def get_chart(self, df, gj):
fig = px.choropleth_mapbox(df,
title="<b>Somministrazioni prima dose</b>",
geojson=gj, color="percentuale1dose",
locations=df.regione, featureidkey="properties.reg_name",
center={"lat": 41.902782, "lon": 12.496366},
mapbox_style="open-street-map", zoom=4.35,
color_continuous_scale="Viridis",
opacity=0.7)
fig.update_layout(margin={"r": 0, "t": 25, "l": 0, "b": 0})
fig.update_layout(title_x=0.5)
fig2 = px.choropleth_mapbox(df,
title="<b>Somministrazioni seconda dose</b>",
geojson=gj, color="percentuale2dose",
locations=df.regione, featureidkey="properties.reg_name",
center={"lat": 41.902782, "lon": 12.496366},
mapbox_style="open-street-map", zoom=4.35,
color_continuous_scale="Viridis",
opacity=0.7)
fig2.update_layout(margin={"r": 0, "t": 25, "l": 0, "b": 0})
fig2.update_layout(title_x=0.5)
fig.update_layout(coloraxis_showscale=False)
fig2.update_layout(coloraxis_showscale=False)
return fig, fig2
def create_map(incremental, metric, n_clicks, map_data, state_filter):
try:
state_filter = int(json.loads(state_filter)['points'][0]['pointIndex'])
except TypeError:
state_filter = "Countrywide"
if metric == "% Positive":
col_to_plot = "%Positive"
colors = "Purpor"
format_type = ":.1f%"
elif metric == "Confirmed Cases":
col_to_plot="Cases"
colors="Greens"
format_type = ":,.0f"
else:
col_to_plot = "Deaths"
colors = "Blues"
format_type = ":,.0f"
map_data = pd.read_json(json.loads(map_data))
df_to_plot = map_data[map_data["Incremental"]==incremental]
df_to_plot['color_field'] = df_to_plot[col_to_plot]/max(df_to_plot[col_to_plot])
if metric == "% Positive":
fig = px.choropleth_mapbox(df_to_plot, geojson=states, locations='id', color='color_field',
color_continuous_scale=colors,
range_color=(0, 0.5),
mapbox_style="carto-positron",
zoom=2.5, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.5,
hover_data={'State':True,
'id':False,
'color_field':False,
col_to_plot:format_type})
else:
fig = px.choropleth_mapbox(df_to_plot, geojson=states, locations='id', color='color_field',
color_continuous_scale=colors,
#range_color=(0, 40),
mapbox_style="carto-positron",
zoom=2.5, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.5,
hover_data={'State':True,
'id':False,
'color_field':False,
col_to_plot:format_type}
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0},
coloraxis_showscale=False)
fig.update_layout(clickmode='event+select')
if dash.callback_context.triggered[0]['prop_id'].split('.')[0] != 'clear-geo':
fig = add_selected_data(fig, state_filter)
return(fig)
def draw_map(sample_data, odm_instance, geo):
map_height = 800
if geo is None:
return px.choropleth_mapbox()
map_center = visualization_helpers.get_map_center(geo)
zoom_level = visualization_helpers.get_zoom_level(geo, map_height)
sample_data["Polygon.name"] = poly_name_from_agg(odm_instance, sample_data)
sample_data["Site.icon"] = sample_data["Site.type"].apply(
lambda x: site_icons.get(x, "marker"))
site_data = sample_data[[
"Site.name", "Site.type", "Site.geoLat", "Site.geoLong", "Site.icon"
]].drop_duplicates()
# print(site_data["Site.icon"].to_list())
# Choropleth layer for sewersheds
fig = px.choropleth_mapbox(
sample_data,
geojson=geo,
locations="Site.polygonID",
featureidkey="properties.polygonID",
