"0.73115987",
"0.72504157",
"0.7124278",
"0.70661306",
"0.7051917",
"0.7039097",
"0.69642985",
"0.69584894",
"0.6954219",
"0.6904534",
"0.68569523",
"0.6851584",
"0.67558306",
"0.6727432",
"0.6726882",
"0.6648573",
"0.6648573",
"0.6648573",
"0.6524289",
"0.64912724",
"0.64359... | 0.0 | -1 |
The secret used to sign the token. (required) | def with_secret(self, secret):
if not isinstance(secret, str):
raise TypeError('Secret must be a string')
self.secret = secret
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def secret(self):\n return self._secret",
"def signing_secret(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"signing_secret\")",
"def secret(self) -> str:\n return pulumi.get(self, \"secret\")",
"def secret(self) -> str:\n return pulumi.get(self, \"secret\")",
... | [
"0.76498646",
"0.75493485",
"0.7544454",
"0.7544454",
"0.75068426",
"0.74685127",
"0.7464547",
"0.7414411",
"0.7377902",
"0.7368211",
"0.71865225",
"0.71200866",
"0.7114842",
"0.7114842",
"0.71128416",
"0.71058685",
"0.7092571",
"0.7067026",
"0.7067026",
"0.70635796",
"0.6999... | 0.0 | -1 |
The backend URI. (optional) | def with_uri(self, uri):
if not isinstance(uri, str):
raise TypeError('URI must be a string')
self.token['uri'] = uri
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getBackend(self):\n return self.header['BACKEND']",
"def backend(self) -> str:\n return self.__class__.BACKEND_NAME",
"def get_backend():\n return _BACKEND",
"def backend(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"backend\")",
"def get_backend():\n return __SETTING... | [
"0.73152083",
"0.7286011",
"0.7179595",
"0.69450295",
"0.680573",
"0.6770195",
"0.67253417",
"0.6694524",
"0.65874857",
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"0.6385044",
"0.638325",
"0.6379678",
"0.6366209",
"0.6364358",
"0.63564456",
"0.63564456... | 0.0 | -1 |
Set a capability for the token, e.g. to publish a stream. (optional) | def with_capability(self, capability):
if not isinstance(capability, str):
raise TypeError('Capability must be a string')
token = self.token
capabilities = set(token['capabilities']) if 'capabilities' in token else set([])
capabilities.add(capability)
self.token['c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_capabilities(self, capabilities: WlSeat.capability) -> None:\n lib.wlr_seat_set_capabilities(self._ptr, capabilities)",
"def addCapability(self, capability):\n self.capabilities.add(capability)",
"def _add_capability(self, msg, identity):\n\n # FIXME retoken on token collision with... | [
"0.62619406",
"0.6175175",
"0.6047601",
"0.5966227",
"0.5726368",
"0.5592567",
"0.5539734",
"0.55156046",
"0.5501202",
"0.5486496",
"0.548406",
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"0.536398",
"0.53598034",
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"0.5254867",
"0.5194692",
"0.5161786",
"0.5160273",
"0.5152895",
"0.5136831",
... | 0.70643526 | 0 |
Expires the token in the given time. | def expires_in_seconds(self, seconds):
if not isinstance(seconds, int):
raise TypeError('Seconds must be an int')
self.token['expires'] = int((datetime.now().timestamp() + seconds)*1000.0)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expire(self):\n logging.debug(\"Expiring token as wanted...\")\n self.expiration = datetime.now() - timedelta(seconds=(10))",
"def _set_token_expiration_time(self, expires_in):\n self.token_expiration_time = dt.datetime.utcnow() + dt.timedelta(0, expires_in) # timedelta(days, seconds)",... | [
"0.76917213",
"0.6979363",
"0.6799405",
"0.67159325",
"0.66909903",
"0.65411913",
"0.6493937",
"0.64727396",
"0.6457241",
"0.6428322",
"0.62765664",
"0.62321156",
"0.61634",
"0.6146268",
"0.61437196",
"0.6119173",
"0.60557485",
"0.6051389",
"0.5999419",
"0.5987485",
"0.596032... | 0.623812 | 11 |
Expires the token at the given dateime | def expires_at(self, ex_datetime):
if not isinstance(ex_datetime, datetime):
raise TypeError('datetime must be a valid date')
self.token['expires'] = int(ex_datetime.timestamp()*1000.0)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expire(self):\n logging.debug(\"Expiring token as wanted...\")\n self.expiration = datetime.now() - timedelta(seconds=(10))",
"def _set_token_expiration_time(self, expires_in):\n self.token_expiration_time = dt.datetime.utcnow() + dt.timedelta(0, expires_in) # timedelta(days, seconds)",... | [
"0.74925524",
"0.6965725",
"0.6959143",
"0.6788541",
"0.6774638",
"0.67563754",
"0.66536474",
"0.6632384",
"0.660434",
"0.6483804",
"0.6436141",
"0.64021134",
"0.6360263",
"0.6308414",
"0.6307253",
"0.6307253",
"0.6266561",
"0.6252453",
"0.62354916",
"0.6176458",
"0.61094713"... | 0.6907802 | 3 |
Limit the token to authentication only. (optional) | def for_authenticate_only(self):
self.token['type'] = 'auth'
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _require_login(self):\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + str(self.token))",
"def auth_token(self):",
"def _validate_token(self):\n if not self.token:\n self.login()\n if not self.token:\n # TODO: create exception for this\n # Access... | [
"0.7105954",
"0.6667656",
"0.6514181",
"0.6469955",
"0.64583325",
"0.63503695",
"0.63232756",
"0.62646914",
"0.6217762",
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"0.6010944",
"0.60016906",
"0.5979614",
"0.5959321",
"0.594469",
"0.5930242",
"0.5928187",... | 0.7511397 | 0 |
Limit the token to streaming only. (optional) | def for_streaming_only(self):
self.token['type'] = 'stream'
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, limitedstream):\n self.limitedstream = limitedstream",
"def server_streaming(self) -> global___Snippet.ServerStreaming:",
"def streaming_request(self) -> global___Snippet.SimpleRequestInitialization:",
"def _limit_helper(stream: Union[BinaryIO, Generator, List], limit: int) -> Gener... | [
"0.60577446",
"0.576815",
"0.57674104",
"0.56763136",
"0.56058735",
"0.5560593",
"0.54444104",
"0.5395247",
"0.53115624",
"0.5293115",
"0.5288135",
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"0.5235168",
"0.52204764",
"0.5201178",
"0.5187488",
"0.5171711",
"0.51633424",
"0.5124375",
"0.5108288",
"0.507085... | 0.7863806 | 0 |
Limit the token to publishing only. (optional) | def for_publishing_only(self):
self.token['type'] = 'publish'
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_add_permission(self, request):\n if not settings.PUBLISHER_CODE:\n return False\n return super().has_add_permission(request)",
"def is_public_token(token):\n return (\n token.payload\n and token.payload.get(\"roles\") == [NONE]\n and token.payload.get(\"co... | [
"0.54844",
"0.54165137",
"0.5385421",
"0.5319278",
"0.53190047",
"0.5302831",
"0.51823705",
"0.5133519",
"0.5131273",
"0.5131162",
"0.51177657",
"0.50790566",
"0.50438625",
"0.5034893",
"0.500718",
"0.4995648",
"0.49872646",
"0.4960588",
"0.4882561",
"0.4860834",
"0.48247495"... | 0.79604816 | 0 |
Limit the token to the specified session ID. (optional) | def for_session(self, session_id):
if not isinstance(session_id, str):
raise TypeError('Session Id must be a string')
self.token['sessionId'] = session_id
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_token(token):\n session.token = token",
"def get_token(self, session, **kwargs):\n return None",
"def login_token(self, token):\n self.token = token # this will also set the refresh_token to None",
"def set_mobile_session(id_token):\n try:\n user = google.oauth2.id_tok... | [
"0.59704345",
"0.5825555",
"0.55893874",
"0.5477177",
"0.536369",
"0.5326675",
"0.52724814",
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"0.52193546",
"0.5193504",
"0.51333624",
"0.5124961",
"0.512454",
"0.5087074",
"0.5080296",
"0.50701594",
"0.5069392",
"0.50401187",
"0.50152",
"0.50027496",
