def test_color_brewer_base():
scheme = ut.color_brewer('YlGnBu', 9)
assert scheme == [
'#ffffd9', '#edf8b1', '#c7e9b4',
'#7fcdbb', '#41b6c4', '#1d91c0',
'#225ea8', '#253494', '#081d58'
]
def choropleth(self, geo_path=None, geo_str=None, data_out='data.json',
data=None, columns=None, key_on=None, threshold_scale=None,
fill_color='blue', fill_opacity=0.6, line_color='black',
line_weight=1, line_opacity=1, legend_name="",
topojson=None, reset=False, smooth_factor=None):
"""
Apply a GeoJSON overlay to the map.
Plot a GeoJSON overlay on the base map. There is no requirement
to bind data (passing just a GeoJSON plots a single-color overlay),
but there is a data binding option to map your columnar data to
different feature objects with a color scale.
If data is passed as a Pandas dataframe, the "columns" and "key-on"
keywords must be included, the first to indicate which DataFrame
columns to use, the second to indicate the layer in the GeoJSON
on which to key the data. The 'columns' keyword does not need to be
passed for a Pandas series.
Colors are generated from color brewer (http://colorbrewer2.org/)
sequential palettes on a D3 threshold scale. The scale defaults to the
following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
passed to `threshold_scale` of length <=6, in order to match the
color brewer range.
TopoJSONs can be passed as "geo_path", but the "topojson" keyword must
also be passed with the reference to the topojson objects to convert.
See the topojson.feature method in the TopoJSON API reference:
https://github.com/mbostock/topojson/wiki/API-Reference
Parameters
----------
geo_path: string, default None
URL or File path to your GeoJSON data
geo_str: string, default None
String of GeoJSON, alternative to geo_path
data_out: string, default 'data.json'
Path to write Pandas DataFrame/Series to JSON if binding data
data: Pandas DataFrame or Series, default None
Data to bind to the GeoJSON.
columns: dict or tuple, default None
If the data is a Pandas DataFrame, the columns of data to be bound.
Must pass column 1 as the key, and column 2 the values.
key_on: string, default None
Variable in the GeoJSON file to bind the data to. Must always
start with 'feature' and be in JavaScript objection notation.
Ex: 'feature.id' or 'feature.properties.statename'.
threshold_scale: list, default None
Data range for D3 threshold scale. Defaults to the following range
of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
fill_color: string, default 'blue'
Area fill color. Can pass a hex code, color name, or if you are
binding data, one of the following color brewer palettes:
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
fill_opacity: float, default 0.6
Area fill opacity, range 0-1.
line_color: string, default 'black'
GeoJSON geopath line color.
line_weight: int, default 1
GeoJSON geopath line weight.
line_opacity: float, default 1
GeoJSON geopath line opacity, range 0-1.
legend_name: string, default empty string
Title for data legend.
topojson: string, default None
If using a TopoJSON, passing "objects.yourfeature" to the topojson
keyword argument will enable conversion to GeoJSON.
reset: boolean, default False
Remove all current geoJSON layers, start with new layer
smooth_factor: float, default None
How much to simplify the polyline on each zoom level. More means
better performance and smoother look, and less means more accurate
representation. Leaflet defaults to 1.0.
Returns
-------
GeoJSON data layer in obj.template_vars
Examples
--------
>>> m.choropleth(geo_path='us-states.json', line_color='blue',
... line_weight=3)
>>> m.choropleth(geo_path='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... threshold_scale=[0, 20, 30, 40, 50, 60])
>>> m.choropleth(geo_path='countries.json',
... topojson='objects.countries')
"""
if threshold_scale and len(threshold_scale) > 6:
raise ValueError
if data is not None and not color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
# Create GeoJson object
if geo_path:
geo_data = open(geo_path)
elif geo_str:
geo_data = geo_str
else:
geo_data = {}
# Create color_data dict
if hasattr(data, 'set_index'):
# This is a pd.DataFrame
color_data = data.set_index(columns[0])[columns[1]].to_dict()
elif hasattr(data, 'to_dict'):
# This is a pd.Series
color_data = data.to_dict()
elif data:
color_data = dict(data)
else:
color_data = None
# Compute color_domain
if threshold_scale:
color_domain = list(threshold_scale)
elif color_data:
# To avoid explicit pandas dependency ; changed default behavior.
warnings.warn("'threshold_scale' default behavior has changed."
