def update_figure(selected):
dff = (
df_india.groupby(["State", "Confirmed", "Recovered", "Deaths"])
.max()
.reset_index()
)
def title(text):
if text == "Confirmed":
return "Confirmed Cases"
elif text == "Deaths":
return "Deaths Cases"
elif text == "Recovered":
return "Recovered Cases"
else:
return "Recovered"
trace = go.Choroplethmapbox(
geojson=states,
featureidkey="properties.State",
locations=dff.State,
z=dff[selected],
colorscale=colormap,
marker_line_width=1,
)
return {
"data": [trace],
"layout": go.Layout(
mapbox_style="mapbox://styles/cocktailsguy/ck8edouiv02lv1io9quvrr36i",
mapbox_accesstoken=token,
mapbox_zoom=3.1,
mapbox_center={"lat": 21, "lon": 78},
margin={"r": 0, "t": 0, "l": 0, "b": 0},
),
}
def map_prep(geojson,
locations,
z,
hovertext,
center_lon,
center_lat,
zoom,
updatetime,
mapstyle="satellite-streets",
title='US'):
# prepare token for Mapbox
token = 'pk.eyJ1IjoiY2h1emhpY29uZyIsImEiOiJjazZoMG5tajIwNmh4M21ueGN0eXdtMmx6In0.kQscrYmKzfyLhN-YgENO0Q'
data = go.Choroplethmapbox(
geojson=geojson,
locations=locations,
z=z,
hovertext=hovertext,
hoverinfo='text',
colorscale='YlOrRd',
marker_line_width=0.5,
marker_line_color='rgb(169, 164, 159)',
colorbar=dict(title="Cases",
titleside="top",
tickmode="array",
tickvals=np.arange(0, 7, 1),
ticktext=["1", "10", "100", "1k", '10k', '100k', '1M'],
ticks="outside"))
layout = go.Layout(
mapbox={
'accesstoken': token,
'center': {
'lon': center_lon,
'lat': center_lat
},
'zoom': zoom,
'style': mapstyle
},
margin={
"r": 1,
"t": 45,
"l": 45,
"b": 30
},
title=title + ' Case HeatMap',
plot_bgcolor='#D3D3D3',
paper_bgcolor='#D3D3D3',
annotations=[
dict(x=0.55,
y=0.03,
xref='paper',
yref='paper',
text='last update on ' + str(updatetime),
showarrow=False)
], #height = graph_height, width = graph_width
)
return data, layout
def display_map(hg, data_json, plot_data_json, hydro_json):
hydro_units = ast.literal_eval(hydro_json)
su_ids = np.sort(hydro_units[hg]['value'])
plot_data = pd.read_json(plot_data_json, orient='split')
plot_data1 = plot_data[plot_data.id.isin(su_ids)]
gjson1 = ast.literal_eval(data_json)
gjson2 = [j for j in gjson1['features'] if j['id'] in su_ids.tolist()]
gjson = {
'type': 'FeatureCollection',
'bbox': gjson1['bbox'],
'features': gjson2
}
fig = go.Figure(go.Choroplethmapbox(
geojson=gjson,
locations=plot_data1['id'],
colorscale=plot_data1['color'].tolist(),
marker_opacity=0.7,
marker_line_width=0,
showscale=False,
text=plot_data1['name'],
z=plot_data1.index,
zmin=plot_data1.index.min(),
zmax=plot_data1.index.max(),
),
layout=map_layout)
return fig
def make_heatmap(df, fig=fig):
# Global heat map
countries = {}
for country in pycountry.countries:
countries[country.name] = country.alpha_3
country_dict = {}
for index, row in df.iterrows():
country_iso3 = str(row["COUNTRY_ISO3"])
duration = int(row["DURATION"])
duration = duration / BYTES_TO_MB
if country_iso3 in country_dict.keys():
country_dict[country_iso3] = country_dict[country_iso3] + duration
else:
country_dict[country_iso3] = duration
with open('heatmap/world_geojson.json') as file:
world = json.load(file)
fig.add_trace(
go.Choroplethmapbox(geojson=world,
