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app.py
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app.py
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# Run this app with `python app.py` and
# visit http://127.0.0.1:8050/ in your web browser.
from dash import Dash, html, dcc, Input, Output
import dash_bootstrap_components as dbc
import plotly.express as px
import numpy as np
import requests
import pickle
from components.base import build_tabs, build_nav
from components.historic import add_hist_filters, add_hist_timeline, build_hist_graphic
from components.map import india_map, build_map, corr_table, add_map_filter
from components.matrix import build_city_matrix, build_matrix_fig, add_matrix_filters
from components.covid import build_covid_graphic, add_covid_gas_filters, build_covid_fig
#from data.loader import load_monthly_with_unemployment, load_grouped_covid_pollution
################################################
## APP INSTANTIATION AND VARIABLES ##
app = Dash(__name__)
app.config.suppress_callback_exceptions=True
server = app.server
covid_df = pickle.load(open('data/covid_grouped.pkl', 'rb'))
df = pickle.load(open('data/month_unemploy.pkl', 'rb'))
DEFAULT_CITY = 'Agartala'
DEFAULT_GAS = 'CO'
THEME = px.colors.qualitative.Plotly
in_outline = requests.get('https://raw.githubusercontent.com/mickeykedia/India-Maps/master/India_Administrative_Maps/country/india_country.geojson')
default_df = df[df['city'] == DEFAULT_CITY]
fig = px.line(default_df, x="date", y="co", color='city', title=f'{DEFAULT_GAS} Levels', color_discrete_sequence=THEME) # add color to be from city dropdown multi select
#########################################
################ LAYOUT #################
#########################################
app.layout = html.Div(className="app-header m-5",
children=[
build_nav(),
html.H1(children='Pollution and Economic Growth Levels in India', id='page-title', style={'padding':'5px'}),
build_tabs(),
html.Div(
id='tab-core',
children=[
add_hist_filters(df, default_city=DEFAULT_CITY, default_df=default_df),
#build_map(map),
build_hist_graphic(),
add_hist_timeline(df),
]
)#,html.Div('Copyright © Ty Martz 2022', style={'align-text': 'center'})
])
#########################################
############### CALLBACKS ###############
#########################################
#### UPDATING TAB 1 WHEN FILTERS CHANGE ####
@app.callback(
Output(component_id='line-graph', component_property='figure'),
Output(component_id='aqi-value', component_property='children'),
Input(component_id='city-filter-value', component_property='value'),
Input(component_id='gas-filter-value', component_property='value'),
Input(component_id='year-filter', component_property='value')
)
def update_line_graph(input_city, input_gas, input_year):
df2 = df.copy()
if input_city is None:
title_city = 'India'
fig = px.line(default_df[default_df['city'] == 'Beverly'], x="date", y="co", color='city', title=f'{DEFAULT_GAS} Levels')
mean_aqi = 'NA'
return fig, mean_aqi
else:
input_list = list(input_city)
if len(input_list) == 1:
title_city = input_list[0]
else:
title_city = 'India'
filt = df2[(df2['city'].isin(input_list)) & (df2['year'] <= input_year)]
mean_aqi = round(np.mean(filt['aqi']), 4)
fig = px.line(filt, x="date", y=input_gas.lower(), color="city", title=f'{input_gas} levels in {title_city}', color_discrete_sequence=THEME)
fig.update_layout(transition_duration=500, yaxis_title=input_gas)
return fig, mean_aqi
#### SWITCHING TABS ####
@app.callback(
Output(component_id='tab-core', component_property='children'),
Input(component_id='app-tabs', component_property='value')
)
def render_tab(tab):
if tab == 'hist':
return html.Div(
id='tab-core',
children=[
add_hist_filters(df, default_city=DEFAULT_CITY),
#build_map(map),
build_hist_graphic(),
add_hist_timeline(df)
]
)
elif tab == 'matrix':
scat, matrix_df = build_matrix_fig()
return html.Div(
id='tab-core',
children=[
add_matrix_filters(matrix_df),
build_city_matrix(scat)
]
)
elif tab == 'map-corr':
map = india_map()
table = corr_table(DEFAULT_CITY)
return html.Div(
id='tab-core',
children=[
add_map_filter(),
build_map(map, table)
]
)
elif tab == 'covid':
return html.Div(
id='tab-core',
children=[
add_covid_gas_filters(),
build_covid_graphic()
]
)
else:
return html.H1(children='Missing Chart', id='missing-msg')
#### Updating TAB 2 FILTERS ####
@app.callback(
Output(component_id='scatter-matrix', component_property='figure'),
Input(component_id='quarter-filter-value', component_property='value'),
Input(component_id='gas-filter-value-matrix', component_property='value'),
Input(component_id='matrix-city-filter-value', component_property='value')
)
def update_matrix(input_quarter, input_gas, input_city):
fig, _ = build_matrix_fig(input_quarter, input_gas, city_choice=input_city)
fig.update_layout(transition_duration=500)
return fig
#### Updating TAB 3 FILTERS ####
@app.callback(
Output(component_id='corr-table-container', component_property='children'),
Input(component_id='map-city-filter-value', component_property='value')
)
def update_map(city):
corrtab = corr_table(city)
items = [
dbc.Label('Correlation with Unemployment', style={'font-size':'24px'}),
dcc.Loading([corrtab]),
dbc.Label(r'*Interpret coefficients: an increase of 1 unit of gas results in a coefficient % change in unemployment', style={'font-size':'10px'})
]
return items
#### Updating TAB 4 COVID FILTERS ####
@app.callback(
Output(component_id='covid-graph', component_property='figure'),
Input(component_id='covid-gas-filter', component_property='value')
)
def update_covid(input_gas):
cov_grp = covid_df.copy()
fig = build_covid_fig(cov_grp, input_gas)
fig.update_layout(transition_duration=500)
return fig
#########################################################################
if __name__ == '__main__':
app.run_server(debug=True)