{"id":1166283,"date":"2025-01-15T15:30:22","date_gmt":"2025-01-15T07:30:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1166283.html"},"modified":"2025-01-15T15:30:24","modified_gmt":"2025-01-15T07:30:24","slug":"python%e5%a6%82%e4%bd%95mask%e9%9d%92%e8%97%8f%e9%ab%98%e5%8e%9f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1166283.html","title":{"rendered":"python\u5982\u4f55mask\u9752\u85cf\u9ad8\u539f"},"content":{"rendered":"

\"python\u5982\u4f55mask\u9752\u85cf\u9ad8\u539f\"<\/p>\n

Python\u4e2dmask\u9752\u85cf\u9ad8\u539f\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u5de5\u5177\u3001\u9065\u611f\u5f71\u50cf\u5904\u7406\u5de5\u5177\u4ee5\u53ca\u6570\u636e\u5904\u7406\u5e93\u7b49\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5229\u7528NumPy\u548cMatplotlib\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528Geopandas\u8fdb\u884c\u5730\u7406\u7a7a\u95f4\u6570\u636e\u5904\u7406\u3001\u4ee5\u53ca\u5229\u7528GDAL\u5e93\u8fdb\u884c\u6805\u683c\u6570\u636e\u5904\u7406\u7b49\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff1a\u5229\u7528NumPy\u548cMatplotlib\u8fdb\u884c\u6570\u636e\u5904\u7406\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u548cMatplotlib\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n

1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4efb\u4f55\u6570\u636e\u5904\u7406\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684Python\u5e93\u3002NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u5904\u7406\u6570\u7ec4\u7684\u5e93\uff0c\u800cMatplotlib\u662f\u4e00\u4e2a\u7528\u4e8e\u7ed8\u5236\u56fe\u5f62\u7684\u5e93\u3002\u6211\u4eec\u8fd8\u9700\u8981\u5b89\u88c5Basemap\u5e93\uff0c\u5b83\u662fMatplotlib\u7684\u4e00\u90e8\u5206\uff0c\u7528\u4e8e\u5904\u7406\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.basemap import Basemap<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u83b7\u53d6\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u8303\u56f4<\/h4>\n<\/p>\n
\u9752\u85cf\u9ad8\u539f\u5927\u81f4\u4f4d\u4e8e\u5317\u7eac26\u00b0\u81f3\u5317\u7eac40\u00b0\u4e4b\u95f4\uff0c\u4e1c\u7ecf73\u00b0\u81f3\u4e1c\u7ecf104\u00b0\u4e4b\u95f4\u3002\u6211\u4eec\u53ef\u4ee5\u7528\u8fd9\u4e9b\u5750\u6807\u6765\u5b9a\u4e49\u9752\u85cf\u9ad8\u539f\u7684\u8303\u56f4\u3002<\/p>\n<\/p>\n
lat_min, lat_max = 26, 40<\/p>\n
lon_min, lon_max = 73, 104<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u521b\u5efa\u4e00\u4e2aBasemap\u5b9e\u4f8b<\/h4>\n<\/p>\n
Basemap\u5e93\u662fMatplotlib\u7684\u4e00\u4e2a\u5de5\u5177\u5305\uff0c\u7528\u4e8e\u7ed8\u5236\u5730\u7406\u6295\u5f71\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Basemap\u521b\u5efa\u4e00\u4e2a\u9752\u85cf\u9ad8\u539f\u8303\u56f4\u5185\u7684\u5730\u56fe\u3002<\/p>\n<\/p>\n
m = Basemap(projection='cyl', llcrnrlat=lat_min, urcrnrlat=lat_max, llcrnrlon=lon_min, urcrnrlon=lon_max, resolution='i')<\/p>\n
m.drawcoastlines()<\/p>\n
m.drawcountries()<\/p>\n
m.drawmapboundary()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u52a0\u8f7d\u548c\u5904\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u8fd9\u91cc\u6211\u4eec\u5047\u8bbe\u6709\u4e00\u4e2a\u5305\u542b\u5168\u7403\u6570\u636e\u7684NumPy\u6570\u7ec4data<\/code>\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u5bf9\u5e94\u4e8e\u4e00\u4e2a\u5730\u7406\u4f4d\u7f6e\u7684\u503c\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u8303\u56f4\u6765\u63a9\u819c\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u5047\u8bbe\u6570\u636e\u662f\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4<\/p>\n
data = np.random.random((180, 360))<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e0e\u6570\u636e\u76f8\u540c\u5927\u5c0f\u7684\u63a9\u819c\u6570\u7ec4<\/strong><\/h2>\n
mask = np.zeros_like(data, dtype=bool)<\/p>\n