"0.4987716"... | 0.6013787 | 0 |
Limit the token to the specified remote address. (optional) | def for_remote_address(self, remote_address):
if not isinstance(remote_address, str):
raise TypeError('Remote Address must be a string')
self.token['remoteAddress'] = remote_address
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_token_info_remote(self, token_info_url):",
"def request(self, token):\n pass",
"def RemoteEndPoint(self) -> _n_5_t_2:",
"def RemoteEndPoint(self) -> _n_5_t_1:",
"def RemoteEndPoint(self) -> _n_5_t_1:",
"def request_token_url(self): # pragma: no cover\n raise NotImplementedError()",... | [
"0.54644215",
"0.5210578",
"0.5100081",
"0.5062072",
"0.5062072",
"0.49818325",
"0.49592954",
"0.4892904",
"0.48131654",
"0.48083392",
"0.47912484",
"0.4739382",
"0.47248766",
"0.47165176",
"0.46989724",
"0.4686481",
"0.4672394",
"0.46668348",
"0.46643445",
"0.46341494",
"0.4... | 0.6179291 | 0 |
Limit the token to the specified origin stream ID. (optional) | def for_origin_stream(self, origin_stream_id):
if not isinstance(origin_stream_id, str):
raise TypeError('Origin Stream Id must be a string')
self.token['originStreamId'] = origin_stream_id
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def for_streaming_only(self):\n self.token['type'] = 'stream'\n\n return self",
"def origin_id(self, origin_id):\n\n self._origin_id = origin_id",
"def token_id_from(self, token_id_from):\n\n self._token_id_from = token_id_from",
"def get_channel_stream(self, stream_args, origin):... | [
"0.5534165",
"0.54561955",
"0.53313065",
"0.49888474",
"0.49621314",
"0.4875266",
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"0.47702262",
"0.47550067",
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"0.46063805",
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"0.45823655",
"0.45739326",
"0.45626962",
"0.4554549",
"0.4541904",
"0.4... | 0.7007976 | 0 |
Limit the token to the specified channel ID. (optional) | def for_channel(self, channel_id):
if not isinstance(channel_id, str):
raise TypeError('Channel ID must be a string')
self.for_tag('channelId:{}'.format(channel_id))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def cclimit(self, ctx, limit_amount: int = None):\n if limit_amount is None:\n return await ctx.send_help()\n if limit_amount < 0:\n return await ctx.send(\"You need to use a number larger than 0.\")\n await self.config.limit.set(limit_amount)\n await ctx.sen... | [
"0.5936086",
"0.58824795",
"0.57529366",
"0.57121193",
"0.57084006",
"0.57061315",
"0.5675261",
"0.55601346",
"0.55402774",
"0.55343854",
"0.544267",
"0.54274446",
"0.53523386",
"0.5313491",
"0.5306997",
"0.52759963",
"0.52693397",
"0.52605224",
"0.5251249",
"0.5242523",
"0.5... | 0.50766903 | 28 |
Limit the token to the specified channel alias. (optional) | def for_channel_alias(self, channel_alias):
if not isinstance(channel_alias, str):
raise TypeError('Channel Alias must be a string')
self.for_tag('channelAlias:{}'.format(channel_alias))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def channel_access_token(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"channel_access_token\")",
"async def cclimit(self, ctx, limit_amount: int = None):\n if limit_amount is None:\n return await ctx.send_help()\n if limit_amount < 0:\n return await ... | [
"0.56562996",
"0.5192465",
"0.5055396",
"0.5030429",
"0.48632553",
"0.4810923",
"0.47978848",
"0.47668964",
"0.4738913",
"0.46901718",
"0.46724027",
"0.4672042",
"0.46369606",
"0.46153092",
"0.45997238",
"0.45752332",
"0.4561803",
"0.4559696",
"0.4552768",
"0.45245716",
"0.45... | 0.5702281 | 0 |
Limit the token to the specified room ID. (optional) | def for_room(self, room_id):
if not isinstance(room_id, str):
raise TypeError('Room ID must be a string')
self.for_tag('roomId:{}'.format(room_id))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def room_to_read_only(self, room_id):\n # set room to read-only\n response = requests.patch(\n f\"{self.uri}/rooms/{room_id}/attribute/id/text\",\n json={\"attribute\": \"readonly\", \"value\": \"True\"},\n headers={\"Authorization\": f\"Bearer {self.token}\"},\n ... | [
"0.55621105",
"0.5426208",
"0.51994264",
"0.51904845",
"0.5157681",
"0.51059484",
"0.50616014",
"0.50450844",
"0.5039581",
"0.50214154",
"0.49993423",
"0.49977985",
"0.49938306",
"0.49395907",
"0.49322966",
"0.48919842",
"0.48741508",
"0.48704398",
"0.48625404",
"0.4838289",
... | 0.5008069 | 10 |
Limit the token to the specified room alias. (optional) | def for_room_alias(self, room_alias):
if not isinstance(room_alias, str):
raise TypeError('Room Alias must be a string')
self.for_tag('roomAlias:{}'.format(room_alias))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def deletealias(self, ctx, alias, mem_id: int, mode=\"idol\"):\n alias = alias.replace(\"_\", \" \")\n if mode.lower() in [\"idol\", \"member\", \"members\", \"idols\"]:\n obj = await self.ex.u_group_members.get_member(mem_id)\n name = f\"{obj.full_name} ({obj.stage_name})... | [
"0.48553815",
"0.47974828",
"0.47712502",
"0.4765508",
"0.47145182",
"0.46774754",
"0.46155646",
"0.46057123",
"0.46009037",
"0.45790952",
"0.45714125",
"0.45700288",
"0.45267746",
"0.4501701",
"0.44909224",
"0.4374284",
"0.43716162",
"0.43599826",
"0.4358302",
"0.43562332",
... | 0.57757866 | 0 |
Limit the token to the specified tag on the origin stream. (optional) | def for_tag(self, tag):
if not isinstance(tag, str):
raise TypeError('Tag must be a string')
self.token['requiredTag'] = tag
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_token(self, context, token):",
"def for_streaming_only(self):\n self.token['type'] = 'stream'\n\n return self",
"def updateTag(self, authenticationToken, tag):\r\n pass",
"def token(self, token):\n\n self._token = token",
"def token(self, token):\n\n self._token = to... | [
"0.57777685",
"0.5351099",
"0.5214884",
"0.5155102",
"0.5155102",
"0.4888451",
"0.4850415",
"0.48312423",
"0.47413683",
"0.47393548",
"0.4733733",
"0.47291398",
"0.4727417",
"0.47127685",
"0.46923047",
"0.46893948",
"0.46803626",
"0.4658257",
"0.4621659",
"0.46118456",
"0.458... | 0.4747601 | 8 |
Apply the tag to the stream when it is setup. (optional) | def apply_tag(self, tag):
if not isinstance(tag, str):
raise TypeError('Tag must be a string')
token = self.token
apply_tags = set(token['applyTags']) if 'applyTags' in token else set()
apply_tags.add(tag)
self.token['applyTags'] = list(apply_tags)
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _HandleTag(self, input_line, tag, output_stream):\n if tag not in self._open_tags:\n self._OpenTag(input_line, tag, output_stream)\n else:\n self._CloseTag(input_line, tag, output_stream)",
"def tag(self, tag):\n self.tag = tag",
"def tag(self, tag):\n\n self._tag = tag",
"d... | [
"0.61478925",
"0.6118014",
"0.6011416",
"0.6011416",
"0.6011416",
"0.6011416",
"0.5999301",
"0.5905584",
"0.58045685",
"0.57613564",
"0.5724124",
"0.56950116",
"0.56664157",
"0.5655396",
"0.56345576",
"0.563412",
"0.5623479",
"0.5594282",
"0.5590867",
"0.55425113",
"0.5542511... | 0.49773872 | 58 |
Build the signed token | def build(self):
token = DigestTokens()
if not self.application_id:
raise ValueError('application_id must be set using the \
"with_application_id" method before calling "build"')
if not self.secret:
raise ValueError('secret must be set using... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_signature(self):\n sig_contents = \\\n self.payload + \".\" + \\\n b64encode(b\"application/xml\").decode(\"ascii\") + \".\" + \\\n b64encode(b\"base64url\").decode(\"ascii\") + \".\" + \\\n b64encode(b\"RSA-SHA256\").decode(\"ascii\")\n sig_hash... | [
"0.6664714",
"0.64297086",
"0.636979",
"0.6257425",
"0.6235636",
"0.62269235",
"0.6215452",
"0.620243",
"0.61873454",
"0.6109761",