" Now you get a linear scale between the 'min' and"
" the 'max' of your data."
" To get former behavior, use"
" folium.utilities.split_six.",
FutureWarning, stacklevel=2)
data_min = min(color_data.values())
data_max = max(color_data.values())
if data_min == data_max:
data_min = (data_min if data_min < 0 else 0
if data_min > 0 else -1)
data_max = (data_max if data_max > 0 else 0
if data_max < 0 else 1)
data_min, data_max = (1.01*data_min-0.01*data_max,
1.01*data_max-0.01*data_min)
nb_class = 6
color_domain = [data_min+i*(data_max-data_min)*1./nb_class
for i in range(1+nb_class)]
else:
color_domain = None
if color_domain and key_on:
key_on = key_on[8:] if key_on.startswith('feature.') else key_on
color_range = color_brewer(fill_color, n=len(color_domain))
def get_by_key(obj, key):
return (obj.get(key, None) if len(key.split('.')) <= 1 else
get_by_key(obj.get(key.split('.')[0], None),
'.'.join(key.split('.')[1:])))
def color_scale_fun(x):
return color_range[len(
[u for u in color_domain if
get_by_key(x, key_on) in color_data and
u <= color_data[get_by_key(x, key_on)]])]
else:
def color_scale_fun(x):
return fill_color
def style_function(x):
return {
"weight": line_weight,
"opactiy": line_opacity,
"color": line_color,
"fillOpacity": fill_opacity,
"fillColor": color_scale_fun(x)
}
if topojson:
geo_json = TopoJson(
geo_data,
topojson,
style_function=style_function,
smooth_factor=smooth_factor)
else:
geo_json = GeoJson(
geo_data,
style_function=style_function,
smooth_factor=smooth_factor)
self.add_child(geo_json)
# Create ColorMap.
if color_domain:
brewed = color_brewer(fill_color, n=len(color_domain))
color_scale = StepColormap(
brewed[1:len(color_domain)],
index=color_domain,
vmin=color_domain[0],
vmax=color_domain[-1],
)
self.add_child(color_scale)
def choropleth(self,
geo_data,
data=None,
columns=None,
key_on=None,
threshold_scale=None,
fill_color='blue',
fill_opacity=0.6,
line_color='black',
line_weight=1,
line_opacity=1,
name=None,
legend_name='',
topojson=None,
reset=False,
smooth_factor=None,
highlight=None):
"""
Apply a GeoJSON overlay to the map.
Plot a GeoJSON overlay on the base map. There is no requirement
to bind data (passing just a GeoJSON plots a single-color overlay),
but there is a data binding option to map your columnar data to
different feature objects with a color scale.
If data is passed as a Pandas DataFrame, the "columns" and "key-on"
keywords must be included, the first to indicate which DataFrame
columns to use, the second to indicate the layer in the GeoJSON
on which to key the data. The 'columns' keyword does not need to be
passed for a Pandas series.
Colors are generated from color brewer (http://colorbrewer2.org/)
sequential palettes on a D3 threshold scale. The scale defaults to the
following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
passed to `threshold_scale` of length <=6, in order to match the
color brewer range.
TopoJSONs can be passed as "geo_data", but the "topojson" keyword must
also be passed with the reference to the topojson objects to convert.
See the topojson.feature method in the TopoJSON API reference:
https://github.com/topojson/topojson/wiki/API-Reference
Parameters
----------
geo_data: string/object
URL, file path, or data (json, dict, geopandas, etc) to your GeoJSON geometries
data: Pandas DataFrame or Series, default None
Data to bind to the GeoJSON.
columns: dict or tuple, default None
If the data is a Pandas DataFrame, the columns of data to be bound.
Must pass column 1 as the key, and column 2 the values.
key_on: string, default None
Variable in the GeoJSON file to bind the data to. Must always
start with 'feature' and be in JavaScript objection notation.
