#!/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__":

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

    fullCmdArguments = sys.argv
    argumentList = fullCmdArguments[1:]

    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

    db_config = {'user': 'my_user',
                 'pwd': 'my_user_password',
                 'host': 'localhost',
                 'port': 5432,
                 'db': 'zen'}

    connection_string = 'postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],
                                    db_config['pwd'],
                                 	db_config['host'],
                                	db_config['port'],
                                 	db_config['db'])
    engine = create_engine(connection_string)

    #Теперь выберем из таблицы только те строки,
    #которые были выпущены между start_dt и end_dt
	query = ''' SELECT event_id, age_segment, event, item_id, item_topic, item_type, source_id, source_topic, source_type, user_id, 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')

    # преобразуем типы данных
    columns_str = ['age_segment', 'event', 'item_topic', 'item_type', 'source_topic', 'source_type']
    columns_numeric = ['item_id', 'source_id', 'user_id ']
    columns_datetime = ['dt']

    for column in columns_str: data_raw[column] = data_raw[column].astype(str)
    for column in columns_numeric: data_raw[column] = pd.to_numeric(data_raw[column], errors='coerce')
    for column in columns_datetime: data_raw[column] = pd.to_datetime(data_raw[column]).dt.round('min')

    # создадим агрегирующие таблицы
    dash_engagement = (data_raw
                       .groupby(['dt', 'item_topic', 'event', 'age_segment'])
                       .agg({'user_id': lambda x: x.nunique()}))
    dash_engagement = dash_engagement.rename(columns = {'user_id': 'unique_users'})

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

    dash_visits = dash_visits.fillna(0).reset_index()
    dash_engagement = dash_engagement.fillna(0).reset_index()

    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)