"0.6107653",
"0.6098172",
"0.605793",
"0.6022945",
"0.5983509",
"0.5927956",
"0.5914759",
"0.5901994",
"0.58837533",
"0.58827204",
"0.5882324",
... | 0.74539524 | 0 |
make sure the shuffled sequence does not lose any elements | def test_decode(self):
self.assertEqual(
hex_to_b64(self.hex_string),
self.expect_result
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_shuffle(self):\n random.shuffle(self.seq)\n self.seq.sort()\n self.assertEqual(self.seq, range(10))",
"def test_shuffle(self):\n r = self.RNA(\"UUUUCCCCAAAAGGGG\")\n s = r.shuffle()\n self.assertNotEqual(r, s)\n self.assertEqualItems(r, s)",
"def test_s... | [
"0.75482666",
"0.74879754",
"0.71228105",
"0.70709866",
"0.7006968",
"0.6793258",
"0.67546386",
"0.6753882",
"0.66764843",
"0.6596472",
"0.6590754",
"0.6590754",
"0.65671426",
"0.6523371",
"0.6473894",
"0.6473151",
"0.6466011",
"0.6466011",
"0.64310604",
"0.64198136",
"0.6419... | 0.0 | -1 |
creates a new compilation engine with the given input and output | def __init__(self, inpt, outpt):
self.tokenizer = JackTokenizer(inpt)
self.symbol_table = SymbolTable()
self.code_writer = VMWriter(outpt)
self.class_name = None
self.label_index = 0
self.compile_class() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_engine(self, input):\n return",
"def __create_execution_engine(self):\n target = self.binding.Target.from_default_triple()\n target_machine = target.create_target_machine()\n backing_mod = binding.parse_assembly(\"\")\n engine = binding.create_mcjit_compiler(backing_... | [
"0.68125397",
"0.65761006",
"0.651195",
"0.651195",
"0.651195",
"0.6220326",
"0.60901946",
"0.6087107",
"0.6024652",
"0.5952252",
"0.5927771",
"0.5926563",
"0.5822195",
"0.5791559",
"0.57531047",
"0.5751278",
"0.5742589",
"0.57242006",
"0.57177174",
"0.5706596",
"0.5705054",
... | 0.58511925 | 12 |
compiles a complete class | def compile_class(self):
self.tokenizer.advance() # ignore 'class' keyword
self.class_name = self.tokenizer.identifier()
self.tokenizer.advance()
self.tokenizer.advance() # ignore '{' symbol
while self.tokenizer.curtok < len(self.tokenizer.tokens) - 1:
dec = s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_class(self):\n\t\t\n\t\txml = '<class>\\n' + self.tokenizer.keyword() + self.tokenizer.identifier() + self.tokenizer.symbol()\n\n\t\tself.outfile.write(xml)",
"def compile(self):\n\n\t\twhile(self.tokenizer.has_more_tokens()):\n\n\t\t\tif self.tokenizer.get_token() == 'class':\n\t\t\t\tself.compile_c... | [
"0.7345077",
"0.70924985",
"0.6796033",
"0.6782586",
"0.658691",
"0.6350718",
"0.6341831",
"0.62512016",
"0.6195713",
"0.6145655",
"0.61391705",
"0.61391705",
"0.61386114",
"0.605397",
"0.605353",
"0.5922235",
"0.59199834",
"0.5835646",
"0.5799892",
"0.57497466",
"0.57380176"... | 0.75520575 | 0 |
compiles a complete method, function or constructor | def compile_subroutine(self):
self.symbol_table.start_subroutine()
func_type = self.tokenizer.key_word()
self.tokenizer.advance()
self.tokenizer.advance() # ignore the return type of the function
func_name = self.class_name + "." + self.tokenizer.identifier()
self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile(self):\n raise NotImplementedError()",
"def __compile_subroutine_body(self):\r\n self.compile_statements()",
"def compile(self):\n\n\t\twhile(self.tokenizer.has_more_tokens()):\n\n\t\t\tif self.tokenizer.get_token() == 'class':\n\t\t\t\tself.compile_class()\n\t\t\telif self.tokenizer.... | [
"0.7122685",
"0.70046073",
"0.6941051",
"0.69288325",
"0.69202787",
"0.6875751",
"0.6820653",
"0.6811628",
"0.66644967",
"0.6570711",
"0.65058327",
"0.6425481",
"0.6422159",
"0.63993543",
"0.6328704",
"0.63063186",
"0.63039833",
"0.6284907",
"0.6284418",
"0.62655216",
"0.6241... | 0.70149505 | 1 |
compiles a static declaration or a field declaration | def compile_var_dec(self):
var_kind = SymbolTable.KEY2ENUM[self.tokenizer.key_word()]
self.tokenizer.advance()
tok_type = self.tokenizer.token_type()
if tok_type == JackTokenizer.KEYWORD_T:
var_type = self.tokenizer.key_word()
else:
var_type = self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_class(self):\r\n self.tokenizer.advance() # ignore 'class' keyword\r\n self.class_name = self.tokenizer.identifier()\r\n self.tokenizer.advance()\r\n self.tokenizer.advance() # ignore '{' symbol\r\n while self.tokenizer.curtok < len(self.tokenizer.tokens) - 1:\r\n ... | [
"0.5971691",
"0.56881565",
"0.5662439",
"0.5661217",
"0.55370194",
"0.54972905",
"0.5491543",
"0.537461",
"0.5179423",
"0.51772547",
"0.51236063",
"0.50624144",
"0.4992937",
"0.49721813",
"0.4969132",
"0.4961225",
"0.49396598",
"0.49110854",
"0.48925692",
"0.48781055",
"0.487... | 0.46924675 | 36 |
compiles a (possibly empty) parameter list not including parentheses | def compile_parameter_list(self):
var_type = None
while True:
self.tokenizer.advance()
tok_type = self.tokenizer.token_type()
if tok_type == JackTokenizer.KEYWORD_T:
var_type = self.tokenizer.key_word()
elif tok_type == JackTokenizer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_parameter_list(self) -> None:\n if self._get_current_token() != ')':\n var_type = self._get_current_token()\n self._consume_type()\n\n self.table.define(self._get_current_token(), var_type, Kind.ARG)\n self._consume(TokenTypes.IDENTIFIER)\n ... | [
"0.70801806",
"0.6877128",
"0.6378339",
"0.6040842",
"0.59524256",
"0.5888855",
"0.58112115",
"0.5777761",
"0.5720453",
"0.5696444",
"0.5536624",
"0.54903793",
"0.5452463",
"0.5431968",
"0.5419633",
"0.54116756",
"0.54111105",
"0.54099673",
"0.5350463",
"0.5343245",
"0.534324... | 0.6088443 | 3 |
compiles a sequence of statements, not including the enclosing "{}" | def compile_statements(self):
tok_type = self.tokenizer.token_type()
while tok_type == JackTokenizer.KEYWORD_T:
key = self.tokenizer.key_word()
if key == "let":
self.compile_let()
elif key == "do":
self.compile_do()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_statements(self):\n\t\n\t\tif self.tokenizer.get_token() == 'do':\n\t\t\tself.compile_do()\n\t\telif self.tokenizer.get_token() == 'let':\n\t\t\tself.compile_let()\n\t\telif self.tokenizer.get_token() == 'while':\n\t\t\tself.compile_while()\n\t\telif self.tokenizer.get_token() == 'return':\n\t\t\tself.... | [
"0.75589675",
"0.7019048",
"0.672143",
"0.6697218",
"0.6517987",
"0.64978373",
"0.64847785",
"0.6292182",
"0.6128917",
"0.5995169",
"0.5977094",
"0.5945606",
"0.5886187",
"0.5850196",
"0.5706215",
"0.57057124",
"0.57051194",
"0.56963694",
"0.56932306",
"0.5675147",
"0.5630193... | 0.69941765 | 2 |
compile a do statement | def compile_do(self):
self.tokenizer.advance() # ignore 'do' keyword
func_name = self.tokenizer.identifier()
var_type = self.symbol_table.type_of(func_name)
n_args = 0
if var_type:
self.code_writer.write_push(self.symbol_table.kind_of(func_name),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_do(self) -> None:\n self._consume('do')\n self.compile_subroutine_call()\n self.writer.write_pop('TEMP', 0) # void method\n self._consume(';')",
"def compile_do(self):\n\n\t\txml = '<doStatement>\\n' + self.tokenizer.keyword() + self.tokenizer.identifier()\n\n\t\tif self.... | [
"0.7731854",
"0.7476447",
"0.65631324",
"0.6170741",
"0.56916237",
"0.5522174",
"0.5387398",
"0.53237134",
"0.5298152",
"0.52630836",
"0.52493423",
"0.5230324",
"0.5228078",
"0.52264786",
"0.51874954",
"0.5172265",
"0.5170777",
"0.5116289",
"0.5071028",
"0.50559103",
"0.50476... | 0.74331874 | 2 |
compiles a let statement | def compile_let(self):
self.tokenizer.advance() # ignore 'let' keyword
var_name = self.tokenizer.identifier()
kind = self.symbol_table.kind_of(var_name)
ind = self.symbol_table.index_of(var_name)
self.tokenizer.advance()
sym = self.tokenizer.symbol()
