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remove bugs from path generation
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ilia
parent
8f80e65261
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270
passfinder/city.txt
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270
passfinder/city.txt
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@ -0,0 +1,270 @@
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Москва
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Санкт-Петербург
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Новосибирск
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Екатеринбург
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Нижний Новгород
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Самара
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Омск
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Казань
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Челябинск
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Ростов-на-Дону
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Уфа
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Волгоград
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Пермь
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Красноярск
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Воронеж
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Саратов
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Краснодар
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Тольятти
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Ижевск
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Ульяновск
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Барнаул
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Владивосток
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Ярославль
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Иркутск
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Тюмень
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Махачкала
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Хабаровск
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Оренбург
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Новокузнецк
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Кемерово
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Рязань
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Томск
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Астрахань
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Пенза
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Набережные Челны
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Липецк
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Тула
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Киров
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Чебоксары
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Калининград
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Брянск
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Курск
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Иваново
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Магнитогорск
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Улан-Удэ
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Тверь
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Ставрополь
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Нижний Тагил
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Белгород
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Архангельск
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Владимир
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Сочи
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Курган
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Смоленск
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Калуга
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Чита
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Ор
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л
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Волжский
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Череповец
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Владикавказ
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Мурманск
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Сургут
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Вологда
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Саранск
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Тамбов
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Стерлитамак
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Грозный
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Якутск
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Кострома
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Комсомольск-на-Амуре
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Петрозаводск
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Таганрог
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Нижневартовск
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Йошкар-Ола
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Братск
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Новороссийск
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Дзержинск
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Шахты
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Нальчик
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Орск
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Сыктывкар
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Нижнекамск
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Ангарск
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Старый Оскол
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Великий Новгород
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Балашиха
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Благовещенск
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Прокопьевск
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Бийск
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Химки
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Псков
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Энгельс
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Рыбинск
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Балаково
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Северодвинск
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Армавир
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Подольск
