def update_global_graph(selected_dropdown_value):
country = selected_dropdown_value
df = make_data_global(country)
return {
'data': [
# {'y': df['recovered'], 'x': df.index, 'type': 'bar', 'name': 'Recovered'},
{
'y': df['confirmed'],
'x': df.index,
'type': 'bar',
'name': 'Confirmed'
},
{
'y': df['deaths'],
'x': df.index,
'type': 'bar',
'name': 'Deaths'
},
],
'layout': {
'title':
'{country} COVID-19 Cases, Last Updated {update}'.format(
country=country,
update=last_update(country).strftime("%B %d, %Y")),
'barmode':
'stack',
#'margin':{'l': 40, 'b': 40, 't': 10, 'r': 10}
}
}
def update_confinement_graph(selected_dropdown_value):
city = selected_dropdown_value
country = 'Sweden' # @TODO: for now we only focus on Sweden anyway
df = make_data_global(country)
return {
'data': [
{
'y': df['confirmed'],
'x': df.index,
'type': 'bar',
'name': 'Confirmed'
},
{
'y': df['deaths'],
'x': df.index,
'type': 'bar',
'name': 'Deaths'
},
],
'layout': {
'title':
'{country} COVID-19 Cases, Last Updated {update}'.format(
country=country,
update=last_update(country).strftime("%B %d, %Y")),
'barmode':
'stack',
'height':
350,
'margin':
dict(l=50, r=50, b=100, t=50, pad=4),
'paper_bgcolor':
"white",
}
}
def update_analysis_graph(selected_dropdown_value):
city = selected_dropdown_value
country = 'Sweden' # @TODO: for now we only focus on Sweden anyway
df_confinement = make_data_confinement(city)
date_list_confinement = list(df_confinement.index)
df_cases = make_data_global(country)
df_cases = df_cases[df_cases.index.isin(date_list_confinement)]
date_list_cases = list(df_cases.index)
df_confinement = df_confinement[df_confinement.index.isin(date_list_cases)]
correlation = compute_correlation(df_confinement['mean_nb_detected'],
df_cases['confirmed'])
return '''
### Spearman Correlation : {:.2f}
This factor assesses how well the relationship between two variables can be described using a monotonic function.
The closest it is from 1, the more data are correlated.
[More info](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient)
'''.format(correlation)
def update_analysis_graph(selected_dropdown_value):
city = selected_dropdown_value
country = 'Sweden' # @TODO: for now we only focus on Sweden anyway
df_confinement = make_data_confinement(city)
date_list_confinement = list(df_confinement.index)
df_cases = make_data_global(country)
df_cases = df_cases[df_cases.index.isin(date_list_confinement)]
date_list_cases = list(df_cases.index)
df_confinement = df_confinement[df_confinement.index.isin(date_list_cases)]
correlation = compute_correlation(df_confinement['mean_nb_detected'],
df_cases['confirmed'])
return {
'data': [{
'x': df_cases['confirmed'],
'y': df_confinement['mean_nb_detected'],
'name': 'Confirmed cases vs Confinement Status',
'text': date_list_cases,
'mode': 'lines+markers',
'opacity': 0.5,
'marker': {
'size': 15,
'line': {
'width': 0.5,
'color': 'white'
},
'color': 'red'
}
}],
'layout': {
'title':
'{country} COVID-19 Cases vs Confinement Status, last update :{update}'
.format(country=country, update=date_list_cases[-1]),
'xaxis': {
'title': 'COVID-19 confirmed cases'
},
'yaxis': {
'title': 'People detected outside'
},
'barmode':
'stack',
'height':
350,
'margin':
dict(
l=50,
r=50,
b=100,
t=50,
pad=4,
),
'hovermode':
'closest',
'paper_bgcolor':
"white",
}
}