locations=list(country_dict.keys()),
z=list(country_dict.values()),
colorscale='Darkmint',
zmin=0,
zmax=90,
marker_opacity=0.7,
marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron", mapbox_zoom=1.5)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
def MakeWildfireMap(self, state, fipsData):
fig = go.Figure(
go.Choroplethmapbox(
geojson=state,
locations=fipsData.fips,
z=fipsData.fire_count,
colorscale="redor",
zmin=0,
zmax=500,
marker_opacity=0.7,
marker_line_width=0,
text=fipsData.county,
hovertemplate="%{text}" + "<extra>%{z}</extra>",
))
fig.update_layout(
mapbox_accesstoken=
"pk.eyJ1IjoiY3NjaHJvZWQiLCJhIjoiY2s3YjJwcWk1MDFyNzNrbnpiaGdlajltbCJ9.8jO290WpRrStFoFl6oXDdA",
mapbox_style="mapbox://styles/cschroed/cknl0nnlf219117qmeizntn9q",
mapbox_zoom=5.20,
mapbox_center={
"lat": 37.502236,
"lon": -120.962930
})
fig_layout = fig["layout"]
fig_layout["paper_bgcolor"] = "rgba(0,0,0,0)"
fig_layout["font"]["color"] = "#fcc9a1"
fig_layout["xaxis"]["tickfont"]["color"] = "#fcc9a1"
fig_layout["yaxis"]["tickfont"]["color"] = "#fcc9a1"
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def usa_map():
with urlopen(
'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
) as response:
counties = json.load(response)
df = getdata.usa_counties()
df.drop([2311], inplace=True)
fig = go.Figure(
go.Choroplethmapbox(geojson=counties,
locations=df.fips,
z=df['cases/capita'],
marker_opacity=0.75,
marker_line_width=0,
colorscale=[[0, '#FFFAF4'], [.005, '#FFE4CC'],
[.030, '#DC654F'], [.060, '#CA3328'],
[.080, '#B80000'], [.100, '#7C100C'],
[.150, '#580000'], [.175, '#300000'],
[1, '#170707']]))
fig.update_layout(mapbox_style='carto-positron',
paper_bgcolor='rgba(0,0,0,0)',
mapbox_zoom=2.75,
mapbox_center={
'lat': 37.0902,
'lon': -95.7129
},
margin=dict(t=0, l=0, r=0, b=0))
plot_div = plot(fig,
include_plotlyjs=False,
output_type='div',
config={'displayModeBar': False})
return plot_div
def get_map_html():
map_plot = go.Choroplethmapbox(geojson=polygons,
hovertemplate=paavo_df.text,
locations=paavo_df['Postal code'],
z=paavo_df['Trend near future'],
colorscale="Viridis",
marker_opacity=0.7,
marker_line_width=0,
showscale=False)
map_layout = go.Layout(width=360,
height=600,
mapbox_style="carto-positron",
mapbox_zoom=4.0,
mapbox_center={
"lat": 65.361064,
"lon": 26.985940
},
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
})
graph = dcc.Graph(id='main_plot',
config={'displayModeBar': False},
figure={
'layout': map_layout,
'data': [map_plot]
},
className="map_main_plot")
text_block = html.Div(children=get_side_analysis(),
className="side_analysis",
id="side_info")
map_html = html.Div(children=[graph, text_block], id="map_html")
return map_html
def map_trace(layout):
trace_map = []
global mapbox_accesstoken
global df
global selection
global US_data
global pl_deep
global app
print(df.head())
for attribute in selection:
trace_map.append(
go.Choroplethmapbox(
geojson=US_data,
# locations=df['PostalCode_y'].tolist(),
locations=df['PostalCode'].tolist(),
z=df[attribute].tolist(),
colorscale=pl_deep,
text=addresses,
colorbar=dict(thickness=20, ticklen=3),