custom_data=["Polygon.name"],
hover_name="Polygon.name",
color="Polygon.name",
center=map_center,
opacity=0.5,
mapbox_style="open-street-map",
zoom=zoom_level,
)
fig.add_scattermapbox(
below="",
name="Sampling Sites",
lat=site_data["Site.geoLat"],
lon=site_data["Site.geoLong"],
customdata=site_data["Site.name"],
mode="markers",
marker=dict(
size=20,
symbol="circle",
color="black",
opacity=1,
),
hoverinfo="text",
text=site_data["Site.name"],
showlegend=True,
)
fig.update_layout(clickmode="event+select",
height=map_height,
legend_orientation="h",
legend_yanchor="top",
legend_xanchor="left")
return fig
def create_zipcode_geomap(covid_df,
zipcode_trip_df,
geo_json,
covid_attr,
zipcode_attr,
zoom=9,
center={
"lat": 40.7,
"lon": -73.99
}):
covid_fig = px.choropleth_mapbox(
covid_df,
geojson=geo_json,
color=covid_attr,
locations="zipcode",
featureidkey="properties.postalCode",
mapbox_style="carto-positron",
hover_data=[
'neighborhood', 'population', 'median_household_income', 'zipcode',
'num_tests'
],
zoom=zoom,
center=center,
opacity=0.5,
color_continuous_scale=px.colors.sequential.Reds,
title="COVID-19 Data",
width=730,
height=600)
zipcode_trip_fig = px.choropleth_mapbox(
zipcode_trip_df,
geojson=geo_json,
color=zipcode_attr,
locations="zipcode",
featureidkey="properties.postalCode",
mapbox_style="carto-positron",
hover_data=[
'num_cash_payment', 'num_card_payment', 'num_dropoff',
'avg_trip_passenger', 'avg_trip_speed_mph', 'avg_trip_distance',
'avg_total_price', 'avg_price_per_mile'
],
zoom=zoom,
center=center,
opacity=0.5,
color_continuous_scale=px.colors.sequential.Reds,
title="NYC Yellow Taxi Data",
width=730,
height=600)
return covid_fig, zipcode_trip_fig
def update_map2p(dep_select_value):
df_map2a = df_map2[df_map2['nombre_dep']==dep_select_value]
coords_dep_f = coords_dep[coords_dep['nombre_dep']==dep_select_value].reset_index()
map2 = px.choropleth_mapbox(df_map2a,
geojson=municipios,
color="Conteo",
color_continuous_scale="brwnyl",
locations="codigo_mun",
featureidkey="properties.MPIO_CCNCT",
mapbox_style="carto-positron",
zoom=7,
center={"lat": coords_dep_f.latitud[0], "lon": coords_dep_f.longitud[0]},
hover_name="nombre_mun",
hover_data={"codigo_dep": False,
"codigo_mun": False},
labels={"Conteo": "Número de <br> sentencias"})
map2.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
map2.update_layout(
autosize=True,
hoverlabel=dict(
bgcolor="#EDEAE8",
font_size=16,
font_color = '#1B1B1B')
)
return [
dcc.Graph( figure = map2 )
]
def update_choropleth(df, geojson):
# Prepare the datasets
primary_df = dp.load_primary_energy_sources(df)
primary_df = dp.data_subset(primary_df, states=[state for state in primary_df["State"].unique(
) if state != "United States"], sectors=["Total"])
primary_df["BTU"] = primary_df["BTU"]/1_000_000
primary_df = primary_df.rename(columns={"BTU": "Quadrillion BTU"})
primary_df = primary_df.groupby(["State", "Year"], as_index=False).sum()
primary_df = primary_df[primary_df["Year"] == 2018]
per_cap_df = dp.data_subset(df, states=[state for state in df["State"].unique(
) if state != "United States"], sources=["Total"], sectors=["Total consumption per capita"])
per_cap_df = per_cap_df.rename(columns={"BTU": "Million BTU per capita"})
max_y = per_cap_df["Million BTU per capita"].max()