Ex: 'feature.id' or 'feature.properties.statename'.
threshold_scale: list, default None
Data range for D3 threshold scale. Defaults to the following range
of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
fill_color: string, default 'blue'
Area fill color. Can pass a hex code, color name, or if you are
binding data, one of the following color brewer palettes:
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
fill_opacity: float, default 0.6
Area fill opacity, range 0-1.
line_color: string, default 'black'
GeoJSON geopath line color.
line_weight: int, default 1
GeoJSON geopath line weight.
line_opacity: float, default 1
GeoJSON geopath line opacity, range 0-1.
legend_name: string, default empty string
Title for data legend.
topojson: string, default None
If using a TopoJSON, passing "objects.yourfeature" to the topojson
keyword argument will enable conversion to GeoJSON.
reset: boolean, default False
Remove all current geoJSON layers, start with new layer
smooth_factor: float, default None
How much to simplify the polyline on each zoom level. More means
better performance and smoother look, and less means more accurate
representation. Leaflet defaults to 1.0.
highlight: boolean, default False
Enable highlight functionality when hovering over a GeoJSON area.
Returns
-------
GeoJSON data layer in obj.template_vars
Examples
--------
>>> m.choropleth(geo_data='us-states.json', line_color='blue',
... line_weight=3)
>>> m.choropleth(geo_data='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... threshold_scale=[0, 20, 30, 40, 50, 60])
>>> m.choropleth(geo_data='countries.json',
... topojson='objects.countries')
>>> m.choropleth(geo_data='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... threshold_scale=[0, 20, 30, 40, 50, 60],
... highlight=True)
"""
if threshold_scale and len(threshold_scale) > 6:
raise ValueError
if data is not None and not color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
# Create color_data dict
if hasattr(data, 'set_index'):
# This is a pd.DataFrame
color_data = data.set_index(columns[0])[columns[1]].to_dict()
elif hasattr(data, 'to_dict'):
# This is a pd.Series
color_data = data.to_dict()
elif data:
color_data = dict(data)
else:
color_data = None
# Compute color_domain
if threshold_scale:
color_domain = list(threshold_scale)
elif color_data:
# To avoid explicit pandas dependency ; changed default behavior.
data_min = min(color_data.values())
data_max = max(color_data.values())
if data_min == data_max:
data_min = (data_min
if data_min < 0 else 0 if data_min > 0 else -1)
data_max = (data_max
if data_max > 0 else 0 if data_max < 0 else 1)
data_min, data_max = (1.01 * data_min - 0.01 * data_max,
1.01 * data_max - 0.01 * data_min)
nb_class = 6
color_domain = [
data_min + i * (data_max - data_min) * 1. / nb_class
for i in range(1 + nb_class)
]
else:
color_domain = None
if color_domain and key_on:
key_on = key_on[8:] if key_on.startswith('feature.') else key_on
color_range = color_brewer(fill_color, n=len(color_domain))
def get_by_key(obj, key):
return (obj.get(key, None) if len(key.split('.')) <= 1 else
get_by_key(obj.get(key.split('.')[0], None), '.'.join(
key.split('.')[1:])))
def color_scale_fun(x):
return color_range[len([
u for u in color_domain
if get_by_key(x, key_on) in color_data
and u <= color_data[get_by_key(x, key_on)]
])]
else:
def color_scale_fun(x):
return fill_color
def style_function(x):
return {
'weight': line_weight,
'opacity': line_opacity,
'color': line_color,
'fillOpacity': fill_opacity,
'fillColor': color_scale_fun(x)
}
def highlight_function(x):
return {
'weight': line_weight + 2,
'fillOpacity': fill_opacity + .2
}
if topojson:
geo_json = TopoJson(geo_data,
topojson,
name=name,
style_function=style_function,
smooth_factor=smooth_factor)
else:
geo_json = GeoJson(
geo_data,
name=name,
style_function=style_function,
smooth_factor=smooth_factor,
highlight_function=highlight_function if highlight else None)
self.add_child(geo_json)
# Create ColorMap.
if color_domain:
brewed = color_brewer(fill_color, n=len(color_domain))
color_scale = StepColormap(
brewed[1:len(color_domain)],
index=color_domain,
vmin=color_domain[0],
vmax=color_domain[-1],
caption=legend_name,
)
self.add_child(color_scale)
def choropleth(self,
geo_data,
data=None,
columns=None,
key_on=None,
bins=6,
fill_color='blue',
nan_fill_color='black',
fill_opacity=0.6,
nan_fill_opacity=None,
line_color='black',
line_weight=1,
line_opacity=1,
name=None,
legend_name='',
topojson=None,
reset=False,
smooth_factor=None,
highlight=None,
**kwargs):
"""
Apply a GeoJSON overlay to the map.
Plot a GeoJSON overlay on the base map. There is no requirement
to bind data (passing just a GeoJSON plots a single-color overlay),
but there is a data binding option to map your columnar data to
different feature objects with a color scale.
