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How to use or_ method in Selene
Best Python code snippet using selene_python
rule.py
Source:rule.py 
...6from Lab2.constants import *7SIMPLE_CITIES = [city.lower() for city in city_test if city.find('-') == -1 and city.find(' ') == -1]8COMPLEX_CITIES = [city.lower() for city in city_test if city.find('-') != -1 or city.find(' ') != -1]9Address = fact('Address', get_address_schema())10CITY_NAME = or_(rule(dictionary(SIMPLE_CITIES)), morph_pipeline(COMPLEX_CITIES)).interpretation(Address.city)11SIMPLE = and_(true(), or_(get_noun_gram(), get_adjective_gram()))12COMPLEX = or_(rule(SIMPLE, is_dash().optional(), SIMPLE),13 rule(true(), is_dash().optional(), caseless('на'), is_dash().optional(), true()))14PERSON_RULE = or_(rule(SIMPLE), COMPLEX)15MAYBE_CITY_NAME = or_(PERSON_RULE, rule(PERSON_RULE, '-', get_int_type())).interpretation(Address.city)16CITY_WORDS = or_(rule(normalized('гоÑод')), rule(caseless('г'), is_dot().optional())).interpretation(17 Address.city_type.const('гоÑод'))18CITY = or_(rule(CITY_NAME), rule(CITY_WORDS, CITY_NAME), rule(CITY_NAME, CITY_WORDS)).interpretation(Address)19SHORT_MODIFIER_WORDS = rule(in_caseless({*ex_word}), is_dash().optional())20MODIFIER_WORDS = or_(SHORT_MODIFIER_WORDS)21LET_WORDS = or_(rule(caseless('леÑ')), rule(is_dash().optional(), caseless('леÑиÑ')))22LET = rule(get_int_type(), LET_WORDS)23MONTH_WORDS = dictionary({*months})24DAY = and_(get_int_type(), gte(1), lte(31))25YEAR = and_(get_int_type(), gte(1), lte(2100))26YEAR_WORDS = normalized('год')27DATE = or_(rule(DAY, MONTH_WORDS), rule(get_adjective_gram(), normalized('дней')), rule(YEAR, YEAR_WORDS))28TITLE_RULE = and_(29 true(),30 not_(get_int_type()),31 not_(get_intj_type()),32 not_(length_eq(3)),33 not_(normalized('пÑоезд')),34 not_(normalized('виднÑй')),35 not_(normalized('кÑÑлова')),36 not_(normalized('пиÑеÑ'))37)38PART = and_(TITLE_RULE, or_(gram('Name'), gram('Surn')))39MAYBE_FIO = or_(rule(gram('Surn')), rule(gram('Name')), rule(TITLE_RULE, PART), rule(PART, TITLE_RULE))40POSITION_WORDS = or_(rule(dictionary(roles_test)))41MAYBE_PERSON = or_(MAYBE_FIO, rule(POSITION_WORDS, MAYBE_FIO))42IMENI_WORDS = or_(rule(caseless('им'), is_dot().optional()), rule(caseless('имени')))43IMENI = rule(IMENI_WORDS.optional(), MAYBE_PERSON)44SIMPLE = and_(or_(get_adjective_gram(), and_(get_noun_gram(), get_genitive())), TITLE_RULE)45COMPLEX = or_(rule(and_(get_adjective_gram(), TITLE_RULE), get_noun_gram()),46 rule(TITLE_RULE, is_dash().optional(), TITLE_RULE))47EXCEPTION = dictionary({'аÑбаÑ', 'ваÑваÑка'})48MAYBE_NAME = or_(rule(SIMPLE), rule(EXCEPTION))49MAIL_STREET = rule(get_int_type(), get_adjective_gram(), get_noun_gram())50LET_NAME = or_(MAYBE_NAME, LET, DATE, IMENI)51MODIFIER_NAME = rule(MODIFIER_WORDS, get_noun_gram())52PERSON_RULE = or_(LET_NAME, MODIFIER_NAME, MAIL_STREET)53ADDR_NAME = PERSON_RULE54SPB_SHORT = rule(normalized('пиÑеÑ')).interpretation(Address.city.const('ÑанкÑ-пеÑеÑбÑÑг'))55SBP_NAME = LET_NAME.interpretation(Address.street)56SPB_STREET = rule(SPB_SHORT, SBP_NAME).interpretation(Address)57STREET_NAME = ADDR_NAME.interpretation(Address.street)58STREET_WORDS = or_(rule(normalized('ÑлиÑа')), rule(normalized('ÑлиÑа'), normalized('знаÑиÑ')),59 rule(caseless('Ñл'), is_dot().optional())60 ).interpretation(Address.street_type.const('ÑлиÑа'))61STREET = or_(rule(STREET_WORDS, STREET_NAME), rule(STREET_NAME, STREET_WORDS)).interpretation(Address)62BOULEVARD_WORDS = or_(rule(caseless('б'), '-', caseless('Ñ')),63 rule(caseless('бÑл'), is_dot().optional())).interpretation(Address.street_type.const('бÑлÑваÑ'))64BOULEVARD_NAME = ADDR_NAME.interpretation(Address.street)65BOULEVARD = or_(rule(BOULEVARD_WORDS, BOULEVARD_NAME), rule(BOULEVARD_NAME, BOULEVARD_WORDS)).interpretation(Address)66HIGHWAY_WORDS = or_(rule(caseless('Ñ'), is_dot()), rule(normalized('ÑоÑÑе'))).interpretation(67 Address.street_type.const('ÑоÑÑе'))68HIGHWAY_NAME = ADDR_NAME.interpretation(Address.street)69HIGHWAY = or_(rule(HIGHWAY_WORDS, HIGHWAY_NAME), rule(HIGHWAY_NAME, HIGHWAY_WORDS)).interpretation(Address)70TRACT_WORDS = or_(rule(caseless('ÑÑ'), is_dot()), rule(normalized('ÑÑакÑ'))).interpretation(71 Address.street_type.const('ÑÑакÑ'))72TRACT_NAME = ADDR_NAME.interpretation(Address.street)73TRACT = or_(rule(TRACT_WORDS, TRACT_NAME), rule(TRACT_NAME, TRACT_WORDS)).interpretation(Address)74GAI_WORDS = rule(normalized('гай'))75GAI_NAME = ADDR_NAME76GAI = or_(rule(GAI_WORDS, GAI_NAME), rule(GAI_NAME, GAI_WORDS)).interpretation(Address.street)77VAL_WORDS = rule(normalized('вал'))78VAL_NAME = ADDR_NAME79VAL = or_(rule(VAL_WORDS, VAL_NAME), rule(VAL_NAME, VAL_WORDS)).interpretation(Address.street)80ALLEY_WORDS = rule(normalized('аллеи')).interpretation(Address.street_type.const('аллеи'))81ALLEY_NAME = ADDR_NAME.interpretation(Address.street)82ALLEY = rule(ALLEY_NAME, ALLEY_WORDS).interpretation(Address)83AVENUE_WORDS = or_(rule(in_caseless({'пÑ', 'пÑоÑп'}), is_dot().optional()),84 rule(caseless('пÑ'), '-', in_caseless({'кÑ', 'Ñ'}), is_dot().optional()),85 rule(normalized('пÑоÑпекÑ'))).interpretation(Address.street_type.const('пÑоÑпекÑ'))86AVENUE_NAME = ADDR_NAME.interpretation(Address.street)87AVENUE = or_(rule(AVENUE_WORDS, AVENUE_NAME), rule(AVENUE_NAME, AVENUE_WORDS)).interpretation(Address)88DISTRICT_WORDS = or_(rule(in_caseless({'мк', 'мкÑ'}), is_dot().optional()),89 rule(caseless('мк'), '-', in_caseless({'Ñн', 'н'}), is_dot().optional()),90 ).interpretation(Address.street_type.const('микÑоÑайон'))91DISTRICT_NAME = ADDR_NAME.interpretation(Address.street)92DISTRICT = or_(rule(DISTRICT_WORDS, DISTRICT_NAME), rule(DISTRICT_NAME, DISTRICT_WORDS)).interpretation(Address)93DRIVEWAY_WORDS = or_(rule(normalized('пÑоезд')), rule(caseless('пÑ'), is_dot().optional())).interpretation(94 Address.street_type.const('пÑоезд'))95DRIVEWAY_NAME = ADDR_NAME.interpretation(Address.street)96DRIVEWAY = or_(rule(DRIVEWAY_NAME, DRIVEWAY_WORDS), rule(DRIVEWAY_WORDS, DRIVEWAY_NAME)).interpretation(Address)97ALLEYWAY_WORDS = or_(rule(caseless('п'), is_dot()), rule(caseless('пеÑ'), is_dot().optional()), ).interpretation(98 Address.street_type.const('пеÑеÑлок'))99ALLEYWAY_NAME = ADDR_NAME.interpretation(Address.street)100ALLEYWAY = or_(rule(ALLEYWAY_WORDS, ALLEYWAY_NAME), rule(ALLEYWAY_NAME, ALLEYWAY_WORDS)).interpretation(Address)101SQUARE_WORDS = or_(rule(caseless('пл'), is_dot().optional())).interpretation(Address.street_type.const('плоÑадÑ'))102SQUARE_NAME = ADDR_NAME.interpretation(Address.street)103SQUARE = or_(rule(SQUARE_WORDS, SQUARE_NAME), rule(SQUARE_NAME, SQUARE_WORDS)).interpretation(Address)104EMBANKMENT_WORDS = or_(rule(caseless('наб'), is_dot().optional())).interpretation(105 Address.street_type.const('набеÑежнаÑ'))106EMBANKMENT_NAME = ADDR_NAME.interpretation(Address.street)107EMBANKMENT = or_(rule(EMBANKMENT_WORDS, EMBANKMENT_NAME), rule(EMBANKMENT_NAME, EMBANKMENT_WORDS)).interpretation(108 Address)109LETTER = in_(ru)110QUOTE = in_(QUOTES)111LETTER = or_(rule(LETTER), rule(QUOTE, LETTER, QUOTE))112SEP = in_(r' /\-')113VALUE = or_(rule(get_int_type(), SEP, LETTER), rule(get_int_type(), LETTER),114 rule(get_int_type(), is_whitespace(), LETTER), rule(get_int_type()),115 rule(get_int_type(), SEP, get_int_type()))116ADDR_VALUE = rule(eq('â').optional(), VALUE)117HOUSE_WORDS = or_(rule(normalized('номеÑ')), rule(normalized('дом')), rule(caseless('д'), is_dot())).interpretation(118 Address.house_type.const('дом'))119HOUSE_VALUE = ADDR_VALUE.interpretation(Address.house)120HOUSE = rule(HOUSE_WORDS, HOUSE_VALUE).interpretation(Address)121APARTMENT_WORDS = or_(rule(in_caseless('кв'), is_dot().optional()), rule(normalized('кваÑÑиÑа'))).interpretation(122 Address.corpus_type.const('коÑпÑÑ'))123APARTMENT_VALUE = ADDR_VALUE.interpretation(Address.apartment)124APARTMENT = or_(rule(APARTMENT_WORDS, APARTMENT_VALUE), rule(APARTMENT_VALUE, APARTMENT_WORDS)).interpretation(Address)125corpus_words_const = {'к', 'коÑп', 'коÑ', 'коÑпÑÑ'}126CORPUS_WORDS = or_(rule(in_caseless(corpus_words_const), is_dot().optional())).interpretation(127 Address.corpus_type.const('коÑпÑÑ'))128CORPUS_VALUE = ADDR_VALUE.interpretation(Address.corpus)129CORPUS = rule(CORPUS_WORDS, CORPUS_VALUE).interpretation(Address)130building_words_const = {'ÑÑ', 'ÑÑÑоение'}131BUILDING_WORDS = or_(132 rule(in_caseless(building_words_const), is_dot().optional())).interpretation(133 Address.building_type.const('ÑÑÑоение'))134BUILDING_VALUE = ADDR_VALUE.interpretation(Address.building)135BUILDING = rule(BUILDING_WORDS, BUILDING_VALUE).interpretation(Address)136STREET_HOUSE_CORPUS = rule(137 CITY.optional(),138 or_(HIGHWAY, STREET, STREET_NAME, AVENUE,139 DRIVEWAY, TRACT, SQUARE, EMBANKMENT, ALLEY,140 BOULEVARD, DISTRICT, GAI, VAL, ALLEYWAY),141 HOUSE_WORDS.optional(),142 HOUSE_VALUE,143 CORPUS_WORDS,144 CORPUS_VALUE145).interpretation(Address)146HOUSE_CORPUS = rule(HOUSE_VALUE, CORPUS_WORDS, CORPUS_VALUE).interpretation(Address)147HOUSE_BUILDING = rule(148 CITY.optional(),149 or_(HIGHWAY, STREET, STREET_NAME, AVENUE,150 DRIVEWAY, TRACT, SQUARE, EMBANKMENT, ALLEY,151 BOULEVARD, DISTRICT, GAI, VAL, ALLEYWAY),152 HOUSE_WORDS.optional(),153 HOUSE_VALUE,154 BUILDING_WORDS,155 BUILDING_VALUE156).interpretation(Address)157HOUSE_STREET = rule(158 CITY.optional(),159 or_(HIGHWAY, STREET, STREET_NAME, AVENUE,160 DRIVEWAY, TRACT, SQUARE, EMBANKMENT, ALLEY,161 BOULEVARD, DISTRICT, GAI, VAL, ALLEYWAY),162 HOUSE_WORDS.optional(),163 HOUSE_VALUE164).interpretation(Address)165VALUE_HOUSE = rule(get_int_type()).interpretation(Address.house)166NUMBER_HOUSE = rule(rule(normalized('номеÑ')), VALUE_HOUSE).interpretation(Address)167TRIPLE_HOUSE = rule(rule(normalized('дом')), VALUE_HOUSE).interpretation(Address)168DOM_APARTMENT = rule(HOUSE_VALUE, APARTMENT_VALUE).interpretation(Address)169ADDRESS_RULE = or_(170 SPB_STREET, CITY, NUMBER_HOUSE, HOUSE_CORPUS, HOUSE_BUILDING, TRIPLE_HOUSE,171 HOUSE_STREET, DOM_APARTMENT, STREET, DRIVEWAY, ALLEYWAY, SQUARE, HIGHWAY, TRACT, EMBANKMENT, VAL, GAI, ALLEY, HOUSE,172 STREET_HOUSE_CORPUS, HOUSE_BUILDING, BOULEVARD, DISTRICT, CORPUS, APARTMENT, BUILDING173).interpretation(Address)174Name = fact('Name', name)175name = and_(gram('Name'), not_(get_abbr_gram()), get_len())176patronymic = and_(gram('Patr'), not_(get_abbr_gram()), get_len())177surname = and_(gram('Surn'), get_len())178FIRST = name.interpretation(Name.first)179FIRST_ABBR = and_(get_abbr_gram(), true()).interpretation(Name.first)180LAST = surname.interpretation(Name.last)181MAYBE_LAST = and_(true(), not_(get_abbr_gram()), get_len()).interpretation(Name.last)182MIDDLE = patronymic.interpretation(Name.middle)183MIDDLE_SHORT = and_(get_abbr_gram(), true()).interpretation(Name.middle)184FIRST_LAST = rule(FIRST, MAYBE_LAST)185LAST_FIRST = rule(MAYBE_LAST, FIRST)186LAST_ABBR_FIRST = rule(MAYBE_LAST, FIRST_ABBR, is_dot())187LAST_ABBR_FIRST_MIDDLE = rule(MAYBE_LAST, FIRST_ABBR, is_dot(), MIDDLE_SHORT, is_dot())188FIRST_MIDDLE = rule(FIRST, MIDDLE)189MIDDLE_FIRST = rule(MIDDLE, FIRST)190FIRST_MIDDLE_LAST = rule(FIRST, MIDDLE, LAST)191LAST_FIRST_MIDDLE = rule(LAST, FIRST, MIDDLE)192SHORT_FIRST_MIDDLE_LAST = rule(FIRST_ABBR, is_dot(), MIDDLE_SHORT, is_dot(), MAYBE_LAST)193SHORT_FIRST_LAST = rule(FIRST_ABBR, is_dot(), MAYBE_LAST)194PERSON_RULE = or_(LAST_FIRST_MIDDLE, FIRST_MIDDLE_LAST, FIRST_MIDDLE, FIRST_LAST, LAST_FIRST, SHORT_FIRST_LAST,...
