def render(self):
#print("Avant de descendre dans le decorateur etc")
geo_file,legend_file = self.get_step(variable= self.variable)
#print("On en revient ici")
geojson_layer = ipyl.GeoJSON(data=geo_file,hover_style={"opacity":1},name="AROME")
if hasattr(self,"geojson_layer"):
if self.geojson_layer in self.m.layers:
self.m.substitute_layer(self.geojson_layer,geojson_layer)
else:
#print("par ici?")
self.m.add_layer(geojson_layer)
else:
self.m.add_layer(geojson_layer)
self.geojson_layer = geojson_layer
self.geojson_layer.on_hover(self.update_html)
legend_file.seek(0)
self.legend.value =legend_file.read()
chor_layer = ipyl.Choropleth(geo_data=self.region_geo,
choro_data=self.da_aggregated.isel(step=self.step).to_pandas().to_dict(),
name="zonage sur temps sensible (WME)",
value_min = self.vmin,
value_max = self.vmax,
colormap=linear.RdBu_03)
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)
chor_layer2 = ipyl.Choropleth(geo_data=self.region_geo2,
choro_data=self.toto,
name="zonage sur homogeneous temps sensible criterion ("+self.distance_choice+")",
value_min = self.vmin,
value_max = self.vmax,
colormap=linear.RdBu_07)
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 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 create_choropleth_layer(self, key):
"""add choropleth layer to map.
Args:
key (str): A selected value from tablemap's layer selection drop down menu.
"""
# vmax_val = max(self.bldg_data_df[key])
vmax_val = 1
temp_id = list(range(len(self.inventory_df['guid'])))
temp_id = [str(i) for i in temp_id]
choro_data = dict(zip(temp_id, self.inventory_df[key]))
try:
self.map.remove_layer(self.layer)
print("removed previous layer")
except Exception:
print("there is no existing layer")
pass
self.layer = ipylft.Choropleth(geo_data=self.inventory_json,
choro_data=choro_data,
colormap=linear.YlOrRd_04,
value_min=0,
value_max=vmax_val,
border_color='black',
style={'fillOpacity': 0.8},
name='dataset map')
self.map.add_layer(self.layer)
print('Done loading layer.')
def build_us_cntymap(dataframe, colname):
global geojson_cnty, this_cnty_colname, cnty_overlay, county_data_dict
global cnty_layer, cnty_legend, cnty_control
global MapsVDict, geojson_cnty, mapcenter, mapzoom, loc_dict
# This function builds a US Choropleth Map (but doesn't display it) for the county-level
# data provided.
# Load data needed to build either kind of map
build_us_genericmap()
# Build location dictionary if doesn't yet exist
try:
loc_dict
except NameError:
loc_dict = BuildLocationDict(dataframe)
# ipyleaflet requires a dictionary for the choro_data field/the variable to be visualized,
# so convert the Pandas data series into the appropriate dictionary setting keys to postal
# codes used in geojson_states
county_data_dict = get_cnty_dict(dataframe, colname)
