def weighted_pointmap_wkb_2(point, c, s, conf=vega):
from arctern import weighted_point_map_layer
return weighted_point_map_layer(conf,
point,
False,
color_weights=c,
size_weights=s)
def test_weighted_point_map():
x_data = []
y_data = []
c_data = []
s_data = []
x_data.append(-73.96524)
x_data.append(-73.96118)
x_data.append(-73.97324)
x_data.append(-73.98456)
y_data.append(40.73747)
y_data.append(40.74507)
y_data.append(40.75890)
y_data.append(40.77654)
c_data.append(1)
c_data.append(2)
c_data.append(3)
c_data.append(4)
s_data.append(4)
s_data.append(6)
s_data.append(8)
s_data.append(10)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
arr_c = pandas.Series(c_data)
arr_s = pandas.Series(s_data)
vega1 = vega_weighted_pointmap(1024, 896, bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816], color_gradient=["#0000FF"], opacity=1.0, coordinate_system="EPSG:4326")
res1 = arctern.weighted_point_map_layer(vega1, points)
save_png(res1, "/tmp/test_weighted_0_0.png")
vega2 = vega_weighted_pointmap(1024, 896, bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816], color_gradient=["#0000FF", "#FF0000"], color_bound=[1, 5], opacity=1.0, coordinate_system="EPSG:4326")
res2 = arctern.weighted_point_map_layer(vega2, points, color_weights=arr_c)
save_png(res2, "/tmp/test_weighted_1_0.png")
vega3 = vega_weighted_pointmap(1024, 896, bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816], color_gradient=["#0000FF"], size_bound=[1, 10], opacity=1.0, coordinate_system="EPSG:4326")
res3 = arctern.weighted_point_map_layer(vega3, points, size_weights=arr_s)
save_png(res3, "/tmp/test_weighted_0_1.png")
vega4 = vega_weighted_pointmap(1024, 896, bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816], color_gradient=["#0000FF", "#FF0000"], color_bound=[1, 5], size_bound=[1, 10], opacity=1.0, coordinate_system="EPSG:4326")
res4 = arctern.weighted_point_map_layer(vega4, points, color_weights=arr_c, size_weights=arr_s)
save_png(res4, "/tmp/test_weighted_1_1.png")
def weighted_pointmap(ax,
points,
color_weights=None,
size_weights=None,
bounding_box=None,
color_gradient=["#115f9a", "#d0f400"],
color_bound=[0, 0],
size_bound=[3],
opacity=1.0,
coordinate_system='EPSG:3857',
**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 point_szie: int
:param point_size: size of point
:type opacity: float
:param opacity: opacity of point
: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_weighted_pointmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
size_bound=size_bound,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.weighted_point_map_layer(vega,
points,
color_weights=color_weights,
size_weights=size_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 weighted_pointmap(ax,
points,
color_weights=None,
size_weights=None,
bounding_box=None,
color_gradient=["#115f9a", "#d0f400"],
color_bound=[0, 0],
size_bound=[3],
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plot weighted pointmap in Matplotlib
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type points: GeoSeries
:param points: Sequence of Points
:type color_weights: Series(dtype: float|int64)
:param color_weights: Weights for point color, default as None
:type size_weights: Series(dtype: float|int64)
:param size_weights: Weights for point size, deciding diameter of point (after bounded by size_bound)
Default as None
:type bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north],
Default as None
:type color_gradient: list
:param color_gradient: Specify range of color gradient.
Either use ["hex_color"] to specify a same color for all points,
or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2"
Default as ["#115f9a", "#d0f400"]
: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 [0, 0]
:type size_bound: list
:param size_bound: Specify range [w1, w2] of size_weights.
When weight < w1 or weight > w2, truncate to w1/w2 accordingly.
Default as [3]
:type opacity: float
:param opacity: Opacity of point, 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 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.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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>>
>>> # plot weighted pointmap with variable color and fixed size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, color_weights=df['color_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#115f9a", "#d0f400"], color_bound=[2.5,15], size_bound=[16], opacity=1.0, coordinate_system="EPSG:4326")
>>> plt.show()
>>>
>>> # plot weighted pointmap with fixed color and variable size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, size_weights=df['size_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#37A2DA"], size_bound=[15, 50], opacity=1.0, coordinate_system="EPSG:4326")
>>> plt.show()
>>>
>>> # plot weighted pointmap with variable color and size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, color_weights=df['color_weights'], size_weights=df['size_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#115f9a", "#d0f400"], color_bound=[2.5,15], size_bound=[15, 50], opacity=1.0, coordinate_system="EPSG:4326")
>>> 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_weighted_pointmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
size_bound=size_bound,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.weighted_point_map_layer(vega,
points,
color_weights=color_weights,
size_weights=size_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 weighted_pointmap(ax,
points,
color_weights=None,
size_weights=None,
bounding_box=None,
color_gradient=["#115f9a", "#d0f400"],
color_bound=[0, 0],
size_bound=[3],
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plots a weighted point map in Matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
points : GeoSeries
Sequence of points.
color_weights : Series, optional
Weights of point color.
size_weights : Series, optional
Weights of point size.
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
color_gradient : list, optional
Range of color gradient, by default ["#115f9a", "#d0f400"].
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``, by default [0, 0].
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.
size_bound : list, optional
Weight range [w1, w2] of ``size_weights``, by default [3]. When weight < w1 or weight > w2, the weight will be truncated to w1 or w2 accordingly.
opacity : float, optional
Opacity of points, 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.
**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.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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>>
>>> # plot weighted pointmap with variable color and fixed size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, color_weights=df['color_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#115f9a", "#d0f400"], color_bound=[2.5,15], size_bound=[16], opacity=1.0, coordinate_system="EPSG:4326")
>>> plt.show()
>>>
>>> # plot weighted pointmap with fixed color and variable size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, size_weights=df['size_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#37A2DA"], size_bound=[15, 50], opacity=1.0, coordinate_system="EPSG:4326")
>>> plt.show()
>>>
>>> # plot weighted pointmap with variable color and size
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.weighted_pointmap(ax, points, color_weights=df['color_weights'], size_weights=df['size_weights'], bounding_box=[-73.99668712186558,40.72972339069935,-73.99045479584949,40.7345193345495], color_gradient=["#115f9a", "#d0f400"], color_bound=[2.5,15], size_bound=[15, 50], opacity=1.0, coordinate_system="EPSG:4326")
>>> 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_weighted_pointmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
size_bound=size_bound,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.weighted_point_map_layer(vega,
points,
color_weights=color_weights,
size_weights=size_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 weighted_pointmap_wkb_0(point, conf=vega):
from arctern import weighted_point_map_layer
return weighted_point_map_layer(conf, point, False)
def weighted_pointmap_wkb_3857_1(point, c, conf=vega):
from arctern import weighted_point_map_layer
return weighted_point_map_layer(conf,
point,
False,
color_weights=c)