is_ar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_let(self) -> None:\n self._consume('let')\n name = self._get_current_token()\n kind = convert_kind(self.table.kind_of(name))\n index = self.table.index_of(name)\n\n self._consume(TokenTypes.IDENTIFIER)\n if self._get_current_token() == '[':\n self._c... | [
"0.73564106",
"0.72491395",
"0.7166526",
"0.6516337",
"0.6221294",
"0.6105278",
"0.6005555",
"0.5933077",
"0.5905761",
"0.5885362",
"0.5634257",
"0.5587453",
"0.5564042",
"0.5523054",
"0.5512737",
"0.5454524",
"0.54349154",
"0.5341546",
"0.5339458",
"0.531304",
"0.530831",
... | 0.7684182 | 0 |
compiles a while statement | def compile_while(self):
lab1 = self.class_name + ".L" + str(self.label_index)
self.label_index += 1
lab2 = self.class_name + ".L" + str(self.label_index)
self.label_index += 1
self.tokenizer.advance() # ignore 'while' keyword
self.tokenizer.advance() # ignore '('... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_while(self) -> None:\n self._consume('while')\n self._consume('(')\n\n while_lbl = f\"WHILE_{self._while_count}\"\n while_false_lbl = f\"WHILE_FALSE{self._while_count}\"\n self._while_count += 1\n self.writer.write_label(while_lbl)\n\n self.compile_expre... | [
"0.7955481",
"0.776042",
"0.7662631",
"0.7374275",
"0.7131139",
"0.71004325",
"0.70531285",
"0.7040795",
"0.6986955",
"0.69267535",
"0.69196093",
"0.69127405",
"0.6842747",
"0.6212009",
"0.61005455",
"0.60468936",
"0.6012953",
"0.5999755",
"0.59282243",
"0.5804145",
"0.578901... | 0.7888832 | 1 |
compiles a return statement | def compile_return(self):
self.tokenizer.advance()
if self.tokenizer.token_type() == JackTokenizer.SYMBOL_T and self.tokenizer.symbol() == ";":
self.code_writer.write_push("constant", 0)
else:
self.compile_expression()
self.code_writer.write_return() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_return(self):\n\n\t\txml = '<returnStatement>\\n' + self.tokenizer.keyword()\n\t\tself.outfile.write(xml)\n\n\t\tif self.tokenizer.get_token() != ';':\n\t\t\tself.compile_expression()\n\n\t\txml = self.tokenizer.symbol() + '</returnStatement>\\n'\n\t\tself.outfile.write(xml)",
"def compile_return(sel... | [
"0.7594602",
"0.75319594",
"0.71452075",
"0.6909647",
"0.68616664",
"0.6687466",
"0.65710676",
"0.652341",
"0.6279737",
"0.6262663",
"0.6223273",
"0.6188292",
"0.617591",
"0.61289674",
"0.59650666",
"0.5962612",
"0.5933766",
"0.5873438",
"0.58618015",
"0.58410645",
"0.5826408... | 0.7245663 | 2 |
compiles an if\else statement | def compile_if(self):
lab1 = self.class_name + ".L" + str(self.label_index)
self.label_index += 1
lab2 = self.class_name + ".L" + str(self.label_index)
self.label_index += 1
self.tokenizer.advance() # ignore 'if' keyword
self.tokenizer.advance() # ignore '(' symbo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_if(self):\r\n else_label = \"ELSE_\" + str(self.__if_count)\r\n end_label = \"END_IF_\" + str(self.__if_count)\r\n self.__if_count += 1\r\n self.__advance(n=2)\r\n self.compile_expression()\r\n self.__vmwriter.write_arithmetic(\"not\")\r\n self.__vmwrite... | [
"0.7727683",
"0.7289224",
"0.72581816",
"0.68175304",
"0.6810607",
"0.661387",
"0.65655905",
"0.6555208",
"0.6516338",
"0.6474446",
"0.6472909",
"0.6414552",
"0.64119923",
"0.6258708",
"0.6228315",
"0.621231",
"0.61874413",
"0.6169468",
"0.6071389",
"0.6052652",
"0.60436004",... | 0.7691382 | 1 |
compiles a (possibly empty) comma separated expression list | def compile_expression_list(self):
n_expressions = 0
while True:
tok_type = self.tokenizer.token_type()
if tok_type == JackTokenizer.SYMBOL_T:
sym = self.tokenizer.symbol()
if sym == ")":
break
elif sym =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_expression_list(self):\n\n\t\tself.compile_expression()\n\n\t\twhile(self.tokenizer.get_token() == ','):\n\t\t\tself.outfile.write(self.tokenizer.symbol())\n\t\t\tself.compile_expression()",
"def compile_expression_list(self) -> int:\n n_args = 0\n\n if self._get_current_token() != ')':... | [
"0.7832312",
"0.71106184",
"0.65448207",
"0.6429112",
"0.62829477",
"0.6227067",
"0.6078659",
"0.60175455",
"0.58949816",
"0.58610445",
"0.5756176",
"0.57423204",
"0.5708315",
"0.5526907",
"0.5511897",
"0.54655975",
"0.54565",
"0.5421775",
"0.54128385",
"0.5408563",
"0.536056... | 0.64618796 | 3 |
Get secret from secret service | def get_secret(self, token, owner_type, resource_type, source, name):
try:
token = "Bearer " + token
metadata = (('authorization', token),)
owner_type = ResourceOwnerType.Value(owner_type)
resource_type = ResourceType.Value(resource_type)
source = Res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_secret(secret):\n if isinstance(secret, AwsSecret):\n result = aws_cli([\n 'secretsmanager',\n '--region', secret.region,\n 'get-secret-value',\n '--secret-id', secret.arn\n ])\n\n \"\"\"\n Result will be of the form:\n {\n... | [
"0.7876364",
"0.77615315",
"0.7694051",
"0.7657492",
"0.75929713",
"0.75847775",
"0.75797004",
"0.75692135",
"0.7512623",
"0.7512623",
"0.74721676",
"0.7460301",
"0.74577284",
"0.74248636",
"0.74248636",
"0.73171264",
"0.7313317",
"0.72957635",
"0.72957635",
"0.71956354",
"0.... | 0.64663106 | 98 |
Return the config. Use this method to get the bot config | def get_config(self) -> Dict[str, Any]:
if self.config is None:
self.config = self.load_config()
return self.config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def config(self):\n if self.__config is None:\n self.__config = self._get_config(self.bot)\n return self.__config",
"def get_config(self):\n return self.config",
"def getConfig(self):\n \n return self.config",
"def get_config():\n return CONFIG",
"def config... | [
"0.8793585",
"0.825753",
"0.8087057",
"0.7923807",
"0.7904086",
"0.78217363",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0.78192705",
"0... | 0.7942068 | 4 |
Extract information for sys.argv and load the bot configuration | def load_config(self) -> Dict[str, Any]:
# Load all configs
config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
return config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_arguments(argv):\n\tif argv[0] in ('1', '2'):\n\t\tconos_config['endpoint'] = endpoint[argv[0]]\n\telse:\n\t\tusage()\n\n\tif argv[1] in ('dev', 'test', 'int', 'prod'):\n\t\tconos_config['environment'] = argv[1]\n\t\tconos_config['sts_url'] = eval(argv[1] + '_sts_url')\n\t\tconos_config['aicuu_url'] = eva... | [
"0.7061687",
"0.64792365",
"0.63791585",
"0.63641816",
"0.6280541",
"0.6233762",
"0.6233762",
"0.61830795",
"0.6156966",
"0.6153224",
"0.6123412",
"0.60791194",
"0.607641",
"0.60395724",
"0.6026321",
"0.60259324",
"0.6018109",
"0.6004645",
"0.600403",
"0.59953845",
"0.5993967... | 0.0 | -1 |
Check that the HoleM51 object is correct | def check(self):
Rbo = self.get_Rbo()
alpha = self.comp_alpha()
if self.W0 < (self.W2 + self.W3):
raise S51_WCheckError("You must have W2+W3 < W0")
if Rbo < self.H0 + self.H2:
raise S51_RHCheckError("You must have H0+H2 < Rbo")
if alpha > pi / 2:
raise S51_AlphaCheckError... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_full_house_properties(self):\n self.assertEqual(self.hand.flushInd, 0)\n self.assertEqual(self.hand.straightInd, 0)\n self.assertEqual(self.hand.straightHead, 0)\n self.assertEqual(self.hand.postHandType, 4)\n self.assertEqual(self.hand.postHandValue, 171)",
"def check... | [
"0.6389238",
"0.6138588",
"0.6076156",
"0.6029584",
"0.60056037",
"0.60027415",
"0.5961152",
"0.5958448",
"0.5958351",
"0.59574455",
"0.5934016",
"0.59121776",
"0.5896677",
"0.58715385",
"0.5870168",
"0.58549494",
"0.58464986",
"0.58349705",
"0.582925",
"0.58279985",
"0.58042... | 0.6223342 | 1 |
Map observation data to empirical CDF quantiles in place. | def _transform_observation_data(
self,
observation_data: List[ObservationData],
) -> List[ObservationData]:
# TODO (jej): Transform covariances.
if self.winsorize:
winsorization_rates = {}
for metric_name, vals in self.percentiles.items():
n = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quantile_transform(X, *, axis=..., n_quantiles=..., output_distribution=..., ignore_implicit_zeros=..., subsample=..., random_state=..., copy=...):\n ...",
"def ecdf_quantiles(data_quantiles, performance_measure, ecdf_parameter):\n from statsmodels.distributions.empirical_distribution import ECDF\n\n ... | [
"0.5898078",
"0.5854797",
"0.58402777",
"0.5831225",
"0.5804016",
"0.571265",
"0.56208575",
"0.5602898",
"0.5595195",
"0.5581166",
"0.5550828",
"0.55504656",
"0.546441",
"0.5462287",
"0.542158",
"0.54096085",
"0.540888",
"0.5403378",
"0.5393875",
"0.5387819",
"0.53789145",
... | 0.60944504 | 0 |
Create a tau analysis algorithm sequence | def makeDiTauAnalysisSequence( dataType, workingPoint,
deepCopyOutput = False, postfix = '' ):
if dataType not in ["data", "mc", "afii"] :
raise ValueError ("invalid data type: " + dataType)
if postfix != '' :
postfix = '_' + postfix
pass
splitWP = wor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_tau_functions(self, **kwargs):\n tau1x,tau2x,dens,col = self.select_data(**kwargs)\n\n # ddens = (10**dens[1]-10**dens[0])\n #dlogdens = (dens[1]-dens[0])\n #dlndens = dlogdens * np.log(10)\n\n def tau(meandens, line=tau1x, sigma=1.0, hightail=False,\n ... | [
"0.6678921",
"0.64726853",
"0.59206873",
"0.58965874",
"0.5889418",
"0.58359766",
"0.58147067",
"0.5737234",
"0.56830996",
"0.56643397",
"0.56588525",
"0.56533486",
"0.56486934",
"0.5611884",
"0.5595678",
"0.55607104",
"0.55576503",
"0.55543387",
"0.55469376",
"0.55081576",
"... | 0.5796416 | 7 |
Import content from fpath and puts it into a Python module. Returns the module. | def load_recipe(root: Path) -> None:
module_name = 'dotops.recipes.' + root.name
fpath = find_recipe(root)
try:
_ast = import_file_to_ast(fpath, module_name)
mod = imp.new_module(module_name)
mod.__file__ = fpath
eval(ast_compile(_ast, fpath, "exec"), _globals(root, mod))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_file(name: Text, file_path: Text):\n\n spec = spec_from_file_location(f\"luh3417.{name}\", file_path)\n module = module_from_spec(spec)\n spec.loader.exec_module(module)\n\n return module",
"def import_module_from_file(f_path, verbose=True):\n # get absolute path\n f_path = os.path.a... | [
"0.68160796",
"0.6811874",
"0.6781548",
"0.6760454",
"0.66912633",
"0.6654619",
"0.6637428",
"0.6599749",
"0.6581477",
"0.6511371",
"0.63773304",
"0.6327028",
"0.62901616",
"0.6283164",
"0.62297213",
"0.6172371",
"0.6172371",
"0.6172371",
"0.6159115",
"0.6159115",
"0.6137258"... | 0.0 | -1 |
Test Users Are Listed on User Page | def test_user_listed(self):
url = reverse('admin:core_user_changelist')
res = self.client.get(url)
#assert are django checks on http request is 200
self.assertContains(res, self.user.name)
self.assertContains(res, self.user.email) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_users_listed(self):\n # the url is defined in django admin documentation\n # it generate the url for the list of user page\n # it is good using that instead of the url in case it changes\n url = reverse('admin:core_user_changelist')\n res = self.client.get(url)\n\n ... | [
"0.8124728",
"0.80238926",
"0.79463685",
"0.7838391",
"0.7786036",
"0.75236106",
"0.74479085",
"0.7121244",
"0.7078165",
"0.69479847",
"0.680003",
"0.66679984",
"0.66044194",
"0.65990674",
"0.6565116",
"0.65329105",
"0.6504518",
"0.6474638",
"0.6463932",
"0.64483243",
"0.6435... | 0.76845825 | 5 |
Test User Edit Page Functioning | def test_user_page_change_page(self):
url = reverse('admin:core_user_change', args=[self.user.id])
# houw args workd
# admin/core/usre/
res = self.client.get(url)
#checking response for 200 ok page works
self.assertEqual(res.status_code, 200) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_provider_user_edit(self):\n browser = self.browser\n browser.get(self.base_url)\n wait = WebDriverWait(browser, 10)\n\n user = self.getUser()\n self.assertTrue(user.login())\n\n browser.get(self.base_url + \"user/me/edit\")\n\n pass1 = wait.until(\n ... | [
"0.7720228",
"0.77047116",
"0.74995023",
"0.74353683",
"0.7335236",
"0.7279572",
"0.72357357",
"0.72062695",
"0.7197021",
"0.71928006",
"0.7165932",
"0.7125301",
"0.7110623",
"0.7105635",
"0.7068543",
"0.70568454",
"0.70303935",
"0.7024331",
"0.70119196",
"0.69808865",
"0.697... | 0.670942 | 40 |
Test Create Page User Is Working | def test_create_user_page(self):
url = reverse('admin:core_user_add')
res = self.client.get(url)
self.assertEqual(res.status_code, 200) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_user_page(self):\n\n # Get the admin url and send a GET request\n url = reverse('admin:core_user_add')\n res = self.client.get(url)\n\n # Assertions\n self.assertEqual(res.status_code, 200)",
"def test_createUser_single(self):\n #TODO: this and other test... | [
"0.7967953",
"0.78794813",
"0.77616847",
"0.77616847",
"0.77616847",
"0.7539795",
"0.7418349",
"0.7406909",
"0.73963964",
"0.7326523",
"0.7249234",
"0.7249234",
"0.72076356",
"0.71943235",
"0.7188387",
"0.718378",
"0.7145582",
"0.7144657",
"0.71303326",
"0.71269745",
"0.71253... | 0.81040174 | 3 |
Return the last five published polls. | def get_queryset(self):
return Poll.objects.order_by('-pub_date')[:5] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def latest_question(questions):\n return questions.order_by('-pub_date')[:5]",
"def get_queryset(self):\n #.1 below code was showing future poll/questions\n #.1 return Question.objects.order_by('-pub_date')[:5]\n\n #re-defining\n \"\"\"\n Return the last five published quest... | [
"0.7059502",
"0.68990624",
"0.6736997",
"0.6610546",
"0.65707564",
"0.64962935",
"0.6420193",
"0.6103467",
"0.6092696",
"0.6053481",
"0.6053481",
"0.6045828",
"0.60050005",
"0.5983273",
"0.5981904",
"0.5981904",
"0.59585816",
"0.592744",
"0.592744",
"0.592744",
"0.592744",
... | 0.70417947 | 1 |
Verifies format_note_line is properly functioning | def test_format_note_line(self, timestamp, note, expectation):
self.assertEqual(
expectation,
Plugin().format_note_line(timestamp, note)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n self.note(\"Test Note\", \"\"\" This is a note.\nsecond line\"\"\", \"date\")",
"def testBadLine(self):\n\n self.assertRaises(\n ValueError,\n tools._trackInfo,\n 'not a real line'\n )",
"def test_text_multiline(self):\n self.assertEqua... | [
"0.63763535",
"0.6088162",
"0.6052769",
"0.5908646",
"0.58638895",
"0.58430326",
"0.58398646",
"0.5815939",
"0.5808479",
"0.5801687",
"0.5766474",
"0.5717834",
"0.5704316",
"0.56848854",
"0.56711686",
"0.5639434",
"0.5638027",
"0.56326956",
"0.5625865",
"0.56150144",
"0.56107... | 0.82377183 | 0 |
Get all the notes and chords from the midi files | def get_notes(n_notes=3):
notes = []
for ii, file in enumerate(glob.glob("data/maestro-v2.0.0/2004/*.midi")):
if ii >= n_notes:
break
pickle_file_name = file[:-4] + 'pkl'
if os.path.isfile(pickle_file_name):
print(f'Reading parsed file: {pickle_file_name}')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_notes():\n notes = []\n durs = []\n\n for file in glob.glob(\"D:\\\\anthems\\\\data\\\\*.mid\"):\n midi = converter.parse(file)\n\n print(\"Parsing %s\" % file)\n\n notes_to_parse = None\n\n try:\n s2 = instrument.partitionByInstrument(midi)\n note... | [
"0.79301363",
"0.7536378",
"0.7083482",
"0.6557107",
"0.65365964",
"0.638125",
"0.6212812",
"0.6065463",
"0.60483277",
"0.5995753",
"0.5991631",
"0.5934174",
"0.5896727",
"0.5851554",
"0.579453",
"0.57905567",
"0.57853305",
"0.57399046",
"0.5710731",
"0.570966",
"0.56802684",... | 0.7196914 | 2 |
Prepare the sequences used by the Neural Network | def prepare_sequences(notes, n_vocab):
sequence_length = GLOBAL_SEQUENCE_LENGTH
# Get all pitch names
pitchnames = sorted(set(item for item in notes))
# Create a dictionary to map pitches to integers
note_to_int = dict((note, number) for number, note in enumerate(pitchnames))
network_input = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_sequences(notes, n_vocab):\n sequence_length = 50\n\n # get all pitch names\n pitchnames = sorted(set(item for item in notes))\n\n # create a dictionary to map pitches to integers\n note_to_int = dict((note, number) for number, note in enumerate(pitchnames))\n\n network_input = []\n ... | [
