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", } }