def test_simple_step():
step = cm.StepColormap(['green', 'yellow', 'red'],
vmin=3.,
vmax=10.,
index=[3, 4, 8, 10],
caption='step')
step = cm.StepColormap(['r', 'y', 'g', 'c', 'b', 'm'])
step._repr_html_()
def update_homo_area(self):
# On change les donnes de Gab
if self.distance_choice in ["compas","compas_asym"]:
variable = "WME"
else:
variable = "W1"
(code,hexc,_) = geo_gv.get_cmap_info(variable)
cmap = cm.StepColormap(hexc.values,index=np.asarray(code).astype(float),vmin=code[0],vmax=code[-1]+1)
chor_layer2 = ipyl.Choropleth(geo_data=self.region_geo2,
choro_data=self.homo_zone,
name="zonage sur homogeneous temps sensible criterion ("+self.distance_choice+")",
value_min = float(code[0]),
value_max = float(code[-1]+1),
colormap = cmap,
border_color="red",
style={'fillOpacity': 0.8, 'dashArray': '5, 5',"opacity":0.5},
hover_style={"opacity":1,"color":"black","weight":4})
chor_layer2.value_min = float(code[0])
chor_layer2.value_max = code[-1]*1.0
if hasattr(self,"chor_layer2"):
if self.chor_layer2 in self.m.layers:
#print("et par la")
self.m.substitute_layer(self.chor_layer2,chor_layer2)
else:
#print('et ici')
self.m.add_layer(chor_layer2)
else:
#print('et ou la')
self.m.add_layer(chor_layer2)
self.chor_layer2 = chor_layer2
self.chor_layer2.on_hover(self.update_chor_html2)
def test_step_to_linear():
step = cm.StepColormap(['green', 'yellow', 'red'],
vmin=3.,
vmax=10.,
index=[3, 4, 8, 10],
caption='step')
step.to_linear()
def colormap(m, index, caption):
"""
Def a color map, need the map m, an array of colors with a matching index and caption for the name.
"""
CB = cm.StepColormap(colors, vmin=0, vmax=50, index=index, caption=caption)
m.add_child(CB)
def add_colorbar(self):
"""add a colorbar at the top right corner of the map"""
vmin, vmax = self.df["price"].quantile(0.005), self.df["price"].max()
# set vmin to price.min() will screw the colorbar scale
colormap = cm.StepColormap(self.color_codes,
index=self.index,
vmin=vmin,
vmax=vmax)
colormap.caption = "Monthly rent (€)"
self.map.add_child(colormap)
def test_max_labels_step(max_labels, expected):
colorbar = cm.StepColormap(['red', 'blue'] * 5,
vmin=0,
vmax=10,
max_labels=max_labels)
try:
colorbar.render()
except AssertionError: # rendering outside parent Figure raises error
pass
assert colorbar.tick_labels == expected
def colormap(m, index, caption):
"""
Def a color map, need the map m, an array of colors with a matching index and caption for the name.
"""
#CB= cm.StepColormap(colors, vmin=0,vmax=10,index=index, caption=caption )
print("in colormap colorbar_values=", colorbar_values[0],
colorbar_max_value)
print(colorbar_values)
CB = cm.StepColormap(colors,
vmin=colorbar_values[0],
vmax=colorbar_max_value,
index=colorbar_values[:],
caption=caption)
m.add_child(CB)
def plot_isochrone(source, source_coords, stations_data, cutoff, threshold, G):
# Plot the isochrone map
color_bar = cm.StepColormap(['blue', 'red', 'yellow', 'green'],
index=[0, 0.25, 0.50, 0.75, 1])
color_bar.caption = 'Trip Uncertainty'
m = folium.Map(source_coords, zoom_start=13, tiles='Stamen toner')
m.add_child(color_bar)
popup_data = {}
for station_data in stations_data:
station_name = station_data[0]
quality = station_data[2]
arrival_time = station_data[3]
lat, long = G.node[station_name]['lat'], G.node[station_name]['lon']
popup_data['station_name'] = station_name
popup_data['arrival_time'] = arrival_time
add_circle(m, (lat, long), quality, time_diff(cutoff, arrival_time),
popup_data, color_bar)
return m
def FoliumMapAB(mapobj,dat,colorFac='IF_Adj',ColBins=7):
'''
mapobj = folium map object
dat = Geopandas dataframe used for plotting
ColBins = # of color bins needed
'''
#Get dat into GeoPandas DataFrame
dat = gpd.GeoDataFrame(dat,crs={'init': 'epsg:4326'})
datJson = dat.to_json() #
###############################
#datJson has "id" node which is the index for the "dat". BUT it is string so convert your