# Determine range of values for colormap, then define colormap (need to also pass
# max/min values to Choropleth builder or they are ignored). Set up legend dictionary
# to show this range.
(minval, maxval) = set_cm_limits(county_data_dict)
cmap = BuildColormap(minval, maxval)
legendDict = BuildLegendDict(minval, maxval, cmap)
# Creating the map
cnty_map = lf.Map(center=mapcenter, zoom=mapzoom)
# Draw a functional counties layer
cnty_layer = lf.Choropleth(
geo_data=geojson_cnty,
choro_data=scrub(county_data_dict),
key_on='id',
# Below here is some formatting/coloring from the documentation
colormap=cmap,
value_min=minval,
value_max=maxval,
border_color=map_border_color,
hover_style=map_hover_style,
style=map_style)
cnty_map.add_layer(cnty_layer)
# Display a legend
cnty_legend = lf.LegendControl(legendDict,
name="Legend",
position="bottomleft")
cnty_map.add_control(cnty_legend)
# Display data in overlay
this_cnty_colname = colname
cnty_overlay = widgets.HTML("Hover over Location for Details")
cnty_control = lf.WidgetControl(widget=cnty_overlay, position='topright')
cnty_map.add_control(cnty_control)
cnty_layer.on_hover(update_cnty_overlay)
return (cnty_map, cnty_legend, cnty_overlay)
def create_choropleth_layer(self, key):
# vmax_val = max(self.bldg_data_df[key])
vmax_val = 1
temp_id = list(range(len(self.bldg_data_df['guid'])))
temp_id = [str(i) for i in temp_id]
choro_data = dict(zip(temp_id, self.bldg_data_df[key]))
layer = ipylft.Choropleth(geo_data=self.bldg_data_json,
choro_data=choro_data,
colormap=linear.YlOrRd_04,
value_min=0,
value_max=vmax_val,
border_color='black',
style={'fillOpacity': 0.8},
name='Tax-Lots')
self.m.add_layer(layer)
# self.m
print('done')
def update_us_cntymap(dataframe, colname, thismap, thislegend, thisoverlay):
global geojson_cnty, this_cnty_colname, county_data_dict, cnty_overlay, loc_dict
# This function updates an existing US County-level Choropleth map
# Build location dictionary if doesn't yet exist
try:
loc_dict
except NameError:
loc_dict = BuildLocationDict(dataframe)
# Load the new data and determine the new colormap limits
county_data_dict = get_cnty_dict(dataframe, colname)
(minval, maxval) = set_cm_limits(county_data_dict)
cmap = BuildColormap(minval, maxval)
legendDict = BuildLegendDict(minval, maxval, cmap)
# Assign updated legend dictionary
thislegend.legends = legendDict
# Draw a functional counties layer
cnty_layer_update = lf.Choropleth(
geo_data=geojson_cnty,
choro_data=scrub(county_data_dict),
key_on='id',
# Below here is some formatting/coloring from the documentation
colormap=cmap,
value_min=minval,
value_max=maxval,
border_color=map_border_color,
hover_style=map_hover_style,
style=map_style)
# Replace existing Choropleth layer (which is always the second layer with new layer
cnty_layer = thismap.layers[1]
thismap.substitute_layer(cnty_layer, cnty_layer_update)
cnty_layer = cnty_layer_update
# Update column name used by state overlay to look up values
this_cnty_colname = colname
thisoverlay.value = "Hover over Location for Details"
cnty_overlay = thisoverlay
cnty_layer_update.on_hover(update_cnty_overlay)
return
def render(self):
# On ajoute le choropleth
chor_layer = ipyl.Choropleth(geo_data=self.region_geo,
choro_data=self.da_aggregated.isel(step=self.step).to_pandas().to_dict(),
name="regional_mean",
value_min = self.vmin,
value_max = self.vmax,
colormap=linear.RdBu_03)
if hasattr(self,"chor_layer"):
if self.chor_layer in self.m.layers:
self.m.substitute_layer(self.chor_layer,chor_layer)
else:
self.m.add_layer(chor_layer)
else:
self.m.add_layer(chor_layer)
self.chor_layer = chor_layer
self.chor_layer.on_hover(self.update_chor_html)
# On ajoute l'interactivité sur click
self.chor_layer.on_click(self.hist_html)
def update_us_statesmap(dataframe, colname, thismap, thislegend, thisoverlay):
global geojson_states, this_state_colname, state_data_dict, state_overlay
# This function updates an existing US State-level Choropleth map
# Load the new data and determine the new colormap limits and set up legend dictionary
# to show this range.
state_data_dict = get_state_dict(dataframe, colname)
(minval, maxval) = set_cm_limits(state_data_dict)
cmap = BuildColormap(minval, maxval)
legendDict = BuildLegendDict(minval, maxval, cmap)
# Assign updated legend dictionary
thislegend.legends = legendDict
# Draw a functional states layer
state_layer_update = lf.Choropleth(
geo_data=geojson_states,
choro_data=scrub(state_data_dict),
key_on='id',
# Below here is some formatting/coloring from the documentation
colormap=cmap,
value_min=minval,
value_max=maxval,
border_color=map_border_color,
hover_style=map_hover_style,
style=map_style)
# Replace existing Choropleth layer (which is always the second layer with new layer
state_layer = thismap.layers[1]
thismap.substitute_layer(state_layer, state_layer_update)
# Update column name used by state overlay to look up values
this_state_colname = colname
thisoverlay.value = "Hover over States for Details"
state_overlay = thisoverlay
state_layer_update.on_hover(update_state_overlay)
return
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)
def plotLeaflet(geoJson: dict,
idToValue: dict,
worldMap: lf.Map = None,
includeEmpty: bool = True,
labelFormat: str = "Id={0} - Value={1}",
mapArgs: dict = {},
heatmapArgs: dict = {}) -> lf.Map:
''' Plot the choropleth on the map using the ipyleaflet library
@idToValue: a dictionary containing mappings of ids to their associated values
@worldMap: ipyleaflet Map object. If this is specified, the new heatmap will be addded
to this object. Else a new Map object will be created
@includeEmpty: true if we want to display the regions that have value 0. False otherwise
@labelFormat: the string format of the text that is displayed when the mouse hovers over a polygon.