"0.6689426",
"0.6478123",
"0.64326185",
"0.64018196",
"0.6347999",
"0.6346381",
"0.619188",
"0.61806005",
"0.61420333",
"0.61329055",
"0.61148167",
"0.6100435",
"0.608873",
"0.60376334",
"0.603599",
"0.601905",
"0.60031384",
"0.5962889",
"0.5943119",
"0.5938684",
"0.59183395"... | 0.65555423 | 1 |
Generate notes from the neural network based on a sequence of notes | def generate_notes(model, network_input, n_vocab):
# pick a random sequence from the input as a starting point for the prediction
start = np.random.randint(0, len(network_input)-1)
# Get pitch names and store in a dictionary
pitchnames = sorted(set(item for item in notes))
int_to_note = dict((n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_notes(model, training_notes, note_translator):\n\n # pick a random sequence from the input as a starting point for the prediction\n sequence_length = model.sequence_length\n note_reverse_translator = {index: note for note, index in note_translator.items()}\n init_state = data_preprocess.pr... | [
"0.75724083",
"0.75357854",
"0.7140033",
"0.7138525",
"0.7112814",
"0.6966233",
"0.69008785",
"0.6879311",
"0.6497627",
"0.61884177",
"0.6184555",
"0.6086288",
"0.59519565",
"0.5931394",
"0.5835027",
"0.5832675",
"0.5720372",
"0.5715914",
"0.56507623",
"0.5642474",
"0.5600574... | 0.7653167 | 0 |
convert the output from the prediction to notes and create a midi file from the notes | def create_midi(prediction_output, filename):
offset = 0
output_notes = []
# create note and chord objects based on the values generated by the model
for item in prediction_output:
pattern = item
# pattern is a chord
if ('.' in pattern) or pattern.isdigit():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_midi(prediction_output, model_file):\n offset = 0\n output_notes = []\n\n # create note and chord objects based on the values generated by the model\n for pattern in prediction_output:\n # pattern is a chord\n if ('.' in pattern) or pattern.isdigit():\n notes_in_chor... | [
"0.79950756",
"0.7954297",
"0.79236674",
"0.7789785",
"0.671896",
"0.660403",
"0.6532247",
"0.6258551",
"0.6235759",
"0.62201077",
"0.6185511",
"0.61558264",
"0.6133344",
"0.60906225",
"0.6084611",
"0.60755897",
"0.60568166",
"0.60479105",
"0.60397",
"0.6035738",
"0.6019836",... | 0.8151729 | 0 |
Convert a metric's OC time series to list of Azure data points. | def create_data_points(self, time_series, metric_descriptor):
data_points = []
for point in time_series.points:
# TODO: Possibly encode namespace in name
data_point = DataPoint(ns=metric_descriptor.name,
name=metric_descriptor.name,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_twodim_list(self):\n if self._timestampFormat is None:\n return self._timeseriesData\n\n datalist = []\n append = datalist.append\n convert = TimeSeries.convert_epoch_to_timestamp\n for entry in self._timeseriesData:\n append([convert(entry[0], sel... | [
"0.5604662",
"0.560111",
"0.5588779",
"0.545325",
"0.5367597",
"0.53425586",
"0.52977",
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"0.52455235",
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"0.49761406",
"0.49100855",
"0.490695",
"0.4901837",
"0.4899198",
"0.48926318",
"0.4870198",
... | 0.5541618 | 3 |
Transmit the data envelopes to the ingestion service. Does not perform retry logic. For partial success and nonretryable failure, simply outputs result to logs. This function should never throw exception. | def _transmit_without_retry(self, envelopes):
# Contains logic from transport._transmit
# TODO: Remove this function from exporter and consolidate with
# transport._transmit to cover all exporter use cases. Uses cases
# pertain to properly handling failures and implementing a retry
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, result):\n\n logger.debug(\"Checking if we should send {}\".format(result))\n if result.is_failure() and self.will_publish(result):\n message = self._construct_message(result)\n headers = {\n \"Content-Type\": \"application/json\"\n }\n\n... | [
"0.5656892",
"0.5287145",
"0.5174026",
"0.515644",
"0.51534945",
"0.5118591",
"0.5063369",
"0.5057553",
"0.50450146",
"0.5035203",
"0.50120544",
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"0.49908283",
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"0.49834234",
"0.49751616",
"0.49745554",
"0.49649167",
"0.49632606",
"0.4957523",
"0.493... | 0.736348 | 0 |
A somewhat strange case where the right hand side is constant and thus we don't need to use the information in y. | def rhs_fenics(y,t):
#print "time: ",t
uprev.vector()[:]=y
f.t = t #dolfin needs to know the current time for cos(t)
uprime_solver.solve()
return uprime_solution.vector().array() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def y(self, x):\n return x",
"def y(self):\n pass",
"def y(self) -> int:",
"def _prepare_y(self,y):\n y_i = np.ones(y.shape, dtype=np.float64, order=\"C\")\n y_i[y != 1] = -1.0\n return y_i",
"def _derY(self, x, y):\n raise NotImplementedError()",
"def y(self):\... | [
"0.7393856",
"0.69137716",
"0.6871106",
"0.6628756",
"0.66229767",
"0.6590499",
"0.6571285",
"0.64413816",
"0.6343707",
"0.6302943",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6266832",
"0.6257375",
... | 0.0 | -1 |
dy/dt = f(y,t) with y(0)=1 dy/dt = 2y > solution y(t) = c exp(2t) | def rhs(y,t):
return math.cos(t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ret_f(t,y):\n\n f = np.zeros(3)\n f[0] = 77.27*(y(1) - y(0)*y(1)+ y(0)-8.375e-6*y(0)*y(0))\n f[1] = (1.0/77.27)*(-y(1)-y(0)*y(1)+y(2))\n f[2] = 0.161*(y(0)-y(2))\n\n return f",
"def dY_dt(self, y, t=0):\n\t\t \n\t\t#variables\n\t\tpSgg = y[0] / float(sum(y))\n\t\tpSgh = y[3] / float(sum(y))... | [
"0.69780666",
"0.6871589",
"0.6852503",
"0.6789334",
"0.6778427",
"0.66323686",
"0.6560891",
"0.6533457",
"0.65250427",
"0.6490271",
"0.64422923",
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"0.6398747",
"0.6392627",
"0.6340595",
"0.63075",
"0.62892526",
"0.62366945",
"0.62335855",
"0.6227496... | 0.0 | -1 |
podmd Accepts a Bycon `byc` object a Bycon `h_o` handover object with its `target_values` representing `_id` objects of a `variants` collection The function creates a basic BED file and returns its local path. A standard use would be to create a link to this file and submit it as `hgt.customText` parameter to the UCSC ... | def _write_variants_bedfile(h_o, p_f, p_t, byc):
local_paths = byc.get("local_paths")
if not local_paths:
return False
tmp_path = Path( path.join( *local_paths[ "server_tmp_dir_loc" ]) )
if not tmp_path.is_dir():
return False
v_ret = 0
v_max = 1000
if len( h_o["target_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_file(material_id):\n apr=get_doc_from_MP(material_id)\n mat_list=generate_matrix(apr)\n formu=POSCAR_title(apr)\n cell_for=generate_cell_formula(apr)\n needed_dos=generate_dos_str(material_id)\n revise_dos=dos_into_string(needed_dos)\n ordered_list=generate_ordered_list(revise_dos... | [
"0.523093",
"0.51998866",
"0.5158229",
"0.5140045",
"0.5118935",
"0.50558704",
"0.5029571",
"0.49992138",
"0.49688032",
"0.48805413",
"0.4840689",
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"0.47752804",
"0.47610596",
"0.47577068",
"0.47528267",
"0.4687908",
"0.46316627",
"0.4619879",
"0.46195143",
"0.46... | 0.5394805 | 0 |
The index of the edge key in an equilibrium structure. | def index_from_model(self, model, structure):
try:
return structure.edge_index[self.key]
except TypeError:
return tuple([structure.edge_index[k] for k in self.key]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def key(self):\r\n\t\treturn ( hashE(self.edge),self.p1,self.p2);",
"def node_index(self, key):\n return self._key_to_node_index[key]",
"def edges_key(graph):\n return tuple(graph.edges())",
"def get_index(self, key):\n return self.keys.index(key)",
"def edge_index(indexed_triangle, ed... | [
"0.69453967",
"0.6640415",
"0.6603783",
"0.6572264",
"0.6566291",
"0.65475166",
"0.65134764",
"0.65052545",
"0.6482312",
"0.64436454",
"0.6403377",
"0.63573134",
"0.6352896",
"0.6352896",
"0.6338956",
"0.63289213",
"0.63231397",
"0.62645197",
"0.62625116",
"0.6258962",
"0.623... | 0.6424732 | 10 |
r"""Compute the ctc loss with batched labels. | def ctc_loss(inputs, padding_mask=-1, **kwargs):
args = ArgHelper.parse(locals())