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Корол
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в
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Южно-Сахалинск
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Петропавловск-Камчатский
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Сызрань
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Норильск
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Златоуст
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Каменск-Уральский
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Мытищи
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Люберцы
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Волгодонск
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Новочеркасск
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Абакан
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Находка
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Уссурийск
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Березники
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Салават
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Электросталь
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Миасс
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Первоуральск
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Рубцовск
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Альметьевск
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Ковров
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Коломна
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Майкоп
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Пятигорск
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Одинцово
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Колпино
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Копейск
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Хасавюрт
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Новомосковск
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Кисловодск
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Серпухов
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Новочебоксарск
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@ -1,226 +1,47 @@
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city_in_hotels = ['Абзаково',
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'Абрамовка',
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'Абрау-Дюрсо',
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'Адлер',
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'Азов',
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'Аксай',
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'Альметьевск',
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'Анапа',
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'Андрианово',
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'Арамиль',
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'Арзамас',
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'Арнеево',
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'Архипо-Осиповка',
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'Бабкино',
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'Базы отдыха ВТО',
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city_in_hotels = {'Астрахань',
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'Балашиха',
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'Батайск',
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'Беличье',
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'Белореченск',
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'Бердск',
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'Бердяш',
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'Березовка',
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'Бжид',
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'Битца',
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'Благовещенская',
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'Болтино',
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'Большой Сочи',
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'Бор, Нижегородская область',
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'Борисово',
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'Борносово',
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'Будённовск',
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'Вардане',
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'Васильево, Ленинградская область',
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'Васкелово',
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'Вербилки',
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'Веселовка, Краснодарский край',
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'Видное',
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'Витязево',
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'Владимировка',
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'Внуково',
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'Воскресенск',
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'Вотря',
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'Всеволожск',
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'Всходы',
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'Выборг',
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'Вырубово',
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'Гатчина',
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'Гвардейское',
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'Геленджик',
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'Голиково',
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'Головинка Краснодарский край',
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'Голубицкая',
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'Горки',
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'Городец',
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'Горячий ключ',
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'Григорчиково',
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'Гуамка',
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'Д/О Авангард',
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'Дагомыс',
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'Джемете',
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'Джубга',
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'Березники',