marker_line_width=0,
marker_opacity=0.7,
visible=False,
subplot='mapbox1'))
trace_map[0]['visible'] = True
return trace_map
def death_map():
with urlopen(
'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
) as response:
counties = json.load(response)
hovertext = covid_deaths["Admin2"] + ", " + covid_deaths["Province_State"]
fig_death = go.Figure(
go.Choroplethmapbox(geojson=counties,
locations=covid_deaths["FIPS"],
z=covid_deaths.iloc[:, -1],
colorscale='Viridis',
zmin=0,
zmax=2000,
marker_opacity=0.5,
marker_line_width=0,
hovertext=hovertext))
fig_death.update_layout(mapbox_style="carto-positron",
mapbox_zoom=4,
title='Latest Map and Total Death Count by County',
mapbox_center={
"lat": 40.785794,
"lon": -89.20973
})
return fig_death
def get_Choropleth(df, geo_data, arg, marker_opacity,
marker_line_width, marker_line_color, fig=None):
# get_Choropleth(df, geo_data, arg, marker_opacity = 0.4, marker_line_width = 1, marker_line_color='#6666cc')
if fig is None:
fig = go.Figure()
fig.add_trace(
go.Choroplethmapbox(
geojson=geo_data,
locations=df['LSOA'],
featureidkey="properties.lsoa11cd",
colorscale=arg['colorscale'],
z=arg['z_vec'],
zmin=arg['min_value'],
zmax=arg['max_value'],
text=arg['text_vec'],
hoverinfo="text",
marker_opacity=marker_opacity,
marker_line_width=marker_line_width,
marker_line_color=marker_line_color,
colorbar_title=arg['title'],
colorbar_title_side='right'
)
)
return fig
def choropleth(geojson, location_id, variable, label):
data = go.Choroplethmapbox(
geojson = geojson,
locations = location_id,
z = variable,
text = label,
colorscale = 'reds',
#colorbar = {'thickness':20, 'ticklen':3},
showscale = False,
marker_line_width=0.5,
marker_opacity=0.5
)
layout = go.Layout(
title_text = '',
autosize = True,
height = 250,
width = 350,
margin = {'l': 10, 'r': 0, 'b': 0, 't': 0},
mapbox = {'center': {'lat':33.677, 'lon':65.21600},
'accesstoken': mapbox_token,
'zoom':4,
'style': 'light'}
)
fig = go.Figure(data=data, layout=layout)
return fig
def boundary_update(boundary, measure, diag, start, end):
# janky method for fixing dates
start = datetime.datetime.strptime(start, '%Y-%m-%d')
start = datetime.datetime(start.year, start.month, start.day)
end = datetime.datetime.strptime(end, '%Y-%m-%d')
end = datetime.datetime(end.year, end.month, end.day)
if measure in cancer_measures:
# TODO add a year filter, currently using all years for measure
df = df_cancer[df_cancer['Measure'] == measure]
df = df[(df['Period'] >= start) & (df['Period'] <= end)]
df = df.groupby([
boundary_codes[boundary],
boundary_names[boundary],
'Measure'
]).agg({'Value': 'sum'}).reset_index()
df.columns = ['Code', 'Name', 'Measure', 'Value']
elif measure in waiting_measures:
df = waiting_times_map(measure, boundary, test=diag, start=start, end=end)
df = df.groupby('Code').sum().reset_index()
map_json = views[boundary]
fig = go.Figure(
go.Choroplethmapbox(
geojson=map_json,
locations=df.Code,
z=df.Value,
colorscale='Viridis',
marker_line_width=0,
marker_opacity=0.5,
)
)