fig = px.choropleth_mapbox(
per_cap_df,
geojson=geojson,
locations="State",
color="Million BTU per capita",
featureidkey="properties.NAME",
color_continuous_scale=plotly.colors.diverging.Temps,
range_color=(0, max_y),
animation_frame="Year",
)
fig.update_layout(
mapbox_style="carto-positron",
mapbox_zoom=2.2,
mapbox_center={"lat": 37.8, "lon": -95.7},
)
fig.update_layout(plotting.CHOROPLETH_COLORS)
# fig.update_layout(plotting.CHOROPLETH_COLORS)
return fig
def _update_map_rating_heat_map(self, trigger):
with open("assets/us-states.json") as geojson:
states = json.load(geojson)
df = self._data_handler.get_data_frame_original
df = df[["state_name",
"student_star"]].groupby("state_name").mean().reset_index()
fig = px.choropleth_mapbox(df,
geojson=states,
locations="state_name",
color="student_star",
color_continuous_scale="tealgrn",
range_color=(0, 5),
center={
"lat": 37.0902,
"lon": -95.7129
},
zoom=3,
opacity=0.5)
fig.update_layout(mapbox=dict(accesstoken=self._mapbox_token,
style=self._mapbox_style),
margin=go.layout.Margin(l=0, r=0, t=0, b=0),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)")
return fig
def plot_unemploy_choro_county(df, rangesize, datatype):
fig_map = px.choropleth_mapbox(
df,
geojson=counties,
locations=df.index,
color=datatype,
color_continuous_scale="Reds",
range_color=rangesize,
mapbox_style="carto-positron",
zoom=3,
center={
"lat": 37.0902,
"lon": -95.7129
},
opacity=0.5,
)
fig_map.update_layout(
title_text='{} by County'.format(datatype),
margin={
"r": 10,
"t": 0,
"l": 0,
"b": 0
},
)
return fig_map
def map_communes():
fig = px.choropleth_mapbox(df_communes_tot,
geojson=geojson_communes,
locations="NIS5",
color='colorbase',
color_continuous_scale="deep",
range_color=(3, 10),
featureidkey="properties.NIS5",
center={
"lat": 50.641111,
"lon": 4.668889
},
hover_name="CASES",
hover_data=["name", "CASES", "NIS5"],
height=500,
mapbox_style="carto-positron",
zoom=6)
fig.update_geos(fitbounds="locations")
NB_TICKS = 12
fig.layout.coloraxis.colorbar = dict(
title=gettext("Number of cases"),
titleside="right",
tickmode="array",
tickvals=list(range(1, NB_TICKS + 1)),
ticktext=[str(2**i) for i in range(1, NB_TICKS + 1)],
ticks="outside")
fig.update_traces(hovertemplate=gettext(
"<b>%{customdata[0]}</b><br>%{customdata[1]} cases"))
fig.update_layout(template="plotly_white", margin=dict(l=0, r=0, t=5, b=0))
return fig
def job():
print("Start job")
#filedata = 'C:/Users/ale/OneDrive/Ale/COVID/grafici/dpc-covid19-ita-province-latest.csv'
filedata = "https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province-latest.csv"
df = pd.read_csv(filedata,
dtype={"sigla_provincia": str},
error_bad_lines=True)
fig1 = px.choropleth_mapbox(df,
geojson=geo,
color="totale_casi",
locations="sigla_provincia",
featureidkey="properties.prov_acr",
center={
"lat": 42.35,
"lon": 12.70
},
mapbox_style="carto-positron",
zoom=5.5,
labels={'totale_casi': 'confirmed cases'})
fig1.update_layout(title_text=tc,
margin={
"r": 0,
"t": 100,
"l": 0,
"b": 0
},
autosize=True,
width=1000,
height=1100)
#plot(fig1)
fig1.show()
plot(fig1, filename='index.html')
def plot_sectorcode_values(hdbSeries, customdata = None, hovertemplate = None):
"""
Plots hdbSeries, which is a pandas Series with values for each sectorcode