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'Благовещенск',
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'Владикавказ',
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'Волгоград',
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'Волгодонск',
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'Волжский',
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'Воронеж',
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'Грозный',
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'Дзержинск',
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'Дивеево',
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'Дивногорье, Краснодарский край',
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'Дивноморское',
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'Дмитров',
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'Домодедово',
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'Дракино',
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'Дранишники',
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'Дубечино',
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'Егорьевск',
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'Ейск',
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'Екатеринбург',
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'Ершово',
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'Ессентуки',
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'Железноводск',
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'Жуковский',
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'За Родину',
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'Звенигород',
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'Зеленая поляна',
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'Ивантеевка',
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'Ильичево',
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'Иннолово ',
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'Иноземцево',
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'Исаково',
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'Истра',
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'Кабардинка',
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'Казань',
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'Каменск-Шахтинский',
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'Каневская',
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'Кингисепп',
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'Златоуст',
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'Ижевск',
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'Каменск-Уральский',
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'Киров',
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'Кисловодск',
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'Клин',
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'Коломна',
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'Коробицыно',
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'Королев',
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'Косулино',
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'Котельники',
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'Красная Горка',
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'Красная Поляна',
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'Красногорск',
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'Краснодар',
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'Красный Колос',
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'Кудряшовский',
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'Курово',
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'Кусимовского Рудника',
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'Кучугуры',
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'Лабинск',
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'Лазаревское',
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'Лермонтово',
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'Лесной городок',
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'Лодейное Поле',
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'Лоо',
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'Лосево',
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'Колпино',
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'Комсомольск-на-Амуре',
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'Копейск',
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'Кострома',
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'Красноярск',
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'Курган',
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'Люберцы',
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'Магнитогорск',
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'Малые Решники',
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'Марьино, Ленинградская область',
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'Маяковского',
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'Мезмай',
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'Мещерино',
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'Миасс',
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'Минеральные Воды',
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'Мистолово',
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'Мишуткино',
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'Можайск',
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'Махачкала',
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'Москва',
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'Мостовской',
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'Мытищи',
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'Набережные Челны',
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'Наро-Фоминск',
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'Нарынка',
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'Небуг',
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'Нестерово',
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'Нижний Новгород',
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'Нальчик',
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'Нижнекамск',
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'Нижний Тагил',
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'Новая',
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'Новоабзаково',