# figure = px.choropleth_mapbox(
# df,
# geojson=map_json,
# locations='Code',
# color='area',
# mapbox_style='light',
# center={'lat': 53, 'lon': -1.5},
# zoom=5,
# hover_name='Name',
# hover_data=['area'],
# )
#
# figure.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.update_layout(
mapbox_style='light',
mapbox_accesstoken=MAPBOX_TOKEN,
mapbox_zoom=5,
mapbox_center={'lat': 53, 'lon': -1.5},
margin={"r": 0, "t": 0, "l": 0, "b": 0},
clickmode='event+select'
)
return fig
def update_voronoi(datePicked, selectedLocation, chosen_tech, chosen_plan):
# Voronoi
with open("tower_vor.json") as jsonfile:
geojson = json.load(jsonfile)
center = {"lat": 25.6823, "lon": -100.3030}
voronoi_fig = go.Figure(
go.Choroplethmapbox(
name="",
geojson=geojson,
locations=voronoi_df.sitio,
z=voronoi_df.sum_bytes,
featureidkey="properties.tower",
colorscale="Viridis",
hoverinfo="location+z",
marker_opacity=0.3,
marker_line_color="#d8d8d8",
marker_line_width=2,
selected_marker_opacity=0.8,
unselected_marker_opacity=0.3,
colorbar=dict(
title="Consumo<br>de Datos",
nticks=24,
tickfont=dict(color="#d8d8d8"),
titlefont=dict(color="#d8d8d8"),
thicknessmode="pixels",
),
))
voronoi_fig.add_trace(
go.Scattermapbox(
lon=voronoi_df.longitud,
lat=voronoi_df.latitud,
mode="markers",
marker=dict(opacity=0.5, size=10),
))
voronoi_fig.update_layout(
title_text="Mapa de radiobases por regiones",
mapbox_style="carto-darkmatter",
mapbox_zoom=12,
mapbox_center=center,
autosize=True,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
uirevision="foo",
clickmode="event+select",
hovermode="closest",
# colorbar={"tickfont": {"color": "white"}},
hoverdistance=2,
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
pitch=40,
),
)
return voronoi_fig
def info_por_municipio(municipios):
data = []
df = get_filtered_rows(municipios)
data.append(
go.Choroplethmapbox(geojson=geojson,
locations=df['MPIOS'],
z=df['DESEMPLEO'],
colorscale='earth',
text=df['NOMBRE_MPI'],
colorbar_title="DESEMPLEO"))
return data
def create_access_map(poi_type, metric, stratum, colorscale, figure):
"""
TODO : write docstring
"""
if "layout" in figure:
lat = figure["layout"]["mapbox"]["center"][
"lat"] # TODO: This can probably be removed now
lon = figure["layout"]["mapbox"]["center"]["lon"]
zoom = figure["layout"]["mapbox"]["zoom"]
else:
lat = float(52.48)
lon = float(-1.89)
zoom = float(8.5)
selected_data = access_map_attr.loc[
(access_map_attr['poi_type'] == poi_type)
& (access_map_attr['metric'] == metric)
& (access_map_attr['stratum'] == stratum)]
data = [
go.Choroplethmapbox(
geojson=oa_boundaries,
locations=selected_data['oa_id'],
z=selected_data['value'],
colorscale=colorscale,
reversescale=True,
zmin=get_scale_extremes(selected_data['value'], metric)[0],
zmax=get_scale_extremes(selected_data['value'], metric)[1],
marker_opacity=0.85,
marker_line_width=0,
colorbar=dict(
title=dict(text=set_metric(metric), side='right'),
titlefont=dict(size=14),
),
),
]
layout = dict(
mapbox=dict(
layers=[],
accesstoken=mapbox_access_token,
center=dict(lat=lat, lon=lon),
zoom=zoom,
),
hovermode="closest",
margin=dict(r=0, l=0, t=0, b=0),
dragmode="lasso",
uirevision="The User is always right",
plot_bgcolor="#F9F9F9",
paper_bgcolor="#F9F9F9",
)
