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app.py
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import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import plotly.graph_objects as go
from dash.dependencies import Input, Output, State
import json
import pandas as pd
import numpy as np
from urllib.request import urlopen
app = dash.Dash(__name__)
server = app.server
geojson_url = 'https://raw.githubusercontent.com/openpolis/geojson-italy/master/geojson/limits_IT_provinces.geojson'
dataset_url = 'https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province.csv'
with urlopen(geojson_url) as response:
prov = json.load(response)
data = pd.read_csv(dataset_url)
month_dict = {'01': 'Gen', '02': 'Feb', '03': 'Mar', '04': 'Apr', '05': 'Mag', '06': 'Giu',
'07': 'Lug', '08': 'Ago', '09': 'Set', '10': 'Ott', '11': 'Nov', '12': 'Dec'}
temp_label = 'In fase di aggiornamento'
data = data.replace('In fase di definizione/aggiornamento', temp_label)
data['data'] = [d.split('T')[0] for d in data['data']]
full_dates = data.data.unique()
full_dates.sort()
date_labels = [month_dict[f.split("-")[1]] + f.split("-")[2] for f in full_dates]
dates = {d: n for n, d in enumerate(full_dates)}
labels = {d: l for d, l in zip(full_dates, date_labels)}
data['date_index'] = data.apply(lambda ind: dates[ind['data']], axis=1)
data['date_labels'] = data.apply(lambda ind: labels[ind['data']], axis=1)
x_max = data['date_index'].max()
y_max = data['totale_casi'].max()
prov_codename = dict(zip(data.codice_provincia, data.denominazione_provincia))
data = data.sort_values(['denominazione_regione', 'denominazione_provincia', 'date_index'])
data['var'] = data['totale_casi'].diff()
data['varper'] = data['totale_casi'].pct_change() * 100
data = data.round(2)
for r in data[data.date_index == 0].index:
data.at[r, 'var'] = np.nan
data.at[r, 'varper'] = np.nan
data = data.replace(np.inf, np.nan)
for r in data[data.denominazione_provincia == temp_label].index:
data.at[r, 'var'] = np.nan
data.at[r, 'varper'] = np.nan
data = data.rename(columns={'totale_casi': 'Totale casi', 'data': 'Data',
'var': 'Variazione (rispetto al giorno precedente)',
'varper': 'Variazione (%)', 'denominazione_regione': 'Regione',
'denominazione_provincia': 'Provincia'})
measures = ['Totale casi', 'Variazione (rispetto al giorno precedente)', 'Variazione (%)']
table_cols = ['Data', 'Regione', 'Provincia', 'Totale casi',
'Variazione (rispetto al giorno precedente)', 'Variazione (%)']
slider_marks = {int(i): {'label': day, 'style': {'transform': 'rotate(45deg)', 'font-size': '16px'}}
if (x_max - int(i)) % 3 == 0 else ''
for i, day in zip(data['date_index'].unique(), data['date_labels'].unique())}
app.layout = html.Div([html.Div(html.H1('COVID-19 in Italia: i contagi per provincia'), className='title'),
html.Div(dcc.Dropdown(id='selectmeasure',
options=[{'label': i, 'value': i} for i in measures],
value=measures[0]), className='dropdown-container'),
html.Div([html.Div([dcc.Graph(id='prov-choropleth',
figure=dict(
data=[],
layout=dict(
mapbox=dict(
layers=[],
style='carto-positron',
center={
'lat': 41.8919,
'lon': 12.5113
},
pitch=0,
zoom=4
),
autosize=True,
margin=dict(r=0, l=0, t=0, b=0)
)))], className='graph'),
html.Div([dcc.Graph(id='plot',
figure=dict(
data=[],
layout=dict(
xaxis=dict(range=[0, x_max]),
yaxis=dict(range=[0, y_max]),
clickmode='event+select',
showlegend=True,
margin=dict(r=50, l=50,
t=20, b=50),
padding=dict(r=0, l=0,
t=0, b=0))))],
className='graph')], className='graph-container'),
html.Div([dcc.Slider(
id='date-slider',
min=data['date_index'].min(),
max=data['date_index'].max(),
value=data['date_index'].max(),
marks=slider_marks,
step=None,
included=False,
updatemode='drag')],
className='slider-container'),
html.Div([dash_table.DataTable(
id='table', columns=[{'name': i, 'id': i} for i in table_cols],
data=[])], className='table')], className='main')
@app.callback(
[Output('prov-choropleth', 'figure'),
Output('table', 'data')],
[Input('date-slider', 'value'),
Input('selectmeasure', 'value')],
[State('prov-choropleth', 'figure')])
def update_figure(selected_date, measure, figure):
filtered_df = data[data.date_index == selected_date]
z_min = filtered_df[filtered_df.Provincia != temp_label][measure].min()
z_max = filtered_df[filtered_df.Provincia != temp_label][measure].max()
table_data = filtered_df.to_dict('rows')
if figure['data']:
figure['data'][0]['z'] = filtered_df[measure]
figure['data'][0]['zmin'] = z_min
figure['data'][0]['zmax'] = z_max
figure['data'][0]['name'] = measure
else:
trace = go.Choroplethmapbox(z=filtered_df[measure], geojson=prov, locations=filtered_df['codice_provincia'],
featureidkey='properties.prov_istat_code_num', zmax=z_max, zmin=z_min, name=measure,
hovertemplate='%{text}<extra>%{z}</extra>',
text=filtered_df['Provincia'] + "<br>" + filtered_df['Data'])
figure['data'] = [trace]
return [figure, table_data]
@app.callback(
Output('plot', 'figure'),
[Input('prov-choropleth', 'clickData'),
Input('plot', 'clickData'),
Input('selectmeasure', 'value')],
[State('plot', 'figure')]
)
def update_state_click(choro_click, plot_click, measure, plot):
ctx = dash.callback_context
y_min = data[data.Provincia != temp_label][measure].min()
y_max = data[data.Provincia != temp_label][measure].max()
if ctx.triggered[0]['prop_id'].split('.')[0] == 'selectmeasure':
plot['data'] = []
plot['layout']['xaxis'] = dict(range=[0, x_max])
plot['layout']['yaxis'] = dict(range=[y_min, y_max])
displayed_provs = [p['name'] for p in plot['data']]
if choro_click is not None:
if prov_codename[choro_click['points'][0]['location']] not in displayed_provs and \
ctx.triggered[0]['prop_id'].split('.')[0] == 'prov-choropleth':
df_prov = data[data.codice_provincia == choro_click['points'][0]['location']].sort_values('date_index')
plot['data'].append(
dict(x=df_prov['date_labels'], y=df_prov[measure], name=df_prov['Provincia'].tolist()[0]))
plot['layout']['yaxis'] = dict(range=[0, y_max])
if plot_click is not None and ctx.triggered[0]['prop_id'].split('.')[0] == 'plot':
plot['data'].pop(plot_click['points'][0]['curveNumber'])
return plot
if __name__ == '__main__':
app.run_server(debug=True)