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'Нововолково',
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'Новомихайловский',
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'Новороссийск',
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'Новосибирск',
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'Новочеркасск',
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'Новый путь',
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'Ногинск',
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'Нурлат',
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'Овсяники',
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'Новочебоксарск',
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'Одинцово',
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'Озеры',
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'Оксино',
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'Октябрьский, Московская область',
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'Ольгинка',
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'Остров, Московская область',
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'Павловск',
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'Падиково',
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'Пересвет',
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'Платформа 69-й километр, Сосновское сельское поселение',
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'Омск',
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'Оренбург',
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'Орск',
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'Пермь',
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'Подольск',
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'Подпорожье',
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'Полтавская',
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'Приморско-Ахтарск',
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'Приозерск',
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'Прохорово',
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'Пушкино',
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'Пятигорск',
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'Раменское',
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'Реутов',
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'Рождествено',
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'Роза Хутор',
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'Ростов-на-Дону',
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'Рощино',
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'Руза',
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'Салават',
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'Санкт-Петербург',
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'Светлое',
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'Светлый',
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'Свирица',
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'Сергиев Посад',
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'Серпухов',
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'Симагино',
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'Сириус',
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'Скоково',
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'Снегири',
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'Солнечногорск',
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'Солохаул',
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'Сосново',
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'Сосновый Бор, Ленинградская область',
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'Сосновый Бор, Московская область',
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'Софрино',
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'Сочи',
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'Ставрополь',
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'Станица Динская',
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'Станица Должанская',
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'Старая Руза',
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'Степаньково',
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'Суйда',
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'Сукко',
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'Супсех',
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'Таганрог',
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'Тарасово',
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'Тимашевск',
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'Тихвин',
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'Тихорецк',
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'Тобольск',
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'Туапсе',
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'Тучково',
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'Томск',
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'Тюмень',
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'Увильды ',
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'Углегорский',
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'Удельная',
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'Усть-Койсуг',
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'Усть-Лабинск',
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'Уфа',
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'Ушаки',
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'Фрязино',
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'Хадыженск',
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'Хасавюрт',
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'Химки',
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'Хоста',
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'Чебаркуль',
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'Челябинск',
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'Чехов, Сахалинская область',
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'Чудская',
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'Шахты',
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'Широкая балка',
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'Щёлково',
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'Эсто-Садок',
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'Якорная щель']