# index to strings.
#Create a key value pair for color coding lines:
dat_dict = dat.set_index(dat.index.astype('str'))[colorFac]
Min1 =dat[colorFac].min()
Max1 =dat[colorFac].max()
##########################################################
#https://github.com/python-visualization/folium/blob/master/examples/Colormaps.ipynb
#Color Code Lines:
#sns.palplot(sns.color_palette("YlOrRd", ColBins-1))
GrYlRe_Pal = ["green"] +sns.color_palette("YlOrRd", ColBins-1)
l1 = np.linspace(7,Max1,ColBins).astype('int').tolist()
l1 = [Min1]+l1
colormap = cm.StepColormap(
GrYlRe_Pal,
vmin=Min1, vmax=Max1,
index=l1,
caption='Adjusted Incident Factor for I-40 and I-95')
#Add lines on map
folium.GeoJson(
datJson, name= 'Adjusted IF Heatmap',
style_function=lambda feature: {
'fillColor': colormap(dat_dict[feature['id']]),
'color': colormap(dat_dict[feature['id']]),
'weight': 4,
'dashArray': '5, 5'
}).add_to(mapobj)
#Add Legend
colormap.add_to(mapobj)
return mapobj
def plot_avg_price_on_map(df):
df['lat'] = [round(x, 2) for x in df['lat']]
df['long'] = [round(x, 2) for x in df['long']]
df = df.groupby(by=['lat', 'long'])['price'].mean().reset_index()
print(len(df))
map_template = folium.Map(tiles='cartodbpositron',
location=[47, -122],
zoom_start=6)
color = list(Spectral9)
colormap = cm.StepColormap(color,
vmin=df["price"].min(),
vmax=df["price"].max())
diff = df["price"].max() - df["price"].min()
range_diff = int(diff / len(color)) + 1
for row_number, row in df.iterrows():
n_color = int(row['price'] / range_diff)
if n_color > 8:
n_color = 8
n_size = row['price'] / 1000000
if n_size < 2:
n_size = 1
folium.CircleMarker([row['lat'], row['long']],
radius=n_size,
popup="average price: " + str(round(row['price'])),
color=color[n_color],
fill_color=color[n_color]).add_to(map_template)
map_template.add_child(colormap)
f = folium.Figure()
string = "Average home price"
f.html.add_child(
folium.Element("<font face='helvetica'><font size='5'><b><p1>" +
string + "</b></font></p1>"))
f.add_child(map_template)
map_template.save('average_home_price_map.html')
def update_HSS_area(self):
# On change les donnees de Mary
if hasattr(self,"dfres"):
# On choisit (par defaut) le code WME a afficher pour Mary
if self.distance_choice in ["compas","compas_asym"]:
variable = "WME"
else:
variable = "W1"
(code,hexc,_) = geo_gv.get_cmap_info(variable)
cmap = cm.StepColormap(hexc.values,index=np.asarray(code).astype(float),vmin=code[0],vmax=code[-1]+1)
chor_layer = ipyl.Choropleth(
geo_data=self.region_geo,
choro_data=self.mary_zone.to_dict(),
name="zonage sur temps sensible (WME)",
value_min = float(code[0]),
value_max = float(code[-1]+1),
colormap = cmap,
border_color='blue',
style={'fillOpacity': 0.8, 'dashArray': '5, 5'},
