<gh_stars>1-10
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import altair as alt
import pandas as pd
import states_choropleth
import state_choropleth
from varieties_heatmap import plot_heatmap
from bar_plots import sort_extract_bar_plot

alt.data_transformers.disable_max_rows()

APP = dash.Dash(__name__,
                assets_folder='assets',
                external_stylesheets=[dbc.themes.LUX,
                                      'jumbotron.css'])

# Set the app title
APP.title = "V is for Vino"

# Read in the DATA
DATA = pd.read_csv('../data/cleaned_data.csv', index_col=0)

# Set NUMBER_OBS for bar plots
NUMBER_OBS = {1: {'label': '1'}}
NUMBER_OBS.update({n: {'label': str(n)} for n in range(5, 51, 5)})

# Get the states dictionary
STATES = DATA[['state', 'state_id']].drop_duplicates(keep='first')
STATES.rename(columns={"state": "label", "state_id": "value"}, inplace=True)
STATES = STATES.to_dict('records')

# Set JUMBOTRON element
JUMBOTRON = dbc.Jumbotron(
    [
        dbc.Col(
            [
                html.H1("V is for Vino", className="display-3"),
                html.P(
                    "Explore the best wines the United States \
                        has to offer using our interactive dashboard",
                    className="lead"),
            ],
            style={"text-align": 'center'})],
    fluid=True,
)

def plot_choropleth(_type, state_id=6):
    """
    Helper function to call the correct Choropleth mapping.

    Parameters:
    -----------
    _type -- (str) string of either 'states' or 'state'.
    state_id -- (int) a state_id (ex. 6 for 'California' - default of 6)
    """
    if _type == 'states':
        return states_choropleth.plot_map(DATA)
    elif _type == 'state':
        return state_choropleth.plot_map(DATA, state_id)