return go.Figure(data=data, layout=layout)
def get_custom_map_chart(geojson, locations, z, text=None):
figure = go.Choroplethmapbox(
geojson=geojson,
locations=locations,
z=z,
colorscale='Blues',
text=text,
hoverinfo="text + z",
)
return figure
def create_population_map(pop_type, colorscale, figure):
"""
TODO : write docstring
"""
if "layout" in figure:
lat = figure["layout"]["mapbox"]["center"]["lat"]
lon = figure["layout"]["mapbox"]["center"]["lon"]
zoom = figure["layout"]["mapbox"]["zoom"]
else:
lat = float(52.48)
lon = float(-1.89)
zoom = float(8.5)
selected_data = population_map_attr.loc[(
population_map_attr['population'] == pop_type)]
zmax = float(selected_data['count'].mean() +
(map_outlier_bound * selected_data['count'].std()))
data = [
go.Choroplethmapbox(
geojson=oa_boundaries,
locations=selected_data['oa_id'],
z=selected_data['count'],
colorscale=colorscale,
reversescale=False,
zmin=0,
zmax=zmax,
marker_opacity=0.85,
marker_line_width=0,
colorbar=dict(
title=dict(text='Population', side='right'),
titlefont=dict(size=14),
),
),
]
layout = dict(
mapbox=dict(
layers=[],
accesstoken=mapbox_access_token,
center=dict(lat=lat, lon=lon),
zoom=zoom,
),
hovermode="closest",
margin=dict(r=0, l=0, t=0, b=0),
dragmode="lasso",
uirevision="The User is always right",
plot_bgcolor="#F9F9F9",
paper_bgcolor="#F9F9F9",
)
return go.Figure(data=data, layout=layout)
def create_at_risk_map(poi_type, metric, stratum, pop_type, click,
access_slider, population_slider):
"""
TODO : write docstring
"""
selected_access_data = access_map_attr.loc[
(access_map_attr['poi_type'] == poi_type)
& (access_map_attr['metric'] == metric)
& (access_map_attr['stratum'] == stratum)]
selected_pop_data = population_map_attr.loc[(
population_map_attr['population'] == pop_type)]
lat = float(52.48)
lon = float(-1.89)
zoom = float(8.5)
layout = dict(
mapbox=dict(
layers=[],
accesstoken=mapbox_access_token,
center=dict(lat=lat, lon=lon),
zoom=zoom,
),
hovermode="closest",
margin=dict(r=0, l=0, t=0, b=0),
dragmode="lasso",
uirevision="The User is always right",
)
at_risk_map_attr = access_map_attr.copy(deep=True)
at_risk_oa_list = list(
filter_extreme_oa(selected_access_data, selected_pop_data,
access_slider, population_slider))
at_risk_map_attr['value'] = at_risk_map_attr['oa_id'].isin(
at_risk_oa_list).astype(int)
data = [
go.Choroplethmapbox(geojson=oa_boundaries,
locations=at_risk_map_attr['oa_id'],
z=at_risk_map_attr['value'],
colorscale='oranges',
reversescale=False,
zmin=False,
zmax=True,
marker_opacity=at_risk_map_attr['value'],
marker_line_width=0,
showscale=False)
]
return go.Figure(data=data, layout=layout)
def choropleth_coronavirus(csv_file):
europe_url = 'https://raw.githubusercontent.com/leakyMirror/'+\
'map-of-europe/master/GeoJSON/europe.geojson'
jdata = read_geojson(europe_url)
jdata['features'][26]['properties']['NAME'] = 'Macedonia'
ids = [feat['id'] for feat in jdata['features']]
names = [feat['properties']['NAME'] for feat in jdata['features']]
d = dict(zip(names, ids))
df = pd.read_csv(csv_file)
countries = list(df['European Region'])
cids = [d[country] for country in countries]