customdata is a 2D array that contains [custom values] for each sectorcode
hovertemplate formats the output data for display
"""
shapefile = Path('/home/jovyan/shared-notebooks/notebooks/HDB_analysis/data/processed/sectorcode/sectorcode.shp')
sgshp = geopandas.read_file(shapefile)
geojson = json.loads(sgshp.to_json())
input_df = hdbSeries.reset_index() #move index to a column
fig = px.choropleth_mapbox(input_df, geojson=geojson,
locations='sc',
featureidkey="properties.sectorcode",
color=hdbSeries.name,
mapbox_style='open-street-map',
center = {"lat": 1.3521, "lon": 103.8198},
opacity = 0.5,
zoom = 10,
custom_data = ['sc'],
height = 600,
color_continuous_scale = 'jet',
range_color = list(hdbSeries.quantile([.1, 0.9]))
)
if customdata is not None:
fig.customdata = customdata
if hovertemplate is None:
hovertemplate = "<b>Sector Code: %{customdata[0]}</b><br>"+"{}".format(hdbSeries.name)+": %{z}<extra></extra>"
fig['data'][0].hovertemplate = hovertemplate
return fig
def update_map_plot(crime_drop, year_drop):
dfmap = dff[(dff['year'] == year_drop) & (dff['crime'].isin(crime_drop))]
dfmap = dfmap.groupby('mapid').sum().reset_index()
Map_fig2 = px.choropleth_mapbox(
dfmap,
locations='mapid',
color='total',
#featureidkey="properties.NOMBRE_DPT",
geojson=geoj,
zoom=7,
mapbox_style="stamen-toner",
center={
"lat": 4.655115,
"lon": -74.055404
}, #Center
color_continuous_scale="matter", #Color Scheme
opacity=0.5,
title='Total crimes in Colombia')
Map_fig2.update_layout(title='Total de Crímenes por Departamento',
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor="#F8F9F9")
return Map_fig2
def update_graph(option_slctd):
print(option_slctd)
print(type(option_slctd))
container = "The year chosen by user was: {}".format(option_slctd)
dff = suicide.copy()
dff = dff[dff["Year"] == option_slctd]
# Plotly Express
fig = px.choropleth_mapbox(
dff,
geojson=counties,
color="Suicide_Death_Rate",
locations="FIPS",
hover_name="County",
hover_data=["Deaths", "Population", "Suicide_Death_Rate"],
center={
"lat": 38.72490,
"lon": -95.61446
},
mapbox_style="carto-positron",
zoom=3)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
# fig.update_layout(
# title_text="Bees Affected by Mites in the USA",
# title_xanchor="center",
# title_font=dict(size=24),
# title_x=0.5,
# geo=dict(scope='usa'),
# )
return container, fig
def get_provincial_map(df_province, province_map_json, cmap):
fig = px.choropleth_mapbox(
data_frame=df_province,
geojson=province_map_json,
locations='sigla_provincia',
featureidkey='properties.prov_acr',
color='totale_casi',
color_continuous_scale=cmap,
range_color=(0, math.ceil(df_province['totale_casi'].max() + 1)),
hover_data=["growth_rate", "increased_cases"],
mapbox_style="carto-positron",
zoom=4,
center={
"lat": 42.00107394,
"lon": 10.3283498
},
#opacity=1,
animation_frame="giorno",
labels={
"giorno": "Giorno",
"totale_casi": "Totale Casi",
'data': 'Data',
"growth_rate": "Growth Rate",
"increased_cases": "Nuovi Casi",
"increased_tamponi": "Nuovi Tamponi Effettuati",
"sigla_provincia": "Sigla Provincia"
},
height=600,
)
return fig
def choropleth_state_v2(df, states_centers, state_name):
lat_lon = states_centers[states_centers['Abbr'] == state_name][['lat', 'lon']]