hover_style={"opacity":1,"color":"black","weight":4})
chor_layer.value_min = float(code[0])
chor_layer.value_max = code[-1]*1.0
if hasattr(self,"chor_layer"):
if self.chor_layer in self.m.layers:
#print("on passe la")
self.m.substitute_layer(self.chor_layer,chor_layer)
else:
#print("ici")
self.m.add_layer(chor_layer)
else:
#print('ou la')
self.m.add_layer(chor_layer)
self.chor_layer = chor_layer
self.chor_layer.on_hover(self.update_chor_html)
else:
if hasattr(self,"chor_layer") and self.chor_layer in self.m.layers:
self.m.remove_layer(self.chor_layer)
del outDataRaster ## delete the data (not the actual geotiff)
gdal.Warp("projected_output.tif","pca_kmeans_output.tif",dstSRS='EPSG:4326')
"""# Visualize classified image on map using Folium"""
with rio.open("projected_output.tif") as src:
dataset = src.read()
meta = src.meta
ymin = src.bounds[1]
xmin = src.bounds[0]
ymax = src.bounds[3]
xmax = src.bounds[2]
dataset = dataset[0,:,]
dataset[np.isnan(dataset)] = 0 ### Handling nan values
dataimage=dataset
step = cm.StepColormap([(255,255,255,1), (56,168,0), (0, 38, 115), (38, 115, 0), (255,211,127), (230, 0, 169),(0, 0, 0)], vmin=0, vmax=6, index=[0,0.1, 1, 2, 3,4,5,6], caption="step")
step.caption = '1-Low Vegetation,2-Waterbodies, 3-Roads, 4-Land surface, 5-Urban,6-Dense Vegetation '
token = "pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw" # your mapbox token
tileurl = 'https://api.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}@2x.png?access_token=' + str(token)
m = folium.Map([(ymax+ymin)/2,(xmax+xmin)/2],
zoom_start=10)
folium.TileLayer(tiles=tileurl,attr='Mapbox',name="Mapbox Satellite Imagery").add_to(m)
folium.raster_layers.ImageOverlay(name="Classified Hyperspectral Image",image=dataimage,
bounds=[[ymin, xmin], [ymax, xmax]],
colormap=lambda x: step.rgba_bytes_tuple(x) ,
opacity = 1,).add_to(m)
m.add_child(step)
folium.LayerControl().add_to(m)
m.save("map.html")
m
"#b15928",
"#FFFFFF",
"#000000",
]
# Trim colour list to number of clusters
if k_opt <= len(color_list):
color_list = color_list[:k_opt]
else:
print(
"Warn: Number of clusters ({}) is larger than colours available ({})".format(
k_opt, len(color_list)
)
) # Warning won't trigger so long kmax <= color_list
color_step = cmp.StepColormap(color_list, vmin=1, vmax=k_opt, caption="Clusters",)
cluster_dict = cluster_geodf.set_index("Subzone")["cluster"]
# %%
# Plot Subzones clusters on Map
m5 = folium.Map(location=[1.3521, 103.8198], tiles="cartodbpositron", zoom_start=11)
# Colouring Subzones based on Clusters
cluster_gj = folium.GeoJson(
cluster_geodf,
style_function=lambda feature: {
"fillColor": color_step(cluster_dict[feature["properties"]["Subzone"]]),