If data is passed as a Pandas DataFrame, the "columns" and "key-on"
keywords must be included, the first to indicate which DataFrame
columns to use, the second to indicate the layer in the GeoJSON
on which to key the data. The 'columns' keyword does not need to be
passed for a Pandas series.
Colors are generated from color brewer (http://colorbrewer2.org/)
sequential palettes. By default, linear binning is used between
the min and the max of the values. Custom binning can be achieved
with the `bins` parameter.
TopoJSONs can be passed as "geo_data", but the "topojson" keyword must
also be passed with the reference to the topojson objects to convert.
See the topojson.feature method in the TopoJSON API reference:
https://github.com/topojson/topojson/wiki/API-Reference
Parameters
----------
geo_data: string/object
URL, file path, or data (json, dict, geopandas, etc) to your GeoJSON
geometries
data: Pandas DataFrame or Series, default None
Data to bind to the GeoJSON.
columns: dict or tuple, default None
If the data is a Pandas DataFrame, the columns of data to be bound.
Must pass column 1 as the key, and column 2 the values.
key_on: string, default None
Variable in the `geo_data` GeoJSON file to bind the data to. Must
start with 'feature' and be in JavaScript objection notation.
Ex: 'feature.id' or 'feature.properties.statename'.
bins: int or sequence of scalars or str, default 6
If `bins` is an int, it defines the number of equal-width
bins between the min and the max of the values.
If `bins` is a sequence, it directly defines the bin edges.
For more information on this parameter, have a look at
numpy.histogram function.
fill_color: string, default 'blue'
Area fill color. Can pass a hex code, color name, or if you are
binding data, one of the following color brewer palettes:
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
nan_fill_color: string, default 'black'
Area fill color for nan or missing values.
Can pass a hex code, color name.
fill_opacity: float, default 0.6
Area fill opacity, range 0-1.
nan_fill_opacity: float, default fill_opacity
Area fill opacity for nan or missing values, range 0-1.
line_color: string, default 'black'
GeoJSON geopath line color.
line_weight: int, default 1
GeoJSON geopath line weight.
line_opacity: float, default 1
GeoJSON geopath line opacity, range 0-1.
legend_name: string, default empty string
Title for data legend.
topojson: string, default None
If using a TopoJSON, passing "objects.yourfeature" to the topojson
keyword argument will enable conversion to GeoJSON.
reset: boolean, default False
Remove all current geoJSON layers, start with new layer
smooth_factor: float, default None
How much to simplify the polyline on each zoom level. More means
better performance and smoother look, and less means more accurate
representation. Leaflet defaults to 1.0.
highlight: boolean, default False
Enable highlight functionality when hovering over a GeoJSON area.
Returns
-------
GeoJSON data layer in obj.template_vars
Examples
--------
>>> m.choropleth(geo_data='us-states.json', line_color='blue',
... line_weight=3)
>>> m.choropleth(geo_data='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... bins=[0, 20, 30, 40, 50, 60])
>>> m.choropleth(geo_data='countries.json',
... topojson='objects.countries')
>>> m.choropleth(geo_data='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... bins=[0, 20, 30, 40, 50, 60],
... highlight=True)
"""
if data is not None and not color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
if nan_fill_opacity is None:
nan_fill_opacity = fill_opacity
if 'threshold_scale' in kwargs:
if kwargs['threshold_scale'] is not None:
bins = kwargs['threshold_scale']
warnings.warn(
'choropleth `threshold_scale` parameter is now depreciated '
'in favor of the `bins` parameter.', DeprecationWarning)
# Create color_data dict
if hasattr(data, 'set_index'):
# This is a pd.DataFrame
color_data = data.set_index(columns[0])[columns[1]].to_dict()
elif hasattr(data, 'to_dict'):
# This is a pd.Series
color_data = data.to_dict()
elif data:
color_data = dict(data)
else:
color_data = None
if color_data is not None and key_on is not None:
real_values = np.array(list(color_data.values()))
real_values = real_values[~np.isnan(real_values)]
_, bin_edges = np.histogram(real_values, bins=bins)
bins_min, bins_max = min(bin_edges), max(bin_edges)
if np.any((real_values < bins_min) | (real_values > bins_max)):
raise ValueError(
'All values are expected to fall into one of the provided '
'bins (or to be Nan). Please check the `bins` parameter '
'and/or your data.')
