Py.Cafe

import dash
from dash import dcc, html, Input, Output
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np
import plotly.figure_factory as ff
import plotly.colors as pc
import plotly.graph_objects as go


# def genSankey(df, cat_cols=[], value_cols='', c_scale='', region ='', year='', shipmode=''):
#     labelList = []
#     colorNumList = []
#     for catCol in cat_cols:
#         labelListTemp = list(set(df[catCol].values))
#         colorNumList.append(len(labelListTemp))
#         labelList = labelList + labelListTemp

#     # Remove duplicates from labelList
#     labelList = list(dict.fromkeys(labelList))

#     # Define base colors using Viridis color scale
#     num_labels = len(labelList)
#     color_scale = getattr(pc.sequential, c_scale)
#     base_colors = pc.sample_colorscale(color_scale, [i / (num_labels - 1) for i in range(num_labels)])
    
#     # Assign colors directly
#     color_dict = {label: color for label, color in zip(labelList, base_colors)}

#     # Transform df into a source-target pair
#     for i in range(len(cat_cols) - 1):
#         if i == 0:
#             sourceTargetDf = df[[cat_cols[i], cat_cols[i + 1], value_cols]]
#             sourceTargetDf.columns = ['source', 'target', 'count']
#         else:
#             tempDf = df[[cat_cols[i], cat_cols[i + 1], value_cols]]
#             tempDf.columns = ['source', 'target', 'count']
#             sourceTargetDf = pd.concat([sourceTargetDf, tempDf])
        
#         sourceTargetDf = sourceTargetDf.groupby(['source', 'target']).agg({'count': lambda x: round(x.sum(), 2)}).reset_index()

#     # Add index for source-target pair
#     sourceTargetDf['sourceID'] = sourceTargetDf['source'].apply(lambda x: labelList.index(x))
#     sourceTargetDf['targetID'] = sourceTargetDf['target'].apply(lambda x: labelList.index(x))

#     # Create a list of colors for the links with alpha 0.5
#     link_colors = [color_dict[src].replace('rgb(', 'rgba(').replace(')', ', 0.5)') for src in sourceTargetDf['source']]

#     # Creating the sankey diagram
#     data = dict(
#         type='sankey',
#         node=dict(
#             pad=15,
#             thickness=20,
#             line=dict(
#                 color="black",
#                 width=0.5
#             ),
#             label=labelList,
#             color=[color_dict[label] for label in labelList]
#         ),
#         link=dict(
#             source=sourceTargetDf['sourceID'],
#             target=sourceTargetDf['targetID'],
#             value=sourceTargetDf['count'].apply(lambda x: float("{:.2f}".format(x))),
#             color=link_colors
#         )
#     )

#     layout = dict(template='seaborn',
#                   title=dict(
#                   text=f'Sankey of {"all years" if year == "All" else year} sales data, '
#                         f'for {"all regions" if region == "All" else "the " + region.lower() + " region"}, '
#                         f'and {"all shipping modes" if shipmode == "All" else shipmode.lower() + " shipping mode"}',
#                     ),
#                     width=900,
#                     height=500,
#                     font=dict(color='#FFFFFF'),
#                     margin=dict(b=35, t=35, l=0, r=0),
#                     paper_bgcolor='rgba(0,0,0,0)',
#                     plot_bgcolor='rgba(0,0,0,0)'
                  
#     )

#     fig = dict(data=[data], layout=layout)

#     return fig


# def sales_map(df,selected_region, selected_year, selected_shipmode):
#     fig = ff.create_hexbin_mapbox(
#         data_frame=df, lat="Lat", lon="Long",
#         nx_hexagon=20,
#         opacity=0.6,
#         color="Sales", 
#         agg_func=np.sum,
#         labels={"color": "Sales"}, 
#         mapbox_style='open-street-map', 
#         min_count=1, 
#         color_continuous_scale='plasma',
#     )

#     fig.update_layout(title=dict(
#                     text=f'Map of {"all years" if selected_year == "All" else selected_year} sales data, '
#                         f'for {"all regions" if selected_region == "All" else "the " + selected_region.lower() + " region"}, '
#                         f'and {"all shipping modes" if selected_shipmode == "All" else selected_shipmode.lower() + " shipping mode"}',
#                     ),
#                     width=900,
#                     height=500,
#                     font=dict(color='#FFFFFF'),
#                     margin=dict(b=35, t=35, l=0, r=0),
#                     paper_bgcolor='rgba(0,0,0,0)',
#                     plot_bgcolor='rgba(0,0,0,0)'
#                     )

#     return fig


# def sankey_figure(df, selected_region, selected_year, selected_shipmode):
#     grouped_df = df.groupby(['Segment','State/Province','Category']).agg({
#         'Sales': 'sum',
#     #     'Profit': 'sum'
#     }).reset_index()

#     cat_columns = grouped_df.columns.tolist()
#     x = len(cat_columns)-1
#     cat_columns.pop(x)
#     cat_columns

#     fig = genSankey(df, cat_cols=cat_columns,value_cols='Sales',c_scale='Darkmint', region=selected_region, year=selected_year, shipmode=selected_shipmode)  

#     return fig

def air_map(df):
    years = list(range(2011, 2022))  # Generate years from 1850 to 2021
    fig = go.Figure()