# Declate CONTENT element for dash
CONTENT = dbc.Container(
    [
        html.P(
            "This app allows you to visualize details of over 50,000 wine reviews from across \
            the United States, using data scraped from Wine Enthusiast on November 22nd, \
            2017. Given the data source, the wines tend to be of relatively high quality, \
            with each receiving a rating score between 80 and 100."
        ),
        html.P(
            "We’ve used these ratings to assign a custom ‘value’ score to each wine, which \
            is a ratio of its rating to price scaled to be out of 100. Buying a high value wine \
            would mean you're getting a high rating score per dollar spent. Each review also contains \
            details such as grape variety, winery, region, county, and state."
        ),
        html.Br(),
        dcc.Tabs(
            id="tabs-with-classes",
            value='tab-1',
            parent_className='custom-tabs',
            className='custom-tabs-container',
            children=[
                dcc.Tab(
                    label='Geographic Analysis',
                    value='tab-1',
                    className='custom-tab',
                    selected_className='custom-tab--selected',
                    children=[
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H1('Wine Reviews by Geographic Location'))),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(
                            dbc.Col(html.P("See how wine is distributed across the U.S. \
                                  Hover over a particular state or county to see some summary \
                                  information for things like average price, points, or value rating. \
                                  Use the dropdown menu to take a closer look at a particular state, \
                                  where you can see a breakdown by county. Hover over a county to get more \
                                  summary information. In no time at all you'll be an expert on where you can \
                                  find the best wine's at the best prices in America."))
                            ),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Total number of wine reviews by state'))),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                           ),
                        dbc.Row(dbc.Col(html.H5('Choose a State'))),
                        dbc.Row(
                            dcc.Dropdown(
                                id='state_id',
                                options=STATES,
                                value=6, 
                                style=dict(width='45%',
                                           verticalAlign='middle',
                                           left=15)
                            )),
                        dbc.Row([
                            html.Iframe(
                                sandbox='allow-scripts',
                                id='state_choropleth',
                                height=500,
                                width=420,
                                style={'border-width': '0'},
                                srcDoc=plot_choropleth('state').to_html()),
                            html.Iframe(
                                sandbox='allow-scripts',
                                id='states_choropleth',
                                height=500,
                                width=720,
                                style={'border-width': '0'},
                                srcDoc=plot_choropleth('states').to_html())
                            ]),
                        ]
                ),
                dcc.Tab(
                    label='Explore Rating, Price & Value of Wines',
                    value='tab-2',
                    className='custom-tab',
                    selected_className='custom-tab--selected',
                    children=[
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H1('Wine Feature Comparisons'))),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(
                            dbc.Col(html.P('These interactive graphs allow you to explore the price, \
                                rating and our calculated value score for different wineries, grape varieties, \
                                and regions.'))
                            ),
                        dbc.Row(
                            dbc.Col(html.P('The first chart shows ranked average values \
                                calculated from your dropdown selections, \
                                while the heat map at the bottom shows how the value scores for popular grape \
                                varieties are distributed across different price points or ratings.'))
                            ),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Wine Rankings'))),
                        dbc.Row(
                            dbc.Col(html.Ul([
                                html.Li(['Explore wines from different regions, wineries, or varieties \
                                using the "Choose X Axis" dropdown menu']), 
                                html.Li(['Change the number of regions, wineries, or varieties you are exploring \
                                using the slider bar']),
                                html.Li(['Find the highest or lowest ranked wines based on rating, price, \
                                or value using the "Choose Y Axis" dropdown menu and the "Choose the \
                                Ranking" toggle button.'])
                            ])
                            )
                        ),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                        ),
                        dbc.Row([
                            dbc.Col(html.H5('Choose X Axis:')),
                            dbc.Col(html.H5('Choose Y Axis:')),
                            dbc.Col(html.H5('Choose Ranking:')),
                            ]),
                        dbc.Row([
                            dbc.Col(
                                dcc.Dropdown(
                                    id='bar-chart-x',
                                    options=[
                                        {'label': 'Region', 'value': 'region_1'},
                                        {'label': 'Winery', 'value': 'winery'},
                                        {'label': 'Grape Variety', 'value': 'variety'},
                                    ],
                                    value='region_1', # setting default when app loads
                                    style=dict(width='80%',
                                               verticalAlign='middle')
                                    )),
                            dbc.Col(
                                dcc.Dropdown(
                                    id='bar-chart-y',
                                    options=[
                                        {'label': 'Rating', 'value': 'points'},
                                        {'label': 'Price', 'value': 'price'},
                                        {'label': 'Value', 'value': 'value_scaled'},
                                    ],
                                    value='points',
                                    style=dict(width='80%',
                                               verticalAlign='middle')
                                    )),
                            dbc.Col(
                                dcc.RadioItems(
                                    id='bar-chart-sort',
                                    options=[
                                        {'label': ' Lowest to Highest  ', 'value': 'asc'},
                                        {'label': ' Highest to Lowest', 'value': 'desc'}
                                    ],
                                    value='desc',
                                    labelStyle={'display': 'block'}
                                    )),
                            ]),
                        dbc.Row(dbc.Col(html.H5('Number of observations:'))),
                        dbc.Row([
                            dbc.Col([
                                dcc.Slider(
                                    id='bar-chart-slider',
                                    min=1,
                                    max=50,
                                    step=1,
                                    value=15,
                                    marks=NUMBER_OBS,
                                    ),
                                ],
                            style={'marginBottom': 50, 'marginTop': 10}),
                            ]),
                        dbc.Row([
                            dbc.Col([
                                html.Iframe(
                                    sandbox='allow-scripts',
                                    id='plot',
                                    height=600,
                                    width=800,
                                    style={'border-width': '0'},
                                    srcDoc=sort_extract_bar_plot(DATA).to_html()
                                )
                            ])
                            ]),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Which Grape Varieties Are The Best Value?'))),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                        ),
                        dbc.Row(dbc.Col(html.H5('Analyze by Price or Rating'))),
                        dbc.Row([
                            dbc.Col([
                                dcc.Dropdown(
                                    id='heatmap_x',
                                    options=[
                                        {'label': 'Rating', 'value': 'points'},
                                        {'label': 'Price', 'value': 'price'},
                                    ],
                                    value='price', 
                                    style=dict(width='45%',
                                               verticalAlign='middle')
                                )
                            ])
                            ]),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row([
                            dbc.Col([
                                html.Iframe(
                                    sandbox='allow-scripts',
                                    id='heatmap_1',
                                    height=600,
                                    width=900,
                                    style={'border-width': '0'},
                                    srcDoc=plot_heatmap(DATA).to_html()
                                )
                            ])
                        ])
                ])
        ])
    ])