\u5c06\u9752\u85cf\u9ad8\u539f\u8303\u56f4\u5185\u7684\u533a\u57df\u8bbe\u7f6e\u4e3aTrue<\/strong><\/h2>\n
lat_indices = np.linspace(-90, 90, data.shape[0])<\/p>\n
lon_indices = np.linspace(-180, 180, data.shape[1])<\/p>\n
for i, lat in enumerate(lat_indices):<\/p>\n
    for j, lon in enumerate(lon_indices):<\/p>\n
        if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:<\/p>\n
            mask[i, j] = True<\/p>\n
\u5e94\u7528\u63a9\u819c<\/strong><\/h2>\n
masked_data = np.ma.masked_where(mask, data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e\uff0c\u4ee5\u53ef\u89c6\u5316\u9752\u85cf\u9ad8\u539f\u533a\u57df\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
m.imshow(masked_data, cmap='viridis', interpolation='nearest')<\/p>\n
plt.colorbar(label='Data Value')<\/p>\n
plt.title('Masked Data for Tibetan Plateau')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Geopandas\u5904\u7406\u5730\u7406\u7a7a\u95f4\u6570\u636e<\/h3>\n<\/p>\n
1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
Geopandas\u662f\u4e00\u4e2a\u5904\u7406\u5730\u7406\u7a7a\u95f4\u6570\u636e\u7684Python\u5e93\uff0c\u5b83\u4f7f\u5f97\u64cd\u4f5c\u5730\u7406\u6570\u636e\u53d8\u5f97\u5bb9\u6613\u3002<\/p>\n<\/p>\n
import geopandas as gpd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u52a0\u8f7d\u5730\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u8fb9\u754c\u6570\u636e\u3002\u53ef\u4ee5\u4ece\u5404\u79cd\u5730\u7406\u6570\u636e\u5e93\u4e0b\u8f7d\u8be5\u6570\u636e\uff0c\u4f8b\u5982Natural Earth\u6216\u8005GADM\u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n
tibet_shapefile = 'path_to_shapefile.shp'<\/p>\n
tibet = gpd.read_file(tibet_shapefile)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u52a0\u8f7d\u6805\u683c\u6570\u636e\u5e76\u5e94\u7528\u63a9\u819c<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u52a0\u8f7d\u4e00\u4e2a\u5305\u542b\u5168\u7403\u6570\u636e\u7684\u6805\u683c\u6587\u4ef6\uff0c\u5e76\u5e94\u7528\u9752\u85cf\u9ad8\u539f\u7684\u63a9\u819c\u3002<\/p>\n<\/p>\n
from osgeo import gdal<\/p>\n
raster = gdal.Open('path_to_raster.tif')<\/p>\n
band = raster.GetRasterBand(1)<\/p>\n
data = band.ReadAsArray()<\/p>\n
\u4f7f\u7528\u9752\u85cf\u9ad8\u539f\u7684\u8fb9\u754c\u63a9\u819c\u6570\u636e<\/strong><\/h2>\n
mask = np.zeros_like(data, dtype=bool)<\/p>\n
for geom in tibet.geometry:<\/p>\n
    mask |= gpd.GeoSeries(geom).to_mask(data.shape, transform=raster.GetGeoTransform())<\/p>\n
masked_data = np.ma.masked_where(mask, data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
plt.imshow(masked_data, cmap='viridis')<\/p>\n
plt.colorbar(label='Data Value')<\/p>\n
plt.title('Masked Data for Tibetan Plateau')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528GDAL\u5e93\u5904\u7406\u6805\u683c\u6570\u636e<\/h3>\n<\/p>\n
1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
GDAL\u662f\u4e00\u4e2a\u5904\u7406\u6805\u683c\u6570\u636e\u7684\u5e93\uff0c\u652f\u6301\u591a\u79cd\u6805\u683c\u6570\u636e\u683c\u5f0f\u3002<\/p>\n<\/p>\n
from osgeo import gdal, ogr<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u52a0\u8f7d\u5730\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u8fb9\u754c\u6570\u636e\u548c\u6805\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
shapefile = 'path_to_shapefile.shp'<\/p>\n
raster = gdal.Open('path_to_raster.tif')<\/p>\n
\u8bfb\u53d6\u9752\u85cf\u9ad8\u539f\u7684\u8fb9\u754c<\/strong><\/h2>\n
driver = ogr.GetDriverByName("ESRI Shapefile")<\/p>\n
dataSource = driver.Open(shapefile, 0)<\/p>\n
layer = dataSource.GetLayer()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5e94\u7528\u63a9\u819c<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u9752\u85cf\u9ad8\u539f\u7684\u8fb9\u754c\u5e94\u7528\u5230\u6805\u683c\u6570\u636e\u4e0a\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6\u6805\u683c\u6570\u636e<\/p>\n