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'Энгельс'}
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@ -65,6 +65,49 @@ class RouteInputSerializer(serializers.Serializer):
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min_length=24, max_length=24, required=False, allow_blank=True, allow_null=True
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)
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movement = serializers.ChoiceField(['walk', 'bike', 'scooter', 'auto'], required=False, allow_blank=True)
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stars = serializers.ListField(
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child=serializers.ChoiceField([1, 2, 3, 4, 5]),
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required=False,
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allow_empty=True,
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allow_null=True
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)
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what_to_see = serializers.ListField(
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child=serializers.ChoiceField(
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[
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'attractions',
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'museum',
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'movie',
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'concert',
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'artwork',
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'plays',
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'shop',
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'gallery',
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'theme_park',
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'viewpoint',
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'zoo'
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]
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),
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required=False,
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allow_empty=True,
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allow_null=True
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)
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where_stay = serializers.ListField(
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child=serializers.ChoiceField([
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'hotel', 'apartment', 'hostel'
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]),
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required=False,
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allow_empty=True,
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allow_null=True
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)
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where_eat = serializers.ListField(
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child=serializers.ChoiceField(['restaurant', 'bar', 'cafe']),
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required=False,
|
||||
allow_empty=True,
|
||||
allow_null=True
|
||||
)
|
||||
with_kids = serializers.BooleanField(required=False, allow_null=True)
|
||||
with_animals = serializers.BooleanField(required=False, allow_null=True)
|
||||
|
||||
|
||||
class CitySerializer(serializers.ModelSerializer):
|
||||
class Meta:
|
||||
|
|
|
|||
|
|
@ -75,6 +75,40 @@ def post(self, request):
|
|||
movement = data['movement']
|
||||
except KeyError:
|
||||
movement = 'walk'
|
||||
|
||||
hotel_stars = data['stars']
|
||||
if hotel_stars is None:
|
||||
hotel_stars = []
|
||||
|
||||
|
||||
hotel_type = data['where_stay']
|
||||
if hotel_type is None:
|
||||
hotel_type = ['hotel']
|
||||
|
||||
where_eat = data['where_eat']
|
||||
if where_eat is None:
|
||||
where_eat = ['restaurant', 'bar', 'cafe']
|
||||
|
||||
what_to_see = data['what_to_see']
|
||||
if what_to_see is None:
|
||||
what_to_see = [
|
||||
'attractions',
|
||||
'museum',
|
||||
'movie',
|
||||
'concert',
|
||||
'artwork',
|
||||
'plays',
|
||||
'shop',
|
||||
'gallery',
|
||||
'theme_park',
|
||||
'viewpoint',
|
||||
'zoo'
|
||||
]
|
||||
|
||||
|
||||
if 'hotel' not in hotel_type:
|
||||
hotel_stars = []
|
||||
|
||||
region = None
|
||||
|
||||
if city_id:
|
||||
|
|
@ -95,7 +129,17 @@ def post(self, request):
|
|||
|
||||
print(request.user, region, start_date, end_date)
|
||||
|
||||
tour = generate_tour(request.user, region, start_date, end_date, movement_mapping[movement])
|
||||
tour = generate_tour(
|
||||
request.user,
|
||||
region,
|
||||
start_date,
|
||||
end_date,
|
||||
avg_velocity=movement_mapping[movement],
|
||||
stars=hotel_stars,
|
||||
hotel_type=hotel_type,
|
||||
where_eat=where_eat,
|
||||
what_to_see=what_to_see
|
||||
)
|
||||
print(len(tour[1]))
|
||||
|
||||
return Response(data=tour[0])
|
||||
|
|
@ -109,9 +153,7 @@ class ListRegionApiView(ListAPIView):
|
|||
class ListCityApiView(ListAPIView):
|
||||
serializer_class = CitySerializer
|
||||
queryset = (
|
||||
City.objects.annotate(points_num=Count("points"))
|
||||
.filter(points_num__gte=100)
|
||||
.order_by("title")
|
||||
City.objects.annotate(points_count=Count('points')).filter(title__in=city_in_hotels).filter(points_count__gt=200).order_by('title')
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from rest_framework import serializers
|
||||
from passfinder.events.api.serializers import EventSerializer, HotelSerializer
|
||||
from passfinder.events.api.serializers import EventSerializer, HotelSerializer, ObjectRouteSerializer
|
||||
|
||||
|
||||
class TinderProceedSerializer(serializers.Serializer):
|
||||
|
|
@ -49,3 +49,23 @@ class DailySelectionSerializer(serializers.Serializer):
|
|||
)
|
||||
)
|
||||
event = EventSerializer()
|
||||
|
||||
|
||||
class StarSelectionSerializer(serializers.Serializer):
|
||||
stars = serializers.ListField(child=serializers.IntegerField(), write_only=True)
|
||||
|
||||
|
||||
class CategorySelectionSerializer(serializers.Serializer):
|
||||
categories = serializers.ListField(child=serializers.ChoiceField(
|
||||
['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
|
||||
))
|
||||
|
||||
|
||||
class RecomendationNode(serializers.Serializer):
|
||||
category = serializers.CharField()
|
||||
events = serializers.ListField(child=ObjectRouteSerializer())
|
||||
|
||||
|
||||
|
||||