center_dict = {'lat': lat_lon['lat'].values[0],'lon': lat_lon['lon'].values[0] }
zoom_dict =get_zoom_level(df)
fig = px.choropleth_mapbox(df,
geojson=df.geometry,
locations=df.index,
color='covid_values',
center = center_dict,
mapbox_style='open-street-map',
zoom=zoom_dict[state_name],
opacity=0.6,
color_continuous_scale='rainbow'
)
fig.update_geos(fitbounds="locations", visible=False),
fig.update_traces(marker_line_width=2, marker_line_color='black')
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0},
geo=dict(bgcolor= config['plot_color']),
coloraxis_colorbar=dict(title=dict(text="Cumulative cases", font=dict(color='white', size=18)), tickfont=dict(size=14, color='white')))
fig.update_layout(dict( paper_bgcolor=config['paper_color']))
return fig
def choropleth_country_fig():
fig = px.choropleth_mapbox(get_country_choropeth_data(),
geojson=chile_g_geojson,
color_continuous_scale="PiYG_r",
color_continuous_midpoint=0.05,
locations="Codigo region",
color='Positividad',
featureidkey="properties.COD_REGI",
hover_data={
"Region": True,
"Positividad": ":.1%",
"Codigo region": False
},
height=800,
mapbox_style="carto-positron",
zoom=3.4,
center={
"lat": -39,
"lon": -70.6653
},
opacity=0.5)
# fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(coloraxis_showscale=False,
margin={
"r": 5,
"t": 0,
"l": 5,
"b": 0
})
return fig
def choropleth_fig():
fig = px.choropleth_mapbox(get_rm_choropleth_data(),
geojson=communes_g,
color_continuous_scale="PiYG_r",
color_continuous_midpoint=0.05,
locations="Comuna norm",
color='Positividad',
featureidkey="properties.NOM_COM_NORM",
hover_data={
"Comuna": True,
"Positividad": ":.1%",
"Comuna norm": False
},
mapbox_style="carto-positron",
zoom=7.5,
center={
"lat": -33.5826900,
"lon": -70.6472400
},
opacity=0.5)
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(coloraxis_showscale=False,
margin={
"r": 5,
"t": 0,
"l": 5,
"b": 0
})
return fig
def paintCarTrafficInZBS(stat):
if not (os.path.exists(
os.path.join(getRootPath(), 'data/trafico/07-2020_zbsMap.csv'))):
preprocessCarTrafficInZBS()
grouped_byzbs_mean = pd.read_csv(
os.path.join(getRootPath(), 'data/trafico/07-2020_zbsMap.csv'))
zbs_json = loadZBSJson(
os.path.join(
getRootPath(),
"data/COVID/zonas_basicas_salud/zonas_basicas_salud.json"))
fig = px.choropleth_mapbox(
grouped_byzbs_mean,
geojson=zbs_json,
featureidkey='properties.codigo_geo',
locations='index',
color=stat,
color_continuous_scale="OrRd",
mapbox_style="carto-positron",
hover_data=[stat],
center={
"lat": 40.417008,
"lon": -3.703795
}
#labels={'casos_confirmados_ultimos_14dias': 'Casos últimos 14 días'}
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def map_communes_cases_per_week():
maxv = df5.CASES.max()
fig = px.choropleth_mapbox(
df5,
geojson=geojson_communes,
locations="NIS5",
color='CASES',
color_continuous_scale="magma_r",
range_color=(0, maxv),
animation_frame="WEEK",
animation_group="NIS5",
featureidkey="properties.NIS5",
center={
"lat": 50.641111,
"lon": 4.668889
},
hover_name="CASES",
hover_data=["CASES_PER_1000HABITANT", "CASES", "name"],
height=600,
mapbox_style="carto-positron",
zoom=6)
fig.update_geos(fitbounds="locations")
fig.layout.coloraxis.colorbar.title = gettext("Number of cases per week")