APP.layout = html.Div([JUMBOTRON, 
                       CONTENT,
                       html.Div(id='tabs-content-classes')])

@APP.callback(
    dash.dependencies.Output('plot', 'srcDoc'),
    [dash.dependencies.Input('bar-chart-x', 'value'),
    dash.dependencies.Input('bar-chart-y', 'value'),
    dash.dependencies.Input('bar-chart-sort', 'value'),
    dash.dependencies.Input('bar-chart-slider', 'value')])
def update_plot(x_name_update, y_name_update, order, obs):
    """
    Takes the x column name and y column name and calls the
    sort_extract_bar_plot() function,
    """
    updated_bar_plot = sort_extract_bar_plot(data=DATA,
                                             y_name=y_name_update,
                                             x_name=x_name_update,
                                             n=obs,
                                             direction=order).to_html()
    return updated_bar_plot

@APP.callback(
    dash.dependencies.Output('state_choropleth', 'srcDoc'),
    [dash.dependencies.Input('state_id', 'value')])
def update_state_call(state_id):
    """
    Takes the state name from the dropdown list and
    updates the state choropleth chart to that state.
    """
    update_state = plot_choropleth('state', state_id).to_html()
    return update_state

@APP.callback(
    dash.dependencies.Output('heatmap_1', 'srcDoc'),
    [dash.dependencies.Input('heatmap_x', 'value')])
def update_heatmap_call(heatmap_x_update):
    """
    Takes the x variable from the dropdown list and
    updates the heatmap according to the user selection.
    """
    update_heatmap = plot_heatmap(DATA, x_name = heatmap_x_update).to_html()
    return update_heatmap

if __name__ == '__main__':
    APP.run_server(debug=True)

import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import altair as alt
import pandas as pd
import states_choropleth
import state_choropleth
from varieties_heatmap import plot_heatmap
from bar_plots import sort_extract_bar_plot

alt.data_transformers.disable_max_rows()

APP = dash.Dash(__name__,
                assets_folder='assets',
                external_stylesheets=[dbc.themes.LUX,
                                      'jumbotron.css'])

# Set the app title
APP.title = "V is for Vino"

# Read in the DATA
DATA = pd.read_csv('../data/cleaned_data.csv', index_col=0)

# Set NUMBER_OBS for bar plots
NUMBER_OBS = {1: {'label': '1'}}
NUMBER_OBS.update({n: {'label': str(n)} for n in range(5, 51, 5)})

# Get the states dictionary
STATES = DATA[['state', 'state_id']].drop_duplicates(keep='first')
STATES.rename(columns={"state": "label", "state_id": "value"}, inplace=True)
STATES = STATES.to_dict('records')

# Set JUMBOTRON element
JUMBOTRON = dbc.Jumbotron(
    [
        dbc.Col(
            [
                html.H1("V is for Vino", className="display-3"),
                html.P(
                    "Explore the best wines the United States \
                        has to offer using our interactive dashboard",
                    className="lead"),
            ],
            style={"text-align": 'center'})],
    fluid=True,
)

def plot_choropleth(_type, state_id=6):
    """
    Helper function to call the correct Choropleth mapping.

    Parameters:
    -----------
    _type -- (str) string of either 'states' or 'state'.
    state_id -- (int) a state_id (ex. 6 for 'California' - default of 6)
    """
    if _type == 'states':
        return states_choropleth.plot_map(DATA)
    elif _type == 'state':
        return state_choropleth.plot_map(DATA, state_id)