class SelfRecomendationSerializer(serializers.Serializer):
|
||||
recomendations = serializers.ListField(child=RecomendationNode(), write_only=True)
|
||||
|
|
@ -49,32 +49,13 @@ class PersonalRecommendation(viewsets.GenericViewSet):
|
|||
model = Event
|
||||
queryset = Event.objects.all()
|
||||
|
||||
@action(methods=['GET'], detail=False)
|
||||
def plays(self, request, *args, **kwargs):
|
||||
recs = get_personal_plays_recommendation(request.user)
|
||||
ans = []
|
||||
for rec in recs:
|
||||
ans.append(EventSerializer(rec[1]).data)
|
||||
return Response(ans, 200)
|
||||
|
||||
|
||||
@action(methods=['GET'], detail=False)
|
||||
def concerts(self, request, *args, **kwargs):
|
||||
recs = get_personal_concerts_recommendation(request.user)
|
||||
ans = []
|
||||
for rec in recs:
|
||||
ans.append(EventSerializer(rec[1]).data)
|
||||
return Response(ans, 200)
|
||||
|
||||
|
||||
@action(methods=['GET'], detail=False)
|
||||
def movies(self, request, *args, **kwargs):
|
||||
recs = get_personal_movies_recommendation(request.user)
|
||||
ans = []
|
||||
for rec in recs:
|
||||
ans.append(EventSerializer(rec[1]).data)
|
||||
return Response(ans, 200)
|
||||
|
||||
@action(methods=['GET'], detail=False, serializer_class=SelfRecomendationSerializer)
|
||||
def recommendations(self, request, *args, **kwargs):
|
||||
return Response(
|
||||
data=get_personal_recomendations(request.user),
|
||||
status=200
|
||||
)
|
||||
|
||||
@action(methods=['GET'], detail=True)
|
||||
def get_nearest_user_distance(self, request, pk, *args, **kwargs):
|
||||
|
|
@ -166,3 +147,17 @@ def add_to_favorites(self, request, pk, *args, **kwargs):
|
|||
pref.save()
|
||||
|
||||
return Response(status=200)
|
||||
|
||||
@action(methods=['POST'], detail=False, serializer_class=StarSelectionSerializer)
|
||||
def set_hotel_stars(self, request, *args, **kwargs):
|
||||
up, _ = UserPreferences.objects.get_or_create(user=request.user)
|
||||
up.preferred_stars = request.data['stars']
|
||||
up.save()
|
||||
return Response(status=200)
|
||||
|
||||
@action(methods=['POST'], detail=False, serializer_class=CategorySelectionSerializer)
|
||||
def set_categories(self, request, *args, **kwargs):
|
||||
up, _ = UserPreferences.objects.get_or_create(user=request.user)
|
||||
up.preferred_categories = request.data['categories']
|
||||
up.save()
|
||||
return Response(status=200)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,31 @@
|
|||
# Generated by Django 4.2.1 on 2023-05-27 10:21
|
||||
|
||||
import django.contrib.postgres.fields
|
||||
from django.db import migrations, models
|
||||
|
||||
|
||||
class Migration(migrations.Migration):
|
||||
|
||||
dependencies = [
|
||||
("recomendations", "0007_nearesteventtorestaurant"),
|
||||
]
|
||||
|
||||
operations = [
|
||||
migrations.AddField(
|
||||
model_name="userpreferences",
|
||||
name="preferred_categories",
|
||||
field=django.contrib.postgres.fields.ArrayField(
|
||||
base_field=models.CharField(max_length=100),
|
||||
blank=True,
|
||||
null=True,
|
||||
size=None,
|
||||
),
|
||||
),
|
||||
migrations.AddField(
|
||||
model_name="userpreferences",
|
||||
name="preferred_stars",
|
||||
field=django.contrib.postgres.fields.ArrayField(
|
||||
base_field=models.IntegerField(), blank=True, null=True, size=None
|
||||
),
|
||||
),
|
||||
]
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
from django.db import models
|
||||
from passfinder.users.models import User
|
||||
from passfinder.events.models import Event, Hotel, Restaurant
|
||||
from django.contrib.postgres.fields import ArrayField
|
||||
|
||||
|
||||
class UserPreferences(models.Model):
|
||||
|
|
@ -21,6 +22,9 @@ class UserPreferences(models.Model):
|
|||
prefferred_museums = models.ManyToManyField(Event, related_name='preffered_users_museums')
|
||||
unprefferred_museums = models.ManyToManyField(Event, related_name='unpreffered_users_museums')
|
||||
|
||||
preferred_categories = ArrayField(base_field=models.CharField(max_length=100), null=True, blank=True)
|
||||
preferred_stars = ArrayField(base_field=models.IntegerField(), null=True, blank=True)
|
||||
|
||||
|
||||
|
||||
class NearestEvent(models.Model):
|
||||
|
|
|
|||
|
|
@ -266,8 +266,7 @@ def dist_func(event1: Event, event2: Event):
|
|||
return dist
|
||||
except:
|
||||
return 1000000
|
||||
#return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
|
||||
# return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
|
||||
return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
|
||||
|
||||
|
||||
def generate_nearest():
|
||||
|
|
@ -349,6 +348,19 @@ def match_points():
|
|||
print(i)
|
||||
|
||||
|
||||
def match_restaurants():
|
||||
regions = list(City.objects.all())
|
||||
|
||||
for i, point in enumerate(Restaurant.objects.all()):
|
||||
s_regions = list(sorted(regions.copy(), key=lambda x: dist_func(point, x)))
|
||||
point.city = s_regions[0]
|
||||
|
||||
point.save()
|
||||
if i % 10 == 0:
|
||||
print(i)
|
||||
|
||||
|
||||
|
||||
def calculate_mean_metric(
|
||||
favorite_events: Iterable[Event],
|
||||
target_event: Event,
|
||||
|
|
@ -393,21 +405,50 @@ def calculate_favorite_metric(event: Event, user: User):
|
|||
def get_exponential_koef(time: timedelta):
|
||||
time = time.seconds
|
||||
if time < 60 * 10:
|
||||
return 1
|
||||
return 2
|
||||
if time < 60 * 20:
|
||||
return 10
|
||||
return 5
|
||||
if time < 60 * 30:
|
||||
return 1000
|
||||
return 10
|
||||
if time < 60 * 40:
|
||||
return 100000
|
||||
return 20
|
||||
return int(1e10)
|
||||
|
||||
|
||||
def get_category_similarity_coef(event, user):
|
||||
up, _ = UserPreferences.objects.get_or_create(user=user)
|
||||
cat = up.preferred_categories
|
||||
if event.type in cat:
|
||||
return 0.7
|
||||
else:
|
||||
return 1.2
|
||||
|
||||
|
||||
def get_nearest_favorite(
|
||||
events: Iterable[Event], user: User, base_event: Event, exclude_events: Iterable[Event] = [], velocity=3.0, top_k=1
|
||||
events: Iterable[Event],
|
||||
user: User,
|
||||
base_event: Event,
|
||||
exclude_events: Iterable[Event] = [],
|
||||
velocity=3.0,
|
||||
top_k=1
|
||||
):
|
||||
|
||||
sorted_events = list(sorted(events, key=lambda event: calculate_favorite_metric(event, user) * get_exponential_koef(time_func(dist_func(event, base_event), velocity))))
|
||||
sorted_events = list(
|
||||
sorted(
|
||||
filter(lambda event: event not in exclude_events, events),
|
||||
key=lambda event:
|
||||
calculate_favorite_metric(event, user) *
|
||||