fig.layout.coloraxis.colorbar.titleside = "right"
fig.layout.coloraxis.colorbar.ticks = "outside"
fig.layout.coloraxis.colorbar.tickmode = "array"
fig.update_traces(hovertemplate=gettext(
"<b>%{customdata[0]} /1000<br><b>%{customdata[1]} cases <br><b>%{customdata[2]}"
))
fig.update_layout(template="plotly_white", margin=dict(l=0, r=0, t=5, b=0))
return fig
def choroplet_plot(plot_dict, geo_data, geo_df, today=None, delay=2, los=8):
_, _, _, y_pred, _, _ = plot_dict["icu_occupancy"][delay][los]
if today is None:
today = n_obs
# Index regions `today`
y_pred = np.array(y_pred)
geo_df['% additional demand'] = y_pred[[3, 4, 3, 2, 1, 2, 0, 5, 6], today].round(2)
fig = px.choropleth_mapbox(
geo_df,
geojson=geo_data,
locations='ID',
color='% additional demand',
color_continuous_scale="Portland",
featureidkey="properties.nuts118cd",
range_color=(0, 100),
mapbox_style="carto-positron",
hover_data=["Region", "% additional demand"],
zoom=4.7,
center={"lat": 53, "lon": -2},
opacity=0.7
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def map_cases_incidence_provinces():
div = 250
fig = px.choropleth_mapbox(
df3d,
geojson=geojson_provinces,
locations="PROV",
color='CASES_PER_100KHABITANT',
range_color=(0, 300),
color_continuous_scale="magma_r",
#color_continuous_scale=[(0, "green"), (15/150, "green"), (15/150, "yellow"),
# (30/150, "yellow"), (30/150, "orange"), (50/150, "orange"),
# (50/150, "red"), (100/150, "red"),(100/150, "black"),(150/150, "black")],
featureidkey="properties.proviso",
center={
"lat": 50.641111,
"lon": 4.668889
},
hover_name="CASES_PER_100KHABITANT",
hover_data=["CASES_PER_100KHABITANT", "POP", "PROVINCE"],
height=600,
mapbox_style="carto-positron",
zoom=6)
fig.update_geos(fitbounds="locations")
fig.layout.coloraxis.colorbar.title = get_translation(
fr="Nombres de cas/100K past [d-17,d-4] days",
en="Number of cases/100K past [d-17,d-4] days")
fig.layout.coloraxis.colorbar.titleside = "right"
fig.layout.coloraxis.colorbar.ticks = "outside"
fig.layout.coloraxis.colorbar.tickmode = "array"
fig.update_traces(hovertemplate=gettext(
gettext(
"incidence:<b>%{customdata[0]}<br>pop:<b>%{customdata[1]}<br><b>%{customdata[2]}"
)))
fig.update_layout(template="plotly_white", margin=dict(l=0, r=0, t=5, b=0))
return fig
def get_regional_map(df_regioni, regions_map_json, cmap, data_selected,
data_selected_label):
fig = px.choropleth_mapbox(
data_frame=df_regioni,
geojson=regions_map_json,
locations='codice_regione',
featureidkey='properties.reg_istat_code_num',
color=data_selected,
color_continuous_scale=cmap,
range_color=(0, math.ceil(df_regioni[data_selected].max() + 1)),
hover_data=["increased_cases", "increased_tamponi", data_selected],
mapbox_style="carto-positron",
zoom=4,
center={
"lat": 42.00107394,
"lon": 10.3283498
},
#opacity=1,
animation_frame="giorno",
labels={
data_selected: data_selected_label,
"giorno": "Giorno",
'data': 'Data',
"growth_rate": "Growth Rate",
"increased_cases": "Nuovi Casi",
"increased_tamponi": "Nuovi Tamponi Effettuati",
"codice_regione": "Codice Regione"
},
height=600,
)
return fig
def map_provinces():
fig = px.choropleth_mapbox(df_prov_tot,
geojson=geojson_provinces,
locations="PROVINCE",
color='CASES_PER_THOUSAND',
color_continuous_scale="deep",
