#!/usr/bin/python
import sys

import getopt
from datetime import datetime

import pandas as pd

from sqlalchemy import create_engine

if __name__ == "__main__":

    unixOptions = "sdt:edt"
    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 = '1981-01-01'
    end_dt = '1998-01-01'
    for currentArgument, currentValue in arguments:
        if currentArgument in ("-sdt", "--start_dt"):
            start_dt = currentValue
        elif currentArgument in ("-edt", "--end_dt"):
            end_dt = currentValue

    db_config = {'user': 'my_user',
                 'pwd': 'my_user_password',
                 'host': 'localhost',
                 'port': 5432,
                 'db': 'games'}
    connection_string = 'postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],
                                                             db_config['pwd'],
                                                             db_config['host'],
                                                             db_config['port'],
                                                             db_config['db'])

    engine = create_engine(connection_string)

    query = ''' SELECT *
                FROM data_raw
                WHERE year_of_release::TIMESTAMP BETWEEN '{}'::TIMESTAMP AND '{}'::TIMESTAMP
            '''.format(start_dt, end_dt)

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

    columns_str = [...]
    columns_numeric = [...]
    columns_datetime = [...]

    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])

    data_raw['total_copies_sold'] = data_raw[[...]].sum(axis=1)

    agg_games_year_genre_platform = data_raw.groupby([...]).agg({'name': 'count', 'total_copies_sold': 'sum'})
    agg_games_year_score = data_raw.groupby([...]).agg(
        {'critic_score': 'mean', 'user_score': 'mean', 'total_copies_sold': 'sum'})

    print(data_raw.info())
    print(agg_games_year_genre_platform.head(5))
    print(agg_games_year_score.head(5))

    print(data_raw.info())