get_exponential_koef(
|
||||
time_func(
|
||||
dist_func(
|
||||
event, base_event
|
||||
),
|
||||
velocity
|
||||
)
|
||||
) *
|
||||
get_category_similarity_coef(event, user)
|
||||
)
|
||||
)
|
||||
|
||||
if top_k == 1:
|
||||
return sorted_events[0]
|
||||
|
|
@ -424,14 +465,6 @@ def time_func(km_distance: float, velocity: float):
|
|||
return timedelta(minutes=(km_distance) / (velocity / 60))
|
||||
|
||||
|
||||
def generate_route(point1: BasePoint, point2: BasePoint, velocity: float):
|
||||
distance = dist_func(point1, point2)
|
||||
time = time_func(distance, velocity)
|
||||
|
||||
|
||||
def time_func(km_distance: float, velocity):
|
||||
return timedelta(minutes=(km_distance) / (velocity / 60))
|
||||
|
||||
|
||||
def generate_route(point1: BasePoint, point2: BasePoint, velocity):
|
||||
distance = dist_func(point1, point2)
|
||||
|
|
@ -470,14 +503,40 @@ def generate_multiple_tours(user: User, city: City, start_date: datetime.date, e
|
|||
return pool.map(generate_tour, [(user, start_date, end_date, hotel) for hotel in hotels])
|
||||
|
||||
|
||||
def generate_tour(user: User, city: City, start_date: datetime.date, end_date: datetime.date, avg_velocity=3.0):
|
||||
def generate_tour(
|
||||
user: User,
|
||||
city: City,
|
||||
start_date: datetime.date,
|
||||
end_date: datetime.date,
|
||||
avg_velocity=3.0,
|
||||
stars=[],
|
||||
hotel_type=['hotel', 'hostel', 'apartment'],
|
||||
where_eat=['restaurant', 'bar', 'cafe'],
|
||||
what_to_see=['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
|
||||
):
|
||||
UserPreferences.objects.get_or_create(user=user)
|
||||
hotel = choice(list(Hotel.objects.filter(city=city)))
|
||||
|
||||
hotels_candidates = Hotel.objects.filter(city=city)
|
||||
if len(hotels_candidates.filter(stars__in=stars)):
|
||||
hotels_candidates = hotels_candidates.filter(stars__in=stars)
|
||||
|
||||
try:
|
||||
hotel = choice(list(hotels_candidates))
|
||||
except:
|
||||
hotel = city
|
||||
current_date = start_date
|
||||
paths, points, disallowed_rest = [], [], []
|
||||
|
||||
while current_date < end_date:
|
||||
local_points, local_paths, local_disallowed_rest = generate_path(user, points, hotel, disallowed_rest, avg_velocity)
|
||||
local_points, local_paths, local_disallowed_rest = generate_path(
|
||||
user,
|
||||
points,
|
||||
hotel,
|
||||
disallowed_rest,
|
||||
avg_velocity,
|
||||
where_eat=where_eat,
|
||||
what_to_see=what_to_see
|
||||
)
|
||||
points.extend(local_points)
|
||||
paths.append(
|
||||
{
|
||||
|
|
@ -514,22 +573,56 @@ def nearest_distance_points(point: BasePoint, user: User, velocity: float=3.0):
|
|||
|
||||
|
||||
|
||||
def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hotel, disallowed_rests: Iterable[Restaurant], avg_velocity: float):
|
||||
# region_events = Event.objects.filter(region=region)
|
||||
|
||||
#candidates = NearestHotel.objects.get(hotel=hotel).nearest_events.all()
|
||||
allowed_types = ['museum', 'attraction']
|
||||
|
||||
start_point = NearestRestaurantToHotel.objects.filter(hotel=hotel).first().restaurants.filter(~Q(oid__in=disallowed_rests)).first()
|
||||
disallowed_rests.append(start_point.oid)
|
||||
candidates = list(filter(lambda x: x not in disallowed_points, hotel.nearest_hotel_rel.all().first().nearest_events.filter(type__in=allowed_types)))
|
||||
#candidates = list(filter(lambda x: x.type in allowed_types, map(lambda x: x.event, start_point.nearestrestauranttoevent_set.all()[0:100])))
|
||||
points = [start_point]
|
||||
path = [
|
||||
generate_hotel(hotel),
|
||||
generate_route(start_point, hotel, avg_velocity),
|
||||
generate_restaurant(start_point)
|
||||
def generate_path(
|
||||
user: User,
|
||||
disallowed_points: Iterable[BasePoint],
|
||||
hotel: Hotel,
|
||||
disallowed_rests: Iterable[Restaurant],
|
||||
avg_velocity: float,
|
||||
where_eat=['restaurant', 'bar', 'cafe'],
|
||||
what_to_see=['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
|
||||
):
|
||||
allowed_types = [
|
||||
'museum',
|
||||
'attraction',
|
||||
'artwork',
|
||||
'shop',
|
||||
'gallery',
|
||||
'theme_park',
|
||||
'zoo',
|
||||
'other',
|
||||
'viewpoint'
|
||||
]
|
||||
if len(set(allowed_types) & set(what_to_see)) == 0:
|
||||
allowed_types = what_to_see
|
||||
else:
|
||||
allowed_types = list(set(allowed_types) & set(what_to_see))
|
||||
print(allowed_types, hotel)
|
||||
if isinstance(hotel, City):
|
||||
start_points_candidate = Restaurant.objects.filter(city=hotel).filter(~Q(oid__in=disallowed_rests))
|
||||
else:
|
||||
start_points_candidate = NearestRestaurantToHotel.objects.filter(hotel=hotel).first().restaurants.filter(~Q(oid__in=disallowed_rests))
|
||||
|
||||
if len(start_points_candidate.filter(type__in=where_eat)):
|
||||
start_points_candidate = start_points_candidate.filter(type__in=where_eat)
|
||||
|
||||
start_point = start_points_candidate[0]
|
||||
disallowed_rests.append(start_point.oid)
|
||||
|
||||
candidates = NearestEventToRestaurant.objects.get(restaurant=start_point).events.all().filter(type__in=allowed_types)
|
||||
|
||||
points = [start_point]
|
||||
|
||||
if isinstance(hotel, Hotel):
|
||||
path = [
|
||||
generate_hotel(hotel),
|
||||
generate_route(start_point, hotel, avg_velocity),
|
||||
generate_restaurant(start_point)
|
||||
]
|
||||
else:
|
||||
path = [
|
||||
generate_restaurant(start_point)
|
||||
]
|
||||
|
||||
start_time = datetime.combine(datetime.now(), time(hour=10))
|
||||
|
||||
|
|
@ -537,32 +630,53 @@ def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hot
|
|||
|
||||
while start_time.hour < 22 and start_time.day == datetime.now().day:
|
||||
if (start_time.hour > 14 and how_many_eat == 1) or (start_time.hour > 20 and how_many_eat == 2):
|
||||
point = NearestRestaurantToEvent.objects.filter(event=points[-1]).first().restaurants.filter(~Q(oid__in=disallowed_rests))[0]
|
||||
disallowed_rests.append(point.oid)
|
||||
points.append(point)
|
||||
# Переделать - сделать еще один прекалк на рестораны с точками
|
||||
candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all().filter(type__in=allowed_types)
|
||||
if len(candidates) < 10:
|
||||
candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all()
|
||||
|
||||
|
||||
path.append(generate_restaurant(points[-1]))
|
||||
start_time += timedelta(seconds=path[-1]['time'])
|
||||
how_many_eat += 1
|
||||
continue
|
||||
print(points, start_time)
|
||||
try:
|
||||