"color": color_step(cluster_dict[feature["properties"]["Subzone"]]),
"fillOpacity": 0.5,
"weight": 1,
# for d in (range(100)): #測試資料筆數 # for m in (range(50)): #找是在哪列 # if lon_min[list1[m]-1]<lon[d] and lon[d]<lon_max[list2[m]-1] and lat_min[list1[m]-1]<lat[d] and lat[d]<lat_max[list2[m]-1]: # for n in range(list1[m],list2[m]+1): #找是在該列的哪個 # if lon_min[n-1]<lon[d] and lon[d]<lon_max[n-1] and lat_min[n-1]<lat[d] and lat[d]<lat_max[n-1]: # ans_Id[d]=Id[n]-1 taichungmap_1x1 = taichungmap_1x1.merge(all_point_data_mix, on='Id') taichungmap_1x1['PM2.5']=taichungmap_1x1['PM2.5'].round() taichung_district = taichung_district.merge(weather, on='區名') #taichung_district #================================================================================================== #folium variable = 'PM2.5' colorList = ['#98fb98','#00ff00','#32cd32','#ffff00','#ffd700','#ffa500','#ff6347','#ff0000','#ba55d3'] map_color = cm.StepColormap(colorList,index=[0,10,20,30,40,50,60,70,80],vmin=0,vmax=100,caption = 'PM2.5') fmap=folium.Map(location=[24.2,120.9], zoom_start=10.5) #fmap.choropleth( # geo_data=taichungmap_1x1, # name='pm2.5', # columns=['Id', 'PM2.5'], # key_on='feature.properties.Id', # data=all_point_data_mix, # #threshold_scale=[], # fill_color='BuGn', # legend_name='pm2.5', # line_opacity=0.5, # fill_opacity=0.8 # ) folium.GeoJson(
color='green' if row.thematic_area
== 'Inspiring Civic Engagement & Service' else 'red'
if row.thematic_area == "Improving Public Health" else 'blue',
prefix='fa',
icon='circle')).add_to(m)
with open('countries.json') as geo_json_data:
geo_json_data = json.load(geo_json_data)
country_dict = df.set_index('state')['number']
country_dict = country_dict[~country_dict.index.duplicated(keep="first")]
import branca.colormap as cm
step = cm.StepColormap(['blue', 'red', 'green', 'purple', 'white'],
vmin=1,
vmax=6,
index=[1, 1.9, 2.9, 3.9, 4.9, 5.9],
caption='step')
colormap = cm.StepColormap(['blue', 'red', 'green', 'purple', 'white'],
vmin=1,
vmax=6,
index=[1, 1.9, 2.9, 3.9, 4.9, 5.9],
caption='step')
def style_function(feature):
countried = country_dict.get(feature['id'], None)
return {
'fillOpacity': 0.5,
'weight': 0.5,
def color_series(value_series,
colors=[rainbow_shades[3], rainbow_shades[0]],
index=None,
max_value=None,
min_value=None,
method='linear',
reversed_colors=False):
"""
:param value_series: the pd.Series that contain the values
:param colors: list containing the colors used to build the color scale ['red', 'blue']
:param index: list containing the value corresponding to each color
:param min_value: the values below min_values are given the same color : colors[0]
:param max_value: value to use as the maximum when normalizing the series (to focus on low values).