range_color=(range_min, range_max),
featureidkey="properties.proviso",
center={
"lat": 50.641111,
"lon": 4.668889
},
height=400,
mapbox_style="carto-positron",
zoom=6,
labels={
'CASES_PER_THOUSAND':
gettext('Cases per thousand inhabitants')
})
fig.update_geos(fitbounds="locations")
fig.layout.coloraxis.colorbar.titleside = 'right'
fig.update_traces(hovertemplate=gettext(
"<b>%{properties.name}</b><br>%{z:.3f} cases per 1000 inhabitants"))
fig.update_layout(template="plotly_white", margin=dict(l=0, r=0, t=0, b=0))
return fig
def get_map(selected_test, selected_year, selected_mod):
query = '''
SELECT *
FROM fact_estu_prgm_departamento
WHERE prueba = '{0}'
AND year = {1}
'''.format(selected_test, selected_year)
map_df = pd.read_sql_query(query, con=engine)
fig = px.choropleth_mapbox(map_df,
geojson=departamentos,
locations='estu_prgm_departamento',
color=selected_mod,
color_continuous_scale="Viridis",
center={
'lat': 4.3,
'lon': -74.1
},
mapbox_style="carto-positron",
zoom=4.2,
labels=dict(presentation_module,
**presentation_column_short_names))
fig.update_layout(title="Colombia",
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor="#F8F9F9",
plot_bgcolor="#F8F9F9")
return fig
def get_map(ix = "r"):
risk_str = ""
if ix == "r":
risk_str = "Risk index"
colormap='Reds'
elif ix == "ic":
risk_str = "Peak ICU/ICU beds"
colormap='Blues'
elif ix == "mi":
risk_str = "Fatalities/population"
colormap='Greens'
elif ix == "tf":
risk_str = "Fatalities"
colormap='Magenta'
fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color=risk_str,
color_continuous_scale = colormap,
range_color=(df.quantile(0.05)[risk_str], df.quantile(0.95)[risk_str]),
mapbox_style="carto-positron",
zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.5,
labels={'risk':'County risk'}
)
fig.update_layout(
margin=dict(l=0, r=0, t=0, b=0),
plot_bgcolor='rgba(0, 0, 0, 0)',
paper_bgcolor='rgba(0, 0, 0, 0)',)
return fig
def _make_map(self, data, geo_data):
fig = px.choropleth_mapbox(data,
geojson=geo_data,
locations='id',
color='DurationOfExistenceInMonths',
hover_data=('DurationOfExistenceInMonths',
'name'),
color_continuous_scale="OrRd",
mapbox_style="carto-positron",
zoom=6,
range_color=(0, 100),
center={
'lat': 52.1,
'lon': 19.4
},
opacity=1,
labels={
'DurationOfExistenceInMonths':
'Średni czas życia',
'name': 'Nazwa'
},
height=842)
fig.update_layout(margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
hoverlabel=dict(bgcolor="white", font_size=16))
return fig
def update_figure(candidate, metric, date):
global dates
date_str = dates[date]
global preprocessed_data
if preprocessed_data is None:
preprocessed_data = prepare_data()
logging.info('Data is ready')
if candidate == candidates[-1]:
candidate = 'ratio'
metric = metric + '_' + candidate
logging.info('Prepearing the figure...')
fig = px.choropleth_mapbox(
preprocessed_data[preprocessed_data['created_at'] == date_str],
geojson=us_states,
locations='state',
color=metric,
#color_continuous_scale="Viridis",
#animation_frame="created_at",
range_color=(0, preprocessed_data[metric].max()),
mapbox_style="carto-positron",
zoom=3,
center={
"lat": 37.0902,
"lon": -95.7129
},
opacity=0.5,
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0}, )
logging.info('Figure is ready')
return [fig]