The format must contain exactly 2 placeholders.
Returns: map object
'''
updatedIdToValue = __syncIdToValueDict(geoJson, idToValue)
# If we don't display empty regions, we need to
# create a new geo JSON that does NOT contain the
# ids that have 0 value
if (not includeEmpty):
origGeoJson = geoJson
geoJson = {"type": "FeatureCollection", "features": []}
for idValue, value in updatedIdToValue.items():
if (value > 0):
feature = getFeatureById(origGeoJson, idValue)
geoJson["features"].append(feature)
# Create the world map object if not specified
if (worldMap is None):
#basemap=lf.basemaps.CartoDB.DarkMatter, center=center, zoom=10
worldMap = lf.Map(**mapArgs)
worldMap.add_control(lf.FullScreenControl())
worldMap.add_control(lf.LayersControl(position="topright"))
# Default heatmap arguments
minVal, maxVal = min(updatedIdToValue.values()), max(
updatedIdToValue.values())
defaultHeatmapArgs = {
"key_on": "id",
"border_color": "black",
"value_min": minVal,
"value_max": maxVal,
"style": {
'fillOpacity': 0.8,
'dashArray': '5, 5'
},
"hover_style": {
'fillColor': 'purple',
'dashArray': '0',
'fillOpacity': 0.5
},
"name": "Choropleth",
"colormap": linear.OrRd_06
}
# Make a copy of the heatmapArgs, because we would add the default arguments
# to this dict if the default arguments are not specified by the caller. Making
# a copy prevents modifying the passed in dict object
heatmapArgs = dict(heatmapArgs)
for k, v in defaultHeatmapArgs.items():
if (k not in heatmapArgs):
heatmapArgs[k] = v
# Create the choropleth layer
choroplethLayer = lf.Choropleth(geo_data=geoJson,
choro_data=updatedIdToValue,
**heatmapArgs)
# Create a label widget to display the currently hovered polygon id &
# the value associated with that id
labelWidget = widgets.Label(value="")
widgetControl = lf.WidgetControl(widget=labelWidget,
position="bottomright")
worldMap.add_control(widgetControl)
def mouseout(*args, **kwargs):
''' Set the label value to empty string when the mouse exits a region '''
labelWidget.value = ""
def hover(*args, **kwargs):
''' Set the label to the id & its associated value '''
idValue = kwargs["id"]
labelWidget.value = labelFormat.format(idValue,
updatedIdToValue[idValue])
# Register callbacks
choroplethLayer.on_mouseout(mouseout)
choroplethLayer.on_hover(hover)
# Remove existing choropleth layer that has the same name
choroplethName = heatmapArgs["name"] if ("name" in heatmapArgs) else ""
removeLayerByType(worldMap, choroplethName, lf.Choropleth)
worldMap.add_layer(choroplethLayer)
return worldMap
def brlmap(data,
pattern="cod",
title="",
colormap=linear.YlOrBr_04,
legendPosition="bottomright",
style={
'fillOpacity': 1,
'dashArray': '5, 5'
}):
geo_json_data = __finddictcities(data.keys(), pattern)
key_max = max(data.keys(), key=(lambda k: data[k]))
key_min = min(data.keys(), key=(lambda k: data[k]))
centerX = (geo_json_data["bounds"][0] + geo_json_data["bounds"][2]) / 2
centerY = (geo_json_data["bounds"][1] + geo_json_data["bounds"][3]) / 2
escala = {
1: 360,
2: 180,
3: 90,
4: 45,
5: 22.5,
6: 11.25,
7: 5.625,
8: 2.8125,
9: 1.40625,
10: 0.703125,
11: 0.3515625,
12: 0.17578125,
13: 0.087890625,
14: 0.0439453125,
15: 0.02197265625,
16: 0.010986328125,
17: 0.0054931640625
}
difX = abs(geo_json_data["bounds"][2] - geo_json_data["bounds"][0])
difY = abs(geo_json_data["bounds"][3] - geo_json_data["bounds"][1])
if (difY > difX):
maiordif = difY * 1.5
else:
maiordif = difX
ind = 17
while (escala[ind] < maiordif):
ind -= 1
m = ipyleaflet.Map(center=(centerY, centerX), zoom=ind)