band = raster.GetRasterBand(1)<\/p>\n
data = band.ReadAsArray()<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u63a9\u819c\u6570\u7ec4<\/strong><\/h2>\n
mask = np.zeros_like(data, dtype=bool)<\/p>\n
\u904d\u5386\u9752\u85cf\u9ad8\u539f\u7684\u8fb9\u754c<\/strong><\/h2>\n
for feature in layer:<\/p>\n
    geom = feature.GetGeometryRef()<\/p>\n
    if geom.Intersects(raster.GetGeoTransform()):<\/p>\n
        mask |= gdal.RasterizeLayer(raster, [1], layer, burn_values=[1])<\/p>\n
masked_data = np.ma.masked_where(mask, data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7ed8\u5236\u63a9\u819c\u540e\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
plt.imshow(masked_data, cmap='viridis')<\/p>\n
plt.colorbar(label='Data Value')<\/p>\n
plt.title('Masked Data for Tibetan Plateau')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u4ee5\u4e0a\u4ecb\u7ecd\u4e86\u4e09\u79cd\u4e0d\u540c\u7684\u65b9\u6cd5\u6765\u5728Python\u4e2d\u63a9\u819c\u9752\u85cf\u9ad8\u539f\u7684\u6570\u636e\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
    \n
  1. NumPy\u548cMatplotlib<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u7b80\u5355\u7684\u4e8c\u7ef4\u6570\u7ec4\u6570\u636e\uff0c\u9002\u5408\u521d\u5b66\u8005\u548c\u5feb\u901f\u539f\u578b\u5f00\u53d1\u3002<\/li>\n
  2. Geopandas<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u77e2\u91cf\u5730\u7406\u6570\u636e\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u8fdb\u884c\u5730\u7406\u6570\u636e\u7684\u64cd\u4f5c\u548c\u53ef\u89c6\u5316\u3002<\/li>\n
  3. GDAL<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6805\u683c\u6570\u636e\uff0c\u652f\u6301\u591a\u79cd\u6805\u683c\u6570\u636e\u683c\u5f0f\uff0c\u9002\u5408\u9700\u8981\u5904\u7406\u5927\u91cf\u5730\u7406\u7a7a\u95f4\u6570\u636e\u7684\u573a\u666f\u3002<\/li>\n<\/ol>\n
    \u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u5728Python\u4e2d\u5bf9\u9752\u85cf\u9ad8\u539f\u8fdb\u884c\u63a9\u819c\u5904\u7406\uff0c\u4ece\u800c\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5730\u7406\u6570\u636e\u5206\u6790\u548c\u7814\u7a76\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u63a9\u819c\u5904\u7406\u9752\u85cf\u9ad8\u539f\u7684\u6570\u636e\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u63a9\u819c\u5904\u7406\u9752\u85cf\u9ad8\u539f\u7684\u6570\u636e\u901a\u5e38\u6d89\u53ca\u4f7f\u7528\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u5e93\uff0c\u5982Geopandas\u6216Rasterio\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u63a9\u819c\u56fe\u5c42\uff08mask layer\uff09\uff0c\u4ec5\u63d0\u53d6\u9752\u85cf\u9ad8\u539f\u533a\u57df\u7684\u6570\u636e\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u52a0\u8f7d\u6570\u636e\u96c6\u3001\u5b9a\u4e49\u9752\u85cf\u9ad8\u539f\u7684\u8fb9\u754c\uff0c\u5e76\u4f7f\u7528\u63a9\u819c\u51fd\u6570\u5c06\u6570\u636e\u9650\u5236\u5728\u8be5\u533a\u57df\u5185\u3002<\/p>\n
    \u5bf9\u4e8e\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u6570\u636e\uff0c\u63a8\u8350\u4f7f\u7528\u54ea\u4e9bPython\u5e93\uff1f<\/strong>
    \u5904\u7406\u9752\u85cf\u9ad8\u539f\u7684\u5730\u7406\u6570\u636e\u65f6\uff0c\u63a8\u8350\u4f7f\u7528\u4ee5\u4e0b\u5e93\uff1aRasterio\u7528\u4e8e\u5904\u7406\u6805\u683c\u6570\u636e\uff0cGeopandas\u7528\u4e8e\u77e2\u91cf\u6570\u636e\uff0cMatplotlib\u7528\u4e8e\u53ef\u89c6\u5316\uff0c\u4ee5\u53caNumpy\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u8fd9\u4e9b\u5de5\u5177\u7ed3\u5408\u4f7f\u7528\u80fd\u591f\u5e2e\u52a9\u60a8\u6709\u6548\u5904\u7406\u548c\u5206\u6790\u9752\u85cf\u9ad8\u539f\u7684\u73af\u5883\u6570\u636e\u3002<\/p>\n
    \u5982\u4f55\u9a8c\u8bc1\u63a9\u819c\u64cd\u4f5c\u662f\u5426\u6210\u529f\uff1f<\/strong>
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