:param method: method used (linear or step)
:return: color_series: pd.Series that contains the colors corresponding to the values
:rtype: pd.Series
example:
::
import pandas as pd
# common visualization library
import matplotlib.pyplot as plt
% matplotlib inline
plt.rcParams['figure.figsize'] = [17,7]
plt.rcParams['font.size'] = 20
# custom visualization library
from syspy_utils import data_visualization, syscolors
data = {'a': 1, 'b': 2, 'c': 3, 'd': 5, 'e': 7, 'f': 10}
series = pd.Series(data)
color_scale = ('#559BB4', '#D22328')
color_series = data_visualization.color_series(series, colors=color_scale, min_value=2, max_value=6)
dataframe = pd.DataFrame({'value': series, 'color': color_series, 'width': width_series})
series.plot(kind='bar', color=color_series)
.. figure:: ./pictures/data_visualization_color_series.png
:width: 25cm
:align: center
:alt: bar plot with color series
:figclass: align-center
bar plot with color series
"""
colors = list(reversed(colors)) if reversed_colors else colors
max_value = max_value if max_value else np.max(list(value_series.values))
min_value = min_value if min_value else np.min(list(value_series.values))
if method == 'linear':
if index == None:
index = np.linspace(min_value, max_value, len(colors))
colormap = cm.LinearColormap(colors, index=index)
else:
if index == None:
index = value_series.quantile(np.linspace(0, 1,
len(colors))).values
colormap = cm.StepColormap(colors, index=index)
return value_series.apply(
lambda x: colormap(max(min(x, max_value), min_value)))
df_loc = pd.DataFrame(addr_loc, columns=['latitude', 'longitude'])
df = pd.concat([df, df_loc], axis=1)
df_col = pd.DataFrame({
'Status': [
"Bardzo dobry", "Dobry", "Umiarkowany", "Dostateczny", "Zły",
"Bardzo zły"
],
'Color': ['blue', 'green', 'yellow', 'orange', 'red', 'black']
})
m = folium.Map(addr_loc[0], zoom_start=13, control_scale=True)
legend = cm.StepColormap(df_col['Color'].values.tolist(), vmin=0, vmax=12)
now = datetime.datetime.now()
last_update = 'Ostatnia aktualizacja: {}'.format(
now.strftime('%Y-%m-%d %H:%M:%S'))
legend_html = '''
<div style="position: fixed;
top: 50px; right: 150px; width: 300px; height: 90px;
border:3px dark grey; z-index:9999; font-size:14px;
"> {} <br>
</div>
'''.format(last_update)
m.get_root().html.add_child(folium.Element(legend_html))
choro_map_data_covidxdep = dict(
zip(xdepartamentos['unique_values'].tolist(),
xdepartamentos['counts'].tolist()))
# In[16]:
colormap = cm.StepColormap(
colors=[
'#FFA07A', '#F08080', '#CD5C5C', '#FF7F50', '#FF6347', '#DC143C',
'#B22222', '#A52A2A'
],
index=[
min(choro_map_data_covidxdep.values()),
np.percentile(list(choro_map_data_covidxdep.values()), 12.5),
np.percentile(list(choro_map_data_covidxdep.values()), 25),
np.percentile(list(choro_map_data_covidxdep.values()), 37.5),
np.median(list(choro_map_data_covidxdep.values())),
np.percentile(list(choro_map_data_covidxdep.values()), 62.5),
np.percentile(list(choro_map_data_covidxdep.values()), 75),
np.percentile(list(choro_map_data_covidxdep.values()), 87.5),
max(choro_map_data_covidxdep.values())
],
vmin=250,
vmax=66000)
# In[17]:
layercoviddepa = ipyleaflet.Choropleth(geo_data=geo_json_data,
choro_data=choro_map_data_covidxdep,