global __data
__data = data
layer = ipyleaflet.Choropleth(geo_data=geo_json_data,
choro_data=data,
colormap=colormap,
border_color='black',
style=style)
legend = colormap.scale(data[key_min], data[key_max])
out = widgets.Output()
with out:
display(legend)
m.add_layer(layer)
stylesheets = HTML('''
<style>
.rigthlgd svg{
float: right;
}
.rigthlgd{
float: right;
text-align: right;
}
.hiddenTitle
{
display: none;
}
</style>
''')
elemTitle = HTML('''
<h3 style="text-align: center;">''' + title + '''</h3>
''')
if (title == ""):
elemTitle.add_class("hiddenTitle")
global labelDados
labelDados = HTML('<h4><h4>')
labelDados.layout.padding = '2px'
labelDados.layout.width = '47%'
box_legenda = Layout(display='flex',
flex_flow='row',
align_items='stretch',
width='100%')
if (legendPosition.find("right") >= 0):
out.add_class("rigthlgd")
boxLgnd = Box(children=[labelDados, out, stylesheets],
layout=box_legenda)
else:
labelDados.add_class("rigthlgd")
boxLgnd = Box(children=[out, labelDados, stylesheets],
layout=box_legenda)
box_layout = Layout(display='flex',
flex_flow='column',
align_items='stretch',
width='100%')
layer.on_hover(__update_html)
if (legendPosition.find("bottom") >= 0):
box = Box(children=[elemTitle, m, boxLgnd], layout=box_layout)
else:
box = Box(children=[elemTitle, boxLgnd, m], layout=box_layout)
return box
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',
style={
'fillOpacity': 0.6,
'dashArray': '5, 5'
})
mapacoviddep = ipyleaflet.Map(center=(-10, -70), zoom=4.9)
mapacoviddep.add_layer(layercoviddepa)
display(mapacoviddep)
# **COVID PER CAPITA**
# In[18]:
xdepartamentos2 = xdepartamentos.sort_values(by='unique_values',
ascending=True).drop([10])
xdepartamentos2 = xdepartamentos2.reset_index()
def show(self, **kwargs):
"""
Generate and return the map object for displaying the map
Parameters
-----------
None
Returns
----------
map:ipyleaflet.Map
"""
self.map = ipyleaflet.Map(
layers=(ipyleaflet.basemap_to_tiles(
ipyleaflet.basemaps.CartoDB.Positron),),
**kwargs
)
choro_layer = ipyleaflet.Choropleth(
name=self.dataset,
geo_data=self.geo_data,
choro_data=self.choro_data[self.selectedAttr][self.selectedYear],
colormap=linear.YlOrRd_04,
style={
'opacity': 1,
'weight': 1.9,
'dashArray': '9',
'fillOpacity': 0.5}
)
def handle_click(**kwargs):
if kwargs['event'] == 'click':
clickedFips = kwargs['properties']['GEOID']
clickedName = self.fipsLookUp[clickedFips]
dataBox.value = f"{clickedName} : {self.choro_data[self.selectedAttr][self.selectedYear][clickedFips]:,}"
self.clickedID = clickedFips
choro_layer.on_click(handle_click)
# Year select
def handle_year_change(change):
new_year = str(change.new)
choro_layer.choro_data = self.choro_data[self.selectedAttr][new_year]
if self.clickedID:
clickedName = self.fipsLookUp[self.clickedID]
dataBox.value = f"{clickedName} : {self.choro_data[self.selectedAttr][new_year][self.clickedID]:,}"
self.selectedYear = new_year
yearList = self.availableYearDict[self.selectedAttr]
yearListNum = list(map(int, yearList))
minYear = min(yearListNum)
maxYear = max(yearListNum)
yearSlider, yearWidget = self.yearSlider(minYear, maxYear, maxYear)
yearSlider.observe(handle_year_change, 'value')
dataBox, dataWidget = self.dataBox()
def handle_attr_change(change):
new_attr = change.new
choro_layer.choro_data = self.choro_data[new_attr][self.selectedYear]
if self.clickedID:
clickedName = self.fipsLookUp[self.clickedID]
dataBox.value = f"{clickedName} : {self.choro_data[new_attr][self.selectedYear][self.clickedID]:,}"
