#!/usr/bin/python
# -*- coding: utf-8 -*-

import sys
import getopt
from datetime import datetime
import pandas as pd
from sqlalchemy import create_engine

if __name__ == "__main__":

    #Задаём входные параметры
    unixOptions = "s:e"
    gnuOptions = ["start_dt=", "end_dt="]

    fullCmdArguments = sys.argv
    argumentList = fullCmdArguments[1:] #excluding script name

    try:
        arguments, values = getopt.getopt(argumentList, unixOptions, gnuOptions)
    except getopt.error as err:
        print (str(err))
        sys.exit(2)

    start_dt = ''
    end_dt = ''
    for currentArgument, currentValue in arguments:
        if currentArgument in ("-s", "--start_dt"):
            start_dt = currentValue
        elif currentArgument in ("-e", "--end_dt"):
            end_dt = currentValue

    # Задаем параметры подключения к БД,
    # их можно узнать у администратора БД.
    db_config = {'user': 'my_user',
                 'pwd': 'my_user_password',
                 'host': 'localhost',
                 'port': 5432,
                 'db': 'zen'}

    # Формируем строку соединения с БД.
    connection_string = 'postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],
                                                             db_config['pwd'],
                                                             db_config['host'],
                                                             db_config['port'],
                                                             db_config['db'])
    # Подключаемся к БД.
    engine = create_engine(connection_string)

    # Теперь выберем из таблицы только те строки,
    # которые были выпущены между start_dt и end_dt
    query = ''' SELECT *, TO_TIMESTAMP(ts / 1000) AT TIME ZONE 'Etc/UTC' AS dt
           FROM log_raw
           WHERE TO_TIMESTAMP(ts / 1000) AT TIME ZONE 'Etc/UTC' BETWEEN '{}'::TIMESTAMP AND '{}'::TIMESTAMP
       '''.format(start_dt, end_dt)

    data_raw = pd.io.sql.read_sql(query, con = engine, index_col = 'event_id')

    data_raw['dt'] = pd.to_datetime(data_raw['dt']).dt.round('min')

    columns_datetime = ['dt']
    columns_numeric = ['user_id']

    for column in columns_datetime: data_raw[column] = pd.to_datetime(data_raw[column])
    for column in columns_numeric: data_raw[column] = pd.to_numeric(data_raw[column], errors='coerce')

    #### агрегирующие таблицы

    dash_visits = data_raw.groupby(['item_topic', 'source_topic', 'age_segment', 'dt']).agg({'event': 'count'}).reset_index()

    dash_visits = dash_visits.rename(columns = {'event': 'visits'})

    dash_engagement = data_raw.groupby(['dt', 'item_topic', 'event', 'age_segment']).agg({'user_id': lambda x: x.nunique()}).reset_index()

    dash_engagement = dash_engagement.rename(columns = {'user_id': 'unique_users'})



    #Удаляем старые записи между start_dt и end_dt
    tables = {'dash_visits': dash_visits,
              'dash_engagement': dash_engagement}

    for table_name, table_data in tables.items():
      query = '''DELETE FROM {} WHERE dt BETWEEN '{}'::TIMESTAMP AND '{}'::TIMESTAMP'''.format(table_name, start_dt, end_dt)
      engine.execute(query)

      table_data.to_sql(name = table_name, con = engine, if_exists = 'append', index = False)

    print(table_data.head(5))
    print()