colormap=colormap,
border_color='black',
def create_covid_viz():
'''
Load and pre-process the geojson file
'''
world_geojson = gpd.read_file(geojson_path)
world_geojson.drop(columns=['ISO_A2'], inplace=True)
'''
Load and pre-process the COVID-19 data
'''
# Load the COVID-19 data
df_covid = pd.read_csv(
os.path.join(data_dir_path, 'covid_' + newest_dataset))
timestamp = df_covid['Last_Update'][0]
# Replace some country names
df_covid.replace(to_replace={'Country_Region': 'US'},
value='United States of America',
inplace=True)
df_covid.replace(to_replace={'Country_Region': 'Bahamas'},
value='The Bahamas',
inplace=True)
df_covid.replace(to_replace={'Country_Region': 'Congo (Brazzaville)'},
value='Republic of Congo',
inplace=True)
df_covid.replace(to_replace={'Country_Region': 'Congo (Kinshasa)'},
value='Democratic Republic of the Congo',
inplace=True)
df_covid.replace(to_replace={'Country_Region': 'Taiwan*'},
value='Taiwan',
inplace=True)
df_covid.replace(to_replace={'Country_Region': "Cote d'Ivoire"},
value='Ivory Coast',
inplace=True)
df_covid.replace(to_replace={'Country_Region': "Czechia"},
value='Czech Republic',
inplace=True)
world_geojson.replace(to_replace={'ADMIN': 'Macedonia'},
value='North Macedonia',
inplace=True)
# Change the name of 'ADMIN' column in the geojson DF to match the one in COVID DF
world_geojson.rename(columns={'ADMIN': 'Country_Region'}, inplace=True)
# Aggregate the data for countries that have regional information
df_covid_agg = df_covid.groupby('Country_Region').agg({
'Confirmed':
'sum',
'Deaths':
'sum',
'Recovered':
'sum',
'Active':
'sum',
'Incident_Rate':
'mean',
'Case_Fatality_Ratio':
'mean'
})
world_geojson = world_geojson.sort_values('Country_Region').reset_index(
drop=True)
# Join the geojson with the DataFrame
df_covid_joined = df_covid_agg.merge(world_geojson,
how='right',
on='Country_Region')
# Count min and max values for specific columns
min_dict, max_dict = {}, {}
column_names = [
'Confirmed', 'Deaths', 'Active', 'Incident_Rate', 'Case_Fatality_Ratio'
]
for name in column_names:
min_dict[name] = min(df_covid_joined[name])
max_dict[name] = max(df_covid_joined[name])
# Replace NaNs in the DataFrame with '-1'
df_covid_joined.fillna(-1, inplace=True)
# Add the data columns to geo json for future popup displaying
world_geojson = world_geojson.assign(
Confirmed=df_covid_joined['Confirmed'],
Deaths=df_covid_joined['Deaths'],
Active=df_covid_joined['Active'],
Incident_Rate=df_covid_joined['Incident_Rate'],
Case_Fatality_Ratio=df_covid_joined['Case_Fatality_Ratio'])
print(world_geojson)
# Set the correct index columns
df_covid_joined.set_index('Country_Region', inplace=True)
# Create a lists of evenly spaced attribute values over computed min-max intervals and assign corresponding colors to the DataFrame
colormap_dict = {}
bins = []
for name in column_names:
# Work-around for geometric space not accepting zeros in the sequence
tmp_min = min_dict[name]
if min_dict[name] < 1:
min_dict[name] = 1
inner_bins = np.geomspace(start=min_dict[name],
stop=max_dict[name],
num=10)
min_dict[name] = tmp_min
inner_bins = np.delete(inner_bins, 0)
inner_bins = np.insert(inner_bins, 0, min_dict[name])
inner_bins = np.insert(inner_bins, 0, -1.)