    # Loop over each year to create a scatter (bubble) map trace
    for year in years:
        fig.add_trace(go.Scattermapbox(
            lat=df['lat'],
            lon=df['long'],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=df[str(year)],  # Size of bubbles based on the year's data
                color=df[str(year)],  # Color based on the year's data
                colorscale='plasma',
                showscale=True,
                sizemode='area',
                sizeref=2.*max(df[str(year)])/100,  # Adjust this for scaling
                sizemin=3
            ),
            # Tooltip text with city, country, and the year's data
            text=df['city'] + ', ' + df['county'] + '<br>' + 'Value: ' + df[str(year)].astype(str),
            hoverinfo="text",
            name=str(year),
            visible=False  # Set all traces to not visible initially
        ))

    # Add a slider to control visibility of different year traces
    steps = []
    for i, year in enumerate(years):
        step = dict(
            method='restyle',
            args=['visible', [False] * len(fig.data)],  # Set all to not visible
        )
        step['args'][1][i] = True  # Make the ith trace visible
        steps.append(step)

    sliders = [dict(
        active=0,
        currentvalue={"prefix": "Year: "},
        pad={"t": 50},
        steps=steps
    )]

    # Update layout for the map and sliders
    fig.update_layout(
        sliders=sliders,
        title=dict(
            text='Bubble Map from 2011 to 2021',
        ),
        width=900,
        height=500,
        font=dict(color='#FFFFFF'),
        margin=dict(b=35, t=35, l=0, r=0),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
        mapbox=dict(
            style="open-street-map",
            zoom=1,
            center=dict(lat=20, lon=0)  # Adjust the center for your data
        )
    )

    # Set the first trace (1850) to be visible
    fig.data[0].visible = True

    return fig

# Load data
data = pd.read_csv('air-pollution2.csv')
amapg1 = air_map(data)
# data_df['Order Date'] = pd.to_datetime(data_df['Order Date'], dayfirst=True)
# data_df['order_year'] = data_df['Order Date'].dt.year

# # Extract unique values for filters
# regions = sorted(data_df['Region'].unique())
# regions.insert(0, 'All')
# orderyears = sorted(data_df['order_year'].unique(), reverse=True)
# orderyear_options = [{'label': str(orderyear), 'value': orderyear} for orderyear in orderyears]
# orderyear_options.insert(0, {'label': 'All', 'value': 'All'})

# shipmodes = sorted(data_df['Ship Mode'].unique())
# shipmodes.insert(0, 'All')

# Initialize the Dash app
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

# Define custom theme
custom_theme = {
    "primary": "#75C9BE",
    "secondary": "#d6e8e7",
    "info": "#3B9ECB",
    "gray": "#adb5bd",
    "success": "#8BE3AA",
    "warning": "#F9F871",
    "danger": "#c0003e",
    "body_bg": "#1F5869",
    "content_bg": "#153F4C",
    "text_color": "#CBE2E2"
}

# Define the layout
app.layout = dbc.Container(
    [
        dbc.Row(
            dbc.Col(
                html.H1(
                    'Figure Friday - Week 36 - Air Pollution Data',
                    style={'textAlign': 'center', 'color': custom_theme["text_color"]}
                ),
                width=12
            ),
            justify='center'
        ),
        dbc.Row(
            [
                dbc.Col(
                    [
                        # html.Label('Select a Region', style={'color': custom_theme["text_color"]}),
                        # dcc.Dropdown(id='region-filter', options=[{'label': region, 'value': region} for region in regions], value='All')
                    ],
                    width=4,
                    className="my-4",
                    style={'padding': '0 5px'}
                ),
                dbc.Col(
                    [
                        # html.Label('Select an Order Year', style={'color': custom_theme["text_color"]}),
                        # dcc.Dropdown(id='sales_year-filter', options=orderyear_options, value='All')
                    ],
                    width=4,
                    className="my-4",
                    style={'padding': '0 5px'}
                ),
                dbc.Col(
                    [
                        # html.Label('Select a Shipping Mode', style={'color': custom_theme["text_color"]}),
                        # dcc.Dropdown(id='ship_mode-filter', options=[{'label': shipmode, 'value': shipmode} for shipmode in shipmodes], value='All')
                    ],
                    width=4,
                    className="my-4",
                    style={'padding': '0 5px'}
                ),
            ],
            justify='center',
            style={'margin-bottom': '20px'}
        ),
        dbc.Row(
            [
                dbc.Col(
                    dcc.Graph(figure=amapg1),#id='airmap_graph1'),
                    width=12, #6
                    className="my-4",
                    style={'padding': '0 5px'}
                ),
                # dbc.Col(
                #     dcc.Graph(id='sankey'),
                #     width=6,
                #     className="my-4",
                #     style={'padding': '0 5px'}
                # ),
            ]
        )
    ],
    fluid=True,
    style={'backgroundColor': custom_theme["body_bg"]}
)



# Define callbacks to update figures based on selected filters
# @app.callback(
#     [Output('sales_graph1', 'figure'),
#      Output('sankey', 'figure')],
#     [Input('region-filter', 'value'),
#      Input('sales_year-filter', 'value'),
#      Input('ship_mode-filter', 'value')]
# )
# def update_figures(selected_region, selected_year, selected_shipmode):
#     filtered_df = data_df
    
#     if selected_region != 'All':
#         filtered_df = filtered_df[filtered_df['Region'] == selected_region]
    
#     if selected_year != 'All':
#         filtered_df = filtered_df[filtered_df['order_year'] == selected_year]
    
#     if selected_shipmode != 'All':
#         filtered_df = filtered_df[filtered_df['Ship Mode'] == selected_shipmode]
    
#     sales_map_fig = sales_map(filtered_df, selected_region, selected_year, selected_shipmode)
#     sankey_fig = sankey_figure(filtered_df, selected_region, selected_year, selected_shipmode)
    
#     return sales_map_fig, sankey_fig


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