def get_stat(df_projections,d, val, scope):
if d is None:
return None
if isinstance(d, str):
d = datetime.datetime.strptime(d, '%Y-%m-%d').date()
if scope == 'US':
df_projections_sub = df_projections.loc[(df_projections.Country == scope) & (df_projections.Province == 'None')]
elif scope =='World':
df_projections_sub = df_projections.loc[df_projections.Continent == 'None']
else:
df_projections_sub = df_projections.loc[(df_projections.Continent == scope) & (df_projections.Country == 'None')]
df_projections_sub = df_projections_sub.loc[df_projections_sub['Day']==d].reset_index()
if df_projections_sub.empty:
return None
card_content = [
dbc.CardHeader(
f'{df_projections_sub.iloc[0][val]:,}',
style={"textAlign":"center","fontSize":30,"fontWeight": "bold","color":'#1E74F0'}
),
dbc.CardBody(
[
html.H5(add_cases(val),id='us-stats-cards'),
]
),
]
return card_content
def build_continent_map(df_continent,PopInfo,map_date,val='Active', continent = 'World', pop = 1):
if continent !='World':
df_continent = df_continent.loc[df_continent.Continent == continent] #Filter by continent
if map_date is None:
return None
if isinstance(map_date, str):
map_date = datetime.datetime.strptime(map_date, '%Y-%m-%d').date()
df_map = df_continent.loc[df_continent['Day'] == map_date]
df_map = df_map.loc[df_map['Province'] == 'None'] #exclude province data
df_map = df_map.loc[df_map['Country'] != 'None'] #exclude global world data
population = np.array([])
for i in df_map['Country']:
ind1 = np.logical_and(PopInfo['Country']==i, PopInfo['Province']=='None')
pop_val = PopInfo.loc[ind1,'pop'].values
population = np.concatenate((population, pop_val),0)
df_map['Population'] =population
df_map['Active Per Million'] = (np.round(1000000*df_map['Active']/df_map['Population'], decimals = 2))
df_map['Total Detected Per Million'] = (np.round(1000000*df_map['Total Detected']/df_map['Population'], decimals = 2))
df_map['Active Hospitalized Per Million'] = (np.round(1000000*df_map['Active Hospitalized']/df_map['Population'], decimals = 2))
df_map['Cumulative Hospitalized Per Million'] = (np.round(1000000*df_map['Cumulative Hospitalized']/df_map['Population'], decimals = 2))
df_map['Total Detected Deaths Per Million'] = (np.round(1000000*df_map['Total Detected Deaths']/df_map['Population'], decimals = 2))
df_map = df_map.applymap(str)
cols = get_cols()
if (val is not None) and (val in cols) and pop == 1:
df_map.loc[:,'text'] = df_map['Country'] + '<br>' + \
'Total Detected ' + df_map['Total Detected'] + '<br>' + \
'Active ' + df_map['Active'] + '<br>' + \
'Active Hospitalized ' + df_map['Active Hospitalized'] + '<br>' + \
'Cumulative Hospitalized ' + df_map['Cumulative Hospitalized'] + '<br>' + \
'Total Detected Deaths ' + df_map['Total Detected Deaths']
zval = df_map[val].astype(float)
if (val is not None) and (val in cols) and pop != 1:
df_map.loc[:,'text'] = df_map['Country'] + '<br>' + \
'Total Detected Per Million ' + df_map['Total Detected Per Million'] + '<br>' + \
'Active Per Million ' + df_map['Active Per Million'] + '<br>' + \
'Active Hospitalized Per Million ' + df_map['Active Hospitalized Per Million'] + '<br>' + \
'Cumulative Hospitalized Per Million ' + df_map['Cumulative Hospitalized Per Million'] + '<br>' + \
'Total Detected Deaths Per Million ' + df_map['Total Detected Deaths Per Million']
zval = df_map[val+ " Per Million"].astype(float)
if (val is not None) and (val in cols):
fig = go.Figure(data=go.Choropleth(
locations=df_map['Country'],
z= zval,
locationmode="country names",
autocolorscale=False,
colorscale='inferno_r',
text=df_map['text'], # hover text
marker_line_color='black', # line markers between states