search.py
Source:search.py
...74 Job.job_id)75 a["results"] += games_and["results"] + companies_and["results"] + jobs_and["results"]76 # contains OR77 print("PARTIAL OR")78 games_queries_or = OrderedDict([("name", or_(* [Game.name.ilike("%" + x + "%") for x in terms])),79 ("deck", or_(* [Game.deck.ilike("%" + x + "%") for x in terms])),80 ("description", or_(* [Game.description.ilike("%" + x + "%") for x in terms]))])81 companies_queries_or = OrderedDict([("name", or_(* [Company.name.ilike("%" + x + "%") for x in terms])),82 ("deck", or_(* [Company.deck.ilike("%" + x + "%") for x in terms])),83 ("description", or_(* [Company.description.ilike("%" + x + "%") for x in terms]))])84 jobs_queries_or = OrderedDict([("job_title", or_(* [Job.job_title.ilike("%" + x + "%") for x in terms])),85 ("description", or_(* [Job.description.ilike("%" + x + "%") for x in terms])),86 ("location", or_(* [Job.location.ilike("%" + x + "%") for x in terms])),87 ("company_name", or_(* [Job.company_name.ilike("%" + x + "%") for x in terms]))])88 games_or = search_models(a, request.args["s"], Game, "games", "partial match OR", "OR", games_queries_or, Game.name,89 Game.game_id)90 companies_or = search_models(a, request.args["s"], Company, "companies", "partial match OR", "OR", companies_queries_or,91 Company.name, Company.company_id)92 jobs_or = search_models(a, request.args["s"], Job, "jobs", "partial match OR", "OR",93 jobs_queries_or, Job.job_title, Job.job_id)94 a["results"] += games_or["results"] + companies_or["results"] + jobs_or["results"]95 return a96# search_string, filter, entities97def search_models(temp_result, search_string, model, type, match_type, result_type, queries, *entities):98 result = {}99 result["results"] = []100 terms = search_string.rsplit(" ")101 for q in queries:...
results.py
Source:results.py
...33avg std min max3496706.3 2651.17 80156 9806235100318 1131.11 91302 10094036100649 1752.85 87910 10176237and_(or_(and_(IN9, or_(IN5, IN3)), and_(or_(and_(or_(and_(IN13, IN12), not_(not_(IN8))), IN16), and_(or_(and_(not_(not_(IN8)), or_(IN2, IN4)), or_(and_(and_(or_(and_(IN7, or_(IN3, IN3)), IN10), or_(and_(and_(not_(not_(IN8)), and_(or_(and_(IN7, or_(and_(IN7, or_(IN3, IN3)), not_(IN8))), IN10), or_(IN5, IN4))), IN16), IN2)), IN16), and_(or_(and_(not_(not_(IN1)), or_(IN2, and_(IN7, or_(or_(IN3, IN5), or_(and_(IN2, IN4), IN10))))), IN15), or_(IN12, and_(IN6, IN11))))), or_(IN2, and_(IN7, or_(or_(IN3, IN5), or_(and_(IN2, IN4), IN10)))))), or_(and_(or_(and_(IN7, or_(and_(IN7, or_(IN3, IN3)), or_(and_(IN6, IN8), IN10))), IN10), or_(IN5, IN4)), and_(or_(and_(or_(IN4, not_(not_(IN8))), IN16), and_(or_(and_(not_(not_(IN8)), or_(IN7, IN4)), IN15), or_(not_(not_(and_(IN13, IN3))), and_(IN6, IN11)))), IN12)))), or_(and_(or_(or_(and_(IN2, IN4), IN10), IN15), or_(not_(not_(and_(IN13, IN12))), and_(IN6, IN11))), or_(and_(and_(or_(and_(IN7, or_(IN3, IN3)), IN10), or_(and_(and_(or_(and_(IN7, or_(IN3, IN3)), IN13), and_(or_(and_(IN7, or_(and_(IN7, or_(IN3, IN3)), not_(IN8))), IN10), or_(and_(or_(and_(IN7, or_(and_(IN7, or_(IN3, IN3)), not_(IN8))), IN10), or_(IN5, IN4)), IN4))), IN16), IN2)), IN16), and_(or_(and_(not_(not_(IN1)), or_(IN2, and_(IN7, or_(or_(IN3, IN5), or_(and_(IN2, IN4), IN10))))), IN15), or_(not_(not_(IN8)), and_(IN6, IN11))))))3898445.8 491.316 95088 988623992616.2 2998.54 74400 938804096612.7 3473.9 73164 984344196469.9 1417.88 87818 976344298469.3 2206.32 87930 996764397442.4 2274.31 83064 98402...
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