self.selectedAttr = new_attr
attrDropdown, attrDropdownWidget = self.attrDropdown()
attrDropdown.observe(handle_attr_change, "value")
# Add to map
self.map.add_layer(choro_layer)
self.map.add_control(ipyleaflet.LayersControl())
self.map.add_control(ipyleaflet.FullScreenControl())
self.map.add_control(attrDropdownWidget)
self.map.add_control(yearWidget)
self.map.add_control(dataWidget)
return self.map
geo_json_data = load_data(
'https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/examples/us-states.json',
'us-states.json', json.load)
unemployment = load_data(
'https://raw.githubusercontent.com/jupyter-widgets/ipyleaflet/master/examples/US_Unemployment_Oct2012.csv',
'US_Unemployment_Oct2012.csv', pd.read_csv)
unemployment = dict(
zip(unemployment['State'].tolist(), unemployment['Unemployment'].tolist()))
layer = ipyleaflet.Choropleth(geo_data=geo_json_data,
choro_data=unemployment,
colormap=linear.YlOrRd_04,
border_color='black',
style={
'fillOpacity': 0.8,
'dashArray': '5, 5'
})
m = ipyleaflet.Map(center=(43, -100), zoom=4)
m.add_layer(layer)
m
import seaborn as sns
sns.set(style="white")
# Load the example mpg dataset
mpg = sns.load_dataset("mpg")
# Plot miles per gallon against horsepower with other semantics
def renderMap(self):
'''creates ipyleaflet map'''
self.m = ipyleaflet.Map(center=(33.66832279243364, 135.8861750364304),
zoom=10)
# self.grid
options = []
options.extend(list(self.all_style_dict.keys()))
option = options[0]
dates = sorted([date for date in self.all_style_dict[option].keys()])
self.layer = ipyleaflet.Choropleth(
geo_data=self.grid,
choro_data=self.all_style_dict[option][str(dates[0])],
colormap=linear.YlOrRd_04,
style={
"fillOpacity": 0.8,
"dashArray": "5, 5"
},
)
self.m.add_layer(self.layer)
self.time_slider = widgets.SelectionSlider(
options=dates,
value=dates[0],
description="TimeStamp",
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
)
self.time_slider.observe(self.on_slide, "value")
widget_control_slider = ipyleaflet.WidgetControl(
widget=self.time_slider, position="bottomright")
self.m.add_control(widget_control_slider)
# widgets.interact(update_map_time, timeStamp = self.time_slider)
self.dropdown = widgets.Dropdown(options=options,
value=option,
description="Select layer")
self.dropdown.observe(self.on_click, "value")
widget_control = ipyleaflet.WidgetControl(widget=self.dropdown,
position="topright")
self.m.add_control(widget_control)
self.color_range = widgets.IntRangeSlider(
value=[self.layer.value_min, self.layer.value_max],
min=self.layer.value_min,
max=self.layer.value_max,
step=1,
description="ColorBar:",
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
readout_format="d",
)
self.zoom_to_location({"new": list(self.BBOXes.keys())[0]})
self.color_range.observe(self.on_selection, "value")
widget_control = ipyleaflet.WidgetControl(widget=self.color_range,
position="bottomright")
self.m.add_control(widget_control)
locations = widgets.Dropdown(
options=self.BBOXes.keys(),
value=list(self.BBOXes.keys())[0],
description="Location:",
)
locations.observe(self.zoom_to_location, "value")
widget_control = ipyleaflet.WidgetControl(widget=locations,
position="topright")
self.m.add_control(widget_control)
self.m.observe(self.zoom_out_to_target_bounds, "bounds")
def compute_style(feature, colormap, choro_data):
return {
"fillColor": colormap(choro_data),
"color": "white",
"weight": random.randint(1, 3),
}
self.layer.style_callback = compute_style