# Declate CONTENT element for dash
CONTENT = dbc.Container(
    [
        html.P(
            "This app allows you to visualize details of over 50,000 wine reviews from across \
            the United States, using data scraped from Wine Enthusiast on November 22nd, \
            2017. Given the data source, the wines tend to be of relatively high quality, \
            with each receiving a rating score between 80 and 100."
        ),
        html.P(
            "We’ve used these ratings to assign a custom ‘value’ score to each wine, which \
            is a ratio of its rating to price scaled to be out of 100. Buying a high value wine \
            would mean you're getting a high rating score per dollar spent. Each review also contains \
            details such as grape variety, winery, region, county, and state."
        ),
        html.Br(),
        dcc.Tabs(
            id="tabs-with-classes",
            value='tab-1',
            parent_className='custom-tabs',
            className='custom-tabs-container',
            children=[
                dcc.Tab(
                    label='Geographic Analysis',
                    value='tab-1',
                    className='custom-tab',
                    selected_className='custom-tab--selected',
                    children=[
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H1('Wine Reviews by Geographic Location'))),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(
                            dbc.Col(html.P("See how wine is distributed across the U.S. \
                                  Hover over a particular state or county to see some summary \
                                  information for things like average price, points, or value rating. \
                                  Use the dropdown menu to take a closer look at a particular state, \
                                  where you can see a breakdown by county. Hover over a county to get more \
                                  summary information. In no time at all you'll be an expert on where you can \
                                  find the best wine's at the best prices in America."))
                            ),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Total number of wine reviews by state'))),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                           ),
                        dbc.Row(dbc.Col(html.H5('Choose a State'))),
                        dbc.Row(
                            dcc.Dropdown(
                                id='state_id',
                                options=STATES,
                                value=6, 
                                style=dict(width='45%',
                                           verticalAlign='middle',
                                           left=15)
                            )),
                        dbc.Row([
                            html.Iframe(
                                sandbox='allow-scripts',
                                id='state_choropleth',
                                height=500,
                                width=420,
                                style={'border-width': '0'},
                                srcDoc=plot_choropleth('state').to_html()),
                            html.Iframe(
                                sandbox='allow-scripts',
                                id='states_choropleth',
                                height=500,
                                width=720,
                                style={'border-width': '0'},
                                srcDoc=plot_choropleth('states').to_html())
                            ]),
                        ]
                ),
                dcc.Tab(
                    label='Explore Rating, Price & Value of Wines',
                    value='tab-2',
                    className='custom-tab',
                    selected_className='custom-tab--selected',
                    children=[
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H1('Wine Feature Comparisons'))),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(
                            dbc.Col(html.P('These interactive graphs allow you to explore the price, \
                                rating and our calculated value score for different wineries, grape varieties, \
                                and regions.'))
                            ),
                        dbc.Row(
                            dbc.Col(html.P('The first chart shows ranked average values \
                                calculated from your dropdown selections, \
                                while the heat map at the bottom shows how the value scores for popular grape \
                                varieties are distributed across different price points or ratings.'))
                            ),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Wine Rankings'))),
                        dbc.Row(
                            dbc.Col(html.Ul([
                                html.Li(['Explore wines from different regions, wineries, or varieties \
                                using the "Choose X Axis" dropdown menu']), 
                                html.Li(['Change the number of regions, wineries, or varieties you are exploring \
                                using the slider bar']),
                                html.Li(['Find the highest or lowest ranked wines based on rating, price, \
                                or value using the "Choose Y Axis" dropdown menu and the "Choose the \
                                Ranking" toggle button.'])
                            ])
                            )
                        ),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                        ),
                        dbc.Row([
                            dbc.Col(html.H5('Choose X Axis:')),
                            dbc.Col(html.H5('Choose Y Axis:')),
                            dbc.Col(html.H5('Choose Ranking:')),
                            ]),
                        dbc.Row([
                            dbc.Col(
                                dcc.Dropdown(
                                    id='bar-chart-x',
                                    options=[
                                        {'label': 'Region', 'value': 'region_1'},
                                        {'label': 'Winery', 'value': 'winery'},
                                        {'label': 'Grape Variety', 'value': 'variety'},
                                    ],
                                    value='region_1', # setting default when app loads
                                    style=dict(width='80%',
                                               verticalAlign='middle')
                                    )),
                            dbc.Col(
                                dcc.Dropdown(
                                    id='bar-chart-y',
                                    options=[
                                        {'label': 'Rating', 'value': 'points'},
                                        {'label': 'Price', 'value': 'price'},
                                        {'label': 'Value', 'value': 'value_scaled'},
                                    ],
                                    value='points',
                                    style=dict(width='80%',
                                               verticalAlign='middle')
                                    )),
                            dbc.Col(
                                dcc.RadioItems(
                                    id='bar-chart-sort',
                                    options=[
                                        {'label': ' Lowest to Highest  ', 'value': 'asc'},
                                        {'label': ' Highest to Lowest', 'value': 'desc'}
                                    ],
                                    value='desc',
                                    labelStyle={'display': 'block'}
                                    )),
                            ]),
                        dbc.Row(dbc.Col(html.H5('Number of observations:'))),
                        dbc.Row([
                            dbc.Col([
                                dcc.Slider(
                                    id='bar-chart-slider',
                                    min=1,
                                    max=50,
                                    step=1,
                                    value=15,
                                    marks=NUMBER_OBS,
                                    ),
                                ],
                            style={'marginBottom': 50, 'marginTop': 10}),
                            ]),
                        dbc.Row([
                            dbc.Col([
                                html.Iframe(
                                    sandbox='allow-scripts',
                                    id='plot',
                                    height=600,
                                    width=800,
                                    style={'border-width': '0'},
                                    srcDoc=sort_extract_bar_plot(DATA).to_html()
                                )
                            ])
                            ]),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row(dbc.Col(html.H3('Which Grape Varieties Are The Best Value?'))),
                        dbc.Row(
                            dbc.Col(html.Hr(hidden=False,
                                            style={'height':1,
                                                   'background-color': '#50107a',
                                                   'margin-top': 0}))
                        ),
                        dbc.Row(dbc.Col(html.H5('Analyze by Price or Rating'))),
                        dbc.Row([
                            dbc.Col([
                                dcc.Dropdown(
                                    id='heatmap_x',
                                    options=[
                                        {'label': 'Rating', 'value': 'points'},
                                        {'label': 'Price', 'value': 'price'},
                                    ],
                                    value='price', 
                                    style=dict(width='45%',
                                               verticalAlign='middle')
                                )
                            ])
                            ]),
                        dbc.Row(dbc.Col(html.Br())),
                        dbc.Row([
                            dbc.Col([
                                html.Iframe(
                                    sandbox='allow-scripts',
                                    id='heatmap_1',
                                    height=600,
                                    width=900,
                                    style={'border-width': '0'},
                                    srcDoc=plot_heatmap(DATA).to_html()
                                )
                            ])
                        ])
                ])
        ])
    ])

