def test_point_map():
x_data = []
y_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)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
vega = vega_pointmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
point_size=10,
point_color="#0000FF",
opacity=1.0,
coordinate_system="EPSG:4326")
curve_z1 = arctern.point_map_layer(vega, points)
save_png(curve_z1, "/tmp/test_curve_z1.png")
def plot_pointmap(ax,
points,
bounding_box,
coordinate_system='EPSG:4326',
point_size=3,
point_color='red',
point_opacity=1.0,
**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 point_szie: int
:param point_size: size of point
:type point_color: str
:param point_color: specify color, using matplotlib.colors
:type opacity: float
:param opacity: opacity of point
: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 colors, pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
color_hex = colors.to_hex(point_color)
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=color_hex,
opacity=point_opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
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 pointmap(ax,
points,
bounding_box,
point_size=3,
point_color='#115f9a',
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plot 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 bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north].
:type point_size: int
:param point_size: Diameter of point, default as 3
:type point_color: str
:param point_color: Specify point color in Hex Color Code, default as "#115f9a"
: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 pointmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.pointmap(ax, points, [-74.01398981737215,40.71353244267465,-73.96979949831308,40.74480271529791], point_size=10, point_color='#115f9a',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_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=point_color,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
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 pointmap(ax,
points,
bounding_box,
point_size=3,
point_color='#115f9a',
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plots a point map in Matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
points : GeoSeries
Sequence of points.
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
point_size : int, optional
Diameter of points, by default 3.
point_color : str, optional
Point color in Hex Color Code, by default '#115f9a'.
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 pointmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.pointmap(ax, points, [-74.01398981737215,40.71353244267465,-73.96979949831308,40.74480271529791], point_size=10, point_color='#115f9a',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_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=point_color,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
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 pointmap_wkb(point, conf=vega):
from arctern import point_map_layer
return point_map_layer(conf, point, False)