dash_app.py

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Output, Input
import plotly.express as px
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.graph_objects as go
import os
from pathlib import Path
import plotly.io as pio

data = str(Path(os.getcwd()+'/data'))
pio.templates.default = "plotly_white"

master_df = pd.DataFrame()
tc_by_pm_df = pd.DataFrame()

years = range(2016,2020)
for year in years:
    #create plant dataframe 
    plant_df = pd.read_excel(data + f'/2___Plant_Y{year}.xlsx', skiprows=1)
    plant_df = plant_df[['Utility ID','Plant Code', 'Plant Name', 'Latitude', 'Longitude']]

    #create generator dataframe
    gen_df = pd.read_excel(data + f'/3_1_Generator_Y{year}.xlsx', skiprows=1)
    gen_df = gen_df[['Utility ID','Plant Code', 'Plant Name','Generator ID', 'Prime Mover', 'Technology', 'Operating Year','Nameplate Capacity (MW)']]

    #merge both dataframes on 'plant code' 
    merged_df = pd.merge(gen_df, plant_df)
    print('-------MERGED DF--------')
    merged_df = merged_df.assign(year=year)
    # Create Total capcity grouped by Prime Mover Dataframe 
    dff = merged_df[['year','Prime Mover','Nameplate Capacity (MW)']].groupby('Prime Mover', as_index=False)['Nameplate Capacity (MW)'].sum()
    dff = dff.assign(year=year)
    tc_by_pm_df = pd.concat([tc_by_pm_df, dff], ignore_index=True)

    master_df = pd.concat([master_df, merged_df], ignore_index=True)

tc_by_pm_df.rename(columns = {'Nameplate Capacity (MW)':'Total Nameplate Capacity'}, inplace = True) 


app = dash.Dash(__name__, external_stylesheets=[dbc.themes.FLATLY])

# ------------APP LAYOUT------------------------------
app.layout = dbc.Container([
    dbc.Row([
        dbc.Col(html.H1("Energy Data Dashboard",
                        className='text-center text-primary mb-4'),
                )
    ]),
        dbc.Row([
        dbc.Col(html.P("Created by Henry Quillin",
                        className='text-center pt-1'),
                )
    ]),
    dbc.Row([
        dbc.Col([
            html.H3('Map of generation units in the United States', style={'textDecoration': 'underline'}, className='text-center'), 
            dcc.Checklist(id='bubble_map_checklist', value=['BA','BT','CE','CS','FW','HY','OT','PS','WT'], options=[{'label':x,'value':x} for x in sorted(master_df['Prime Mover'].unique())],className='text-center',labelClassName='mr-3 '),
            dcc.Graph(id='bubble_chart', figure={})
        ], 
        xs=12, sm=12, md=12, lg=10, xl=10),
    ], justify='around'),
    dbc.Row([
        dbc.Col([
            html.H4("Total Nameplate Capacity by Prime Mover (Line Chart)", className='text-center text-primary mb-4'),
            dcc.Dropdown(id='dpdn1', multi=True, value=['WT','CT','CA','HY'], options=[{'label':x, 'value':x} for x in master_df['Prime Mover'].unique()],
                         ),
            dcc.Graph(id='line-fig', figure={})
        ], 
        xs=12, sm=12, md=12, lg=5, xl=5),
        dbc.Col([
            html.H4("Total Nameplate Capacity by Prime Mover (Bar Chart)", className='text-center text-primary mb-4'),
            dcc.Dropdown(id='dpdn2', multi=True, value=['BA','BT','CE','CS','FW','HY','OT','PS','WT'], options=[{'label':x, 'value':x} for x in sorted(master_df['Prime Mover'].unique())],
                         ),
            dcc.Graph(id='bar-chart', figure={})
        ], 
        xs=12, sm=12, md=12, lg=5, xl=5),
    ],justify='around')
],fluid=True)

print('---------------CREATED LAYOUT-------------------')

# Updating Line Fig 1 
@app.callback(
    [Output('line-fig','figure')],
    [Input('dpdn1','value')]
)
def update_fig1(dpdn_val):
    dff = tc_by_pm_df[tc_by_pm_df['Prime Mover'].isin(dpdn_val)]
    fig = px.line(dff, x='year', y='Total Nameplate Capacity', color='Prime Mover')
    return [fig]


# Updating Bar Chart
@app.callback(
    [Output('bar-chart','figure')],
    [Input('dpdn2','value')]
)
def update_bar_chart(dpdn_val):
    dff = tc_by_pm_df[tc_by_pm_df['Prime Mover'].isin(dpdn_val)]
    line_fig = px.bar(dff, x='year', y='Total Nameplate Capacity', color='Prime Mover')
    return [line_fig]


# Updating Map 
@app.callback(
    [Output('bubble_chart','figure')],
    [Input('bubble_map_checklist','value')]
)
def update_map(dpdn_val):
    dff = master_df[master_df['Prime Mover'].isin(dpdn_val)]
    fig1 = px.scatter_geo(dff,
                     lat='Latitude',
                     lon='Longitude',
                     color='Prime Mover',
                     scope='usa',
                     size='Nameplate Capacity (MW)',
                     width=1200,
                     height=600
                     )

    return[fig1]


print('---------------RAN SERVER-------------------')
if __name__ == '__main__':
    app.run_server(debug=True)