在指定的 json 边界内绘制 hexbin 截止

Question

我正在绘制一个单独的 hexbin 图和 json 边界文件。尽管 hexbin 网格与边界文件重叠。我只对展示非洲大陆感兴趣。我的目标是切断或子集 hexbin 网格within 非洲大陆。因此不应在边界文件之外显示任何网格正方形。有没有办法使用 Plotly 来实现这一点？

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
import geopandas as gpd
import json

data = pd.DataFrame({
    'LAT': [1,5,6,7,5,6,7,5,6,7,5,6,7,12,-40,50],
    'LON': [10,10,11,12,10,11,12,10,11,12,10,11,12,-20,40,50],
    })

gdf_poly = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
gdf_poly = gdf_poly.drop('name', axis = 1)

Afr_gdf_area = gdf_poly[gdf_poly['continent'] == 'Africa'].reset_index(drop = True)

fig = ff.create_hexbin_mapbox(data_frame=data,
                       lat="LAT", 
                       lon="LON",
                       nx_hexagon=25,
                       opacity=0.4,
                       labels={"color": "Point Count"},
                       mapbox_style='carto-positron',
                       zoom = 1
                       )

fig.update_layout(mapbox={
        "layers": [
            {"source": json.loads(Afr_gdf_area.geometry.to_json()),
                "below": "traces",
                "type": "fill",
                "color": "orange",
                "opacity" : 0.1,
                "line": {"width": 1}
            },
        ],
    })   

fig.show()

预期的输出是切断或剪掉非洲满足感之外的方块，这是橙色的。

Answer 1

如果你往里面看

fig.data[0]

，它是一个

Choroplethmapbox

，有几个字段，包括

customdata

和

geojson

。 geojson 包含绘制 hexbins 所需的所有信息，包括坐标和每个六边形的唯一

id

。 customdata 是一个形状为

[n_hexbins x 3]

的数组，其中数组的每个元素都包含 id 和用于绘制确定每个 hexbin 颜色的数值。

'customdata': array([[0.0, '-0.3490658516205964,-0.7648749219440846', 0],
                         [0.0, '-0.3490658516205964,-0.6802309514438665', 0],
                         [0.0, '-0.3490658516205964,-0.5955869809436484', 0],
                         ...,
                         [0.0, '0.8482300176421051,0.8010385323099501', 0],
                         [0.0, '0.8482300176421051,0.8856825028101681', 0],
                         [0.0, '0.8482300176421051,0.9703264733103861', 0]], dtype=object),
    'geojson': {'features': [{'geometry': {'coordinates': [[[-20.00000007,
                                                           -41.31174966478728],
                                                           [-18.6000000672,
                                                           -40.70179509236059],
                                                           [-18.6000000672,
                                                           -39.464994178287064],
                                                           [-20.00000007,
                                                           -38.838189880150665],
                                                           [-21.4000000728,
                                                           -39.464994178287064],
                                                           [-21.4000000728,
                                                           -40.70179509236059],
                                                           [-20.00000007,
                                                           -41.31174966478728]]],
                                           'type': 'Polygon'},
                              'id': '-0.3490658516205964,-0.7648749219440846',
                              'type': 'Feature'},
                             {'geometry': {'coordinates': [[[-20.00000007,
                                                           -37.56790013078226],
                                                           [-18.6000000672,
                                                           -36.924474103794715],
                                                           [-18.6000000672,
                                                           -35.62123099996148],
                                                           [-20.00000007,
                                                           -34.96149172026768],
                                                           [-21.4000000728,
                                                           -35.62123099996148],
                                                           [-21.4000000728,
                                                           -36.924474103794715],
                                                           [-20.00000007,
                                                           -37.56790013078226]]],
                                           'type': 'Polygon'},
                              'id': '-0.3490658516205964,-0.6802309514438665',
                              'type': 'Feature'},
                             {'geometry': {'coordinates
...

要选择指定边界内的 hexbins，我们可以从 plotly 生成的 fig.data[0] 中的 customdata 和 geojson 中提取信息开始，并创建一个 geopandas 数据框。然后我们可以创建一个名为

hexbins_in_afr

的新 geopandas 数据框，它是我们新的 hexbin 的 gdf 和

Afr_gdf_area

之间的内部连接（这样我们就可以删除 Afr_gdf_area 之外的所有 hexbin）。

我们从

geojson

和

hexbins_in_afr

中提取

customdata

信息后，我们可以在

fig.data[0]

中显式设置以下字段：

fig.data[0]['geojson']['features'] = new_geojson
fig.data[0]['customdata'] = hexbins_in_afr['customdata']

这里是经过必要修改的代码：

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
import geopandas as gpd
from geopandas.tools import sjoin
from shapely.geometry import Polygon
import json


data = pd.DataFrame({
    'LAT': [1,5,6,7,5,6,7,5,6,7,5,6,7,12,-40,50],
    'LON': [10,10,11,12,10,11,12,10,11,12,10,11,12,-20,40,50],
    })

gdf_poly = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
gdf_poly = gdf_poly.drop('name', axis = 1)

Afr_gdf_area = gdf_poly[gdf_poly['continent'] == 'Africa'].reset_index(drop = True)

fig = ff.create_hexbin_mapbox(data_frame=data,
                       lat="LAT", 
                       lon="LON",
                       nx_hexagon=25,
                       opacity=0.4,
                       labels={"color": "Point Count"},
                       mapbox_style='carto-positron',
                       zoom = 1
                       )

gdf = gpd.GeoDataFrame({
    'customdata': fig.data[0]['customdata'].tolist(),
    'id':[item['id'] for item in fig.data[0]['geojson']['features']],
    'geometry':[Polygon(item['geometry']['coordinates'][0]) for item in fig.data[0]['geojson']['features']]
})
gdf.set_crs(epsg=4326, inplace=True)

hexbins_in_afr = sjoin(gdf, Afr_gdf_area, how='inner')

def get_coordinates(polygon):
    return [[list(i) for i in polygon.exterior.coords]]

hexbins_in_afr['coordinates'] = hexbins_in_afr['geometry'].apply(lambda x: get_coordinates(x))

## create a new geojson that matches the structure of fig.data[0]['geojson']['features']
new_geojson = [{
    'type': 'Feature', 
    'id': id, 
    'geometry': {
        'type': 'Polygon', 
        'coordinates': coordinate
    }
} for id, coordinate in zip(hexbins_in_afr['id'],hexbins_in_afr['coordinates'])]

fig.data[0]['geojson']['features'] = new_geojson
fig.data[0]['customdata'] = hexbins_in_afr['customdata']

fig.update_layout(mapbox={
        "layers": [
            {"source": json.loads(Afr_gdf_area.geometry.to_json()),
                "below": "traces",
                "type": "fill",
                "color": "orange",
                "opacity" : 0.1,
                "line": {"width": 1}
            },
        ],
    })   

fig.show()