def test_choropleth_map():
wkt_data = []
count_data = []
wkt_data.append("POLYGON (("
"-73.97324 40.73747, "
"-73.96524 40.74507, "
"-73.96118 40.75890, "
"-73.95556 40.77654, "
"-73.97324 40.73747))")
count_data.append(5.0)
arr_wkt = pandas.Series(wkt_data)
arr_wkb = arctern.ST_GeomFromText(arr_wkt)
arr_count = pandas.Series(count_data)
vega = vega_choroplethmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
color_gradient=["#0000FF", "#FF0000"],
color_bound=[2.5, 5],
opacity=1.0,
coordinate_system='EPSG:4326')
choropleth_map1 = arctern.choropleth_map_layer(vega, arr_wkb, arr_count)
save_png(choropleth_map1, "/tmp/test_choropleth_map1.png")
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type points: Series(dtype: object)
:param points: Points in WKB form
:type bounding_box: (float, float, float, float)
:param bounding_box: The bounding rectangle, as a [left, upper, right, lower]-tuple.
value should be of :coordinate_system:
:type coordinate_system: str
:param coordinate_system: either 'EPSG:4326' or 'EPSG:3857'
:type extra_contextily_params: dict
:param extra_contextily_params: extra parameters for contextily.add_basemap.
See https://contextily.readthedocs.io/en/latest/reference.html
"""
from matplotlib import pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Render region boundaries in matplotlib
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type region_boundaries: GeoSeries
:param region_boundaries: Sequence of polygons, as region boundaries to plot.
:type weights: Series(dtype: float|int64)
:param weights: Color weights for polygons
:type bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north].
:type color_gradient: list
:param color_gradient: Specify range of color gradient.
Either use ["hex_color"] to specify a same color for all polygons,
or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2"
:type color_bound: list
:param color_bound: Specify weight range [w1, w2] binding to color_gradient.
Needed only when color_gradient has two value ["color1", "color2"].
Bind w1 to "color1", and w2 to "color2".
When weight < w1 or weight > w2, truncate to w1/w2 accordingly.
Default as None
:type opacity: float
:param opacity: Opacity of polygons, ranged from 0.0 to 1.0, default as 1.0
:type coordinate_system: str
:param coordinate_system: Coordinate Reference System of the geometry objects.
Must be SRID formed, e.g. 'EPSG:4326' or 'EPSG:3857'
Default as 'EPSG:3857'
:type aggregation_type: str
:param aggregation_type: Aggregation type of data processing. Default as 'max'
:type extra_contextily_params: dict
:param extra_contextily_params: Extra parameters will be passed to contextily.add_basemap.
See https://contextily.readthedocs.io/en/latest/reference.html for details
:example:
>>> import pandas as pd
>>> import numpy as np
>>> import arctern
>>> import matplotlib.pyplot as plt
>>> # read from test_data.csv
>>> # Download link: https://raw.githubusercontent.com/arctern-io/arctern-resources/benchmarks/benchmarks/dataset/layer_rendering_test_data/test_data.csv
>>> df = pd.read_csv("/path/to/test_data.csv", dtype={'longitude':np.float64, 'latitude':np.float64, 'color_weights':np.float64, 'size_weights':np.float64, 'region_boundaries':np.object})
>>> input = df[pd.notna(df['region_boundaries'])].groupby(['region_boundaries']).mean().reset_index()
>>> polygon = arctern.GeoSeries(input['region_boundaries'])
>>> # plot choroplethmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.choroplethmap(ax, polygon, input['color_weights'], bounding_box=[-74.01124953254566,40.73413446570038,-73.96238859103838,40.766161712662296], color_gradient=["#115f9a","#d0f400"], color_bound=[5,18], opacity=1.0, coordinate_system='EPSG:4326', aggregation_type="mean")
>>> plt.show()
"""
from matplotlib import pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
ax.axis('off')
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Plots a choropleth map in matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
region_boundaries : GeoSeries
Sequence of polygons, as region boundaries to plot.
weights : Series
Color weights for polygons
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
color_gradient : list
Range of color gradient.
Either use ["hex_color"] to specify a same color for all geometries, or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2".
color_bound : list, optional
Weight range [w1, w2] of ``color_gradient``.
Needed only when ``color_gradient`` has two values ["color1", "color2"]. Binds w1 to "color1", and w2 to "color2". When weight < w1 or weight > w2, the weight will be truncated to w1 or w2 accordingly.
opacity : float, optional
Opacity of polygons, ranged from 0.0 to 1.0, by default 1.0.
coordinate_system : str, optional
The Coordinate Reference System (CRS) set to all geometries, by default 'EPSG:3857'.
Only supports SRID as a WKT representation of CRS by now.
aggregation_type : str, optional
Aggregation type, by default 'max'.
**extra_contextily_params: dict
Extra parameters passed to `contextily.add_basemap. <https://contextily.readthedocs.io/en/latest/reference.html>`_
Examples
-------
>>> import pandas as pd
>>> import numpy as np
>>> import arctern
>>> import matplotlib.pyplot as plt
>>> # read from test_data.csv
>>> # Download link: https://raw.githubusercontent.com/arctern-io/arctern-resources/benchmarks/benchmarks/dataset/layer_rendering_test_data/test_data.csv
>>> df = pd.read_csv("/path/to/test_data.csv", dtype={'longitude':np.float64, 'latitude':np.float64, 'color_weights':np.float64, 'size_weights':np.float64, 'region_boundaries':np.object})
>>> input = df[pd.notna(df['region_boundaries'])].groupby(['region_boundaries']).mean().reset_index()
>>> polygon = arctern.GeoSeries(input['region_boundaries'])
>>> # plot choroplethmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.choroplethmap(ax, polygon, input['color_weights'], bounding_box=[-74.01124953254566,40.73413446570038,-73.96238859103838,40.766161712662296], color_gradient=["#115f9a","#d0f400"], color_bound=[5,18], opacity=1.0, coordinate_system='EPSG:4326', aggregation_type="mean")
>>> plt.show()
"""
from matplotlib import pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
ax.axis('off')
def choroplethmap_wkb(wkb, w, conf=vega):
from arctern import choropleth_map_layer
return choropleth_map_layer(conf, wkb, w, False)
