def test_heat_map():
x_data = []
y_data = []
c_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(10)
c_data.append(20)
c_data.append(30)
c_data.append(40)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
arr_c = pandas.Series(c_data)
points = arctern.ST_Point(arr_x, arr_y)
vega = vega_heatmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
map_zoom_level=13.0,
coordinate_system='EPSG:4326')
heat_map1 = arctern.heat_map_layer(vega, points, arr_c)
save_png(heat_map1, "/tmp/test_heat_map1.png")
def heatmap(ax,
points,
weights,
bounding_box,
map_zoom_level=None,
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])
if map_zoom_level is None:
map_zoom_level = _calc_zoom(bounding_box, coordinate_system)
vega = vega_heatmap(w,
h,
bounding_box=bounding_box,
map_zoom_level=map_zoom_level,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.heat_map_layer(vega, points, 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 heatmap(ax,
points,
weights,
bounding_box,
map_zoom_level=None,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Plot heatmap in matplotlibs
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type points: GeoSeries
:param points: Sequence of Points
:type weights: Series(dtype: float|int64)
:param weights: Weights of point intensity
:type bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north].
:type map_zoom_level: int
:param map_zoom_level: Zoom level of heatmap. Default as auto.
: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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>>
>>> # plot heatmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.heatmap(ax, points, df['color_weights'], bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], 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])
if map_zoom_level is None:
map_zoom_level = _calc_zoom(bounding_box, coordinate_system)
vega = vega_heatmap(w,
h,
bounding_box=bounding_box,
map_zoom_level=map_zoom_level,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.heat_map_layer(vega, points, 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 heatmap(ax,
points,
weights,
bounding_box,
map_zoom_level=None,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Plots a heat map in matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
points : GeoSeries
Sequence of points.
weights : Series
Weights of point intensity.
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
map_zoom_level : [type], optional
Zoom level of the map by default 'auto'.
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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>>
>>> # plot heatmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.heatmap(ax, points, df['color_weights'], bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], 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])
if map_zoom_level is None:
map_zoom_level = _calc_zoom(bounding_box, coordinate_system)
vega = vega_heatmap(w,
h,
bounding_box=bounding_box,
map_zoom_level=map_zoom_level,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.heat_map_layer(vega, points, 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 heatmap_wkb(point, w, conf=vega):
from arctern import heat_map_layer
return heat_map_layer(conf, point, w, False)