df['cids'] = cids
dfs = df.sort_values(by=['cids'])
jdata['features'][26]['properties']['NAME'] = 'Macedonia'
#my mapbox_access_token
mapboxt = open(".mapbox_token").read().rstrip()
locations = dfs['cids'].tolist()
z = [np.log (cf+0.001) for cf in dfs['total confirmed']]
fig = go.Figure(go.Choroplethmapbox(
z=z,
locations=locations,
coloraxis='coloraxis',
geojson=jdata,
marker_line_width=0.75,
marker_line_color='#eeeeee',
marker_opacity=0.85))
fig.update_layout(width=1000, height=800,
font_family='Balto',
coloraxis=dict(colorscale='Reds',
showscale=False),
mapbox = dict(center= dict(lat= 55.499998,
lon=17.3833318),
accesstoken= mapboxt,
zoom=2.55,
style='basic'
))
fig.data[0].customdata = np.stack((dfs['European Region'],
dfs['total confirmed'],
dfs['total deaths'],
dfs['transmission']), axis=-1)
fig.data[0].hovertemplate = \
"<b>%{customdata[0]}</b>"+\
"<br><b>Total confirmed cases</b>: %{customdata[1]}"+\
"<br><b>Total deaths </b>: %{customdata[2]}"+\
"<br><b>Transmission </b>: %{customdata[3]}<extra></extra>"
return fig
def display_map(hg, sw_data_json, gw_data_json, s_info_json):
s_info = pd.read_json(s_info_json, orient='split')
if hg == 'Groundwater':
s_data = ast.literal_eval(gw_data_json)
else:
s_data = ast.literal_eval(sw_data_json)
sp_names = [n['id'] for n in s_data['features']]
t_data = s_info[s_info.SpatialUnitName.isin(sp_names)]
fig = go.Figure(go.Choroplethmapbox(geojson=s_data, locations=t_data['SpatialUnitName'], z=t_data.index, colorscale=t_data['color'].tolist(), zmin=t_data.index.min(), zmax=t_data.index.max(), marker_opacity=0.7, marker_line_width=0, showscale=False), layout=map_layout)
return fig
def generate_choropleth(time_values, selector, segment, kategori):
if time_values is not None:
min_date, max_date = time_slider_to_date(time_values)
filtered_df = retailer_df.sort_values("Siparis_Tarihi").set_index(
"Siparis_Tarihi")[min_date:max_date]
filtered_df = filtered_df[filtered_df["Segment"].isin(segment)
& filtered_df["Kategori"].isin(kategori)]
df = pd.DataFrame(
filtered_df.groupby(["Plaka_kodu"]).sum().reset_index())
# print(df.head(10))
# hovertemplate = "<br> %{z} TL"
if selector == "siparis":
z_val = "Siparis_Miktari"
elif selector == "satis":
z_val = "Satis"
else:
z_val = "Kar"
fig = go.Figure(
go.Choroplethmapbox(
geojson=tr_sehirler,
locations=df.Plaka_kodu,
z=df[z_val],
colorscale=[[0, "#65aaf7"], [1, "#012d6b"]],
colorbar=dict(thickness=20, ticklen=3),
#hovertemplate = hovertemplate,
#hoverinfo=z,
zmin=df[z_val].min(),
zmax=df[z_val].max(),
below=True,
marker_opacity=0.6,
marker_line_width=0.2))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=4.5,
mapbox_center={
"lat": 39.822060,
"lon": 34.808132
})
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
return {"data": []}
def update_graph(metric):
if metric == 'Confirmed':
#cs = "reds"
cs = "inferno"
rs = True
elif metric == 'Active':
#cs = "blues"
cs = "plotly3"
rs = False
elif metric == 'Recovered':
#cs = "greens"
cs = "viridis"
rs = False
else:
#cs = "oranges"
cs = "RdYlGn"
rs = False
fig = go.Figure(
go.Choroplethmapbox(geojson=indian_states,
locations=indian_map_data.fid.tolist(),