# We add the colorscale
nb_bins = len(bin_edges) - 1
color_range = color_brewer(fill_color, n=nb_bins)
color_scale = StepColormap(color_range,
index=bin_edges,
vmin=bins_min,
vmax=bins_max,
caption=legend_name)
self.add_child(color_scale)
# then we 'correct' the last edge for numpy digitize
# (we add a very small amount to fake an inclusive right interval)
increasing = bin_edges[0] <= bin_edges[-1]
bin_edges[-1] = np.nextafter(bin_edges[-1],
(1 if increasing else -1) * np.inf)
key_on = key_on[8:] if key_on.startswith('feature.') else key_on
def get_by_key(obj, key):
return (obj.get(key, None) if len(key.split('.')) <= 1 else
get_by_key(obj.get(key.split('.')[0], None), '.'.join(
key.split('.')[1:])))
def color_scale_fun(x):
key_of_x = get_by_key(x, key_on)
if key_of_x not in color_data.keys():
return nan_fill_color, nan_fill_opacity
value_of_x = color_data[key_of_x]
if np.isnan(value_of_x):
return nan_fill_color, nan_fill_opacity
color_idx = np.digitize(value_of_x, bin_edges, right=False) - 1
return color_range[color_idx], fill_opacity
else:
def color_scale_fun(x):
return fill_color, fill_opacity
def style_function(x):
color, opacity = color_scale_fun(x)
return {
'weight': line_weight,
'opacity': line_opacity,
'color': line_color,
'fillOpacity': opacity,
'fillColor': color
}
def highlight_function(x):
return {
'weight': line_weight + 2,
'fillOpacity': fill_opacity + .2
}
if topojson:
geo_json = TopoJson(geo_data,
topojson,
name=name,
style_function=style_function,
smooth_factor=smooth_factor)
else:
geo_json = GeoJson(
geo_data,
name=name,
style_function=style_function,
smooth_factor=smooth_factor,
highlight_function=highlight_function if highlight else None)
self.add_child(geo_json)
def choropleth(self,
geo_path=None,
geo_str=None,
data_out='data.json',
data=None,
columns=None,
key_on=None,
threshold_scale=None,
fill_color='blue',
fill_opacity=0.6,
line_color='black',
line_weight=1,
line_opacity=1,
legend_name="",
topojson=None,
popup_function=None,
reset=False):
"""
Apply a GeoJSON overlay to the map.
Plot a GeoJSON overlay on the base map. There is no requirement
to bind data (passing just a GeoJSON plots a single-color overlay),
but there is a data binding option to map your columnar data to
different feature objects with a color scale.
If data is passed as a Pandas dataframe, the "columns" and "key-on"
keywords must be included, the first to indicate which DataFrame
columns to use, the second to indicate the layer in the GeoJSON
on which to key the data. The 'columns' keyword does not need to be
passed for a Pandas series.
Colors are generated from color brewer (http://colorbrewer2.org/)
sequential palettes on a D3 threshold scale. The scale defaults to the
following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
passed to `threshold_scale` of length <=6, in order to match the
color brewer range.
TopoJSONs can be passed as "geo_path", but the "topojson" keyword must
also be passed with the reference to the topojson objects to convert.
See the topojson.feature method in the TopoJSON API reference:
https://github.com/mbostock/topojson/wiki/API-Reference
Parameters
----------
geo_path: string, default None
URL or File path to your GeoJSON data
geo_str: string, default None
String of GeoJSON, alternative to geo_path
data_out: string, default 'data.json'
Path to write Pandas DataFrame/Series to JSON if binding data
data: Pandas DataFrame or Series, default None
Data to bind to the GeoJSON.
columns: dict or tuple, default None
If the data is a Pandas DataFrame, the columns of data to be bound.
Must pass column 1 as the key, and column 2 the values.
key_on: string, default None
Variable in the GeoJSON file to bind the data to. Must always
start with 'feature' and be in JavaScript objection notation.