point_candidates = NearestRestaurantToEvent.objects.filter(event=points[-1]).first().restaurants.filter(~Q(oid__in=disallowed_rests))
|
||||
if len(point_candidates.filter(type__in=where_eat)):
|
||||
point_candidates = point_candidates.filter(type__in=where_eat)
|
||||
point = point_candidates[0]
|
||||
|
||||
disallowed_rests.append(point.oid)
|
||||
points.append(point)
|
||||
|
||||
candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all().filter(type__in=allowed_types)
|
||||
if len(candidates) < 2:
|
||||
candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all()
|
||||
|
||||
path.append(generate_restaurant(points[-1]))
|
||||
start_time += timedelta(seconds=path[-1]['time'])
|
||||
how_many_eat += 1
|
||||
continue
|
||||
except:
|
||||
return points, path, disallowed_rests
|
||||
|
||||
|
||||
if start_time.hour > 17:
|
||||
allowed_types = ['play', 'concert', 'movie']
|
||||
allowed_types = [
|
||||
'play',
|
||||
'concert',
|
||||
'movie',
|
||||
'shop',
|
||||
'gallery',
|
||||
'theme_park',
|
||||
'viewpoint'
|
||||
]
|
||||
if len(set(allowed_types) & set(what_to_see)) == 0:
|
||||
allowed_types = what_to_see
|
||||
else:
|
||||
allowed_types = list(set(allowed_types) & set(what_to_see))
|
||||
|
||||
|
||||
if candidates is None:
|
||||
candidates = NearestEvent.objects.get(event=points[-1]).nearest.filter(type__in=allowed_types)
|
||||
if len(candidates) < 10:
|
||||
if len(candidates) < 2:
|
||||
candidates = NearestEvent.objects.get(event=points[-1]).nearest.all()
|
||||
|
||||
try:
|
||||
points.append(get_nearest_favorite(candidates, user, points[-1], points + disallowed_points))
|
||||
|
||||
except AttributeError:
|
||||
except:
|
||||
points.append(get_nearest_favorite(candidates, user, points[-1], points))
|
||||
|
||||
transition_route = generate_route(points[-1], points[-2], avg_velocity)
|
||||
|
|
@ -576,15 +690,6 @@ def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hot
|
|||
return points, path, disallowed_rests
|
||||
|
||||
|
||||
def calculate_distance(sample1: Event, samples: Iterable[Event], model: AnnoyIndex, rev_mapping):
|
||||
metrics = []
|
||||
|
||||
for sample in samples:
|
||||
metrics.append(model.get_distance(rev_mapping[sample1.oid], rev_mapping[sample.oid]))
|
||||
|
||||
return sum(metrics) / len(metrics)
|
||||
|
||||
|
||||
def calculate_distance(
|
||||
sample1: Event, samples: Iterable[Event], model: AnnoyIndex, rev_mapping
|
||||
):
|
||||
|
|
@ -627,11 +732,6 @@ def get_onboarding_hotels(stars=Iterable[int]):
|
|||
|
||||
|
||||
def generate_points_path(user: User, points: Iterable[Event], velocity=3.0):
|
||||
"""
|
||||
Дописать
|
||||
1) генерить маршруты от многих точек (не только по 2) (salesman problem)
|
||||
2) Если в маршруте до 7 точек - добавлять похожие пока не станет 7 точек
|
||||
"""
|
||||
if len(points) < 7:
|
||||
candidates = NearestEvent.objects.get(event=points[0]).nearest.all()
|
||||
points.extend(list(get_nearest_favorite(candidates, user, points[0], [], velocity, 7-len(points))))
|
||||
|
|
@ -662,4 +762,115 @@ def generate_points_path(user: User, points: Iterable[Event], velocity=3.0):
|
|||
])
|
||||
visited_points.append(pt)
|
||||
|
||||
return res
|
||||
return res
|
||||
|
||||
|
||||
def flat_list(lst):
|
||||
res = []
|
||||
for i in lst:
|
||||
res.extend(i)
|
||||
return res
|
||||
|
||||
|
||||
def range_candidates(candidates, user, favorite_events):
|
||||
model_mappings = {
|
||||
'attraction': [attracion_model, rev_attraction_mapping],
|
||||
'museum': [mus_model, rev_mus_mapping],
|
||||
'movie': [cinema_model, rev_cinema_mapping],
|
||||
'concert': [concert_model, rev_concert_mapping],
|
||||
'plays': [plays_model, rev_plays_mapping]
|
||||
}
|
||||
|
||||
if candidates[0].type in ['attraction', 'museum', 'movie', 'concert', 'plays']:
|
||||
candidates = sorted(
|
||||
candidates,
|
||||
key=lambda cand: calculate_mean_metric(
|
||||
favorite_events,
|
||||
cand,
|
||||
*model_mappings[cand.type]
|
||||
)
|
||||
)
|
||||
return candidates[0:10]
|
||||
return sample(candidates, 10)
|
||||
|
||||
|
||||
def get_personal_recomendations(user):
|
||||
up, _ = UserPreferences.objects.get_or_create(user=user)
|
||||
candidates_generate_strategy = {
|
||||
'plays': [lambda pref: flat_list(
|
||||
list(
|
||||
map(
|
||||
lambda cand: nearest_plays(
|
||||
cand, 30
|
||||
),
|
||||
pref.preffered_plays.all()
|
||||
)
|
||||
),
|
||||
), lambda pref: pref.preffered_plays.all()],
|
||||
'movie': [lambda pref: flat_list(
|
||||
list(
|
||||
map(
|
||||
lambda cand: nearest_movie(
|
||||
cand, 30
|
||||
),
|
||||
pref.preffered_movies.all()
|
||||
)
|
||||
),
|
||||
), lambda pref: pref.preffered_movies.all()],
|
||||
'concert': [lambda pref: flat_list(
|
||||
list(
|
||||
map(
|
||||
lambda cand: nearest_concert(
|
||||
cand, 30
|
||||
),
|
||||
pref.preferred_concerts.all()
|
||||
)
|
||||
),
|
||||
), lambda pref: pref.preferred_concerts.all()],
|
||||
'attractions': [lambda pref: flat_list(
|
||||
list(
|
||||
map(
|
||||
lambda cand: nearest_attraction(
|
||||
cand, 30
|
||||
),
|
||||
pref.prefferred_attractions.all()
|
||||
)
|
||||
),
|
||||
), lambda pref: pref.prefferred_attractions.all()],
|
||||
'museum': [lambda pref: flat_list(
|
||||
list(
|
||||
map(
|
||||
lambda cand: nearest_mus(
|
||||
cand, 30
|
||||
),
|
||||
pref.prefferred_museums.all()
|
||||
)
|
||||
),
|
||||
), lambda pref: pref.prefferred_museums.all()],
|
||||
'shop': [lambda pref: sample(list(Event.objects.filter(type='shop')), 10), lambda x: []],
|
||||
'gallery': [lambda pref: sample(list(Event.objects.filter(type='gallery')), 10), lambda x: []],
|
||||
'theme_park': [lambda pref: sample(list(Event.objects.filter(type='theme_park')), 10), lambda x: []],
|
||||
'viewpoint': [lambda pref: sample(list(Event.objects.filter(type='viewpoint')), 10), lambda x: []],
|
||||
'zoo': [lambda pref: sample(list(Event.objects.filter(type='zoo')), 10), lambda x: []],
|
||||
}
|
||||
|
||||
res = []
|
||||
for category_candidate in up.preferred_categories:
|
||||
candidates = candidates_generate_strategy[category_candidate][0](up)
|
||||
ranged = range_candidates(
|
||||
candidates,
|
||||
user,
|
||||
candidates_generate_strategy[category_candidate][1](up)
|
||||
)
|
||||
res.append(
|
||||
{
|
||||
'category': category_candidate,
|
||||
'events': list(
|
||||
map(
|
||||
lambda x: ObjectRouteSerializer(x).data,
|
||||
ranged
|
||||
)
|
||||
)
|
||||
}
|
||||
)
|
||||
return res
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user