inner_bins = inner_bins.tolist()
# Round the inner_bins values before appending to the bins list
if name in ['Confirmed', 'Deaths', 'Active']:
inner_bins = [int(round(bin, 0)) for bin in inner_bins]
else:
inner_bins = [round(bin, 2) for bin in inner_bins]
bins.append(inner_bins)
colormap_dict[name] = cm.StepColormap(colors=color_dict[name],
index=inner_bins,
vmin=min_dict[name],
vmax=max_dict[name])
df_covid_joined[name + '_color'] = df_covid_joined[name].map(
lambda x: colormap_dict[name].rgb_hex_str(x))
'''
Initialize the map
'''
map_covid = folium.Map(location=[0, 0],
zoom_start=4,
max_bounds=True,
tiles=None)
base_map = folium.FeatureGroup(name='Basemap', overlay=True, control=False)
folium.TileLayer(min_zoom=3, tiles='OpenStreetMap').add_to(base_map)
base_map.add_to(map_covid)
'''
Create the content of the map
'''
# Create FeatureGroups to group the data
feature_groups = []
for category, _ in color_dict.items():
group = folium.FeatureGroup(category, overlay=False)
feature_groups.append(group)
# Create the choropleths
choropleth_confirmed = folium.GeoJson(
data=world_geojson,
zoom_on_click=False,
name='Confirmed Cases',
style_function=lambda x: {
'fillColor':
df_covid_joined['Confirmed_color'][x['properties']['Country_Region'
]],
'fillOpacity':
0.7,
'color':
'black',
'weight':
0.5
}).add_to(feature_groups[0])
popup_confirmed = folium.GeoJsonPopup(
fields=['Country_Region', 'Confirmed'], labels=False)
popup_confirmed.add_to(choropleth_confirmed)
choropleth_deaths = folium.GeoJson(
data=world_geojson,
name='Deaths',
style_function=lambda x: {
'fillColor':
df_covid_joined['Deaths_color'][x['properties']['Country_Region']],
'fillOpacity':
0.7,
'color':
'black',
'weight':
1
}).add_to(feature_groups[1])
popup_deaths = folium.GeoJsonPopup(fields=['Country_Region', 'Deaths'],
labels=False)
popup_deaths.add_to(choropleth_deaths)
choropleth_active = folium.GeoJson(
data=world_geojson,
name='Active Cases',
style_function=lambda x: {
'fillColor':
df_covid_joined['Active_color'][x['properties']['Country_Region']],
'fillOpacity':
0.7,
'color':
'black',
'weight':
1
}).add_to(feature_groups[2])
popup_active = folium.GeoJsonPopup(fields=['Country_Region', 'Active'],
labels=False)
popup_active.add_to(choropleth_active)
choropleth_incident_rate = folium.GeoJson(
data=world_geojson,
name='Incident Rate',
style_function=lambda x: {
'fillColor':
df_covid_joined['Incident_Rate_color'][x['properties'][
'Country_Region']],
'fillOpacity':
0.7,
'color':
'black',
'weight':
1
}).add_to(feature_groups[3])
popup_incident_rate = folium.GeoJsonPopup(
fields=['Country_Region', 'Incident_Rate'], labels=False)
popup_incident_rate.add_to(choropleth_incident_rate)
choropleth_case_fatality_ratio = folium.GeoJson(
data=world_geojson,
name='Case Fatality Ratio',
style_function=lambda x: {
'fillColor':
df_covid_joined['Case_Fatality_Ratio_color'][x['properties'][
'Country_Region']],
'fillOpacity':
0.7,
'color':
'black',
'weight':
1
}).add_to(feature_groups[4])
popup_case_fatality_ratio = folium.GeoJsonPopup(
fields=['Country_Region', 'Case_Fatality_Ratio'], labels=False)
popup_case_fatality_ratio.add_to(choropleth_case_fatality_ratio)
# Create the map legends templates
legend_str_dict = {}
for i, (k, v) in enumerate(color_dict.items()):
legend_labels_dict = {}
j = 0
for color in v:
if j == 0:
legend_labels_dict[color] = 'No data'
elif j == len(v) - 1:
legend_labels_dict[color] = '> ' + str(bins[i][j])
break
else:
legend_labels_dict[color] = str(bins[i][j]) + ' - ' + str(
bins[i][j + 1])
j += 1
legend_str_dict[k] = legend_labels_dict
template = utils.create_legend(caption='COVID-19 status as of: ' +
str(timestamp) + ' UTC',
legend_labels=legend_str_dict)
macro = MacroElement()
macro._template = Template(template)
map_covid.get_root().add_child(macro)
for feature_group in feature_groups:
feature_group.add_to(map_covid)
# Activate Layer Control
folium.LayerControl(collapsed=True).add_to(map_covid)
'''
Save completed map viz to an appropriate folder
'''
map_covid.save(
os.path.join(script_dir_path, '..', 'webapp', 'templates',
'COVID-19_viz.html'))
print('Successfully created the COVID-19 viz!')