Ex: 'feature.id' or 'feature.properties.statename'.
threshold_scale: list, default None
Data range for D3 threshold scale. Defaults to the following range
of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
fill_color: string, default 'blue'
Area fill color. Can pass a hex code, color name, or if you are
binding data, one of the following color brewer palettes:
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
fill_opacity: float, default 0.6
Area fill opacity, range 0-1.
line_color: string, default 'black'
GeoJSON geopath line color.
line_weight: int, default 1
GeoJSON geopath line weight.
line_opacity: float, default 1
GeoJSON geopath line opacity, range 0-1.
legend_name: string, default empty string
Title for data legend.
topojson: string, default None
If using a TopoJSON, passing "objects.yourfeature" to the topojson
keyword argument will enable conversion to GeoJSON.
popup_function: string or function, default None
If using GeoJson a string or function to pass through to the
map to create popups based on feature attributes. Ignored for
topojson.
reset: boolean, default False
Remove all current geoJSON layers, start with new layer
Returns
-------
GeoJSON data layer in obj.template_vars
Examples
--------
>>> m.choropleth(geo_path='us-states.json', line_color='blue',
... line_weight=3)
>>> m.choropleth(geo_path='geo.json', data=df,
... columns=['Data 1', 'Data 2'],
... key_on='feature.properties.myvalue',
... fill_color='PuBu',
... threshold_scale=[0, 20, 30, 40, 50, 60])
>>> m.choropleth(geo_path='countries.json',
... topojson='objects.countries')
"""
if threshold_scale and len(threshold_scale) > 6:
raise ValueError
if data is not None and not color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
# Create GeoJson object
if geo_path:
geo_data = open(geo_path)
elif geo_str:
geo_data = geo_str
else:
geo_data = {}
# Create color_data dict
if hasattr(data, 'set_index'):
# This is a pd.DataFrame
color_data = data.set_index(columns[0])[columns[1]].to_dict()
elif hasattr(data, 'to_dict'):
# This is a pd.Series
color_data = data.to_dict()
elif data:
color_data = dict(data)
else:
color_data = None
# Compute color_domain
if threshold_scale:
color_domain = list(threshold_scale)
elif color_data:
# To avoid explicit pandas dependency ; changed default behavior.
warnings.warn(
"'threshold_scale' default behavior has changed."
" Now you get a linear scale between the 'min' and"
" the 'max' of your data."
" To get former behavior, use"
" folium.utilities.split_six.",
FutureWarning,
stacklevel=2)
data_min = min(color_data.values())
data_max = max(color_data.values())
if data_min == data_max:
data_min = (data_min
if data_min < 0 else 0 if data_min > 0 else -1)
data_max = (data_max
if data_max > 0 else 0 if data_max < 0 else 1)
data_min, data_max = (1.01 * data_min - 0.01 * data_max,
1.01 * data_max - 0.01 * data_min)
nb_class = 6
color_domain = [
data_min + i * (data_max - data_min) * 1. / nb_class
for i in range(1 + nb_class)
]
else:
color_domain = None
if color_domain and key_on:
key_on = key_on[8:] if key_on.startswith('feature.') else key_on
color_range = color_brewer(fill_color, n=len(color_domain))
def get_by_key(obj, key):
return (obj.get(key, None) if len(key.split('.')) <= 1 else
get_by_key(obj.get(key.split('.')[0], None), '.'.join(
key.split('.')[1:])))
def color_scale_fun(x):
return color_range[len([
u for u in color_domain
if u <= color_data[get_by_key(x, key_on)]
])]
else:
def color_scale_fun(x):
return fill_color
def style_function(x):
return {
"weight": line_weight,
"opactiy": line_opacity,
"color": line_color,
"fillOpacity": fill_opacity,
"fillColor": color_scale_fun(x)
}
if topojson:
geo_json = TopoJson(geo_data,
topojson,
style_function=style_function) # noqa
else:
geo_json = GeoJson(geo_data,
style_function=style_function,
popup_function=popup_function)
self.add_children(geo_json)
# Create ColorMap.
if color_domain:
brewed = color_brewer(fill_color, n=len(color_domain))
color_scale = StepColormap(
brewed[1:len(color_domain)],
index=color_domain,
vmin=color_domain[0],
vmax=color_domain[-1],
)
self.add_children(color_scale)
def test_color_brewer_base():
scheme = ut.color_brewer('YlGnBu', 9)
assert scheme == [
'#ffffcc', '#d5eeba', '#a3dbb7', '#6fc7bd', '#41b6c4', '#269ac1',
'#1f77b4', '#1c519f', '#0c2c84'
]
def test_color_brewer_reverse():
scheme = ut.color_brewer('YlGnBu')
scheme_r = ut.color_brewer('YlGnBu_r')
assert scheme[::-1] == scheme_r
def __init__(self, geo_data, data=None, columns=None, key_on=None, # noqa
bins=6, fill_color='blue', nan_fill_color='black',
fill_opacity=0.6, nan_fill_opacity=None, line_color='black',
line_weight=1, line_opacity=1, name=None, legend_name='',
overlay=True, control=True, show=True,
topojson=None, smooth_factor=None, highlight=None,
**kwargs):
super(Choropleth, self).__init__(name=name, overlay=overlay,
control=control, show=show)
self._name = 'Choropleth'
if data is not None and not color_brewer(fill_color):
raise ValueError('Please pass a valid color brewer code to '
'fill_local. See docstring for valid codes.')