colorbar_title='<br>'.join(wrap(''.join(['{}'.format(add_cases(val))]), width=10))
))
fig.update_layout(
margin=dict(l=10, r=10, t=50, b=50),
title_text=add_cases('{} Predicted {} {}'.format(map_date.strftime('%b %d,%Y'), continent, val)),
geo = dict(
scope= continent.lower() if continent is not None else None,
projection=go.layout.geo.Projection(type = 'natural earth'),
showlakes=True, # lakes
lakecolor='rgb(255, 255, 255)',
countrycolor='lightgray',
landcolor='whitesmoke',
showland=True,
showframe = False,
showcoastlines = True,
showcountries=True,
visible = False,
),
modebar={
'orientation': 'v',
'bgcolor': 'rgba(0,0,0,0)',
'color': 'lightgray',
'activecolor': 'gray'
}
)
graph = dcc.Graph(
id='continent-projection-map',
figure=fig,
)
return graph
return
def build_us_map(df_projections,PopInfo,map_date,val='Active', pop = 1):
if map_date is None:
return None
if isinstance(map_date, str):
map_date = datetime.datetime.strptime(map_date, '%Y-%m-%d').date()
df_us = df_projections.loc[(df_projections.Country == "US") & (df_projections.Province != 'None')]
df_map = df_us.loc[df_us['Day']==map_date]
df_map = df_map.loc[df_us['Province']!='US']
states = get_states()
df_map.loc[:,'code'] = df_map.Province.apply(lambda x: states[x])
population = np.array([])
for i in df_map['Province']:
pop_val = PopInfo.loc[PopInfo['Province']==i,'pop'].values
population = np.concatenate((population, pop_val),0)
df_map['Population'] =population
df_map['Active Per Million'] = (np.round(1000000*df_map['Active']/df_map['Population'], decimals = 2))
df_map['Total Detected Per Million'] = (np.round(1000000*df_map['Total Detected']/df_map['Population'], decimals = 2))
df_map['Active Hospitalized Per Million'] = (np.round(1000000*df_map['Active Hospitalized']/df_map['Population'], decimals = 2))
df_map['Cumulative Hospitalized Per Million'] = (np.round(1000000*df_map['Cumulative Hospitalized']/df_map['Population'], decimals = 2))
df_map['Total Detected Deaths Per Million'] = (np.round(1000000*df_map['Total Detected Deaths']/df_map['Population'], decimals = 2))
df_map = df_map.applymap(str)
cols = get_cols()
if (val is not None) and (val in cols) and pop == 1:
df_map.loc[:,'text'] = df_map['Province'] + '<br>' + \
'Total Detected ' + df_map['Total Detected'] + '<br>' + \
'Active ' + df_map['Active'] + '<br>' + \
'Active Hospitalized ' + df_map['Active Hospitalized'] + '<br>' + \
'Cumulative Hospitalized ' + df_map['Cumulative Hospitalized'] + '<br>' + \
'Total Detected Deaths ' + df_map['Total Detected Deaths']
z_val = df_map[val].astype(float)
if (val is not None) and (val in cols) and pop != 1:
df_map.loc[:,'text'] = df_map['Province'] + '<br>' + \
'Total Detected Per Million ' + df_map['Total Detected Per Million'] + '<br>' + \
'Active Per Million ' + df_map['Active Per Million'] + '<br>' + \
'Active Hospitalized Per Million ' + df_map['Active Hospitalized Per Million'] + '<br>' + \
'Cumulative Hospitalized Per Million ' + df_map['Cumulative Hospitalized Per Million'] + '<br>' + \
'Total Detected Deaths Per Million ' + df_map['Total Detected Deaths Per Million']
z_val =df_map[val+ " Per Million"].astype(float)
if (val is not None) and (val in cols):
fig = go.Figure(data=go.Choropleth(
locations=df_map['code'],
z=z_val,
locationmode='USA-states',
colorscale='inferno_r',
autocolorscale=False,
text=df_map['text'], # hover text
marker_line_color='white' , # line markers between states
colorbar_title='<br>'.join(wrap(''.join(['{}'.format(add_cases(val))]), width=10))
))
fig.update_layout(
margin=dict(l=10, r=10, t=50, b=50),
title_text=add_cases('{} Predicted US {}'.format(map_date.strftime('%b %d,%Y'), val)),