inputs[0] = activation_ops.softmax(inputs[0], axis=2)
op_lib = loss_ops_lib.Operator
if context.executing_eagerly():
raise NotImplementedError
else:
return op_lib.blend('CTCLoss', **args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ctc_loss(label_length, ctc_input_length, labels, probs):\n label_length = tf.cast(tf.squeeze(label_length), dtype=tf.int32)\n ctc_input_length = tf.cast(tf.squeeze(ctc_input_length), dtype=tf.int32)\n sparse_labels = tf.cast(tf.keras.backend.ctc_label_dense_to_sparse(labels, label_length), dtype=tf.in... | [
"0.6970509",
"0.68200433",
"0.6804985",
"0.6726371",
"0.6681177",
"0.6646176",
"0.6607578",
"0.6524093",
"0.64642483",
"0.6374277",
"0.61923236",
"0.6092338",
"0.6085526",
"0.60759014",
"0.60613245",
"0.6043316",
"0.6035013",
"0.59991264",
"0.5978405",
"0.5954415",
"0.5942769... | 0.70889 | 0 |
r"""Compute the elementwise absolute value difference. | def l1_loss(inputs, reduction='mean', **kwargs):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.L1Loss
if context.executing_eagerly():
return op_lib \
.instantiate(reduction=args['reduction']) \
.apply(inputs)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def abs(array):\n return np.abs(array)",
"def absolute_value(x):\n x_star = x.clone()\n x_star[1] *= -1\n return elementwise_mult(x, x_star)[0].sqrt_()",
"def abs_(a):",
"def absolute_difference(x1: np.ndarray, x2: np.ndarray) -> float:\n assert isinstance(x1, np.ndarray) and isinstance(x2, np... | [
"0.69788164",
"0.6964427",
"0.6954321",
"0.68909156",
"0.68321335",
"0.6825986",
"0.67846584",
"0.676503",
"0.665343",
"0.6612451",
"0.66115755",
"0.66112155",
"0.6566696",
"0.6485016",
"0.64668524",
"0.64620847",
"0.6413898",
"0.6408128",
"0.64043254",
"0.6324162",
"0.630435... | 0.0 | -1 |
r"""Compute the elementwise squared error. | def l2_loss(inputs, reduction='mean', **kwargs):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.L2Loss
if context.executing_eagerly():
return op_lib \
.instantiate(reduction=args['reduction']) \
.apply(inputs)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def squared_error(a, b):\n return (a - b)**2",
"def rms_error(self, X, y) :\n ### ========== TODO : START ========== ###\n # part h: compute RMSE\n n, d = X.shape\n error = np.sqrt(self.cost(X,y)/n)\n ### ========== TODO : END ========== ###\n return error",
"def Me... | [
"0.70697165",
"0.6690482",
"0.6645244",
"0.6612147",
"0.65794015",
"0.65507746",
"0.6526567",
"0.6521677",
"0.64709365",
"0.6470684",
"0.6470531",
"0.6406789",
"0.6403659",
"0.63983536",
"0.6370437",
"0.6363914",
"0.635806",
"0.633491",
"0.6334258",
"0.6305452",
"0.6280436",
... | 0.0 | -1 |
r"""Compute the negative likelihood loss with sparse labels. | def nll_loss(
inputs,
axis=1,
ignore_index=None,
reduction='valid',
**kwargs
):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.NLLLoss
if context.executing_eagerly():
return op_lib \
.instantiate(
axis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sparse_nll_loss(probs, labels, epsilon=1e-9, scope=None):\n with tf.name_scope(scope, \"log_loss\"):\n labels = tf.one_hot(labels, tf.shape(probs)[1], axis=1)\n losses = - tf.reduce_sum(labels * tf.log(probs + epsilon), 1)\n return losses",
"def sparse_nll_... | [
"0.7014988",
"0.7014988",
"0.7014988",
"0.7014988",
"0.69838434",
"0.6676288",
"0.65585726",
"0.6471683",
"0.6253885",
"0.6216614",
"0.6169275",
"0.61666584",
"0.6152545",
"0.61438704",
"0.61434895",
"0.6123603",
"0.6062964",
"0.6035631",
"0.6029464",
"0.5992437",
"0.5945079"... | 0.0 | -1 |
r"""Compute the sigmoid cross entropy with contiguous targets. | def sigmoid_cross_entropy(inputs, reduction='valid', **kwargs):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.SigmoidCrossEntropy
if context.executing_eagerly():
return op_lib \
.instantiate(reduction=args['reduction']) \
.ap... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sigmoid_cross_entropy(self):\n loss_op = pointwise_losses.SigmoidCrossEntropy()\n\n y_pred = loss_op.final_activation_op({\n \"logits\": self.logits,\n \"metadata\": {\n \"mask\": self.mask\n }\n })\n assert np.isclose(y_pred[0][0... | [
"0.733087",
"0.7251085",
"0.70497286",
"0.7024441",
"0.68964267",
"0.6850176",
"0.6775583",
"0.6740799",
"0.6706655",
"0.6696851",
"0.66841483",
"0.66245735",
"0.6619512",
"0.6565874",
"0.65273166",
"0.6526585",
"0.6509253",
"0.6495321",
"0.6495321",
"0.64779925",
"0.6461931"... | 0.6762066 | 7 |
r"""Compute the sigmoid focal loss with sparse labels. | def sigmoid_focal_loss(
inputs,
axis=1,
alpha=0.25,
gamma=2.,
negative_index=None,
reduction='valid',
**kwargs
):
args = ArgHelper.parse(locals())
args['alpha'] = float(args['alpha'])
args['gamma'] = float(args['gamma'])
args['reduction'] = reduction.upper()
op_lib = loss... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def focal_loss_sigmoid(labels, logits, alpha=0.5, gamma=2):\n\n prob = logits.sigmoid()\n labels = torch.nn.functional.one_hot(labels.squeeze().long(), num_classes=prob.shape[1])\n\n cross_ent = torch.clamp(logits, min=0) - logits * labels + torch.log(1+torch.exp(-torch.abs(logits)))\n prob_t = (la... | [
"0.7132021",
"0.7093548",
"0.66306",
"0.6588871",
"0.65455276",
"0.6520407",
"0.64797497",
"0.6420658",
"0.6377443",
"0.63494956",
"0.63168055",
"0.62914336",
"0.62914336",
"0.6280836",
"0.6270551",
"0.6268474",
"0.6265231",
"0.6265231",
"0.6265231",
"0.6265231",
"0.6265231",... | 0.65818846 | 4 |
r"""Compute the elementwise error transited from L1 and L2. | def smooth_l1_loss(inputs, beta=1., reduction='mean', **kwargs):
args = ArgHelper.parse(locals())
args['beta'] = float(args['beta'])
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.SmoothL1Loss
if context.executing_eagerly():
return op_lib \
.instantiate(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_error(d1, d2):\n square_sum = 0.0\n for i in range(len(d1)):\n square_sum += pow( distributions_EMD(d1[i], d2[i]), 2)\n return math.sqrt(square_sum)",
"def errorEMat(E1, E2):\n E1_normalized = E1 / E1[2][2];\n E2_normalized = E2 / E2[2][2];\n return torch.norm(E1_normalized... | [
"0.66480964",
"0.6408133",
"0.63674414",
"0.6302799",
"0.62619704",
"0.62610716",
"0.625721",
"0.62301993",
"0.61891586",
"0.61241555",
"0.6061378",
"0.6036818",
"0.60175693",
"0.5936132",
"0.58897555",
"0.584485",
"0.57753235",
"0.57597107",
"0.57388484",
"0.5727462",
"0.572... | 0.0 | -1 |
r"""Compute the softmax cross entropy with contiguous targets. | def softmax_cross_entropy(inputs, axis=1, reduction='mean', **kwargs):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.SoftmaxCrossEntropy
if context.executing_eagerly():
return op_lib \
.instantiate(
axis=axis,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cross_entropy(input: Tensor, target: Tensor) -> Tensor:\n norm_log = log_softmax(input, 1)\n\n np_one_hot = np.eye(input.shape[1])[target.data]\n tensor_one_hot = tensor(np_one_hot, 'one-hot', False, True)\n\n mask = -norm_log * tensor_one_hot\n mask_sum = sum(mask, 1)\n loss = sum(mask_sum, ... | [
"0.7206213",
"0.71602994",
"0.71359116",
"0.7133334",
"0.7103154",
"0.6957571",
"0.6934931",
"0.6868393",
"0.6858417",
"0.6841772",
"0.6818581",
"0.68184334",
"0.67718226",
"0.6725869",
"0.67039573",
"0.66911614",
"0.668195",
"0.665026",
"0.6638869",
"0.6636618",
"0.65985984"... | 0.67265826 | 13 |
r"""Compute the softmax cross entropy with sparse labels. | def sparse_softmax_cross_entropy(
inputs,
axis=1,
ignore_index=None,
reduction='valid',
**kwargs
):
args = ArgHelper.parse(locals())
args['reduction'] = reduction.upper()
op_lib = loss_ops_lib.SparseSoftmaxCrossEntropy
if context.executing_eagerly():
return op_lib \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sparse_cross_entropy(true, pred, axis=-1, epsilon=1e-7):\n true = ivy.one_hot(true, pred.shape[axis])\n return cross_entropy(true, pred, axis, epsilon)",
"def sparse_cross_entropy_with_probs(probs, labels, name=None):\n with tf.name_scope(name, \"SparseSoftmaxCrossEntropyWithProb\", [probs, labels])... | [
"0.75729984",
"0.7483392",
"0.7342698",
"0.7261253",
"0.71876013",
"0.71847457",
"0.7153395",
"0.708201",
"0.70781726",
"0.707635",
"0.7052675",
"0.7032999",
"0.6964066",
"0.69560903",
"0.69532275",
"0.6948643",
"0.69474673",
"0.6936967",
"0.6919669",
"0.6887562",
"0.68827564... | 0.75418687 | 1 |
Get a normalized path and create a directory if it doesn't exist | def get_path(dir_name, file_format, args):