if nan_fill_opacity is None:
nan_fill_opacity = fill_opacity
if 'threshold_scale' in kwargs:
if kwargs['threshold_scale'] is not None:
bins = kwargs['threshold_scale']
warnings.warn(
'choropleth `threshold_scale` parameter is now depreciated '
'in favor of the `bins` parameter.', DeprecationWarning)
# Create color_data dict
if hasattr(data, 'set_index'):
# This is a pd.DataFrame
color_data = data.set_index(columns[0])[columns[1]].to_dict()
elif hasattr(data, 'to_dict'):
# This is a pd.Series
color_data = data.to_dict()
elif data:
color_data = dict(data)
else:
color_data = None
self.color_scale = None
if color_data is not None and key_on is not None:
real_values = np.array(list(color_data.values()))
real_values = real_values[~np.isnan(real_values)]
_, bin_edges = np.histogram(real_values, bins=bins)
bins_min, bins_max = min(bin_edges), max(bin_edges)
if np.any((real_values < bins_min) | (real_values > bins_max)):
raise ValueError(
'All values are expected to fall into one of the provided '
'bins (or to be Nan). Please check the `bins` parameter '
'and/or your data.')
# We add the colorscale
nb_bins = len(bin_edges) - 1
color_range = color_brewer(fill_color, n=nb_bins)
self.color_scale = StepColormap(
color_range,
index=bin_edges,
vmin=bins_min,
vmax=bins_max,
caption=legend_name)
# then we 'correct' the last edge for numpy digitize
# (we add a very small amount to fake an inclusive right interval)
increasing = bin_edges[0] <= bin_edges[-1]
bin_edges[-1] = np.nextafter(
bin_edges[-1],
(1 if increasing else -1) * np.inf)
key_on = key_on[8:] if key_on.startswith('feature.') else key_on
def get_by_key(obj, key):
return (obj.get(key, None) if len(key.split('.')) <= 1 else
get_by_key(obj.get(key.split('.')[0], None),
'.'.join(key.split('.')[1:])))
def color_scale_fun(x):
key_of_x = get_by_key(x, key_on)
if key_of_x not in color_data.keys():
return nan_fill_color, nan_fill_opacity
value_of_x = color_data[key_of_x]
if np.isnan(value_of_x):
return nan_fill_color, nan_fill_opacity
color_idx = np.digitize(value_of_x, bin_edges, right=False) - 1
return color_range[color_idx], fill_opacity
else:
def color_scale_fun(x):
return fill_color, fill_opacity
def style_function(x):
color, opacity = color_scale_fun(x)
return {
'weight': line_weight,
'opacity': line_opacity,
'color': line_color,
'fillOpacity': opacity,
'fillColor': color
}
def highlight_function(x):
return {
'weight': line_weight + 2,
'fillOpacity': fill_opacity + .2
}
if topojson:
self.geojson = TopoJson(
geo_data,
topojson,
style_function=style_function,
smooth_factor=smooth_factor)
else:
self.geojson = GeoJson(
geo_data,
style_function=style_function,
smooth_factor=smooth_factor,
highlight_function=highlight_function if highlight else None)
self.add_child(self.geojson)
if self.color_scale:
self.add_child(self.color_scale)
def test_color_brewer_reverse():
scheme = ut.color_brewer('YlGnBu')
scheme_r = ut.color_brewer('YlGnBu_r')
assert scheme[::-1] == scheme_r
def test_color_brewer_base():
scheme = ut.color_brewer('YlGnBu', 9)
assert scheme == [
'#ffffd9', '#edf8b1', '#c7e9b4', '#7fcdbb', '#41b6c4', '#1d91c0',
'#225ea8', '#253494', '#081d58'
]
