f_path = r"E:\gra_thesis\sum_pre_data_new\grid_nc\AMJ_pre_total_precip.nc" f = xr.open_dataset(f_path) f # %% lon = f['lon'] lat = f['lat'] data= f['precip'] data_mean = np.mean(data, 0) # %% shp_path = r"C:\Users\86133\Desktop\thesis\2020国家级行政边界\China_province.shp" sf = shapefile.Reader(shp_path) shp_reader = Reader(shp_path) sf.records() region_list = [110000, 120000, 130000,140000,150000,210000,220000, 230000, 310000, 320000,330000,340000,350000,360000, 370000, 410000, 420000,430000,440000,450000,460000, 500000, 510000, 520000,530000,540000,610000,620000, 630000, 640000, 650000,710000,810000,820000] # %% proj = ccrs.PlateCarree() extent = [105, 125, 15, 30] fig, ax = plt.subplots(1, 1, subplot_kw={'projection': proj}) ax.set_extent(extent, proj) # ax.add_feature(cfeature.LAND, fc='0.8', zorder=1) ax.add_feature(cfeature.COASTLINE, lw=1, ec="k", zorder=2) ax.add_feature(cfeature.OCEAN, fc='white', zorder=2) ax.add_geometries(shp_reader.geometries(), fc="None", ec="k", lw=1, crs=proj, zorder=2) ax.spines['geo'].set_linewidth(0.8) ax.tick_params(axis='both',which='major',labelsize=9, direction='out',length=2.5,width=0.8,pad=1.5, bottom=True, left=True) ax.tick_params(axis='both',which='minor',direction='out',width=0.5,bottom=True,left=True) ax.set_xticks(np.arange(105, 130, 5)) ax.set_yticks(np.arange(15, 40, 5)) ax.xaxis.set_major_formatter(LongitudeFormatter()) ax.yaxis.set_major_formatter(LatitudeFormatter()) cf = ax.contourf(lon, lat, data_mean, extend='both', cmap='RdBu') cb = fig.colorbar(cf, shrink=0.9, pad=0.05)解释这段代码
时间: 2023-06-03 16:07:03 浏览: 76
这是Python中的代码。它使用xarray库中的open_dataset函数来打开一个名为AMJ_pre_total_precip.nc的NetCDF文件。文件位于E:\gra_thesis\sum_pre_data_new\grid_nc目录中。函数返回一个数据集,命名为f。
相关问题
years = np.arange(1979, 2020, 1) year_str = [str(i) for i in years] x_min = 70 x_max = 140 y_min = 10 y_max = 55 grid_lon = np.arange(x_min, x_max+1, 1.0) grid_lat = np.arange(y_min, y_max+1, 1.0) nyears = years.shape[0] nlon = grid_lon.shape[0] nlat = grid_lat.shape[0] total_grid_data = np.zeros((nyears, nlat, nlon), dtype=float) i = 0 for year in year_str: f_path = r'E:\gra_thesis\sum_pre_data_new/AMJ_pre_data/'+year+'_AMJ_sumPre.xlsx' df = pd.read_excel(f_path) df = df.dropna(axis=0) lon = df['X'] lat = df['Y'] data= df['总降水量'] Krin = pykrige.OrdinaryKriging(lon, lat, data, variogram_model="gaussian", nlags=6) grid_data, ssl = Krin.execute('grid', grid_lon, grid_lat) grid_data = np.array(grid_data) total_grid_data[i, :, :] = grid_data i = i + 1 # %% # # save data data_nc = xr.Dataset( { "precip":(("time", "lat","lon"), total_grid_data) }, coords={ "time":years, "lat":grid_lat, "lon":grid_lon, } ) data_nc.attrs["long_name"] = "total_grid_precip" data_nc.to_netcdf("E:"+"JJA_pre_total_precip.nc") 解释这段代码i
这段代码是用于读取地理数据,并对其进行插值,得到一个三维数组。years变量存储了年份序列,year_str变量是将年份序列转换为字符串序列。grid_lon变量和grid_lat变量分别是经度和纬度序列。nyears、nlon和nlat是计算数组维度的变量。total_grid_data是三维数组,用于存储地理数据的插值结果。代码使用了pykrige包进行插值,其中OrdinaryKriging()函数是调用普通克里金插值方法进行计算,variogram_model指定了变异函数的类型,nlags是变异函数的参数。for循环逐个读取每个年份的数据,同时将插值结果存储在total_grid_data中。最终代码的结果是得到了一个三维数组,其中每个元素值是地理数据的插值结果。
python gdal 重采样_Python遥感影像重采样
对于Python遥感影像重采样,可以使用GDAL(Geospatial Data Abstraction Library)库来实现。GDAL是一个开源的地理信息系统(GIS)库,它提供了许多用于处理栅格数据的功能,包括重采样。
下面是一个简单的示例代码,演示如何使用GDAL库进行遥感影像重采样:
```python
from osgeo import gdal
def resample_image(input_path, output_path, pixel_size):
# 打开输入影像
input_ds = gdal.Open(input_path)
# 获取输入影像的投影和仿射变换参数
projection = input_ds.GetProjection()
geotransform = input_ds.GetGeoTransform()
# 获取输入影像的宽度和高度
width = input_ds.RasterXSize
height = input_ds.RasterYSize
# 计算重采样后的宽度和高度
new_width = int(width / pixel_size)
new_height = int(height / pixel_size)
# 创建输出影像
driver = gdal.GetDriverByName('GTiff')
output_ds = driver.Create(output_path, new_width, new_height, 1, gdal.GDT_Float32)
# 设置输出影像的投影和仿射变换参数
output_ds.SetProjection(projection)
output_ds.SetGeoTransform((geotransform[0], pixel_size, 0, geotransform[3], 0, -pixel_size))
# 执行重采样
gdal.ReprojectImage(input_ds, output_ds, None, None, gdal.GRA_Bilinear)
# 关闭数据集
input_ds = None
output_ds = None
# 使用示例
input_path = 'input_image.tif'
output_path = 'resampled_image.tif'
pixel_size = 10 # 新的像素大小(单位:米)
resample_image(input_path, output_path, pixel_size)
```
在上面的示例中,`input_path`是输入影像的路径,`output_path`是重采样后的输出影像的路径,`pixel_size`是新的像素大小,用于指定重采样后每个像素的大小(单位:米)。代码将使用双线性插值进行重采样操作,并将结果保存为GeoTIFF格式的影像文件。
请注意,执行此代码需要安装GDAL库。你可以使用pip安装它:`pip install gdal`。
希望这个示例对你有帮助!如果你有任何其他问题,请随时提问。