z=indian_map_data[metric].tolist(),
featureidkey="id",
hovertext=indian_map_data['hovertext'],
colorscale=cs,
reversescale=rs,
zmin=indian_map_data[metric].min(),
zmax=indian_map_data[metric].max(),
marker_opacity=0.8,
marker_line_width=1))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=4.22,
mapbox_center={
"lat": 22.5458,
"lon": 82.0882
},
xaxis=dict(fixedrange=True),
yaxis=dict(fixedrange=True),
dragmode=False)
fig.update_layout(height=800,
width=800,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
})
return fig
def mapa_comunas(request):
fecha_ult_act_chile = data_crec_por_dia.columns[-1]
fig = go.Figure(
go.Choroplethmapbox(
geojson=geo_comunas,
locations=data_comunas.Comuna,
z=data_comunas.Casos,
colorscale="Viridis",
zmin=0,
zmax=1000,
featureidkey="properties.NOM_COM",
colorbar=dict(thickness=20, ticklen=3),
marker_opacity=0.2,
marker_line_width=0,
text=data_comunas['Poblacion'],
hovertemplate='<b>Región</b>: <b>' + data_comunas['Region'] +
'</b>' + '<br><b>Comuna </b>: %{properties.NOM_COM}<br>' +
'<b>Población: </b>:%{text}<br>' + '<b>Casos </b>: %{z}<br>'))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=3,
height=600,
mapbox_center={
"lat": -30.0000000,
"lon": -71.0000000
})
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig2 = casos_regiones_ac_act()
graph1 = fig.to_html(full_html=False)
graph2 = fig2.to_html(full_html=False)
fig3 = grafico_Update_Dropdown_chile()
graph3 = fig3.to_html(full_html=False)
return render(
request, "mapa_casos_comunas.html", {
"fecha_Act_chile": fecha_ult_act_chile,
"fecha_comuna": fecha,
"num_recuFIS": num_recuFIS,
"grafico1": graph1,
"grafico2": graph2,
"grafico3": graph3,
"fecha_casos": fecha_casos,
"n_casos": num_cases_cl,
"num_rec": casos_act,
"num_death": num_death
})
def dibujar_mapa(primer_ageb):
primer_ageb_str = str(primer_ageb)
geodf_jdata = obtenergeo_json_df(dataframe, geodataframe, 'CVE_AGEB',
primer_ageb)
geodf = geodf_jdata[0]
jdata = geodf_jdata[1]
z = geodf[primer_ageb_str].tolist()
locations = geodf['CVE_AGEB'].tolist()
trace_mapa = go.Choroplethmapbox(z=z,
locations=locations,
colorscale='magma',
colorbar={
'thicknessmode': 'pixels',
'thickness': 7,
'outlinecolor': 'white',
'title': {
'text': 'km',
'side': 'bottom'
}
},
reversescale=True,
geojson=jdata,
hoverinfo='location',
marker_line_width=0.1,
marker_opacity=0.7)
layout_mapa = go.Layout(mapbox={
'center': {
'lat': 19.404730,
'lon': -99.172845
},
'style': 'carto-positron',
'zoom': 12
},
margin={
'l': 0,
'r': 0,
't': 5,
'b': 15
},
clickmode='event+select')
figura_mapa = go.Figure(data=[trace_mapa], layout=layout_mapa)
return figura_mapa
def info_por_municipio(municipios):
data =[]
# print(municipios)
df = get_filtered_rows(municipios)
# print(df)
data.append(go.Choroplethmapbox(
geojson=geojson,
locations=df['MPIOS'],
z=df['HECTARES'],
colorscale='earth',
text=df['NOMBRE_MPI'],
colorbar_title="HECTAREAS"
))
# print(data)
return data
def juris_highlighter(juris_name):
df['selected']=0
df.loc[df['jurisdiction']==juris_name, 'selected']=1
fig = go.Figure(go.Choroplethmapbox(geojson=counties,
locations=df['FIPS'],