geo = dict(
scope='usa',
projection=go.layout.geo.Projection(type = 'albers usa'),
showlakes=True, # lakes
lakecolor='rgb(255, 255, 255)'
),
modebar={
'orientation': 'v',
'bgcolor': 'rgba(0,0,0,0)',
'color': 'lightgray',
'activecolor': 'gray'
}
)
graph = dcc.Graph(
id='us-projection-map',
figure=fig
)
return graph
return
def get_bottom_visual():
df_projections = get_df_projections()
cols = get_cols()
bottom_visual = \
[
dbc.Row(
[
dbc.Col(
[
html.H5('Use the tool below to explore our predictions for different locations.'),
]
),
],
)
] + \
[
dbc.Row(
[
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown("What value would you like to plot?"),
dbc.Row(
[
dbc.Col(dcc.Markdown("**Predicted \n Value:**"),width="auto"),
dbc.Col(
html.Div(
dcc.Dropdown(
id = 'predicted_timeline',
options = [{'label': add_cases(x), 'value': x} for x in cols.keys()],
value = ['Active'],
multi=True,
),
id = "p2-transfer-dropdown-wrapper",
),
width=True
),
]
),
],
),
],
className="projections-general-card h-100",
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
html.Div(
id = 'province-card-title',
style={'paddingBottom':10}
),
dbc.Row(
[
dbc.Col([dcc.Markdown("**Country:**"),dcc.Markdown("**Province / State:**")]),
dbc.Col(
html.Div(
[
dcc.Dropdown(
id = 'country_dropdown',
options = [{'label': x, 'value': x} for x in df_projections[df_projections['Continent'] == 'North America'].Country.unique()],
),
dcc.Dropdown(
id = 'province_dropdown',
),
html.Div(id = "p2-transfer-dropdown-wrapper"),
]),
),
]
),
],
),
],
className="projections-general-card h-100",
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
],
style={'marginBottom':20,'marginTop':20},
)
] + \
[
dbc.Row(
[
dbc.Col(
[
html.Div(
id = 'state_projection_graph',
children = [],
style={
'width': '100%',
'display': 'inline-block',
'paddingTop': 20,
}
),
]
)
],
)
] + \
[
dbc.Row([
dbc.Col(
html.Div(
html.A(
"Download the Data",
id="download-link",
href="https://raw.githubusercontent.com/COVIDAnalytics/website/master/data/predicted/Global.csv",
),
style={'textAlign':"center"}
)
),
]
),
]
return bottom_visual
def get_top_visual():
map_locations = [
'US', "Europe", "Asia", "North America", "South America", "Africa",
'World'
]
cols = get_cols()
today = pd.Timestamp('today')
oneWeekFromNow = datetime.date.today() + datetime.timedelta(days=7)
df_projections = pd.read_csv('data/predicted/Global.csv',
sep=",",
parse_dates=['Day'])
df_projections.loc[:, 'Day'] = pd.to_datetime(df_projections['Day'],
format='y%m%d').dt.date
df_projections = df_projections.loc[df_projections['Day'] >= today]
top_visual = [
dbc.Row(
[
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown("For what date do you want to see projections?"),
dbc.Row(
[
dbc.Col(
html.Div(
dcc.DatePickerSingle(
id='us-map-date-picker-range',
min_date_allowed=today,
max_date_allowed=max(df_projections.Day.values),
date=oneWeekFromNow,
initial_visible_month=oneWeekFromNow,
),
id="date-projections-picker-div"
),
),
]
),
],
),
],
className="projections-general-card h-100"
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown("And for which area?"),
dbc.Row(
[
dbc.Col(
html.Div(
[
dcc.Dropdown(
id = 'location_map_dropdown',
options = [{'label': x, 'value': x} for x in map_locations],
value = 'US',
clearable=False
),
]),
),
]
),
],
),
],
className="projections-general-card h-100"
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
],
style={'marginBottom':20,'marginTop':20},
)
] + \
[
dbc.Row(
[
html.Div(
id='us-stats-title',
style={
'width': '100%',
'color': 'black',
'textAlign': 'center',
'fontSize': 30,
'fontWeight':'bold'
}