fname = "{exp}-excl-{excl}".format(exp=args.exp,
excl=args.feature_set)
path = os.path.join(SAVE_ROOT, dir_name, args.rbp,
fname + file_format)
# make the directory if it doesn't exist_ok
os.makedi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_dir_if_needed(path):\n\n if not os.path.exists(path):\n os.makedirs(path)\n return path",
"def fix_path(path):\n path = os.path.normpath(path)\n os.makedirs(path, exist_ok=True)\n return path",
"def _normalize_path(path):\n if path is None:\n directory = BASE_PATH\n ... | [
"0.73886275",
"0.7341739",
"0.7287668",
"0.7276798",
"0.72392887",
"0.72309655",
"0.721909",
"0.7198315",
"0.71643466",
"0.71285844",
"0.7112931",
"0.70983285",
"0.70932275",
"0.70858145",
"0.70818454",
"0.7024364",
"0.7011929",
"0.70076835",
"0.70046",
"0.69750345",
"0.69701... | 0.0 | -1 |
Initialize the object for use. | def __init__(self):
# regular expressions used for matching non-standard date formats
# TODO: move to separate file
self.regexes = {
'match': {},
'substitution': {},
'capture': {}
}
# years before 0
self.regexes['match']['suffix-b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def initialize(self):\n pass... | [
"0.8585764",
"0.8585764",
"0.8585764",
"0.8585764",
"0.8585764",
"0.8585764",
"0.8585764",
"0.8585764",
"0.84923893",
"0.84923893",
"0.84923893",
"0.84923893",
"0.84923893",
"0.8478491",
"0.8464105",
"0.8458038",
"0.8458038",
"0.8438537",
"0.8438537",
"0.8438537",
"0.8401424"... | 0.0 | -1 |
Returns a set of decades that covers all of the years and year ranges in the data. data can be a single string or a set of strings disjoint whether or not to exclude decades in the interim between the earliest and latest decades | def decades(self, data, disjoint=True):
decade_set = set()
try:
# multi valued
assert isinstance(data, set)
for datum in data:
preprocessed_year_data = self._extract_year_data(datum)
decade_set = decade_set | self._enumerate_decades(pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def years(self, data, disjoint=True):\n\n year_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n year_set = year_set | self._enumerate_years... | [
"0.6242524",
"0.584959",
"0.58418256",
"0.5749594",
"0.55666363",
"0.55094856",
"0.54740614",
"0.54646933",
"0.5412162",
"0.5407114",
"0.5374574",
"0.53573066",
"0.53103226",
"0.5309206",
"0.5307339",
"0.5235848",
"0.5222081",
"0.5171749",
"0.51681775",
"0.5146663",
"0.513659... | 0.7365615 | 0 |
Returns a set of years that covers all of the years and year ranges in the data. data can be a single string or a set of strings disjoint whether or not to exclude years in the interim between the earliest and latest years | def years(self, data, disjoint=True):
year_set = set()
try:
# multi valued
assert isinstance(data, set)
for datum in data:
preprocessed_year_data = self._extract_year_data(datum)
year_set = year_set | self._enumerate_years(preprocessed... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def t_range_years(t_range):\r\n start_year = int(t_range[0].split(\"-\")[0])\r\n end_year = int(t_range[1].split(\"-\")[0])\r\n end_month = int(t_range[1].split(\"-\")[1])\r\n end_day = int(t_range[1].split(\"-\")[2])\r\n if end_month == 1 and end_day == 1:\r\n year_range_list = np.arange(sta... | [
"0.6668446",
"0.64742446",
"0.6461352",
"0.6017229",
"0.60148984",
"0.5969544",
"0.593733",
"0.59081143",
"0.57882893",
"0.5774196",
"0.5734059",
"0.56695604",
"0.56593835",
"0.56580514",
"0.5633372",
"0.5602902",
"0.55264854",
"0.55252516",
"0.55181205",
"0.5500564",
"0.5471... | 0.7573435 | 0 |
Maps a match of regexes['match']['date'] to a tuple of years, or a single year. m the re.match object | def _date_match_to_int_or_tuple(self, m):
years = set()
try:
if m[0] != '':
# yyyy-dd-mm
years = int(m[0][0:4])
elif m[1] != '':
# year-range derived from a century
century = int(re.match(re.compile('\\d+'), m[1]).g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _year_range(m):\n return (m.group(1), m.group(2))",
"def _extract_year_data(self, date_string):\n\n try:\n # first see if dateutil can parse the date string\n # simplest case, a single year\n return {parse(date_string).year}\n\n except ValueError:\n ... | [
"0.7254186",
"0.6608783",
"0.6011262",
"0.5957045",
"0.59214723",
"0.59050083",
"0.5880262",
"0.58387053",
"0.5810087",
"0.58070123",
"0.57679784",
"0.57420975",
"0.57067704",
"0.5700978",
"0.56853163",
"0.5677188",
"0.56761444",
"0.56604224",
"0.56588614",
"0.5644384",
"0.56... | 0.7606871 | 0 |
Return a set of decades. If disjoint is false, returns a set of decades that spans the entire range. preprocessed_data a heterogeneous set of ints and tuples of ints | def _enumerate_decades(self, preprocessed_data, disjoint):
if disjoint:
decades = set()
for decade in preprocessed_data:
if isinstance(decade, int):
decades.add(decade // 10 * 10)
elif isinstance(decade, tuple):
dec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decades(self, data, disjoint=True):\n\n decade_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n decade_set = decade_set | self._enumera... | [
"0.7483984",
"0.51474196",
"0.5103328",
"0.5055087",
"0.50201726",
"0.50139266",
"0.49108776",
"0.48341992",
"0.4811175",
"0.4799761",
"0.47267008",
"0.46881026",
"0.46321952",
"0.4605094",
"0.45930317",
"0.45531568",
"0.4522701",
"0.45008975",
"0.44996762",
"0.44854528",
"0.... | 0.7381708 | 1 |
Return a set of years. If disjoint is false, returns a set of years that spans the entire range. preprocessed_data a heterogeneous set of ints and tuples of ints | def _enumerate_years(self, preprocessed_data, disjoint):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def years(self, data, disjoint=True):\n\n year_set = set()\n try:\n # multi valued\n assert isinstance(data, set)\n for datum in data:\n preprocessed_year_data = self._extract_year_data(datum)\n year_set = year_set | self._enumerate_years... | [
"0.80802757",
"0.6195092",
"0.6097575",
"0.6085509",
"0.5956418",
"0.57805914",
"0.5775583",
"0.57030433",
"0.56905186",
"0.55768806",
"0.5571027",
"0.55363435",
"0.5514044",
"0.54679793",
"0.54667073",
"0.54420847",
"0.5440692",
"0.54319644",
"0.5420999",
"0.5370453",
"0.531... | 0.7213386 | 1 |
Extracts the year(s) and/or year range(s) embedded in the date_string, and return a set of ints (year) and/or tuples of ints (year range, start/end). date_string the string containing the dirty date | def _extract_year_data(self, date_string):
try:
# first see if dateutil can parse the date string
# simplest case, a single year
return {parse(date_string).year}
except ValueError:
try:
# strip alphabetical chars and spaces from the left ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def string_to_years(s):\n pattern = r'\\d\\d\\d\\d'\n r = re.compile(pattern)\n min_year = 1960\n max_year = datetime.now().year + 1\n return list(filter(lambda y: y >= min_year and y <= max_year, map(int, r.findall(s))))",
"def _year_range(m):\n return (m.group(1), m.group(2))",
"def parse_y... | [
"0.71493185",
"0.7043156",
"0.68464625",
"0.67900616",
"0.63941294",
"0.62800896",
"0.6208942",
"0.6202308",
"0.6173974",
"0.60687256",
"0.6009146",
"0.5986021",
"0.5980821",
"0.59670657",
"0.59482944",
"0.5917469",
"0.58205444",
"0.57989943",
"0.574035",
"0.5707159",
"0.5699... | 0.8044839 | 0 |
i is a string that represents a year with a possibly missing ones value, like "199?" or "199?". Round down to the nearest decade. | def _resolve_unknown_ones(self, i):
m = re.compile('(^\\d{4}$)').match(i)
if m is not None:
return int(m.group(1))
else:
m = re.compile('(^\\d{1,3})[-*?]$').match(i)
return i if m is None else int(m.group(1) + '0') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decade(year):\r\n # get the first 3 digits of the year\r\n partial = (year[0]//10).item()\r\n # add a 0 to the end, return as decade\r\n return partial * 10",
"def translate_years(val):\n if val.find(\"-\") > 0:\n tokens = re.findall(\"[0-9]+\", val)\n one = int(tokens[0])\n ... | [
"0.6792026",
"0.65144795",
"0.64973646",
"0.64911294",
"0.63963395",
"0.6301948",
"0.6170404",
"0.61197406",
"0.6097923",
"0.6078981",
"0.6054153",
"0.598611",
"0.5985679",
"0.5947528",
"0.5931075",
"0.5913055",
"0.5898607",
"0.5834762",
"0.5772921",
"0.5737773",
"0.57252026"... | 0.5021449 | 90 |
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