z=df['selected'],
colorscale=['Gray', 'Yellow'],
# text=df['county_name'],
zmin=0,
zmax=1,
marker_line_width=.5))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=5.8,
mapbox_center = {"lat": 38.0293, "lon": -79.4428})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
def display_predictions():
mapbox_key = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNqdnBvNDMyaTAxYzkzeW5ubWdpZ2VjbmMifQ.TXcBE-xg9BFdV2ocecc_7g"
database = Connection()
predicted_merged_2 = prediction_pull(database)
colors = ["blue", "purple", "red"]
with urlopen(
'https://raw.githubusercontent.com/mikeypatt/json_file_crime/master/LSOA_2011.json'
) as response:
j_file = json.load(response)
i = 1
for feature in j_file["features"]:
feature['id'] = str(i).zfill(2)
i += 1
print(j_file["features"])
print(predicted_merged_2['id'])
fig = go.Figure(
go.Choroplethmapbox(geojson=j_file,
locations=predicted_merged_2.id,
z=predicted_merged_2.prediction,
showscale=False,
colorscale=colors,
zmin=0,
zmax=20,
ids=predicted_merged_2.LSOA11NM,
text=predicted_merged_2.LSOA11NM,
hoverinfo='z+text'))
fig.update_layout(mapbox_style="dark",
mapbox_accesstoken=mapbox_key,
mapbox_zoom=10,
mapbox_center={
"lat": 51.5074,
"lon": -0.1278
})
fig.update_layout(autosize=True,
height=1300,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
})
return fig
def map_prep(geojson, locations, z, hovertext, center_lon, center_lat, zoom,
title, updatetime):
# prepare token for Mapbox
token = 'pk.eyJ1IjoiY2h1emhpY29uZyIsImEiOiJjazZoMG5tajIwNmh4M21ueGN0eXdtMmx6In0.kQscrYmKzfyLhN-YgENO0Q'
data = go.Choroplethmapbox(
geojson=geojson,
locations=locations,
z=z,
hovertext=hovertext,
hoverinfo='text',
colorscale='YlOrRd',
marker_line_width=0.5,
marker_line_color='rgb(169, 164, 159)',
colorbar=dict(title="确诊病例数",
titleside="top",
tickmode="array",
tickvals=np.arange(0, 5, 1),
ticktext=["1", "10", "100", "1k", '10k', '100k'],
ticks="outside"))
layout = go.Layout(mapbox={
'accesstoken': token,
'center': {
'lon': center_lon,
'lat': center_lat
},
'zoom': zoom
},
margin={
"r": 1,
"t": 45,
"l": 45,
"b": 30
},
title='累计确诊' + title + '分布图',
annotations=[
dict(x=0.55,
y=0.03,
xref='paper',
yref='paper',
text='数据更新于 ' + str(updatetime),
showarrow=False)
],
height=500)
return data, layout
def juris_highlighter(juris_name):
df['selected']=np.where(df['jurisdiction']==juris_name, 1, 0)
fig = go.Figure(go.Choroplethmapbox(geojson=counties,
locations=df['FIPS'],
z=df['selected'],
# colorscale=['blues'],
text=df['county_name'],
hoverinfo='text',
zmin=0,
zmax=1,
marker_line_width=.5
))
fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=5.8,
mapbox_center = {"lat": 38.0293, "lon": -79.4428})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
return fig
def update_map_fig(county, state):
map_fig = go.Figure(
go.Choroplethmapbox(geojson=counties,
locations=df["FIPS"],
z=df["10/2/20"],
colorscale="Viridis",
marker_opacity=0.5,
marker_line_width=0,
hovertext=hovertext))
map_fig.update_layout(mapbox_style="carto-positron",
mapbox_zoom=3,
mapbox_center={
"lat": 37.0902,
"lon": -95.7129
},
margin=dict(l=20, r=20, t=20, b=20))
return map_fig