APP.layout = html.Div([JUMBOTRON, 
                       CONTENT,
                       html.Div(id='tabs-content-classes')])

@APP.callback(
    dash.dependencies.Output('plot', 'srcDoc'),
    [dash.dependencies.Input('bar-chart-x', 'value'),
    dash.dependencies.Input('bar-chart-y', 'value'),
    dash.dependencies.Input('bar-chart-sort', 'value'),
    dash.dependencies.Input('bar-chart-slider', 'value')])
def update_plot(x_name_update, y_name_update, order, obs):
    """
    Takes the x column name and y column name and calls the
    sort_extract_bar_plot() function,
    """
    updated_bar_plot = sort_extract_bar_plot(data=DATA,
                                             y_name=y_name_update,
                                             x_name=x_name_update,
                                             n=obs,
                                             direction=order).to_html()
    return updated_bar_plot

@APP.callback(
    dash.dependencies.Output('state_choropleth', 'srcDoc'),
    [dash.dependencies.Input('state_id', 'value')])
def update_state_call(state_id):
    """
    Takes the state name from the dropdown list and
    updates the state choropleth chart to that state.
    """
    update_state = plot_choropleth('state', state_id).to_html()
    return update_state

@APP.callback(
    dash.dependencies.Output('heatmap_1', 'srcDoc'),
    [dash.dependencies.Input('heatmap_x', 'value')])
def update_heatmap_call(heatmap_x_update):
    """
    Takes the x variable from the dropdown list and
    updates the heatmap according to the user selection.
    """
    update_heatmap = plot_heatmap(DATA, x_name = heatmap_x_update).to_html()
    return update_heatmap

if __name__ == '__main__':
    APP.run_server(debug=True)
# Set the app title # Read in the DATA # Set NUMBER_OBS for bar plots # Get the states dictionary # Set JUMBOTRON element Helper function to call the correct Choropleth mapping.      Parameters:     -----------     _type -- (str) string of either 'states' or 'state'.     state_id -- (int) a state_id (ex. 6 for 'California' - default of 6) # Declate CONTENT element for dash # setting default when app loads Takes the x column name and y column name and calls the     sort_extract_bar_plot() function, Takes the state name from the dropdown list and     updates the state choropleth chart to that state. Takes the x variable from the dropdown list and     updates the heatmap according to the user selection.