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

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

import plotly.graph_objs as go

from datetime import datetime

import pandas as pd

# defining the data to be displayed
from sqlalchemy import create_engine

# sample code for connecting to the database with PostgreSQL
# db_config = {'user': 'my_user',
#             'pwd': 'my_user_password',
#             'host': 'localhost',
#             'port': 5432,
#             'db': 'games'}
# engine = create_engine('postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],
#                                                            db_config['pwd'],
#                                                            db_config['host'],
#                                                            db_config['port'],
#                                                            db_config['db']))
# sample code for connecting to the database with SQLite
engine = create_engine('sqlite:////db/games.db', echo = False)

# obtaining raw data
query = '''
            SELECT * FROM data_raw
        '''
games_raw = pd.io.sql.read_sql(query, con = engine)

# converting types
games_raw['year_of_release'] = pd.to_datetime(games_raw['year_of_release'])
columns = ['na_players', 'eu_players', 'jp_players', 'other_players']
for column in columns: games_raw[column] = pd.to_numeric(games_raw[column], errors = 'coerce')

# defining the data for the report
games_grouped = (games_raw.groupby(['year_of_release', 'genre'])
                          .agg({'name': 'nunique'})
                          .reset_index()
                          .rename(columns = {'name': 'games_launched'})
                )

# defining the layout
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets,compress=False)
app.layout = html.Div(children=[

    # forming a header with an HTML tag
    html.H1(children = 'Games released by year'),

    # selecting the time range
    html.Label('Time range:'),
    dcc.DatePickerRange(
        start_date = games_raw['year_of_release'].dt.date.min(),
        end_date = datetime(2016,1,1).strftime('%Y-%m-%d'),
        display_format = 'YYYY-MM-DD',
        id = 'dt_selector',
    ),

    # graph of games released by year
    dcc.Graph(
        id = 'launches_by_year'
    ),

])

# dashboard logic
@app.callback(
    [Output('launches_by_year', 'figure'),
    ],
    [Input('dt_selector', 'start_date'),
     Input('dt_selector', 'end_date'),
    ])
def update_figures(start_date, end_date):
    # converting input parameters to the required types
    start_date = datetime.strptime(start_date, '%Y-%m-%d')
    end_date = datetime.strptime(end_date, '%Y-%m-%d')

    # applying filters
    filtered_data = games_grouped.query('year_of_release >= @start_date and year_of_release <= @end_date')

    # building graphs to be displayed
    data = []
    for genre in filtered_data['genre'].unique():
        data += [go.Scatter(x = filtered_data.query('genre == @genre')['year_of_release'],
                          y = filtered_data.query('genre == @genre')['games_launched'],
                          mode = 'lines',
                          stackgroup = 'one',
                          name = genre)]

    # forming the result to be displayed
    return (
        {
                'data': data,
                'layout': go.Layout(xaxis = {'title': 'Date and time'},
                                    yaxis = {'title': 'Games released'})
        },
    )

# dashboard logic
if __name__ == '__main__':
    app.run_server(host='0.0.0.0', port=3000)