),
],
)
] + \
[
dbc.Row(
[
build_card('us_tot_det'),
build_card('us_tot_death'),
build_card('us_active'),
build_card('us_active_hosp')
],
align="center"
)
] + \
[
dbc.Row(
[
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown('Predicted Value:'),
dbc.Row(
[
dbc.Col(
html.Div(
dcc.Dropdown(
id = 'us_map_dropdown',
options = [{'label': add_cases(x), 'value': x} for x in cols.keys()],
value = 'Total Detected',
),
id="predicted-value-projections-picker-div",
),
),
]
),
],
),
],
className="projections-general-card h-100"
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown("And for which population type?"),
dbc.Row(
[
dbc.Col(
html.Div(
[
dcc.RadioItems(
options=[
{'label': ' Full population', 'value': 1},
{'label': ' Value per million', 'value': 2}
],
value=1,
id = 'radio_botton',
labelStyle={'display': 'inline-block',
'margin-right': '20px'
},
style={'textAlign': 'center'}
),
]),
),
]
),
],
),
],
className="projections-general-card h-100"
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
],
style={'marginBottom':20,'marginTop':20},
)
] + \
[
dbc.Row(
[
dbc.Col(
[
html.Div(
id = 'map_projections',
children = [],
),
html.P(
children = [],
style={'color':'gray'},
id='grey-countries-text'
),
]
),
],
)
]
return top_visual
def get_bottom_visual():
north_america_countries = ['Canada', 'Costa Rica', 'Cuba', 'Dominican Republic',
'El Salvador', 'Guatemala', 'Honduras', 'Mexico', 'Panama',
'US']
df_projections = get_df_projections()
data_csv_string = df_projections.to_csv(index=False, encoding='utf-8')
data_csv_string = "data:text/csv;charset=utf-8," + urllib.parse.quote(data_csv_string)
cols = get_cols()
bottom_visual = \
[
dbc.Row(
[
dbc.Col(
[
html.H5('Use the tool below to explore our predictions for different locations.'),
]
),
],
)
] + \
[
dbc.Row(
[
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown("What value would you like to plot?"),
dbc.Row(
[
dbc.Col(dcc.Markdown("**Predicted \n Value:**"),width="auto"),
dbc.Col(
html.Div(
dcc.Dropdown(
id = 'predicted_timeline',
options = [{'label': add_cases(x), 'value': x} for x in cols.keys()],
value = ['Total Detected'],
multi=True,
),
id = "p2-transfer-dropdown-wrapper",
),
width=True
),
]
),
],
),
],
className="projections-general-card h-100",
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
html.Div(
id = 'province-card-title',
style={'paddingBottom':10}
),
dbc.Row(
[
dbc.Col([dcc.Markdown("**Country:**"),dcc.Markdown("**Province / State:**")]),
dbc.Col(
html.Div(
[
dcc.Dropdown(
id = 'country_dropdown',
options = [{'label': x, 'value': x} for x in north_america_countries],
),
dcc.Dropdown(
id = 'province_dropdown',
),
html.Div(id = "p2-transfer-dropdown-wrapper"),
]),
),
]
),
],
),
],
className="projections-general-card h-100",
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
],
style={'marginBottom':20,'marginTop':20},
)
] + \
[
dbc.Row(
[
dbc.Col(
[
html.Div(
id = 'state_projection_graph',
children = [],
style={
'width': '100%',
'display': 'inline-block',
'paddingTop': 20,
}
),
]
)
],
)
] + \
[
dbc.Row([
dbc.Col(
html.Div(
html.A(
"Download Most Recent Predictions",
id="download-link",
download="covid_analytics_projections.csv",
href=data_csv_string,
target="_blank"
),
style={'textAlign':"center"}
)
),
]
),
]
return bottom_visual
dbc.Row(
[
dbc.Col(
[
dbc.Card(
[
dbc.CardBody(
[
dcc.Markdown('Predicted Value:'),
dbc.Row(
[
dbc.Col(
html.Div(
dcc.Dropdown(
id = 'us_map_dropdown',
options = [{'label': add_cases(x), 'value': x} for x in cols.keys()],
value = 'Active',
),
id="predicted-value-projections-picker-div",
),
),
]
),
],
),
],
className="projections-general-card h-100"
),
],
xs=12,
